[Mlir-commits] [mlir] [MLIR][TOSA] Enable strict property assembly format (PR #217291)
Mehdi Amini
llvmlistbot at llvm.org
Wed Aug 26 04:01:38 PDT 2026
https://github.com/joker-eph updated https://github.com/llvm/llvm-project/pull/217291
>From c63404469ea7b088fe0c27835bf0ed58e25ece3a Mon Sep 17 00:00:00 2001
From: Mehdi Amini <joker.eph at gmail.com>
Date: Wed, 6 May 2026 14:27:07 -0700
Subject: [PATCH] [MLIR][TOSA] Enable strict property assembly format
Enable strict properties-in-assembly-format mode for TOSA. Bind TOSA
operation properties directly in declarative assembly formats so strict
parsing does not rely on property dictionaries.
Teach generic optional attribute parsing to diagnose attribute wrappers
without a static name. This lets TOSA BoolAttr properties use the declarative
parser without custom parser/printer hooks.
Update the TOSA tests to use the direct property spelling.
Assisted-by: Codex
---
.../mlir/Dialect/Tosa/IR/TosaOpBase.td | 6 +-
mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td | 182 ++++-
.../mlir/Dialect/Tosa/IR/TosaShapeOps.td | 13 +
.../mlir/Dialect/Tosa/IR/TosaUtilOps.td | 4 +-
mlir/include/mlir/IR/OpImplementation.h | 10 +-
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp | 298 -------
.../TosaToArith/tosa-to-arith-invalid.mlir | 2 +-
.../Conversion/TosaToArith/tosa-to-arith.mlir | 12 +-
.../TosaToLinalg/tosa-to-linalg-invalid.mlir | 18 +-
.../TosaToLinalg/tosa-to-linalg-named.mlir | 90 +-
.../TosaToLinalg/tosa-to-linalg-pipeline.mlir | 6 +-
.../TosaToLinalg/tosa-to-linalg-resize.mlir | 152 ++--
.../TosaToLinalg/tosa-to-linalg.mlir | 294 +++----
.../Conversion/TosaToSCF/tosa-to-scf.mlir | 4 +-
.../custom-op-domain-to-opcode-invalid.mlir | 2 +-
.../custom-op-domain-to-opcode.mlir | 6 +-
.../TosaToSPIRVTosa/graph-constant-mark.mlir | 4 +-
.../TosaToSPIRVTosa/tosa-to-spirv.mlir | 48 +-
.../TosaToTensor/tosa-to-tensor-invalid.mlir | 4 +-
.../TosaToTensor/tosa-to-tensor.mlir | 104 +--
mlir/test/Dialect/MemRef/resolve-dim-ops.mlir | 4 +-
mlir/test/Dialect/Tosa/canonicalize.mlir | 766 +++++++++---------
.../Tosa/constant-reciprocal-fold.mlir | 18 +-
mlir/test/Dialect/Tosa/constant_folding.mlir | 514 ++++++------
mlir/test/Dialect/Tosa/dynamic_extension.mlir | 40 +-
mlir/test/Dialect/Tosa/error_if_check.mlir | 160 ++--
mlir/test/Dialect/Tosa/inlining.mlir | 2 +-
mlir/test/Dialect/Tosa/invalid.mlir | 602 +++++++-------
mlir/test/Dialect/Tosa/invalid_extension.mlir | 202 ++---
mlir/test/Dialect/Tosa/level_check.mlir | 438 +++++-----
mlir/test/Dialect/Tosa/ops.mlir | 584 ++++++-------
.../Dialect/Tosa/profile_all_unsupported.mlir | 52 +-
.../Tosa/profile_pro_fp_unsupported.mlir | 62 +-
.../Tosa/profile_pro_int_unsupported.mlir | 76 +-
mlir/test/Dialect/Tosa/quant-test.mlir | 6 +-
...tosa-convert-integer-type-to-signless.mlir | 14 +-
.../Tosa/tosa-decompose-depthwise.mlir | 50 +-
.../Tosa/tosa-decompose-transpose-conv.mlir | 128 ++-
.../Tosa/tosa-downgrade-1-1-to-1-0.mlir | 16 +-
...a-infer-shapes-fold-shape-expressions.mlir | 18 +-
mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir | 604 +++++++-------
...ayerwise-constant-fold-dense-resource.mlir | 14 +-
.../Tosa/tosa-layerwise-constant-fold.mlir | 154 ++--
.../tosa-narrow-f64-to-f32-aggressive.mlir | 2 +-
.../Dialect/Tosa/tosa-narrow-f64-to-f32.mlir | 22 +-
.../tosa-narrow-i64-to-i32-aggressive.mlir | 12 +-
.../Dialect/Tosa/tosa-narrow-i64-to-i32.mlir | 54 +-
.../Dialect/Tosa/tosa-reduce-transposes.mlir | 274 +++----
.../Tosa/tosa-validation-valid-strict.mlir | 4 +-
.../Dialect/Tosa/tosa-validation-valid.mlir | 10 +-
...ation-validate-func-signature-invalid.mlir | 2 +-
.../tosa-validation-version-1p0-invalid.mlir | 62 +-
.../tosa-validation-version-1p1-invalid.mlir | 20 +-
...a-validation-version-1p1-pro-fp-valid.mlir | 4 +-
.../tosa-validation-version-1p1-valid.mlir | 154 ++--
mlir/test/Dialect/Tosa/transpose-fold.mlir | 18 +-
mlir/test/Dialect/Tosa/verifier.mlir | 580 ++++++-------
mlir/test/IR/invalid-custom-print-parse.mlir | 3 +-
.../regalloc-eviction-test-model-tosa.mlir | 2 +-
59 files changed, 3423 insertions(+), 3583 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
index 63acbb09b7a04..9733982a6ef0e 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
@@ -53,6 +53,7 @@ def Tosa_Dialect : Dialect {
let hasConstantMaterializer = 1;
let useDefaultAttributePrinterParser = 1;
let useDefaultTypePrinterParser = 1;
+ let useStrictPropertiesInAssemblyFormat = 1;
}
//===----------------------------------------------------------------------===//
@@ -486,9 +487,6 @@ class Tosa_InferShapedTypeOp<string mnemonic, list<Trait> traits = []>
// for multiple zero points in convolution ops.
class Tosa_ConvOp<string mnemonic, list<Trait> traits = []>
: Tosa_InferShapedTypeOp<mnemonic, !listconcat(traits,
- [SameVariadicOperandSize, NoMemoryEffect])> {
- let assemblyFormat =
- "operands attr-dict `:` functional-type(operands, results)";
-}
+ [SameVariadicOperandSize, NoMemoryEffect])> {}
#endif // TOSA_OP_BASE
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index 2edacfa02b576..3749b24ab607d 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -56,7 +56,11 @@ def Tosa_ArgMaxOp : Tosa_InferShapedTypeOp<"argmax", [NoMemoryEffect]> {
let hasFolder = 1;
let hasVerifier = 1;
- let hasCustomAssemblyFormat = 1;
+
+ let assemblyFormat =
+ "operands `axis` `(` $axis `)` "
+ "oilist(`nan_mode` `` $nan_mode) attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -109,7 +113,11 @@ def Tosa_AvgPool2dOp : Tosa_InferShapedTypeOp<"avg_pool2d", [NoMemoryEffect]> {
let hasCanonicalizer = 1;
let assemblyFormat =
- "operands attr-dict `:` functional-type(operands, results)";
+ "operands `kernel` `(` $kernel `)` "
+ "`stride` `(` $stride `)` "
+ "`pad` `(` $pad `)` "
+ "`acc_type` `(` $acc_type `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -149,7 +157,8 @@ def Tosa_AvgPool2dAdaptiveOp
let hasCanonicalizer = 1;
let assemblyFormat =
- "operands attr-dict `:` functional-type(operands, results)";
+ "operands `acc_type` `(` $acc_type `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -196,6 +205,15 @@ def Tosa_Conv2DOp : Tosa_ConvOp<"conv2d"> {
let hasCanonicalizer = 1;
let hasVerifier = 1;
+
+ let assemblyFormat =
+ "operands `pad` `(` $pad `)` "
+ "`stride` `(` $stride `)` "
+ "`dilation` `(` $dilation `)` "
+ "`acc_type` `(` $acc_type `)` "
+ "oilist(`local_bound` `(` $local_bound `)`) "
+ "attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -234,7 +252,10 @@ def Tosa_Conv2DBlockScaledOp : Tosa_InferShapedTypeOp<"conv2d_block_scaled", [No
);
let hasVerifier = 1;
- let hasCustomAssemblyFormat = 1;
+
+ let assemblyFormat =
+ "operands `block_size` `` $block_size attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -278,6 +299,15 @@ def Tosa_Conv3DOp : Tosa_ConvOp<"conv3d"> {
let builders = [Tosa_ConvOpQuantInfoBuilder];
let hasVerifier = 1;
+
+ let assemblyFormat =
+ "operands `pad` `(` $pad `)` "
+ "`stride` `(` $stride `)` "
+ "`dilation` `(` $dilation `)` "
+ "`acc_type` `(` $acc_type `)` "
+ "oilist(`local_bound` `(` $local_bound `)`) "
+ "attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -324,6 +354,15 @@ def Tosa_DepthwiseConv2DOp : Tosa_ConvOp<"depthwise_conv2d"> {
let hasCanonicalizer = 1;
let hasVerifier = 1;
+
+ let assemblyFormat =
+ "operands `pad` `(` $pad `)` "
+ "`stride` `(` $stride `)` "
+ "`dilation` `(` $dilation `)` "
+ "`acc_type` `(` $acc_type `)` "
+ "oilist(`local_bound` `(` $local_bound `)`) "
+ "attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -364,14 +403,18 @@ def Tosa_FFT2dOp : Tosa_InferShapedTypeOp<"fft2d", [
);
let assemblyFormat = [{
- $input_real `,` $input_imag attr-dict `:` `(` type($input_real) `,`
+ $input_real `,` $input_imag `inverse` `(` $inverse `)`
+ oilist(`local_bound` `(` $local_bound `)`) attr-dict `:` `(` type($input_real) `,`
type($input_imag) `)` `->` `(` type($output_real) `,` type($output_imag) `)`
}];
let hasVerifier = 1;
let assemblyFormat =
- "operands attr-dict `:` functional-type(operands, results)";
+ "operands `inverse` `(` $inverse `)` "
+ "oilist(`local_bound` `(` $local_bound `)`) "
+ "attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -477,7 +520,10 @@ def Tosa_MatmulTBlockScaledOp : Tosa_InferShapedTypeOp<"matmul_t_block_scaled",
);
let hasVerifier = 1;
- let hasCustomAssemblyFormat = 1;
+
+ let assemblyFormat =
+ "operands `block_size` `` $block_size attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -508,7 +554,11 @@ def Tosa_MaxPool2dOp : Tosa_InferShapedTypeOp<"max_pool2d", [Pure]> {
let hasCanonicalizer = 1;
let hasVerifier = 1;
- let hasCustomAssemblyFormat = 1;
+
+ let assemblyFormat =
+ "operands `kernel` `(` $kernel `)` `stride` `(` $stride `)` "
+ "`pad` `(` $pad `)` oilist(`nan_mode` `` $nan_mode) "
+ "attr-dict `:` functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -538,7 +588,10 @@ def Tosa_MaxPool2dAdaptiveOp
let hasVerifier = 1;
let hasCanonicalizer = 1;
- let hasCustomAssemblyFormat = 1;
+
+ let assemblyFormat =
+ "operands oilist(`nan_mode` `` $nan_mode) attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -578,13 +631,15 @@ def Tosa_RFFT2dOp : Tosa_InferShapedTypeOp<"rfft2d", [
);
let assemblyFormat = [{
- $input_real attr-dict `:` `(` type($input_real) `)` `->` `(` type($output_real) `,` type($output_imag) `)`
+ $input_real oilist(`local_bound` `(` $local_bound `)`) attr-dict `:`
+ `(` type($input_real) `)` `->` `(` type($output_real) `,` type($output_imag) `)`
}];
let hasVerifier = 1;
let assemblyFormat =
- "operands attr-dict `:` functional-type(operands, results)";
+ "operands oilist(`local_bound` `(` $local_bound `)`) attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -628,6 +683,14 @@ def Tosa_TransposeConv2DOp : Tosa_ConvOp<"transpose_conv2d"> {
let builders = [Tosa_TransConvOpQuantInfoBuilder];
let hasVerifier = 1;
+
+ let assemblyFormat =
+ "operands `out_pad` `(` $out_pad `)` "
+ "`stride` `(` $stride `)` "
+ "`acc_type` `(` $acc_type `)` "
+ "oilist(`local_bound` `(` $local_bound `)`) "
+ "attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -661,7 +724,11 @@ def Tosa_ClampOp : Tosa_ElementwiseUnaryOp<"clamp", [Pure]> {
let hasCanonicalizer = 1;
let hasVerifier = 1;
- let hasCustomAssemblyFormat = 1;
+
+ let assemblyFormat =
+ "operands `min_val` `(` $min_val `)` `max_val` `(` $max_val `)` "
+ "oilist(`nan_mode` `` $nan_mode) attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -811,7 +878,9 @@ def Tosa_ArithmeticRightShiftOp : Tosa_ElementwiseOp<"arithmetic_right_shift",
Tosa_Tensor:$output
);
- let assemblyFormat = "operands attr-dict `:` functional-type(operands, results)";
+ let assemblyFormat =
+ "operands `round` `(` $round `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -1079,7 +1148,10 @@ def Tosa_MaximumOp : Tosa_ElementwiseOp<"maximum", [
let results = (outs
Tosa_Tensor:$output
);
- let hasCustomAssemblyFormat = 1;
+
+ let assemblyFormat =
+ "operands oilist(`nan_mode` `` $nan_mode) attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -1106,7 +1178,9 @@ def Tosa_MinimumOp : Tosa_ElementwiseOp<"minimum", [
Tosa_Tensor:$output
);
- let hasCustomAssemblyFormat = 1;
+ let assemblyFormat =
+ "operands oilist(`nan_mode` `` $nan_mode) attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -1734,7 +1808,9 @@ def Tosa_ReduceAllOp : Tosa_InferTensorTypeOp<"reduce_all", [Pure]> {
}
}];
- let assemblyFormat = "operands attr-dict `:` functional-type(operands, results)";
+ let assemblyFormat =
+ "operands `axis` `(` $axis `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -1770,7 +1846,9 @@ def Tosa_ReduceAnyOp : Tosa_InferTensorTypeOp<"reduce_any", [Pure]> {
}
}];
- let assemblyFormat = "operands attr-dict `:` functional-type(operands, results)";
+ let assemblyFormat =
+ "operands `axis` `(` $axis `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -1807,7 +1885,10 @@ def Tosa_ReduceMaxOp : Tosa_InferTensorTypeOp<"reduce_max", [Pure]> {
}
}];
- let hasCustomAssemblyFormat = 1;
+ let assemblyFormat =
+ "operands `axis` `(` $axis `)` "
+ "oilist(`nan_mode` `` $nan_mode) attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -1844,7 +1925,10 @@ def Tosa_ReduceMinOp : Tosa_InferTensorTypeOp<"reduce_min", [Pure]> {
}
}];
- let hasCustomAssemblyFormat = 1;
+ let assemblyFormat =
+ "operands `axis` `(` $axis `)` "
+ "oilist(`nan_mode` `` $nan_mode) attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -1883,7 +1967,9 @@ def Tosa_ReduceProductOp : Tosa_InferTensorTypeOp<"reduce_product", [NoMemoryEff
}
}];
- let assemblyFormat = "operands attr-dict `:` functional-type(operands, results)";
+ let assemblyFormat =
+ "operands `axis` `(` $axis `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -1922,7 +2008,9 @@ def Tosa_ReduceSumOp : Tosa_InferTensorTypeOp<"reduce_sum", [NoMemoryEffect]> {
}
}];
- let assemblyFormat = "operands attr-dict `:` functional-type(operands, results)";
+ let assemblyFormat =
+ "operands `axis` `(` $axis `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -1958,7 +2046,9 @@ def Tosa_ConcatOp : Tosa_InferTensorTypeOp<"concat", [Pure]> {
static bool isCompatibleReturnTypes(TypeRange l, TypeRange r);
}];
- let assemblyFormat = "operands attr-dict `:` functional-type(operands, results)";
+ let assemblyFormat =
+ "operands `axis` `(` $axis `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -2075,7 +2165,8 @@ def Tosa_ReshapeBlockScaledOp
}];
let assemblyFormat =
- "operands attr-dict `:` functional-type(operands, results)";
+ "operands `block_size` `` $block_size attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -2108,7 +2199,9 @@ def Tosa_ReverseOp: Tosa_Op<"reverse", [
let hasFolder = 1;
let hasVerifier = 1;
- let assemblyFormat = "operands attr-dict `:` functional-type(operands, results)";
+ let assemblyFormat =
+ "operands `axis` `(` $axis `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -2201,7 +2294,8 @@ def Tosa_TransposeOp : Tosa_InferShapedTypeOp<"transpose",
let hasVerifier = 1;
let assemblyFormat =
- "operands attr-dict `:` functional-type(operands, results)";
+ "operands `perms` `(` $perms `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -2304,7 +2398,8 @@ def Tosa_RowGatherBlockScaledOp
let hasVerifier = 1;
let assemblyFormat =
- "operands attr-dict `:` functional-type(operands, results)";
+ "operands `block_size` `` $block_size attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -2394,7 +2489,10 @@ def Tosa_ResizeOp : Tosa_InferShapedTypeOp<"resize", [Pure]> {
let hasFolder = 1;
let hasVerifier = 1;
- let hasCustomAssemblyFormat = 1;
+
+ let assemblyFormat =
+ "operands `mode` `` $mode attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -2423,7 +2521,9 @@ def Tosa_CastOp: Tosa_Op<"cast", [Pure, SameOperandsAndResultShape,
Tosa_Tensor:$output
);
- let assemblyFormat = "operands attr-dict `:` functional-type(operands, results)";
+ let assemblyFormat =
+ "operands oilist(`input_unsigned` `(` $input_unsigned `)`) "
+ "attr-dict `:` functional-type(operands, results)";
let hasFolder = 1;
let hasCanonicalizer = 1;
@@ -2457,7 +2557,10 @@ def Tosa_CastFromBlockScaledOp: Tosa_InferShapedTypeOp<"cast_from_block_scaled",
);
let hasVerifier = 1;
- let hasCustomAssemblyFormat = 1;
+
+ let assemblyFormat =
+ "operands `block_size` `` $block_size attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -2489,7 +2592,10 @@ def Tosa_CastToBlockScaledOp : Tosa_InferShapedTypeOp<"cast_to_block_scaled", [P
let hasCanonicalizer = 1;
let hasVerifier = 1;
- let hasCustomAssemblyFormat = 1;
+
+ let assemblyFormat =
+ "operands `block_size` `` $block_size attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -2561,7 +2667,15 @@ def Tosa_RescaleOp : Tosa_InferShapedTypeOp<"rescale", [NoMemoryEffect]> {
}];
let hasVerifier = 1;
- let hasCustomAssemblyFormat = 1;
+
+ let assemblyFormat =
+ "operands `scale32` `(` $scale32 `)` "
+ "`rounding_mode` `` $rounding_mode "
+ "`per_channel` `(` $per_channel `)` "
+ "`input_unsigned` `(` $input_unsigned `)` "
+ "`output_unsigned` `(` $output_unsigned `)` "
+ "attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -2673,7 +2787,11 @@ def Tosa_CustomOp : Tosa_Op<"custom"> {
Variadic<Tosa_Tensor>:$output_list
);
- let assemblyFormat = "operands attr-dict `:` functional-type(operands, results)";
+ let assemblyFormat =
+ "operands `operator_name` `(` $operator_name `)` "
+ "`domain_name` `(` $domain_name `)` "
+ "`implementation_attrs` `(` $implementation_attrs `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaShapeOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaShapeOps.td
index 1bbfdc5776f2e..80a1d881360d1 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaShapeOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaShapeOps.td
@@ -112,6 +112,10 @@ def Tosa_ConstShapeOp : Tosa_ShapeOp<"const_shape", [ConstantLike, Pure]> {
let hasVerifier = 1;
let hasFolder = 1;
+
+ let assemblyFormat =
+ "operands `values` `(` $values `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -134,6 +138,10 @@ def Tosa_DimOp : Tosa_ShapeOp<"dim", [Pure]> {
let hasVerifier = 1;
let hasFolder = 1;
+
+ let assemblyFormat =
+ "operands `axis` `(` $axis `)` attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
@@ -372,6 +380,11 @@ def Tosa_AssertEqualShapeOp
BoolAttr:$allow_broadcast);
let results = (outs);
+
+ let assemblyFormat =
+ "operands `allow_broadcast` `(` $allow_broadcast "
+ "`)` attr-dict `:` "
+ "functional-type(operands, results)";
}
#endif // TOSA_SHAPE_OPS
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaUtilOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaUtilOps.td
index 4c71089c50fba..fd23ff7879866 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaUtilOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaUtilOps.td
@@ -55,7 +55,9 @@ def Tosa_ApplyScaleOp :
std::optional<SmallVector<int64_t, 4>> getShapeForUnroll();
}];
- let hasCustomAssemblyFormat = 1;
+ let assemblyFormat =
+ "operands `rounding_mode` `` $rounding_mode attr-dict `:` "
+ "functional-type(operands, results)";
}
//===----------------------------------------------------------------------===//
diff --git a/mlir/include/mlir/IR/OpImplementation.h b/mlir/include/mlir/IR/OpImplementation.h
index 6e0f001f83ba9..187b31073e94e 100644
--- a/mlir/include/mlir/IR/OpImplementation.h
+++ b/mlir/include/mlir/IR/OpImplementation.h
@@ -1171,9 +1171,13 @@ class AsmParser {
if (!parseResult.has_value() || failed(*parseResult))
return parseResult;
result = dyn_cast<AttrType>(attr);
- if (!result)
- return emitError(loc) << "expected attribute of type '" << AttrType::name
- << "', but found attribute '" << attr << "'";
+ if (!result) {
+ InFlightDiagnostic diag =
+ emitError(loc, "invalid kind of attribute specified");
+ if constexpr (HasStaticName<AttrType>::value)
+ diag << ": expected " << AttrType::name << ", but found " << attr;
+ return diag;
+ }
return success();
}
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 46f84940d4718..8873e7bffbef2 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -272,304 +272,6 @@ void mlir::tosa::printVariableOpTypeOrInitialValue(
}
}
-namespace {
-
-// parse attributes with special handling for tosa enum attributes
-template <typename EnumType>
-ParseResult parseAttrEntryWithEnumHandling(OpAsmParser &parser,
- NamedAttrList &outAttrs) {
- llvm::StringRef name;
- if (parser.parseOptionalKeyword(&name) || parser.parseEqual())
- return failure();
-
- // special handling: rounding_mode accepts a *bare* RoundingMode enum
- // keyword.
- llvm::StringRef kw;
- if constexpr (std::is_same_v<EnumType, tosa::RoundingMode>) {
- if (name == "rounding_mode" &&
- succeeded(parser.parseOptionalKeyword(&kw))) {
- auto sym = symbolizeRoundingMode(kw);
- if (!sym)
- return parser.emitError(parser.getCurrentLocation())
- << "invalid rounding_mode value: " << kw;
- auto attr = RoundingModeAttr::get(parser.getContext(), sym.value());
- outAttrs.push_back(NamedAttribute(name, attr));
- return success();
- }
- }
- // special handling: mode accepts a *bare* ResizeMode enum keyword.
- if constexpr (std::is_same_v<EnumType, tosa::ResizeMode>) {
- if (name == "mode" && succeeded(parser.parseOptionalKeyword(&kw))) {
- auto sym = symbolizeResizeMode(kw);
- if (!sym)
- return parser.emitError(parser.getCurrentLocation())
- << "invalid resize mode value: " << kw;
- auto attr = ResizeModeAttr::get(parser.getContext(), sym.value());
- outAttrs.push_back(NamedAttribute(name, attr));
- return success();
- }
- }
- // special handling: nan_mode accepts a *bare* NanPropagationMode enum
- // keyword.
- if constexpr (std::is_same_v<EnumType, tosa::NanPropagationMode>) {
- if (name == "nan_mode" && succeeded(parser.parseOptionalKeyword(&kw))) {
- auto sym = symbolizeNanPropagationMode(kw);
- if (!sym)
- return parser.emitError(parser.getCurrentLocation())
- << "invalid nan_mode value: " << kw;
- auto attr = NanPropagationModeAttr::get(parser.getContext(), sym.value());
- outAttrs.push_back(NamedAttribute(name, attr));
- return success();
- }
- }
-
- // special handling: block_size accepts a *bare* BlockSizeMode enum
- if constexpr (std::is_same_v<EnumType, tosa::BlockSize>) {
- if (name == "block_size" && succeeded(parser.parseOptionalKeyword(&kw))) {
- auto sym = symbolizeBlockSize(kw);
- if (!sym)
- return parser.emitError(parser.getCurrentLocation())
- << "invalid block_size value: " << kw;
- auto attr = BlockSizeAttr::get(parser.getContext(), sym.value());
- outAttrs.push_back(NamedAttribute(name, attr));
- return success();
- }
- }
-
- // Default path: parse any normal attribute literal, including fully qualified
- // enum keyword
- Attribute attr;
- return parser.parseAttribute(attr, name, outAttrs);
-}
-
-template <typename EnumType>
-ParseResult parseWithEnumHandling(OpAsmParser &parser, OperationState &result) {
- // parse operands
- SmallVector<OpAsmParser::UnresolvedOperand, 5> operands;
- if (parser.parseCommaSeparatedList(
- [&]() { return parser.parseOperand(operands.emplace_back()); }))
- return failure();
-
- // Parse { attr-dict } with special handling for enum bare token
- NamedAttrList attrs;
- if (succeeded(parser.parseOptionalLBrace()) &&
- failed(parser.parseOptionalRBrace())) {
- do {
- if (parseAttrEntryWithEnumHandling<EnumType>(parser, attrs))
- return failure();
- } while (succeeded(parser.parseOptionalComma()));
- if (parser.parseRBrace())
- return failure();
- }
-
- FunctionType fnTy;
- if (parser.parseColonType(fnTy))
- return failure();
-
- // Resolve operands and types
- if (failed(parser.resolveOperands(operands, fnTy.getInputs(),
- parser.getCurrentLocation(),
- result.operands)))
- return failure();
-
- result.addTypes(fnTy.getResults());
- result.addAttributes(attrs);
-
- return success();
-}
-
-void printNamedAttr(OpAsmPrinter &parser, const NamedAttribute namedAttr) {
- parser << namedAttr.getName().strref() << " = ";
- auto attr = namedAttr.getValue();
- if (auto roundingModeAttr = dyn_cast<tosa::RoundingModeAttr>(attr)) {
- parser << roundingModeAttr.getValue();
- } else if (auto resizeModeAttr = dyn_cast<tosa::ResizeModeAttr>(attr)) {
- parser << resizeModeAttr.getValue();
- } else if (auto nanPropagationModeAttr =
- dyn_cast<tosa::NanPropagationModeAttr>(attr)) {
- parser << nanPropagationModeAttr.getValue();
- } else if (auto blockSizeAttr = dyn_cast<tosa::BlockSizeAttr>(attr)) {
- parser << blockSizeAttr.getValue();
- } else {
- parser.printAttribute(attr);
- }
-}
-
-// print with special handling for default valued NanPropagationMode attribute
-void printWithNanPropagationHandling(OpAsmPrinter &parser, Operation *op) {
- parser << " ";
- parser.printOperands(op->getOperands());
-
- NamedAttrList toPrint(op->getAttrs());
- // remove default NanPropagate attribute
- const auto kDefaultNanValue = NanPropagationMode::PROPAGATE;
- for (auto attr : op->getAttrs()) {
- if (auto nanAttr = dyn_cast<NanPropagationModeAttr>(attr.getValue())) {
- if (nanAttr.getValue() == kDefaultNanValue) {
- // elide from toPrint
- toPrint.erase(attr.getName());
- break;
- }
- }
- }
-
- if (!toPrint.empty()) {
- parser << " {";
- llvm::interleaveComma(toPrint, parser, [&](const NamedAttribute namedAttr) {
- printNamedAttr(parser, namedAttr);
- });
- parser << "}";
- }
-
- parser << " : ";
- parser.printFunctionalType(op);
-}
-
-// print with special handling for enums: RoundingMode, ResizeMode
-void printWithEnumHandling(OpAsmPrinter &parser, Operation *op) {
- parser << " ";
- parser.printOperands(op->getOperands());
-
- if (!op->getAttrs().empty()) {
- parser << " {";
- llvm::interleaveComma(op->getAttrs(), parser,
- [&](const NamedAttribute namedAttr) {
- printNamedAttr(parser, namedAttr);
- });
- parser << "}";
- }
-
- parser << " : ";
- parser.printFunctionalType(op);
-}
-
-} // namespace
-
-ParseResult RescaleOp::parse(OpAsmParser &parser, OperationState &result) {
- return parseWithEnumHandling<tosa::RoundingMode>(parser, result);
-}
-
-void RescaleOp::print(OpAsmPrinter &parser) {
- printWithEnumHandling(parser, *this);
-}
-
-ParseResult ApplyScaleOp::parse(OpAsmParser &parser, OperationState &result) {
- return parseWithEnumHandling<tosa::RoundingMode>(parser, result);
-}
-
-void ApplyScaleOp::print(OpAsmPrinter &parser) {
- printWithEnumHandling(parser, *this);
-}
-
-ParseResult ResizeOp::parse(OpAsmParser &parser, OperationState &result) {
- return parseWithEnumHandling<tosa::ResizeMode>(parser, result);
-}
-
-void ResizeOp::print(OpAsmPrinter &parser) {
- printWithEnumHandling(parser, *this);
-}
-
-ParseResult ArgMaxOp::parse(OpAsmParser &parser, OperationState &result) {
- return parseWithEnumHandling<tosa::NanPropagationMode>(parser, result);
-}
-
-void ArgMaxOp::print(OpAsmPrinter &parser) {
- printWithNanPropagationHandling(parser, *this);
-}
-
-ParseResult MaxPool2dOp::parse(OpAsmParser &parser, OperationState &result) {
- return parseWithEnumHandling<tosa::NanPropagationMode>(parser, result);
-}
-
-void MaxPool2dOp::print(OpAsmPrinter &parser) {
- printWithNanPropagationHandling(parser, *this);
-}
-
-ParseResult MaxPool2dAdaptiveOp::parse(OpAsmParser &parser,
- OperationState &result) {
- return parseWithEnumHandling<tosa::NanPropagationMode>(parser, result);
-}
-
-void MaxPool2dAdaptiveOp::print(OpAsmPrinter &parser) {
- printWithNanPropagationHandling(parser, *this);
-}
-
-ParseResult ClampOp::parse(OpAsmParser &parser, OperationState &result) {
- return parseWithEnumHandling<tosa::NanPropagationMode>(parser, result);
-}
-
-void ClampOp::print(OpAsmPrinter &parser) {
- printWithNanPropagationHandling(parser, *this);
-}
-
-ParseResult MaximumOp::parse(OpAsmParser &parser, OperationState &result) {
- return parseWithEnumHandling<tosa::NanPropagationMode>(parser, result);
-}
-
-void MaximumOp::print(OpAsmPrinter &parser) {
- printWithNanPropagationHandling(parser, *this);
-}
-
-ParseResult MinimumOp::parse(OpAsmParser &parser, OperationState &result) {
- return parseWithEnumHandling<tosa::NanPropagationMode>(parser, result);
-}
-
-void MinimumOp::print(OpAsmPrinter &parser) {
- printWithNanPropagationHandling(parser, *this);
-}
-
-ParseResult ReduceMaxOp::parse(OpAsmParser &parser, OperationState &result) {
- return parseWithEnumHandling<tosa::NanPropagationMode>(parser, result);
-}
-
-void ReduceMaxOp::print(OpAsmPrinter &parser) {
- printWithNanPropagationHandling(parser, *this);
-}
-
-ParseResult ReduceMinOp::parse(OpAsmParser &parser, OperationState &result) {
- return parseWithEnumHandling<tosa::NanPropagationMode>(parser, result);
-}
-
-void ReduceMinOp::print(OpAsmPrinter &parser) {
- printWithNanPropagationHandling(parser, *this);
-}
-
-ParseResult MatmulTBlockScaledOp::parse(OpAsmParser &parser,
- OperationState &result) {
- return parseWithEnumHandling<tosa::BlockSize>(parser, result);
-}
-
-void MatmulTBlockScaledOp::print(OpAsmPrinter &parser) {
- printWithEnumHandling(parser, *this);
-}
-
-ParseResult CastFromBlockScaledOp::parse(OpAsmParser &parser,
- OperationState &result) {
- return parseWithEnumHandling<tosa::BlockSize>(parser, result);
-}
-
-void CastFromBlockScaledOp::print(OpAsmPrinter &parser) {
- printWithEnumHandling(parser, *this);
-}
-
-ParseResult CastToBlockScaledOp::parse(OpAsmParser &parser,
- OperationState &result) {
- return parseWithEnumHandling<tosa::BlockSize>(parser, result);
-}
-
-void CastToBlockScaledOp::print(OpAsmPrinter &parser) {
- printWithEnumHandling(parser, *this);
-}
-
-ParseResult Conv2DBlockScaledOp::parse(OpAsmParser &parser,
- OperationState &result) {
- return parseWithEnumHandling<tosa::BlockSize>(parser, result);
-}
-
-void Conv2DBlockScaledOp::print(OpAsmPrinter &parser) {
- printWithEnumHandling(parser, *this);
-}
-
//===----------------------------------------------------------------------===//
// Tosa utilities.
//===----------------------------------------------------------------------===//
diff --git a/mlir/test/Conversion/TosaToArith/tosa-to-arith-invalid.mlir b/mlir/test/Conversion/TosaToArith/tosa-to-arith-invalid.mlir
index 749c833398d47..aa6481da53615 100644
--- a/mlir/test/Conversion/TosaToArith/tosa-to-arith-invalid.mlir
+++ b/mlir/test/Conversion/TosaToArith/tosa-to-arith-invalid.mlir
@@ -3,6 +3,6 @@
// CHECK-LABEL: @apply_scale_unsupported_inexact_round
func.func @apply_scale_unsupported_inexact_round(%arg0 : i64, %arg1 : i32, %arg2 : i8) -> (i32) {
// expected-error at +1 {{failed to legalize operation 'tosa.apply_scale'}}
- %res = tosa.apply_scale %arg0, %arg1, %arg2 {rounding_mode = INEXACT_ROUND} : (i64, i32, i8) -> i32
+ %res = tosa.apply_scale %arg0, %arg1, %arg2 rounding_mode<INEXACT_ROUND> : (i64, i32, i8) -> i32
return %res : i32
}
diff --git a/mlir/test/Conversion/TosaToArith/tosa-to-arith.mlir b/mlir/test/Conversion/TosaToArith/tosa-to-arith.mlir
index 48d8d52ddc39a..a2c83812cfbbe 100644
--- a/mlir/test/Conversion/TosaToArith/tosa-to-arith.mlir
+++ b/mlir/test/Conversion/TosaToArith/tosa-to-arith.mlir
@@ -4,7 +4,7 @@
// CHECK-LABEL: func @const_test
func.func @const_test() -> (tensor<i32>) {
// CHECK: [[C3:%.+]] = arith.constant dense<3> : tensor<i32>
- %result = "tosa.const"() {values = dense<3> : tensor<i32>} : () -> tensor<i32>
+ %result = "tosa.const"() <{values = dense<3> : tensor<i32>}> : () -> tensor<i32>
// CHECK: return [[C3]]
return %result : tensor<i32>
@@ -67,7 +67,7 @@ func.func @apply_scale_test_i32(%arg0 : i32, %arg1 : i32, %arg2 : i8) -> (i32) {
// CHECK-DAG: %[[LOWALIGN:.+]] = arith.select %[[OVER31]], %[[C0]], %[[LOR]]
// CHECK-DAG: %[[RESULT:.+]] = arith.addi %[[LOWALIGN]], %[[HIALIGN]]
// CHECK: return %[[RESULT]]
- %res = tosa.apply_scale %arg0, %arg1, %arg2 {rounding_mode = DOUBLE_ROUND} : (i32, i32, i8) -> i32
+ %res = tosa.apply_scale %arg0, %arg1, %arg2 rounding_mode<DOUBLE_ROUND> : (i32, i32, i8) -> i32
return %res : i32
}
@@ -77,7 +77,7 @@ func.func @apply_scale_test_i32(%arg0 : i32, %arg1 : i32, %arg2 : i8) -> (i32) {
// SCALE: tosa.apply_scale
func.func @apply_scale_test_vector(%arg0 : vector<4xi32>, %arg1 : vector<4xi32>, %arg2 : vector<4xi8>) -> (vector<4xi32>) {
// CHECK-NOT: "tosa.apply_scale"
- %res = tosa.apply_scale %arg0, %arg1, %arg2 {rounding_mode = DOUBLE_ROUND} : (vector<4xi32>, vector<4xi32>, vector<4xi8>) -> vector<4xi32>
+ %res = tosa.apply_scale %arg0, %arg1, %arg2 rounding_mode<DOUBLE_ROUND> : (vector<4xi32>, vector<4xi32>, vector<4xi8>) -> vector<4xi32>
return %res : vector<4xi32>
}
@@ -87,7 +87,7 @@ func.func @apply_scale_test_vector(%arg0 : vector<4xi32>, %arg1 : vector<4xi32>,
// SCALE: tosa.apply_scale
func.func @apply_scale_test_vector_rank_0(%arg0 : vector<i32>, %arg1 : vector<i32>, %arg2 : vector<i8>) -> (vector<i32>) {
// CHECK-NOT: "tosa.apply_scale"
- %res = tosa.apply_scale %arg0, %arg1, %arg2 {rounding_mode = DOUBLE_ROUND} : (vector<i32>, vector<i32>, vector<i8>) -> vector<i32>
+ %res = tosa.apply_scale %arg0, %arg1, %arg2 rounding_mode<DOUBLE_ROUND> : (vector<i32>, vector<i32>, vector<i8>) -> vector<i32>
return %res : vector<i32>
}
@@ -125,7 +125,7 @@ func.func @apply_scale_test_i48(%arg0 : i48, %arg1 : i32, %arg2 : i8) -> (i32) {
// CHECK-DAG: %[[SHR:.+]] = arith.shrsi %[[RES64]], %[[S64]]
// CHECK-DAG: %[[TRUNC:.+]] = arith.trunci %[[SHR]] : i64 to i32
// CHECK: return %[[TRUNC]]
- %res = tosa.apply_scale %arg0, %arg1, %arg2 {rounding_mode = DOUBLE_ROUND} : (i48, i32, i8) -> i32
+ %res = tosa.apply_scale %arg0, %arg1, %arg2 rounding_mode<DOUBLE_ROUND> : (i48, i32, i8) -> i32
return %res : i32
}
@@ -162,6 +162,6 @@ func.func @apply_scale_test_i64(%arg0 : i64, %arg1 : i32, %arg2 : i8) -> (i32) {
// CHECK-DAG: %[[SHR:.+]] = arith.shrsi %[[RES64]], %[[S64]]
// CHECK-DAG: %[[TRUNC:.+]] = arith.trunci %[[SHR]] : i64 to i32
// CHECK: return %[[TRUNC]]
- %res = tosa.apply_scale %arg0, %arg1, %arg2 {rounding_mode = DOUBLE_ROUND} : (i64, i32, i8) -> i32
+ %res = tosa.apply_scale %arg0, %arg1, %arg2 rounding_mode<DOUBLE_ROUND> : (i64, i32, i8) -> i32
return %res : i32
}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir
index ecfd953f9ef1f..d3882e1cede0a 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir
@@ -3,7 +3,7 @@
// CHECK-LABEL: @avg_pool2d_with_unsupported_quant_type
func.func @avg_pool2d_with_unsupported_quant_type(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, %arg1: tensor<1xi8>, %arg2: tensor<1xi8>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>> {
// expected-error at +1 {{failed to legalize operation 'tosa.avg_pool2d'}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}> : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
return %0 : tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
}
@@ -21,10 +21,10 @@ func.func @tensor_with_unknown_rank(%arg0: tensor<*xi8>) -> tensor<*xi8> {
// CHECK-LABEL: @unranked_add
func.func @unranked_add(%arg0 : tensor<10x10xf32> , %arg1 : tensor<10x10xf32>, %arg2 : tensor<*xf32>) -> (tensor<10x10xf32>) {
// expected-error at +3 {{failed to legalize operation 'tosa.add'}}
- %reduce = tosa.reduce_max %arg0 {axis = 1 : i32} : (tensor<10x10xf32>) -> tensor<10x1xf32>
+ %reduce = tosa.reduce_max %arg0 axis(1) : (tensor<10x10xf32>) -> tensor<10x1xf32>
%1 = tosa.add %reduce, %arg1 : (tensor<10x1xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
%0 = tosa.add %1, %arg2 : (tensor<10x10xf32>, tensor<*xf32>) -> tensor<*xf32>
- %s = tosa.const_shape {values = dense<[10, 10]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[10, 10]> : tensor<2xindex>) : () -> !tosa.shape<2>
%2 = tosa.reshape %0, %s : (tensor<*xf32>, !tosa.shape<2>) -> tensor<10x10xf32>
return %2 : tensor<10x10xf32>
}
@@ -33,12 +33,12 @@ func.func @unranked_add(%arg0 : tensor<10x10xf32> , %arg1 : tensor<10x10xf32>, %
// CHECK-LABEL: @rescale_unsupported_type
func.func @rescale_unsupported_type(%arg0: tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<127> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<-1> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<127> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<-1> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{failed to legalize operation 'tosa.rescale'}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = true, output_unsigned = false} : (tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
return %0 : tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
}
@@ -62,7 +62,7 @@ func.func @cast_unsupported_type(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3x!
func.func @unranked_reduce(%arg0: tensor<*xf32>) -> tensor<*xf32> {
// expected-error at +1 {{failed to legalize operation 'tosa.reduce_sum'}}
- %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<*xf32>) -> tensor<*xf32>
+ %0 = tosa.reduce_sum %arg0 axis(0) : (tensor<*xf32>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
index 801144c01829a..0dab4cd11ab5b 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -102,7 +102,7 @@ func.func @max_pool(%arg0: tensor<1x6x34x62xf32>) -> () {
// CHECK-DAG: [[FILL:%.+]] = linalg.fill ins([[CONST]]{{.*}}outs([[INIT]]
// CHECK-DAG: [[KERNEL:%.+]] = tensor.empty()
// CHECK: linalg.pooling_nhwc_max {dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>} ins(%arg0, [[KERNEL]] : tensor<1x6x34x62xf32>, tensor<3x3xf32>) outs([[FILL]] : tensor<1x4x32x62xf32>)
- %0 = tosa.max_pool2d %arg0 {pad = array<i64: 0, 0, 0, 0>, kernel = array<i64: 3, 3>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xf32>) -> tensor<1x4x32x62xf32>
+ %0 = tosa.max_pool2d %arg0 kernel([3, 3]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x6x34x62xf32>) -> tensor<1x4x32x62xf32>
return
}
@@ -116,7 +116,7 @@ func.func @max_pool_padded(%arg0: tensor<1x6x34x62xf32>) -> () {
// CHECK-DAG: [[FILL:%.+]] = linalg.fill ins([[INITVAL]]{{.*}}outs([[INIT]]
// CHECK-DAG: [[KERNEL:%.+]] = tensor.empty()
// CHECK: linalg.pooling_nhwc_max {dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>} ins([[PAD]], [[KERNEL]] : tensor<1x6x35x62xf32>, tensor<3x3xf32>) outs([[FILL]] : tensor<1x4x33x62xf32>)
- %0 = tosa.max_pool2d %arg0 {pad = array<i64: 0, 0, 0, 1>, kernel = array<i64: 3, 3>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xf32>) -> tensor<1x4x33x62xf32>
+ %0 = tosa.max_pool2d %arg0 kernel([3, 3]) stride([1, 1]) pad([0, 0, 0, 1]) : (tensor<1x6x34x62xf32>) -> tensor<1x4x33x62xf32>
return
}
@@ -129,7 +129,7 @@ func.func @max_pool_dyn(%arg0: tensor<?x6x34x62xf32>) -> () {
// CHECK: %[[FILL:.+]] = linalg.fill ins(%[[CONST]]{{.*}}outs(%[[INIT]]
// CHECK: %[[KERNEL:.+]] = tensor.empty()
// CHECK: linalg.pooling_nhwc_max {dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>} ins(%arg0, %[[KERNEL]] : tensor<?x6x34x62xf32>, tensor<3x3xf32>) outs(%[[FILL]] : tensor<?x4x32x62xf32>)
- %0 = tosa.max_pool2d %arg0 {pad = array<i64: 0, 0, 0, 0>, kernel = array<i64: 3, 3>, stride = array<i64: 1, 1>} : (tensor<?x6x34x62xf32>) -> tensor<?x4x32x62xf32>
+ %0 = tosa.max_pool2d %arg0 kernel([3, 3]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<?x6x34x62xf32>) -> tensor<?x4x32x62xf32>
return
}
@@ -137,7 +137,7 @@ func.func @max_pool_dyn(%arg0: tensor<?x6x34x62xf32>) -> () {
func.func @max_pool_i8(%arg0: tensor<1x6x34x62xi8>) -> () {
// CHECK: arith.constant -128
// CHECK: linalg.pooling_nhwc_max
- %0 = tosa.max_pool2d %arg0 {pad = array<i64: 0, 0, 0, 0>, kernel = array<i64: 3, 3>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xi8>) -> tensor<1x4x32x62xi8>
+ %0 = tosa.max_pool2d %arg0 kernel([3, 3]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x6x34x62xi8>) -> tensor<1x4x32x62xi8>
return
}
@@ -150,7 +150,7 @@ func.func @max_pool_ui8(%arg0: tensor<1x6x34x62xui8>) -> tensor<1x4x32x62xui8> {
// CHECK-SAME: outs({{.*}} : tensor<1x4x32x62xi8>)
// CHECK-SAME: -> tensor<1x4x32x62xi8>
// CHECK: builtin.unrealized_conversion_cast {{.*}} : tensor<1x4x32x62xi8> to tensor<1x4x32x62xui8>
- %0 = tosa.max_pool2d %arg0 {pad = array<i64: 0, 0, 0, 0>, kernel = array<i64: 3, 3>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xui8>) -> tensor<1x4x32x62xui8>
+ %0 = tosa.max_pool2d %arg0 kernel([3, 3]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x6x34x62xui8>) -> tensor<1x4x32x62xui8>
return %0 : tensor<1x4x32x62xui8>
}
@@ -158,7 +158,7 @@ func.func @max_pool_ui8(%arg0: tensor<1x6x34x62xui8>) -> tensor<1x4x32x62xui8> {
func.func @max_pool_i16(%arg0: tensor<1x6x34x62xi16>) -> () {
// CHECK: arith.constant -32768
// CHECK: linalg.pooling_nhwc_max
- %0 = tosa.max_pool2d %arg0 {pad = array<i64: 0, 0, 0, 0>, kernel = array<i64: 3, 3>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xi16>) -> tensor<1x4x32x62xi16>
+ %0 = tosa.max_pool2d %arg0 kernel([3, 3]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x6x34x62xi16>) -> tensor<1x4x32x62xi16>
return
}
@@ -166,7 +166,7 @@ func.func @max_pool_i16(%arg0: tensor<1x6x34x62xi16>) -> () {
func.func @max_pool_i32(%arg0: tensor<1x6x34x62xi32>) -> () {
// CHECK: arith.constant -2147483648
// CHECK: linalg.pooling_nhwc_max
- %0 = tosa.max_pool2d %arg0 {pad = array<i64: 0, 0, 0, 0>, kernel = array<i64: 3, 3>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xi32>) -> tensor<1x4x32x62xi32>
+ %0 = tosa.max_pool2d %arg0 kernel([3, 3]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x6x34x62xi32>) -> tensor<1x4x32x62xi32>
return
}
@@ -219,7 +219,7 @@ func.func @max_pool_all_dynamic(%arg0: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf3
// CHECK-CSE: %[[OUT:.+]] = linalg.pooling_nhwc_max {dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>} ins(%[[PADDED]], %[[FAKE_WINDOW]] : tensor<?x?x?x?xf32>, tensor<2x5xf32>) outs(%[[FILL]] : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
// CHECK-CSE: return %[[OUT]]
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 2, 5>, pad = array<i64: 0, 0, 2, 2>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.max_pool2d %arg0 kernel([2, 5]) stride([1, 1]) pad([0, 0, 2, 2]) : (tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
return %0 : tensor<?x?x?x?xf32>
}
@@ -304,7 +304,7 @@ func.func @avg_pool_f32(%arg0: tensor<1x6x34x62xf32>) -> (tensor<1x5x33x62xf32>)
// CHECK: linalg.yield %[[DIV]]
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x33x62xf32>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([4, 4]) stride([1, 1]) pad([1, 1, 1, 1]) acc_type(f32) : (tensor<1x6x34x62xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x33x62xf32>
return %0 : tensor<1x5x33x62xf32>
}
@@ -391,7 +391,7 @@ func.func @avg_pool_f16_f32acc(%arg0: tensor<1x6x34x62xf16>) -> (tensor<1x5x33x6
// CHECK: linalg.yield %[[TRUNC]]
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x5x33x62xf16>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([4, 4]) stride([1, 1]) pad([1, 1, 1, 1]) acc_type(f32) : (tensor<1x6x34x62xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x5x33x62xf16>
return %0 : tensor<1x5x33x62xf16>
}
@@ -423,7 +423,7 @@ func.func @avg_pool_i8(%arg0: tensor<1x6x34x62xi8>) -> (tensor<1x5x33x62xi8>) {
// CHECK: %[[TRUNC_SHIFT:.+]] = arith.trunci %[[SUB]]
// CHECK: %[[C30:.+]] = arith.constant 30
// CHECK: %[[SHIFT:.+]] = arith.addi %[[TRUNC_SHIFT]], %[[C30]] : i8
- // CHECK: %[[SCALED:.+]] = tosa.apply_scale %[[IN]], %[[TRUNC_MUL]], %[[SHIFT]] {rounding_mode = SINGLE_ROUND}
+ // CHECK: %[[SCALED:.+]] = tosa.apply_scale %[[IN]], %[[TRUNC_MUL]], %[[SHIFT]] rounding_mode<SINGLE_ROUND>
// Perform the normalization.
// CHECK: %[[CMIN:.+]] = arith.constant -128
@@ -434,7 +434,7 @@ func.func @avg_pool_i8(%arg0: tensor<1x6x34x62xi8>) -> (tensor<1x5x33x62xi8>) {
// CHECK: linalg.yield %[[TRUNC]]
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = i32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x5x33x62xi8>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([4, 4]) stride([1, 1]) pad([1, 1, 1, 1]) acc_type(i32) : (tensor<1x6x34x62xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x5x33x62xi8>
return %0 : tensor<1x5x33x62xi8>
}
@@ -459,7 +459,7 @@ func.func @avg_pool_dyn(%arg0: tensor<?x6x34x62xf32>) -> (tensor<?x5x33x62xf32>)
// CHECK: %[[GENERIC:.+]] = linalg.generic
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<?x6x34x62xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x5x33x62xf32>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([4, 4]) stride([1, 1]) pad([1, 1, 1, 1]) acc_type(f32) : (tensor<?x6x34x62xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x5x33x62xf32>
return %0 : tensor<?x5x33x62xf32>
}
@@ -474,7 +474,7 @@ func.func @conv2d_scalar_bias_f32(%input: tensor<1x49x42x27xf32>, %weights: tens
// CHECK: %[[BROADCAST:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<1xf32>) outs(%[[INIT]] : tensor<1x45x40x28xf32>) {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x45x40x28xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([2, 1]) acc_type(f32) : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x45x40x28xf32>
return
}
@@ -496,7 +496,7 @@ func.func @conv2d_i8(%input: tensor<1x49x42x27xi8>, %weights: tensor<28x1x1x27xi
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = i32, dilation = array<i64: 2, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x49x42x27xi8>, tensor<28x1x1x27xi8>, tensor<28xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x49x42x28xi32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([2, 1]) acc_type(i32) : (tensor<1x49x42x27xi8>, tensor<28x1x1x27xi8>, tensor<28xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x49x42x28xi32>
return
}
@@ -518,7 +518,7 @@ func.func @conv2d_f32(%input: tensor<1x49x42x27xf32>, %weights: tensor<28x3x3x27
// HWCF: linalg.conv_2d_nhwc_hwcf {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %[[TRANSPOSE]] : tensor<1x49x42x27xf32>, tensor<3x3x27x28xf32>) outs(%{{[a-zA-Z0-9_]*}} : tensor<1x45x40x28xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x45x40x28xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([2, 1]) acc_type(f32) : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x45x40x28xf32>
return
}
@@ -539,7 +539,7 @@ func.func @conv2d_dyn(%input: tensor<?x49x42x27xf32>, %weights: tensor<28x3x3x27
// CHECK: linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<?x49x42x27xf32>, tensor<28x3x3x27xf32>) outs(%[[BROADCAST]] : tensor<?x45x40x28xf32>) -> tensor<?x45x40x28xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<?x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x45x40x28xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([2, 1]) acc_type(f32) : (tensor<?x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x45x40x28xf32>
return
}
@@ -597,7 +597,7 @@ func.func @conv2d_dyn_w_h(%input: tensor<1x?x?x27xf32>, %weights: tensor<28x3x3x
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x?x?x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x?x?x28xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([2, 1]) acc_type(f32) : (tensor<1x?x?x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x?x?x28xf32>
return
}
@@ -622,7 +622,7 @@ func.func @conv2d_dyn_output(%input: tensor<2x6x5x4xf32>, %weights: tensor<4x3x3
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x6x5x4xf32>, tensor<4x3x3x4xf32>, tensor<4xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x4x3x4xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<2x6x5x4xf32>, tensor<4x3x3x4xf32>, tensor<4xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x4x3x4xf32>
return
}
@@ -636,7 +636,7 @@ func.func @conv2d_padded_f32(%input: tensor<1x47x40x28xf32>, %weights: tensor<28
// CHECK: linalg.conv_2d_nhwc_fhwc
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>}
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([1, 1, 1, 1]) stride([1, 1]) dilation([2, 1]) acc_type(f32)
: (tensor<1x47x40x28xf32>, tensor<28x3x3x28xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x45x40x28xf32>
return
}
@@ -651,7 +651,7 @@ func.func @conv2d_quant(%arg0 : tensor<1x12x12x1xi8>, %arg1 : tensor<1024x3x3x1x
// CHECK: linalg.conv_2d_nhwc_fhwc_q
%input_zp = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([1, 1, 1, 1]) stride([1, 1]) dilation([1, 1]) acc_type(i32)
: (tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, tensor<1024xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x12x12x1024xi32>
return
}
@@ -666,7 +666,7 @@ func.func @conv2d_f16_f32_acc(%input: tensor<1x49x42x27xf16>, %weights: tensor<2
// CHECK: arith.extf %{{.*}} : f16 to f32
// CHECK: %[[CONV:.*]] = linalg.conv_2d_nhwc_fhwc {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x49x42x27xf16>, tensor<28x3x3x27xf16>) outs(%{{.*}} : tensor<1x45x40x28xf32>) -> tensor<1x45x40x28xf32>
// CHECK: tosa.cast %[[CONV]] : (tensor<1x45x40x28xf32>) -> tensor<1x45x40x28xf16>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x49x42x27xf16>, tensor<28x3x3x27xf16>, tensor<28xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x45x40x28xf16>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([2, 1]) acc_type(f32) : (tensor<1x49x42x27xf16>, tensor<28x3x3x27xf16>, tensor<28xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x45x40x28xf16>
return
}
@@ -677,7 +677,7 @@ func.func @conv2d_f8_f32_acc(%input: tensor<1x49x42x27xf8E5M2>, %weights: tensor
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
// CHECK: %[[CONV:.*]] = linalg.conv_2d_nhwc_fhwc {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x49x42x27xf8E5M2>, tensor<28x3x3x27xf8E5M2>) outs(%{{.*}} : tensor<1x45x40x28xf32>) -> tensor<1x45x40x28xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x49x42x27xf8E5M2>, tensor<28x3x3x27xf8E5M2>, tensor<28xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x45x40x28xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([2, 1]) acc_type(f32) : (tensor<1x49x42x27xf8E5M2>, tensor<28x3x3x27xf8E5M2>, tensor<28xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x45x40x28xf32>
return
}
@@ -701,7 +701,7 @@ func.func @depthwise_conv(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tensor<3x1x3x11xf
// CHECK: } -> tensor<1x5x5x33xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
return
}
@@ -719,7 +719,7 @@ func.func @depthwise_conv_scalar_bias(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tenso
// CHECK: } -> tensor<1x5x5x33xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
return
}
@@ -745,7 +745,7 @@ func.func @depthwise_conv_dyn(%arg0 : tensor<?x7x5x3xf32>, %arg1 : tensor<3x1x3x
// CHECK: } -> tensor<?x5x5x33xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<?x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x5x5x33xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<?x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x5x5x33xf32>
return
}
@@ -769,7 +769,7 @@ func.func @depthwise_conv_strides(%arg0 : tensor<1x11x9x3xf32>, %arg1 : tensor<3
// CHECK: } -> tensor<1x5x5x33xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1> } : (tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([2, 2]) dilation([1, 1]) acc_type(f32) : (tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
return
}
@@ -799,7 +799,7 @@ func.func @depthwise_conv_quant(%arg0 : tensor<1x12x12x4xi8>, %arg1 : tensor<3x3
// CHECK: } -> tensor<1x12x12x512xi32>
%input_zp = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x12x12x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x12x12x512xi32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([1, 1, 1, 1]) stride([1, 1]) dilation([1, 1]) acc_type(i32) : (tensor<1x12x12x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x12x12x512xi32>
return
}
@@ -825,7 +825,7 @@ func.func @depthwise_conv_quant_dilations(%arg0 : tensor<1x14x14x4xi8>, %arg1 :
// CHECK: } -> tensor<1x10x10x512xi32>
%input_zp = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 2> } : (tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x10x10x512xi32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([2, 2]) acc_type(i32) : (tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x10x10x512xi32>
return
}
@@ -843,7 +843,7 @@ func.func @depthwise_conv2d_dyn_w_h(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<3x
// CHECK: %[[COLLAPSED:.+]] = tensor.collapse_shape %[[CONV]] {{\[}}[0], [1], [2], [3, 4]]
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 1, 2, 3, 4>, dilation = array<i64: 2, 1>, stride = array<i64: 1, 2>} : (tensor<2x?x?x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([1, 2, 3, 4]) stride([1, 2]) dilation([2, 1]) acc_type(f32) : (tensor<2x?x?x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x15xf32>
return
}
@@ -867,7 +867,7 @@ func.func @depthwise_int_conv_zero_zp(%arg0 : tensor<1x7x5x3xi8>, %arg1 : tensor
// CHECK: } -> tensor<1x5x5x33xi32>
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xi8>, tensor<3x1x3x11xi8>, tensor<33xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x5x5x33xi32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) : (tensor<1x7x5x3xi8>, tensor<3x1x3x11xi8>, tensor<33xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x5x5x33xi32>
return
}
@@ -879,7 +879,7 @@ func.func @depthwise_conv2d_f16_f32_acc(%arg0 : tensor<1x7x5x3xf16>, %arg1 : ten
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
// CHECK: %[[CONV:.*]] = linalg.depthwise_conv_2d_nhwc_hwcm {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x7x5x3xf16>, tensor<3x1x3x11xf16>) outs(%{{.*}} : tensor<1x5x5x3x11xf32>) -> tensor<1x5x5x3x11xf32>
// CHECK: tosa.cast %[[CONV]] : (tensor<1x5x5x3x11xf32>) -> tensor<1x5x5x3x11xf16>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf16>, tensor<3x1x3x11xf16>, tensor<33xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x5x5x33xf16>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x7x5x3xf16>, tensor<3x1x3x11xf16>, tensor<33xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x5x5x33xf16>
return
}
@@ -890,7 +890,7 @@ func.func @depthwise_conv2d_f8_f32_acc(%arg0 : tensor<1x7x5x3xf8E5M2>, %arg1 : t
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
// CHECK: %[[CONV:.*]] = linalg.depthwise_conv_2d_nhwc_hwcm {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x7x5x3xf8E5M2>, tensor<3x1x3x11xf8E5M2>) outs(%{{.*}} : tensor<1x5x5x3x11xf32>) -> tensor<1x5x5x3x11xf32>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf8E5M2>, tensor<3x1x3x11xf8E5M2>, tensor<33xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x5x5x33xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x7x5x3xf8E5M2>, tensor<3x1x3x11xf8E5M2>, tensor<33xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x5x5x33xf32>
return
}
@@ -915,7 +915,7 @@ func.func @conv3d_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<43x3x4
// CHECK-SAME: outs(%[[BROADCAST]] : tensor<1x47x45x43x43xf32>) -> tensor<1x47x45x43x43xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf32>, tensor<43x3x4x5x27xf32>, tensor<43xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x47x45x43x43xf32>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<1x49x48x47x27xf32>, tensor<43x3x4x5x27xf32>, tensor<43xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x47x45x43x43xf32>
return
}
@@ -931,7 +931,7 @@ func.func @conv3d_scalar_bias_f32(%input: tensor<1x49x48x47x27xf32>, %weights: t
// CHECK-SAME: {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf32>, tensor<28x3x4x5x27xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x47x45x43x28xf32>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<1x49x48x47x27xf32>, tensor<28x3x4x5x27xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x47x45x43x28xf32>
return
}
@@ -960,7 +960,7 @@ func.func @conv3d_i8(%input: tensor<1x49x48x47x27xi8>, %weights: tensor<43x3x4x5
%input_zp = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xi8>, tensor<43x3x4x5x27xi8>, tensor<43xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x47x45x43x43xi32>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(i32) : (tensor<1x49x48x47x27xi8>, tensor<43x3x4x5x27xi8>, tensor<43xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x47x45x43x43xi32>
return
}
@@ -974,7 +974,7 @@ func.func @conv3d_f16_f32_acc(%input: tensor<1x49x48x47x27xf16>, %weights: tenso
// CHECK: arith.extf %{{.*}} : f16 to f32
// CHECK: %[[CONV:.*]] = linalg.conv_3d_ndhwc_dhwcf {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x49x48x47x27xf16>, tensor<3x4x5x27x43xf16>) outs(%{{.*}} : tensor<1x47x45x43x43xf32>) -> tensor<1x47x45x43x43xf32>
// CHECK: tosa.cast %[[CONV]] : (tensor<1x47x45x43x43xf32>) -> tensor<1x47x45x43x43xf16>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf16>, tensor<43x3x4x5x27xf16>, tensor<43xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x47x45x43x43xf16>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<1x49x48x47x27xf16>, tensor<43x3x4x5x27xf16>, tensor<43xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x47x45x43x43xf16>
return
}
@@ -985,7 +985,7 @@ func.func @conv3d_f8_f32_acc(%input: tensor<1x49x48x47x27xf8E5M2>, %weights: ten
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
// CHECK: %[[CONV:.*]] = linalg.conv_3d_ndhwc_dhwcf {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x49x48x47x27xf8E5M2>, tensor<3x4x5x27x43xf8E5M2>) outs(%{{.*}} : tensor<1x47x45x43x43xf32>) -> tensor<1x47x45x43x43xf32>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf8E5M2>, tensor<43x3x4x5x27xf8E5M2>, tensor<43xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x47x45x43x43xf32>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<1x49x48x47x27xf8E5M2>, tensor<43x3x4x5x27xf8E5M2>, tensor<43xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x47x45x43x43xf32>
return
}
@@ -996,7 +996,7 @@ func.func @conv3d_f8_f32_acc(%input: tensor<1x49x48x47x27xf8E5M2>, %weights: ten
func.func @test_transpose(%arg0: tensor<1x2x3xi32>) -> () {
// CHECK: %[[INIT:.+]] = tensor.empty() : tensor<2x3x1xi32>
// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[ARG0]] : tensor<1x2x3xi32>) outs(%[[INIT]] : tensor<2x3x1xi32>) permutation = [1, 2, 0]
- %1 = tosa.transpose %arg0 {perms = array<i32: 1, 2, 0>}: (tensor<1x2x3xi32>) -> tensor<2x3x1xi32>
+ %1 = tosa.transpose %arg0 perms([1, 2, 0]) : (tensor<1x2x3xi32>) -> tensor<2x3x1xi32>
return
}
@@ -1009,7 +1009,7 @@ func.func @test_transpose_dyn(%arg0: tensor<1x?x3x4xi32>) -> () {
// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG0]], %[[C1]]
// CHECK: %[[INIT:.+]] = tensor.empty(%[[DIM]]) : tensor<?x4x1x3xi32>
// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[ARG0]] : tensor<1x?x3x4xi32>) outs(%[[INIT]] : tensor<?x4x1x3xi32>) permutation = [1, 3, 0, 2]
- %1 = tosa.transpose %arg0 {perms = array<i32: 1, 3, 0, 2>}: (tensor<1x?x3x4xi32>) -> tensor<?x4x1x3xi32>
+ %1 = tosa.transpose %arg0 perms([1, 3, 0, 2]) : (tensor<1x?x3x4xi32>) -> tensor<?x4x1x3xi32>
return
}
@@ -1024,7 +1024,7 @@ func.func @test_transpose_dyn_multiple_2d(%arg0: tensor<?x?xf32>) -> () {
// CHECK-DAG: %[[DIM1:.+]] = tensor.dim %[[ARG0]], %[[C1]]
// CHECK: %[[INIT:.+]] = tensor.empty(%[[DIM1]], %[[DIM0]])
// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[ARG0]] : tensor<?x?xf32>) outs(%[[INIT]] : tensor<?x?xf32>) permutation = [1, 0]
- %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<?x?xf32>) -> tensor<?x?xf32>
+ %1 = tosa.transpose %arg0 perms([1, 0]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
return
}
@@ -1041,7 +1041,7 @@ func.func @test_transpose_dyn_multiple_3d(%arg0: tensor<?x?x?xf32>) {
// CHECK-DAG: %[[DIM2:.*]] = tensor.dim %[[ARG0]], %[[C2]] : tensor<?x?x?xf32>
// CHECK: %[[INIT:.*]] = tensor.empty(%[[DIM2]], %[[DIM0]], %[[DIM1]]) : tensor<?x?x?xf32>
// CHECK: %[[TRANSPOSE:.*]] = linalg.transpose ins(%[[ARG0]] : tensor<?x?x?xf32>) outs(%[[INIT]] : tensor<?x?x?xf32>) permutation = [2, 0, 1]
- %1 = "tosa.transpose"(%arg0) {perms = array<i32: 2, 0, 1>} : (tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
+ %1 = "tosa.transpose"(%arg0) <{perms = array<i32: 2, 0, 1>}> : (tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
return
}
@@ -1051,7 +1051,7 @@ func.func @test_transpose_dyn_multiple_3d(%arg0: tensor<?x?x?xf32>) {
func.func @max_pool2d_nan_propagate(%arg0: tensor<1x6x34x62xf32>) -> (tensor<1x4x32x62xf32>) {
// CHECK: linalg.pooling_nhwc_max
// CHECK-NOT: linalg.generic
- %0 = tosa.max_pool2d %arg0 {pad = array<i64: 0, 0, 0, 0>, kernel = array<i64: 3, 3>, stride = array<i64: 1, 1>, nan_mode = PROPAGATE} : (tensor<1x6x34x62xf32>) -> tensor<1x4x32x62xf32>
+ %0 = tosa.max_pool2d %arg0 kernel([3, 3]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x6x34x62xf32>) -> tensor<1x4x32x62xf32>
return %0 : tensor<1x4x32x62xf32>
}
@@ -1061,7 +1061,7 @@ func.func @max_pool2d_nan_propagate(%arg0: tensor<1x6x34x62xf32>) -> (tensor<1x4
func.func @max_pool2d_nan_ignore_int(%arg0: tensor<1x6x34x62xi8>) -> (tensor<1x4x32x62xi8>) {
// CHECK: linalg.pooling_nhwc_max
// CHECK-NOT: linalg.generic
- %0 = tosa.max_pool2d %arg0 {pad = array<i64: 0, 0, 0, 0>, kernel = array<i64: 3, 3>, stride = array<i64: 1, 1>, nan_mode = IGNORE} : (tensor<1x6x34x62xi8>) -> tensor<1x4x32x62xi8>
+ %0 = tosa.max_pool2d %arg0 kernel([3, 3]) stride([1, 1]) pad([0, 0, 0, 0]) nan_mode<IGNORE> : (tensor<1x6x34x62xi8>) -> tensor<1x4x32x62xi8>
return %0: tensor<1x4x32x62xi8>
}
@@ -1075,6 +1075,6 @@ func.func @max_pool2d_nan_ignore(%arg0: tensor<1x6x34x62xf32>) -> (tensor<1x4x32
// CHECK: arith.cmpf uno
// CHECK: arith.select
// CHECK: linalg.yield
- %0 = tosa.max_pool2d %arg0 {pad = array<i64: 0, 0, 0, 0>, kernel = array<i64: 3, 3>, stride = array<i64: 1, 1>, nan_mode = IGNORE} : (tensor<1x6x34x62xf32>) -> tensor<1x4x32x62xf32>
+ %0 = tosa.max_pool2d %arg0 kernel([3, 3]) stride([1, 1]) pad([0, 0, 0, 0]) nan_mode<IGNORE> : (tensor<1x6x34x62xf32>) -> tensor<1x4x32x62xf32>
return %0: tensor<1x4x32x62xf32>
}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-pipeline.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-pipeline.mlir
index 373b5b71cb68b..9e41bb711f8fa 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-pipeline.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-pipeline.mlir
@@ -25,7 +25,7 @@ func.func @test_avg_pool2d_zero_dim_input(%arg0: tensor<1x0x?x9xf32>, %arg1: ten
// check that --tosa-to-linalg kick in
func.func @avg_pool2d_with_unsupported_quant_type(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, %arg1: tensor<1xi8>, %arg2: tensor<1xi8>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>> {
// expected-error at +1 {{failed to legalize operation 'tosa.avg_pool2d'}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}> : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
return %0 : tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
}
@@ -38,7 +38,7 @@ func.func @rescale_doubleround(%arg0: tensor<8x9x7x14xi32>) -> tensor<8x9x7x14xi
%2 = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
%3 = "tosa.const"() <{values = dense<-5> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: tosa.apply_scale
- %4 = tosa.rescale %arg0, %0, %1, %2, %3 {input_unsigned = false, output_unsigned = false, per_channel = true, rounding_mode = DOUBLE_ROUND, scale32 = true} : (tensor<8x9x7x14xi32>, tensor<14xi32>, tensor<14xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<8x9x7x14xi8>
+ %4 = tosa.rescale %arg0, %0, %1, %2, %3 scale32(true) rounding_mode<DOUBLE_ROUND> per_channel(true) input_unsigned(false) output_unsigned(false) : (tensor<8x9x7x14xi32>, tensor<14xi32>, tensor<14xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<8x9x7x14xi8>
return %4 : tensor<8x9x7x14xi8>
}
@@ -50,6 +50,6 @@ func.func @rescale_inexactround(%arg0: tensor<8x9x7x14xi32>) -> tensor<8x9x7x14x
%2 = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
%3 = "tosa.const"() <{values = dense<-5> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op failed attribute check: rounding_mode = INEXACT_ROUND requires extension [inexactround]}}
- %4 = tosa.rescale %arg0, %0, %1, %2, %3 {input_unsigned = false, output_unsigned = false, per_channel = true, rounding_mode = INEXACT_ROUND, scale32 = true} : (tensor<8x9x7x14xi32>, tensor<14xi32>, tensor<14xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<8x9x7x14xi8>
+ %4 = tosa.rescale %arg0, %0, %1, %2, %3 scale32(true) rounding_mode<INEXACT_ROUND> per_channel(true) input_unsigned(false) output_unsigned(false) : (tensor<8x9x7x14xi32>, tensor<14xi32>, tensor<14xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<8x9x7x14xi8>
return %4 : tensor<8x9x7x14xi8>
}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-resize.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-resize.mlir
index 2959cf59e953a..e603991c7aaeb 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-resize.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-resize.mlir
@@ -2,10 +2,10 @@
// CHECK-LABEL: @unary_resize_nearest_fp32
func.func @unary_resize_nearest_fp32(%arg0 : tensor<3x1x1x7xf32>) -> tensor<3x1x1x7xf32> {
- %scale = tosa.const_shape { values = dense<[2, 2, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<3x1x1x7xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xf32>
+ %scale = tosa.const_shape values(dense<[2, 2, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<3x1x1x7xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xf32>
// CHECK: return %arg0
return %resize : tensor<3x1x1x7xf32>
}
@@ -14,10 +14,10 @@ func.func @unary_resize_nearest_fp32(%arg0 : tensor<3x1x1x7xf32>) -> tensor<3x1x
// CHECK-LABEL: @unary_resize_nearest_bf16
func.func @unary_resize_nearest_bf16(%arg0 : tensor<3x1x1x7xbf16>) -> tensor<3x1x1x7xbf16> {
- %scale = tosa.const_shape { values = dense<[2, 2, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<3x1x1x7xbf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xbf16>
+ %scale = tosa.const_shape values(dense<[2, 2, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<3x1x1x7xbf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xbf16>
// CHECK: return %arg0
return %resize : tensor<3x1x1x7xbf16>
}
@@ -26,10 +26,10 @@ func.func @unary_resize_nearest_bf16(%arg0 : tensor<3x1x1x7xbf16>) -> tensor<3x1
// CHECK-LABEL: @unary_resize_nearest_fp16
func.func @unary_resize_nearest_fp16(%arg0 : tensor<3x1x1x7xf16>) -> tensor<3x1x1x7xf16> {
- %scale = tosa.const_shape { values = dense<[2, 2, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<3x1x1x7xf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xf16>
+ %scale = tosa.const_shape values(dense<[2, 2, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<3x1x1x7xf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xf16>
// CHECK: return %arg0
return %resize : tensor<3x1x1x7xf16>
}
@@ -38,10 +38,10 @@ func.func @unary_resize_nearest_fp16(%arg0 : tensor<3x1x1x7xf16>) -> tensor<3x1x
// CHECK-LABEL: @unary_resize_bilinear_fp32
func.func @unary_resize_bilinear_fp32(%arg0 : tensor<3x1x1x7xf32>) -> tensor<3x1x1x7xf32> {
- %scale = tosa.const_shape { values = dense<[2, 2, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<3x1x1x7xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xf32>
+ %scale = tosa.const_shape values(dense<[2, 2, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<3x1x1x7xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xf32>
// CHECK: return %arg0
return %resize : tensor<3x1x1x7xf32>
}
@@ -50,10 +50,10 @@ func.func @unary_resize_bilinear_fp32(%arg0 : tensor<3x1x1x7xf32>) -> tensor<3x1
// CHECK-LABEL: @unary_resize_bilinear_bf16
func.func @unary_resize_bilinear_bf16(%arg0 : tensor<3x1x1x7xbf16>) -> tensor<3x1x1x7xbf16> {
- %scale = tosa.const_shape { values = dense<[2, 2, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<3x1x1x7xbf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xbf16>
+ %scale = tosa.const_shape values(dense<[2, 2, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<3x1x1x7xbf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xbf16>
// CHECK: return %arg0
return %resize : tensor<3x1x1x7xbf16>
}
@@ -62,10 +62,10 @@ func.func @unary_resize_bilinear_bf16(%arg0 : tensor<3x1x1x7xbf16>) -> tensor<3x
// CHECK-LABEL: @unary_resize_bilinear_fp16
func.func @unary_resize_bilinear_fp16(%arg0 : tensor<3x1x1x7xf16>) -> tensor<3x1x1x7xf16> {
- %scale = tosa.const_shape { values = dense<[2, 2, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<3x1x1x7xf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xf16>
+ %scale = tosa.const_shape values(dense<[2, 2, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<3x1x1x7xf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xf16>
// CHECK: return %arg0
return %resize : tensor<3x1x1x7xf16>
}
@@ -74,10 +74,10 @@ func.func @unary_resize_bilinear_fp16(%arg0 : tensor<3x1x1x7xf16>) -> tensor<3x1
// CHECK-LABEL: @unary_resize_nearest_i8
func.func @unary_resize_nearest_i8(%arg0 : tensor<3x1x1x7xi8>) -> tensor<3x1x1x7xi8> {
- %scale = tosa.const_shape { values = dense<[2, 1, 3, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<3x1x1x7xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xi8>
+ %scale = tosa.const_shape values(dense<[2, 1, 3, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<3x1x1x7xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xi8>
// CHECK: return %arg0
return %resize : tensor<3x1x1x7xi8>
}
@@ -94,10 +94,10 @@ func.func @broadcast_resize_nearest_bf16(%arg0 : tensor<3x1x1x7xbf16>) -> tensor
// CHECK-SAME: ins(%[[COLLAPSE]] : tensor<3x7xbf16>) outs(%[[EMPTY]] : tensor<3x1x5x7xbf16>)
// CHECK: ^bb0(%[[IN:.+]]: bf16, %[[OUT:.+]]: bf16):
// CHECK: linalg.yield %[[IN]] : bf16
- %scale = tosa.const_shape { values = dense<[2, 1, 3, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<3x1x1x7xbf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x5x7xbf16>
+ %scale = tosa.const_shape values(dense<[2, 1, 3, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<3x1x1x7xbf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x5x7xbf16>
return %resize : tensor<3x1x5x7xbf16>
}
@@ -114,10 +114,10 @@ func.func @broadcast_resize_nearest_f32(%arg0 : tensor<3x1x1x7xf32>) -> tensor<3
// CHECK-SAME: ins(%[[COLLAPSE]] : tensor<3x7xf32>) outs(%[[EMPTY]] : tensor<3x1x5x7xf32>)
// CHECK: ^bb0(%[[IN:.+]]: f32, %[[OUT:.+]]: f32):
// CHECK: linalg.yield %[[IN]] : f32
- %scale = tosa.const_shape { values = dense<[2, 1, 3, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<3x1x1x7xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x5x7xf32>
+ %scale = tosa.const_shape values(dense<[2, 1, 3, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<3x1x1x7xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x5x7xf32>
return %resize : tensor<3x1x5x7xf32>
}
@@ -151,10 +151,10 @@ func.func @broadcast_resize_bilinear_i8(%arg0 : tensor<3x1x1x7xi8>) -> tensor<3x
// CHECK-SAME: ins(%[[COLLAPSE_0]] : tensor<3x7xi32>) outs(%[[EMPTY_0]] : tensor<3x4x5x7xi32>) {
// CHECK: ^bb0(%[[IN:.+]]: i32, %[[OUT:.+]]: i32):
// CHECK: linalg.yield %[[IN]] : i32
- %scale = tosa.const_shape { values = dense<[2, 1, 3, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<3x1x1x7xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x4x5x7xi32>
+ %scale = tosa.const_shape values(dense<[2, 1, 3, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<3x1x1x7xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x4x5x7xi32>
return %resize : tensor<3x4x5x7xi32>
}
@@ -180,10 +180,10 @@ func.func @unary_resize_bilinear_i32(%arg0 : tensor<3x1x1x7xi8>) -> tensor<3x1x1
// CHECK: } -> tensor<3x7xi32>
// CHECK: %[[EXPAND:.+]] = tensor.expand_shape %[[GENERIC:.+]]
// CHECK-SAME{literal} [[0], [1, 2, 3]] : tensor<3x7xi32> into tensor<3x1x1x7xi32>
- %scale = tosa.const_shape { values = dense<[2, 1, 2, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<3x1x1x7xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xi32>
+ %scale = tosa.const_shape values(dense<[2, 1, 2, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<3x1x1x7xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x1x7xi32>
// CHECK: return %[[EXPAND]]
return %resize : tensor<3x1x1x7xi32>
@@ -251,10 +251,10 @@ func.func @resize_nearest_int(%arg0: tensor<1x15x13x1xi8>) -> () {
// CHECK: linalg.yield %[[EXTRACT]]
// Round to the nearest index.
- %scale = tosa.const_shape { values = dense<[11, 7, 89, 6]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x15x13x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x23x179x1xi8>
+ %scale = tosa.const_shape values(dense<[11, 7, 89, 6]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x15x13x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x23x179x1xi8>
return
}
@@ -355,10 +355,10 @@ func.func @resize_bilinear_int(%arg0: tensor<1x19x20x1xi8>) {
// CHECK: linalg.yield %[[RESULT]]
// Round to the nearest index.
- %scale = tosa.const_shape { values = dense<[16, 1, 16, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<1x19x20x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x289x305x1xi48>
+ %scale = tosa.const_shape values(dense<[16, 1, 16, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x19x20x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x289x305x1xi48>
return
}
@@ -424,10 +424,10 @@ func.func @resize_nearest_fp32(%input: tensor<1x50x48x1xf32>) -> () {
// CHECK: %[[EXTRACT:.+]] = tensor.extract %arg0[%[[IDX0]], %[[IDY]], %[[IDX]], %[[IDX3]]]
// CHECK: linalg.yield %[[EXTRACT]]
- %scale = tosa.const_shape { values = dense<[64, 2, 64, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-31, -31]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[31, 31]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %output = tosa.resize %input, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x50x48x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x1600x1536x1xf32>
+ %scale = tosa.const_shape values(dense<[64, 2, 64, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-31, -31]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[31, 31]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %output = tosa.resize %input, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x50x48x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x1600x1536x1xf32>
return
}
@@ -521,10 +521,10 @@ func.func @resize_bilinear_fp(%input: tensor<1x23x24x1xf32>) -> () {
// CHECK: linalg.yield %[[RESULT]]
// Round by bilinear interpolation
- %scale = tosa.const_shape { values = dense<[4, 1, 4, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %output = tosa.resize %input, %scale, %offset, %border {mode = BILINEAR} : (tensor<1x23x24x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x89x93x1xf32>
+ %scale = tosa.const_shape values(dense<[4, 1, 4, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %output = tosa.resize %input, %scale, %offset, %border mode<BILINEAR> : (tensor<1x23x24x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x89x93x1xf32>
return
}
@@ -538,10 +538,10 @@ func.func @resize_dyn(%input: tensor<?x2x2x1xi8>) -> () {
// CHECK: %[[BATCH:.+]] = tensor.dim %arg0, %[[C0]]
// CHECK: %[[INIT:.+]] = tensor.empty(%[[BATCH]]) : tensor<?x4x4x1xi32>
// CHECK: %[[GENERIC:.+]] = linalg.generic
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %output = tosa.resize %input, %scale, %offset, %border { mode = BILINEAR } : (tensor<?x2x2x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> (tensor<?x4x4x1xi32>)
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %output = tosa.resize %input, %scale, %offset, %border mode<BILINEAR> : (tensor<?x2x2x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> (tensor<?x4x4x1xi32>)
return
}
@@ -549,10 +549,10 @@ func.func @resize_dyn(%input: tensor<?x2x2x1xi8>) -> () {
// CHECK-LABEL: @resize_bilinear_int48
func.func @resize_bilinear_int48(%arg0: tensor<1x19x19x1xi16>) {
- %scale = tosa.const_shape { values = dense<[16, 1, 16, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<1x19x19x1xi16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x289x289x1xi48>
+ %scale = tosa.const_shape values(dense<[16, 1, 16, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x19x19x1xi16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x289x289x1xi48>
return
}
@@ -575,10 +575,10 @@ func.func @skip_interpolate_bilinear_i8(%arg0 : tensor<3x1x2x7xi8>) -> tensor<3x
// CHECK: %[[ADD:.+]] = arith.addi %[[MUL0]], %[[MUL1]]
// CHECK: %[[RES:.+]] = arith.muli %[[ADD]], %[[C2]]
// CHECK: linalg.yield %[[RES]]
- %scale = tosa.const_shape { values = dense<[2, 1, 3, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<3x1x2x7xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x4x7xi32>
+ %scale = tosa.const_shape values(dense<[2, 1, 3, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<3x1x2x7xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x4x7xi32>
// CHECK: return %[[GENERIC]]
return %resize : tensor<3x1x4x7xi32>
@@ -597,10 +597,10 @@ func.func @skip_interpolate_bilinear_f32(%arg0 : tensor<3x1x2x7xf32>) -> tensor<
// CHECK: %[[MUL1:.+]] = arith.mulf %[[EXTRACT1]], %[[DX]]
// CHECK: %[[ADD:.+]] = arith.addf %[[MUL0]], %[[MUL1]]
// CHECK: linalg.yield %[[ADD]]
- %scale = tosa.const_shape { values = dense<[2, 1, 3, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<3x1x2x7xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x4x7xf32>
+ %scale = tosa.const_shape values(dense<[2, 1, 3, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<3x1x2x7xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x4x7xf32>
// CHECK: return %[[GENERIC]]
return %resize : tensor<3x1x4x7xf32>
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
index a803ee7d99153..ce95560ca5be5 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
@@ -532,7 +532,7 @@ func.func @test_simple_f32(%arg0: tensor<1xf32>) -> () {
// CHECK: linalg.generic
// CHECK: arith.minimumf
// CHECK: arith.maximumf
- %18 = tosa.clamp %0 {min_val = 1.0 : f32, max_val = 5.0 : f32} : (tensor<1xf32>) -> tensor<1xf32>
+ %18 = tosa.clamp %0 min_val(1.0 : f32) max_val(5.0 : f32) : (tensor<1xf32>) -> tensor<1xf32>
// CHECK: linalg.generic
// CHECK: arith.negf
@@ -699,7 +699,7 @@ func.func @test_simple_i32(%arg0: tensor<1xi32>, %unsigned: tensor<1xui32>, %uns
// CHECK: linalg.generic
// CHECK: arith.shrsi
- %11 = tosa.arithmetic_right_shift %arg0, %arg0 {round = 0 : i1} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
+ %11 = tosa.arithmetic_right_shift %arg0, %arg0 round(false) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
// CHECK: linalg.generic
// CHECK: arith.constant 1
@@ -714,7 +714,7 @@ func.func @test_simple_i32(%arg0: tensor<1xi32>, %unsigned: tensor<1xui32>, %uns
// CHECK: arith.select
// CHECK: arith.extui
// CHECK: arith.addi
- %12 = tosa.arithmetic_right_shift %arg0, %arg0 {round = 1 : i1} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
+ %12 = tosa.arithmetic_right_shift %arg0, %arg0 round(true) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
// CHECK: math.ctlz
%13 = tosa.clz %arg0 : (tensor<1xi32>) -> tensor<1xi32>
@@ -742,14 +742,14 @@ func.func @test_simple_i32(%arg0: tensor<1xi32>, %unsigned: tensor<1xui32>, %uns
// CHECK: linalg.generic
// CHECK-DAG: arith.maxsi
// CHECK-DAG: arith.minsi
- %19 = tosa.clamp %0 {min_val = 1 : i32, max_val = 5 : i32} : (tensor<1xi32>) -> tensor<1xi32>
+ %19 = tosa.clamp %0 min_val(1 : i32) max_val(5 : i32) : (tensor<1xi32>) -> tensor<1xi32>
// CHECK: linalg.generic
// CHECK-DAG: %[[LB:.*]] = arith.constant 4 : i32
// CHECK-DAG: %[[UB:.*]] = arith.constant 32 : i32
// CHECK-DAG: arith.maxui %[[LB]],
// CHECK-DAG: arith.minui %[[UB]],
- %u0 = tosa.clamp %unsigned {min_val = 4 : ui32, max_val = 32 : ui32} : (tensor<1xui32>) -> tensor<1xui32>
+ %u0 = tosa.clamp %unsigned min_val(4 : ui32) max_val(32 : ui32) : (tensor<1xui32>) -> tensor<1xui32>
// CHECK: linalg.generic
// CHECK: arith.trunci
@@ -799,7 +799,7 @@ func.func @test_i8(%arg0: tensor<1xi8>) -> () {
// CHECK-DAG: %[[C126:.+]] = arith.constant 126
// CHECK-DAG: %[[LOWER:.+]] = arith.maxsi %[[C127]], %[[ARG1]]
// CHECK-DAG: %[[CLAMPED:.+]] = arith.minsi %[[C126]], %[[LOWER]]
- %0 = tosa.clamp %arg0 {min_val = -127 : i8, max_val = 126 : i8} : (tensor<1xi8>) -> tensor<1xi8>
+ %0 = tosa.clamp %arg0 min_val(-127 : i8) max_val(126 : i8) : (tensor<1xi8>) -> tensor<1xi8>
return
}
@@ -814,7 +814,7 @@ func.func @test_i64(%arg0: tensor<1xi64>) -> () {
// CHECK-DAG: %[[C126:.+]] = arith.constant 9223372036854775807
// CHECK-DAG: %[[LOWER:.+]] = arith.maxsi %[[C127]], %[[ARG1]]
// CHECK-DAG: %[[CLAMPED:.+]] = arith.minsi %[[C126]], %[[LOWER]]
- %0 = tosa.clamp %arg0 {min_val = -9223372036854775808 : i64, max_val = 9223372036854775807 : i64} : (tensor<1xi64>) -> tensor<1xi64>
+ %0 = tosa.clamp %arg0 min_val(-9223372036854775808 : i64) max_val(9223372036854775807 : i64) : (tensor<1xi64>) -> tensor<1xi64>
return
}
@@ -829,7 +829,7 @@ func.func @test_clamp_f16(%arg0: tensor<1xf16>) -> () {
// CHECK-DAG: %[[C6:.+]] = arith.constant 6.0
// CHECK-DAG: %[[MIN:.+]] = arith.minimumf %[[ARG1]], %[[C6]]
// CHECK-DAG: %[[MAX:.+]] = arith.maximumf %[[MIN]], %[[C0]]
- %0 = tosa.clamp %arg0 {min_val = 0.0 : f16, max_val = 6.0 : f16} : (tensor<1xf16>) -> tensor<1xf16>
+ %0 = tosa.clamp %arg0 min_val(0.0 : f16) max_val(6.0 : f16) : (tensor<1xf16>) -> tensor<1xf16>
return
}
@@ -977,7 +977,7 @@ func.func @reduce_bf16(%arg0: tensor<5x4xbf16>) -> () {
// CHECK: linalg.yield [[TRUNCF]] : bf16
// CHECK: }
// CHECK: tensor.expand_shape [[RES]] {{\[}}[0, 1]] output_shape [1, 4] : tensor<4xbf16> into tensor<1x4xbf16>
- %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<5x4xbf16>) -> tensor<1x4xbf16>
+ %0 = tosa.reduce_sum %arg0 axis(0) : (tensor<5x4xbf16>) -> tensor<1x4xbf16>
return
}
@@ -995,7 +995,7 @@ func.func @reduce_float(%arg0: tensor<5x4xf32>) -> () {
// CHECK: linalg.yield [[RES]] : f32
// CHECK: }
// CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [1, 4] : tensor<4xf32> into tensor<1x4xf32>
- %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<5x4xf32>) -> tensor<1x4xf32>
+ %0 = tosa.reduce_sum %arg0 axis(0) : (tensor<5x4xf32>) -> tensor<1x4xf32>
// CHECK: [[INIT:%.+]] = tensor.empty() : tensor<5xf32>
// CHECK: [[CST0:%.+]] = arith.constant 0.0
@@ -1006,25 +1006,25 @@ func.func @reduce_float(%arg0: tensor<5x4xf32>) -> () {
// CHECK: linalg.yield [[RES]] : f32
// CHECK: }
// CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [5, 1] : tensor<5xf32> into tensor<5x1xf32>
- %1 = tosa.reduce_sum %arg0 {axis = 1 : i32} : (tensor<5x4xf32>) -> tensor<5x1xf32>
+ %1 = tosa.reduce_sum %arg0 axis(1) : (tensor<5x4xf32>) -> tensor<5x1xf32>
// CHECK: arith.constant 1.0
// CHECK: linalg.fill
// CHECK: linalg.reduce
// CHECK: arith.mulf
- %2 = tosa.reduce_product %arg0 {axis = 0 : i32} : (tensor<5x4xf32>) -> tensor<1x4xf32>
+ %2 = tosa.reduce_product %arg0 axis(0) : (tensor<5x4xf32>) -> tensor<1x4xf32>
// CHECK: arith.constant 3.40282347E+38 : f32
// CHECK: linalg.fill
// CHECK: linalg.reduce
// CHECK: arith.minimumf
- %3 = tosa.reduce_min %arg0 {axis = 0 : i32} : (tensor<5x4xf32>) -> tensor<1x4xf32>
+ %3 = tosa.reduce_min %arg0 axis(0) : (tensor<5x4xf32>) -> tensor<1x4xf32>
// CHECK: arith.constant -3.40282347E+38 : f32
// CHECK: linalg.fill
// CHECK: linalg.reduce
// CHECK: arith.maximumf
- %4 = tosa.reduce_max %arg0 {axis = 0 : i32} : (tensor<5x4xf32>) -> tensor<1x4xf32>
+ %4 = tosa.reduce_max %arg0 axis(0) : (tensor<5x4xf32>) -> tensor<1x4xf32>
return
}
@@ -1047,7 +1047,7 @@ func.func @reduce_float_dyn(%arg0: tensor<?x5x4xf32>) -> () {
// CHECK: %[[DIM_1:.+]] = tensor.dim %[[REDUCE]], %[[C0_0]] : tensor<?x4xf32>
// CHECK: %[[C1:.+]] = arith.constant 1 : index
// CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0], [1, 2]] output_shape [%[[DIM_1]], 1, 4] : tensor<?x4xf32> into tensor<?x1x4xf32>
- %0 = tosa.reduce_sum %arg0 {axis = 1 : i32} : (tensor<?x5x4xf32>) -> tensor<?x1x4xf32>
+ %0 = tosa.reduce_sum %arg0 axis(1) : (tensor<?x5x4xf32>) -> tensor<?x1x4xf32>
return
}
@@ -1065,7 +1065,7 @@ func.func @reduce_float_dyn_rank_1(%arg0: tensor<?xf32>) -> () {
// CHECK: linalg.yield %[[RES]] : f32
// CHECK: }
// CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}] output_shape [1] : tensor<f32> into tensor<1xf32>
- %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<?xf32>) -> tensor<1xf32>
+ %0 = tosa.reduce_sum %arg0 axis(0) : (tensor<?xf32>) -> tensor<1xf32>
return
}
@@ -1088,7 +1088,7 @@ func.func @reduce_float_dyn_nonzero_batch(%arg0: tensor<5x?x4xf32>) -> () {
// CHECK: %[[DIM_1:.+]] = tensor.dim %[[REDUCE]], %[[C1_0]] : tensor<5x?xf32>
// CHECK: %[[C1_2:.+]] = arith.constant 1 : index
// CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0], [1, 2]] output_shape [5, %[[DIM_1]], 1] : tensor<5x?xf32> into tensor<5x?x1xf32>
- %0 = tosa.reduce_product %arg0 {axis = 2 : i32} : (tensor<5x?x4xf32>) -> tensor<5x?x1xf32>
+ %0 = tosa.reduce_product %arg0 axis(2) : (tensor<5x?x4xf32>) -> tensor<5x?x1xf32>
return
}
@@ -1111,7 +1111,7 @@ func.func @reduce_float_dyn_multiple(%arg0: tensor<?x?xf32>) -> () {
// CHECK: %[[DIM_1:.+]] = tensor.dim %[[REDUCE]], %[[C0_0]] : tensor<?xf32>
// CHECK: %[[C1_2:.+]] = arith.constant 1 : index
// CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0, 1]] output_shape [%[[DIM_1]], 1] : tensor<?xf32> into tensor<?x1xf32>
- %0 = tosa.reduce_max %arg0 {axis = 1 : i32} : (tensor<?x?xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_max %arg0 axis(1) : (tensor<?x?xf32>) -> tensor<?x1xf32>
return
}
@@ -1129,7 +1129,7 @@ func.func @reduce_int(%arg0: tensor<5x4xi32>) -> () {
// CHECK: linalg.yield [[RES]] : i32
// CHECK: }
// CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [1, 4] : tensor<4xi32> into tensor<1x4xi32>
- %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<1x4xi32>
+ %0 = tosa.reduce_sum %arg0 axis(0) : (tensor<5x4xi32>) -> tensor<1x4xi32>
// CHECK: [[INIT:%.+]] = tensor.empty()
// CHECK: [[CST0:%.+]] = arith.constant 0
@@ -1140,25 +1140,25 @@ func.func @reduce_int(%arg0: tensor<5x4xi32>) -> () {
// CHECK: linalg.yield [[RES]] : i32
// CHECK: }
// CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [5, 1] : tensor<5xi32> into tensor<5x1xi32>
- %1 = tosa.reduce_sum %arg0 {axis = 1 : i32} : (tensor<5x4xi32>) -> tensor<5x1xi32>
+ %1 = tosa.reduce_sum %arg0 axis(1) : (tensor<5x4xi32>) -> tensor<5x1xi32>
// CHECK: arith.constant 1
// CHECK: linalg.fill
// CHECK: linalg.reduce
// CHECK: arith.muli
- %2 = tosa.reduce_product %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<1x4xi32>
+ %2 = tosa.reduce_product %arg0 axis(0) : (tensor<5x4xi32>) -> tensor<1x4xi32>
// CHECK: arith.constant 2147483647 : i32
// CHECK: linalg.fill
// CHECK: linalg.reduce
// CHECK: arith.minsi
- %3 = tosa.reduce_min %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<1x4xi32>
+ %3 = tosa.reduce_min %arg0 axis(0) : (tensor<5x4xi32>) -> tensor<1x4xi32>
// CHECK: arith.constant -2147483648 : i32
// CHECK: linalg.fill
// CHECK: linalg.reduce
// CHECK: arith.maxsi
- %4 = tosa.reduce_max %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<1x4xi32>
+ %4 = tosa.reduce_max %arg0 axis(0) : (tensor<5x4xi32>) -> tensor<1x4xi32>
return
}
@@ -1176,13 +1176,13 @@ func.func @reduce_bool(%arg0: tensor<5x4xi1>) -> () {
// CHECK: linalg.yield [[RES]] : i1
// CHECK: }
// CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [1, 4] : tensor<4xi1> into tensor<1x4xi1>
- %0 = tosa.reduce_all %arg0 {axis = 0 : i32} : (tensor<5x4xi1>) -> tensor<1x4xi1>
+ %0 = tosa.reduce_all %arg0 axis(0) : (tensor<5x4xi1>) -> tensor<1x4xi1>
// CHECK: arith.constant false
// CHECK: linalg.fill
// CHECK: linalg.reduce
// CHECK: or
- %1 = tosa.reduce_any %arg0 {axis = 0 : i32} : (tensor<5x4xi1>) -> tensor<1x4xi1>
+ %1 = tosa.reduce_any %arg0 axis(0) : (tensor<5x4xi1>) -> tensor<1x4xi1>
return
}
@@ -1202,7 +1202,7 @@ func.func @rescale_i8(%arg0 : tensor<2xi8>) -> () {
// CHECK: [[C22:%.+]] = arith.constant 22
// CHECK-DAG: [[IN32:%.+]] = arith.extsi [[IN]]
// CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C17]]
- // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] {rounding_mode = SINGLE_ROUND}
+ // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] rounding_mode<SINGLE_ROUND>
// CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C22]]
// CHECK-DAG: [[CMIN:%.+]] = arith.constant -128
// CHECK-DAG: [[CMAX:%.+]] = arith.constant 127
@@ -1210,11 +1210,11 @@ func.func @rescale_i8(%arg0 : tensor<2xi8>) -> () {
// CHECK-DAG: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]]
// CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]]
// CHECK-DAG: linalg.yield [[TRUNC]]
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16>} : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<15> : tensor<1xi8>} : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<17> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<22> : tensor<1xi8>} : () -> tensor<1xi8>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<15> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<17> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<22> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
// CHECK: return
return
@@ -1235,7 +1235,7 @@ func.func @rescale_i8_unsigned_output_explicit(%arg0 : tensor<2xi8>) -> () {
// CHECK-DAG: [[C234:%.+]] = arith.constant 234
// CHECK-DAG: [[IN32:%.+]] = arith.extsi [[IN]]
// CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C17]]
- // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] {rounding_mode = SINGLE_ROUND}
+ // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] rounding_mode<SINGLE_ROUND>
// CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C234]]
// CHECK-DAG: [[CMIN:%.+]] = arith.constant 0
// CHECK-DAG: [[CMAX:%.+]] = arith.constant 255
@@ -1244,11 +1244,11 @@ func.func @rescale_i8_unsigned_output_explicit(%arg0 : tensor<2xi8>) -> () {
// CHECK: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]]
// CHECK: [[TRUNC_ITOU:%.+]] = builtin.unrealized_conversion_cast [[TRUNC]] : i8 to ui8
// CHECK: linalg.yield [[TRUNC_ITOU]]
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<17> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<-22> : tensor<1xi8>} : () -> tensor<1xi8>
- %1 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xui8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<15> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<17> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %1 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xui8>
// CHECK: return
return
@@ -1269,7 +1269,7 @@ func.func @rescale_i8_unsigned_output_implicit(%arg0 : tensor<2xi8>) -> () {
// CHECK-DAG: [[C234:%.+]] = arith.constant 234
// CHECK-DAG: [[IN32:%.+]] = arith.extsi [[IN]]
// CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C17]]
- // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] {rounding_mode = SINGLE_ROUND}
+ // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] rounding_mode<SINGLE_ROUND>
// CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C234]]
// CHECK-DAG: [[CMIN:%.+]] = arith.constant 0
// CHECK-DAG: [[CMAX:%.+]] = arith.constant 255
@@ -1278,11 +1278,11 @@ func.func @rescale_i8_unsigned_output_implicit(%arg0 : tensor<2xi8>) -> () {
// CHECK: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]]
// CHECK-NOT: builtin.unrealized_conversion_cast [[TRUNC]]
// CHECK: linalg.yield [[TRUNC]]
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<17> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<-22> : tensor<1xi8>} : () -> tensor<1xi8>
- %1 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<15> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<17> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %1 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
// CHECK: return
return
@@ -1303,7 +1303,7 @@ func.func @rescale_i48_unsigned_output_implicit(%arg0 : tensor<2xi48>) -> () {
// CHECK-DAG: [[C0:%.+]] = arith.constant 0
// CHECK-DAG: [[C234:%.+]] = arith.constant 234
// CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN]], [[C0]]
- // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C19689]], [[C15]] {rounding_mode = SINGLE_ROUND}
+ // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C19689]], [[C15]] rounding_mode<SINGLE_ROUND>
// CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C234]]
// CHECK-DAG: [[CMIN:%.+]] = arith.constant 0
// CHECK-DAG: [[CMAX:%.+]] = arith.constant 255
@@ -1311,11 +1311,11 @@ func.func @rescale_i48_unsigned_output_implicit(%arg0 : tensor<2xi48>) -> () {
// CHECK-DAG: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]]
// CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]]
// CHECK: linalg.yield [[TRUNC]]
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi48>} : () -> tensor<1xi48>
- %output_zp = "tosa.const"() {values = dense<-22> : tensor<1xi8>} : () -> tensor<1xi8>
- %1 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<2xi48>, tensor<1xi16>, tensor<1xi8>, tensor<1xi48>, tensor<1xi8>) -> tensor<2xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<15> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi48>}> : () -> tensor<1xi48>
+ %output_zp = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %1 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<2xi48>, tensor<1xi16>, tensor<1xi8>, tensor<1xi48>, tensor<1xi8>) -> tensor<2xi8>
// CHECK: return
return
@@ -1328,21 +1328,21 @@ func.func @rescale_i48_unsigned_output_implicit(%arg0 : tensor<2xi48>) -> () {
// CHECK-LABEL: @rescale_i8_dyn_batch
// CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]:
func.func @rescale_i8_dyn_batch(%arg0 : tensor<?x2xi8>) -> () {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16>} : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<15> : tensor<1xi8>} : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<17> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<22> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<15> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<17> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<22> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[C0:.+]] = arith.constant 0
// CHECK: %[[BATCH:.+]] = tensor.dim %[[ARG0]], %[[C0]]
// CHECK: %[[INIT:.+]] = tensor.empty(%[[BATCH]]) : tensor<?x2xi8>
// CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]] : tensor<?x2xi8>) outs(%[[INIT]] : tensor<?x2xi8>)
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<?x2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<?x2xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<?x2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<?x2xi8>
// CHECK: %[[C0:.+]] = arith.constant 0
// CHECK: %[[BATCH:.+]] = tensor.dim %[[ARG0]], %[[C0]]
// CHECK: %[[INIT:.+]] = tensor.empty(%[[BATCH]]) : tensor<?x2xi8>
// CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]] : tensor<?x2xi8>) outs(%[[INIT]] : tensor<?x2xi8>)
- %1 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<?x2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<?x2xi8>
+ %1 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<?x2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<?x2xi8>
return
}
@@ -1354,17 +1354,17 @@ func.func @rescale_i8_dyn_batch(%arg0 : tensor<?x2xi8>) -> () {
// CHECK-LABEL: @rescale_dyn
// CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]:
func.func @rescale_dyn(%arg0 : tensor<1x?x?x32xi32>) -> () {
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[C1:.+]] = arith.constant 1
// CHECK: %[[DIM1:.+]] = tensor.dim %[[ARG0]], %[[C1]]
// CHECK: %[[C2:.+]] = arith.constant 2
// CHECK: %[[DIM2:.+]] = tensor.dim %[[ARG0]], %[[C2]]
// CHECK: %[[INIT:.+]] = tensor.empty(%[[DIM1]], %[[DIM2]])
// CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<1x?x?x32xi32>) outs(%[[INIT]] : tensor<1x?x?x32xi8>)
- %multiplier = "tosa.const"() {values = dense<1376784203> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<38> : tensor<1xi8> } : () -> tensor<1xi8>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = DOUBLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<1x?x?x32xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<1x?x?x32xi8>
+ %multiplier = "tosa.const"() <{values = dense<1376784203> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<38> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<DOUBLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<1x?x?x32xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<1x?x?x32xi8>
return
}
@@ -1384,7 +1384,7 @@ func.func @rescale_i8_unsigned_input_explicit(%arg0 : tensor<2xui8>) -> () {
// CHECK-DAG: [[IN_UTOI:%.+]] = builtin.unrealized_conversion_cast [[IN]] : ui8 to i8
// CHECK-DAG: [[IN32:%.+]] = arith.extui [[IN_UTOI]]
// CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C17]]
- // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] {rounding_mode = SINGLE_ROUND}
+ // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] rounding_mode<SINGLE_ROUND>
// CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C22]]
// CHECK-DAG: [[CMIN:%.+]] = arith.constant -128
// CHECK-DAG: [[CMAX:%.+]] = arith.constant 127
@@ -1392,11 +1392,11 @@ func.func @rescale_i8_unsigned_input_explicit(%arg0 : tensor<2xui8>) -> () {
// CHECK: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]]
// CHECK: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]]
// CHECK: linalg.yield [[TRUNC]]
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<17> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<22> : tensor<1xi8>} : () -> tensor<1xi8>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = true, output_unsigned = false} : (tensor<2xui8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<15> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<17> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<22> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<2xui8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
return
}
@@ -1417,7 +1417,7 @@ func.func @rescale_i8_unsigned_input_implicit(%arg0 : tensor<2xi8>) -> () {
// CHECK-DAG: [[C22:%.+]] = arith.constant 22
// CHECK-DAG: [[IN32:%.+]] = arith.extui [[IN]]
// CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C128]]
- // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] {rounding_mode = SINGLE_ROUND}
+ // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] rounding_mode<SINGLE_ROUND>
// CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C22]]
// CHECK-DAG: [[CMIN:%.+]] = arith.constant -128
// CHECK-DAG: [[CMAX:%.+]] = arith.constant 127
@@ -1425,11 +1425,11 @@ func.func @rescale_i8_unsigned_input_implicit(%arg0 : tensor<2xi8>) -> () {
// CHECK-DAG: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]]
// CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]]
// CHECK: linalg.yield [[TRUNC]]
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<-128> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<22> : tensor<1xi8>} : () -> tensor<1xi8>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = true, output_unsigned = false} : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<15> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<22> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
return
}
@@ -1450,7 +1450,7 @@ func.func @rescale_i8_unsigned_input_output_explicit(%arg0 : tensor<2xui8>) -> (
// CHECK-DAG: [[IN_UTOI:%.+]] = builtin.unrealized_conversion_cast [[IN]] : ui8 to i8
// CHECK-DAG: [[IN32:%.+]] = arith.extui [[IN_UTOI]]
// CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C17]]
- // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] {rounding_mode = SINGLE_ROUND}
+ // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] rounding_mode<SINGLE_ROUND>
// CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C22]]
// CHECK-DAG: [[CMIN:%.+]] = arith.constant -128
// CHECK-DAG: [[CMAX:%.+]] = arith.constant 127
@@ -1459,11 +1459,11 @@ func.func @rescale_i8_unsigned_input_output_explicit(%arg0 : tensor<2xui8>) -> (
// CHECK: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]]
// CHECK: [[TRUNC_ITOU:%.+]] = builtin.unrealized_conversion_cast [[TRUNC]] : i8 to ui8
// CHECK: linalg.yield [[TRUNC_ITOU]]
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<17> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<22> : tensor<1xi8>} : () -> tensor<1xi8>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = true, output_unsigned = false} : (tensor<2xui8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xui8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<15> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<17> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<22> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<2xui8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xui8>
return
}
@@ -1485,7 +1485,7 @@ func.func @rescale_per_channel(%arg0 : tensor<3xi8>) -> (tensor<3xi8>) {
// CHECK-DAG: [[IN32:%.+]] = arith.extsi [[IN]]
// CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C243]]
- // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[MULTIPLIER]], [[SHIFT]] {rounding_mode = SINGLE_ROUND}
+ // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[MULTIPLIER]], [[SHIFT]] rounding_mode<SINGLE_ROUND>
// CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C252]]
// CHECK-DAG: [[CMIN:%.+]] = arith.constant -128
// CHECK-DAG: [[CMAX:%.+]] = arith.constant 127
@@ -1493,11 +1493,11 @@ func.func @rescale_per_channel(%arg0 : tensor<3xi8>) -> (tensor<3xi8>) {
// CHECK-DAG: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]]
// CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]]
// CHECK-DAG: linalg.yield [[TRUNC]]
- %multiplier = "tosa.const"() {values = dense<[42, 43, 44]> : tensor<3xi16>} : () -> tensor<3xi16>
- %shift = "tosa.const"() {values = dense<[14, 15, 64]> : tensor<3xi8>} : () -> tensor<3xi8>
- %input_zp = "tosa.const"() {values = dense<43> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<52> : tensor<1xi8>} : () -> tensor<1xi8>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = true, input_unsigned = false, output_unsigned = false} : (tensor<3xi8>, tensor<3xi16>, tensor<3xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<3xi8>
+ %multiplier = "tosa.const"() <{values = dense<[42, 43, 44]> : tensor<3xi16>}> : () -> tensor<3xi16>
+ %shift = "tosa.const"() <{values = dense<[14, 15, 64]> : tensor<3xi8>}> : () -> tensor<3xi8>
+ %input_zp = "tosa.const"() <{values = dense<43> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<52> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(true) input_unsigned(false) output_unsigned(false) : (tensor<3xi8>, tensor<3xi16>, tensor<3xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<3xi8>
// CHECK: return [[GENERIC]]
return %0 : tensor<3xi8>
@@ -1507,15 +1507,15 @@ func.func @rescale_per_channel(%arg0 : tensor<3xi8>) -> (tensor<3xi8>) {
// CHECK-LABEL: @rescaleDoubleRound
func.func @rescaleDoubleRound(%arg0 : tensor<2xi8>) -> (tensor<2xi8>) {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32>} : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<33> : tensor<1xi8>} : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<43> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<52> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<33> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<43> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<52> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: linalg.generic
// CHECK: tosa.apply_scale
- // CHECK-SAME: {rounding_mode = DOUBLE_ROUND}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = true, rounding_mode = DOUBLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
+ // CHECK-SAME: rounding_mode<DOUBLE_ROUND>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<DOUBLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
return %0 : tensor<2xi8>
}
@@ -1523,27 +1523,27 @@ func.func @rescaleDoubleRound(%arg0 : tensor<2xi8>) -> (tensor<2xi8>) {
// CHECK-LABEL: @rescaleUnnecessaryDoubleRound
func.func @rescaleUnnecessaryDoubleRound(%arg0 : tensor<2xi8>) -> (tensor<2xi8>) {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32>} : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<15> : tensor<1xi8>} : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<43> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<52> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<15> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<43> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<52> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: linalg.generic
// CHECK: tosa.apply_scale
- // CHECK-SAME: {rounding_mode = SINGLE_ROUND}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = true, rounding_mode = DOUBLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
+ // CHECK-SAME: rounding_mode<SINGLE_ROUND>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<DOUBLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
return %0 : tensor<2xi8>
}
// -----
func.func @unsupportedRescaleInexactRound(%arg0 : tensor<2xi8>) -> (tensor<2xi8>) {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<33> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<33> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{failed to legalize operation 'tosa.rescale'}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = true, rounding_mode = INEXACT_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<INEXACT_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
return %0 : tensor<2xi8>
}
@@ -1565,13 +1565,13 @@ func.func @rescale_no_const(%arg0 : tensor<2xi8>, %multiplier : tensor<1xi32>, %
// CHECK: [[OUTPUT_ZP_I32:%.+]] = arith.extsi [[ARG4]] : i8 to i32
// CHECK: [[ARG0_I32:%.+]] = arith.extsi [[ARG0]] : i8 to i32
// CHECK: [[TMP1:%.+]] = arith.subi [[ARG0_I32]], [[INPUT_ZP_I32]] : i32
- // CHECK: [[TMP2:%.+]] = tosa.apply_scale [[TMP1]], [[ARG1]], [[ARG2]] {rounding_mode = DOUBLE_ROUND} : (i32, i32, i8) -> i32
+ // CHECK: [[TMP2:%.+]] = tosa.apply_scale [[TMP1]], [[ARG1]], [[ARG2]] rounding_mode<DOUBLE_ROUND> : (i32, i32, i8) -> i32
// CHECK: [[TMP3:%.+]] = arith.addi [[TMP2]], [[OUTPUT_ZP_I32]] : i32
// CHECK: %c-128_i32 = arith.constant -128 : i32
// CHECK: %c127_i32 = arith.constant 127 : i32
// CHECK: [[MAX:%.+]] = arith.maxsi %c-128_i32, [[TMP3]] : i32
// CHECK: [[MIN:%.+]] = arith.minsi %c127_i32, [[MAX]] : i32
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = true, rounding_mode = DOUBLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<DOUBLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
return %0 : tensor<2xi8>
}
@@ -1593,13 +1593,13 @@ func.func @rescale_no_const_per_channel(%arg0 : tensor<2xi8>, %arg1 : tensor<2xi
// CHECK: [[OUTPUT_ZP_I32:%.+]] = arith.extsi [[ARG4]] : i8 to i32
// CHECK: [[ARG0_I32:%.+]] = arith.extsi [[ARG0]] : i8 to i32
// CHECK: [[TMP1:%.+]] = arith.subi [[ARG0_I32]], [[INPUT_ZP_I32]] : i32
- // CHECK: [[TMP2:%.+]] = tosa.apply_scale [[TMP1]], [[ARG1]], [[ARG2]] {rounding_mode = DOUBLE_ROUND} : (i32, i32, i8) -> i32
+ // CHECK: [[TMP2:%.+]] = tosa.apply_scale [[TMP1]], [[ARG1]], [[ARG2]] rounding_mode<DOUBLE_ROUND> : (i32, i32, i8) -> i32
// CHECK: [[TMP3:%.+]] = arith.addi [[TMP2]], [[OUTPUT_ZP_I32]] : i32
// CHECK: %c-128_i32 = arith.constant -128 : i32
// CHECK: %c127_i32 = arith.constant 127 : i32
// CHECK: [[MAX:%.+]] = arith.maxsi %c-128_i32, [[TMP3]] : i32
// CHECK: [[MIN:%.+]] = arith.minsi %c127_i32, [[MAX]] : i32
- %0 = tosa.rescale %arg0, %arg1, %arg2, %input_zp, %output_zp {scale32 = true, rounding_mode = DOUBLE_ROUND, per_channel = true, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<2xi32>, tensor<2xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
+ %0 = tosa.rescale %arg0, %arg1, %arg2, %input_zp, %output_zp scale32(true) rounding_mode<DOUBLE_ROUND> per_channel(true) input_unsigned(false) output_unsigned(false) : (tensor<2xi8>, tensor<2xi32>, tensor<2xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
return %0 : tensor<2xi8>
}
@@ -1623,13 +1623,13 @@ func.func @rescale_no_const_per_channel_input_output_zp_ui8(%arg0 : tensor<2xi8>
// CHECK: [[OUTPUT_ZP_I32:%.+]] = arith.extui [[OUTPUT_ZP_I8]] : i8 to i32
// CHECK: [[ARG0_I32:%.+]] = arith.extsi [[ARG0]] : i8 to i32
// CHECK: [[TMP1:%.+]] = arith.subi [[ARG0_I32]], [[INPUT_ZP_I32]] : i32
- // CHECK: [[TMP2:%.+]] = tosa.apply_scale [[TMP1]], [[ARG1]], [[ARG2]] {rounding_mode = DOUBLE_ROUND} : (i32, i32, i8) -> i32
+ // CHECK: [[TMP2:%.+]] = tosa.apply_scale [[TMP1]], [[ARG1]], [[ARG2]] rounding_mode<DOUBLE_ROUND> : (i32, i32, i8) -> i32
// CHECK: [[TMP3:%.+]] = arith.addi [[TMP2]], [[OUTPUT_ZP_I32]] : i32
// CHECK: %c0_i32 = arith.constant 0 : i32
// CHECK: %c255_i32 = arith.constant 255 : i32
// CHECK: [[MAX:%.+]] = arith.maxsi %c0_i32, [[TMP3]] : i32
// CHECK: [[MIN:%.+]] = arith.minsi %c255_i32, [[MAX]] : i32
- %0 = tosa.rescale %arg0, %arg1, %arg2, %input_zp, %output_zp {scale32 = true, rounding_mode = DOUBLE_ROUND, per_channel = true, input_unsigned = false, output_unsigned = true} : (tensor<2xi8>, tensor<2xi32>, tensor<2xi8>, tensor<1xui8>, tensor<1xui8>) -> tensor<2xui8>
+ %0 = tosa.rescale %arg0, %arg1, %arg2, %input_zp, %output_zp scale32(true) rounding_mode<DOUBLE_ROUND> per_channel(true) input_unsigned(false) output_unsigned(true) : (tensor<2xi8>, tensor<2xi32>, tensor<2xi8>, tensor<1xui8>, tensor<1xui8>) -> tensor<2xui8>
return %0 : tensor<2xui8>
}
@@ -1651,7 +1651,7 @@ func.func @reverse(%arg0: tensor<5x4xi32>) -> () {
// CHECK-DAG: %[[READ_DIM:.+]] = arith.subi %[[RDIM_MINUS_C1]], %[[I0]]
// CHECK-DAG: %[[EXTRACT:.+]] = tensor.extract %arg0[%[[READ_DIM]], %[[I1]]] : tensor<5x4xi32>
// CHECK: linalg.yield %[[EXTRACT]]
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<5x4xi32>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<5x4xi32>) -> tensor<5x4xi32>
// CHECK: %[[C1:.+]] = arith.constant 1
// CHECK: %[[RDIM:.+]] = tensor.dim %[[ARG0]], %[[C1]]
@@ -1664,7 +1664,7 @@ func.func @reverse(%arg0: tensor<5x4xi32>) -> () {
// CHECK-DAG: %[[READ_DIM:.+]] = arith.subi %[[RDIM_MINUS_C1]], %[[I1]]
// CHECK-DAG: %[[EXTRACT:.+]] = tensor.extract %arg0[%[[I0]], %[[READ_DIM]]] : tensor<5x4xi32>
// CHECK: linalg.yield %[[EXTRACT]]
- %1 = tosa.reverse %arg0 {axis = 1 : i32} : (tensor<5x4xi32>) -> tensor<5x4xi32>
+ %1 = tosa.reverse %arg0 axis(1) : (tensor<5x4xi32>) -> tensor<5x4xi32>
return
}
@@ -1687,7 +1687,7 @@ func.func @reverse_dyn(%arg0: tensor<?xi32>) -> () {
// CHECK-DAG: %[[READ_DIM:.+]] = arith.subi %[[RDIM_MINUS_C1]], %[[I0]]
// CHECK-DAG: %[[EXTRACT:.+]] = tensor.extract %arg0[%[[READ_DIM]]] : tensor<?xi32>
// CHECK: linalg.yield %[[EXTRACT]]
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<?xi32>) -> tensor<?xi32>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<?xi32>) -> tensor<?xi32>
return
}
@@ -1703,26 +1703,26 @@ func.func @tile(%arg0 : tensor<2x3xi8>) -> () {
// CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<2x3xi8>) outs([[INIT]] : tensor<2x2x1x3xi8>)
// CHECK: ^bb0(%[[ARG1:[0-9a-zA-Z_]+]]: i8
// CHECK: linalg.yield %[[ARG1]] : i8
- // CHECK: [[CONST3:%.+]] = tosa.const_shape {values = dense<[4, 3]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ // CHECK: [[CONST3:%.+]] = tosa.const_shape values(dense<[4, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: tosa.reshape [[GENERIC]], [[CONST3]]
- %cst21 = tosa.const_shape { values = dense<[2, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %cst21 = tosa.const_shape values(dense<[2, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = tosa.tile %arg0, %cst21: (tensor<2x3xi8>, !tosa.shape<2>) -> tensor<4x3xi8>
// CHECK: [[INIT:%.+]] = tensor.empty()
// CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<2x3xi8>) outs([[INIT]] : tensor<1x2x2x3xi8>)
// CHECK: ^bb0(%[[ARG1:[0-9a-zA-Z_]+]]: i8
// CHECK: linalg.yield %[[ARG1]] : i8
- // CHECK: [[CONST8:%.+]] = tosa.const_shape {values = dense<[2, 6]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ // CHECK: [[CONST8:%.+]] = tosa.const_shape values(dense<[2, 6]> : tensor<2xindex>) : () -> !tosa.shape<2>
// tosa.reshape [[GENERIC]], [[CONST8]]
- %cst12 = tosa.const_shape { values = dense<[1, 2]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %cst12 = tosa.const_shape values(dense<[1, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
%1 = tosa.tile %arg0, %cst12: (tensor<2x3xi8>, !tosa.shape<2>) -> tensor<2x6xi8>
// CHECK: [[INIT:%.+]] = tensor.empty()
// CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<2x3xi8>) outs([[INIT]] : tensor<5x2x7x3xi8>)
// CHECK: ^bb0(%[[ARG1:[0-9a-zA-Z_]+]]: i8
// CHECK: linalg.yield %[[ARG1]] : i8
- %cst57 = tosa.const_shape { values = dense<[5, 7]> : tensor<2xindex> } : () -> !tosa.shape<2>
- // CHECK: [[CONST13:%.+]] = tosa.const_shape {values = dense<[10, 21]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %cst57 = tosa.const_shape values(dense<[5, 7]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ // CHECK: [[CONST13:%.+]] = tosa.const_shape values(dense<[10, 21]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: tosa.reshape [[GENERIC]], [[CONST13]]
%2 = tosa.tile %arg0, %cst57: (tensor<2x3xi8>, !tosa.shape<2>) -> tensor<10x21xi8>
@@ -1743,9 +1743,9 @@ func.func @tile_dyn_input(%arg0 : tensor<?x3xi8>) -> () {
// CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<?x3xi8>) outs(%[[INIT]] : tensor<2x?x1x3xi8>)
// CHECK: ^bb0(%[[ARG1:.+]]: i8,
// CHECK: linalg.yield %[[ARG1]] : i8
- // CHECK: %[[CONST3:.+]] = tosa.const_shape {values = dense<[-1, 3]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ // CHECK: %[[CONST3:.+]] = tosa.const_shape values(dense<[-1, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: tosa.reshape %[[GENERIC]], %[[CONST3]]
- %cst21 = tosa.const_shape { values = dense<[2, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %cst21 = tosa.const_shape values(dense<[2, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = tosa.tile %arg0, %cst21: (tensor<?x3xi8>, !tosa.shape<2>) -> tensor<?x3xi8>
return
@@ -1765,9 +1765,9 @@ func.func @tile_dyn_multiples(%arg0 : tensor<2x3xi8>) -> () {
// CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<2x3xi8>) outs(%[[INIT]] : tensor<2x2x?x3xi8>)
// CHECK: ^bb0(%[[ARG1:.+]]: i8,
// CHECK: linalg.yield %[[ARG1]] : i8
- // CHECK: %[[CONST2:.+]] = tosa.const_shape {values = dense<[2, -1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ // CHECK: %[[CONST2:.+]] = tosa.const_shape values(dense<[2, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: tosa.reshape %[[GENERIC]], %[[CONST2]]
- %cst = tosa.const_shape { values = dense<[2, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %cst = tosa.const_shape values(dense<[2, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = tosa.tile %arg0, %cst: (tensor<2x3xi8>, !tosa.shape<2>) -> tensor<2x?xi8>
return
@@ -1796,7 +1796,7 @@ func.func @argmax(%arg0 : tensor<3x2xi32>, %arg1 : tensor<6xf32>) -> () {
// CHECK: [[SELECT_VAL:%.+]] = arith.select [[CMP]], %[[ARG1]], %[[ARG3]]
// CHECK: [[SELECT_IDX:%.+]] = arith.select [[CMP]], [[CAST]], %[[ARG2]]
// CHECK: linalg.yield [[SELECT_IDX]], [[SELECT_VAL]]
- %0 = tosa.argmax %arg0 { axis = 0 : i32} : (tensor<3x2xi32>) -> tensor<2xi32>
+ %0 = tosa.argmax %arg0 axis(0) : (tensor<3x2xi32>) -> tensor<2xi32>
// CHECK: [[IDX_INIT:%.+]] = tensor.empty()
// CHECK: [[IDX_MIN:%.+]] = arith.constant 0 : i32
@@ -1812,7 +1812,7 @@ func.func @argmax(%arg0 : tensor<3x2xi32>, %arg1 : tensor<6xf32>) -> () {
// CHECK: [[SELECT_VAL:%.+]] = arith.select [[CMP]], %[[ARG1]], %[[ARG3]]
// CHECK: [[SELECT_IDX:%.+]] = arith.select [[CMP]], [[CAST]], %[[ARG2]]
// CHECK: linalg.yield [[SELECT_IDX]], [[SELECT_VAL]]
- %1 = tosa.argmax %arg0 { axis = 1 : i32} : (tensor<3x2xi32>) -> tensor<3xi32>
+ %1 = tosa.argmax %arg0 axis(1) : (tensor<3x2xi32>) -> tensor<3xi32>
// CHECK: arith.constant -3.40282347E+38 : f32
// CHECK: linalg.index
@@ -1823,7 +1823,7 @@ func.func @argmax(%arg0 : tensor<3x2xi32>, %arg1 : tensor<6xf32>) -> () {
// CHECK: select
// CHECK: select
// CHECK: linalg.yield
- %2 = tosa.argmax %arg1 { axis = 0 : i32} : (tensor<6xf32>) -> tensor<i32>
+ %2 = tosa.argmax %arg1 axis(0) : (tensor<6xf32>) -> tensor<i32>
return
}
@@ -1850,7 +1850,7 @@ func.func @argmax_dyn_non_axis(%arg0 : tensor<3x?xi32>) -> () {
// CHECK: %[[SELECT_VAL:.+]] = arith.select %[[CMP]], %[[ARG1]], %[[ARG3]]
// CHECK: %[[SELECT_IDX:.+]] = arith.select %[[CMP]], %[[CAST]], %[[ARG2]]
// CHECK: linalg.yield %[[SELECT_IDX]], %[[SELECT_VAL]]
- %0 = tosa.argmax %arg0 { axis = 0 : i32} : (tensor<3x?xi32>) -> tensor<?xi32>
+ %0 = tosa.argmax %arg0 axis(0) : (tensor<3x?xi32>) -> tensor<?xi32>
return
}
@@ -1873,7 +1873,7 @@ func.func @argmax_dyn_axis(%arg0 : tensor<3x?xi32>) -> () {
// CHECK: %[[SELECT_VAL:.+]] = arith.select %[[CMP]], %[[ARG1]], %[[ARG3]]
// CHECK: %[[SELECT_IDX:.+]] = arith.select %[[CMP]], %[[CAST]], %[[ARG2]]
// CHECK: linalg.yield %[[SELECT_IDX]], %[[SELECT_VAL]]
- %0 = tosa.argmax %arg0 { axis = 1 : i32} : (tensor<3x?xi32>) -> tensor<3xi32>
+ %0 = tosa.argmax %arg0 axis(1) : (tensor<3x?xi32>) -> tensor<3xi32>
return
}
@@ -2114,7 +2114,7 @@ func.func @table8_dyn_table(%arg0: tensor<6xi8>, %arg1: tensor<?xi8>) -> () {
// CHECK: return %[[VAL_3:.*]]#0, %[[VAL_3]]#1 : tensor<5x4x5xf32>, tensor<5x4x5xf32>
// CHECK: }
func.func @test_static_rfft2d(%arg0: tensor<5x4x8xf32>) -> (tensor<5x4x5xf32>, tensor<5x4x5xf32>) {
- %output_real, %output_imag = "tosa.rfft2d"(%arg0) {} : (tensor<5x4x8xf32>) -> (tensor<5x4x5xf32>, tensor<5x4x5xf32>)
+ %output_real, %output_imag = "tosa.rfft2d"(%arg0) <{}> : (tensor<5x4x8xf32>) -> (tensor<5x4x5xf32>, tensor<5x4x5xf32>)
return %output_real, %output_imag : tensor<5x4x5xf32>, tensor<5x4x5xf32>
}
@@ -2191,7 +2191,7 @@ func.func @test_static_rfft2d(%arg0: tensor<5x4x8xf32>) -> (tensor<5x4x5xf32>, t
// CHECK: return %[[VAL_3:.*]]#0, %[[VAL_3]]#1 : tensor<?x?x?xf32>, tensor<?x?x?xf32>
// CHECK: }
func.func @test_dynamic_rfft2d(%arg0: tensor<?x?x?xf32>) -> (tensor<?x?x?xf32>, tensor<?x?x?xf32>) {
- %output_real, %output_imag = "tosa.rfft2d"(%arg0) {} : (tensor<?x?x?xf32>) -> (tensor<?x?x?xf32>, tensor<?x?x?xf32>)
+ %output_real, %output_imag = "tosa.rfft2d"(%arg0) <{}> : (tensor<?x?x?xf32>) -> (tensor<?x?x?xf32>, tensor<?x?x?xf32>)
return %output_real, %output_imag : tensor<?x?x?xf32>, tensor<?x?x?xf32>
}
@@ -2251,7 +2251,7 @@ func.func @test_dynamic_rfft2d(%arg0: tensor<?x?x?xf32>) -> (tensor<?x?x?xf32>,
// CHECK: return %[[VAL_48:.*]]#0, %[[VAL_48]]#1 : tensor<8x8x8xf32>, tensor<8x8x8xf32>
// CHECK: }
func.func @test_static_fft2d(%arg0: tensor<8x8x8xf32>, %arg1: tensor<8x8x8xf32>) -> (tensor<8x8x8xf32>, tensor<8x8x8xf32>) {
- %output_real, %output_imag = "tosa.fft2d"(%arg0, %arg1) {inverse=false} : (tensor<8x8x8xf32>, tensor<8x8x8xf32>) -> (tensor<8x8x8xf32>, tensor<8x8x8xf32>)
+ %output_real, %output_imag = "tosa.fft2d"(%arg0, %arg1) <{inverse=false}> : (tensor<8x8x8xf32>, tensor<8x8x8xf32>) -> (tensor<8x8x8xf32>, tensor<8x8x8xf32>)
return %output_real, %output_imag : tensor<8x8x8xf32>, tensor<8x8x8xf32>
}
@@ -2319,7 +2319,7 @@ func.func @test_static_fft2d(%arg0: tensor<8x8x8xf32>, %arg1: tensor<8x8x8xf32>)
// CHECK: return %[[VAL_56:.*]]#0, %[[VAL_56]]#1 : tensor<?x?x?xf32>, tensor<?x?x?xf32>
// CHECK: }
func.func @test_dynamic_fft2d(%arg0: tensor<?x?x?xf32>, %arg1: tensor<?x?x?xf32>) -> (tensor<?x?x?xf32>, tensor<?x?x?xf32>) {
- %output_real, %output_imag = "tosa.fft2d"(%arg0, %arg1) {inverse = true} : (tensor<?x?x?xf32>, tensor<?x?x?xf32>) -> (tensor<?x?x?xf32>, tensor<?x?x?xf32>)
+ %output_real, %output_imag = "tosa.fft2d"(%arg0, %arg1) <{inverse = true}> : (tensor<?x?x?xf32>, tensor<?x?x?xf32>) -> (tensor<?x?x?xf32>, tensor<?x?x?xf32>)
return %output_real, %output_imag : tensor<?x?x?xf32>, tensor<?x?x?xf32>
}
@@ -2364,7 +2364,7 @@ func.func @reduce_min_nan_propagate(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf3
// CHECK-NOT: tensor.empty()
// CHECK-NOT: select
// CHECK: return
- %3 = tosa.reduce_min %arg0 {axis = 0 : i32, nan_mode = PROPAGATE} : (tensor<5x4xf32>) -> tensor<1x4xf32>
+ %3 = tosa.reduce_min %arg0 axis(0) : (tensor<5x4xf32>) -> tensor<1x4xf32>
return
}
@@ -2383,7 +2383,7 @@ func.func @reduce_max_nan_propagate(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf3
// CHECK-NOT: tensor.empty()
// CHECK-NOT: select
// CHECK: return
- %4 = tosa.reduce_max %arg0 {axis = 0 : i32, nan_mode = PROPAGATE} : (tensor<5x4xf32>) -> tensor<1x4xf32>
+ %4 = tosa.reduce_max %arg0 axis(0) : (tensor<5x4xf32>) -> tensor<1x4xf32>
return
}
@@ -2402,7 +2402,7 @@ func.func @reduce_min_nan_ignore_int(%arg0: tensor<5x4xi8>, %arg1: tensor<5x4xi8
// CHECK-NOT: tensor.empty()
// CHECK-NOT: select
// CHECK: return
- %5 = tosa.reduce_min %arg0 {axis = 0 : i32, nan_mode = IGNORE} : (tensor<5x4xi8>) -> tensor<1x4xi8>
+ %5 = tosa.reduce_min %arg0 axis(0) nan_mode<IGNORE> : (tensor<5x4xi8>) -> tensor<1x4xi8>
return
}
@@ -2421,7 +2421,7 @@ func.func @reduce_max_nan_ignore_int(%arg0: tensor<5x4xi8>, %arg1: tensor<5x4xi8
// CHECK-NOT: tensor.empty()
// CHECK-NOT: select
// CHECK: return
- %6 = tosa.reduce_max %arg0 {axis = 0 : i32, nan_mode = IGNORE} : (tensor<5x4xi8>) -> tensor<1x4xi8>
+ %6 = tosa.reduce_max %arg0 axis(0) nan_mode<IGNORE> : (tensor<5x4xi8>) -> tensor<1x4xi8>
return
}
@@ -2439,7 +2439,7 @@ func.func @reduce_min_nan_ignore(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>)
// CHECK: linalg.fill
// CHECK: tensor.empty()
// CHECK: select
- %5 = tosa.reduce_min %arg0 {axis = 0 : i32, nan_mode = IGNORE} : (tensor<5x4xf32>) -> tensor<1x4xf32>
+ %5 = tosa.reduce_min %arg0 axis(0) nan_mode<IGNORE> : (tensor<5x4xf32>) -> tensor<1x4xf32>
return
}
@@ -2457,7 +2457,7 @@ func.func @reduce_max_nan_ignore(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>)
// CHECK: linalg.fill
// CHECK: tensor.empty()
// CHECK: select
- %6 = tosa.reduce_max %arg0 {axis = 0 : i32, nan_mode = IGNORE} : (tensor<5x4xf32>) -> tensor<1x4xf32>
+ %6 = tosa.reduce_max %arg0 axis(0) nan_mode<IGNORE> : (tensor<5x4xf32>) -> tensor<1x4xf32>
return
}
@@ -2470,7 +2470,7 @@ func.func @minimum_nan_propagate(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>)
// CHECK-NOT: arith.cmpf uno
// CHECK-NOT: arith.select
// CHECK: linalg.yield
- %7 = tosa.minimum %arg0, %arg1 {nan_mode = PROPAGATE} : (tensor<5x4xf32>, tensor<5x4xf32>) -> tensor<5x4xf32>
+ %7 = tosa.minimum %arg0, %arg1 : (tensor<5x4xf32>, tensor<5x4xf32>) -> tensor<5x4xf32>
return
}
@@ -2483,7 +2483,7 @@ func.func @maximum_nan_propagate(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>)
// CHECK-NOT: arith.cmpf uno
// CHECK-NOT: arith.select
// CHECK: linalg.yield
- %8 = tosa.maximum %arg0, %arg1 {nan_mode = PROPAGATE} : (tensor<5x4xf32>, tensor<5x4xf32>) -> tensor<5x4xf32>
+ %8 = tosa.maximum %arg0, %arg1 : (tensor<5x4xf32>, tensor<5x4xf32>) -> tensor<5x4xf32>
return
}
@@ -2496,7 +2496,7 @@ func.func @minimum_nan_ignore_int(%arg0: tensor<5x4xi8>, %arg1: tensor<5x4xi8>)
// CHECK-NOT: arith.cmpf uno
// CHECK-NOT: arith.select
// CHECK: linalg.yield
- %9 = tosa.minimum %arg0, %arg1 {nan_mode = IGNORE} : (tensor<5x4xi8>, tensor<5x4xi8>) -> tensor<5x4xi8>
+ %9 = tosa.minimum %arg0, %arg1 nan_mode<IGNORE> : (tensor<5x4xi8>, tensor<5x4xi8>) -> tensor<5x4xi8>
return
}
@@ -2509,7 +2509,7 @@ func.func @maximum_nan_ignore_int(%arg0: tensor<5x4xi8>, %arg1: tensor<5x4xi8>)
// CHECK-NOT: arith.cmpf uno
// CHECK-NOT: arith.select
// CHECK: linalg.yield
- %10 = tosa.maximum %arg0, %arg1 {nan_mode = IGNORE} : (tensor<5x4xi8>, tensor<5x4xi8>) -> tensor<5x4xi8>
+ %10 = tosa.maximum %arg0, %arg1 nan_mode<IGNORE> : (tensor<5x4xi8>, tensor<5x4xi8>) -> tensor<5x4xi8>
return
}
@@ -2524,7 +2524,7 @@ func.func @minimum_nan_ignore(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) ->
// CHECK: arith.select
// CHECK: arith.select
// CHECK: linalg.yield
- %9 = tosa.minimum %arg0, %arg1 {nan_mode = IGNORE} : (tensor<5x4xf32>, tensor<5x4xf32>) -> tensor<5x4xf32>
+ %9 = tosa.minimum %arg0, %arg1 nan_mode<IGNORE> : (tensor<5x4xf32>, tensor<5x4xf32>) -> tensor<5x4xf32>
return
}
@@ -2539,7 +2539,7 @@ func.func @maximum_nan_ignore(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) ->
// CHECK: arith.select
// CHECK: arith.select
// CHECK: linalg.yield
- %10 = tosa.maximum %arg0, %arg1 {nan_mode = IGNORE} : (tensor<5x4xf32>, tensor<5x4xf32>) -> tensor<5x4xf32>
+ %10 = tosa.maximum %arg0, %arg1 nan_mode<IGNORE> : (tensor<5x4xf32>, tensor<5x4xf32>) -> tensor<5x4xf32>
return
}
@@ -2556,7 +2556,7 @@ func.func @argmax_nan_propagate(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>)
// CHECK-NOT: arith.cmpf uno
// CHECK-NOT: arith.select
// CHECK: linalg.yield
- %11 = tosa.argmax %arg0 {axis = 0 : i32, nan_mode = PROPAGATE} : (tensor<5x4xf32>) -> tensor<4xi32>
+ %11 = tosa.argmax %arg0 axis(0) : (tensor<5x4xf32>) -> tensor<4xi32>
return
}
@@ -2573,7 +2573,7 @@ func.func @argmax_nan_ignore_int(%arg0: tensor<5x4xi8>, %arg1: tensor<5x4xi8>) -
// CHECK-NOT: arith.select
// CHECK-NOT: arith.select
// CHECK: linalg.yield
- %12 = tosa.argmax %arg0 {axis = 0 : i32, nan_mode = IGNORE} : (tensor<5x4xi8>) -> tensor<4xi32>
+ %12 = tosa.argmax %arg0 axis(0) nan_mode<IGNORE> : (tensor<5x4xi8>) -> tensor<4xi32>
return
}
@@ -2586,7 +2586,7 @@ func.func @argmax_nan_ignore(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) ->
// CHECK: arith.select
// CHECK: arith.select
// CHECK: linalg.yield
- %12 = tosa.argmax %arg0 {axis = 0 : i32, nan_mode = IGNORE} : (tensor<5x4xf32>) -> tensor<4xi32>
+ %12 = tosa.argmax %arg0 axis(0) nan_mode<IGNORE> : (tensor<5x4xf32>) -> tensor<4xi32>
return
}
@@ -2600,7 +2600,7 @@ func.func @clamp_nan_propagate(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -
// CHECK-NOT: arith.cmpf uno
// CHECK-NOT: arith.select
// CHECK: linalg.yield
- %13 = tosa.clamp %arg0 {min_val = 1.0 : f32, max_val = 5.0 : f32, nan_mode = PROPAGATE} : (tensor<5x4xf32>) -> tensor<5x4xf32>
+ %13 = tosa.clamp %arg0 min_val(1.0 : f32) max_val(5.0 : f32) : (tensor<5x4xf32>) -> tensor<5x4xf32>
return
}
@@ -2614,7 +2614,7 @@ func.func @clamp_nan_ignore_int(%arg0: tensor<5x4xi8>, %arg1: tensor<5x4xi8>) ->
// CHECK-NOT: arith.cmpf uno
// CHECK-NOT: arith.select
// CHECK: linalg.yield
- %14 = tosa.clamp %arg0 {min_val = 1 : i8, max_val = 5 : i8, nan_mode = IGNORE} : (tensor<5x4xi8>) -> tensor<5x4xi8>
+ %14 = tosa.clamp %arg0 min_val(1 : i8) max_val(5 : i8) nan_mode<IGNORE> : (tensor<5x4xi8>) -> tensor<5x4xi8>
return
}
@@ -2628,7 +2628,7 @@ func.func @clamp_nan_ignore(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> (
// CHECK: arith.cmpf uno
// CHECK: arith.select
// CHECK: linalg.yield
- %14 = tosa.clamp %arg0 {min_val = 1.0 : f32, max_val = 5.0 : f32, nan_mode = IGNORE} : (tensor<5x4xf32>) -> tensor<5x4xf32>
+ %14 = tosa.clamp %arg0 min_val(1.0 : f32) max_val(5.0 : f32) nan_mode<IGNORE> : (tensor<5x4xf32>) -> tensor<5x4xf32>
return
}
diff --git a/mlir/test/Conversion/TosaToSCF/tosa-to-scf.mlir b/mlir/test/Conversion/TosaToSCF/tosa-to-scf.mlir
index b6f2383ac81fc..6b53030609f6d 100644
--- a/mlir/test/Conversion/TosaToSCF/tosa-to-scf.mlir
+++ b/mlir/test/Conversion/TosaToSCF/tosa-to-scf.mlir
@@ -6,7 +6,7 @@ func.func @while_test(%arg0 : tensor<i32>) -> (tensor<i32>) {
// CHECK: [[WHILE:%.+]] = scf.while ([[ARG1:%.+]] = [[ARG0]])
%0 = tosa.while_loop (%arg1 = %arg0) : (tensor<i32>) -> tensor<i32> {
// CHECK: tosa.const
- %1 = "tosa.const"() {values = dense<3> : tensor<i32>} : () -> tensor<i32>
+ %1 = "tosa.const"() <{values = dense<3> : tensor<i32>}> : () -> tensor<i32>
// CHECK: [[COMPARE:%.+]] = tosa.greater_equal
%2 = tosa.greater_equal %1, %arg1 : (tensor<i32>, tensor<i32>) -> tensor<i1>
@@ -18,7 +18,7 @@ func.func @while_test(%arg0 : tensor<i32>) -> (tensor<i32>) {
// CHECK: ^bb0([[ARG1:%.+]]: tensor<i32>)
^bb0(%arg1: tensor<i32>):
// CHECK: tosa.const
- %1 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %1 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
// CHECK: [[ADD:%.+]] = tosa.add
%2 = tosa.add %arg1, %1 : (tensor<i32>, tensor<i32>) -> tensor<i32>
diff --git a/mlir/test/Conversion/TosaToSPIRVTosa/custom-op-domain-to-opcode-invalid.mlir b/mlir/test/Conversion/TosaToSPIRVTosa/custom-op-domain-to-opcode-invalid.mlir
index e300603d4844a..5dd5aae7077e8 100644
--- a/mlir/test/Conversion/TosaToSPIRVTosa/custom-op-domain-to-opcode-invalid.mlir
+++ b/mlir/test/Conversion/TosaToSPIRVTosa/custom-op-domain-to-opcode-invalid.mlir
@@ -2,6 +2,6 @@
func.func @zero_results(%arg0: tensor<1x16xf32>) {
// CHECK: 'tosa.custom' op with mapped domain requires at least one result
- tosa.custom %arg0 {domain_name = "test", implementation_attrs = "{}", operator_name = "NoResult"} : (tensor<1x16xf32>) -> ()
+ tosa.custom %arg0 operator_name("NoResult") domain_name("test") implementation_attrs("{}") : (tensor<1x16xf32>) -> ()
return
}
diff --git a/mlir/test/Conversion/TosaToSPIRVTosa/custom-op-domain-to-opcode.mlir b/mlir/test/Conversion/TosaToSPIRVTosa/custom-op-domain-to-opcode.mlir
index 15161f584d275..7aa47355af091 100644
--- a/mlir/test/Conversion/TosaToSPIRVTosa/custom-op-domain-to-opcode.mlir
+++ b/mlir/test/Conversion/TosaToSPIRVTosa/custom-op-domain-to-opcode.mlir
@@ -13,7 +13,7 @@ func.func @mapped_custom(%arg0: tensor<1x16xf32>, %arg1: tensor<1x16xf32>) -> te
// CHECK: %[[CALL:.*]] = spirv.ExperimentalML.Call opcode = 0, %[[OP_NAME]], %[[IMPLEMENTATION_ATTRS]], %arg0, %arg1 : (!spirv.array<6 x i8>, !spirv.array<17 x i8>, !spirv.arm.tensor<1x16xf32>, !spirv.arm.tensor<1x16xf32>) -> !spirv.arm.tensor<1x16xf32>
// OVERRIDE-LABEL: spirv.ARM.Graph @mapped_custom
// OVERRIDE: spirv.ExperimentalML.Call opcode = 7,
- %0 = tosa.custom %arg0, %arg1 {domain_name = "my:custom", implementation_attrs = "{\"param\":\"value\"}", operator_name = "TestOp"} : (tensor<1x16xf32>, tensor<1x16xf32>) -> tensor<1x16xf32>
+ %0 = tosa.custom %arg0, %arg1 operator_name("TestOp") domain_name("my:custom") implementation_attrs("{\"param\":\"value\"}") : (tensor<1x16xf32>, tensor<1x16xf32>) -> tensor<1x16xf32>
// CHECK: spirv.ARM.GraphOutputs %[[CALL]] : !spirv.arm.tensor<1x16xf32>
return %0 : tensor<1x16xf32>
}
@@ -26,7 +26,7 @@ func.func @other_custom(%arg0: tensor<1x16xf32>) -> tensor<1x16xf32> {
// CHECK: %[[OP_NAME:.*]] = spirv.Constant [69 : i8, 120 : i8, 97 : i8, 109 : i8, 112 : i8, 108 : i8, 101 : i8, 79 : i8, 112 : i8] : !spirv.array<9 x i8>
// CHECK: %[[IMPLEMENTATION_ATTRS:.*]] = spirv.Constant [123 : i8, 125 : i8] : !spirv.array<2 x i8>
// CHECK: %[[CALL:.*]] = spirv.ExperimentalML.Call opcode = 42, %[[OP_NAME]], %[[IMPLEMENTATION_ATTRS]], %arg0 : (!spirv.array<9 x i8>, !spirv.array<2 x i8>, !spirv.arm.tensor<1x16xf32>) -> !spirv.arm.tensor<1x16xf32>
- %0 = tosa.custom %arg0 {domain_name = "com.example.accel", implementation_attrs = "{}", operator_name = "ExampleOp"} : (tensor<1x16xf32>) -> tensor<1x16xf32>
+ %0 = tosa.custom %arg0 operator_name("ExampleOp") domain_name("com.example.accel") implementation_attrs("{}") : (tensor<1x16xf32>) -> tensor<1x16xf32>
// CHECK: spirv.ARM.GraphOutputs %[[CALL]] : !spirv.arm.tensor<1x16xf32>
return %0 : tensor<1x16xf32>
}
@@ -39,7 +39,7 @@ func.func @empty_strings(%arg0: tensor<1x16xf32>) -> tensor<1x16xf32> {
// CHECK: %[[OP_NAME:.*]] = spirv.Constant [0 : i8] : !spirv.array<1 x i8>
// CHECK: %[[IMPLEMENTATION_ATTRS:.*]] = spirv.Constant [0 : i8] : !spirv.array<1 x i8>
// CHECK: %[[CALL:.*]] = spirv.ExperimentalML.Call opcode = 0, %[[OP_NAME]], %[[IMPLEMENTATION_ATTRS]], %arg0 : (!spirv.array<1 x i8>, !spirv.array<1 x i8>, !spirv.arm.tensor<1x16xf32>) -> !spirv.arm.tensor<1x16xf32>
- %0 = tosa.custom %arg0 {domain_name = "my:custom", implementation_attrs = "", operator_name = ""} : (tensor<1x16xf32>) -> tensor<1x16xf32>
+ %0 = tosa.custom %arg0 operator_name("") domain_name("my:custom") implementation_attrs("") : (tensor<1x16xf32>) -> tensor<1x16xf32>
// CHECK: spirv.ARM.GraphOutputs %[[CALL]] : !spirv.arm.tensor<1x16xf32>
return %0 : tensor<1x16xf32>
}
diff --git a/mlir/test/Conversion/TosaToSPIRVTosa/graph-constant-mark.mlir b/mlir/test/Conversion/TosaToSPIRVTosa/graph-constant-mark.mlir
index b788bf2163806..ceab987ab08b5 100644
--- a/mlir/test/Conversion/TosaToSPIRVTosa/graph-constant-mark.mlir
+++ b/mlir/test/Conversion/TosaToSPIRVTosa/graph-constant-mark.mlir
@@ -20,7 +20,7 @@ func.func @small_const() -> tensor<16xi32> {
// CHECK-LABEL: func.func @large_const_shape
func.func @large_const_shape() -> !tosa.shape<33> {
- // CHECK: tosa.const_shape {grapharm.graph_constant_id = 0 : i32, values = {{.*}}} : () -> !tosa.shape<33>
+ // CHECK: tosa.const_shape values({{.*}}) {grapharm.graph_constant_id = 0 : i32} : () -> !tosa.shape<33>
%res = "tosa.const_shape"() <{values = dense<[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]> : tensor<33xindex>}> : () -> !tosa.shape<33>
return %res : !tosa.shape<33>
}
@@ -42,7 +42,7 @@ func.func @mixed_large_constants() -> (tensor<17xi32>, tensor<18xi32>, !tosa.sha
%const0 = "tosa.const"() <{values = dense<[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]> : tensor<17xi32>}> : () -> tensor<17xi32>
// CHECK: "tosa.const"() <{values = {{.*}}> {grapharm.graph_constant_id = 1 : i32} : () -> tensor<18xi32>
%const1 = "tosa.const"() <{values = dense<[20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37]> : tensor<18xi32>}> : () -> tensor<18xi32>
- // CHECK: tosa.const_shape {grapharm.graph_constant_id = 2 : i32, values = {{.*}}} : () -> !tosa.shape<33>
+ // CHECK: tosa.const_shape values({{.*}}) {grapharm.graph_constant_id = 2 : i32} : () -> !tosa.shape<33>
%shape = "tosa.const_shape"() <{values = dense<[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]> : tensor<33xindex>}> : () -> !tosa.shape<33>
return %const0, %const1, %shape : tensor<17xi32>, tensor<18xi32>, !tosa.shape<33>
}
diff --git a/mlir/test/Conversion/TosaToSPIRVTosa/tosa-to-spirv.mlir b/mlir/test/Conversion/TosaToSPIRVTosa/tosa-to-spirv.mlir
index d9ac75833511f..e8b5c67db4cd5 100644
--- a/mlir/test/Conversion/TosaToSPIRVTosa/tosa-to-spirv.mlir
+++ b/mlir/test/Conversion/TosaToSPIRVTosa/tosa-to-spirv.mlir
@@ -7,7 +7,7 @@
// CHECK-LABEL: spirv.ARM.Graph @argmax_int
func.func @argmax_int(%arg0: tensor<2x3x4xi8>) -> tensor<2x4xi32> {
// CHECK: %[[ARGMAX:.*]] = spirv.Tosa.ArgMax axis = 1, nan_mode = <Propagate>, %arg0 : !spirv.arm.tensor<2x3x4xi8> -> !spirv.arm.tensor<2x4xi32>
- %res = tosa.argmax %arg0 {axis = 1 : i32, nan_mode = PROPAGATE} : (tensor<2x3x4xi8>) -> tensor<2x4xi32>
+ %res = tosa.argmax %arg0 axis(1) : (tensor<2x3x4xi8>) -> tensor<2x4xi32>
return %res : tensor<2x4xi32>
}
@@ -20,7 +20,7 @@ func.func @argmax_int(%arg0: tensor<2x3x4xi8>) -> tensor<2x4xi32> {
// CHECK-LABEL: spirv.ARM.Graph @avg_pool2d_int
func.func @avg_pool2d_int(%arg0: tensor<1x4x4x1xi8>, %arg1: tensor<1xi8>, %arg2: tensor<1xi8>) -> tensor<1x2x2x1xi8> {
// CHECK: %[[AVG_POOL:.*]] = spirv.Tosa.AvgPool2D kernel = [2, 2], stride = [2, 2], pad = [0, 0, 0, 0], acc_type = <INT32>, %arg0, %arg1, %arg2 : !spirv.arm.tensor<1x4x4x1xi8>, !spirv.arm.tensor<1xi8>, !spirv.arm.tensor<1xi8> -> !spirv.arm.tensor<1x2x2x1xi8>
- %res = tosa.avg_pool2d %arg0, %arg1, %arg2 {kernel = array<i64: 2, 2>, stride = array<i64: 2, 2>, pad = array<i64: 0, 0, 0, 0>, acc_type = i32} : (tensor<1x4x4x1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x2x2x1xi8>
+ %res = tosa.avg_pool2d %arg0, %arg1, %arg2 kernel([2, 2]) stride([2, 2]) pad([0, 0, 0, 0]) acc_type(i32) : (tensor<1x4x4x1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x2x2x1xi8>
return %res : tensor<1x2x2x1xi8>
}
@@ -33,7 +33,7 @@ func.func @avg_pool2d_int(%arg0: tensor<1x4x4x1xi8>, %arg1: tensor<1xi8>, %arg2:
// CHECK-LABEL: spirv.ARM.Graph @conv2d_int
func.func @conv2d_int(%arg0: tensor<1x4x4x1xi8>, %arg1: tensor<2x3x3x1xi8>, %arg2: tensor<2xi32>, %arg3: tensor<1xi8>, %arg4: tensor<1xi8>) -> tensor<1x2x2x2xi32> {
// CHECK: %[[CONV2D:.*]] = spirv.Tosa.Conv2D pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1], acc_type = <INT32>, local_bound = false, %arg0, %arg1, %arg2, %arg3, %arg4 : !spirv.arm.tensor<1x4x4x1xi8>, !spirv.arm.tensor<2x3x3x1xi8>, !spirv.arm.tensor<2xi32>, !spirv.arm.tensor<1xi8>, !spirv.arm.tensor<1xi8> -> !spirv.arm.tensor<1x2x2x2xi32>
- %res = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>, acc_type = i32, local_bound = false} : (tensor<1x4x4x1xi8>, tensor<2x3x3x1xi8>, tensor<2xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x2x2x2xi32>
+ %res = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) : (tensor<1x4x4x1xi8>, tensor<2x3x3x1xi8>, tensor<2xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x2x2x2xi32>
return %res : tensor<1x2x2x2xi32>
}
@@ -46,7 +46,7 @@ func.func @conv2d_int(%arg0: tensor<1x4x4x1xi8>, %arg1: tensor<2x3x3x1xi8>, %arg
// CHECK-LABEL: spirv.ARM.Graph @conv3d_int
func.func @conv3d_int(%arg0: tensor<1x4x4x4x1xi8>, %arg1: tensor<2x2x2x2x1xi8>, %arg2: tensor<2xi32>, %arg3: tensor<1xi8>, %arg4: tensor<1xi8>) -> tensor<1x3x3x3x2xi32> {
// CHECK: %[[CONV3D:.*]] = spirv.Tosa.Conv3D pad = [0, 0, 0, 0, 0, 0], stride = [1, 1, 1], dilation = [1, 1, 1], acc_type = <INT32>, local_bound = false, %arg0, %arg1, %arg2, %arg3, %arg4 : !spirv.arm.tensor<1x4x4x4x1xi8>, !spirv.arm.tensor<2x2x2x2x1xi8>, !spirv.arm.tensor<2xi32>, !spirv.arm.tensor<1xi8>, !spirv.arm.tensor<1xi8> -> !spirv.arm.tensor<1x3x3x3x2xi32>
- %res = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>, acc_type = i32, local_bound = false} : (tensor<1x4x4x4x1xi8>, tensor<2x2x2x2x1xi8>, tensor<2xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x3x3x3x2xi32>
+ %res = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(i32) : (tensor<1x4x4x4x1xi8>, tensor<2x2x2x2x1xi8>, tensor<2xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x3x3x3x2xi32>
return %res : tensor<1x3x3x3x2xi32>
}
@@ -59,7 +59,7 @@ func.func @conv3d_int(%arg0: tensor<1x4x4x4x1xi8>, %arg1: tensor<2x2x2x2x1xi8>,
// CHECK-LABEL: spirv.ARM.Graph @depthwise_conv2d_int
func.func @depthwise_conv2d_int(%arg0: tensor<1x4x4x1xi8>, %arg1: tensor<3x3x1x2xi8>, %arg2: tensor<2xi32>, %arg3: tensor<1xi8>, %arg4: tensor<1xi8>) -> tensor<1x2x2x2xi32> {
// CHECK: %[[DEPTHWISE_CONV2D:.*]] = spirv.Tosa.DepthwiseConv2D pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1], acc_type = <INT32>, local_bound = false, %arg0, %arg1, %arg2, %arg3, %arg4 : !spirv.arm.tensor<1x4x4x1xi8>, !spirv.arm.tensor<3x3x1x2xi8>, !spirv.arm.tensor<2xi32>, !spirv.arm.tensor<1xi8>, !spirv.arm.tensor<1xi8> -> !spirv.arm.tensor<1x2x2x2xi32>
- %res = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>, acc_type = i32, local_bound = false} : (tensor<1x4x4x1xi8>, tensor<3x3x1x2xi8>, tensor<2xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x2x2x2xi32>
+ %res = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) : (tensor<1x4x4x1xi8>, tensor<3x3x1x2xi8>, tensor<2xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x2x2x2xi32>
return %res : tensor<1x2x2x2xi32>
}
@@ -100,7 +100,7 @@ func.func @matmul_int(%arg0: tensor<1x2x3xi8>, %arg1: tensor<1x3x4xi8>, %arg2: t
// CHECK-LABEL: spirv.ARM.Graph @max_pool2d_fp
func.func @max_pool2d_fp(%arg0: tensor<1x4x4x1xf32>) -> tensor<1x2x2x1xf32> {
// CHECK: %[[MAX_POOL:.*]] = spirv.Tosa.MaxPool2D kernel = [2, 2], stride = [2, 2], pad = [0, 0, 0, 0], nan_mode = <Propagate>, %arg0 : !spirv.arm.tensor<1x4x4x1xf32> -> !spirv.arm.tensor<1x2x2x1xf32>
- %res = tosa.max_pool2d %arg0 {kernel = array<i64: 2, 2>, stride = array<i64: 2, 2>, pad = array<i64: 0, 0, 0, 0>, nan_mode = PROPAGATE} : (tensor<1x4x4x1xf32>) -> tensor<1x2x2x1xf32>
+ %res = tosa.max_pool2d %arg0 kernel([2, 2]) stride([2, 2]) pad([0, 0, 0, 0]) : (tensor<1x4x4x1xf32>) -> tensor<1x2x2x1xf32>
return %res : tensor<1x2x2x1xf32>
}
@@ -128,7 +128,7 @@ func.func @rfft2d_fp(%arg0: tensor<1x32x32xf32>) -> (tensor<1x32x17xf32>, tensor
// CHECK-LABEL: spirv.ARM.Graph @transpose_conv2d_int
func.func @transpose_conv2d_int(%arg0: tensor<1x2x2x1xi8>, %arg1: tensor<2x3x3x1xi8>, %arg2: tensor<2xi32>, %arg3: tensor<1xi8>, %arg4: tensor<1xi8>) -> tensor<1x4x4x2xi32> {
// CHECK: %[[TRANSPOSE_CONV2D:.*]] = spirv.Tosa.TransposeConv2D out_pad = [0, 0, 0, 0], stride = [1, 1], acc_type = <INT32>, local_bound = false, %arg0, %arg1, %arg2, %arg3, %arg4 : !spirv.arm.tensor<1x2x2x1xi8>, !spirv.arm.tensor<2x3x3x1xi8>, !spirv.arm.tensor<2xi32>, !spirv.arm.tensor<1xi8>, !spirv.arm.tensor<1xi8> -> !spirv.arm.tensor<1x4x4x2xi32>
- %res = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = i32, local_bound = false} : (tensor<1x2x2x1xi8>, tensor<2x3x3x1xi8>, tensor<2xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x2xi32>
+ %res = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(i32) : (tensor<1x2x2x1xi8>, tensor<2x3x3x1xi8>, tensor<2xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x2xi32>
return %res : tensor<1x4x4x2xi32>
}
@@ -141,7 +141,7 @@ func.func @transpose_conv2d_int(%arg0: tensor<1x2x2x1xi8>, %arg1: tensor<2x3x3x1
// CHECK-LABEL: spirv.ARM.Graph @clamp_int
func.func @clamp_int(%arg0: tensor<4x8xi8>) -> tensor<4x8xi8> {
// CHECK: %[[CLAMP:.*]] = spirv.Tosa.Clamp min_val = -2 : i8, max_val = 3 : i8, nan_mode = <Propagate>, %arg0 : !spirv.arm.tensor<4x8xi8> -> !spirv.arm.tensor<4x8xi8>
- %res = tosa.clamp %arg0 {min_val = -2 : i8, max_val = 3 : i8, nan_mode = PROPAGATE} : (tensor<4x8xi8>) -> tensor<4x8xi8>
+ %res = tosa.clamp %arg0 min_val(-2 : i8) max_val(3 : i8) : (tensor<4x8xi8>) -> tensor<4x8xi8>
return %res : tensor<4x8xi8>
}
@@ -206,7 +206,7 @@ func.func @add_int(%arg0: tensor<4x7x3x10xi32>, %arg1: tensor<4x7x3x1xi32>) -> t
// CHECK-LABEL: spirv.ARM.Graph @arithmetic_right_shift_int
func.func @arithmetic_right_shift_int(%arg0: tensor<1x4xi16>, %arg1: tensor<3x4xi16>) -> tensor<3x4xi16> {
// CHECK: %[[SHIFT:.*]] = spirv.Tosa.ArithmeticRightShift round = true, %arg0, %arg1 : !spirv.arm.tensor<1x4xi16>, !spirv.arm.tensor<3x4xi16> -> !spirv.arm.tensor<3x4xi16>
- %res = tosa.arithmetic_right_shift %arg0, %arg1 {round = true} : (tensor<1x4xi16>, tensor<3x4xi16>) -> tensor<3x4xi16>
+ %res = tosa.arithmetic_right_shift %arg0, %arg1 round(true) : (tensor<1x4xi16>, tensor<3x4xi16>) -> tensor<3x4xi16>
return %res : tensor<3x4xi16>
}
@@ -336,7 +336,7 @@ func.func @logicalxor_any(%arg0: tensor<11x4x9x12xi1>, %arg1: tensor<11x4x9x1xi1
// CHECK-LABEL: spirv.ARM.Graph @maximum_int
func.func @maximum_int(%arg0: tensor<1x2x65533x1xi32>, %arg1: tensor<1x2x65533x2xi32>) -> tensor<1x2x65533x2xi32> {
// CHECK: %[[MAXIMUM:.*]] = spirv.Tosa.Maximum nan_mode = <Propagate>, %arg0, %arg1 : !spirv.arm.tensor<1x2x65533x1xi32>, !spirv.arm.tensor<1x2x65533x2xi32> -> !spirv.arm.tensor<1x2x65533x2xi32>
- %res = tosa.maximum %arg0, %arg1 {nan_mode = PROPAGATE} : (tensor<1x2x65533x1xi32>, tensor<1x2x65533x2xi32>) -> tensor<1x2x65533x2xi32>
+ %res = tosa.maximum %arg0, %arg1 : (tensor<1x2x65533x1xi32>, tensor<1x2x65533x2xi32>) -> tensor<1x2x65533x2xi32>
return %res : tensor<1x2x65533x2xi32>
}
@@ -349,7 +349,7 @@ func.func @maximum_int(%arg0: tensor<1x2x65533x1xi32>, %arg1: tensor<1x2x65533x2
// CHECK-LABEL: spirv.ARM.Graph @minimum_int
func.func @minimum_int(%arg0: tensor<15x2x10x11xi32>, %arg1: tensor<15x1x10x11xi32>) -> tensor<15x2x10x11xi32> {
// CHECK: %[[MINIMUM:.*]] = spirv.Tosa.Minimum nan_mode = <Propagate>, %arg0, %arg1 : !spirv.arm.tensor<15x2x10x11xi32>, !spirv.arm.tensor<15x1x10x11xi32> -> !spirv.arm.tensor<15x2x10x11xi32>
- %res = tosa.minimum %arg0, %arg1 {nan_mode = PROPAGATE} : (tensor<15x2x10x11xi32>, tensor<15x1x10x11xi32>) -> tensor<15x2x10x11xi32>
+ %res = tosa.minimum %arg0, %arg1 : (tensor<15x2x10x11xi32>, tensor<15x1x10x11xi32>) -> tensor<15x2x10x11xi32>
return %res : tensor<15x2x10x11xi32>
}
@@ -635,7 +635,7 @@ func.func @greaterequal_int(%arg0: tensor<10x17x7x1xi32>, %arg1: tensor<10x17x7x
// CHECK-LABEL: spirv.ARM.Graph @reduce_all
func.func @reduce_all(%arg0: tensor<2x3x4xi1>) -> tensor<2x1x4xi1> {
// CHECK: %[[REDUCE:.*]] = spirv.Tosa.ReduceAll axis = 1, %arg0 : !spirv.arm.tensor<2x3x4xi1> -> !spirv.arm.tensor<2x1x4xi1>
- %res = tosa.reduce_all %arg0 {axis = 1 : i32} : (tensor<2x3x4xi1>) -> tensor<2x1x4xi1>
+ %res = tosa.reduce_all %arg0 axis(1) : (tensor<2x3x4xi1>) -> tensor<2x1x4xi1>
return %res : tensor<2x1x4xi1>
}
@@ -648,7 +648,7 @@ func.func @reduce_all(%arg0: tensor<2x3x4xi1>) -> tensor<2x1x4xi1> {
// CHECK-LABEL: spirv.ARM.Graph @reduce_any
func.func @reduce_any(%arg0: tensor<2x3x4xi1>) -> tensor<2x1x4xi1> {
// CHECK: %[[REDUCE:.*]] = spirv.Tosa.ReduceAny axis = 1, %arg0 : !spirv.arm.tensor<2x3x4xi1> -> !spirv.arm.tensor<2x1x4xi1>
- %res = tosa.reduce_any %arg0 {axis = 1 : i32} : (tensor<2x3x4xi1>) -> tensor<2x1x4xi1>
+ %res = tosa.reduce_any %arg0 axis(1) : (tensor<2x3x4xi1>) -> tensor<2x1x4xi1>
return %res : tensor<2x1x4xi1>
}
@@ -661,7 +661,7 @@ func.func @reduce_any(%arg0: tensor<2x3x4xi1>) -> tensor<2x1x4xi1> {
// CHECK-LABEL: spirv.ARM.Graph @reduce_max_int
func.func @reduce_max_int(%arg0: tensor<2x3x4xi8>) -> tensor<2x1x4xi8> {
// CHECK: %[[REDUCE:.*]] = spirv.Tosa.ReduceMax axis = 1, nan_mode = <Propagate>, %arg0 : !spirv.arm.tensor<2x3x4xi8> -> !spirv.arm.tensor<2x1x4xi8>
- %res = tosa.reduce_max %arg0 {axis = 1 : i32, nan_mode = PROPAGATE} : (tensor<2x3x4xi8>) -> tensor<2x1x4xi8>
+ %res = tosa.reduce_max %arg0 axis(1) : (tensor<2x3x4xi8>) -> tensor<2x1x4xi8>
return %res : tensor<2x1x4xi8>
}
@@ -674,7 +674,7 @@ func.func @reduce_max_int(%arg0: tensor<2x3x4xi8>) -> tensor<2x1x4xi8> {
// CHECK-LABEL: spirv.ARM.Graph @reduce_min_int
func.func @reduce_min_int(%arg0: tensor<2x3x4xi8>) -> tensor<2x1x4xi8> {
// CHECK: %[[REDUCE:.*]] = spirv.Tosa.ReduceMin axis = 1, nan_mode = <Propagate>, %arg0 : !spirv.arm.tensor<2x3x4xi8> -> !spirv.arm.tensor<2x1x4xi8>
- %res = tosa.reduce_min %arg0 {axis = 1 : i32, nan_mode = PROPAGATE} : (tensor<2x3x4xi8>) -> tensor<2x1x4xi8>
+ %res = tosa.reduce_min %arg0 axis(1) : (tensor<2x3x4xi8>) -> tensor<2x1x4xi8>
return %res : tensor<2x1x4xi8>
}
@@ -687,7 +687,7 @@ func.func @reduce_min_int(%arg0: tensor<2x3x4xi8>) -> tensor<2x1x4xi8> {
// CHECK-LABEL: spirv.ARM.Graph @reduce_product_fp
func.func @reduce_product_fp(%arg0: tensor<2x3x4xf32>) -> tensor<2x1x4xf32> {
// CHECK: %[[REDUCE:.*]] = spirv.Tosa.ReduceProduct axis = 1, %arg0 : !spirv.arm.tensor<2x3x4xf32> -> !spirv.arm.tensor<2x1x4xf32>
- %res = tosa.reduce_product %arg0 {axis = 1 : i32} : (tensor<2x3x4xf32>) -> tensor<2x1x4xf32>
+ %res = tosa.reduce_product %arg0 axis(1) : (tensor<2x3x4xf32>) -> tensor<2x1x4xf32>
return %res : tensor<2x1x4xf32>
}
@@ -700,7 +700,7 @@ func.func @reduce_product_fp(%arg0: tensor<2x3x4xf32>) -> tensor<2x1x4xf32> {
// CHECK-LABEL: spirv.ARM.Graph @reduce_sum_int
func.func @reduce_sum_int(%arg0: tensor<2x3x4xi32>) -> tensor<2x1x4xi32> {
// CHECK: %[[REDUCE:.*]] = spirv.Tosa.ReduceSum axis = 1, %arg0 : !spirv.arm.tensor<2x3x4xi32> -> !spirv.arm.tensor<2x1x4xi32>
- %res = tosa.reduce_sum %arg0 {axis = 1 : i32} : (tensor<2x3x4xi32>) -> tensor<2x1x4xi32>
+ %res = tosa.reduce_sum %arg0 axis(1) : (tensor<2x3x4xi32>) -> tensor<2x1x4xi32>
return %res : tensor<2x1x4xi32>
}
@@ -713,7 +713,7 @@ func.func @reduce_sum_int(%arg0: tensor<2x3x4xi32>) -> tensor<2x1x4xi32> {
// CHECK-LABEL: spirv.ARM.Graph @concat_int
func.func @concat_int(%arg0: tensor<2x3xi8>, %arg1: tensor<2x4xi8>) -> tensor<2x7xi8> {
// CHECK: %[[CONCAT:.*]] = spirv.Tosa.Concat axis = 1, %arg0, %arg1 : !spirv.arm.tensor<2x3xi8>, !spirv.arm.tensor<2x4xi8> -> !spirv.arm.tensor<2x7xi8>
- %res = tosa.concat %arg0, %arg1 {axis = 1 : i32} : (tensor<2x3xi8>, tensor<2x4xi8>) -> tensor<2x7xi8>
+ %res = tosa.concat %arg0, %arg1 axis(1) : (tensor<2x3xi8>, tensor<2x4xi8>) -> tensor<2x7xi8>
return %res : tensor<2x7xi8>
}
@@ -748,7 +748,7 @@ func.func @concat_split_int(%arg0: tensor<1xi8>, %arg1: tensor<1xi8>, %arg2: ten
%arg40, %arg41, %arg42, %arg43, %arg44, %arg45, %arg46, %arg47,
%arg48, %arg49, %arg50, %arg51, %arg52, %arg53, %arg54, %arg55,
%arg56, %arg57, %arg58, %arg59, %arg60, %arg61, %arg62, %arg63,
- %arg64 {axis = 0 : i32}
+ %arg64 axis(0)
: (tensor<1xi8>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>,
tensor<1xi8>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>,
tensor<1xi8>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>,
@@ -775,7 +775,7 @@ func.func @concat_split_int(%arg0: tensor<1xi8>, %arg1: tensor<1xi8>, %arg2: ten
func.func @concat_split_dynamic(%arg0: tensor<?xi8>) -> tensor<?xi8> {
// CHECK: %[[CONCAT0:.*]] = spirv.Tosa.Concat axis = 0, %arg0, %arg0{{.*}} -> !spirv.arm.tensor<?xi8>
// CHECK: %[[CONCAT1:.*]] = spirv.Tosa.Concat axis = 0, %[[CONCAT0]], %arg0 : !spirv.arm.tensor<?xi8>, !spirv.arm.tensor<?xi8> -> !spirv.arm.tensor<?xi8>
- %res = tosa.concat %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0 {axis = 0 : i32} : (tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>) -> tensor<?xi8>
+ %res = tosa.concat %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0 axis(0) : (tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>, tensor<?xi8>) -> tensor<?xi8>
return %res : tensor<?xi8>
}
@@ -820,7 +820,7 @@ func.func @reshape_int(%arg0: tensor<25x6x29x35xi16>) -> tensor<125x6x7x29xi16>
// CHECK-LABEL: spirv.ARM.Graph @reverse_int
func.func @reverse_int(%arg0: tensor<20x5x28x31xi32>) -> tensor<20x5x28x31xi32> {
// CHECK: %[[REVERSE:.*]] = spirv.Tosa.Reverse axis = 2, %arg0 : !spirv.arm.tensor<20x5x28x31xi32> -> !spirv.arm.tensor<20x5x28x31xi32>
- %res = tosa.reverse %arg0 {axis = 2 : i32} : (tensor<20x5x28x31xi32>) -> tensor<20x5x28x31xi32>
+ %res = tosa.reverse %arg0 axis(2) : (tensor<20x5x28x31xi32>) -> tensor<20x5x28x31xi32>
return %res : tensor<20x5x28x31xi32>
}
@@ -865,7 +865,7 @@ func.func @tile_int(%arg0: tensor<10x28x21xi16>) -> tensor<10x28x63xi16> {
// CHECK-LABEL: spirv.ARM.Graph @transpose_int
func.func @transpose_int(%arg0: tensor<14x28x1x61xi16>) -> tensor<1x14x28x61xi16> {
// CHECK: %[[TRANSPOSE:.*]] = spirv.Tosa.Transpose perms = [2, 0, 1, 3], %arg0 : !spirv.arm.tensor<14x28x1x61xi16> -> !spirv.arm.tensor<1x14x28x61xi16>
- %res = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1, 3>} : (tensor<14x28x1x61xi16>) -> tensor<1x14x28x61xi16>
+ %res = tosa.transpose %arg0 perms([2, 0, 1, 3]) : (tensor<14x28x1x61xi16>) -> tensor<1x14x28x61xi16>
return %res : tensor<1x14x28x61xi16>
}
@@ -910,7 +910,7 @@ func.func @resize_int(%arg0: tensor<1x1x31x55xi8>) -> tensor<1x1x278x55xi8> {
// CHECK: %[[OFFSET:.*]] = spirv.Constant dense<0> : !spirv.arm.tensor<2xi32>
// CHECK: %[[BORDER:.*]] = spirv.Constant dense<[0, 7]> : !spirv.arm.tensor<2xi32>
// CHECK: %[[RESIZE:.*]] = spirv.Tosa.Resize mode = <NearestNeighbor>, %arg0, %[[SCALE]], %[[OFFSET]], %[[BORDER]] : !spirv.arm.tensor<1x1x31x55xi8>, !spirv.arm.tensor<4xi32>, !spirv.arm.tensor<2xi32>, !spirv.arm.tensor<2xi32> -> !spirv.arm.tensor<1x1x278x55xi8>
- %res = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x1x31x55xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x1x278x55xi8>
+ %res = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x1x31x55xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x1x278x55xi8>
return %res : tensor<1x1x278x55xi8>
}
@@ -944,7 +944,7 @@ func.func @rescale_int(%arg0: tensor<2x3xi16>) -> tensor<2x3xi16> {
// CHECK: %[[INPUT_ZP:.*]] = spirv.Constant dense<0> : !spirv.arm.tensor<1xi16>
// CHECK: %[[OUTPUT_ZP:.*]] = spirv.Constant dense<0> : !spirv.arm.tensor<1xi16>
// CHECK: %[[RESCALE:.*]] = spirv.Tosa.Rescale scale32 = true, rounding_mode = <DoubleRound>, per_channel = false, input_unsigned = false, output_unsigned = false, %arg0, %[[MULTIPLIER]], %[[SHIFT]], %[[INPUT_ZP]], %[[OUTPUT_ZP]] : !spirv.arm.tensor<2x3xi16>, !spirv.arm.tensor<1xi32>, !spirv.arm.tensor<1xi8>, !spirv.arm.tensor<1xi16>, !spirv.arm.tensor<1xi16> -> !spirv.arm.tensor<2x3xi16>
- %res = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = true, rounding_mode = DOUBLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2x3xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<2x3xi16>
+ %res = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<DOUBLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<2x3xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<2x3xi16>
return %res : tensor<2x3xi16>
}
diff --git a/mlir/test/Conversion/TosaToTensor/tosa-to-tensor-invalid.mlir b/mlir/test/Conversion/TosaToTensor/tosa-to-tensor-invalid.mlir
index 3e7c72c974bbf..5e33ce2a2fcce 100644
--- a/mlir/test/Conversion/TosaToTensor/tosa-to-tensor-invalid.mlir
+++ b/mlir/test/Conversion/TosaToTensor/tosa-to-tensor-invalid.mlir
@@ -2,8 +2,8 @@
// CHECK-LABEL: @slice_resultType_unranked
func.func @slice_resultType_unranked(%arg0: tensor<?xf32>) -> (tensor<*xf32>) {
- %size = tosa.const_shape {values = dense<2> : tensor<1xindex>} : () -> !tosa.shape<1>
- %start = tosa.const_shape {values = dense<0> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %size = tosa.const_shape values(dense<2> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %start = tosa.const_shape values(dense<0> : tensor<1xindex>) : () -> !tosa.shape<1>
// expected-error at +1 {{failed to legalize operation 'tosa.slice'}}
%2 = tosa.slice %arg0, %start, %size : (tensor<?xf32>, !tosa.shape<1>, !tosa.shape<1>) -> tensor<*xf32>
return %2 : tensor<*xf32>
diff --git a/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir b/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
index 0a276e2a5c3d2..c4e98bf7bece9 100644
--- a/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
+++ b/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
@@ -6,7 +6,7 @@
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<f32>
// CHECK: return %[[ARG_0]] : tensor<f32>
func.func @test_reshape_0d_same_s2s_explicit(%arg0: tensor<f32>) -> tensor<f32> {
- %s = tosa.const_shape { values = dense<> : tensor<0xindex> } : () -> !tosa.shape<0>
+ %s = tosa.const_shape values(dense<> : tensor<0xindex>) : () -> !tosa.shape<0>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<f32>, !tosa.shape<0>) -> tensor<f32>
return %0 : tensor<f32>
}
@@ -19,7 +19,7 @@ func.func @test_reshape_0d_same_s2s_explicit(%arg0: tensor<f32>) -> tensor<f32>
// CHECK: %[[VAL_1:.*]] = tensor.cast %[[VAL_0]] : tensor<1xf32> to tensor<?xf32>
// CHECK: return %[[VAL_1]] : tensor<?xf32>
func.func @test_reshape_0d_up_s2d_auto(%arg0: tensor<f32>) -> tensor<?xf32> {
- %s = tosa.const_shape { values = dense<-1> : tensor<1xindex> } : () -> !tosa.shape<1>
+ %s = tosa.const_shape values(dense<-1> : tensor<1xindex>) : () -> !tosa.shape<1>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<f32>, !tosa.shape<1>) -> tensor<?xf32>
return %0 : tensor<?xf32>
}
@@ -32,7 +32,7 @@ func.func @test_reshape_0d_up_s2d_auto(%arg0: tensor<f32>) -> tensor<?xf32> {
// CHECK: %[[VAL_1:.*]] = tensor.cast %[[VAL_0]] : tensor<1xf32> to tensor<?xf32>
// CHECK: return %[[VAL_1]] : tensor<?xf32>
func.func @test_reshape_0d_up_s2d_explicit(%arg0: tensor<f32>) -> tensor<?xf32> {
- %s = tosa.const_shape { values = dense<1> : tensor<1xindex> } : () -> !tosa.shape<1>
+ %s = tosa.const_shape values(dense<1> : tensor<1xindex>) : () -> !tosa.shape<1>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<f32>, !tosa.shape<1>) -> tensor<?xf32>
return %0 : tensor<?xf32>
}
@@ -44,7 +44,7 @@ func.func @test_reshape_0d_up_s2d_explicit(%arg0: tensor<f32>) -> tensor<?xf32>
// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] [] output_shape [1] : tensor<f32> into tensor<1xf32>
// CHECK: return %[[VAL_0]] : tensor<1xf32>
func.func @test_reshape_0d_up_s2s_auto(%arg0: tensor<f32>) -> tensor<1xf32> {
- %s = tosa.const_shape { values = dense<-1> : tensor<1xindex> } : () -> !tosa.shape<1>
+ %s = tosa.const_shape values(dense<-1> : tensor<1xindex>) : () -> !tosa.shape<1>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<f32>, !tosa.shape<1>) -> tensor<1xf32>
return %0 : tensor<1xf32>
}
@@ -56,7 +56,7 @@ func.func @test_reshape_0d_up_s2s_auto(%arg0: tensor<f32>) -> tensor<1xf32> {
// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] [] output_shape [1] : tensor<f32> into tensor<1xf32>
// CHECK: return %[[VAL_0]] : tensor<1xf32>
func.func @test_reshape_0d_up_s2s_explicit(%arg0: tensor<f32>) -> tensor<1xf32> {
- %s = tosa.const_shape { values = dense<1> : tensor<1xindex> } : () -> !tosa.shape<1>
+ %s = tosa.const_shape values(dense<1> : tensor<1xindex>) : () -> !tosa.shape<1>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<f32>, !tosa.shape<1>) -> tensor<1xf32>
return %0 : tensor<1xf32>
}
@@ -69,7 +69,7 @@ func.func @test_reshape_0d_up_s2s_explicit(%arg0: tensor<f32>) -> tensor<1xf32>
// CHECK: %[[VAL_1:.*]] = tensor.collapse_shape %[[VAL_0]] [] : tensor<1xf32> into tensor<f32>
// CHECK: return %[[VAL_1]] : tensor<f32>
func.func @test_reshape_1d_down_d2s_explicit(%arg0: tensor<?xf32>) -> tensor<f32> {
- %s = tosa.const_shape { values = dense<> : tensor<0xindex> } : () -> !tosa.shape<0>
+ %s = tosa.const_shape values(dense<> : tensor<0xindex>) : () -> !tosa.shape<0>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?xf32>, !tosa.shape<0>) -> tensor<f32>
return %0 : tensor<f32>
}
@@ -81,7 +81,7 @@ func.func @test_reshape_1d_down_d2s_explicit(%arg0: tensor<?xf32>) -> tensor<f32
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] [] : tensor<1xf32> into tensor<f32>
// CHECK: return %[[VAL_0]] : tensor<f32>
func.func @test_reshape_1d_down_s2s_explicit(%arg0: tensor<1xf32>) -> tensor<f32> {
- %s = tosa.const_shape { values = dense<> : tensor<0xindex> } : () -> !tosa.shape<0>
+ %s = tosa.const_shape values(dense<> : tensor<0xindex>) : () -> !tosa.shape<0>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<1xf32>, !tosa.shape<0>) -> tensor<f32>
return %0 : tensor<f32>
}
@@ -97,7 +97,7 @@ func.func @test_reshape_1d_down_s2s_explicit(%arg0: tensor<1xf32>) -> tensor<f32
// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[ARG_0]] {{\[\[}}0, 1]] output_shape [2, %[[VAL_0]]] : tensor<?xf32> into tensor<2x?xf32>
// CHECK: return %[[EXPANDED]] : tensor<2x?xf32>
func.func @test_reshape_1d_up_d2d_auto(%arg0: tensor<?xf32>) -> tensor<2x?xf32> {
- %s = tosa.const_shape { values = dense<[2, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?xf32>, !tosa.shape<2>) -> tensor<2x?xf32>
return %0 : tensor<2x?xf32>
}
@@ -109,7 +109,7 @@ func.func @test_reshape_1d_up_d2d_auto(%arg0: tensor<?xf32>) -> tensor<2x?xf32>
// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] {{\[\[}}0, 1]] output_shape [2, 3] : tensor<6xf32> into tensor<2x3xf32>
// CHECK: return %[[VAL_0]] : tensor<2x3xf32>
func.func @test_reshape_1d_up_s2s_explicit(%arg0: tensor<6xf32>) -> tensor<2x3xf32> {
- %s = tosa.const_shape { values = dense<[2, 3]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<6xf32>, !tosa.shape<2>) -> tensor<2x3xf32>
return %0 : tensor<2x3xf32>
}
@@ -121,7 +121,7 @@ func.func @test_reshape_1d_up_s2s_explicit(%arg0: tensor<6xf32>) -> tensor<2x3xf
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1]] : tensor<2x?xf32> into tensor<?xf32>
// CHECK: return %[[VAL_0]] : tensor<?xf32>
func.func @test_reshape_2d_down_d2d_auto(%arg0: tensor<2x?xf32>) -> tensor<?xf32> {
- %s = tosa.const_shape { values = dense<-1> : tensor<1xindex> } : () -> !tosa.shape<1>
+ %s = tosa.const_shape values(dense<-1> : tensor<1xindex>) : () -> !tosa.shape<1>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<2x?xf32>, !tosa.shape<1>) -> tensor<?xf32>
return %0 : tensor<?xf32>
}
@@ -133,7 +133,7 @@ func.func @test_reshape_2d_down_d2d_auto(%arg0: tensor<2x?xf32>) -> tensor<?xf32
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1]] : tensor<2x3xf32> into tensor<6xf32>
// CHECK: return %[[VAL_0]] : tensor<6xf32>
func.func @test_reshape_2d_down_s2s_explicit(%arg0: tensor<2x3xf32>) -> tensor<6xf32> {
- %s = tosa.const_shape { values = dense<6> : tensor<1xindex> } : () -> !tosa.shape<1>
+ %s = tosa.const_shape values(dense<6> : tensor<1xindex>) : () -> !tosa.shape<1>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<2x3xf32>, !tosa.shape<1>) -> tensor<6xf32>
return %0 : tensor<6xf32>
}
@@ -150,7 +150,7 @@ func.func @test_reshape_2d_down_s2s_explicit(%arg0: tensor<2x3xf32>) -> tensor<6
// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1]] output_shape [2, %[[DIV]]] : tensor<?xf32> into tensor<2x?xf32>
// CHECK: return %[[EXPANDED]] : tensor<2x?xf32>
func.func @test_reshape_2d_same_d2d_auto(%arg0: tensor<?x2xf32>) -> tensor<2x?xf32> {
- %s = tosa.const_shape { values = dense<[2, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?x2xf32>, !tosa.shape<2>) -> tensor<2x?xf32>
return %0 : tensor<2x?xf32>
}
@@ -164,7 +164,7 @@ func.func @test_reshape_2d_same_d2d_auto(%arg0: tensor<?x2xf32>) -> tensor<2x?xf
// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<4x2xf32> to tensor<?x2xf32>
// CHECK: return %[[VAL_2]] : tensor<?x2xf32>
func.func @test_reshape_2d_same_s2d_auto(%arg0: tensor<2x4xf32>) -> tensor<?x2xf32> {
- %s = tosa.const_shape { values = dense<[-1, 2]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[-1, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<2x4xf32>, !tosa.shape<2>) -> tensor<?x2xf32>
return %0 : tensor<?x2xf32>
}
@@ -179,7 +179,7 @@ func.func @test_reshape_2d_same_s2d_auto(%arg0: tensor<2x4xf32>) -> tensor<?x2xf
// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<4x2xf32> to tensor<?x2xf32>
// CHECK: return %[[VAL_2]] : tensor<?x2xf32>
func.func @test_reshape_2d_same_s2d_explicit(%arg0: tensor<2x4xf32>) -> tensor<?x2xf32> {
- %s = tosa.const_shape { values = dense<[4, 2]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[4, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<2x4xf32>, !tosa.shape<2>) -> tensor<?x2xf32>
return %0 : tensor<?x2xf32>
}
@@ -192,7 +192,7 @@ func.func @test_reshape_2d_same_s2d_explicit(%arg0: tensor<2x4xf32>) -> tensor<?
// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1]] output_shape [2, 3] : tensor<6xf32> into tensor<2x3xf32>
// CHECK: return %[[VAL_1]] : tensor<2x3xf32>
func.func @test_reshape_2d_same_s2s_explicit(%arg0: tensor<3x2xf32>) -> tensor<2x3xf32> {
- %s = tosa.const_shape { values = dense<[2, 3]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<3x2xf32>, !tosa.shape<2>) -> tensor<2x3xf32>
return %0 : tensor<2x3xf32>
}
@@ -210,7 +210,7 @@ func.func @test_reshape_2d_same_s2s_explicit(%arg0: tensor<3x2xf32>) -> tensor<2
// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<0x3x?xf32> to tensor<?x?x?xf32>
// CHECK: return %[[VAL_2]] : tensor<?x?x?xf32>
func.func @test_reshape_3d_same_d2d_auto_empty(%arg0: tensor<3x2x?xf32>) -> tensor<?x?x?xf32> {
- %s = tosa.const_shape { values = dense<[0, 3, -1]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[0, 3, -1]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<3x2x?xf32>, !tosa.shape<3>) -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
@@ -228,7 +228,7 @@ func.func @test_reshape_3d_same_d2d_auto_empty(%arg0: tensor<3x2x?xf32>) -> tens
// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<2x?x4xf32> to tensor<?x?x?xf32>
// CHECK: return %[[VAL_2]] : tensor<?x?x?xf32>
func.func @test_reshape_3d_same_d2d_auto(%arg0: tensor<2x?x?xf32>) -> tensor<?x?x?xf32> {
- %s = tosa.const_shape { values = dense<[2, -1, 4]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[2, -1, 4]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<2x?x?xf32>, !tosa.shape<3>) -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
@@ -245,7 +245,7 @@ func.func @test_reshape_3d_same_d2d_auto(%arg0: tensor<2x?x?xf32>) -> tensor<?x?
// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] output_shape [2, 3, %[[DIV]]] : tensor<?xf32> into tensor<2x3x?xf32>
// CHECK: return %[[VAL_1]] : tensor<2x3x?xf32>
func.func @test_reshape_3d_same_d2d_auto_identity(%arg0: tensor<?x3x4xf32>) -> tensor<2x3x?xf32> {
- %s = tosa.const_shape { values = dense<[2, 3, -1]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[2, 3, -1]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?x3x4xf32>, !tosa.shape<3>) -> tensor<2x3x?xf32>
return %0 : tensor<2x3x?xf32>
}
@@ -263,7 +263,7 @@ func.func @test_reshape_3d_same_d2d_auto_identity(%arg0: tensor<?x3x4xf32>) -> t
// CHECK: %[[VAL_2:.*]] = tensor.cast %[[EXPANDED]] : tensor<?x3x2xf32> to tensor<?x?x?xf32>
// CHECK: return %[[VAL_2]] : tensor<?x?x?xf32>
func.func @test_reshape_3d_same_d2d_explicit_empty(%arg0: tensor<3x2x?xf32>) -> tensor<?x?x?xf32> {
- %s = tosa.const_shape { values = dense<[0, 3, 2]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[0, 3, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<3x2x?xf32>, !tosa.shape<3>) -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
@@ -281,7 +281,7 @@ func.func @test_reshape_3d_same_d2d_explicit_empty(%arg0: tensor<3x2x?xf32>) ->
// CHECK: %[[VAL_2:.*]] = tensor.cast %[[EXPANDED]] : tensor<?x3x4xf32> to tensor<?x?x?xf32>
// CHECK: return %[[VAL_2]] : tensor<?x?x?xf32>
func.func @test_reshape_3d_same_d2d_explicit(%arg0: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
- %s = tosa.const_shape { values = dense<[2, 3, 4]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[2, 3, 4]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?x?x?xf32>, !tosa.shape<3>) -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
@@ -293,7 +293,7 @@ func.func @test_reshape_3d_same_d2d_explicit(%arg0: tensor<?x?x?xf32>) -> tensor
// CHECK: %[[VAL_0:.*]] = tensor.cast %[[ARG_0]] : tensor<?x3x4xf32> to tensor<2x3x?xf32>
// CHECK: return %[[VAL_0]] : tensor<2x3x?xf32>
func.func @test_reshape_3d_same_d2d_explicit_identity(%arg0: tensor<?x3x4xf32>) -> tensor<2x3x?xf32> {
- %s = tosa.const_shape { values = dense<[2, 3, 4]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[2, 3, 4]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?x3x4xf32>, !tosa.shape<3>) -> tensor<2x3x?xf32>
return %0 : tensor<2x3x?xf32>
}
@@ -311,7 +311,7 @@ func.func @test_reshape_3d_same_d2d_explicit_identity(%arg0: tensor<?x3x4xf32>)
// CHECK: %[[VAL_2:.*]] = tensor.cast %[[EXPANDED]] : tensor<2x?x4xf32> to tensor<2x3x4xf32>
// CHECK: return %[[VAL_2]] : tensor<2x3x4xf32>
func.func @test_reshape_3d_same_d2s_auto(%arg0: tensor<?x?x?xf32>) -> tensor<2x3x4xf32> {
- %s = tosa.const_shape { values = dense<[2, -1, 4]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[2, -1, 4]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?x?x?xf32>, !tosa.shape<3>) -> tensor<2x3x4xf32>
return %0 : tensor<2x3x4xf32>
}
@@ -329,7 +329,7 @@ func.func @test_reshape_3d_same_d2s_auto(%arg0: tensor<?x?x?xf32>) -> tensor<2x3
// CHECK: %[[VAL_2:.*]] = tensor.cast %[[EXPANDED]] : tensor<?x3x4xf32> to tensor<2x3x4xf32>
// CHECK: return %[[VAL_2]] : tensor<2x3x4xf32>
func.func @test_reshape_3d_same_d2s_explicit(%arg0: tensor<?x?x?xf32>) -> tensor<2x3x4xf32> {
- %s = tosa.const_shape { values = dense<[2, 3, 4]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[2, 3, 4]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?x?x?xf32>, !tosa.shape<3>) -> tensor<2x3x4xf32>
return %0 : tensor<2x3x4xf32>
}
@@ -340,7 +340,7 @@ func.func @test_reshape_3d_same_d2s_explicit(%arg0: tensor<?x?x?xf32>) -> tensor
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<2x3x4xf32>
// CHECK: return %[[ARG_0]] : tensor<2x3x4xf32>
func.func @test_reshape_3d_same_s2s_explicit_identity(%arg0: tensor<2x3x4xf32>) -> tensor<2x3x4xf32> {
- %s = tosa.const_shape { values = dense<[2, 3, 4]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[2, 3, 4]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<2x3x4xf32>, !tosa.shape<3>) -> tensor<2x3x4xf32>
return %0 : tensor<2x3x4xf32>
}
@@ -358,7 +358,7 @@ func.func @test_reshape_3d_same_s2s_explicit_identity(%arg0: tensor<2x3x4xf32>)
// CHECK: %[[CAST:.*]] = tensor.cast %[[EXPANDED]] : tensor<?x3x2x1xf32> to tensor<1x3x2x1xf32>
// CHECK: return %[[CAST]] : tensor<1x3x2x1xf32>
func.func @test_reshape_3d_up_d2s_explicit(%input: tensor<?x?x?xf32>) -> tensor<1x3x2x1xf32> {
- %s = tosa.const_shape { values = dense<[1, 3, 2, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
+ %s = tosa.const_shape values(dense<[1, 3, 2, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%0 = tosa.reshape %input, %s : (tensor<?x?x?xf32>, !tosa.shape<4>) -> tensor<1x3x2x1xf32>
return %0 : tensor<1x3x2x1xf32>
}
@@ -371,7 +371,7 @@ func.func @test_reshape_3d_up_d2s_explicit(%input: tensor<?x?x?xf32>) -> tensor<
// CHECK: %[[VAL_1:.*]] = tensor.collapse_shape %[[VAL_0]] [] : tensor<1x1x1x1xf32> into tensor<f32>
// CHECK: return %[[VAL_1]] : tensor<f32>
func.func @test_reshape_4d_down_d2s_explicit(%arg0: tensor<?x?x?x?xf32>) -> tensor<f32> {
- %s = tosa.const_shape { values = dense<> : tensor<0xindex> } : () -> !tosa.shape<0>
+ %s = tosa.const_shape values(dense<> : tensor<0xindex>) : () -> !tosa.shape<0>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?x?x?x?xf32>, !tosa.shape<0>) -> tensor<f32>
return %0 : tensor<f32>
}
@@ -388,7 +388,7 @@ func.func @test_reshape_4d_down_d2s_explicit(%arg0: tensor<?x?x?x?xf32>) -> tens
// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[COLLAPSED]] {{\[\[}}0, 1, 2]] output_shape [%[[VAL_0]], 2, 3] : tensor<?xf32> into tensor<?x2x3xf32>
// CHECK: return %[[EXPANDED]] : tensor<?x2x3xf32>
func.func @test_reshape_5d_down_d2d_auto(%arg0: tensor<?x?x?x2x3xf32>) -> tensor<?x2x3xf32> {
- %s = tosa.const_shape { values = dense<[-1, 2, 3]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[-1, 2, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?x?x?x2x3xf32>, !tosa.shape<3>) -> tensor<?x2x3xf32>
return %0 : tensor<?x2x3xf32>
}
@@ -405,7 +405,7 @@ func.func @test_reshape_5d_down_d2d_auto(%arg0: tensor<?x?x?x2x3xf32>) -> tensor
// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[COLLAPSED]] {{\[\[}}0, 1, 2]] output_shape [%[[VAL_0]], 5, 77] : tensor<?xf32> into tensor<?x5x77xf32>
// CHECK: return %[[EXPANDED]] : tensor<?x5x77xf32>
func.func @test_reshape_6d_down_d2d_auto(%arg0: tensor<1x2x?x5x7x11xf32>) -> tensor<?x5x77xf32> {
- %s = tosa.const_shape { values = dense<[-1, 5, 77]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[-1, 5, 77]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<1x2x?x5x7x11xf32>, !tosa.shape<3>) -> tensor<?x5x77xf32>
return %0 : tensor<?x5x77xf32>
}
@@ -417,7 +417,7 @@ func.func @test_reshape_6d_down_d2d_auto(%arg0: tensor<1x2x?x5x7x11xf32>) -> ten
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2], [3], [4, 5]] : tensor<1x2x3x5x7x11xf32> into tensor<6x5x77xf32>
// CHECK: return %[[VAL_0]] : tensor<6x5x77xf32>
func.func @test_reshape_6d_down_s2s_auto(%arg0: tensor<1x2x3x5x7x11xf32>) -> tensor<6x5x77xf32> {
- %s = tosa.const_shape { values = dense<[6, 5, -1]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[6, 5, -1]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<1x2x3x5x7x11xf32>, !tosa.shape<3>) -> tensor<6x5x77xf32>
return %0 : tensor<6x5x77xf32>
}
@@ -435,7 +435,7 @@ func.func @test_reshape_6d_down_s2s_auto(%arg0: tensor<1x2x3x5x7x11xf32>) -> ten
// CHECK-LABEL: reshape_bug_fix
// CHECK: tensor.expand_shape
func.func @reshape_bug_fix(%arg0: tensor<?xf32>) -> tensor<1x1x1x?xf32> {
- %1 = "tosa.const_shape"() {values = dense<[1, 1, 1, -1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %1 = "tosa.const_shape"() <{values = dense<[1, 1, 1, -1]> : tensor<4xindex>}> : () -> !tosa.shape<4>
%0 = "tosa.reshape"(%arg0, %1) : (tensor<?xf32>, !tosa.shape<4>) -> tensor<1x1x1x?xf32>
return %0 : tensor<1x1x1x?xf32>
}
@@ -447,7 +447,7 @@ func.func @reshape_bug_fix(%arg0: tensor<?xf32>) -> tensor<1x1x1x?xf32> {
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2], [3], [4, 5]] : tensor<1x2x3x5x7x11xf32> into tensor<6x5x77xf32>
// CHECK: return %[[VAL_0]] : tensor<6x5x77xf32>
func.func @test_reshape_6d_down_s2s_explicit(%arg0: tensor<1x2x3x5x7x11xf32>) -> tensor<6x5x77xf32> {
- %s = tosa.const_shape { values = dense<[6, 5, 77]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[6, 5, 77]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = "tosa.reshape"(%arg0, %s) : (tensor<1x2x3x5x7x11xf32>, !tosa.shape<3>) -> tensor<6x5x77xf32>
return %0 : tensor<6x5x77xf32>
}
@@ -461,7 +461,7 @@ func.func @test_reshape_samerank_unsigned(%arg0: tensor<3x2xui8>) -> tensor<2x3x
// CHECK: %[[RESHAPE1:.*]] = tensor.collapse_shape %[[CAST1]] {{\[}}[0, 1]] : tensor<3x2xi8> into tensor<6xi8>
// CHECK: %[[RESHAPE2:.*]] = tensor.expand_shape %[[RESHAPE1]] {{\[}}[0, 1]] output_shape {{\[}}2, 3] : tensor<6xi8> into tensor<2x3xi8>
// CHECK: %[[CAST2:.*]] = builtin.unrealized_conversion_cast %[[RESHAPE2]] : tensor<2x3xi8> to tensor<2x3xui8
- %s = tosa.const_shape { values = dense<[2, 3]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = "tosa.reshape"(%arg0, %s): (tensor<3x2xui8>, !tosa.shape<2>) -> tensor<2x3xui8>
return %0 : tensor<2x3xui8>
}
@@ -471,8 +471,8 @@ func.func @test_reshape_samerank_unsigned(%arg0: tensor<3x2xui8>) -> tensor<2x3x
// CHECK-LABEL: func @slice
func.func @slice(%arg0: tensor<6xf32>) ->() {
// CHECK: [[SLICE:%.+]] = tensor.extract_slice %arg0[2] [1] [1]
- %0 = tosa.const_shape {values = dense<2> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1 = tosa.const_shape {values = dense<1> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %0 = tosa.const_shape values(dense<2> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1 = tosa.const_shape values(dense<1> : tensor<1xindex>) : () -> !tosa.shape<1>
%2 = tosa.slice %arg0, %0, %1 : (tensor<6xf32>, !tosa.shape<1>, !tosa.shape<1>) -> tensor<1xf32>
return
}
@@ -486,8 +486,8 @@ func.func @slice_dyn(%arg0: tensor<?xf32>) -> (tensor<?xf32>) {
// CHECK: %[[C2:.+]] = arith.constant 2 : index
// CHECK: %[[SUB:.+]] = arith.subi %[[DIM]], %[[C2]]
// CHECK: tensor.extract_slice %arg0[2] [%[[SUB]]] [1]
- %0 = tosa.const_shape {values = dense<2> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1 = tosa.const_shape {values = dense<-1> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %0 = tosa.const_shape values(dense<2> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1 = tosa.const_shape values(dense<-1> : tensor<1xindex>) : () -> !tosa.shape<1>
%2 = tosa.slice %arg0, %0, %1 : (tensor<?xf32>, !tosa.shape<1>, !tosa.shape<1>) -> tensor<?xf32>
return %2 : tensor<?xf32>
}
@@ -497,8 +497,8 @@ func.func @slice_dyn(%arg0: tensor<?xf32>) -> (tensor<?xf32>) {
// CHECK-LABEL: @pad_float
// CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]:
func.func @pad_float(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
- %0 = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %0 = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK-DAG: [[INDEX1:%.+]] = arith.constant 1 : index
// CHECK-DAG: [[INDEX2:%.+]] = arith.constant 2 : index
// CHECK-DAG: [[INDEX3:%.+]] = arith.constant 3 : index
@@ -513,8 +513,8 @@ func.func @pad_float(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
// -----
func.func @pad_int(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) {
- %0 = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {values = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %pad_const = "tosa.const"() <{values = dense<3> : tensor<1xi32>}> : () -> tensor<1xi32>
// CHECK: [[CST:%.+]] = arith.constant 3 : i32
// CHECK: tensor.pad
// CHECK: tensor.yield [[CST]]
@@ -524,8 +524,8 @@ func.func @pad_int(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) {
// -----
func.func @pad_quant(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) {
- %0 = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %pad_const = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// CHECK: [[CST:%.+]] = arith.constant 0 : i32
// CHECK: tensor.pad
// CHECK: tensor.yield [[CST]]
@@ -536,7 +536,7 @@ func.func @pad_quant(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) {
// -----
func.func @pad_float_explicit(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
- %0 = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %0 = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK-DAG: [[INDEX1:%.+]] = arith.constant 1 : index
// CHECK-DAG: [[INDEX2:%.+]] = arith.constant 2 : index
// CHECK-DAG: [[INDEX3:%.+]] = arith.constant 3 : index
@@ -553,8 +553,8 @@ func.func @pad_float_explicit(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
// -----
func.func @pad_dyn_input(%arg0 : tensor<?x2xf32>) -> (tensor<?x9xf32>) {
- %0 = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %0 = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK-DAG: [[INDEX1:%.+]] = arith.constant 1 : index
// CHECK-DAG: [[INDEX2:%.+]] = arith.constant 2 : index
// CHECK-DAG: [[INDEX3:%.+]] = arith.constant 3 : index
@@ -569,8 +569,8 @@ func.func @pad_dyn_input(%arg0 : tensor<?x2xf32>) -> (tensor<?x9xf32>) {
// -----
func.func @pad_dyn_padding(%arg0 : tensor<1x2xf32>) -> (tensor<?x9xf32>) {
- %0 = tosa.const_shape {values = dense<[-1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %0 = tosa.const_shape values(dense<[-1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK-DAG: [[INDEX1:%.+]] = arith.constant -1 : index
// CHECK-DAG: [[INDEX2:%.+]] = arith.constant 2 : index
// CHECK-DAG: [[INDEX3:%.+]] = arith.constant 3 : index
@@ -592,12 +592,12 @@ func.func @concat(%arg0: tensor<5x1xf32>, %arg1: tensor<6x1xf32>) -> () {
// CHECK-DAG: [[INIT:%.+]] = tensor.empty() : tensor<11x1xf32>
// CHECK-DAG: [[INSERT0:%.+]] = tensor.insert_slice %[[ARG0]] into [[INIT]][0, 0] [5, 1] [1, 1]
// CHECK-DAG: [[INSERT1:%.+]] = tensor.insert_slice %[[ARG1]] into [[INSERT0]][5, 0] [6, 1] [1, 1]
- %0 = "tosa.concat"(%arg0, %arg1) { axis = 0 : i32} : (tensor<5x1xf32>, tensor<6x1xf32>) -> (tensor<11x1xf32>)
+ %0 = "tosa.concat"(%arg0, %arg1) <{ axis = 0 : i32}> : (tensor<5x1xf32>, tensor<6x1xf32>) -> (tensor<11x1xf32>)
// CHECK-DAG: [[INIT:%.+]] = tensor.empty() : tensor<5x2xf32>
// CHECK-DAG: [[INSERT0:%.+]] = tensor.insert_slice %[[ARG0]] into [[INIT]][0, 0] [5, 1] [1, 1]
// CHECK: [[INSERT1:%.+]] = tensor.insert_slice %[[ARG0]] into [[INSERT0]][0, 1] [5, 1] [1, 1]
- %1 = "tosa.concat"(%arg0, %arg0) { axis = 1 : i32} : (tensor<5x1xf32>, tensor<5x1xf32>) -> (tensor<5x2xf32>)
+ %1 = "tosa.concat"(%arg0, %arg0) <{ axis = 1 : i32}> : (tensor<5x1xf32>, tensor<5x1xf32>) -> (tensor<5x2xf32>)
return
}
@@ -617,7 +617,7 @@ func.func @concat_non_axis_dyn(%arg0: tensor<5x?xf32>, %arg1: tensor<6x?xf32>) -
// CHECK-DAG: %[[IDX1_2:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[DIM2:.+]] = tensor.dim %[[ARG1]], %[[IDX1_2]] : tensor<6x?xf32>
// CHECK: %[[INSERT1:.+]] = tensor.insert_slice %[[ARG1]] into %[[INSERT0]][5, 0] [6, %[[DIM2]]] [1, 1]
- %0 = "tosa.concat"(%arg0, %arg1) { axis = 0 : i32} : (tensor<5x?xf32>, tensor<6x?xf32>) -> (tensor<11x?xf32>)
+ %0 = "tosa.concat"(%arg0, %arg1) <{ axis = 0 : i32}> : (tensor<5x?xf32>, tensor<6x?xf32>) -> (tensor<11x?xf32>)
return
}
@@ -640,7 +640,7 @@ func.func @concat_axis_dyn(%arg0: tensor<?x3xf32>, %arg1: tensor<?x3xf32>) -> ()
// CHECK-DAG: %[[DIM3:.+]] = tensor.dim %[[ARG1]], %[[IDX0_2]] : tensor<?x3xf32>
// CHECK: %[[INSERT1:.+]] = tensor.insert_slice %[[ARG1]] into %[[INSERT0]][%[[DIM0]], 0] [%[[DIM3]], 3] [1, 1] : tensor<?x3xf32> into tensor<?x3xf32>
- %0 = "tosa.concat"(%arg0, %arg1) { axis = 0 : i32} : (tensor<?x3xf32>, tensor<?x3xf32>) -> (tensor<?x3xf32>)
+ %0 = "tosa.concat"(%arg0, %arg1) <{ axis = 0 : i32}> : (tensor<?x3xf32>, tensor<?x3xf32>) -> (tensor<?x3xf32>)
return
}
@@ -718,7 +718,7 @@ func.func @pad_variable_pad_const(%arg0: tensor<2x2xi32>, %pad_input_tensor: ten
// CHECK: tensor.yield %[[EXTRACTED_PAD_VAL]] : i32
// CHECK: } : tensor<2x2xi32> to tensor<4x5xi32>
- %padding_indices = tosa.const_shape {values = dense<[1, 1, 0, 3]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %padding_indices = tosa.const_shape values(dense<[1, 1, 0, 3]> : tensor<4xindex>) : () -> !tosa.shape<4>
%result = "tosa.pad"(%arg0, %padding_indices, %pad_input_tensor) : (tensor<2x2xi32>, !tosa.shape<4>, tensor<1xi32>) -> tensor<4x5xi32>
return %result : tensor<4x5xi32>
}
diff --git a/mlir/test/Dialect/MemRef/resolve-dim-ops.mlir b/mlir/test/Dialect/MemRef/resolve-dim-ops.mlir
index 374e47fb34b48..0d42fe2e0d231 100644
--- a/mlir/test/Dialect/MemRef/resolve-dim-ops.mlir
+++ b/mlir/test/Dialect/MemRef/resolve-dim-ops.mlir
@@ -34,7 +34,7 @@ func.func @dim_out_of_bounds_2(%idx1 : index, %idx2 : index) -> index {
// CHECK-NEXT: tensor.dim %[[arg]], %[[c2]]
// CHECK-NEXT: return
func.func @dynamic_dim_of_transpose_op(%arg0: tensor<1x2x?x8xi8>) -> index {
- %1 = tosa.transpose %arg0 { perms = array<i32: 0, 3, 1, 2> }: (tensor<1x2x?x8xi8>) -> tensor<1x8x2x?xi8>
+ %1 = tosa.transpose %arg0 perms([0, 3, 1, 2]) : (tensor<1x2x?x8xi8>) -> tensor<1x8x2x?xi8>
%c3 = arith.constant 3 : index
%dim = tensor.dim %1, %c3 : tensor<1x8x2x?xi8>
return %dim : index
@@ -46,7 +46,7 @@ func.func @dynamic_dim_of_transpose_op(%arg0: tensor<1x2x?x8xi8>) -> index {
// CHECK: arith.constant 100 : index
// CHECK: return
func.func @static_dim_of_transpose_op(%arg0: tensor<1x100x?x8xi8>) -> index {
- %1 = tosa.transpose %arg0 { perms = array<i32: 0, 3, 1, 2> }: (tensor<1x100x?x8xi8>) -> tensor<1x8x100x?xi8>
+ %1 = tosa.transpose %arg0 perms([0, 3, 1, 2]) : (tensor<1x100x?x8xi8>) -> tensor<1x8x100x?xi8>
%c2 = arith.constant 2 : index
%dim = tensor.dim %1, %c2 : tensor<1x8x100x?xi8>
return %dim : index
diff --git a/mlir/test/Dialect/Tosa/canonicalize.mlir b/mlir/test/Dialect/Tosa/canonicalize.mlir
index cda5630b2f84a..90e16f9d2f5be 100644
--- a/mlir/test/Dialect/Tosa/canonicalize.mlir
+++ b/mlir/test/Dialect/Tosa/canonicalize.mlir
@@ -3,7 +3,7 @@
// CHECK-LABEL: @argmax_nofold
func.func @argmax_nofold(%arg0: tensor<?x1xf32>) -> tensor<1xi32> {
// CHECK: tosa.argmax
- %0 = tosa.argmax %arg0 {axis = 0 : i32}: (tensor<?x1xf32>) -> tensor<1xi32>
+ %0 = tosa.argmax %arg0 axis(0): (tensor<?x1xf32>) -> tensor<1xi32>
return %0 : tensor<1xi32>
}
@@ -12,7 +12,7 @@ func.func @argmax_nofold(%arg0: tensor<?x1xf32>) -> tensor<1xi32> {
// CHECK-LABEL: @test_argmax_fold_i64_index
func.func @test_argmax_fold_i64_index(%arg0: tensor<1xi8>) -> tensor<i64> {
// CHECK: "tosa.const"() <{values = dense<0> : tensor<i64>}> : () -> tensor<i64>
- %0 = tosa.argmax %arg0 {axis = 0 : i32} : (tensor<1xi8>) -> tensor<i64>
+ %0 = tosa.argmax %arg0 axis(0) : (tensor<1xi8>) -> tensor<i64>
return %0 : tensor<i64>
}
@@ -52,13 +52,13 @@ func.func @row_gather_non_const_row_count_no_fold(%arg0: tensor<13x21x3xf32>, %a
func.func @pad_wh_avg_pool2d_nofold(%input: tensor<1x10x8x3xf32>) -> tensor<1x6x5x3xf32> {
// CHECK: tosa.pad
// CHECK: tosa.avg_pool2d
- // CHECK-SAME: pad = array<i64: 0, 1, 0, 1>
- %pad_shape = tosa.const_shape { values = dense<[0, 0, 1, 0, 1, 0, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-SAME: pad([0, 1, 0, 1])
+ %pad_shape = tosa.const_shape values(dense<[0, 0, 1, 0, 1, 0, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%pad_const = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%padded = tosa.pad %input, %pad_shape, %pad_const : (tensor<1x10x8x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x11x9x3xf32>
- %pool = tosa.avg_pool2d %padded, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 2, 2>} : (tensor<1x11x9x3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x6x5x3xf32>
+ %pool = tosa.avg_pool2d %padded, %input_zp, %output_zp kernel([2, 2]) stride([2, 2]) pad([0, 1, 0, 1]) acc_type(f32) : (tensor<1x11x9x3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x6x5x3xf32>
return %pool : tensor<1x6x5x3xf32>
}
@@ -68,13 +68,13 @@ func.func @pad_wh_avg_pool2d_nofold(%input: tensor<1x10x8x3xf32>) -> tensor<1x6x
func.func @pad_wh_avg_pool2d_nofold_pad_const(%input: tensor<1x10x8x3xi8>) -> tensor<1x6x5x3xi8> {
// CHECK: tosa.pad
// CHECK: tosa.avg_pool2d
- // CHECK-SAME: pad = array<i64: 0, 1, 0, 1>
- %pad_shape = tosa.const_shape { values = dense<[0, 0, 1, 0, 1, 0, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-SAME: pad([0, 1, 0, 1])
+ %pad_shape = tosa.const_shape values(dense<[0, 0, 1, 0, 1, 0, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%pad_const = "tosa.const"() <{values = dense<15> : tensor<1xi8>}> : ()-> tensor<1xi8>
%input_zp = "tosa.const"() <{values = dense<10> : tensor<1xi8>}> : ()-> tensor<1xi8>
%output_zp = "tosa.const"() <{values = dense<20> : tensor<1xi8>}> : ()-> tensor<1xi8>
%padded = tosa.pad %input, %pad_shape, %pad_const : (tensor<1x10x8x3xi8>, !tosa.shape<8>, tensor<1xi8>) -> tensor<1x11x9x3xi8>
- %pool = tosa.avg_pool2d %padded, %input_zp, %output_zp {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 2, 2>} : (tensor<1x11x9x3xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x6x5x3xi8>
+ %pool = tosa.avg_pool2d %padded, %input_zp, %output_zp kernel([2, 2]) stride([2, 2]) pad([0, 1, 0, 1]) acc_type(i32) : (tensor<1x11x9x3xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x6x5x3xi8>
return %pool : tensor<1x6x5x3xi8>
}
@@ -84,12 +84,12 @@ func.func @pad_wh_avg_pool2d_nofold_pad_const(%input: tensor<1x10x8x3xi8>) -> te
func.func @pad_wh_avg_pool2d_nofold_pad_larger_than_kernel(%input: tensor<1x10x8x3xf32>) -> tensor<1x7x5x3xf32> {
// CHECK: tosa.pad
// CHECK: tosa.avg_pool2d
- %pad_shape = tosa.const_shape { values = dense<[0, 0, 3, 0, 1, 0, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ %pad_shape = tosa.const_shape values(dense<[0, 0, 3, 0, 1, 0, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%pad_const = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%padded = tosa.pad %input, %pad_shape, %pad_const : (tensor<1x10x8x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x13x9x3xf32>
- %pool = tosa.avg_pool2d %padded, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 2, 2>} : (tensor<1x13x9x3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x5x3xf32>
+ %pool = tosa.avg_pool2d %padded, %input_zp, %output_zp kernel([2, 2]) stride([2, 2]) pad([0, 1, 0, 1]) acc_type(f32) : (tensor<1x13x9x3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x5x3xf32>
return %pool : tensor<1x7x5x3xf32>
}
@@ -99,13 +99,13 @@ func.func @pad_wh_avg_pool2d_nofold_pad_larger_than_kernel(%input: tensor<1x10x8
func.func @pad_wh_conv2d_fold(%input: tensor<1x8x4x3xf32>, %weight: tensor<1x3x3x3xf32>, %bias: tensor<1xf32>) -> tensor<1x10x8x1xf32> {
// CHECK-NOT: tosa.pad
// CHECK: tosa.conv2d
- // CHECK-SAME: pad = array<i64: 2, 2, 3, 3>
- %pad_shape = tosa.const_shape { values = dense<[0, 0, 1, 1, 2, 2, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-SAME: pad([2, 2, 3, 3])
+ %pad_shape = tosa.const_shape values(dense<[0, 0, 1, 1, 2, 2, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%pad_const = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%padded = tosa.pad %input, %pad_shape, %pad_const : (tensor<1x8x4x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x10x8x3xf32>
- %conv = tosa.conv2d %padded, %weight, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<1x10x8x3xf32>, tensor<1x3x3x3xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x10x8x1xf32>
+ %conv = tosa.conv2d %padded, %weight, %bias, %input_zp, %weight_zp pad([1, 1, 1, 1]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x10x8x3xf32>, tensor<1x3x3x3xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x10x8x1xf32>
return %conv : tensor<1x10x8x1xf32>
}
@@ -115,13 +115,13 @@ func.func @pad_wh_conv2d_fold(%input: tensor<1x8x4x3xf32>, %weight: tensor<1x3x3
func.func @pad_bwh_conv2d_nofold(%input: tensor<1x8x4x3xf32>, %weight: tensor<1x3x3x3xf32>, %bias: tensor<1xf32>) -> tensor<3x10x8x1xf32> {
// CHECK: tosa.pad
// CHECK: tosa.conv2d
- // CHECK-SAME: pad = array<i64: 1, 1, 1, 1>
- %pad_shape = tosa.const_shape { values = dense<[1, 1, 1, 1, 2, 2, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-SAME: pad([1, 1, 1, 1])
+ %pad_shape = tosa.const_shape values(dense<[1, 1, 1, 1, 2, 2, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%pad_const = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%padded = tosa.pad %input, %pad_shape, %pad_const : (tensor<1x8x4x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<3x10x8x3xf32>
- %conv = tosa.conv2d %padded, %weight, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<3x10x8x3xf32>, tensor<1x3x3x3xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<3x10x8x1xf32>
+ %conv = tosa.conv2d %padded, %weight, %bias, %input_zp, %weight_zp pad([1, 1, 1, 1]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<3x10x8x3xf32>, tensor<1x3x3x3xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<3x10x8x1xf32>
return %conv : tensor<3x10x8x1xf32>
}
@@ -131,13 +131,13 @@ func.func @pad_bwh_conv2d_nofold(%input: tensor<1x8x4x3xf32>, %weight: tensor<1x
func.func @pad_wh_conv2d_nofold_pad_const(%input: tensor<1x8x4x3xf32>, %weight: tensor<1x3x3x3xf32>, %bias: tensor<1xf32>) -> tensor<1x10x8x1xf32> {
// CHECK: tosa.pad
// CHECK: tosa.conv2d
- // CHECK-SAME: pad = array<i64: 1, 1, 1, 1>
- %pad_shape = tosa.const_shape { values = dense<[0, 0, 1, 1, 2, 2, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-SAME: pad([1, 1, 1, 1])
+ %pad_shape = tosa.const_shape values(dense<[0, 0, 1, 1, 2, 2, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%pad_const = "tosa.const"() <{values = dense<1.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%padded = tosa.pad %input, %pad_shape, %pad_const : (tensor<1x8x4x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x10x8x3xf32>
- %conv = tosa.conv2d %padded, %weight, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<1x10x8x3xf32>, tensor<1x3x3x3xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x10x8x1xf32>
+ %conv = tosa.conv2d %padded, %weight, %bias, %input_zp, %weight_zp pad([1, 1, 1, 1]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x10x8x3xf32>, tensor<1x3x3x3xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x10x8x1xf32>
return %conv : tensor<1x10x8x1xf32>
}
@@ -147,13 +147,13 @@ func.func @pad_wh_conv2d_nofold_pad_const(%input: tensor<1x8x4x3xf32>, %weight:
func.func @pad_wh_depthwise_conv2d_fold(%input: tensor<1x8x4x3xf32>, %weight: tensor<3x3x3x1xf32>, %bias: tensor<3xf32>) -> tensor<1x10x8x3xf32> {
// CHECK-NOT: tosa.pad
// CHECK: tosa.depthwise_conv2d
- // CHECK-SAME: pad = array<i64: 2, 2, 3, 3>
- %pad_shape = tosa.const_shape { values = dense<[0, 0, 1, 1, 2, 2, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-SAME: pad([2, 2, 3, 3])
+ %pad_shape = tosa.const_shape values(dense<[0, 0, 1, 1, 2, 2, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%pad_const = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%padded = tosa.pad %input, %pad_shape, %pad_const : (tensor<1x8x4x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x10x8x3xf32>
- %conv = tosa.depthwise_conv2d %padded, %weight, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<1x10x8x3xf32>, tensor<3x3x3x1xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x10x8x3xf32>
+ %conv = tosa.depthwise_conv2d %padded, %weight, %bias, %input_zp, %weight_zp pad([1, 1, 1, 1]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x10x8x3xf32>, tensor<3x3x3x1xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x10x8x3xf32>
return %conv : tensor<1x10x8x3xf32>
}
@@ -163,11 +163,11 @@ func.func @pad_wh_depthwise_conv2d_fold(%input: tensor<1x8x4x3xf32>, %weight: te
func.func @pad_wh_max_pool2d_fold(%input: tensor<1x10x8x3xf32>) -> tensor<1x6x5x3xf32> {
// CHECK-NOT: tosa.pad
// CHECK: tosa.max_pool2d
- // CHECK-SAME: pad = array<i64: 1, 1, 1, 1>
- %pad_shape = tosa.const_shape { values = dense<[0, 0, 1, 0, 1, 0, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-SAME: pad([1, 1, 1, 1])
+ %pad_shape = tosa.const_shape values(dense<[0, 0, 1, 0, 1, 0, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%pad_const = "tosa.const"() <{values = dense<-3.4028235e+38> : tensor<1xf32>}> : ()-> tensor<1xf32>
%padded = tosa.pad %input, %pad_shape, %pad_const : (tensor<1x10x8x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x11x9x3xf32>
- %pool = tosa.max_pool2d %padded {kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 2, 2>} : (tensor<1x11x9x3xf32>) -> tensor<1x6x5x3xf32>
+ %pool = tosa.max_pool2d %padded kernel([2, 2]) stride([2, 2]) pad([0, 1, 0, 1]) : (tensor<1x11x9x3xf32>) -> tensor<1x6x5x3xf32>
return %pool : tensor<1x6x5x3xf32>
}
@@ -177,11 +177,11 @@ func.func @pad_wh_max_pool2d_fold(%input: tensor<1x10x8x3xf32>) -> tensor<1x6x5x
func.func @pad_wh_max_pool2d_nofold_pad_const(%input: tensor<1x10x8x3xf32>) -> tensor<1x6x5x3xf32> {
// CHECK: tosa.pad
// CHECK: tosa.max_pool2d
- // CHECK-SAME: pad = array<i64: 0, 1, 0, 1>
- %pad_shape = tosa.const_shape { values = dense<[0, 0, 1, 0, 1, 0, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-SAME: pad([0, 1, 0, 1])
+ %pad_shape = tosa.const_shape values(dense<[0, 0, 1, 0, 1, 0, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%pad_const = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%padded = tosa.pad %input, %pad_shape, %pad_const : (tensor<1x10x8x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x11x9x3xf32>
- %pool = tosa.max_pool2d %padded {kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 2, 2>} : (tensor<1x11x9x3xf32>) -> tensor<1x6x5x3xf32>
+ %pool = tosa.max_pool2d %padded kernel([2, 2]) stride([2, 2]) pad([0, 1, 0, 1]) : (tensor<1x11x9x3xf32>) -> tensor<1x6x5x3xf32>
return %pool : tensor<1x6x5x3xf32>
}
@@ -191,10 +191,10 @@ func.func @pad_wh_max_pool2d_nofold_pad_const(%input: tensor<1x10x8x3xf32>) -> t
func.func @pad_wh_max_pool2d_no_fold_8k_limit(%input: tensor<1x10x8x3xf32>) -> tensor<1x6x4101x3xf32> {
// CHECK: tosa.pad
// CHECK: tosa.max_pool2d
- %pad_shape = tosa.const_shape { values = dense<[0, 0, 1, 0, 8193, 0, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ %pad_shape = tosa.const_shape values(dense<[0, 0, 1, 0, 8193, 0, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%pad_const = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%padded = tosa.pad %input, %pad_shape, %pad_const : (tensor<1x10x8x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x11x8201x3xf32>
- %pool = tosa.max_pool2d %padded {kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 2, 2>} : (tensor<1x11x8201x3xf32>) -> tensor<1x6x4101x3xf32>
+ %pool = tosa.max_pool2d %padded kernel([2, 2]) stride([2, 2]) pad([0, 1, 0, 1]) : (tensor<1x11x8201x3xf32>) -> tensor<1x6x4101x3xf32>
return %pool : tensor<1x6x4101x3xf32>
}
@@ -204,7 +204,7 @@ func.func @pad_wh_max_pool2d_no_fold_8k_limit(%input: tensor<1x10x8x3xf32>) -> t
func.func @add_bcast_zero_int(%arg0: tensor<4x2x3xi32>) -> tensor<4x2x3xi32> {
// CHECK-NOT: tosa.add
// CHECK: return %arg0
- %zeros = "tosa.const"() {values = dense<0> : tensor<1x1x1xi32>} : () -> tensor<1x1x1xi32>
+ %zeros = "tosa.const"() <{values = dense<0> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
%1 = tosa.add %arg0, %zeros : (tensor<4x2x3xi32>, tensor<1x1x1xi32>) -> tensor<4x2x3xi32>
return %1 : tensor<4x2x3xi32>
}
@@ -215,7 +215,7 @@ func.func @add_bcast_zero_int(%arg0: tensor<4x2x3xi32>) -> tensor<4x2x3xi32> {
func.func @add_zero_int(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> {
// CHECK: return %arg0
// CHECK-NOT: tosa.add
- %zeros = "tosa.const"() {values = dense<0> : tensor<2x3xi32>} : () -> tensor<2x3xi32>
+ %zeros = "tosa.const"() <{values = dense<0> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
%1 = tosa.add %arg0, %zeros : (tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
return %1 : tensor<2x3xi32>
}
@@ -225,7 +225,7 @@ func.func @add_zero_int(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> {
// CHECK-LABEL: @cast_fold
func.func @cast_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.cast %arg0 : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -234,7 +234,7 @@ func.func @cast_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @cast_nofold
func.func @cast_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xi32> {
// CHECK: tosa.cast
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<?x1xf32>) -> tensor<?x1xi32>
+ %0 = tosa.cast %arg0 : (tensor<?x1xf32>) -> tensor<?x1xi32>
return %0 : tensor<?x1xi32>
}
@@ -243,7 +243,7 @@ func.func @cast_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xi32> {
// CHECK-LABEL: @clamp_i32_not_noop
func.func @clamp_i32_not_noop(%arg0: tensor<4xi32>) -> tensor<4xi32> {
// CHECK: tosa.clamp
- %0 = tosa.clamp %arg0 {min_val = 1 : i32, max_val = 4 : i32} : (tensor<4xi32>) -> tensor<4xi32>
+ %0 = tosa.clamp %arg0 min_val(1 : i32) max_val(4 : i32) : (tensor<4xi32>) -> tensor<4xi32>
return %0 : tensor<4xi32>
}
@@ -252,7 +252,7 @@ func.func @clamp_i32_not_noop(%arg0: tensor<4xi32>) -> tensor<4xi32> {
// CHECK-LABEL: @clamp_f32_not_noop
func.func @clamp_f32_not_noop(%arg0: tensor<4xf32>) -> tensor<4xf32> {
// CHECK: tosa.clamp
- %0 = tosa.clamp %arg0 {min_val = -3.40282347E+38 : f32, max_val = 3.40282347E+38 : f32} : (tensor<4xf32>) -> tensor<4xf32>
+ %0 = tosa.clamp %arg0 min_val(-3.40282347E+38 : f32) max_val(3.40282347E+38 : f32) : (tensor<4xf32>) -> tensor<4xf32>
return %0 : tensor<4xf32>
}
@@ -263,7 +263,7 @@ func.func @clamp_f16_is_noop(%arg0: tensor<4xf16>) -> tensor<4xf16> {
// CHECK: return %arg0
// CHECK-NOT: "tosa.clamp"
// 0x7C00 and 0xFC00 are respectively positive and negative F32 infinity.
- %0 = tosa.clamp %arg0 {max_val = 0x7C00 : f16, min_val = 0xFC00 : f16} : (tensor<4xf16>) -> tensor<4xf16>
+ %0 = tosa.clamp %arg0 min_val(0xFC00 : f16) max_val(0x7C00 : f16) : (tensor<4xf16>) -> tensor<4xf16>
return %0 : tensor<4xf16>
}
@@ -274,7 +274,7 @@ func.func @clamp_f32_is_noop(%arg0: tensor<4xf32>) -> tensor<4xf32> {
// CHECK: return %arg0
// CHECK-NOT: "tosa.clamp"
// 0xFF800000 and 0x7F800000 are respectively negative and positive F32 infinity.
- %0 = tosa.clamp %arg0 {min_val = 0xFF800000 : f32, max_val = 0x7F800000 : f32} : (tensor<4xf32>) -> tensor<4xf32>
+ %0 = tosa.clamp %arg0 min_val(0xFF800000 : f32) max_val(0x7F800000 : f32) : (tensor<4xf32>) -> tensor<4xf32>
return %0 : tensor<4xf32>
}
@@ -284,7 +284,7 @@ func.func @clamp_f32_is_noop(%arg0: tensor<4xf32>) -> tensor<4xf32> {
func.func @clamp_boolean_is_noop(%arg0: tensor<4xi1>) -> tensor<4xi1> {
// CHECK: return %arg0
// CHECK-NOT: tosa.clamp
- %0 = tosa.clamp %arg0 {min_val = false, max_val = true} : (tensor<4xi1>) -> tensor<4xi1>
+ %0 = tosa.clamp %arg0 min_val(false) max_val(true) : (tensor<4xi1>) -> tensor<4xi1>
return %0 : tensor<4xi1>
}
@@ -294,7 +294,7 @@ func.func @clamp_boolean_is_noop(%arg0: tensor<4xi1>) -> tensor<4xi1> {
func.func @clamp_boolean_dynamic_is_noop(%arg0: tensor<?xi1>) -> tensor<?xi1> {
// CHECK: return %arg0
// CHECK-NOT: tosa.clamp
- %0 = tosa.clamp %arg0 {min_val = false, max_val = true} : (tensor<?xi1>) -> tensor<?xi1>
+ %0 = tosa.clamp %arg0 min_val(false) max_val(true) : (tensor<?xi1>) -> tensor<?xi1>
return %0 : tensor<?xi1>
}
@@ -304,7 +304,7 @@ func.func @clamp_boolean_dynamic_is_noop(%arg0: tensor<?xi1>) -> tensor<?xi1> {
func.func @clamp_int8_is_noop(%arg0: tensor<4xi8>) -> tensor<4xi8> {
// CHECK: return %arg0
// CHECK-NOT: tosa.clamp
- %0 = tosa.clamp %arg0 {min_val = -128 : i8, max_val = 127 : i8} : (tensor<4xi8>) -> tensor<4xi8>
+ %0 = tosa.clamp %arg0 min_val(-128 : i8) max_val(127 : i8) : (tensor<4xi8>) -> tensor<4xi8>
return %0 : tensor<4xi8>
}
@@ -314,7 +314,7 @@ func.func @clamp_int8_is_noop(%arg0: tensor<4xi8>) -> tensor<4xi8> {
func.func @clamp_int16_is_noop(%arg0: tensor<4xi16>) -> tensor<4xi16> {
// CHECK: return %arg0
// CHECK-NOT: tosa.clamp
- %0 = tosa.clamp %arg0 {min_val = -32768 : i16, max_val = 32767 : i16} : (tensor<4xi16>) -> tensor<4xi16>
+ %0 = tosa.clamp %arg0 min_val(-32768 : i16) max_val(32767 : i16) : (tensor<4xi16>) -> tensor<4xi16>
return %0 : tensor<4xi16>
}
@@ -324,7 +324,7 @@ func.func @clamp_int16_is_noop(%arg0: tensor<4xi16>) -> tensor<4xi16> {
func.func @clamp_uint8_is_noop(%arg0: tensor<4xui8>) -> tensor<4xui8> {
// CHECK: return %arg0
// CHECK-NOT: tosa.clamp
- %0 = tosa.clamp %arg0 {min_val = 0 : ui8, max_val = 255 : ui8} : (tensor<4xui8>) -> tensor<4xui8>
+ %0 = tosa.clamp %arg0 min_val(0 : ui8) max_val(255 : ui8) : (tensor<4xui8>) -> tensor<4xui8>
return %0 : tensor<4xui8>
}
@@ -332,9 +332,9 @@ func.func @clamp_uint8_is_noop(%arg0: tensor<4xui8>) -> tensor<4xui8> {
// CHECK-LABEL: @clamp_twice_is_single_clamp
func.func @clamp_twice_is_single_clamp(%arg0: tensor<4xi8>) -> tensor<4xi8> {
- // CHECK: tosa.clamp %arg0 {max_val = 2 : i8, min_val = -2 : i8}
- %0 = tosa.clamp %arg0 {max_val = 4 : i8, min_val = -2 : i8} : (tensor<4xi8>) -> tensor<4xi8>
- %1 = tosa.clamp %0 {max_val = 2 : i8, min_val = -4 : i8} : (tensor<4xi8>) -> tensor<4xi8>
+ // CHECK: tosa.clamp %arg0 min_val(-2 : i8) max_val(2 : i8)
+ %0 = tosa.clamp %arg0 min_val(-2 : i8) max_val(4 : i8) : (tensor<4xi8>) -> tensor<4xi8>
+ %1 = tosa.clamp %0 min_val(-4 : i8) max_val(2 : i8) : (tensor<4xi8>) -> tensor<4xi8>
return %1 : tensor<4xi8>
}
@@ -342,10 +342,10 @@ func.func @clamp_twice_is_single_clamp(%arg0: tensor<4xi8>) -> tensor<4xi8> {
// CHECK: @disjoint_clamp_twice_is_not_single_clamp(%[[INPUT:.*]]: tensor<4xi8>)
func.func @disjoint_clamp_twice_is_not_single_clamp(%arg0: tensor<4xi8>) -> tensor<4xi8> {
- // CHECK: %[[CLAMP_1:.*]] = tosa.clamp %[[INPUT]] {max_val = -5 : i8, min_val = -10 : i8} : (tensor<4xi8>) -> tensor<4xi8>
- // CHECK-NEXT: tosa.clamp %[[CLAMP_1]] {max_val = 5 : i8, min_val = 1 : i8} : (tensor<4xi8>) -> tensor<4xi8>
- %0 = tosa.clamp %arg0 {max_val = -5 : i8, min_val = -10 : i8} : (tensor<4xi8>) -> tensor<4xi8>
- %1 = tosa.clamp %0 {max_val = 5 : i8, min_val = 1 : i8} : (tensor<4xi8>) -> tensor<4xi8>
+ // CHECK: %[[CLAMP_1:.*]] = tosa.clamp %[[INPUT]] min_val(-10 : i8) max_val(-5 : i8) : (tensor<4xi8>) -> tensor<4xi8>
+ // CHECK-NEXT: tosa.clamp %[[CLAMP_1]] min_val(1 : i8) max_val(5 : i8) : (tensor<4xi8>) -> tensor<4xi8>
+ %0 = tosa.clamp %arg0 min_val(-10 : i8) max_val(-5 : i8) : (tensor<4xi8>) -> tensor<4xi8>
+ %1 = tosa.clamp %0 min_val(1 : i8) max_val(5 : i8) : (tensor<4xi8>) -> tensor<4xi8>
return %1 : tensor<4xi8>
}
@@ -353,9 +353,9 @@ func.func @disjoint_clamp_twice_is_not_single_clamp(%arg0: tensor<4xi8>) -> tens
// CHECK-LABEL: @clamp_twice_with_nan_propagate_is_single_clamp
func.func @clamp_twice_with_nan_propagate_is_single_clamp(%arg0: tensor<4xi8>) -> tensor<4xi8> {
- // CHECK: tosa.clamp %arg0 {max_val = 2 : i8, min_val = -2 : i8}
- %0 = tosa.clamp %arg0 {max_val = 4 : i8, min_val = -2 : i8, nan_mode = PROPAGATE} : (tensor<4xi8>) -> tensor<4xi8>
- %1 = tosa.clamp %0 {max_val = 2 : i8, min_val = -4 : i8, nan_mode = PROPAGATE} : (tensor<4xi8>) -> tensor<4xi8>
+ // CHECK: tosa.clamp %arg0 min_val(-2 : i8) max_val(2 : i8)
+ %0 = tosa.clamp %arg0 min_val(-2 : i8) max_val(4 : i8) : (tensor<4xi8>) -> tensor<4xi8>
+ %1 = tosa.clamp %0 min_val(-4 : i8) max_val(2 : i8) : (tensor<4xi8>) -> tensor<4xi8>
return %1 : tensor<4xi8>
}
@@ -363,9 +363,9 @@ func.func @clamp_twice_with_nan_propagate_is_single_clamp(%arg0: tensor<4xi8>) -
// CHECK-LABEL: @clamp_twice_with_nan_ignore_is_single_clamp
func.func @clamp_twice_with_nan_ignore_is_single_clamp(%arg0: tensor<4xi8>) -> tensor<4xi8> {
- // CHECK: tosa.clamp %arg0 {max_val = 2 : i8, min_val = -2 : i8, nan_mode = IGNORE}
- %0 = tosa.clamp %arg0 {max_val = 4 : i8, min_val = -2 : i8, nan_mode = IGNORE} : (tensor<4xi8>) -> tensor<4xi8>
- %1 = tosa.clamp %0 {max_val = 2 : i8, min_val = -4 : i8, nan_mode = IGNORE} : (tensor<4xi8>) -> tensor<4xi8>
+ // CHECK: tosa.clamp %arg0 min_val(-2 : i8) max_val(2 : i8) nan_mode<IGNORE>
+ %0 = tosa.clamp %arg0 min_val(-2 : i8) max_val(4 : i8) nan_mode<IGNORE> : (tensor<4xi8>) -> tensor<4xi8>
+ %1 = tosa.clamp %0 min_val(-4 : i8) max_val(2 : i8) nan_mode<IGNORE> : (tensor<4xi8>) -> tensor<4xi8>
return %1 : tensor<4xi8>
}
@@ -373,9 +373,9 @@ func.func @clamp_twice_with_nan_ignore_is_single_clamp(%arg0: tensor<4xi8>) -> t
// CHECK-LABEL: @clamp_twice_with_nan_ignore_propagate_is_single_clamp
func.func @clamp_twice_with_nan_ignore_propagate_is_single_clamp(%arg0: tensor<4xi8>) -> tensor<4xi8> {
- // CHECK: tosa.clamp %arg0 {max_val = 2 : i8, min_val = -2 : i8, nan_mode = IGNORE}
- %0 = tosa.clamp %arg0 {max_val = 4 : i8, min_val = -2 : i8, nan_mode = IGNORE} : (tensor<4xi8>) -> tensor<4xi8>
- %1 = tosa.clamp %0 {max_val = 2 : i8, min_val = -4 : i8, nan_mode = PROPAGATE} : (tensor<4xi8>) -> tensor<4xi8>
+ // CHECK: tosa.clamp %arg0 min_val(-2 : i8) max_val(2 : i8) nan_mode<IGNORE>
+ %0 = tosa.clamp %arg0 min_val(-2 : i8) max_val(4 : i8) nan_mode<IGNORE> : (tensor<4xi8>) -> tensor<4xi8>
+ %1 = tosa.clamp %0 min_val(-4 : i8) max_val(2 : i8) : (tensor<4xi8>) -> tensor<4xi8>
return %1 : tensor<4xi8>
}
@@ -383,39 +383,39 @@ func.func @clamp_twice_with_nan_ignore_propagate_is_single_clamp(%arg0: tensor<4
// CHECK: @clamp_twice_with_nan_propagate_ignore_is_not_single_clamp(%[[INPUT:.*]]: tensor<4xi8>)
func.func @clamp_twice_with_nan_propagate_ignore_is_not_single_clamp(%arg0: tensor<4xi8>) -> tensor<4xi8> {
- // CHECK: %[[CLAMP_1:.*]] = tosa.clamp %[[INPUT]] {max_val = 4 : i8, min_val = -2 : i8} : (tensor<4xi8>) -> tensor<4xi8>
- // CHECK-NEXT: tosa.clamp %[[CLAMP_1]] {max_val = 2 : i8, min_val = -4 : i8, nan_mode = IGNORE} : (tensor<4xi8>) -> tensor<4xi8>
- %0 = tosa.clamp %arg0 {max_val = 4 : i8, min_val = -2 : i8, nan_mode = PROPAGATE} : (tensor<4xi8>) -> tensor<4xi8>
- %1 = tosa.clamp %0 {max_val = 2 : i8, min_val = -4 : i8, nan_mode = IGNORE} : (tensor<4xi8>) -> tensor<4xi8>
+ // CHECK: %[[CLAMP_1:.*]] = tosa.clamp %[[INPUT]] min_val(-2 : i8) max_val(4 : i8) : (tensor<4xi8>) -> tensor<4xi8>
+ // CHECK-NEXT: tosa.clamp %[[CLAMP_1]] min_val(-4 : i8) max_val(2 : i8) nan_mode<IGNORE> : (tensor<4xi8>) -> tensor<4xi8>
+ %0 = tosa.clamp %arg0 min_val(-2 : i8) max_val(4 : i8) : (tensor<4xi8>) -> tensor<4xi8>
+ %1 = tosa.clamp %0 min_val(-4 : i8) max_val(2 : i8) nan_mode<IGNORE> : (tensor<4xi8>) -> tensor<4xi8>
return %1 : tensor<4xi8>
}
// -----
// CHECK-LABEL: @clamp_twice_with_unsigned_quantized_is_single_clamp
-// CHECK: tosa.clamp %arg0 {max_val = 230 : ui8, min_val = 10 : ui8}
+// CHECK: tosa.clamp %arg0 min_val(10 : ui8) max_val(230 : ui8)
func.func @clamp_twice_with_unsigned_quantized_is_single_clamp(%arg0:tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) -> (tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) {
- %0 = tosa.clamp %arg0 {max_val = 240 : ui8, min_val = 10 : ui8} : (tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>
- %1 = tosa.clamp %0 {max_val = 230 : ui8, min_val = 5 : ui8} : (tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>
+ %0 = tosa.clamp %arg0 min_val(10 : ui8) max_val(240 : ui8) : (tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>
+ %1 = tosa.clamp %0 min_val(5 : ui8) max_val(230 : ui8) : (tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>
return %1 : tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>
}
// -----
// CHECK-LABEL: @clamp_twice_with_signed_quantized_is_single_clamp
-// CHECK: tosa.clamp %arg0 {max_val = 110 : i8, min_val = -5 : i8}
+// CHECK: tosa.clamp %arg0 min_val(-5 : i8) max_val(110 : i8)
func.func @clamp_twice_with_signed_quantized_is_single_clamp(%arg0:tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>) -> (tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>) {
- %0 = tosa.clamp %arg0 {max_val = 110 : i8, min_val = -10 : i8} : (tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>
- %1 = tosa.clamp %0 {max_val = 120 : i8, min_val = -5 : i8} : (tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>
+ %0 = tosa.clamp %arg0 min_val(-10 : i8) max_val(110 : i8) : (tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>
+ %1 = tosa.clamp %0 min_val(-5 : i8) max_val(120 : i8) : (tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>
return %1 : tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>
}
// CHECK-LABEL: @clamp_twice_with_signed_quantized_non_overlap_is_not_single_clamp
-// CHECK: %[[CLAMP_1:.*]] = tosa.clamp %arg0 {max_val = 50 : i8, min_val = -10 : i8}
-// CHECK-NEXT: tosa.clamp %[[CLAMP_1]] {max_val = 120 : i8, min_val = 60 : i8}
+// CHECK: %[[CLAMP_1:.*]] = tosa.clamp %arg0 min_val(-10 : i8) max_val(50 : i8)
+// CHECK-NEXT: tosa.clamp %[[CLAMP_1]] min_val(60 : i8) max_val(120 : i8)
func.func @clamp_twice_with_signed_quantized_non_overlap_is_not_single_clamp(%arg0:tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>) -> (tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>) {
- %0 = tosa.clamp %arg0 {max_val = 50 : i8, min_val = -10 : i8} : (tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>
- %1 = tosa.clamp %0 {max_val = 120 : i8, min_val = 60 : i8} : (tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>
+ %0 = tosa.clamp %arg0 min_val(-10 : i8) max_val(50 : i8) : (tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>
+ %1 = tosa.clamp %0 min_val(60 : i8) max_val(120 : i8) : (tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>
return %1 : tensor<?x112x112x32x!quant.uniform<i8:f32, 0.023529412224888802:-128>>
}
@@ -425,7 +425,7 @@ func.func @clamp_twice_with_signed_quantized_non_overlap_is_not_single_clamp(%ar
// CHECK-LABEL: @concat_fold
func.func @concat_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
- %0 = tosa.concat %arg0 {axis = 0 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.concat %arg0 axis(0) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -435,7 +435,7 @@ func.func @concat_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
func.func @concat_fold_cast(%arg0: tensor<?x1xf32>) -> tensor<?x?xf32> {
// CHECK: %[[VAR0:.*]] = tensor.cast %arg0
// CHECK: return %[[VAR0]]
- %0 = tosa.concat %arg0 {axis = 0 : i32}: (tensor<?x1xf32>) -> tensor<?x?xf32>
+ %0 = tosa.concat %arg0 axis(0) : (tensor<?x1xf32>) -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
@@ -444,11 +444,11 @@ func.func @concat_fold_cast(%arg0: tensor<?x1xf32>) -> tensor<?x?xf32> {
// CHECK-LABEL: @conv2d_stride_2
func.func @conv2d_stride_2(%arg0: tensor<4x11x11x2xf32>) -> tensor<4x6x6x3xf32> {
// CHECK: tosa.conv2d
- %weight = "tosa.const"() {values = dense<[[[[1.0, 1.0]]], [[[1.0, 1.0]]], [[[1.0, 1.0]]]]> : tensor<3x1x1x2xf32>} : ()-> tensor<3x1x1x2xf32>
- %bias = "tosa.const"() {values = dense<0.0> : tensor<3xf32>} : ()-> tensor<3xf32>
+ %weight = "tosa.const"() <{values = dense<[[[[1.0, 1.0]]], [[[1.0, 1.0]]], [[[1.0, 1.0]]]]> : tensor<3x1x1x2xf32>}> : ()-> tensor<3x1x1x2xf32>
+ %bias = "tosa.const"() <{values = dense<0.0> : tensor<3xf32>}> : ()-> tensor<3xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
- %0 = tosa.conv2d %arg0, %weight, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1>} : (tensor<4x11x11x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x6x6x3xf32>
+ %0 = tosa.conv2d %arg0, %weight, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([2, 2]) dilation([1, 1]) acc_type(f32) : (tensor<4x11x11x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x6x6x3xf32>
return %0 : tensor<4x6x6x3xf32>
}
@@ -457,11 +457,11 @@ func.func @conv2d_stride_2(%arg0: tensor<4x11x11x2xf32>) -> tensor<4x6x6x3xf32>
// CHECK-LABEL: @conv2d_weight_2x2
func.func @conv2d_weight_2x2(%arg0: tensor<4x10x10x1xf32>) -> tensor<4x9x9x1xf32> {
// CHECK: tosa.conv2d
- %weight = "tosa.const"() {values = dense<[[[[1.0], [1.0]], [[1.0], [1.0]]]]> : tensor<1x2x2x1xf32>} : ()-> tensor<1x2x2x1xf32>
- %bias = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : ()-> tensor<1xf32>
+ %weight = "tosa.const"() <{values = dense<[[[[1.0], [1.0]], [[1.0], [1.0]]]]> : tensor<1x2x2x1xf32>}> : ()-> tensor<1x2x2x1xf32>
+ %bias = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
- %0 = tosa.conv2d %arg0, %weight, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x1xf32>, tensor<1x2x2x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x9x9x1xf32>
+ %0 = tosa.conv2d %arg0, %weight, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<4x10x10x1xf32>, tensor<1x2x2x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x9x9x1xf32>
return %0 : tensor<4x9x9x1xf32>
}
@@ -470,7 +470,7 @@ func.func @conv2d_weight_2x2(%arg0: tensor<4x10x10x1xf32>) -> tensor<4x9x9x1xf32
// CHECK-LABEL: @depthwise_conv2d_stride_2
func.func @depthwise_conv2d_stride_2(%arg0: tensor<4x11x11x2xf32>, %arg1: tensor<1x1x2x3xf32>, %arg2: tensor<6xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<4x6x6x6xf32> {
// CHECK: tosa.depthwise_conv2d
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1>} : (tensor<4x11x11x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x6x6x6xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([2, 2]) dilation([1, 1]) acc_type(f32) : (tensor<4x11x11x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x6x6x6xf32>
return %0 : tensor<4x6x6x6xf32>
}
@@ -479,7 +479,7 @@ func.func @depthwise_conv2d_stride_2(%arg0: tensor<4x11x11x2xf32>, %arg1: tensor
// CHECK-LABEL: @depthwise_conv2d_weight_2x2
func.func @depthwise_conv2d_weight_2x2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<2x2x2x3xf32>, %arg2: tensor<6xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<4x9x9x6xf32> {
// CHECK: tosa.depthwise_conv2d
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<2x2x2x3xf32>, tensor<6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x9x9x6xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<4x10x10x2xf32>, tensor<2x2x2x3xf32>, tensor<6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x9x9x6xf32>
return %0 : tensor<4x9x9x6xf32>
}
@@ -489,7 +489,7 @@ func.func @depthwise_conv2d_weight_2x2(%arg0: tensor<4x10x10x2xf32>, %arg1: tens
func.func @max_pool2d_is_noop(%arg0: tensor<10x1x1x3xf32>) -> tensor<10x1x1x3xf32> {
// CHECK-NOT: tosa.max_pool2d
// CHECK: return %arg0
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<10x1x1x3xf32>) -> tensor<10x1x1x3xf32>
+ %0 = tosa.max_pool2d %arg0 kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) {dilation = array<i64: 1, 1>} : (tensor<10x1x1x3xf32>) -> tensor<10x1x1x3xf32>
return %0 : tensor<10x1x1x3xf32>
}
@@ -498,8 +498,8 @@ func.func @max_pool2d_is_noop(%arg0: tensor<10x1x1x3xf32>) -> tensor<10x1x1x3xf3
// CHECK-LABEL: @pad_noop
func.func @pad_noop(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: return %arg0
- %0 = tosa.const_shape { values = dense<0> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %0 = tosa.const_shape values(dense<0> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
%1 = tosa.pad %arg0, %0, %pad_const : (tensor<?x?xf32>, !tosa.shape<4>, tensor<1xf32>) -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}
@@ -510,8 +510,8 @@ func.func @pad_noop(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
func.func @pad_noop_padding_mismatch_nofold(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: %[[PAD:.+]] = tosa.pad
// CHECK: return %[[PAD]]
- %shape = tosa.const_shape { values = dense<[1, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %shape = tosa.const_shape values(dense<[1, 0, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
%1 = tosa.pad %arg0, %shape, %pad_const : (tensor<?x?xf32>, !tosa.shape<4>, tensor<1xf32>) -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}
@@ -522,8 +522,8 @@ func.func @pad_noop_padding_mismatch_nofold(%arg0: tensor<?x?xf32>) -> tensor<?x
func.func @pad_noop_type_mismatch_nofold(%arg0: tensor<10xf32>) -> tensor<?xf32> {
// CHECK: %[[PAD:.+]] = tosa.pad
// CHECK: return %[[PAD]]
- %shape = tosa.const_shape { values = dense<[1, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %shape = tosa.const_shape values(dense<[1, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
%0 = tosa.pad %arg0, %shape, %pad_const : (tensor<10xf32>, !tosa.shape<2>, tensor<1xf32>) -> tensor<?xf32>
return %0 : tensor<?xf32>
}
@@ -533,10 +533,10 @@ func.func @pad_noop_type_mismatch_nofold(%arg0: tensor<10xf32>) -> tensor<?xf32>
// CHECK-LABEL: @pad_determine_val_i32
func.func @pad_determine_val_i32(%arg0: tensor<?x?xi32>, %arg1 : tensor<2x2xi32>) -> tensor<?x?xi32> {
// CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{values = dense<0> : tensor<1xi32>}
- // CHECK-DAG: %[[PADDING:.+]] = tosa.const_shape {values = dense<[1, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ // CHECK-DAG: %[[PADDING:.+]] = tosa.const_shape values(dense<[1, 0, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK: tosa.pad %arg0, %[[PADDING]], %[[ZERO]]
- %pad_const = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
- %0 = tosa.const_shape { values = dense<[1, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %pad_const = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %0 = tosa.const_shape values(dense<[1, 0, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%1 = tosa.pad %arg0, %0, %pad_const : (tensor<?x?xi32>, !tosa.shape<4>, tensor<1xi32>) -> tensor<?x?xi32>
return %1 : tensor<?x?xi32>
}
@@ -546,10 +546,10 @@ func.func @pad_determine_val_i32(%arg0: tensor<?x?xi32>, %arg1 : tensor<2x2xi32>
// CHECK-LABEL: @pad_determine_val_f32
func.func @pad_determine_val_f32(%arg0: tensor<?x?xf32>, %arg1 : tensor<2x2xi32>) -> tensor<?x?xf32> {
// CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{values = dense<3.140000e+00> : tensor<1xf32>}
- // CHECK-DAG: %[[PADDING:.+]] = tosa.const_shape {values = dense<[1, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ // CHECK-DAG: %[[PADDING:.+]] = tosa.const_shape values(dense<[1, 0, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK: tosa.pad %arg0, %[[PADDING]], %[[ZERO]]
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
- %0 = tosa.const_shape { values = dense<[1, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.const_shape values(dense<[1, 0, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%1 = tosa.pad %arg0, %0, %pad_const : (tensor<?x?xf32>, !tosa.shape<4>, tensor<1xf32>) -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}
@@ -559,10 +559,10 @@ func.func @pad_determine_val_f32(%arg0: tensor<?x?xf32>, %arg1 : tensor<2x2xi32>
// CHECK-LABEL: @pad_determine_val_quant
func.func @pad_determine_val_quant(%arg0: tensor<?x?xi32>, %arg1 : tensor<2x2xi32>) -> tensor<?x?xi32> {
// CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{values = dense<3> : tensor<1xi32>}
- // CHECK-DAG: %[[PADDING:.+]] = tosa.const_shape {values = dense<[1, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ // CHECK-DAG: %[[PADDING:.+]] = tosa.const_shape values(dense<[1, 0, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK: tosa.pad %arg0, %[[PADDING]], %[[ZERO]]
- %pad_const = "tosa.const"() {values =dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
- %0 = tosa.const_shape { values = dense<[1, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %pad_const = "tosa.const"() <{values =dense<3> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %0 = tosa.const_shape values(dense<[1, 0, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%1 = tosa.pad %arg0, %0, %pad_const {input_zp = 42 : i32} : (tensor<?x?xi32>, !tosa.shape<4>, tensor<1xi32>) -> tensor<?x?xi32>
return %1 : tensor<?x?xi32>
}
@@ -582,7 +582,7 @@ func.func @select_not_pred(%arg0: tensor<2x3xi1>, %arg1: tensor<2x3xi32>, %arg2:
// CHECK-LABEL: @reduce_all_fold
func.func @reduce_all_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
- %0 = tosa.reduce_all %arg0 {axis = 1 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_all %arg0 axis(1) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -591,7 +591,7 @@ func.func @reduce_all_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reduce_all_nofold
func.func @reduce_all_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: tosa.reduce_all
- %0 = tosa.reduce_all %arg0 {axis = 0 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_all %arg0 axis(0) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -600,7 +600,7 @@ func.func @reduce_all_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reduce_any_fold
func.func @reduce_any_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
- %0 = tosa.reduce_any %arg0 {axis = 1 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_any %arg0 axis(1) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -609,7 +609,7 @@ func.func @reduce_any_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reduce_any_nofold
func.func @reduce_any_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: tosa.reduce_any
- %0 = tosa.reduce_any %arg0 {axis = 0 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_any %arg0 axis(0) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -618,7 +618,7 @@ func.func @reduce_any_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reduce_max_fold
func.func @reduce_max_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
- %0 = tosa.reduce_max %arg0 {axis = 1 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_max %arg0 axis(1) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -627,7 +627,7 @@ func.func @reduce_max_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reduce_max_nofold
func.func @reduce_max_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: tosa.reduce_max
- %0 = tosa.reduce_max %arg0 {axis = 0 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_max %arg0 axis(0) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -636,7 +636,7 @@ func.func @reduce_max_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reduce_min_fold
func.func @reduce_min_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
- %0 = tosa.reduce_min %arg0 {axis = 1 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_min %arg0 axis(1) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -645,7 +645,7 @@ func.func @reduce_min_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reduce_min_nofold
func.func @reduce_min_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: tosa.reduce_min
- %0 = tosa.reduce_min %arg0 {axis = 0 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_min %arg0 axis(0) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -654,7 +654,7 @@ func.func @reduce_min_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reduce_product_fold
func.func @reduce_product_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
- %0 = tosa.reduce_product %arg0 {axis = 1 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_product %arg0 axis(1) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -663,7 +663,7 @@ func.func @reduce_product_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reduce_product_nofold
func.func @reduce_product_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: tosa.reduce_product
- %0 = tosa.reduce_product %arg0 {axis = 0 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_product %arg0 axis(0) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -672,7 +672,7 @@ func.func @reduce_product_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reduce_sum_fold
func.func @reduce_sum_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
- %0 = tosa.reduce_sum %arg0 {axis = 1 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_sum %arg0 axis(1) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -681,7 +681,7 @@ func.func @reduce_sum_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reduce_sum_nofold
func.func @reduce_sum_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: tosa.reduce_sum
- %0 = tosa.reduce_sum %arg0 {axis = 0 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+ %0 = tosa.reduce_sum %arg0 axis(0) : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %0 : tensor<?x1xf32>
}
@@ -690,7 +690,7 @@ func.func @reduce_sum_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reshape_canonicalize
func.func @reshape_canonicalize(%arg0: tensor<?x10xf32>) -> tensor<?x10xf32> {
// CHECK: return %arg0
- %0 = "tosa.const_shape"() {values = dense<[-1, 10]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = "tosa.const_shape"() <{values = dense<[-1, 10]> : tensor<2xindex>}> : () -> !tosa.shape<2>
%1 = tosa.reshape %arg0, %0 : (tensor<?x10xf32>, !tosa.shape<2>) -> tensor<?x10xf32>
return %1 : tensor<?x10xf32>
}
@@ -699,10 +699,10 @@ func.func @reshape_canonicalize(%arg0: tensor<?x10xf32>) -> tensor<?x10xf32> {
// CHECK-LABEL: @reshape_canonicalize_dyn_nofold
func.func @reshape_canonicalize_dyn_nofold(%arg0: tensor<?x?x10xf32>) -> tensor<?x?x10xf32> {
- // CHECK: %[[SHAPE:.+]] = tosa.const_shape {values = dense<[-1, 2, 10]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ // CHECK: %[[SHAPE:.+]] = tosa.const_shape values(dense<[-1, 2, 10]> : tensor<3xindex>) : () -> !tosa.shape<3>
// CHECK: %[[VAR0:.+]] = tosa.reshape %arg0, %[[SHAPE]] : (tensor<?x?x10xf32>, !tosa.shape<3>) -> tensor<?x?x10xf32>
// CHECK: return %[[VAR0]] : tensor<?x?x10xf32>
- %s = "tosa.const_shape"() {values = dense<[-1, 2, 10]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %s = "tosa.const_shape"() <{values = dense<[-1, 2, 10]> : tensor<3xindex>}> : () -> !tosa.shape<3>
%0 = tosa.reshape %arg0, %s : (tensor<?x?x10xf32>, !tosa.shape<3>) -> tensor<?x?x10xf32>
return %0 : tensor<?x?x10xf32>
}
@@ -711,7 +711,7 @@ func.func @reshape_canonicalize_dyn_nofold(%arg0: tensor<?x?x10xf32>) -> tensor<
// CHECK-LABEL: @reshape_canonicalize_double
func.func @reshape_canonicalize_double(%arg0: tensor<?x10xf32>) -> tensor<?x5xf32> {
- // CHECK: %[[VAL_0:.*]] = tosa.const_shape {values = dense<[-1, 5]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ // CHECK: %[[VAL_0:.*]] = tosa.const_shape values(dense<[-1, 5]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: %[[VAL_1:.*]] = tosa.reshape %arg0, %[[VAL_0]]
// CHECK: return %[[VAL_1]]
%cst0 = "tosa.const_shape"() <{values = dense<[5, -1]> : tensor<2xindex>}> : () -> !tosa.shape<2>
@@ -727,8 +727,8 @@ func.func @reshape_canonicalize_double(%arg0: tensor<?x10xf32>) -> tensor<?x5xf3
func.func @reshape_canonicalize_const() -> tensor<1x5xi32> {
// CHECK: %[[VAR0:.+]] = "tosa.const"() <{values = dense<{{\[\[}}0, 1, 2, 3, 4]]> : tensor<1x5xi32>}
// CHECK: return %[[VAR0]]
- %0 = "tosa.const"() {values = dense<[0, 1, 2, 3, 4]> : tensor<5xi32>} : () -> tensor<5xi32>
- %1 = "tosa.const_shape"() {values = dense<[1, 5]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = "tosa.const"() <{values = dense<[0, 1, 2, 3, 4]> : tensor<5xi32>}> : () -> tensor<5xi32>
+ %1 = "tosa.const_shape"() <{values = dense<[1, 5]> : tensor<2xindex>}> : () -> !tosa.shape<2>
%2 = tosa.reshape %0, %1 : (tensor<5xi32>, !tosa.shape<2>) -> tensor<1x5xi32>
return %2 : tensor<1x5xi32>
}
@@ -738,8 +738,8 @@ func.func @reshape_canonicalize_const() -> tensor<1x5xi32> {
// CHECK-LABEL: @reshape_canonicalize_const_dynamic
func.func @reshape_canonicalize_const_dynamic() -> tensor<1x?xi32> {
// CHECK: tosa.reshape
- %0 = "tosa.const"() {values = dense<[0, 1, 2, 3, 4]> : tensor<5xi32>} : () -> tensor<5xi32>
- %2 = "tosa.const_shape"() {values = dense<[1, 5]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = "tosa.const"() <{values = dense<[0, 1, 2, 3, 4]> : tensor<5xi32>}> : () -> tensor<5xi32>
+ %2 = "tosa.const_shape"() <{values = dense<[1, 5]> : tensor<2xindex>}> : () -> !tosa.shape<2>
%1 = tosa.reshape %0, %2 : (tensor<5xi32>, !tosa.shape<2>) -> tensor<1x?xi32>
return %1 : tensor<1x?xi32>
}
@@ -751,8 +751,8 @@ func.func @reshape_canonicalize_const_splat() -> (tensor<10xi32>, tensor<1x10xi3
// CHECK-DAG: %[[VAR0:.+]] = "tosa.const"() <{values = dense<0> : tensor<10xi32>}
// CHECK-DAG: %[[VAR1:.+]] = "tosa.const"() <{values = dense<0> : tensor<1x10xi32>}
// CHECK: return %[[VAR0]], %[[VAR1]]
- %0 = "tosa.const"() {values = dense<0> : tensor<10xi32>} : () -> tensor<10xi32>
- %2 = "tosa.const_shape"() {values = dense<[1, 10]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %2 = "tosa.const_shape"() <{values = dense<[1, 10]> : tensor<2xindex>}> : () -> !tosa.shape<2>
%1 = tosa.reshape %0, %2 : (tensor<10xi32>, !tosa.shape<2>) -> tensor<1x10xi32>
return %0 , %1 : tensor<10xi32>, tensor<1x10xi32>
}
@@ -762,8 +762,8 @@ func.func @reshape_canonicalize_const_splat() -> (tensor<10xi32>, tensor<1x10xi3
// CHECK-LABEL: @reshape_canonicalize_const_sparse
func.func @reshape_canonicalize_const_sparse() -> (tensor<3xi32>, tensor<1x3xi32>) {
// CHECK: tosa.reshape
- %0 = "tosa.const"() {values = dense<[1, 2, 3]> : tensor<3xi32>} : ()-> tensor<3xi32>
- %2 = "tosa.const_shape"() {values = dense<[1, 3]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = "tosa.const"() <{values = dense<[1, 2, 3]> : tensor<3xi32>}> : ()-> tensor<3xi32>
+ %2 = "tosa.const_shape"() <{values = dense<[1, 3]> : tensor<2xindex>}> : () -> !tosa.shape<2>
%1 = tosa.reshape %0, %2 : (tensor<3xi32>, !tosa.shape<2>) -> tensor<1x3xi32>
return %0 , %1 : tensor<3xi32>, tensor<1x3xi32>
}
@@ -775,8 +775,8 @@ func.func @reshape_canonicalize_quant_nofold() -> (tensor<1x3x!quant.uniform<i8:
// disabled folding for quantized element types
// CHECK{LITERAL}: "tosa.const"() <{values = dense<[1, 2, 3]> : tensor<3xi8>}> : () -> tensor<3x!quant.uniform<i8:f32, 1.000000e+00>>
// CHECK{LITERAL}: tosa.reshape %0, %1 : (tensor<3x!quant.uniform<i8:f32, 1.000000e+00>>, !tosa.shape<2>) -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e+00>>
- %0 = "tosa.const"() {values = dense<[1, 2, 3]> : tensor<3xi8>} : ()-> tensor<3x!quant.uniform<i8:f32, 1.000000e+00>>
- %2 = "tosa.const_shape"() {values = dense<[1, 3]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = "tosa.const"() <{values = dense<[1, 2, 3]> : tensor<3xi8>}> : ()-> tensor<3x!quant.uniform<i8:f32, 1.000000e+00>>
+ %2 = "tosa.const_shape"() <{values = dense<[1, 3]> : tensor<2xindex>}> : () -> !tosa.shape<2>
%1 = tosa.reshape %0, %2 : (tensor<3x!quant.uniform<i8:f32, 1.000000e+00>>, !tosa.shape<2>) -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e+00>>
return %1 : tensor<1x3x!quant.uniform<i8:f32, 1.000000e+00>>
}
@@ -785,11 +785,11 @@ func.func @reshape_canonicalize_quant_nofold() -> (tensor<1x3x!quant.uniform<i8:
// CHECK-LABEL: @transpose_canonicalize_strip_quant
func.func @transpose_canonicalize_strip_quant() -> (tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>) {
- // CHECK-DAG: %[[SHAPE:.*]] = tosa.const_shape {values = dense<[2, 1, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ // CHECK-DAG: %[[SHAPE:.*]] = tosa.const_shape values(dense<[2, 1, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
// CHECK-DAG: %[[CONST:.*]] = "tosa.const"() <{values = dense<0> : tensor<1x2x3xi8>}> : () -> tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>
// CHECK: tosa.reshape %[[CONST]], %[[SHAPE]] : (tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>, !tosa.shape<3>) -> tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
- %0 = "tosa.const"() {values = dense<0> : tensor<1x2x3xi8>} : ()-> tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>
- %1 = tosa.transpose %0 { perms = array<i32: 1, 0, 2> }: (tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>) -> tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1x2x3xi8>}> : ()-> tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>
+ %1 = tosa.transpose %0 perms([1, 0, 2]) : (tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>) -> tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
return %1 : tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
}
@@ -797,8 +797,8 @@ func.func @transpose_canonicalize_strip_quant() -> (tensor<2x1x3x!quant.uniform<
// CHECK-LABEL: @slice_fold
func.func @slice_fold(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
- %0 = tosa.const_shape {values = dense<[0, 0]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = tosa.const_shape {values = dense<[3, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<[0, 0]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[3, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: return %arg0
%3 = tosa.slice %arg0, %0, %1 : (tensor<3x4xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x4xf32>
return %3 : tensor<3x4xf32>
@@ -808,8 +808,8 @@ func.func @slice_fold(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
// CHECK-LABEL: @slice_nofold
func.func @slice_nofold(%arg0: tensor<?x4xf32>) -> tensor<?x4xf32> {
- %0 = tosa.const_shape {values = dense<[0, 0]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = tosa.const_shape {values = dense<[3, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<[0, 0]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[3, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: tosa.slice
%3 = tosa.slice %arg0, %0, %1 : (tensor<?x4xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x4xf32>
return %3 : tensor<?x4xf32>
@@ -818,8 +818,8 @@ func.func @slice_nofold(%arg0: tensor<?x4xf32>) -> tensor<?x4xf32> {
// CHECK-LABEL: @slice_fold_dynamic
func.func @slice_fold_dynamic(%arg0: tensor<?x4xf32>) -> tensor<?x4xf32> {
- %0 = tosa.const_shape {values = dense<[0, 0]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = tosa.const_shape {values = dense<[-1, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<[0, 0]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[-1, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: return %arg0
%3 = tosa.slice %arg0, %0, %1 : (tensor<?x4xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x4xf32>
return %3 : tensor<?x4xf32>
@@ -829,8 +829,8 @@ func.func @slice_fold_dynamic(%arg0: tensor<?x4xf32>) -> tensor<?x4xf32> {
// CHECK-LABEL: @slice_fold_static_dynamic
func.func @slice_fold_static_dynamic(%arg0: tensor<?x4xf32>) -> tensor<?x4xf32> {
- %0 = tosa.const_shape {values = dense<[0, 0]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = tosa.const_shape {values = dense<[-1, -1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<[0, 0]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: return %arg0
%3 = tosa.slice %arg0, %0, %1 : (tensor<?x4xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x4xf32>
return %3 : tensor<?x4xf32>
@@ -840,8 +840,8 @@ func.func @slice_fold_static_dynamic(%arg0: tensor<?x4xf32>) -> tensor<?x4xf32>
// CHECK-LABEL: @slice_nofold_static
func.func @slice_nofold_static(%arg0: tensor<3x4xf32>) -> tensor<3x2xf32> {
- %0 = tosa.const_shape {values = dense<[0, 0]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = tosa.const_shape {values = dense<[3, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<[0, 0]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[3, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: tosa.slice
%3 = tosa.slice %arg0, %0, %1 : (tensor<3x4xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x2xf32>
return %3 : tensor<3x2xf32>
@@ -853,7 +853,7 @@ func.func @slice_nofold_static(%arg0: tensor<3x4xf32>) -> tensor<3x2xf32> {
// CHECK-LABEL: @tile_fold
func.func @tile_fold(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
// CHECK: return %arg0
- %cst = tosa.const_shape { values = dense<1> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %cst = tosa.const_shape values(dense<1> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = tosa.tile %arg0, %cst: (tensor<3x4xf32>, !tosa.shape<2>) -> tensor<3x4xf32>
return %0 : tensor<3x4xf32>
}
@@ -863,7 +863,7 @@ func.func @tile_fold(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
// CHECK-LABEL: @tile_nofold
func.func @tile_nofold(%arg0: tensor<3x4xf32>) -> tensor<3x8xf32> {
// CHECK: tosa.tile
- %cst = tosa.const_shape { values = dense<[1, 2]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %cst = tosa.const_shape values(dense<[1, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = tosa.tile %arg0, %cst: (tensor<3x4xf32>, !tosa.shape<2>) -> tensor<3x8xf32>
return %0 : tensor<3x8xf32>
}
@@ -874,7 +874,7 @@ func.func @tile_nofold(%arg0: tensor<3x4xf32>) -> tensor<3x8xf32> {
func.func @transpose_no_op(%arg0: tensor<3x4x5x6xf32>) -> tensor<3x4x5x6xf32> {
// CHECK: return %arg0
// CHECK-NOT: tosa.transpose
- %1 = tosa.transpose %arg0 { perms = array<i32: 0, 1, 2, 3> }: (tensor<3x4x5x6xf32>) -> tensor<3x4x5x6xf32>
+ %1 = tosa.transpose %arg0 perms([0, 1, 2, 3]) : (tensor<3x4x5x6xf32>) -> tensor<3x4x5x6xf32>
return %1 : tensor<3x4x5x6xf32>
}
@@ -882,9 +882,9 @@ func.func @transpose_no_op(%arg0: tensor<3x4x5x6xf32>) -> tensor<3x4x5x6xf32> {
// CHECK-LABEL: @transpose_is_reshape
func.func @transpose_is_reshape(%arg0: tensor<1x4x5x1xf32>) -> tensor<1x4x1x5xf32> {
- // CHECK: %[[CONST0:.+]] = tosa.const_shape {values = dense<[1, 4, 1, 5]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ // CHECK: %[[CONST0:.+]] = tosa.const_shape values(dense<[1, 4, 1, 5]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK: tosa.reshape %arg0, %[[CONST0]]
- %0 = tosa.transpose %arg0 { perms = array<i32: 3, 1, 0, 2> }: (tensor<1x4x5x1xf32>) -> tensor<1x4x1x5xf32>
+ %0 = tosa.transpose %arg0 perms([3, 1, 0, 2]) : (tensor<1x4x5x1xf32>) -> tensor<1x4x1x5xf32>
return %0 : tensor<1x4x1x5xf32>
}
@@ -892,9 +892,9 @@ func.func @transpose_is_reshape(%arg0: tensor<1x4x5x1xf32>) -> tensor<1x4x1x5xf3
// CHECK-LABEL: @transpose_is_reshape_unknown_dim
func.func @transpose_is_reshape_unknown_dim(%arg0: tensor<1x4x?x1xf32>) -> tensor<1x4x1x?xf32> {
- // CHECK: %[[CONST0:.+]] = tosa.const_shape {values = dense<[1, 4, 1, -1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ // CHECK: %[[CONST0:.+]] = tosa.const_shape values(dense<[1, 4, 1, -1]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK: tosa.reshape %arg0, %[[CONST0]]
- %0 = tosa.transpose %arg0 { perms = array<i32: 3, 1, 0, 2> }: (tensor<1x4x?x1xf32>) -> tensor<1x4x1x?xf32>
+ %0 = tosa.transpose %arg0 perms([3, 1, 0, 2]) : (tensor<1x4x?x1xf32>) -> tensor<1x4x1x?xf32>
return %0 : tensor<1x4x1x?xf32>
}
@@ -915,10 +915,10 @@ func.func @single_bit_reshape() -> tensor<1xi1> {
// CHECK-LABEL: @fold_resize_nearest
func.func @fold_resize_nearest(%arg0 : tensor<1x15x13x1xi8>) -> tensor<1x15x13x1xi8> {
// CHECK: return %arg0
- %scale = tosa.const_shape { values = dense<[2, 2, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x15x13x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x15x13x1xi8>
+ %scale = tosa.const_shape values(dense<[2, 2, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x15x13x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x15x13x1xi8>
return %resize : tensor<1x15x13x1xi8>
}
@@ -927,10 +927,10 @@ func.func @fold_resize_nearest(%arg0 : tensor<1x15x13x1xi8>) -> tensor<1x15x13x1
// CHECK-LABEL: @fold_resize_bilinear
func.func @fold_resize_bilinear(%arg0 : tensor<1x15x13x1xi8>) -> tensor<1x15x13x1xi8> {
// CHECK: return %arg0
- %scale = tosa.const_shape { values = dense<[2, 2, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<1x15x13x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x15x13x1xi8>
+ %scale = tosa.const_shape values(dense<[2, 2, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x15x13x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x15x13x1xi8>
return %resize : tensor<1x15x13x1xi8>
}
@@ -940,10 +940,10 @@ func.func @fold_resize_bilinear(%arg0 : tensor<1x15x13x1xi8>) -> tensor<1x15x13x
// CHECK-LABEL: @fold_resize_identity_scale
func.func @fold_resize_identity_scale(%arg0 : tensor<1x15x13x1xf32>) -> tensor<1x15x13x1xf32> {
// CHECK-NOT: tosa.resize
- %scale = tosa.const_shape { values = dense<[1, 1, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x15x13x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x15x13x1xf32>
+ %scale = tosa.const_shape values(dense<[1, 1, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x15x13x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x15x13x1xf32>
return %resize : tensor<1x15x13x1xf32>
}
@@ -951,10 +951,10 @@ func.func @fold_resize_identity_scale(%arg0 : tensor<1x15x13x1xf32>) -> tensor<1
// CHECK-LABEL: @fold_resize_identity_scale_to_unranked
func.func @fold_resize_identity_scale_to_unranked(%arg0 : tensor<1x15x13x1xf32>) -> tensor<*xf32> {
// CHECK: tosa.resize
- %scale = tosa.const_shape { values = dense<[1, 1, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x15x13x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %scale = tosa.const_shape values(dense<[1, 1, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x15x13x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %resize : tensor<*xf32>
}
@@ -964,10 +964,10 @@ func.func @fold_resize_identity_scale_to_unranked(%arg0 : tensor<1x15x13x1xf32>)
// CHECK-LABEL: @resize_nofold_asymmetric_y_scale
func.func @resize_nofold_asymmetric_y_scale(%arg0 : tensor<1x15x13x1xf32>) -> tensor<1x29x13x1xf32> {
// CHECK: tosa.resize
- %scale = tosa.const_shape { values = dense<[4, 2, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x15x13x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x29x13x1xf32>
+ %scale = tosa.const_shape values(dense<[4, 2, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x15x13x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x29x13x1xf32>
return %resize : tensor<1x29x13x1xf32>
}
@@ -976,7 +976,7 @@ func.func @resize_nofold_asymmetric_y_scale(%arg0 : tensor<1x15x13x1xf32>) -> te
// CHECK-LABEL: @dont_canonicalize_unranked_clamp
func.func @dont_canonicalize_unranked_clamp(%arg0 : tensor<*xf32>) -> tensor<*xf32> {
// CHECK: tosa.clamp
- %0 = tosa.clamp %arg0 {min_val = 0.0 : f32, max_val = 1.0 : f32} : (tensor<*xf32>) -> tensor<*xf32>
+ %0 = tosa.clamp %arg0 min_val(0.0 : f32) max_val(1.0 : f32) : (tensor<*xf32>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -985,7 +985,7 @@ func.func @dont_canonicalize_unranked_clamp(%arg0 : tensor<*xf32>) -> tensor<*xf
// CHECK-LABEL: @dont_canonicalize_unranked_to_ranked_clamp
func.func @dont_canonicalize_unranked_to_ranked_clamp(%arg0 : tensor<*xf32>) -> tensor<1xf32> {
// CHECK: tosa.clamp
- %0 = tosa.clamp %arg0 {min_val = 0.0 : f32, max_val = 1.0 : f32} : (tensor<*xf32>) -> tensor<1xf32>
+ %0 = tosa.clamp %arg0 min_val(0.0 : f32) max_val(1.0 : f32) : (tensor<*xf32>) -> tensor<1xf32>
return %0 : tensor<1xf32>
}
// -----
@@ -993,8 +993,8 @@ func.func @dont_canonicalize_unranked_to_ranked_clamp(%arg0 : tensor<*xf32>) ->
// CHECK-LABEL: @dont_canonicalize_unranked_slice_dynamic_size
func.func @dont_canonicalize_unranked_slice_dynamic_size(%arg0: tensor<1x4xf32>) -> tensor<*xf32> {
// CHECK: tosa.slice
- %start = tosa.const_shape {values = dense<[0, 0]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %size = tosa.const_shape {values = dense<[1, -1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %start = tosa.const_shape values(dense<[0, 0]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %size = tosa.const_shape values(dense<[1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = tosa.slice %arg0, %start, %size : (tensor<1x4xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -1005,10 +1005,10 @@ func.func @dont_canonicalize_unranked_slice_dynamic_size(%arg0: tensor<1x4xf32>)
// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x12x12x1xf32>, %[[VAL_1:.*]]: tensor<1x12x12x1xf32>
// CHECK: return %[[VAL_0]], %[[VAL_1]] : tensor<1x12x12x1xf32>, tensor<1x12x12x1xf32>
func.func @canonicalize_concat_slice_final_axis(%arg0 : tensor<1x12x12x1xf32>, %arg1 : tensor<1x12x12x1xf32>) -> (tensor<1x12x12x1xf32>, tensor<1x12x12x1xf32>) {
- %0 = tosa.concat %arg0, %arg1 {axis = 3 : i32} : (tensor<1x12x12x1xf32>, tensor<1x12x12x1xf32>) -> tensor<1x12x12x2xf32>
- %1 = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %2 = tosa.const_shape {values = dense<[0, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %3 = tosa.const_shape {values = dense<[1, 12, 12, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %0 = tosa.concat %arg0, %arg1 axis(3) : (tensor<1x12x12x1xf32>, tensor<1x12x12x1xf32>) -> tensor<1x12x12x2xf32>
+ %1 = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %2 = tosa.const_shape values(dense<[0, 0, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %3 = tosa.const_shape values(dense<[1, 12, 12, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%4 = tosa.slice %0, %1, %3 : (tensor<1x12x12x2xf32>, !tosa.shape<4>, !tosa.shape<4>) -> tensor<1x12x12x1xf32>
%5 = tosa.slice %0, %2, %3 : (tensor<1x12x12x2xf32>, !tosa.shape<4>, !tosa.shape<4>) -> tensor<1x12x12x1xf32>
return %4, %5 : tensor<1x12x12x1xf32>, tensor<1x12x12x1xf32>
@@ -1020,10 +1020,10 @@ func.func @canonicalize_concat_slice_final_axis(%arg0 : tensor<1x12x12x1xf32>, %
// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x12x12xf32>, %[[VAL_1:.*]]: tensor<1x12x12xf32>
// CHECK: return %[[VAL_0]], %[[VAL_1]] : tensor<1x12x12xf32>, tensor<1x12x12xf32>
func.func @canonicalize_concat_slice_middle_axis(%arg0 : tensor<1x12x12xf32>, %arg1 : tensor<1x12x12xf32>) -> (tensor<1x12x12xf32>, tensor<1x12x12xf32>) {
- %0 = tosa.concat %arg0, %arg1 {axis = 1 : i32} : (tensor<1x12x12xf32>, tensor<1x12x12xf32>) -> tensor<1x24x12xf32>
- %1 = tosa.const_shape {values = dense<[0, 0, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %2 = tosa.const_shape {values = dense<[0, 12, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %3 = tosa.const_shape {values = dense<[1, 12, 12]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %0 = tosa.concat %arg0, %arg1 axis(1) : (tensor<1x12x12xf32>, tensor<1x12x12xf32>) -> tensor<1x24x12xf32>
+ %1 = tosa.const_shape values(dense<[0, 0, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %2 = tosa.const_shape values(dense<[0, 12, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %3 = tosa.const_shape values(dense<[1, 12, 12]> : tensor<3xindex>) : () -> !tosa.shape<3>
%4 = tosa.slice %0, %1, %3 : (tensor<1x24x12xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<1x12x12xf32>
%5 = tosa.slice %0, %2, %3 : (tensor<1x24x12xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<1x12x12xf32>
return %4, %5 : tensor<1x12x12xf32>, tensor<1x12x12xf32>
@@ -1033,20 +1033,20 @@ func.func @canonicalize_concat_slice_middle_axis(%arg0 : tensor<1x12x12xf32>, %a
// CHECK-LABEL: @canonicalize_cross_concat_inputs
// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x12x12xf32>, %[[VAL_1:.*]]: tensor<1x12x12xf32>
-// CHECK-DAG: %[[VAL_2:.*]] = tosa.const_shape {values = dense<[1, 12, 20]> : tensor<3xindex>}
-// CHECK-DAG: %[[VAL_3:.*]] = tosa.const_shape {values = dense<[1, 12, 15]> : tensor<3xindex>}
-// CHECK-DAG: %[[VAL_4:.*]] = tosa.const_shape {values = dense<[0, 0, 4]> : tensor<3xindex>}
-// CHECK-DAG: %[[VAL_5:.*]] = tosa.const_shape {values = dense<0> : tensor<3xindex>}
-// CHECK: %[[VAL_6:.*]] = tosa.concat %[[VAL_0]], %[[VAL_1]] {axis = 2 : i32} : (tensor<1x12x12xf32>, tensor<1x12x12xf32>) -> tensor<1x12x24xf32>
+// CHECK-DAG: %[[VAL_2:.*]] = tosa.const_shape values(dense<[1, 12, 20]> : tensor<3xindex>)
+// CHECK-DAG: %[[VAL_3:.*]] = tosa.const_shape values(dense<[1, 12, 15]> : tensor<3xindex>)
+// CHECK-DAG: %[[VAL_4:.*]] = tosa.const_shape values(dense<[0, 0, 4]> : tensor<3xindex>)
+// CHECK-DAG: %[[VAL_5:.*]] = tosa.const_shape values(dense<0> : tensor<3xindex>)
+// CHECK: %[[VAL_6:.*]] = tosa.concat %[[VAL_0]], %[[VAL_1]] axis(2) : (tensor<1x12x12xf32>, tensor<1x12x12xf32>) -> tensor<1x12x24xf32>
// CHECK: %[[VAL_7:.*]] = tosa.slice %[[VAL_6]], %[[VAL_5]], %[[VAL_3]]
// CHECK: %[[VAL_8:.*]] = tosa.slice %[[VAL_6]], %[[VAL_4]], %[[VAL_2]]
// CHECK: return %[[VAL_7]], %[[VAL_8]] : tensor<1x12x15xf32>, tensor<1x12x20xf32>
func.func @canonicalize_cross_concat_inputs(%arg0 : tensor<1x12x12xf32>, %arg1 : tensor<1x12x12xf32>) -> (tensor<1x12x15xf32>, tensor<1x12x20xf32>) {
- %0 = tosa.concat %arg0, %arg1 {axis = 2 : i32} : (tensor<1x12x12xf32>, tensor<1x12x12xf32>) -> tensor<1x12x24xf32>
- %1 = tosa.const_shape {values = dense<[0, 0, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %2 = tosa.const_shape {values = dense<[0, 0, 4]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %3 = tosa.const_shape {values = dense<[1, 12, 15]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %4 = tosa.const_shape {values = dense<[1, 12, 20]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %0 = tosa.concat %arg0, %arg1 axis(2) : (tensor<1x12x12xf32>, tensor<1x12x12xf32>) -> tensor<1x12x24xf32>
+ %1 = tosa.const_shape values(dense<[0, 0, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %2 = tosa.const_shape values(dense<[0, 0, 4]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %3 = tosa.const_shape values(dense<[1, 12, 15]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %4 = tosa.const_shape values(dense<[1, 12, 20]> : tensor<3xindex>) : () -> !tosa.shape<3>
%5 = tosa.slice %0, %1, %3 : (tensor<1x12x24xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<1x12x15xf32>
%6 = tosa.slice %0, %2, %4 : (tensor<1x12x24xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<1x12x20xf32>
return %5, %6 : tensor<1x12x15xf32>, tensor<1x12x20xf32>
@@ -1056,19 +1056,19 @@ func.func @canonicalize_cross_concat_inputs(%arg0 : tensor<1x12x12xf32>, %arg1 :
// CHECK-LABEL: @canonicalize_concat_slice_on_non_concat_axis
// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x12x12xf32>, %[[VAL_1:.*]]: tensor<1x12x12xf32>
-// CHECK-DAG: %[[VAL_2:.*]] = tosa.const_shape {values = dense<[0, 3, 0]> : tensor<3xindex>}
-// CHECK-DAG: %[[VAL_3:.*]] = tosa.const_shape {values = dense<[1, 3, 12]> : tensor<3xindex>}
-// CHECK-DAG: %[[VAL_4:.*]] = tosa.const_shape {values = dense<0> : tensor<3xindex>}
-// CHECK-DAG: %[[VAL_5:.*]] = tosa.const_shape {values = dense<[1, 6, 12]> : tensor<3xindex>}
+// CHECK-DAG: %[[VAL_2:.*]] = tosa.const_shape values(dense<[0, 3, 0]> : tensor<3xindex>)
+// CHECK-DAG: %[[VAL_3:.*]] = tosa.const_shape values(dense<[1, 3, 12]> : tensor<3xindex>)
+// CHECK-DAG: %[[VAL_4:.*]] = tosa.const_shape values(dense<0> : tensor<3xindex>)
+// CHECK-DAG: %[[VAL_5:.*]] = tosa.const_shape values(dense<[1, 6, 12]> : tensor<3xindex>)
// CHECK: %[[VAL_6:.*]] = tosa.slice %[[VAL_0]], %[[VAL_4]], %[[VAL_5]]
// CHECK: %[[VAL_7:.*]] = tosa.slice %[[VAL_1]], %[[VAL_2]], %[[VAL_3]]
// CHECK: return %[[VAL_6]], %[[VAL_7]] : tensor<1x6x12xf32>, tensor<1x3x12xf32>
func.func @canonicalize_concat_slice_on_non_concat_axis(%arg0 : tensor<1x12x12xf32>, %arg1 : tensor<1x12x12xf32>) -> (tensor<1x6x12xf32>, tensor<1x3x12xf32>) {
- %0 = tosa.concat %arg0, %arg1 {axis = 2 : i32} : (tensor<1x12x12xf32>, tensor<1x12x12xf32>) -> tensor<1x12x24xf32>
- %1 = tosa.const_shape {values = dense<[0, 0, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %2 = tosa.const_shape {values = dense<[1, 6, 12]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %3 = tosa.const_shape {values = dense<[0, 3, 12]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %4 = tosa.const_shape {values = dense<[1, 3, 12]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %0 = tosa.concat %arg0, %arg1 axis(2) : (tensor<1x12x12xf32>, tensor<1x12x12xf32>) -> tensor<1x12x24xf32>
+ %1 = tosa.const_shape values(dense<[0, 0, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %2 = tosa.const_shape values(dense<[1, 6, 12]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %3 = tosa.const_shape values(dense<[0, 3, 12]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %4 = tosa.const_shape values(dense<[1, 3, 12]> : tensor<3xindex>) : () -> !tosa.shape<3>
%5 = tosa.slice %0, %1, %2 : (tensor<1x12x24xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<1x6x12xf32>
%6 = tosa.slice %0, %3, %4 : (tensor<1x12x24xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<1x3x12xf32>
return %5, %6 : tensor<1x6x12xf32>, tensor<1x3x12xf32>
@@ -1078,17 +1078,17 @@ func.func @canonicalize_concat_slice_on_non_concat_axis(%arg0 : tensor<1x12x12xf
// CHECK-LABEL: @canonicalize_pad_slice_overlap
// CHECK-DAG: %[[PAD_CONST:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
-// CHECK-DAG: %[[ZERO:.*]] = tosa.const_shape {values = dense<0> : tensor<4xindex>} : () -> !tosa.shape<4>
-// CHECK-DAG: %[[PADDING:.*]] = tosa.const_shape {values = dense<[0, 0, 0, 0, 1, 1, 0, 0]> : tensor<8xindex>}
-// CHECK-DAG: %[[SLICE_SIZE:.*]] = tosa.const_shape {values = dense<[-1, 14, 18, 3]> : tensor<4xindex>}
+// CHECK-DAG: %[[ZERO:.*]] = tosa.const_shape values(dense<0> : tensor<4xindex>) : () -> !tosa.shape<4>
+// CHECK-DAG: %[[PADDING:.*]] = tosa.const_shape values(dense<[0, 0, 0, 0, 1, 1, 0, 0]> : tensor<8xindex>)
+// CHECK-DAG: %[[SLICE_SIZE:.*]] = tosa.const_shape values(dense<[-1, 14, 18, 3]> : tensor<4xindex>)
// CHECK: %[[PADDED:.*]] = tosa.pad %arg0, %[[PADDING]], %[[PAD_CONST]]
// CHECK: %[[SLICED:.*]] = tosa.slice %[[PADDED]], %[[ZERO]], %[[SLICE_SIZE]]
func.func @canonicalize_pad_slice_overlap(%arg0: tensor<?x16x16x3xf32>) -> tensor<?x14x18x3xf32> {
%pad_const = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
- %padding = tosa.const_shape {values = dense<[0, 0, 0, 0, 2, 2, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ %padding = tosa.const_shape values(dense<[0, 0, 0, 0, 2, 2, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%padded = tosa.pad %arg0, %padding, %pad_const : (tensor<?x16x16x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<?x16x20x3xf32>
- %start = tosa.const_shape {values = dense<[0, 0, 1, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %size = tosa.const_shape {values = dense<[-1, 14, 18, 3]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %start = tosa.const_shape values(dense<[0, 0, 1, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %size = tosa.const_shape values(dense<[-1, 14, 18, 3]> : tensor<4xindex>) : () -> !tosa.shape<4>
%sliced = tosa.slice %padded, %start, %size : (tensor<?x16x20x3xf32>, !tosa.shape<4>, !tosa.shape<4>) -> tensor<?x14x18x3xf32>
return %sliced : tensor<?x14x18x3xf32>
}
@@ -1096,16 +1096,16 @@ func.func @canonicalize_pad_slice_overlap(%arg0: tensor<?x16x16x3xf32>) -> tenso
// -----
// CHECK-LABEL: @canonicalize_pad_slice_inside
-// CHECK-DAG: %[[SLICE_START:.*]] = tosa.const_shape {values = dense<[0, 1, 2, 0]> : tensor<4xindex>}
-// CHECK-DAG: %[[SLICE_SIZE:.*]] = tosa.const_shape {values = dense<[1, 14, 10, 3]> : tensor<4xindex>}
+// CHECK-DAG: %[[SLICE_START:.*]] = tosa.const_shape values(dense<[0, 1, 2, 0]> : tensor<4xindex>)
+// CHECK-DAG: %[[SLICE_SIZE:.*]] = tosa.const_shape values(dense<[1, 14, 10, 3]> : tensor<4xindex>)
// CHECK-NOT: tosa.pad
// CHECK: %[[SLICED:.*]] = tosa.slice %arg0, %[[SLICE_START]], %[[SLICE_SIZE]]
func.func @canonicalize_pad_slice_inside(%arg0: tensor<1x16x16x3xf32>) -> tensor<1x14x10x3xf32> {
%pad_const = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
- %padding = tosa.const_shape {values = dense<[0, 0, 0, 0, 2, 2, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ %padding = tosa.const_shape values(dense<[0, 0, 0, 0, 2, 2, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%padded = tosa.pad %arg0, %padding, %pad_const : (tensor<1x16x16x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x16x20x3xf32>
- %start = tosa.const_shape {values = dense<[0, 1, 4, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %size = tosa.const_shape {values = dense<[1, 14, 10, 3]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %start = tosa.const_shape values(dense<[0, 1, 4, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %size = tosa.const_shape values(dense<[1, 14, 10, 3]> : tensor<4xindex>) : () -> !tosa.shape<4>
%sliced = tosa.slice %padded, %start, %size : (tensor<1x16x20x3xf32>, !tosa.shape<4>, !tosa.shape<4>) -> tensor<1x14x10x3xf32>
return %sliced : tensor<1x14x10x3xf32>
}
@@ -1114,17 +1114,17 @@ func.func @canonicalize_pad_slice_inside(%arg0: tensor<1x16x16x3xf32>) -> tensor
// CHECK-LABEL: func @canonicalize_pad_slice_exact
// CHECK-DAG: %[[PAD_CONST:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
-// CHECK-DAG: %[[ZERO:.*]] = tosa.const_shape {values = dense<0> : tensor<4xindex>} : () -> !tosa.shape<4>
-// CHECK-DAG: %[[PADDING:.*]] = tosa.const_shape {values = dense<[0, 0, 0, 0, 2, 2, 0, 0]> : tensor<8xindex>}
-// CHECK-DAG: %[[SLICE_SIZE:.*]] = tosa.const_shape {values = dense<[1, 16, 20, 2]> : tensor<4xindex>}
+// CHECK-DAG: %[[ZERO:.*]] = tosa.const_shape values(dense<0> : tensor<4xindex>) : () -> !tosa.shape<4>
+// CHECK-DAG: %[[PADDING:.*]] = tosa.const_shape values(dense<[0, 0, 0, 0, 2, 2, 0, 0]> : tensor<8xindex>)
+// CHECK-DAG: %[[SLICE_SIZE:.*]] = tosa.const_shape values(dense<[1, 16, 20, 2]> : tensor<4xindex>)
// CHECK: %[[PADDED:.*]] = tosa.pad %arg0, %[[PADDING]], %[[PAD_CONST]]
// CHECK: %[[SLICED:.*]] = tosa.slice %[[PADDED]], %[[ZERO]], %[[SLICE_SIZE]]
func.func @canonicalize_pad_slice_exact(%arg0: tensor<1x16x16x3xf32>) -> tensor<1x16x20x2xf32> {
%pad_const = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
- %padding = tosa.const_shape {values = dense<[0, 0, 0, 0, 2, 2, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ %padding = tosa.const_shape values(dense<[0, 0, 0, 0, 2, 2, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%padded = tosa.pad %arg0, %padding, %pad_const : (tensor<1x16x16x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x16x20x3xf32>
- %start = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %size = tosa.const_shape {values = dense<[1, 16, 20, 2]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %start = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %size = tosa.const_shape values(dense<[1, 16, 20, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
%sliced = tosa.slice %padded, %start, %size : (tensor<1x16x20x3xf32>, !tosa.shape<4>, !tosa.shape<4>) -> tensor<1x16x20x2xf32>
return %sliced : tensor<1x16x20x2xf32>
}
@@ -1132,16 +1132,16 @@ func.func @canonicalize_pad_slice_exact(%arg0: tensor<1x16x16x3xf32>) -> tensor<
// -----
// CHECK-LABEL: func @canonicalize_pad_slice_dynamic_noupdate
-// CHECK-DAG: tosa.const_shape {values = dense<[0, 0, 0, 0, 2, 2, 0, 0]> : tensor<8xindex>}
-// CHECK-DAG: tosa.const_shape {values = dense<[1, 16, 15, 2]> : tensor<4xindex>}
+// CHECK-DAG: tosa.const_shape values(dense<[0, 0, 0, 0, 2, 2, 0, 0]> : tensor<8xindex>)
+// CHECK-DAG: tosa.const_shape values(dense<[1, 16, 15, 2]> : tensor<4xindex>)
// CHECK: tosa.pad
// CHECK: tosa.slice
func.func @canonicalize_pad_slice_dynamic_noupdate(%arg0: tensor<1x16x?x3xf32>) -> tensor<1x16x?x2xf32> {
%pad_const = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
- %padding = tosa.const_shape {values = dense<[0, 0, 0, 0, 2, 2, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ %padding = tosa.const_shape values(dense<[0, 0, 0, 0, 2, 2, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
%padded = tosa.pad %arg0, %padding, %pad_const : (tensor<1x16x?x3xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x16x?x3xf32>
- %start = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %size = tosa.const_shape {values = dense<[1, 16, 15, 2]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %start = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %size = tosa.const_shape values(dense<[1, 16, 15, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
%sliced = tosa.slice %padded, %start, %size : (tensor<1x16x?x3xf32>, !tosa.shape<4>, !tosa.shape<4>) -> tensor<1x16x?x2xf32>
return %sliced : tensor<1x16x?x2xf32>
}
@@ -1213,14 +1213,14 @@ func.func @fold_abs_abs(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK-LABEL: @reshape_quant_nofold
// check that segfault is fixed
func.func @reshape_quant_nofold() -> tensor<1x1x1x1xi32> {
- %0 = "tosa.const"() {values = dense<127> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
- %cst0 = "tosa.const_shape"() {values = dense<[1, 1, 1, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %0 = "tosa.const"() <{values = dense<127> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %cst0 = "tosa.const_shape"() <{values = dense<[1, 1, 1, 1]> : tensor<4xindex>}> : () -> !tosa.shape<4>
%1 = tosa.reshape %0, %cst0 : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, !tosa.shape<4>) -> tensor<1x1x1x1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<-128> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
- %2 = tosa.rescale %1, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = DOUBLE_ROUND, scale32 = true, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<1x1x1x1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi32>) -> tensor<1x1x1x1xi32>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %2 = tosa.rescale %1, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<DOUBLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<1x1x1x1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi32>) -> tensor<1x1x1x1xi32>
return %2 : tensor<1x1x1x1xi32>
}
@@ -1229,7 +1229,7 @@ func.func @reshape_quant_nofold() -> tensor<1x1x1x1xi32> {
// CHECK-LABEL: @add_quant_nofold
// check that segfault is fixed
func.func @add_quant_nofold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
- %0 = "tosa.const"() {values = dense<127> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %0 = "tosa.const"() <{values = dense<127> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
%1 = tosa.add %0, %0 : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
return %1 : tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
}
@@ -1239,7 +1239,7 @@ func.func @add_quant_nofold() -> tensor<!quant.uniform<i8:f32, 3.075740460189990
// CHECK-LABEL: @sub_quant_nofold
// check that segfault is fixed
func.func @sub_quant_nofold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
- %0 = "tosa.const"() {values = dense<127> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %0 = "tosa.const"() <{values = dense<127> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
%1 = tosa.sub %0, %0 : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
return %1 : tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
}
@@ -1248,7 +1248,7 @@ func.func @sub_quant_nofold() -> tensor<!quant.uniform<i8:f32, 3.075740460189990
// CHECK-LABEL: @greater_quant_fold
func.func @greater_quant_fold() -> tensor<i1> {
- %0 = "tosa.const"() {values = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
// CHECK: "tosa.const"() <{values = dense<false>
%2 = "tosa.greater"(%0, %0) : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<i1>
return %2 : tensor<i1>
@@ -1258,7 +1258,7 @@ func.func @greater_quant_fold() -> tensor<i1> {
// CHECK-LABEL: @greater_equal_quant_fold
func.func @greater_equal_quant_fold() -> tensor<i1> {
- %0 = "tosa.const"() {values = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
// CHECK: "tosa.const"() <{values = dense<true>
%2 = "tosa.greater_equal"(%0, %0) : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<i1>
return %2 : tensor<i1>
@@ -1268,7 +1268,7 @@ func.func @greater_equal_quant_fold() -> tensor<i1> {
// CHECK-LABEL: @equal_quant_fold
func.func @equal_quant_fold() -> tensor<i1> {
- %0 = "tosa.const"() {values = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
// CHECK: "tosa.const"() <{values = dense<true>
%2 = "tosa.equal"(%0, %0) : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<i1>
return %2 : tensor<i1>
@@ -1290,8 +1290,8 @@ func.func @cast_quant_nofold() -> tensor<!quant.uniform<i8:f32, 3.07574046018999
func.func @reverse_quant_fold() -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
// CHECK: %[[CST:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
// CHECK: return %[[CST]]
- %0 = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
- %1 = "tosa.reverse"(%0) { axis = 0 : i32 } : (tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %1 = "tosa.reverse"(%0) <{ axis = 0 : i32 }> : (tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
return %1 : tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
}
@@ -1301,7 +1301,7 @@ func.func @reverse_quant_fold() -> tensor<1x!quant.uniform<i8:f32, 3.07574046018
// CHECK-LABEL: @reverse_nofold_unranked_operand
func.func @reverse_nofold_unranked_operand(%arg0: tensor<*xf32>) -> tensor<*xf32> {
// CHECK: tosa.reverse
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<*xf32>) -> tensor<*xf32>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<*xf32>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -1311,7 +1311,7 @@ func.func @reverse_nofold_unranked_operand(%arg0: tensor<*xf32>) -> tensor<*xf32
// CHECK-LABEL: @reverse_nofold_unit_dim_unranked_result
func.func @reverse_nofold_unit_dim_unranked_result(%arg0: tensor<1x4xf32>) -> tensor<*xf32> {
// CHECK: tosa.reverse
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<1x4xf32>) -> tensor<*xf32>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<1x4xf32>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -1322,7 +1322,7 @@ func.func @reverse_nofold_unit_dim_unranked_result(%arg0: tensor<1x4xf32>) -> te
func.func @reverse_nofold_splat_type_unmatch() -> tensor<*xf32> {
// CHECK: tosa.reverse
%0 = "tosa.const"() <{values = dense<1.0> : tensor<4xf32>}> : () -> tensor<4xf32>
- %1 = tosa.reverse %0 {axis = 0 : i32} : (tensor<4xf32>) -> tensor<*xf32>
+ %1 = tosa.reverse %0 axis(0) : (tensor<4xf32>) -> tensor<*xf32>
return %1 : tensor<*xf32>
}
@@ -1332,9 +1332,9 @@ func.func @reverse_nofold_splat_type_unmatch() -> tensor<*xf32> {
func.func @select_quant_fold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
// CHECK: %[[CONST_0:.*]] = "tosa.const"() <{values = dense<0> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
// CHECK: return %[[CONST_0]]
- %0 = "tosa.const"() {values = dense<true> : tensor<i1>} : () -> tensor<i1>
- %1 = "tosa.const"() {values = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
- %2 = "tosa.const"() {values = dense<127> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %0 = "tosa.const"() <{values = dense<true> : tensor<i1>}> : () -> tensor<i1>
+ %1 = "tosa.const"() <{values = dense<0> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %2 = "tosa.const"() <{values = dense<127> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
%3 = "tosa.select"(%0, %1, %2) : (tensor<i1>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
return %3 : tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
}
@@ -1344,8 +1344,8 @@ func.func @select_quant_fold() -> tensor<!quant.uniform<i8:f32, 3.07574046018999
// CHECK-LABEL: @mul_quant_nofold
func.func @mul_quant_nofold() -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
// CHECK: tosa.mul
- %0 = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
- %1 = "tosa.const"() {values = dense<1> : tensor<1xi8>} : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %1 = "tosa.const"() <{values = dense<1> : tensor<1xi8>}> : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%2 = tosa.mul %0, %1, %shift : (tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<1xi8>) -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
return %2 : tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
@@ -1359,7 +1359,7 @@ func.func nested @fold_reciprocal() -> tensor<3x600x1200xf32> {
// CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<8.620690e-03> : tensor<3x600x1200xf32>}> : () -> tensor<3x600x1200xf32>
// CHECK: return %[[VAL_0]] : tensor<3x600x1200xf32>
// CHECK: }
- %0 = "tosa.const"(){ values = dense<116.0>: tensor<3x600x1200xf32> }: () -> tensor<3x600x1200xf32>
+ %0 = "tosa.const"()<{ values = dense<116.0>: tensor<3x600x1200xf32> }>: () -> tensor<3x600x1200xf32>
%1 = "tosa.reciprocal"(%0): (tensor<3x600x1200xf32>) -> tensor<3x600x1200xf32>
return %1 : tensor<3x600x1200xf32>
}
@@ -1369,7 +1369,7 @@ func.func nested @fold_reciprocal() -> tensor<3x600x1200xf32> {
// CHECK-LABEL: @do_not_fold_reciprocal_int
func.func nested @do_not_fold_reciprocal_int() -> tensor<3x600x1200xi32> {
// CHECK: tosa.reciprocal
- %0 = "tosa.const"(){ values = dense<11>: tensor<3x600x1200xi32> }: () -> tensor<3x600x1200xi32>
+ %0 = "tosa.const"()<{ values = dense<11>: tensor<3x600x1200xi32> }>: () -> tensor<3x600x1200xi32>
%1 = "tosa.reciprocal"(%0): (tensor<3x600x1200xi32>) -> tensor<3x600x1200xi32>
return %1 : tensor<3x600x1200xi32>
}
@@ -1389,13 +1389,13 @@ func.func @do_not_fold_intdiv_division_by_0() -> tensor<1x24x2xi32> {
// -----
// CHECK-LABEL: func.func @slice_dynamic_size_static_output_canonicalize(
// CHECK-SAME: %[[ARG0:.*]]: tensor<2x60x59x?xf32>) -> tensor<2x60x58x?xf32> {
-// CHECK: %[[START:.*]] = tosa.const_shape {values = dense<0> : tensor<4xindex>} : () -> !tosa.shape<4>
-// CHECK: %[[SIZE:.*]] = tosa.const_shape {values = dense<[2, 60, 58, -1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+// CHECK: %[[START:.*]] = tosa.const_shape values(dense<0> : tensor<4xindex>) : () -> !tosa.shape<4>
+// CHECK: %[[SIZE:.*]] = tosa.const_shape values(dense<[2, 60, 58, -1]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK: %[[SLICE:.*]] = tosa.slice %[[ARG0]], %[[START]], %[[SIZE]] : (tensor<2x60x59x?xf32>, !tosa.shape<4>, !tosa.shape<4>) -> tensor<2x60x58x?xf32>
// CHECK: return %[[SLICE]]
func.func @slice_dynamic_size_static_output_canonicalize(%arg0: tensor<2x60x59x?xf32>) -> tensor<2x60x58x?xf32> {
- %0 = tosa.const_shape {values = dense<0> : tensor<4xindex>} : () -> !tosa.shape<4>
- %1 = tosa.const_shape {values = dense<[-1, 60, 58, -1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %0 = tosa.const_shape values(dense<0> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %1 = tosa.const_shape values(dense<[-1, 60, 58, -1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%2 = tosa.slice %arg0, %0, %1 : (tensor<2x60x59x?xf32>, !tosa.shape<4>, !tosa.shape<4>) -> tensor<2x60x58x?xf32>
return %2 : tensor<2x60x58x?xf32>
}
@@ -1492,8 +1492,8 @@ func.func @test_fold_i32_to_i1_cast() -> tensor<i1> {
// CHECK: %[[OUT:.*]] = tosa.cast %arg0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xi32>
// CHECK: return %[[OUT]] : tensor<13x21x3xi32>
func.func @test_canonicalize_non_narrowing_cast_i8_to_i32(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi32> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi16>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xi16>) -> tensor<13x21x3xi32>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xi16>
+ %1 = tosa.cast %0 : (tensor<13x21x3xi16>) -> tensor<13x21x3xi32>
return %1 : tensor<13x21x3xi32>
}
@@ -1504,19 +1504,19 @@ func.func @test_canonicalize_non_narrowing_cast_i8_to_i32(%arg0: tensor<13x21x3x
// CHECK %1 {input_unsigned = true} : (tensor<13x21x3xi16>) -> tensor<13x21x3xi32>
// CHECK: return %1 : tensor<13x21x3xi32>
func.func @test_canonicalize_non_narrowing_non_folding_cast_i8_to_i32_input_unsigned(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi32> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi16>
- %1 = tosa.cast %0 {input_unsigned = true} : (tensor<13x21x3xi16>) -> tensor<13x21x3xi32>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xi16>
+ %1 = tosa.cast %0 input_unsigned(true) : (tensor<13x21x3xi16>) -> tensor<13x21x3xi32>
return %1 : tensor<13x21x3xi32>
}
// -----
// CHECK-LABEL: @test_canonicalize_non_narrowing_cast_i8_to_i32_input_unsigned
-// CHECK: %[[OUT:.*]] = tosa.cast %arg0 {input_unsigned = true} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi32>
+// CHECK: %[[OUT:.*]] = tosa.cast %arg0 input_unsigned(true) : (tensor<13x21x3xi8>) -> tensor<13x21x3xi32>
// CHECK: return %[[OUT]] : tensor<13x21x3xi32>
func.func @test_canonicalize_non_narrowing_cast_i8_to_i32_input_unsigned(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi32> {
- %0 = tosa.cast %arg0 {input_unsigned = true} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi16>
- %1 = tosa.cast %0 {input_unsigned = true} : (tensor<13x21x3xi16>) -> tensor<13x21x3xi32>
+ %0 = tosa.cast %arg0 input_unsigned(true) : (tensor<13x21x3xi8>) -> tensor<13x21x3xi16>
+ %1 = tosa.cast %0 input_unsigned(true) : (tensor<13x21x3xi16>) -> tensor<13x21x3xi32>
return %1 : tensor<13x21x3xi32>
}
@@ -1525,8 +1525,8 @@ func.func @test_canonicalize_non_narrowing_cast_i8_to_i32_input_unsigned(%arg0:
// CHECK-LABEL: @test_canonicalize_non_narrowing_cast_i8_to_i8
// CHECK: return %arg0 : tensor<13x21x3xi8>
func.func @test_canonicalize_non_narrowing_cast_i8_to_i8(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi8> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi16>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xi16>) -> tensor<13x21x3xi8>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xi16>
+ %1 = tosa.cast %0 : (tensor<13x21x3xi16>) -> tensor<13x21x3xi8>
return %1 : tensor<13x21x3xi8>
}
@@ -1536,8 +1536,8 @@ func.func @test_canonicalize_non_narrowing_cast_i8_to_i8(%arg0: tensor<13x21x3xi
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_non_narrowing_cast_f32_to_f8(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf8E5M2> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf16>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xf16>) -> tensor<13x21x3xf8E5M2>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf32>) -> tensor<13x21x3xf16>
+ %1 = tosa.cast %0 : (tensor<13x21x3xf16>) -> tensor<13x21x3xf8E5M2>
return %1 : tensor<13x21x3xf8E5M2>
}
@@ -1547,8 +1547,8 @@ func.func @test_canonicalize_non_narrowing_cast_f32_to_f8(%arg0: tensor<13x21x3x
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_narrowing_cast_i32_to_i8(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xi8> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi32>) -> tensor<13x21x3xi16>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xi16>) -> tensor<13x21x3xi8>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi32>) -> tensor<13x21x3xi16>
+ %1 = tosa.cast %0 : (tensor<13x21x3xi16>) -> tensor<13x21x3xi8>
return %1 : tensor<13x21x3xi8>
}
@@ -1558,8 +1558,8 @@ func.func @test_canonicalize_narrowing_cast_i32_to_i8(%arg0: tensor<13x21x3xi32>
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_narrowing_cast_i32_to_i8_to_i16(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xi16> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi32>) -> tensor<13x21x3xi8>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi32>) -> tensor<13x21x3xi8>
+ %1 = tosa.cast %0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xi16>
return %1 : tensor<13x21x3xi16>
}
@@ -1569,8 +1569,8 @@ func.func @test_canonicalize_narrowing_cast_i32_to_i8_to_i16(%arg0: tensor<13x21
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_narrowing_cast_i8_to_ui16_to_i8(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi8> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi8>) -> tensor<13x21x3xui16>
- %1 = tosa.cast %0 {input_unsigned = true} : (tensor<13x21x3xui16>) -> tensor<13x21x3xi8>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xui16>
+ %1 = tosa.cast %0 input_unsigned(true) : (tensor<13x21x3xui16>) -> tensor<13x21x3xi8>
return %1 : tensor<13x21x3xi8>
}
@@ -1579,8 +1579,8 @@ func.func @test_canonicalize_narrowing_cast_i8_to_ui16_to_i8(%arg0: tensor<13x21
// CHECK-LABEL: @test_canonicalize_non_narrowing_cast_f8_to_f16_to_f8
// CHECK: return %arg0
func.func @test_canonicalize_non_narrowing_cast_f8_to_f16_to_f8(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf8E4M3FN> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xf16>) -> tensor<13x21x3xf8E4M3FN>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16>
+ %1 = tosa.cast %0 : (tensor<13x21x3xf16>) -> tensor<13x21x3xf8E4M3FN>
return %1 : tensor<13x21x3xf8E4M3FN>
}
@@ -1590,8 +1590,8 @@ func.func @test_canonicalize_non_narrowing_cast_f8_to_f16_to_f8(%arg0: tensor<13
// CHECK: %[[OUT:.+]] = tosa.cast %arg0 : (tensor<13x21x3xf8E4M3FN>)
// CHECK: return %[[OUT]] : tensor<13x21x3xf16>
func.func @test_canonicalize_non_narrowing_cast_f8_to_f8E4M3FN_to_f16(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf32>
+ %1 = tosa.cast %0 : (tensor<13x21x3xf32>) -> tensor<13x21x3xf16>
return %1 : tensor<13x21x3xf16>
}
@@ -1601,8 +1601,8 @@ func.func @test_canonicalize_non_narrowing_cast_f8_to_f8E4M3FN_to_f16(%arg0: ten
// CHECK: %[[OUT:.+]] = tosa.cast %arg0 : (tensor<13x21x3xf8E5M2>) -> tensor<13x21x3xf16>
// CHECK: return %[[OUT]] : tensor<13x21x3xf16>
func.func @test_canonicalize_non_narrowing_cast_f8_to_f32_to_f16(%arg0: tensor<13x21x3xf8E5M2>) -> tensor<13x21x3xf16> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf8E5M2>) -> tensor<13x21x3xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf8E5M2>) -> tensor<13x21x3xf32>
+ %1 = tosa.cast %0 : (tensor<13x21x3xf32>) -> tensor<13x21x3xf16>
return %1 : tensor<13x21x3xf16>
}
@@ -1612,8 +1612,8 @@ func.func @test_canonicalize_non_narrowing_cast_f8_to_f32_to_f16(%arg0: tensor<1
// CHECK: %[[OUT:.+]] = tosa.cast %arg0 : (tensor<13x21x3xf16>) -> tensor<13x21x3xf8E5M2>
// CHECK: return %[[OUT]] : tensor<13x21x3xf8E5M2>
func.func @test_canonicalize_non_narrowing_cast_f16_to_f32_to_f8(%arg0: tensor<13x21x3xf16>) -> tensor<13x21x3xf8E5M2> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf16>) -> tensor<13x21x3xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf8E5M2>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf16>) -> tensor<13x21x3xf32>
+ %1 = tosa.cast %0 : (tensor<13x21x3xf32>) -> tensor<13x21x3xf8E5M2>
return %1 : tensor<13x21x3xf8E5M2>
}
@@ -1623,8 +1623,8 @@ func.func @test_canonicalize_non_narrowing_cast_f16_to_f32_to_f8(%arg0: tensor<1
// CHECK: %[[OUT:.+]] = tosa.cast %arg0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xf16>
// CHECK: return %[[OUT]] : tensor<13x21x3xf16>
func.func @test_canonicalize_non_narrowing_cast_i8_to_i32_to_f16(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xf16> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xi32>) -> tensor<13x21x3xf16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xi32>
+ %1 = tosa.cast %0 : (tensor<13x21x3xi32>) -> tensor<13x21x3xf16>
return %1 : tensor<13x21x3xf16>
}
@@ -1634,8 +1634,8 @@ func.func @test_canonicalize_non_narrowing_cast_i8_to_i32_to_f16(%arg0: tensor<1
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_non_narrowing_cast_f8E4M3FN_to_f16_to_f8E5M2(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf8E5M2> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xf16>) -> tensor<13x21x3xf8E5M2>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16>
+ %1 = tosa.cast %0 : (tensor<13x21x3xf16>) -> tensor<13x21x3xf8E5M2>
return %1 : tensor<13x21x3xf8E5M2>
}
@@ -1645,8 +1645,8 @@ func.func @test_canonicalize_non_narrowing_cast_f8E4M3FN_to_f16_to_f8E5M2(%arg0:
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_non_narrowing_cast_f8E4M3FN_to_f16_to_f8E4M3(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf8E4M3> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xf16>) -> tensor<13x21x3xf8E4M3>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16>
+ %1 = tosa.cast %0 : (tensor<13x21x3xf16>) -> tensor<13x21x3xf8E4M3>
return %1 : tensor<13x21x3xf8E4M3>
}
@@ -1656,8 +1656,8 @@ func.func @test_canonicalize_non_narrowing_cast_f8E4M3FN_to_f16_to_f8E4M3(%arg0:
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_non_narrowing_cast_f8E4M3_to_f8E4M3FN_to_f16(%arg0: tensor<13x21x3xf8E4M3>) -> tensor<13x21x3xf16> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf8E4M3>) -> tensor<13x21x3xf8E4M3FN>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf8E4M3>) -> tensor<13x21x3xf8E4M3FN>
+ %1 = tosa.cast %0 : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16>
return %1 : tensor<13x21x3xf16>
}
@@ -1667,8 +1667,8 @@ func.func @test_canonicalize_non_narrowing_cast_f8E4M3_to_f8E4M3FN_to_f16(%arg0:
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_non_narrowing_cast_f6E3M2FN_to_f8E4M3FNUZ_to_f16(%arg0: tensor<13x21x3xf6E3M2FN>) -> tensor<13x21x3xf16> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf6E3M2FN>) -> tensor<13x21x3xf8E4M3FNUZ>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xf8E4M3FNUZ>) -> tensor<13x21x3xf16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf6E3M2FN>) -> tensor<13x21x3xf8E4M3FNUZ>
+ %1 = tosa.cast %0 : (tensor<13x21x3xf8E4M3FNUZ>) -> tensor<13x21x3xf16>
return %1 : tensor<13x21x3xf16>
}
@@ -1678,8 +1678,8 @@ func.func @test_canonicalize_non_narrowing_cast_f6E3M2FN_to_f8E4M3FNUZ_to_f16(%a
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_non_narrowing_cast_f6E3M2FN_to_f8E4M3FN_to_f16_unsupported(%arg0: tensor<13x21x3xf6E3M2FN>) -> tensor<13x21x3xf16> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf6E3M2FN>) -> tensor<13x21x3xf8E4M3FN>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf6E3M2FN>) -> tensor<13x21x3xf8E4M3FN>
+ %1 = tosa.cast %0 : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16>
return %1 : tensor<13x21x3xf16>
}
@@ -1689,8 +1689,8 @@ func.func @test_canonicalize_non_narrowing_cast_f6E3M2FN_to_f8E4M3FN_to_f16_unsu
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_non_narrowing_cast_i1_to_f32_unsupported(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xf32> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi1>) -> tensor<13x21x3xi8>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xi8>) -> tensor<13x21x3xf32>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi1>) -> tensor<13x21x3xi8>
+ %1 = tosa.cast %0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xf32>
return %1 : tensor<13x21x3xf32>
}
@@ -1700,8 +1700,8 @@ func.func @test_canonicalize_non_narrowing_cast_i1_to_f32_unsupported(%arg0: ten
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_non_narrowing_cast_i8_to_i64_unsupported(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi64> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xi32>) -> tensor<13x21x3xi64>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xi32>
+ %1 = tosa.cast %0 : (tensor<13x21x3xi32>) -> tensor<13x21x3xi64>
return %1 : tensor<13x21x3xi64>
}
@@ -1711,8 +1711,8 @@ func.func @test_canonicalize_non_narrowing_cast_i8_to_i64_unsupported(%arg0: ten
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_non_narrowing_cast_f16_to_bf16_unsupported(%arg0: tensor<13x21x3xf16>) -> tensor<13x21x3xbf16> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf16>) -> tensor<13x21x3xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xf32>) -> tensor<13x21x3xbf16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf16>) -> tensor<13x21x3xf32>
+ %1 = tosa.cast %0 : (tensor<13x21x3xf32>) -> tensor<13x21x3xbf16>
return %1 : tensor<13x21x3xbf16>
}
@@ -1722,8 +1722,8 @@ func.func @test_canonicalize_non_narrowing_cast_f16_to_bf16_unsupported(%arg0: t
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_canonicalize_non_narrowing_cast_i8_to_f8E4M3FN_unsupported(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xf8E4M3FN> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi8>) -> tensor<13x21x3xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf8E4M3FN>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xf32>
+ %1 = tosa.cast %0 : (tensor<13x21x3xf32>) -> tensor<13x21x3xf8E4M3FN>
return %1 : tensor<13x21x3xf8E4M3FN>
}
@@ -1732,8 +1732,8 @@ func.func @test_canonicalize_non_narrowing_cast_i8_to_f8E4M3FN_unsupported(%arg0
// CHECK-LABEL: @test_canonicalize_cast_from_cast_to_block_scaled_type_f4E2M1_through_f32
// CHECK: return %arg0 : tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
func.func @test_canonicalize_cast_from_cast_to_block_scaled_type_f4E2M1_through_f32(%arg0: tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<15x3x2x256xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<15x3x2x256xf32>) -> tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
+ %0 = tosa.cast %arg0 : (tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<15x3x2x256xf32>
+ %1 = tosa.cast %0 : (tensor<15x3x2x256xf32>) -> tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %1 : tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
}
@@ -1742,8 +1742,8 @@ func.func @test_canonicalize_cast_from_cast_to_block_scaled_type_f4E2M1_through_
// CHECK-LABEL: @test_canonicalize_cast_from_cast_to_block_scaled_type_f8E5M2_through_f32
// CHECK: return %arg0 : tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>
func.func @test_canonicalize_cast_from_cast_to_block_scaled_type_f8E5M2_through_f32(%arg0: tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<160xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<160xf32>) -> tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>
+ %0 = tosa.cast %arg0 : (tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<160xf32>
+ %1 = tosa.cast %0 : (tensor<160xf32>) -> tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>
return %1 : tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>
}
@@ -1754,8 +1754,8 @@ func.func @test_canonicalize_cast_from_cast_to_block_scaled_type_f8E5M2_through_
// CHECK: %[[block_scaled:.+]] = tosa.cast %[[values]]
// CHECK: return %[[block_scaled]] : tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_type_different_types_f8E5M2_f6E2M3_through_f32(%arg0: tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<160xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<160xf32>) -> tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
+ %0 = tosa.cast %arg0 : (tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<160xf32>
+ %1 = tosa.cast %0 : (tensor<160xf32>) -> tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
return %1 : tensor<160x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
}
@@ -1766,8 +1766,8 @@ func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_type_differen
// CHECK: %[[block_scaled:.+]] = tosa.cast %[[values]]
// CHECK: return %[[block_scaled]] : tensor<32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>
func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_type_different_types_f6E2M3_f6E3M2_through_f32(%arg0: tensor<32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>) -> tensor<32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>) -> tensor<32xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<32xf32>) -> tensor<32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>
+ %0 = tosa.cast %arg0 : (tensor<32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>) -> tensor<32xf32>
+ %1 = tosa.cast %0 : (tensor<32xf32>) -> tensor<32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>
return %1 : tensor<32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>
}
@@ -1778,8 +1778,8 @@ func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_type_differen
// CHECK: %[[block_scaled:.+]] = tosa.cast %[[values]]
// CHECK: return %[[block_scaled]] : tensor<*x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_type_unranked(%arg0: tensor<3x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>) -> tensor<*x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<3x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>) -> tensor<*xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<*xf32>) -> tensor<*x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
+ %0 = tosa.cast %arg0 : (tensor<3x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>) -> tensor<*xf32>
+ %1 = tosa.cast %0 : (tensor<*xf32>) -> tensor<*x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
return %1 : tensor<*x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
}
@@ -1789,8 +1789,8 @@ func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_type_unranked
// CHECK: tosa.cast
// CHECK: tosa.cast
func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_type_f8E5M2_f8E4M3(%arg0: tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<15x3x2x256xf8E4M3FN>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<15x3x2x256xf8E4M3FN>) -> tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>
+ %0 = tosa.cast %arg0 : (tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<15x3x2x256xf8E4M3FN>
+ %1 = tosa.cast %0 : (tensor<15x3x2x256xf8E4M3FN>) -> tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>
return %1 : tensor<15x3x2x256x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>
}
@@ -1833,8 +1833,8 @@ func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_type_f4E2M1FN
// CHECK-LABEL: @test_canonicalize_cast_from_cast_to_block_scaled_f4E2M1
// CHECK: return %arg0, %arg1 : tensor<15x3x2x256xf4E2M1FN>, tensor<15x3x2x8xf8E8M0FNU>
func.func @test_canonicalize_cast_from_cast_to_block_scaled_f4E2M1(%arg0: tensor<15x3x2x256xf4E2M1FN>, %arg1: tensor<15x3x2x8xf8E8M0FNU>) -> (tensor<15x3x2x256xf4E2M1FN>, tensor<15x3x2x8xf8E8M0FNU>) {
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = BLOCK_SIZE_32} : (tensor<15x3x2x256xf4E2M1FN>, tensor<15x3x2x8xf8E8M0FNU>) -> tensor<15x3x2x256xf32>
- %1, %2 = tosa.cast_to_block_scaled %0 {block_size = BLOCK_SIZE_32} : (tensor<15x3x2x256xf32>) -> (tensor<15x3x2x256xf4E2M1FN>, tensor<15x3x2x8xf8E8M0FNU>)
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<15x3x2x256xf4E2M1FN>, tensor<15x3x2x8xf8E8M0FNU>) -> tensor<15x3x2x256xf32>
+ %1, %2 = tosa.cast_to_block_scaled %0 block_size<BLOCK_SIZE_32> : (tensor<15x3x2x256xf32>) -> (tensor<15x3x2x256xf4E2M1FN>, tensor<15x3x2x8xf8E8M0FNU>)
return %1, %2 : tensor<15x3x2x256xf4E2M1FN>, tensor<15x3x2x8xf8E8M0FNU>
}
@@ -1843,8 +1843,8 @@ func.func @test_canonicalize_cast_from_cast_to_block_scaled_f4E2M1(%arg0: tensor
// CHECK-LABEL: @test_canonicalize_cast_from_cast_to_block_scaled_f8E5M2
// CHECK: return %arg0, %arg1 : tensor<160xf8E5M2>, tensor<5xf8E8M0FNU>
func.func @test_canonicalize_cast_from_cast_to_block_scaled_f8E5M2(%arg0: tensor<160xf8E5M2>, %arg1: tensor<5xf8E8M0FNU>) -> (tensor<160xf8E5M2>, tensor<5xf8E8M0FNU>) {
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = BLOCK_SIZE_32} : (tensor<160xf8E5M2>, tensor<5xf8E8M0FNU>) -> tensor<160xf32>
- %1, %2 = tosa.cast_to_block_scaled %0 {block_size = BLOCK_SIZE_32} : (tensor<160xf32>) -> (tensor<160xf8E5M2>, tensor<5xf8E8M0FNU>)
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<160xf8E5M2>, tensor<5xf8E8M0FNU>) -> tensor<160xf32>
+ %1, %2 = tosa.cast_to_block_scaled %0 block_size<BLOCK_SIZE_32> : (tensor<160xf32>) -> (tensor<160xf8E5M2>, tensor<5xf8E8M0FNU>)
return %1, %2 : tensor<160xf8E5M2>, tensor<5xf8E8M0FNU>
}
@@ -1855,8 +1855,8 @@ func.func @test_canonicalize_cast_from_cast_to_block_scaled_f8E5M2(%arg0: tensor
// CHECK: %[[data:.+]], %[[scales:.+]] = tosa.cast_to_block_scaled %[[values]]
// CHECK: return %[[data]], %[[scales]] : tensor<160xf6E2M3FN>, tensor<5xf8E8M0FNU>
func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_different_types_f8E5M2_f6E2M3(%arg0: tensor<160xf8E5M2>, %arg1: tensor<5xf8E8M0FNU>) -> (tensor<160xf6E2M3FN>, tensor<5xf8E8M0FNU>) {
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = BLOCK_SIZE_32} : (tensor<160xf8E5M2>, tensor<5xf8E8M0FNU>) -> tensor<160xf32>
- %1, %2 = tosa.cast_to_block_scaled %0 {block_size = BLOCK_SIZE_32} : (tensor<160xf32>) -> (tensor<160xf6E2M3FN>, tensor<5xf8E8M0FNU>)
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<160xf8E5M2>, tensor<5xf8E8M0FNU>) -> tensor<160xf32>
+ %1, %2 = tosa.cast_to_block_scaled %0 block_size<BLOCK_SIZE_32> : (tensor<160xf32>) -> (tensor<160xf6E2M3FN>, tensor<5xf8E8M0FNU>)
return %1, %2 : tensor<160xf6E2M3FN>, tensor<5xf8E8M0FNU>
}
@@ -1867,8 +1867,8 @@ func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_different_typ
// CHECK: %[[data:.+]], %[[scales:.+]] = tosa.cast_to_block_scaled %[[values]]
// CHECK: return %[[data]], %[[scales]] : tensor<32xf6E3M2FN>, tensor<1xf8E8M0FNU>
func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_different_types_f6E2M3_f6E3M2(%arg0: tensor<32xf6E2M3FN>, %arg1: tensor<1xf8E8M0FNU>) -> (tensor<32xf6E3M2FN>, tensor<1xf8E8M0FNU>) {
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = BLOCK_SIZE_32} : (tensor<32xf6E2M3FN>, tensor<1xf8E8M0FNU>) -> tensor<32xf32>
- %1, %2 = tosa.cast_to_block_scaled %0 {block_size = BLOCK_SIZE_32} : (tensor<32xf32>) -> (tensor<32xf6E3M2FN>, tensor<1xf8E8M0FNU>)
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<32xf6E2M3FN>, tensor<1xf8E8M0FNU>) -> tensor<32xf32>
+ %1, %2 = tosa.cast_to_block_scaled %0 block_size<BLOCK_SIZE_32> : (tensor<32xf32>) -> (tensor<32xf6E3M2FN>, tensor<1xf8E8M0FNU>)
return %1, %2 : tensor<32xf6E3M2FN>, tensor<1xf8E8M0FNU>
}
@@ -1879,8 +1879,8 @@ func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_different_typ
// CHECK: %[[data:.+]], %[[scales:.+]] = tosa.cast_to_block_scaled %[[values]]
// CHECK: return %[[data]], %[[scales]] : tensor<*xf6E2M3FN>, tensor<*xf8E8M0FNU>
func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_unranked(%arg0: tensor<3x64xf6E2M3FN>, %arg1: tensor<3x2xf8E8M0FNU>) -> (tensor<*xf6E2M3FN>, tensor<*xf8E8M0FNU>) {
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = BLOCK_SIZE_32} : (tensor<3x64xf6E2M3FN>, tensor<3x2xf8E8M0FNU>) -> tensor<*xf32>
- %1, %2 = tosa.cast_to_block_scaled %0 {block_size = BLOCK_SIZE_32} : (tensor<*xf32>) -> (tensor<*xf6E2M3FN>, tensor<*xf8E8M0FNU>)
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<3x64xf6E2M3FN>, tensor<3x2xf8E8M0FNU>) -> tensor<*xf32>
+ %1, %2 = tosa.cast_to_block_scaled %0 block_size<BLOCK_SIZE_32> : (tensor<*xf32>) -> (tensor<*xf6E2M3FN>, tensor<*xf8E8M0FNU>)
return %1, %2 : tensor<*xf6E2M3FN>, tensor<*xf8E8M0FNU>
}
@@ -1892,7 +1892,7 @@ func.func @test_do_not_canonicalize_cast_from_cast_to_block_scaled_unranked(%arg
func.func @canonicalize_unit_avg_pool2d(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1904,7 +1904,7 @@ func.func @canonicalize_unit_avg_pool2d(%arg0: tensor<1x32x32x8xf32>) -> tensor<
func.func @dont_canonicalize_unit_avg_pool2d_integer(%arg0: tensor<1x32x32x8xi8>) -> tensor<1x32x32x8xi8> {
%input_zp = "tosa.const"() <{values = dense<1> : tensor<1xi8>}> : () -> tensor<1xi8>
%output_zp = "tosa.const"() <{values = dense<1> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = i32, kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) acc_type(i32) :
(tensor<1x32x32x8xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x32x32x8xi8>
return %0 : tensor<1x32x32x8xi8>
}
@@ -1916,7 +1916,7 @@ func.func @dont_canonicalize_unit_avg_pool2d_integer(%arg0: tensor<1x32x32x8xi8>
func.func @dont_canonicalize_unit_avg_pool2d_non_zero_padding(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x31x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 1, 0, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([2, 2]) stride([1, 1]) pad([1, 0, 0, 0]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x31x8xf32>
return %0 : tensor<1x32x31x8xf32>
}
@@ -1928,7 +1928,7 @@ func.func @dont_canonicalize_unit_avg_pool2d_non_zero_padding(%arg0: tensor<1x32
func.func @dont_canonicalize_unit_avg_pool2d_non_unit_stride(%arg0: tensor<1x33x32x8xf32>) -> tensor<1x17x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 1>} :
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([1, 1]) stride([2, 1]) pad([0, 0, 0, 0]) acc_type(f32) :
(tensor<1x33x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x17x32x8xf32>
return %0 : tensor<1x17x32x8xf32>
}
@@ -1940,7 +1940,7 @@ func.func @dont_canonicalize_unit_avg_pool2d_non_unit_stride(%arg0: tensor<1x33x
func.func @dont_canonicalize_unit_avg_pool2d_non_unit_kernel(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x31x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 2, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([2, 1]) stride([1, 1]) pad([0, 0, 0, 0]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x31x32x8xf32>
return %0 : tensor<1x31x32x8xf32>
}
@@ -1952,7 +1952,7 @@ func.func @dont_canonicalize_unit_avg_pool2d_non_unit_kernel(%arg0: tensor<1x32x
func.func @dont_canonicalize_unit_avg_pool2d_non_unit_kernel(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x?x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x?x32x8xf32>
return %0 : tensor<1x?x32x8xf32>
}
@@ -1962,7 +1962,7 @@ func.func @dont_canonicalize_unit_avg_pool2d_non_unit_kernel(%arg0: tensor<1x32x
// CHECK-LABEL: @dont_canonicalize_unit_avg_pool2d_dynamic_zp
// CHECK: tosa.avg_pool2d
func.func @dont_canonicalize_unit_avg_pool2d_dynamic_zp(%arg0: tensor<1x32x32x8xf32>, %zp: tensor<1xf32>) -> tensor<1x?x32x8xf32> {
- %0 = tosa.avg_pool2d %arg0, %zp, %zp {acc_type = f32, kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.avg_pool2d %arg0, %zp, %zp kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x?x32x8xf32>
return %0 : tensor<1x?x32x8xf32>
}
@@ -1973,7 +1973,7 @@ func.func @dont_canonicalize_unit_avg_pool2d_dynamic_zp(%arg0: tensor<1x32x32x8x
// CHECK-NOT: tosa.max_pool2d
// CHECK: return %arg0 : tensor<1x32x32x8xf32>
func.func @canonicalize_unit_max_pool2d(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, nan_mode = PROPAGATE} :
+ %0 = tosa.max_pool2d %arg0 kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) :
(tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1983,7 +1983,7 @@ func.func @canonicalize_unit_max_pool2d(%arg0: tensor<1x32x32x8xf32>) -> tensor<
// CHECK-LABEL: @dont_canonicalize_unit_max_pool2d_ignore_nan
// CHECK: tosa.max_pool2d
func.func @dont_canonicalize_unit_max_pool2d_ignore_nan(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 1, 1>, nan_mode = IGNORE, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.max_pool2d %arg0 kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) nan_mode<IGNORE> :
(tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1993,7 +1993,7 @@ func.func @dont_canonicalize_unit_max_pool2d_ignore_nan(%arg0: tensor<1x32x32x8x
// CHECK-LABEL: @dont_canonicalize_unit_max_pool2d_non_zero_padding
// CHECK: tosa.max_pool2d
func.func @dont_canonicalize_unit_max_pool2d_non_zero_padding(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x31x8xf32> {
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 2, 2>, pad = array<i64: 1, 0, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.max_pool2d %arg0 kernel([2, 2]) stride([1, 1]) pad([1, 0, 0, 0]) :
(tensor<1x32x32x8xf32>) -> tensor<1x32x31x8xf32>
return %0 : tensor<1x32x31x8xf32>
}
@@ -2003,7 +2003,7 @@ func.func @dont_canonicalize_unit_max_pool2d_non_zero_padding(%arg0: tensor<1x32
// CHECK-LABEL: @dont_canonicalize_unit_max_pool2d_non_unit_stride
// CHECK: tosa.max_pool2d
func.func @dont_canonicalize_unit_max_pool2d_non_unit_stride(%arg0: tensor<1x33x32x8xf32>) -> tensor<1x17x32x8xf32> {
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 1>} :
+ %0 = tosa.max_pool2d %arg0 kernel([1, 1]) stride([2, 1]) pad([0, 0, 0, 0]) :
(tensor<1x33x32x8xf32>) -> tensor<1x17x32x8xf32>
return %0 : tensor<1x17x32x8xf32>
}
@@ -2013,7 +2013,7 @@ func.func @dont_canonicalize_unit_max_pool2d_non_unit_stride(%arg0: tensor<1x33x
// CHECK-LABEL: @dont_canonicalize_unit_max_pool2d_non_unit_kernel
// CHECK: tosa.max_pool2d
func.func @dont_canonicalize_unit_max_pool2d_non_unit_kernel(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x31x32x8xf32> {
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 2, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.max_pool2d %arg0 kernel([2, 1]) stride([1, 1]) pad([0, 0, 0, 0]) :
(tensor<1x32x32x8xf32>) -> tensor<1x31x32x8xf32>
return %0 : tensor<1x31x32x8xf32>
}
@@ -2021,13 +2021,13 @@ func.func @dont_canonicalize_unit_max_pool2d_non_unit_kernel(%arg0: tensor<1x32x
// -----
// CHECK-LABEL: @canonicalize_max_pool2d_adaptive
-// CHECK: %[[POOL:.+]] = tosa.max_pool2d %arg0 {kernel = array<i64: 2, 1>, nan_mode = IGNORE, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>) -> tensor<1x31x32x8xf32>
+// CHECK: %[[POOL:.+]] = tosa.max_pool2d %arg0 kernel([2, 1]) stride([1, 1]) pad([0, 0, 0, 0]) nan_mode<IGNORE> : (tensor<1x32x32x8xf32>) -> tensor<1x31x32x8xf32>
// CHECK: return %[[POOL]]
func.func @canonicalize_max_pool2d_adaptive(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x31x32x8xf32> {
- %kernel = tosa.const_shape {values = dense<[2, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad {nan_mode = IGNORE} :
+ %kernel = tosa.const_shape values(dense<[2, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad nan_mode<IGNORE> :
(tensor<1x32x32x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x31x32x8xf32>
return %0 : tensor<1x31x32x8xf32>
}
@@ -2035,13 +2035,13 @@ func.func @canonicalize_max_pool2d_adaptive(%arg0: tensor<1x32x32x8xf32>) -> ten
// -----
// CHECK-LABEL: @canonicalize_avg_pool2d_adaptive
-// CHECK: %[[POOL:.+]] = tosa.avg_pool2d %arg0, %{{.*}}, %{{.*}} {acc_type = f32, kernel = array<i64: 3, 3>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf32>
+// CHECK: %[[POOL:.+]] = tosa.avg_pool2d %arg0, %{{.*}}, %{{.*}} kernel([3, 3]) stride([1, 1]) pad([1, 1, 1, 1]) acc_type(f32) : (tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf32>
// CHECK: return %[[POOL]]
func.func @canonicalize_avg_pool2d_adaptive(%arg0: tensor<1x7x7x9xf32>, %input_zp: tensor<1xf32>, %output_zp: tensor<1xf32>) -> tensor<1x7x7x9xf32> {
- %kernel = tosa.const_shape {values = dense<[3, 3]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[1, 1, 1, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = f32} :
+ %kernel = tosa.const_shape values(dense<[3, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[1, 1, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(f32) :
(tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xf32>
return %0 : tensor<1x7x7x9xf32>
}
@@ -2051,12 +2051,12 @@ func.func @canonicalize_avg_pool2d_adaptive(%arg0: tensor<1x7x7x9xf32>, %input_z
// CHECK-LABEL: @dont_canonicalize_non_const_max_pool2d_adaptive
// CHECK: tosa.max_pool2d_adaptive
func.func @dont_canonicalize_non_const_max_pool2d_adaptive(%arg0: tensor<1x?x?x8xf32>) -> tensor<1x?x?x8xf32> {
- %dim1 = tosa.dim %arg0 {axis = 1 : i32} : (tensor<1x?x?x8xf32>) -> !tosa.shape<1>
- %dim2 = tosa.dim %arg0 {axis = 2 : i32} : (tensor<1x?x?x8xf32>) -> !tosa.shape<1>
+ %dim1 = tosa.dim %arg0 axis(1) : (tensor<1x?x?x8xf32>) -> !tosa.shape<1>
+ %dim2 = tosa.dim %arg0 axis(2) : (tensor<1x?x?x8xf32>) -> !tosa.shape<1>
%kernel = tosa.concat_shape %dim1, %dim2 : (!tosa.shape<1>, !tosa.shape<1>) -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad {nan_mode = IGNORE} :
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad nan_mode<IGNORE> :
(tensor<1x?x?x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x?x?x8xf32>
return %0 : tensor<1x?x?x8xf32>
}
@@ -2066,12 +2066,12 @@ func.func @dont_canonicalize_non_const_max_pool2d_adaptive(%arg0: tensor<1x?x?x8
// CHECK-LABEL: @dont_canonicalize_non_const_avg_pool2d_adaptive
// CHECK: tosa.avg_pool2d_adaptive
func.func @dont_canonicalize_non_const_avg_pool2d_adaptive(%arg0: tensor<1x?x?x8xf32>, %input_zp: tensor<1xf32>, %output_zp: tensor<1xf32>) -> tensor<1x?x?x8xf32> {
- %dim1 = tosa.dim %arg0 {axis = 1 : i32} : (tensor<1x?x?x8xf32>) -> !tosa.shape<1>
- %dim2 = tosa.dim %arg0 {axis = 2 : i32} : (tensor<1x?x?x8xf32>) -> !tosa.shape<1>
+ %dim1 = tosa.dim %arg0 axis(1) : (tensor<1x?x?x8xf32>) -> !tosa.shape<1>
+ %dim2 = tosa.dim %arg0 axis(2) : (tensor<1x?x?x8xf32>) -> !tosa.shape<1>
%kernel = tosa.concat_shape %dim1, %dim2 : (!tosa.shape<1>, !tosa.shape<1>) -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = f32} :
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(f32) :
(tensor<1x?x?x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x?x?x8xf32>
return %0 : tensor<1x?x?x8xf32>
}
@@ -2084,7 +2084,7 @@ func.func @dont_canonicalize_non_const_avg_pool2d_adaptive(%arg0: tensor<1x?x?x8
// CHECK: %[[SUB:.+]] = tosa.sub %[[ARG0]], %[[ARG1]] : (tensor<96x56x56x96xf32>, tensor<1x56x56x1xf32>) -> tensor<96x56x56x96xf32>
// CHECK: return %[[SUB]]
func.func @canonicalize_tile_broadcast_sub(%arg0: tensor<96x56x56x96xf32>, %arg1: tensor<1x56x56x1xf32>) -> tensor<96x56x56x96xf32> {
- %shape = tosa.const_shape {values = dense<[96, 1, 1, 96]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %shape = tosa.const_shape values(dense<[96, 1, 1, 96]> : tensor<4xindex>) : () -> !tosa.shape<4>
%tile = tosa.tile %arg1, %shape : (tensor<1x56x56x1xf32>, !tosa.shape<4>) -> tensor<96x56x56x96xf32>
%sub = tosa.sub %arg0, %tile : (tensor<96x56x56x96xf32>, tensor<96x56x56x96xf32>) -> tensor<96x56x56x96xf32>
return %sub : tensor<96x56x56x96xf32>
@@ -2098,7 +2098,7 @@ func.func @canonicalize_tile_broadcast_sub(%arg0: tensor<96x56x56x96xf32>, %arg1
// CHECK: %[[GT:.+]] = tosa.greater %[[ARG0]], %[[ARG1]] : (tensor<1x197x768xf32>, tensor<1x197x1xf32>) -> tensor<1x197x768xi1>
// CHECK: return %[[GT]]
func.func @canonicalize_tile_broadcast_greater(%arg0: tensor<1x197x768xf32>, %arg1: tensor<1x197x1xf32>) -> tensor<1x197x768xi1> {
- %shape = tosa.const_shape {values = dense<[1, 1, 768]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %shape = tosa.const_shape values(dense<[1, 1, 768]> : tensor<3xindex>) : () -> !tosa.shape<3>
%tile = tosa.tile %arg1, %shape : (tensor<1x197x1xf32>, !tosa.shape<3>) -> tensor<1x197x768xf32>
%gt = tosa.greater %arg0, %tile : (tensor<1x197x768xf32>, tensor<1x197x768xf32>) -> tensor<1x197x768xi1>
return %gt : tensor<1x197x768xi1>
@@ -2112,7 +2112,7 @@ func.func @canonicalize_tile_broadcast_greater(%arg0: tensor<1x197x768xf32>, %ar
// CHECK: %[[MUL:.+]] = tosa.mul %[[ARG0]], %[[ARG1]], %[[SHIFT]] : (tensor<1x197x768xf32>, tensor<1x197x1xf32>, tensor<1xi8>) -> tensor<1x197x768xf32>
// CHECK: return %[[MUL]]
func.func @canonicalize_tile_broadcast_mul(%arg0: tensor<1x197x768xf32>, %arg1: tensor<1x197x1xf32>, %shift: tensor<1xi8>) -> tensor<1x197x768xf32> {
- %shape = tosa.const_shape {values = dense<[1, 1, 768]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %shape = tosa.const_shape values(dense<[1, 1, 768]> : tensor<3xindex>) : () -> !tosa.shape<3>
%tile = tosa.tile %arg1, %shape : (tensor<1x197x1xf32>, !tosa.shape<3>) -> tensor<1x197x768xf32>
%mul = tosa.mul %arg0, %tile, %shift : (tensor<1x197x768xf32>, tensor<1x197x768xf32>, tensor<1xi8>) -> tensor<1x197x768xf32>
return %mul : tensor<1x197x768xf32>
@@ -2125,7 +2125,7 @@ func.func @canonicalize_tile_broadcast_mul(%arg0: tensor<1x197x768xf32>, %arg1:
// CHECK: tosa.tile
// CHECK: tosa.sub
func.func @dont_canonicalize_tile_when_result_no_longer_broadcastable(%arg0: tensor<2x1xf32>) -> tensor<2x4xf32> {
- %shape = tosa.const_shape {values = dense<[1, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %shape = tosa.const_shape values(dense<[1, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
%tile = tosa.tile %arg0, %shape : (tensor<2x1xf32>, !tosa.shape<2>) -> tensor<2x4xf32>
%sub = tosa.sub %tile, %arg0 : (tensor<2x4xf32>, tensor<2x1xf32>) -> tensor<2x4xf32>
return %sub : tensor<2x4xf32>
@@ -2140,7 +2140,7 @@ func.func @dont_canonicalize_tile_when_result_no_longer_broadcastable(%arg0: ten
// CHECK: %[[ADD:.+]] = tosa.add %[[ARG0]], %[[TILE]] : (tensor<2x1xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
// CHECK: return %[[ADD]]
func.func @dont_canonicalize_second_tile_when_result_no_longer_broadcastable(%arg0: tensor<2x1xf32>) -> tensor<2x4xf32> {
- %shape = tosa.const_shape {values = dense<[1, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %shape = tosa.const_shape values(dense<[1, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
%tile0 = tosa.tile %arg0, %shape : (tensor<2x1xf32>, !tosa.shape<2>) -> tensor<2x4xf32>
%tile1 = tosa.tile %arg0, %shape : (tensor<2x1xf32>, !tosa.shape<2>) -> tensor<2x4xf32>
%add = tosa.add %tile0, %tile1 : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
@@ -2156,7 +2156,7 @@ func.func @dont_canonicalize_second_tile_when_result_no_longer_broadcastable(%ar
// CHECK: %[[SUB:.+]] = tosa.sub %[[ARG1]], %[[TILE]] : (tensor<*xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
// CHECK: return %[[SUB]]
func.func @dont_canonicalize_tile_with_unranked_other_operand(%arg0: tensor<2x1xf32>, %arg1: tensor<*xf32>) -> tensor<2x4xf32> {
- %shape = tosa.const_shape {values = dense<[1, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %shape = tosa.const_shape values(dense<[1, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
%tile = tosa.tile %arg0, %shape : (tensor<2x1xf32>, !tosa.shape<2>) -> tensor<2x4xf32>
%sub = tosa.sub %arg1, %tile : (tensor<*xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
return %sub : tensor<2x4xf32>
@@ -2168,7 +2168,7 @@ func.func @dont_canonicalize_tile_with_unranked_other_operand(%arg0: tensor<2x1x
// CHECK: tosa.tile
// CHECK: tosa.sub
func.func @dont_canonicalize_tile_non_singleton_expansion(%arg0: tensor<1x56x56x96xf32>, %arg1: tensor<1x56x56x2xf32>) -> tensor<1x56x56x96xf32> {
- %shape = tosa.const_shape {values = dense<[1, 1, 1, 48]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %shape = tosa.const_shape values(dense<[1, 1, 1, 48]> : tensor<4xindex>) : () -> !tosa.shape<4>
%tile = tosa.tile %arg1, %shape : (tensor<1x56x56x2xf32>, !tosa.shape<4>) -> tensor<1x56x56x96xf32>
%sub = tosa.sub %arg0, %tile : (tensor<1x56x56x96xf32>, tensor<1x56x56x96xf32>) -> tensor<1x56x56x96xf32>
return %sub : tensor<1x56x56x96xf32>
@@ -2180,7 +2180,7 @@ func.func @dont_canonicalize_tile_non_singleton_expansion(%arg0: tensor<1x56x56x
// CHECK: tosa.tile
// CHECK: tosa.sub
func.func @dont_canonicalize_tile_dynamic_expanded_dim(%arg0: tensor<2x?xf32>, %arg1: tensor<2x4xf32>) -> tensor<2x4xf32> {
- %shape = tosa.const_shape {values = dense<[1, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %shape = tosa.const_shape values(dense<[1, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
%tile = tosa.tile %arg0, %shape : (tensor<2x?xf32>, !tosa.shape<2>) -> tensor<2x4xf32>
%sub = tosa.sub %arg1, %tile : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
return %sub : tensor<2x4xf32>
@@ -2192,7 +2192,7 @@ func.func @dont_canonicalize_tile_dynamic_expanded_dim(%arg0: tensor<2x?xf32>, %
// CHECK: tosa.tile
// CHECK: tosa.sub
func.func @dont_canonicalize_tile_dynamic_output(%arg0: tensor<2x?xf32>, %arg1: tensor<2x1xf32>) -> tensor<2x?xf32> {
- %shape = tosa.const_shape {values = dense<[1, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %shape = tosa.const_shape values(dense<[1, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
%tile = tosa.tile %arg1, %shape : (tensor<2x1xf32>, !tosa.shape<2>) -> tensor<2x?xf32>
%sub = tosa.sub %arg0, %tile : (tensor<2x?xf32>, tensor<2x?xf32>) -> tensor<2x?xf32>
return %sub : tensor<2x?xf32>
@@ -2205,7 +2205,7 @@ func.func @dont_canonicalize_tile_dynamic_output(%arg0: tensor<2x?xf32>, %arg1:
// CHECK: tosa.sub
// CHECK: tosa.add
func.func @dont_canonicalize_tile_multi_use(%arg0: tensor<1x56x56x96xf32>, %arg1: tensor<1x56x56x1xf32>) -> (tensor<1x56x56x96xf32>, tensor<1x56x56x96xf32>) {
- %shape = tosa.const_shape {values = dense<[1, 1, 1, 96]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %shape = tosa.const_shape values(dense<[1, 1, 1, 96]> : tensor<4xindex>) : () -> !tosa.shape<4>
%tile = tosa.tile %arg1, %shape : (tensor<1x56x56x1xf32>, !tosa.shape<4>) -> tensor<1x56x56x96xf32>
%sub = tosa.sub %arg0, %tile : (tensor<1x56x56x96xf32>, tensor<1x56x56x96xf32>) -> tensor<1x56x56x96xf32>
%add = tosa.add %arg0, %tile : (tensor<1x56x56x96xf32>, tensor<1x56x56x96xf32>) -> tensor<1x56x56x96xf32>
@@ -2218,7 +2218,7 @@ func.func @dont_canonicalize_tile_multi_use(%arg0: tensor<1x56x56x96xf32>, %arg1
// CHECK: tosa.tile
// CHECK: tosa.abs
func.func @dont_canonicalize_tile_unary_elementwise(%arg0: tensor<2x1xf32>) -> tensor<2x4xf32> {
- %shape = tosa.const_shape {values = dense<[1, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %shape = tosa.const_shape values(dense<[1, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
%tile = tosa.tile %arg0, %shape : (tensor<2x1xf32>, !tosa.shape<2>) -> tensor<2x4xf32>
%abs = tosa.abs %tile : (tensor<2x4xf32>) -> tensor<2x4xf32>
return %abs : tensor<2x4xf32>
@@ -2230,7 +2230,7 @@ func.func @dont_canonicalize_tile_unary_elementwise(%arg0: tensor<2x1xf32>) -> t
// CHECK: tosa.tile
// CHECK: tosa.mul
func.func @dont_canonicalize_tile_used_as_mul_shift(%lhs: tensor<1xf32>, %rhs: tensor<1xf32>, %shift: tensor<?xi8>) -> tensor<1xf32> {
- %multiples = tosa.const_shape {values = dense<[1]> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %multiples = tosa.const_shape values(dense<[1]> : tensor<1xindex>) : () -> !tosa.shape<1>
%shift_static = tosa.tile %shift, %multiples : (tensor<?xi8>, !tosa.shape<1>) -> tensor<1xi8>
%mul = tosa.mul %lhs, %rhs, %shift_static : (tensor<1xf32>, tensor<1xf32>, tensor<1xi8>) -> tensor<1xf32>
return %mul : tensor<1xf32>
@@ -2239,22 +2239,22 @@ func.func @dont_canonicalize_tile_used_as_mul_shift(%lhs: tensor<1xf32>, %rhs: t
// -----
// CHECK-LABEL: test_single_concat
-// CHECK: %[[VAL_1:.*]] = tosa.concat %arg0, %arg0 {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
+// CHECK: %[[VAL_1:.*]] = tosa.concat %arg0, %arg0 axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
// CHECK: return %[[VAL_1]] : tensor<1x2x7x7xf32>
func.func @test_single_concat(%arg0: tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32> {
- %0 = tosa.concat %arg0, %arg0 {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
+ %0 = tosa.concat %arg0, %arg0 axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
return %0 : tensor<1x2x7x7xf32>
}
// -----
// CHECK-LABEL: test_concat_different_axis
-// CHECK: %[[VAL_1:.*]] = tosa.concat %arg0, %arg0 {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
-// CHECK: %[[VAL_2:.*]] = tosa.concat %[[VAL_1]], %[[VAL_1]] {axis = 0 : i32} : (tensor<1x2x7x7xf32>, tensor<1x2x7x7xf32>) -> tensor<2x2x7x7xf32>
+// CHECK: %[[VAL_1:.*]] = tosa.concat %arg0, %arg0 axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
+// CHECK: %[[VAL_2:.*]] = tosa.concat %[[VAL_1]], %[[VAL_1]] axis(0) : (tensor<1x2x7x7xf32>, tensor<1x2x7x7xf32>) -> tensor<2x2x7x7xf32>
// CHECK: return %[[VAL_2]] : tensor<2x2x7x7xf32>
func.func @test_concat_different_axis(%arg0: tensor<1x1x7x7xf32>) -> tensor<2x2x7x7xf32> {
- %0 = tosa.concat %arg0, %arg0 {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
- %1 = tosa.concat %0, %0 {axis = 0 : i32} : (tensor<1x2x7x7xf32>, tensor<1x2x7x7xf32>) -> tensor<2x2x7x7xf32>
+ %0 = tosa.concat %arg0, %arg0 axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
+ %1 = tosa.concat %0, %0 axis(0) : (tensor<1x2x7x7xf32>, tensor<1x2x7x7xf32>) -> tensor<2x2x7x7xf32>
return %1 : tensor<2x2x7x7xf32>
}
@@ -2262,12 +2262,12 @@ func.func @test_concat_different_axis(%arg0: tensor<1x1x7x7xf32>) -> tensor<2x2x
// CHECK-LABEL: test_fold_concats
// CHECK: %[[VAL_1:.*]] = tensor.empty() : tensor<1x1x7x7xf32>
-// CHECK: %[[VAL_2:.*]] = tosa.concat %[[VAL_1]], %arg0, %arg0, %[[VAL_1]] {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x4x7x7xf32>
+// CHECK: %[[VAL_2:.*]] = tosa.concat %[[VAL_1]], %arg0, %arg0, %[[VAL_1]] axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x4x7x7xf32>
// CHECK: return %[[VAL_2]] : tensor<1x4x7x7xf32>
func.func @test_fold_concats(%arg0: tensor<1x1x7x7xf32>) -> tensor<1x4x7x7xf32> {
%tmp = tensor.empty() : tensor<1x1x7x7xf32>
- %0 = tosa.concat %arg0, %arg0 {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
- %1 = tosa.concat %tmp, %0, %tmp {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x2x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x4x7x7xf32>
+ %0 = tosa.concat %arg0, %arg0 axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
+ %1 = tosa.concat %tmp, %0, %tmp axis(1) : (tensor<1x1x7x7xf32>, tensor<1x2x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x4x7x7xf32>
return %1 : tensor<1x4x7x7xf32>
}
@@ -2275,26 +2275,26 @@ func.func @test_fold_concats(%arg0: tensor<1x1x7x7xf32>) -> tensor<1x4x7x7xf32>
// CHECK-LABEL: test_nested_fold
// CHECK: %[[VAL_1:.*]] = tensor.empty() : tensor<1x1x7x7xf32>
-// CHECK: %[[VAL_2:.*]] = tosa.concat %[[VAL_1]], %arg0, %arg0, %[[VAL_1]], %[[VAL_1]], %arg0, %arg0, %[[VAL_1]] {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x8x7x7xf32>
+// CHECK: %[[VAL_2:.*]] = tosa.concat %[[VAL_1]], %arg0, %arg0, %[[VAL_1]], %[[VAL_1]], %arg0, %arg0, %[[VAL_1]] axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x8x7x7xf32>
// CHECK: return %[[VAL_2]] : tensor<1x8x7x7xf32>
func.func @test_nested_fold(%arg0: tensor<1x1x7x7xf32>) -> tensor<1x8x7x7xf32> {
%tmp = tensor.empty() : tensor<1x1x7x7xf32>
- %0 = tosa.concat %arg0, %arg0 {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
- %1 = tosa.concat %tmp, %0, %tmp {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x2x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x4x7x7xf32>
- %2 = tosa.concat %1, %1 {axis = 1 : i32} : (tensor<1x4x7x7xf32>, tensor<1x4x7x7xf32>) -> tensor<1x8x7x7xf32>
+ %0 = tosa.concat %arg0, %arg0 axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
+ %1 = tosa.concat %tmp, %0, %tmp axis(1) : (tensor<1x1x7x7xf32>, tensor<1x2x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x4x7x7xf32>
+ %2 = tosa.concat %1, %1 axis(1) : (tensor<1x4x7x7xf32>, tensor<1x4x7x7xf32>) -> tensor<1x8x7x7xf32>
return %2 : tensor<1x8x7x7xf32>
}
// -----
// CHECK-LABEL: test_nested_fold_too_many_operands
-// CHECK: %[[VAL_1:.*]] = tosa.concat %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0 {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x8x7x7xf32>
-// CHECK: %[[VAL_2:.*]] = tosa.concat %[[VAL_1]], %[[VAL_1]], %[[VAL_1]], %[[VAL_1]], %[[VAL_1]], %[[VAL_1]], %[[VAL_1]], %[[VAL_1]], %arg0 {axis = 1 : i32} : (tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x65x7x7xf32>
+// CHECK: %[[VAL_1:.*]] = tosa.concat %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0 axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x8x7x7xf32>
+// CHECK: %[[VAL_2:.*]] = tosa.concat %[[VAL_1]], %[[VAL_1]], %[[VAL_1]], %[[VAL_1]], %[[VAL_1]], %[[VAL_1]], %[[VAL_1]], %[[VAL_1]], %arg0 axis(1) : (tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x65x7x7xf32>
// CHECK: return %[[VAL_2]] : tensor<1x65x7x7xf32>
module attributes {tosa.target_env = #tosa.target_env<specification_version = "1.0", level = "8k", profiles = [pro_fp], extensions = [int16]>} {
func.func @test_nested_fold_too_many_operands(%arg0: tensor<1x1x7x7xf32>) -> tensor<1x65x7x7xf32> {
- %0 = tosa.concat %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0 {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x8x7x7xf32>
- %1 = tosa.concat %0, %0, %0, %0, %0, %0, %0, %0, %arg0 {axis = 1 : i32} : (tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x65x7x7xf32>
+ %0 = tosa.concat %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0 axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x8x7x7xf32>
+ %1 = tosa.concat %0, %0, %0, %0, %0, %0, %0, %0, %arg0 axis(1) : (tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x8x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x65x7x7xf32>
return %1 : tensor<1x65x7x7xf32>
}
}
@@ -2302,24 +2302,24 @@ module attributes {tosa.target_env = #tosa.target_env<specification_version = "1
// -----
// CHECK-LABEL: test_wide_fold
-// CHECK: %[[VAL_2:.*]] = tosa.concat %arg0, %arg0, %arg1, %arg1 {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x4x7x7xf32>
+// CHECK: %[[VAL_2:.*]] = tosa.concat %arg0, %arg0, %arg1, %arg1 axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x4x7x7xf32>
// CHECK: return %[[VAL_2]] : tensor<1x4x7x7xf32>
func.func @test_wide_fold(%arg0: tensor<1x1x7x7xf32>, %arg1: tensor<1x1x7x7xf32>) -> tensor<1x4x7x7xf32> {
- %0 = tosa.concat %arg0, %arg0 {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
- %1 = tosa.concat %arg1, %arg1 {axis = 1 : i32} : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
- %2 = tosa.concat %0, %1 {axis = 1 : i32} : (tensor<1x2x7x7xf32>, tensor<1x2x7x7xf32>) -> tensor<1x4x7x7xf32>
+ %0 = tosa.concat %arg0, %arg0 axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
+ %1 = tosa.concat %arg1, %arg1 axis(1) : (tensor<1x1x7x7xf32>, tensor<1x1x7x7xf32>) -> tensor<1x2x7x7xf32>
+ %2 = tosa.concat %0, %1 axis(1) : (tensor<1x2x7x7xf32>, tensor<1x2x7x7xf32>) -> tensor<1x4x7x7xf32>
return %2 : tensor<1x4x7x7xf32>
}
// -----
// CHECK-LABEL: test_partially_foldable
-// CHECK: %[[VAL_2:.*]] = tosa.concat %arg1, %arg1 {axis = 2 : i32} : (tensor<1x2x4x8xf32>, tensor<1x2x4x8xf32>) -> tensor<1x2x8x8xf32>
-// CHECK: %[[VAL_3:.*]] = tosa.concat %arg0, %arg0, %[[VAL_2]] {axis = 1 : i32} : (tensor<1x1x8x8xf32>, tensor<1x1x8x8xf32>, tensor<1x2x8x8xf32>) -> tensor<1x4x8x8xf32>
+// CHECK: %[[VAL_2:.*]] = tosa.concat %arg1, %arg1 axis(2) : (tensor<1x2x4x8xf32>, tensor<1x2x4x8xf32>) -> tensor<1x2x8x8xf32>
+// CHECK: %[[VAL_3:.*]] = tosa.concat %arg0, %arg0, %[[VAL_2]] axis(1) : (tensor<1x1x8x8xf32>, tensor<1x1x8x8xf32>, tensor<1x2x8x8xf32>) -> tensor<1x4x8x8xf32>
// CHECK: return %[[VAL_3]] : tensor<1x4x8x8xf32>
func.func @test_partially_foldable(%arg0: tensor<1x1x8x8xf32>, %arg1: tensor<1x2x4x8xf32>) -> tensor<1x4x8x8xf32> {
- %0 = tosa.concat %arg0, %arg0 {axis = 1 : i32} : (tensor<1x1x8x8xf32>, tensor<1x1x8x8xf32>) -> tensor<1x2x8x8xf32>
- %1 = tosa.concat %arg1, %arg1 {axis = 2 : i32} : (tensor<1x2x4x8xf32>, tensor<1x2x4x8xf32>) -> tensor<1x2x8x8xf32>
- %2 = tosa.concat %0, %1 {axis = 1 : i32} : (tensor<1x2x8x8xf32>, tensor<1x2x8x8xf32>) -> tensor<1x4x8x8xf32>
+ %0 = tosa.concat %arg0, %arg0 axis(1) : (tensor<1x1x8x8xf32>, tensor<1x1x8x8xf32>) -> tensor<1x2x8x8xf32>
+ %1 = tosa.concat %arg1, %arg1 axis(2) : (tensor<1x2x4x8xf32>, tensor<1x2x4x8xf32>) -> tensor<1x2x8x8xf32>
+ %2 = tosa.concat %0, %1 axis(1) : (tensor<1x2x8x8xf32>, tensor<1x2x8x8xf32>) -> tensor<1x4x8x8xf32>
return %2 : tensor<1x4x8x8xf32>
}
diff --git a/mlir/test/Dialect/Tosa/constant-reciprocal-fold.mlir b/mlir/test/Dialect/Tosa/constant-reciprocal-fold.mlir
index 12a3aee428ac6..30bb77e84f846 100644
--- a/mlir/test/Dialect/Tosa/constant-reciprocal-fold.mlir
+++ b/mlir/test/Dialect/Tosa/constant-reciprocal-fold.mlir
@@ -5,7 +5,7 @@ func.func @reciprocal_fold_single_valued() -> tensor<f32> {
// CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}2.5{{0*}}e-01{{.*}}tensor<f32>
// CHECK-NOT: tosa.reciprocal
// CHECK: return [[RES]]
- %0 = "tosa.const"() {values = dense<4.0> : tensor<f32>} : () -> tensor<f32>
+ %0 = "tosa.const"() <{values = dense<4.0> : tensor<f32>}> : () -> tensor<f32>
%1 = "tosa.reciprocal"(%0) : (tensor<f32>) -> tensor<f32>
return %1 : tensor<f32>
}
@@ -15,7 +15,7 @@ func.func @reciprocal_fold_splat() -> tensor<12x7xf32> {
// CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}2.5{{0*}}e-01{{.*}}tensor<12x7xf32>
// CHECK-NOT: tosa.reciprocal
// CHECK: return [[RES]]
- %0 = "tosa.const"() {values = dense<4.0> : tensor<12x7xf32>} : () -> tensor<12x7xf32>
+ %0 = "tosa.const"() <{values = dense<4.0> : tensor<12x7xf32>}> : () -> tensor<12x7xf32>
%1 = "tosa.reciprocal"(%0) : (tensor<12x7xf32>) -> tensor<12x7xf32>
return %1 : tensor<12x7xf32>
}
@@ -26,7 +26,7 @@ func.func @reciprocal_div_zero() -> tensor<f32> {
// CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}0x7F800000
// CHECK-NOT: tosa.reciprocal
// CHECK: return [[RES]]
- %0 = "tosa.const"() {values = dense<0.0> : tensor<f32>} : () -> tensor<f32>
+ %0 = "tosa.const"() <{values = dense<0.0> : tensor<f32>}> : () -> tensor<f32>
%1 = "tosa.reciprocal"(%0) : (tensor<f32>) -> tensor<f32>
return %1 : tensor<f32>
}
@@ -37,7 +37,7 @@ func.func @reciprocal_div_neg_zero() -> tensor<f32> {
// CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}0xFF800000
// CHECK-NOT: tosa.reciprocal
// CHECK: return [[RES]]
- %0 = "tosa.const"() {values = dense<-0.0> : tensor<f32>} : () -> tensor<f32>
+ %0 = "tosa.const"() <{values = dense<-0.0> : tensor<f32>}> : () -> tensor<f32>
%1 = "tosa.reciprocal"(%0) : (tensor<f32>) -> tensor<f32>
return %1 : tensor<f32>
}
@@ -48,7 +48,7 @@ func.func @reciprocal_div_nan() -> tensor<f32> {
// CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}0x7FC00000
// CHECK-NOT: tosa.reciprocal
// CHECK: return [[RES]]
- %0 = "tosa.const"() {values = dense<0x7FC00000> : tensor<f32>} : () -> tensor<f32>
+ %0 = "tosa.const"() <{values = dense<0x7FC00000> : tensor<f32>}> : () -> tensor<f32>
%1 = "tosa.reciprocal"(%0) : (tensor<f32>) -> tensor<f32>
return %1 : tensor<f32>
}
@@ -58,7 +58,7 @@ func.func @reciprocal_div_infinity() -> tensor<f32> {
// CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}<0.{{0*}}e+00>
// CHECK-NOT: tosa.reciprocal
// CHECK: return [[RES]]
- %0 = "tosa.const"() {values = dense<0x7F800000> : tensor<f32>} : () -> tensor<f32>
+ %0 = "tosa.const"() <{values = dense<0x7F800000> : tensor<f32>}> : () -> tensor<f32>
%1 = "tosa.reciprocal"(%0) : (tensor<f32>) -> tensor<f32>
return %1 : tensor<f32>
}
@@ -68,7 +68,7 @@ func.func @reciprocal_div_neg_infinity() -> tensor<f32> {
// CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}<-0.{{0*}}e+00>
// CHECK-NOT: tosa.reciprocal
// CHECK: return [[RES]]
- %0 = "tosa.const"() {values = dense<0xFF800000> : tensor<f32>} : () -> tensor<f32>
+ %0 = "tosa.const"() <{values = dense<0xFF800000> : tensor<f32>}> : () -> tensor<f32>
%1 = "tosa.reciprocal"(%0) : (tensor<f32>) -> tensor<f32>
return %1 : tensor<f32>
}
@@ -78,7 +78,7 @@ func.func @reciprocal_div_underflow() -> tensor<2xf16> {
// CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}-0.{{0*}}e+00, 0.{{0*}}e+00
// CHECK-NOT: tosa.reciprocal
// CHECK: return [[RES]]
- %0 = "tosa.const"() {values = dense<[-6.0e+15, 6.0e+15]> : tensor<2xf16>} : () -> tensor<2xf16>
+ %0 = "tosa.const"() <{values = dense<[-6.0e+15, 6.0e+15]> : tensor<2xf16>}> : () -> tensor<2xf16>
%1 = "tosa.reciprocal"(%0) : (tensor<2xf16>) -> tensor<2xf16>
return %1 : tensor<2xf16>
}
@@ -88,7 +88,7 @@ func.func @reciprocal_div_overflow() -> tensor<2xf16> {
// CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}0x7C00, 0xFC00
// CHECK-NOT: tosa.reciprocal
// CHECK: return [[RES]]
- %0 = "tosa.const"() {values = dense<[0.0000001, -0.0000001]> : tensor<2xf16>} : () -> tensor<2xf16>
+ %0 = "tosa.const"() <{values = dense<[0.0000001, -0.0000001]> : tensor<2xf16>}> : () -> tensor<2xf16>
%1 = "tosa.reciprocal"(%0) : (tensor<2xf16>) -> tensor<2xf16>
return %1 : tensor<2xf16>
}
diff --git a/mlir/test/Dialect/Tosa/constant_folding.mlir b/mlir/test/Dialect/Tosa/constant_folding.mlir
index 678ad8cbde413..ecbf9228e3cab 100644
--- a/mlir/test/Dialect/Tosa/constant_folding.mlir
+++ b/mlir/test/Dialect/Tosa/constant_folding.mlir
@@ -3,7 +3,7 @@
// CHECK-LABEL: func @test_const
func.func @test_const(%arg0 : index) -> tensor<4xi32> {
// CHECK: tosa.const
- %0 = "tosa.const"() {values = dense<[3, 0, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
+ %0 = "tosa.const"() <{values = dense<[3, 0, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
return %0 : tensor<4xi32>
}
@@ -12,7 +12,7 @@ func.func @test_const(%arg0 : index) -> tensor<4xi32> {
// CHECK-LABEL: func @test_const_i64
func.func @test_const_i64(%arg0 : index) -> tensor<4xi64> {
// CHECK: tosa.const
- %0 = "tosa.const"() {values = dense<[3, 0, 1, 2]> : tensor<4xi64>} : () -> tensor<4xi64>
+ %0 = "tosa.const"() <{values = dense<[3, 0, 1, 2]> : tensor<4xi64>}> : () -> tensor<4xi64>
return %0 : tensor<4xi64>
}
@@ -43,7 +43,7 @@ func.func @try_fold_unranked_constant_results() {
%0 = tosa.equal %lhs, %rhs : (tensor<1xi32>, tensor<1xi32>) -> tensor<*xi1>
%1 = tosa.greater %lhs, %rhs : (tensor<1xi32>, tensor<1xi32>) -> tensor<*xi1>
%2 = tosa.greater_equal %lhs, %rhs : (tensor<1xi32>, tensor<1xi32>) -> tensor<*xi1>
- %3 = tosa.cast %lhs {input_unsigned = false} : (tensor<1xi32>) -> tensor<*xf32>
+ %3 = tosa.cast %lhs : (tensor<1xi32>) -> tensor<*xf32>
%4 = tosa.reciprocal %f : (tensor<1xf32>) -> tensor<*xf32>
%5 = tosa.abs %f : (tensor<1xf32>) -> tensor<*xf32>
return
@@ -59,8 +59,8 @@ func.func @try_fold_unranked_identity_results(%arg0: tensor<1xf32>) {
// CHECK: tosa.negate
// CHECK-NEXT: return
%zp = arith.constant dense<0.0> : tensor<1xf32>
- %0 = tosa.transpose %arg0 { perms = array<i32: 0> } : (tensor<1xf32>) -> tensor<*xf32>
- %1 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<1xf32>) -> tensor<*xf32>
+ %0 = tosa.transpose %arg0 perms([0]) : (tensor<1xf32>) -> tensor<*xf32>
+ %1 = tosa.reverse %arg0 axis(0) : (tensor<1xf32>) -> tensor<*xf32>
%3 = tosa.abs %arg0 : (tensor<1xf32>) -> tensor<*xf32>
%4 = tosa.negate %arg0, %zp, %zp : (tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<*xf32>
return
@@ -70,7 +70,7 @@ func.func @try_fold_unranked_identity_results(%arg0: tensor<1xf32>) {
// CHECK-LABEL: @fold_add_zero_rhs_f32
func.func @fold_add_zero_rhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
- %zero = "tosa.const"() {values = dense<0.0> : tensor<f32>} : () -> tensor<f32>
+ %zero = "tosa.const"() <{values = dense<0.0> : tensor<f32>}> : () -> tensor<f32>
%add = tosa.add %arg0, %zero : (tensor<f32>, tensor<f32>) -> tensor<f32>
// CHECK: return %arg0
return %add : tensor<f32>
@@ -80,7 +80,7 @@ func.func @fold_add_zero_rhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
// CHECK-LABEL: @fold_add_zero_lhs_f32
func.func @fold_add_zero_lhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
- %zero = "tosa.const"() {values = dense<0.0> : tensor<f32>} : () -> tensor<f32>
+ %zero = "tosa.const"() <{values = dense<0.0> : tensor<f32>}> : () -> tensor<f32>
%add = tosa.add %zero, %arg0 : (tensor<f32>, tensor<f32>) -> tensor<f32>
// CHECK: return %arg0
return %add : tensor<f32>
@@ -90,7 +90,7 @@ func.func @fold_add_zero_lhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
// CHECK-LABEL: @fold_add_zero_rhs_i32
func.func @fold_add_zero_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
%add = tosa.add %arg0, %zero : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK: return %arg0
return %add : tensor<i32>
@@ -100,7 +100,7 @@ func.func @fold_add_zero_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
// CHECK-LABEL: @fold_add_zero_lhs_i32
func.func @fold_add_zero_lhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
%add = tosa.add %zero, %arg0 : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK: return %arg0
return %add : tensor<i32>
@@ -110,8 +110,8 @@ func.func @fold_add_zero_lhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
// CHECK-LABEL: @fold_add_splat_i32
func.func @fold_add_splat_i32() -> tensor<10xi32> {
- %one = "tosa.const"() {values = dense<1> : tensor<10xi32>} : () -> tensor<10xi32>
- %two = "tosa.const"() {values = dense<2> : tensor<10xi32>} : () -> tensor<10xi32>
+ %one = "tosa.const"() <{values = dense<1> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %two = "tosa.const"() <{values = dense<2> : tensor<10xi32>}> : () -> tensor<10xi32>
%add = tosa.add %one, %two : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32>
// CHECK: %[[THREE:.+]] = "tosa.const"() <{values = dense<3> : tensor<10xi32>}
// CHECK: return %[[THREE]]
@@ -122,8 +122,8 @@ func.func @fold_add_splat_i32() -> tensor<10xi32> {
// CHECK-LABEL: @fold_add_splat_f32
func.func @fold_add_splat_f32() -> tensor<10xf32> {
- %one = "tosa.const"() {values = dense<1.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %two = "tosa.const"() {values = dense<2.0> : tensor<10xf32>} : () -> tensor<10xf32>
+ %one = "tosa.const"() <{values = dense<1.0> : tensor<10xf32>}> : () -> tensor<10xf32>
+ %two = "tosa.const"() <{values = dense<2.0> : tensor<10xf32>}> : () -> tensor<10xf32>
%add = tosa.add %one, %two : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>
// CHECK: %[[THREE:.+]] = "tosa.const"() <{values = dense<3.000000e+00>
// CHECK: return %[[THREE]]
@@ -134,8 +134,8 @@ func.func @fold_add_splat_f32() -> tensor<10xf32> {
// CHECK-LABEL: @fold_add_splat_i32_positive_overflow
func.func @fold_add_splat_i32_positive_overflow() -> tensor<10xi32> {
- %one = "tosa.const"() {values = dense<2147483647> : tensor<10xi32>} : () -> tensor<10xi32>
- %two = "tosa.const"() {values = dense<1> : tensor<10xi32>} : () -> tensor<10xi32>
+ %one = "tosa.const"() <{values = dense<2147483647> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %two = "tosa.const"() <{values = dense<1> : tensor<10xi32>}> : () -> tensor<10xi32>
// CHECK: tosa.add
%add = tosa.add %one, %two : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32>
return %add : tensor<10xi32>
@@ -145,8 +145,8 @@ func.func @fold_add_splat_i32_positive_overflow() -> tensor<10xi32> {
// CHECK-LABEL: @fold_add_splat_i32_negative_overflow
func.func @fold_add_splat_i32_negative_overflow() -> tensor<10xi32> {
- %one = "tosa.const"() {values = dense<-1> : tensor<10xi32>} : () -> tensor<10xi32>
- %two = "tosa.const"() {values = dense<-2147483648> : tensor<10xi32>} : () -> tensor<10xi32>
+ %one = "tosa.const"() <{values = dense<-1> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %two = "tosa.const"() <{values = dense<-2147483648> : tensor<10xi32>}> : () -> tensor<10xi32>
// CHECK: tosa.add
%add = tosa.add %one, %two : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32>
return %add : tensor<10xi32>
@@ -156,8 +156,8 @@ func.func @fold_add_splat_i32_negative_overflow() -> tensor<10xi32> {
// CHECK-LABEL: @fold_add_splat_ui8
func.func @fold_add_splat_ui8() -> tensor<10xui8> {
- %one = "tosa.const"() {values = dense<1> : tensor<10xui8>} : () -> tensor<10xui8>
- %two = "tosa.const"() {values = dense<254> : tensor<10xui8>} : () -> tensor<10xui8>
+ %one = "tosa.const"() <{values = dense<1> : tensor<10xui8>}> : () -> tensor<10xui8>
+ %two = "tosa.const"() <{values = dense<254> : tensor<10xui8>}> : () -> tensor<10xui8>
// CHECK: "tosa.const"() <{values = dense<255> : tensor<10xui8>}> : () -> tensor<10xui8>
%add = tosa.add %one, %two : (tensor<10xui8>, tensor<10xui8>) -> tensor<10xui8>
return %add : tensor<10xui8>
@@ -167,8 +167,8 @@ func.func @fold_add_splat_ui8() -> tensor<10xui8> {
// CHECK-LABEL: @fold_add_splat_ui8_overflow
func.func @fold_add_splat_ui8_overflow() -> tensor<10xui8> {
- %one = "tosa.const"() {values = dense<2> : tensor<10xui8>} : () -> tensor<10xui8>
- %two = "tosa.const"() {values = dense<254> : tensor<10xui8>} : () -> tensor<10xui8>
+ %one = "tosa.const"() <{values = dense<2> : tensor<10xui8>}> : () -> tensor<10xui8>
+ %two = "tosa.const"() <{values = dense<254> : tensor<10xui8>}> : () -> tensor<10xui8>
// CHECK: tosa.add
%add = tosa.add %one, %two : (tensor<10xui8>, tensor<10xui8>) -> tensor<10xui8>
return %add : tensor<10xui8>
@@ -178,7 +178,7 @@ func.func @fold_add_splat_ui8_overflow() -> tensor<10xui8> {
// CHECK-LABEL: @no_fold_add_unknown_broadcast_zero_lhs
func.func @no_fold_add_unknown_broadcast_zero_lhs(%arg0: tensor<1x4xi32>) -> tensor<2x4xi32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<2x1xi32>} : () -> tensor<2x1xi32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
%div = tosa.add %zero, %arg0 : (tensor<2x1xi32>, tensor<1x4xi32>) -> tensor<2x4xi32>
// CHECK: tosa.add
return %div : tensor<2x4xi32>
@@ -188,7 +188,7 @@ func.func @no_fold_add_unknown_broadcast_zero_lhs(%arg0: tensor<1x4xi32>) -> ten
// CHECK-LABEL: @no_fold_dynamic_add_unknown_broadcast_zero_lhs
func.func @no_fold_dynamic_add_unknown_broadcast_zero_lhs(%arg0: tensor<?x4xi32>) -> tensor<?x4xi32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<2x1xi32>} : () -> tensor<2x1xi32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
%div = tosa.add %zero, %arg0 : (tensor<2x1xi32>, tensor<?x4xi32>) -> tensor<?x4xi32>
// CHECK: tosa.add
return %div : tensor<?x4xi32>
@@ -198,7 +198,7 @@ func.func @no_fold_dynamic_add_unknown_broadcast_zero_lhs(%arg0: tensor<?x4xi32>
// CHECK-LABEL: @fold_dynamic_add_broadcast_zero_lhs
func.func @fold_dynamic_add_broadcast_zero_lhs(%arg0: tensor<?x17xi32>) -> tensor<?x17xi32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<1x1xi32>}> : () -> tensor<1x1xi32>
%div = tosa.add %zero, %arg0 : (tensor<1x1xi32>, tensor<?x17xi32>) -> tensor<?x17xi32>
// CHECK: return %arg0
return %div : tensor<?x17xi32>
@@ -208,7 +208,7 @@ func.func @fold_dynamic_add_broadcast_zero_lhs(%arg0: tensor<?x17xi32>) -> tenso
// CHECK-LABEL: @no_fold_dynamic_add_unknown_broadcast_zero_rhs
func.func @no_fold_dynamic_add_unknown_broadcast_zero_rhs(%arg0: tensor<?x4xi32>) -> tensor<?x4xi32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<2x1xi32>} : () -> tensor<2x1xi32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
%div = tosa.add %arg0, %zero : (tensor<?x4xi32>, tensor<2x1xi32>) -> tensor<?x4xi32>
// CHECK: tosa.add
return %div : tensor<?x4xi32>
@@ -218,7 +218,7 @@ func.func @no_fold_dynamic_add_unknown_broadcast_zero_rhs(%arg0: tensor<?x4xi32>
// CHECK-LABEL: @fold_dynamic_add_broadcast_zero_rhs
func.func @fold_dynamic_add_broadcast_zero_rhs(%arg0: tensor<?x17xi32>) -> tensor<?x17xi32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<1x17xi32>} : () -> tensor<1x17xi32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<1x17xi32>}> : () -> tensor<1x17xi32>
%div = tosa.add %arg0, %zero : (tensor<?x17xi32>, tensor<1x17xi32>) -> tensor<?x17xi32>
// CHECK: return %arg0
return %div : tensor<?x17xi32>
@@ -250,7 +250,7 @@ func.func @fold_div_zero_lhs_nonzero_splat_rhs_i32() -> tensor<i32> {
// CHECK-LABEL: @no_fold_dynamic_div_zero_lhs
func.func @no_fold_dynamic_div_zero_lhs(%arg0: tensor<?x4xi32>) -> tensor<?x4xi32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<1x1xi32>}> : () -> tensor<1x1xi32>
// CHECK: tosa.intdiv
%div = tosa.intdiv %zero, %arg0 : (tensor<1x1xi32>, tensor<?x4xi32>) -> tensor<?x4xi32>
return %div : tensor<?x4xi32>
@@ -270,7 +270,7 @@ func.func @no_fold_div_zero_lhs_broadcast(%arg0: tensor<2x4xi32>) -> tensor<2x4x
// CHECK-LABEL: @no_fold_div_unknown_broadcast_one_rhs
func.func @no_fold_div_unknown_broadcast_one_rhs(%arg0: tensor<1x4xi32>) -> tensor<2x4xi32> {
- %one = "tosa.const"() {values = dense<1> : tensor<2x1xi32>} : () -> tensor<2x1xi32>
+ %one = "tosa.const"() <{values = dense<1> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
// CHECK: tosa.intdiv
%div = tosa.intdiv %arg0, %one : (tensor<1x4xi32>, tensor<2x1xi32>) -> tensor<2x4xi32>
return %div : tensor<2x4xi32>
@@ -280,7 +280,7 @@ func.func @no_fold_div_unknown_broadcast_one_rhs(%arg0: tensor<1x4xi32>) -> tens
// CHECK-LABEL: @fold_div_broadcast_one_rhs
func.func @fold_div_broadcast_one_rhs(%arg0: tensor<?x17xi32>) -> tensor<?x17xi32> {
- %one = "tosa.const"() {values = dense<1> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
+ %one = "tosa.const"() <{values = dense<1> : tensor<1x1xi32>}> : () -> tensor<1x1xi32>
%div = tosa.intdiv %arg0, %one : (tensor<?x17xi32>, tensor<1x1xi32>) -> tensor<?x17xi32>
// CHECK: return %arg0
return %div : tensor<?x17xi32>
@@ -290,7 +290,7 @@ func.func @fold_div_broadcast_one_rhs(%arg0: tensor<?x17xi32>) -> tensor<?x17xi3
// CHECK-LABEL: @fold_div_one_rhs_i32
func.func @fold_div_one_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
- %one = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %one = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%div = tosa.intdiv %arg0, %one : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK: return %arg0
return %div : tensor<i32>
@@ -300,7 +300,7 @@ func.func @fold_div_one_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
// CHECK-LABEL: @no_fold_dynamic_div_unknown_broadcast_one_rhs
func.func @no_fold_dynamic_div_unknown_broadcast_one_rhs(%arg0: tensor<?x4xi32>) -> tensor<?x4xi32> {
- %one = "tosa.const"() {values = dense<1> : tensor<2x1xi32>} : () -> tensor<2x1xi32>
+ %one = "tosa.const"() <{values = dense<1> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
// CHECK: tosa.intdiv
%div = tosa.intdiv %arg0, %one : (tensor<?x4xi32>, tensor<2x1xi32>) -> tensor<?x4xi32>
return %div : tensor<?x4xi32>
@@ -310,8 +310,8 @@ func.func @no_fold_dynamic_div_unknown_broadcast_one_rhs(%arg0: tensor<?x4xi32>)
// CHECK-LABEL: @fold_div_splat_i32
func.func @fold_div_splat_i32() -> tensor<i32> {
- %lhs = "tosa.const"() {values = dense<10> : tensor<i32>} : () -> tensor<i32>
- %rhs = "tosa.const"() {values = dense<-3> : tensor<i32>} : () -> tensor<i32>
+ %lhs = "tosa.const"() <{values = dense<10> : tensor<i32>}> : () -> tensor<i32>
+ %rhs = "tosa.const"() <{values = dense<-3> : tensor<i32>}> : () -> tensor<i32>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<-3>
%div = tosa.intdiv %lhs, %rhs : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK: return %[[SPLAT]]
@@ -355,7 +355,7 @@ func.func @no_fold_div_zero_lhs_non_splat_rhs_i32() -> tensor<2xi32> {
// CHECK-LABEL: @fold_mul_zero_rhs_f32
func.func @fold_mul_zero_rhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
- %zero = "tosa.const"() {values = dense<0.0> : tensor<f32>} : () -> tensor<f32>
+ %zero = "tosa.const"() <{values = dense<0.0> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[ZERO:.+]] = "tosa.const"() <{values = dense<0.000000e+00>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%mul = tosa.mul %arg0, %zero, %shift : (tensor<f32>, tensor<f32>, tensor<1xi8>) -> tensor<f32>
@@ -367,7 +367,7 @@ func.func @fold_mul_zero_rhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
// CHECK-LABEL: @fold_mul_zero_lhs_f32
func.func @fold_mul_zero_lhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
- %zero = "tosa.const"() {values = dense<0.0> : tensor<f32>} : () -> tensor<f32>
+ %zero = "tosa.const"() <{values = dense<0.0> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[ZERO:.+]] = "tosa.const"() <{values = dense<0.000000e+00>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%mul = tosa.mul %zero, %arg0, %shift : (tensor<f32>, tensor<f32>, tensor<1xi8>) -> tensor<f32>
@@ -379,7 +379,7 @@ func.func @fold_mul_zero_lhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
// CHECK-LABEL: @fold_mul_zero_rhs_i32
func.func @fold_mul_zero_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[ZERO:.+]] = "tosa.const"() <{values = dense<0>
%mul = tosa.mul %arg0, %zero, %shift : (tensor<i32>, tensor<i32>, tensor<1xi8>) -> tensor<i32>
@@ -391,7 +391,7 @@ func.func @fold_mul_zero_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
// CHECK-LABEL: @fold_mul_zero_lhs_i32
func.func @fold_mul_zero_lhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[ZERO:.+]] = "tosa.const"() <{values = dense<0>
%mul = tosa.mul %zero, %arg0, %shift : (tensor<i32>, tensor<i32>, tensor<1xi8>) -> tensor<i32>
@@ -403,7 +403,7 @@ func.func @fold_mul_zero_lhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
// CHECK-LABEL: @fold_mul_one_rhs_f32
func.func @fold_mul_one_rhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
- %one = "tosa.const"() {values = dense<1.0> : tensor<f32>} : () -> tensor<f32>
+ %one = "tosa.const"() <{values = dense<1.0> : tensor<f32>}> : () -> tensor<f32>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%mul = tosa.mul %arg0, %one, %shift : (tensor<f32>, tensor<f32>, tensor<1xi8>) -> tensor<f32>
// CHECK: return %arg0
@@ -414,7 +414,7 @@ func.func @fold_mul_one_rhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
// CHECK-LABEL: @no_fold_mul_unknown_broadcast_one_rhs_f32
func.func @no_fold_mul_unknown_broadcast_one_rhs_f32(%arg0: tensor<1x4xf32>) -> tensor<2x4xf32> {
- %one = "tosa.const"() {values = dense<1.0> : tensor<2x1xf32>} : () -> tensor<2x1xf32>
+ %one = "tosa.const"() <{values = dense<1.0> : tensor<2x1xf32>}> : () -> tensor<2x1xf32>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: tosa.mul
%mul = tosa.mul %arg0, %one, %shift : (tensor<1x4xf32>, tensor<2x1xf32>, tensor<1xi8>) -> tensor<2x4xf32>
@@ -425,7 +425,7 @@ func.func @no_fold_mul_unknown_broadcast_one_rhs_f32(%arg0: tensor<1x4xf32>) ->
// CHECK-LABEL: @no_fold_dynamic_mul_unknown_broadcast_one_rhs_f32
func.func @no_fold_dynamic_mul_unknown_broadcast_one_rhs_f32(%arg0: tensor<?x4xf32>) -> tensor<?x4xf32> {
- %one = "tosa.const"() {values = dense<1.0> : tensor<2x1xf32>} : () -> tensor<2x1xf32>
+ %one = "tosa.const"() <{values = dense<1.0> : tensor<2x1xf32>}> : () -> tensor<2x1xf32>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: tosa.mul
%mul = tosa.mul %arg0, %one, %shift : (tensor<?x4xf32>, tensor<2x1xf32>, tensor<1xi8>) -> tensor<?x4xf32>
@@ -436,7 +436,7 @@ func.func @no_fold_dynamic_mul_unknown_broadcast_one_rhs_f32(%arg0: tensor<?x4xf
// CHECK-LABEL: @fold_mul_one_lhs_f32
func.func @fold_mul_one_lhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
- %one = "tosa.const"() {values = dense<1.0> : tensor<f32>} : () -> tensor<f32>
+ %one = "tosa.const"() <{values = dense<1.0> : tensor<f32>}> : () -> tensor<f32>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%mul = tosa.mul %one, %arg0, %shift : (tensor<f32>, tensor<f32>, tensor<1xi8>) -> tensor<f32>
// CHECK: return %arg0
@@ -447,8 +447,8 @@ func.func @fold_mul_one_lhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
// CHECK-LABEL: @fold_mul_one_rhs_i32
func.func @fold_mul_one_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
- %one = "tosa.const"() {values = dense<64> : tensor<i32>} : () -> tensor<i32>
- %shift = "tosa.const"() {values = dense<6> : tensor<1xi8>} : () -> tensor<1xi8>
+ %one = "tosa.const"() <{values = dense<64> : tensor<i32>}> : () -> tensor<i32>
+ %shift = "tosa.const"() <{values = dense<6> : tensor<1xi8>}> : () -> tensor<1xi8>
%mul = tosa.mul %arg0, %one, %shift : (tensor<i32>, tensor<i32>, tensor<1xi8>) -> tensor<i32>
// CHECK: return %arg0
return %mul : tensor<i32>
@@ -458,8 +458,8 @@ func.func @fold_mul_one_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
// CHECK-LABEL: @fold_mul_one_lhs_i32
func.func @fold_mul_one_lhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
- %one = "tosa.const"() {values = dense<64> : tensor<i32>} : () -> tensor<i32>
- %shift = "tosa.const"() {values = dense<6> : tensor<1xi8>} : () -> tensor<1xi8>
+ %one = "tosa.const"() <{values = dense<64> : tensor<i32>}> : () -> tensor<i32>
+ %shift = "tosa.const"() <{values = dense<6> : tensor<1xi8>}> : () -> tensor<1xi8>
%mul = tosa.mul %one, %arg0, %shift : (tensor<i32>, tensor<i32>, tensor<1xi8>) -> tensor<i32>
// CHECK: return %arg0
return %mul : tensor<i32>
@@ -469,7 +469,7 @@ func.func @fold_mul_one_lhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
// CHECK-LABEL: @no_fold_mul_unknown_broadcast_one_lhs_f32
func.func @no_fold_mul_unknown_broadcast_one_lhs_f32(%arg0: tensor<1x4xf32>) -> tensor<2x4xf32> {
- %one = "tosa.const"() {values = dense<1.0> : tensor<2x1xf32>} : () -> tensor<2x1xf32>
+ %one = "tosa.const"() <{values = dense<1.0> : tensor<2x1xf32>}> : () -> tensor<2x1xf32>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: tosa.mul
%mul = tosa.mul %one, %arg0, %shift : (tensor<2x1xf32>, tensor<1x4xf32>, tensor<1xi8>) -> tensor<2x4xf32>
@@ -480,7 +480,7 @@ func.func @no_fold_mul_unknown_broadcast_one_lhs_f32(%arg0: tensor<1x4xf32>) ->
// CHECK-LABEL: @no_fold_dynamic_mul_unknown_broadcast_one_lhs_f32
func.func @no_fold_dynamic_mul_unknown_broadcast_one_lhs_f32(%arg0: tensor<?x4xf32>) -> tensor<?x4xf32> {
- %one = "tosa.const"() {values = dense<1.0> : tensor<2x1xf32>} : () -> tensor<2x1xf32>
+ %one = "tosa.const"() <{values = dense<1.0> : tensor<2x1xf32>}> : () -> tensor<2x1xf32>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: tosa.mul
%mul = tosa.mul %one, %arg0, %shift : (tensor<2x1xf32>, tensor<?x4xf32>, tensor<1xi8>) -> tensor<?x4xf32>
@@ -494,7 +494,7 @@ func.func @fold_mul_one_float(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
// CHECK: return %arg0
// CHECK-NOT: tosa.mul
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %ones = "tosa.const"() {values = dense<1.0> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
+ %ones = "tosa.const"() <{values = dense<1.0> : tensor<2x3xf32>}> : () -> tensor<2x3xf32>
%1 = tosa.mul %arg0, %ones, %shift : (tensor<2x3xf32>, tensor<2x3xf32>, tensor<1xi8>) -> tensor<2x3xf32>
return %1 : tensor<2x3xf32>
}
@@ -505,7 +505,7 @@ func.func @fold_mul_one_float(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
func.func @fold_mul_bcast_one_float(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
// CHECK: return %arg0
// CHECK-NOT: tosa.mul
- %ones = "tosa.const"() {values = dense<1.0> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
+ %ones = "tosa.const"() <{values = dense<1.0> : tensor<1x1xf32>}> : () -> tensor<1x1xf32>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%1 = tosa.mul %ones, %arg0, %shift : (tensor<1x1xf32>, tensor<2x3xf32>, tensor<1xi8>) -> tensor<2x3xf32>
return %1 : tensor<2x3xf32>
@@ -518,7 +518,7 @@ func.func @fold_mul_one_int(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> {
// CHECK: return %arg0
// CHECK-NOT: tosa.mul
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %ones = "tosa.const"() {values = dense<1> : tensor<2x3xi32>} : () -> tensor<2x3xi32>
+ %ones = "tosa.const"() <{values = dense<1> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
%1 = tosa.mul %arg0, %ones, %shift : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<1xi8>) -> tensor<2x3xi32>
return %1 : tensor<2x3xi32>
}
@@ -530,7 +530,7 @@ func.func @fold_mul_one_int_and_shift(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK-DAG: %[[VAL_1:.*]] = "tosa.const"() <{values = dense<1> : tensor<2x3xi32>}>
// CHECK-DAG: %[[VAL_2:.*]] = "tosa.const"() <{values = dense<31> : tensor<1xi8>}>
// CHECK: %[[VAL_3:.*]] = tosa.mul %arg0, %[[VAL_1]], %[[VAL_2]] : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<1xi8>)
- %ones = "tosa.const"() {values = dense<1> : tensor<2x3xi32>} : () -> tensor<2x3xi32>
+ %ones = "tosa.const"() <{values = dense<1> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
%shift = "tosa.const"() <{values = dense<31> : tensor<1xi8>}> : () -> tensor<1xi8>
%1 = tosa.mul %arg0, %ones, %shift : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<1xi8>) -> tensor<2x3xi32>
return %1 : tensor<2x3xi32>
@@ -542,7 +542,7 @@ func.func @fold_mul_one_int_and_shift(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32>
func.func @fold_mul_zero_broadcast(%arg0: tensor<2x3xf32>) -> (tensor<2x3xf32>, tensor<2x3xf32>) {
// CHECK: %[[ZERO:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<2x3xf32>}
// CHECK-NOT: tosa.mul
- %zeros = "tosa.const"() {values = dense<0.0> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
+ %zeros = "tosa.const"() <{values = dense<0.0> : tensor<1x1xf32>}> : () -> tensor<1x1xf32>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%1 = tosa.mul %arg0, %zeros, %shift : (tensor<2x3xf32>, tensor<1x1xf32>, tensor<1xi8>) -> tensor<2x3xf32>
@@ -583,9 +583,9 @@ func.func @fold_mul_one_dynamic_fold(%arg0: tensor<?x17xf32>) -> tensor<?x17xf32
// CHECK-LABEL: @fold_mul_splat_i8
func.func @fold_mul_splat_i8() -> tensor<10xi32> {
- %one = "tosa.const"() {values = dense<17> : tensor<10xi8>} : () -> tensor<10xi8>
- %two = "tosa.const"() {values = dense<32> : tensor<10xi8>} : () -> tensor<10xi8>
- %shift = "tosa.const"() {values = dense<3> : tensor<1xi8>} : () -> tensor<1xi8>
+ %one = "tosa.const"() <{values = dense<17> : tensor<10xi8>}> : () -> tensor<10xi8>
+ %two = "tosa.const"() <{values = dense<32> : tensor<10xi8>}> : () -> tensor<10xi8>
+ %shift = "tosa.const"() <{values = dense<3> : tensor<1xi8>}> : () -> tensor<1xi8>
%mul = tosa.mul %one, %two, %shift : (tensor<10xi8>, tensor<10xi8>, tensor<1xi8>) -> tensor<10xi32>
// CHECK: %[[SIXTY_EIGHT:.+]] = "tosa.const"() <{values = dense<68> : tensor<10xi32>}
// CHECK: return %[[SIXTY_EIGHT]]
@@ -596,8 +596,8 @@ func.func @fold_mul_splat_i8() -> tensor<10xi32> {
// CHECK-LABEL: @fold_mul_splat_f32
func.func @fold_mul_splat_f32() -> tensor<10xf32> {
- %one = "tosa.const"() {values = dense<3.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %two = "tosa.const"() {values = dense<2.0> : tensor<10xf32>} : () -> tensor<10xf32>
+ %one = "tosa.const"() <{values = dense<3.0> : tensor<10xf32>}> : () -> tensor<10xf32>
+ %two = "tosa.const"() <{values = dense<2.0> : tensor<10xf32>}> : () -> tensor<10xf32>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%mul = tosa.mul %one, %two, %shift : (tensor<10xf32>, tensor<10xf32>, tensor<1xi8>) -> tensor<10xf32>
// CHECK: %[[SIX:.+]] = "tosa.const"() <{values = dense<6.000000e+00> : tensor<10xf32>}
@@ -609,7 +609,7 @@ func.func @fold_mul_splat_f32() -> tensor<10xf32> {
// CHECK-LABEL: @fold_sub_zero_rhs_f32
func.func @fold_sub_zero_rhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
- %zero = "tosa.const"() {values = dense<0.0> : tensor<f32>} : () -> tensor<f32>
+ %zero = "tosa.const"() <{values = dense<0.0> : tensor<f32>}> : () -> tensor<f32>
%sub = tosa.sub %arg0, %zero : (tensor<f32>, tensor<f32>) -> tensor<f32>
// CHECK: return %arg0
return %sub : tensor<f32>
@@ -619,7 +619,7 @@ func.func @fold_sub_zero_rhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
// CHECK-LABEL: @fold_sub_zero_rhs_i32
func.func @fold_sub_zero_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
%sub = tosa.sub %arg0, %zero : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK: return %arg0
return %sub : tensor<i32>
@@ -629,7 +629,7 @@ func.func @fold_sub_zero_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
// CHECK-LABEL: @no_fold_sub_unknown_broadcast_zero_rhs
func.func @no_fold_sub_unknown_broadcast_zero_rhs(%arg0: tensor<1x4xi32>) -> tensor<2x4xi32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<2x1xi32>} : () -> tensor<2x1xi32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
%div = tosa.sub %arg0, %zero : (tensor<1x4xi32>, tensor<2x1xi32>) -> tensor<2x4xi32>
// CHECK: tosa.sub
return %div : tensor<2x4xi32>
@@ -639,7 +639,7 @@ func.func @no_fold_sub_unknown_broadcast_zero_rhs(%arg0: tensor<1x4xi32>) -> ten
// CHECK-LABEL: @no_fold_dynamic_sub_unknown_broadcast_zero_rhs
func.func @no_fold_dynamic_sub_unknown_broadcast_zero_rhs(%arg0: tensor<?x4xi32>) -> tensor<?x4xi32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<2x1xi32>} : () -> tensor<2x1xi32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
%div = tosa.sub %arg0, %zero : (tensor<?x4xi32>, tensor<2x1xi32>) -> tensor<?x4xi32>
// CHECK: tosa.sub
return %div : tensor<?x4xi32>
@@ -649,7 +649,7 @@ func.func @no_fold_dynamic_sub_unknown_broadcast_zero_rhs(%arg0: tensor<?x4xi32>
// CHECK-LABEL: @fold_dynamic_sub_broadcast_zero_rhs
func.func @fold_dynamic_sub_broadcast_zero_rhs(%arg0: tensor<?x17xi32>) -> tensor<?x17xi32> {
- %zero = "tosa.const"() {values = dense<0> : tensor<1x17xi32>} : () -> tensor<1x17xi32>
+ %zero = "tosa.const"() <{values = dense<0> : tensor<1x17xi32>}> : () -> tensor<1x17xi32>
%div = tosa.sub %arg0, %zero : (tensor<?x17xi32>, tensor<1x17xi32>) -> tensor<?x17xi32>
// CHECK: return %arg0
return %div : tensor<?x17xi32>
@@ -659,8 +659,8 @@ func.func @fold_dynamic_sub_broadcast_zero_rhs(%arg0: tensor<?x17xi32>) -> tenso
// CHECK-LABEL: @fold_sub_splat_i32
func.func @fold_sub_splat_i32() -> tensor<10xi32> {
- %one = "tosa.const"() {values = dense<1> : tensor<10xi32>} : () -> tensor<10xi32>
- %two = "tosa.const"() {values = dense<2> : tensor<10xi32>} : () -> tensor<10xi32>
+ %one = "tosa.const"() <{values = dense<1> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %two = "tosa.const"() <{values = dense<2> : tensor<10xi32>}> : () -> tensor<10xi32>
%sub = tosa.sub %one, %two : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32>
// CHECK: %[[NEGATIVE_ONE:.+]] = "tosa.const"() <{values = dense<-1> : tensor<10xi32>}
// CHECK: return %[[NEGATIVE_ONE]]
@@ -671,8 +671,8 @@ func.func @fold_sub_splat_i32() -> tensor<10xi32> {
// CHECK-LABEL: @fold_sub_splat_f32
func.func @fold_sub_splat_f32() -> tensor<10xf32> {
- %one = "tosa.const"() {values = dense<1.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %two = "tosa.const"() {values = dense<2.0> : tensor<10xf32>} : () -> tensor<10xf32>
+ %one = "tosa.const"() <{values = dense<1.0> : tensor<10xf32>}> : () -> tensor<10xf32>
+ %two = "tosa.const"() <{values = dense<2.0> : tensor<10xf32>}> : () -> tensor<10xf32>
%sub = tosa.sub %one, %two : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>
// CHECK: %[[NEGATIVE_ONE:.+]] = "tosa.const"() <{values = dense<-1.000000e+00> : tensor<10xf32>}
// CHECK: return %[[NEGATIVE_ONE]]
@@ -683,8 +683,8 @@ func.func @fold_sub_splat_f32() -> tensor<10xf32> {
// CHECK-LABEL: @fold_sub_splat_i32_positive_overflow
func.func @fold_sub_splat_i32_positive_overflow() -> tensor<10xi32> {
- %one = "tosa.const"() {values = dense<2147483647> : tensor<10xi32>} : () -> tensor<10xi32>
- %two = "tosa.const"() {values = dense<-1> : tensor<10xi32>} : () -> tensor<10xi32>
+ %one = "tosa.const"() <{values = dense<2147483647> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %two = "tosa.const"() <{values = dense<-1> : tensor<10xi32>}> : () -> tensor<10xi32>
// CHECK: tosa.sub
%sub = tosa.sub %one, %two : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32>
return %sub : tensor<10xi32>
@@ -694,8 +694,8 @@ func.func @fold_sub_splat_i32_positive_overflow() -> tensor<10xi32> {
// CHECK-LABEL: @fold_sub_splat_i32_negative_overflow
func.func @fold_sub_splat_i32_negative_overflow() -> tensor<10xi32> {
- %one = "tosa.const"() {values = dense<-2147483648> : tensor<10xi32>} : () -> tensor<10xi32>
- %two = "tosa.const"() {values = dense<1> : tensor<10xi32>} : () -> tensor<10xi32>
+ %one = "tosa.const"() <{values = dense<-2147483648> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %two = "tosa.const"() <{values = dense<1> : tensor<10xi32>}> : () -> tensor<10xi32>
// CHECK: tosa.sub
%sub = tosa.sub %one, %two : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32>
return %sub : tensor<10xi32>
@@ -705,8 +705,8 @@ func.func @fold_sub_splat_i32_negative_overflow() -> tensor<10xi32> {
// CHECK-LABEL: @fold_sub_splat_ui8
func.func @fold_sub_splat_ui8() -> tensor<10xui8> {
- %one = "tosa.const"() {values = dense<255> : tensor<10xui8>} : () -> tensor<10xui8>
- %two = "tosa.const"() {values = dense<253> : tensor<10xui8>} : () -> tensor<10xui8>
+ %one = "tosa.const"() <{values = dense<255> : tensor<10xui8>}> : () -> tensor<10xui8>
+ %two = "tosa.const"() <{values = dense<253> : tensor<10xui8>}> : () -> tensor<10xui8>
// CHECK: "tosa.const"() <{values = dense<2> : tensor<10xui8>}> : () -> tensor<10xui8>
%sub = tosa.sub %one, %two : (tensor<10xui8>, tensor<10xui8>) -> tensor<10xui8>
return %sub : tensor<10xui8>
@@ -716,8 +716,8 @@ func.func @fold_sub_splat_ui8() -> tensor<10xui8> {
// CHECK-LABEL: @fold_sub_splat_ui8_overflow
func.func @fold_sub_splat_ui8_overflow() -> tensor<10xui8> {
- %one = "tosa.const"() {values = dense<1> : tensor<10xui8>} : () -> tensor<10xui8>
- %two = "tosa.const"() {values = dense<253> : tensor<10xui8>} : () -> tensor<10xui8>
+ %one = "tosa.const"() <{values = dense<1> : tensor<10xui8>}> : () -> tensor<10xui8>
+ %two = "tosa.const"() <{values = dense<253> : tensor<10xui8>}> : () -> tensor<10xui8>
// CHECK: tosa.sub
%sub = tosa.sub %one, %two : (tensor<10xui8>, tensor<10xui8>) -> tensor<10xui8>
return %sub : tensor<10xui8>
@@ -727,10 +727,10 @@ func.func @fold_sub_splat_ui8_overflow() -> tensor<10xui8> {
// CHECK-LABEL: @fold_greater_splat_f32
func.func @fold_greater_splat_f32() -> (tensor<10xi1>, tensor<10xi1>) {
- %0 = "tosa.const"() {values = dense<4.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %1 = "tosa.const"() {values = dense<2.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %2 = "tosa.const"() {values = dense<1.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %3 = "tosa.const"() {values = dense<2.0> : tensor<10xf32>} : () -> tensor<10xf32>
+ %0 = "tosa.const"() <{values = dense<4.0> : tensor<10xf32>}> : () -> tensor<10xf32>
+ %1 = "tosa.const"() <{values = dense<2.0> : tensor<10xf32>}> : () -> tensor<10xf32>
+ %2 = "tosa.const"() <{values = dense<1.0> : tensor<10xf32>}> : () -> tensor<10xf32>
+ %3 = "tosa.const"() <{values = dense<2.0> : tensor<10xf32>}> : () -> tensor<10xf32>
%true = tosa.greater %0, %1 : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xi1>
%false = tosa.greater %2, %3 : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xi1>
// CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
@@ -743,10 +743,10 @@ func.func @fold_greater_splat_f32() -> (tensor<10xi1>, tensor<10xi1>) {
// CHECK-LABEL: @fold_greater_splat_i32
func.func @fold_greater_splat_i32() -> (tensor<10xi1>, tensor<10xi1>) {
- %0 = "tosa.const"() {values = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
- %1 = "tosa.const"() {values = dense<8> : tensor<10xi32>} : () -> tensor<10xi32>
- %2 = "tosa.const"() {values = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
- %3 = "tosa.const"() {values = dense<-12> : tensor<10xi32>} : () -> tensor<10xi32>
+ %0 = "tosa.const"() <{values = dense<-10> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %1 = "tosa.const"() <{values = dense<8> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %2 = "tosa.const"() <{values = dense<-10> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %3 = "tosa.const"() <{values = dense<-12> : tensor<10xi32>}> : () -> tensor<10xi32>
%false = tosa.greater %0, %1 : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi1>
%true = tosa.greater %2, %3 : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi1>
// CHECK-DAG: %[[FALSE:.+]] = "tosa.const"() <{values = dense<false> : tensor<10xi1>}
@@ -759,10 +759,10 @@ func.func @fold_greater_splat_i32() -> (tensor<10xi1>, tensor<10xi1>) {
// CHECK-LABEL: @fold_greater_splat_ui8
func.func @fold_greater_splat_ui8() -> (tensor<10xi1>, tensor<10xi1>, tensor<10xi1>) {
- %0 = "tosa.const"() {values = dense<1> : tensor<10xui8>} : () -> tensor<10xui8>
- %1 = "tosa.const"() {values = dense<1> : tensor<10xui8>} : () -> tensor<10xui8>
- %2 = "tosa.const"() {values = dense<246> : tensor<10xui8>} : () -> tensor<10xui8>
- %3 = "tosa.const"() {values = dense<245> : tensor<10xui8>} : () -> tensor<10xui8>
+ %0 = "tosa.const"() <{values = dense<1> : tensor<10xui8>}> : () -> tensor<10xui8>
+ %1 = "tosa.const"() <{values = dense<1> : tensor<10xui8>}> : () -> tensor<10xui8>
+ %2 = "tosa.const"() <{values = dense<246> : tensor<10xui8>}> : () -> tensor<10xui8>
+ %3 = "tosa.const"() <{values = dense<245> : tensor<10xui8>}> : () -> tensor<10xui8>
%true = tosa.greater %2, %3 : (tensor<10xui8>, tensor<10xui8>) -> tensor<10xi1>
%false = tosa.greater %0, %1 : (tensor<10xui8>, tensor<10xui8>) -> tensor<10xi1>
%false2 = tosa.greater %0, %2 : (tensor<10xui8>, tensor<10xui8>) -> tensor<10xi1>
@@ -776,10 +776,10 @@ func.func @fold_greater_splat_ui8() -> (tensor<10xi1>, tensor<10xi1>, tensor<10x
// CHECK-LABEL: @fold_greater_eq_splat_f32
func.func @fold_greater_eq_splat_f32() -> (tensor<10xi1>, tensor<10xi1>) {
- %0 = "tosa.const"() {values = dense<4.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %1 = "tosa.const"() {values = dense<4.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %2 = "tosa.const"() {values = dense<1.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %3 = "tosa.const"() {values = dense<2.0> : tensor<10xf32>} : () -> tensor<10xf32>
+ %0 = "tosa.const"() <{values = dense<4.0> : tensor<10xf32>}> : () -> tensor<10xf32>
+ %1 = "tosa.const"() <{values = dense<4.0> : tensor<10xf32>}> : () -> tensor<10xf32>
+ %2 = "tosa.const"() <{values = dense<1.0> : tensor<10xf32>}> : () -> tensor<10xf32>
+ %3 = "tosa.const"() <{values = dense<2.0> : tensor<10xf32>}> : () -> tensor<10xf32>
%true = tosa.greater_equal %0, %1 : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xi1>
%false = tosa.greater_equal %2, %3 : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xi1>
// CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
@@ -792,10 +792,10 @@ func.func @fold_greater_eq_splat_f32() -> (tensor<10xi1>, tensor<10xi1>) {
// CHECK-LABEL: @fold_greater_eq_splat_i32
func.func @fold_greater_eq_splat_i32() -> (tensor<10xi1>, tensor<10xi1>) {
- %0 = "tosa.const"() {values = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
- %1 = "tosa.const"() {values = dense<8> : tensor<10xi32>} : () -> tensor<10xi32>
- %2 = "tosa.const"() {values = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
- %3 = "tosa.const"() {values = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
+ %0 = "tosa.const"() <{values = dense<-10> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %1 = "tosa.const"() <{values = dense<8> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %2 = "tosa.const"() <{values = dense<-10> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %3 = "tosa.const"() <{values = dense<-10> : tensor<10xi32>}> : () -> tensor<10xi32>
%true = tosa.greater_equal %2, %3 : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi1>
%false = tosa.greater_equal %0, %1 : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi1>
// CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
@@ -808,10 +808,10 @@ func.func @fold_greater_eq_splat_i32() -> (tensor<10xi1>, tensor<10xi1>) {
// CHECK-LABEL: @fold_greater_eq_splat_ui8
func.func @fold_greater_eq_splat_ui8() -> (tensor<10xi1>, tensor<10xi1>) {
- %0 = "tosa.const"() {values = dense<1> : tensor<10xui8>} : () -> tensor<10xui8>
- %1 = "tosa.const"() {values = dense<255> : tensor<10xui8>} : () -> tensor<10xui8>
- %2 = "tosa.const"() {values = dense<245> : tensor<10xui8>} : () -> tensor<10xui8>
- %3 = "tosa.const"() {values = dense<245> : tensor<10xui8>} : () -> tensor<10xui8>
+ %0 = "tosa.const"() <{values = dense<1> : tensor<10xui8>}> : () -> tensor<10xui8>
+ %1 = "tosa.const"() <{values = dense<255> : tensor<10xui8>}> : () -> tensor<10xui8>
+ %2 = "tosa.const"() <{values = dense<245> : tensor<10xui8>}> : () -> tensor<10xui8>
+ %3 = "tosa.const"() <{values = dense<245> : tensor<10xui8>}> : () -> tensor<10xui8>
%true = tosa.greater_equal %2, %3 : (tensor<10xui8>, tensor<10xui8>) -> tensor<10xi1>
%false = tosa.greater_equal %0, %1 : (tensor<10xui8>, tensor<10xui8>) -> tensor<10xi1>
// CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
@@ -824,10 +824,10 @@ func.func @fold_greater_eq_splat_ui8() -> (tensor<10xi1>, tensor<10xi1>) {
// CHECK-LABEL: @fold_eq_splat_f32
func.func @fold_eq_splat_f32() -> (tensor<10xi1>, tensor<10xi1>) {
- %0 = "tosa.const"() {values = dense<4.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %1 = "tosa.const"() {values = dense<4.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %2 = "tosa.const"() {values = dense<1.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %3 = "tosa.const"() {values = dense<2.0> : tensor<10xf32>} : () -> tensor<10xf32>
+ %0 = "tosa.const"() <{values = dense<4.0> : tensor<10xf32>}> : () -> tensor<10xf32>
+ %1 = "tosa.const"() <{values = dense<4.0> : tensor<10xf32>}> : () -> tensor<10xf32>
+ %2 = "tosa.const"() <{values = dense<1.0> : tensor<10xf32>}> : () -> tensor<10xf32>
+ %3 = "tosa.const"() <{values = dense<2.0> : tensor<10xf32>}> : () -> tensor<10xf32>
%true = tosa.equal %0, %1 : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xi1>
%false = tosa.equal %2, %3 : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xi1>
// CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
@@ -854,10 +854,10 @@ func.func @fold_compare_nan_f32() -> (tensor<10xi1>, tensor<10xi1>, tensor<10xi1
// CHECK-LABEL: @fold_eq_splat_i32
func.func @fold_eq_splat_i32() -> (tensor<10xi1>, tensor<10xi1>) {
- %0 = "tosa.const"() {values = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
- %1 = "tosa.const"() {values = dense<8> : tensor<10xi32>} : () -> tensor<10xi32>
- %2 = "tosa.const"() {values = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
- %3 = "tosa.const"() {values = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
+ %0 = "tosa.const"() <{values = dense<-10> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %1 = "tosa.const"() <{values = dense<8> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %2 = "tosa.const"() <{values = dense<-10> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %3 = "tosa.const"() <{values = dense<-10> : tensor<10xi32>}> : () -> tensor<10xi32>
%true = tosa.equal %2, %3 : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi1>
%false = tosa.equal %0, %1 : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi1>
// CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
@@ -880,8 +880,8 @@ func.func @fold_eq_i32(%arg0 : tensor<10xi32>) -> (tensor<10xi1>) {
func.func @reshape_splat() -> tensor<6x5x4xi32> {
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<42> : tensor<6x5x4xi32>}
- %splat = "tosa.const"() {values = dense<42> : tensor<4x5x6xi32>} : () -> tensor<4x5x6xi32>
- %const = tosa.const_shape {values = dense<[6, 5, 4]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %splat = "tosa.const"() <{values = dense<42> : tensor<4x5x6xi32>}> : () -> tensor<4x5x6xi32>
+ %const = tosa.const_shape values(dense<[6, 5, 4]> : tensor<3xindex>) : () -> !tosa.shape<3>
%reshape = tosa.reshape %splat, %const : (tensor<4x5x6xi32>, !tosa.shape<3>) -> tensor<6x5x4xi32>
// CHECK: return %[[SPLAT]]
return %reshape : tensor<6x5x4xi32>
@@ -893,7 +893,7 @@ func.func @reshape_splat() -> tensor<6x5x4xi32> {
func.func @reshape_dense_resource() -> tensor<4xf32> {
// CHECK: %[[RESHAPED:.+]] = "tosa.const"() <{values = dense_resource<reshape_resource> : tensor<4xf32>}> : () -> tensor<4xf32>
%input = "tosa.const"() <{values = dense_resource<reshape_resource> : tensor<2x2xf32>}> : () -> tensor<2x2xf32>
- %shape = tosa.const_shape {values = dense<4> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %shape = tosa.const_shape values(dense<4> : tensor<1xindex>) : () -> !tosa.shape<1>
%reshape = tosa.reshape %input, %shape : (tensor<2x2xf32>, !tosa.shape<1>) -> tensor<4xf32>
// CHECK: return %[[RESHAPED]]
return %reshape : tensor<4xf32>
@@ -911,9 +911,9 @@ func.func @reshape_dense_resource() -> tensor<4xf32> {
// CHECK-LABEL: @slice_splat
func.func @slice_splat() -> tensor<1x1x1xi32> {
// CHECK: %[[SLICE:.+]] = "tosa.const"() <{values = dense<42> : tensor<1x1x1xi32>}
- %splat = "tosa.const"() {values = dense<42> : tensor<4x5x6xi32>} : () -> tensor<4x5x6xi32>
- %start = tosa.const_shape {values = dense<[1, 2, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %size = tosa.const_shape {values = dense<[1, 1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %splat = "tosa.const"() <{values = dense<42> : tensor<4x5x6xi32>}> : () -> tensor<4x5x6xi32>
+ %start = tosa.const_shape values(dense<[1, 2, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[1, 1, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
%slice= tosa.slice %splat, %start, %size : (tensor<4x5x6xi32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<1x1x1xi32>
// CHECK: return %[[SLICE]]
@@ -924,10 +924,10 @@ func.func @slice_splat() -> tensor<1x1x1xi32> {
// CHECK-LABEL: @slice_singleton
func.func @slice_singleton() -> tensor<1x1xi32> {
- %splat = "tosa.const"() {values = dense<[[0, 1, 2], [3, 4, 5], [6, 7 ,8]]> : tensor<3x3xi32>} : () -> tensor<3x3xi32>
+ %splat = "tosa.const"() <{values = dense<[[0, 1, 2], [3, 4, 5], [6, 7 ,8]]> : tensor<3x3xi32>}> : () -> tensor<3x3xi32>
// CHECK: %[[SLICE:.+]] = "tosa.const"() <{values = dense<4> : tensor<1x1xi32>}
- %start = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %size = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %start = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %size = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%slice= tosa.slice %splat, %start, %size : (tensor<3x3xi32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x1xi32>
// CHECK: return %[[SLICE]]
return %slice : tensor<1x1xi32>
@@ -937,9 +937,9 @@ func.func @slice_singleton() -> tensor<1x1xi32> {
// CHECK-LABEL: @test_slice_resource_no_fold
func.func @test_slice_resource_no_fold() -> tensor<1x1xi32> {
- %input = "tosa.const"() {values = dense_resource<slice_resource> : tensor<3x4xi32>} : () -> tensor<3x4xi32>
- %start = tosa.const_shape {values = dense<[1, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %size = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %input = "tosa.const"() <{values = dense_resource<slice_resource> : tensor<3x4xi32>}> : () -> tensor<3x4xi32>
+ %start = tosa.const_shape values(dense<[1, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %size = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: tosa.slice
%slice= tosa.slice %input, %start, %size : (tensor<3x4xi32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x1xi32>
return %slice : tensor<1x1xi32>
@@ -957,9 +957,9 @@ func.func @test_slice_resource_no_fold() -> tensor<1x1xi32> {
// CHECK: func.func @cast_float_to_float
func.func @cast_float_to_float() -> tensor<f16> {
- %splat = "tosa.const"() {values = dense<42.0> : tensor<f32>} : () -> tensor<f32>
+ %splat = "tosa.const"() <{values = dense<42.0> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<4.200000e+01> : tensor<f16>}
- %cast = tosa.cast %splat {input_unsigned = false} : (tensor<f32>) -> tensor<f16>
+ %cast = tosa.cast %splat : (tensor<f32>) -> tensor<f16>
// CHECK: return %[[SPLAT]]
return %cast : tensor<f16>
}
@@ -968,9 +968,9 @@ func.func @cast_float_to_float() -> tensor<f16> {
// CHECK: func.func @cast_int_to_float
func.func @cast_int_to_float() -> tensor<f16> {
- %splat = "tosa.const"() {values = dense<4> : tensor<i32>} : () -> tensor<i32>
+ %splat = "tosa.const"() <{values = dense<4> : tensor<i32>}> : () -> tensor<i32>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<4.000000e+00> : tensor<f16>}
- %cast = tosa.cast %splat {input_unsigned = false} : (tensor<i32>) -> tensor<f16>
+ %cast = tosa.cast %splat : (tensor<i32>) -> tensor<f16>
// CHECK: return %[[SPLAT]]
return %cast : tensor<f16>
}
@@ -981,7 +981,7 @@ func.func @cast_int_to_float() -> tensor<f16> {
func.func @cast_signless_to_float_input_unsigned() -> tensor<f16> {
%splat = "tosa.const"() {values = dense<200> : tensor<i8>} : () -> tensor<i8>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<2.000000e+02> : tensor<f16>}
- %cast = tosa.cast %splat {input_unsigned = true} : (tensor<i8>) -> tensor<f16>
+ %cast = tosa.cast %splat input_unsigned(true) : (tensor<i8>) -> tensor<f16>
// CHECK: return %[[SPLAT]]
return %cast : tensor<f16>
}
@@ -992,7 +992,7 @@ func.func @cast_signless_to_float_input_unsigned() -> tensor<f16> {
func.func @cast_int_to_int_input_unsigned() -> tensor<i32> {
%splat = "tosa.const"() {values = dense<200> : tensor<i8>} : () -> tensor<i8>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<200> : tensor<i32>}
- %cast = tosa.cast %splat {input_unsigned = true} : (tensor<i8>) -> tensor<i32>
+ %cast = tosa.cast %splat input_unsigned(true) : (tensor<i8>) -> tensor<i32>
// CHECK: return %[[SPLAT]]
return %cast : tensor<i32>
}
@@ -1001,9 +1001,9 @@ func.func @cast_int_to_int_input_unsigned() -> tensor<i32> {
// CHECK: func.func @cast_float_to_int
func.func @cast_float_to_int() -> tensor<i16> {
- %splat = "tosa.const"() {values = dense<-4.0> : tensor<f32>} : () -> tensor<f32>
+ %splat = "tosa.const"() <{values = dense<-4.0> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<-4> : tensor<i16>}
- %cast = tosa.cast %splat {input_unsigned = false} : (tensor<f32>) -> tensor<i16>
+ %cast = tosa.cast %splat : (tensor<f32>) -> tensor<i16>
// CHECK: return %[[SPLAT]]
return %cast : tensor<i16>
}
@@ -1012,9 +1012,9 @@ func.func @cast_float_to_int() -> tensor<i16> {
// CHECK: func.func @cast_float_to_int_round
func.func @cast_float_to_int_round() -> tensor<i16> {
- %splat = "tosa.const"() {values = dense<-3.5> : tensor<f32>} : () -> tensor<f32>
+ %splat = "tosa.const"() <{values = dense<-3.5> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<-4> : tensor<i16>}
- %cast = tosa.cast %splat {input_unsigned = false} : (tensor<f32>) -> tensor<i16>
+ %cast = tosa.cast %splat : (tensor<f32>) -> tensor<i16>
// CHECK: return %[[SPLAT]]
return %cast : tensor<i16>
}
@@ -1025,7 +1025,7 @@ func.func @cast_float_to_int_round() -> tensor<i16> {
func.func @cast_float_to_int_saturates_high() -> tensor<i8> {
%splat = "tosa.const"() {values = dense<1.000000e+20> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<127> : tensor<i8>}
- %cast = tosa.cast %splat {input_unsigned = false} : (tensor<f32>) -> tensor<i8>
+ %cast = tosa.cast %splat : (tensor<f32>) -> tensor<i8>
// CHECK: return %[[SPLAT]]
return %cast : tensor<i8>
}
@@ -1036,7 +1036,7 @@ func.func @cast_float_to_int_saturates_high() -> tensor<i8> {
func.func @cast_float_to_int_saturates_low() -> tensor<i8> {
%splat = "tosa.const"() {values = dense<-1.000000e+20> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<-128> : tensor<i8>}
- %cast = tosa.cast %splat {input_unsigned = false} : (tensor<f32>) -> tensor<i8>
+ %cast = tosa.cast %splat : (tensor<f32>) -> tensor<i8>
// CHECK: return %[[SPLAT]]
return %cast : tensor<i8>
}
@@ -1047,7 +1047,7 @@ func.func @cast_float_to_int_saturates_low() -> tensor<i8> {
func.func @cast_float_to_unsigned_int_saturates_low() -> tensor<ui8> {
%splat = "tosa.const"() {values = dense<-1.000000e+20> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<0> : tensor<ui8>}
- %cast = tosa.cast %splat {input_unsigned = false} : (tensor<f32>) -> tensor<ui8>
+ %cast = tosa.cast %splat : (tensor<f32>) -> tensor<ui8>
// CHECK: return %[[SPLAT]]
return %cast : tensor<ui8>
}
@@ -1058,7 +1058,7 @@ func.func @cast_float_to_unsigned_int_saturates_low() -> tensor<ui8> {
func.func @cast_float_to_unsigned_int_saturates_high() -> tensor<ui8> {
%splat = "tosa.const"() {values = dense<1.000000e+20> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<255> : tensor<ui8>}
- %cast = tosa.cast %splat {input_unsigned = false} : (tensor<f32>) -> tensor<ui8>
+ %cast = tosa.cast %splat : (tensor<f32>) -> tensor<ui8>
// CHECK: return %[[SPLAT]]
return %cast : tensor<ui8>
}
@@ -1067,9 +1067,9 @@ func.func @cast_float_to_unsigned_int_saturates_high() -> tensor<ui8> {
// CHECK: func.func @cast_int_to_int_trunc
func.func @cast_int_to_int_trunc() -> tensor<i16> {
- %splat = "tosa.const"() {values = dense<-1> : tensor<i32>} : () -> tensor<i32>
+ %splat = "tosa.const"() <{values = dense<-1> : tensor<i32>}> : () -> tensor<i32>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<-1> : tensor<i16>}
- %cast = tosa.cast %splat {input_unsigned = false} : (tensor<i32>) -> tensor<i16>
+ %cast = tosa.cast %splat : (tensor<i32>) -> tensor<i16>
// CHECK: return %[[SPLAT]]
return %cast : tensor<i16>
}
@@ -1078,9 +1078,9 @@ func.func @cast_int_to_int_trunc() -> tensor<i16> {
// CHECK: func.func @cast_int_to_int_sign
func.func @cast_int_to_int_sign() -> tensor<i32> {
- %splat = "tosa.const"() {values = dense<-1> : tensor<i16>} : () -> tensor<i16>
+ %splat = "tosa.const"() <{values = dense<-1> : tensor<i16>}> : () -> tensor<i16>
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<-1> : tensor<i32>}
- %cast = tosa.cast %splat {input_unsigned = false} : (tensor<i16>) -> tensor<i32>
+ %cast = tosa.cast %splat : (tensor<i16>) -> tensor<i32>
// CHECK: return %[[SPLAT]]
return %cast : tensor<i32>
}
@@ -1090,8 +1090,8 @@ func.func @cast_int_to_int_sign() -> tensor<i32> {
// CHECK-LABEL: @reverse_splat
func.func @reverse_splat() -> tensor<10xi32> {
// CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<42> : tensor<10xi32>}
- %splat = "tosa.const"() {values = dense<42> : tensor<10xi32>} : () -> tensor<10xi32>
- %reverse = tosa.reverse %splat { axis = 0 : i32 } : (tensor<10xi32>) -> tensor<10xi32>
+ %splat = "tosa.const"() <{values = dense<42> : tensor<10xi32>}> : () -> tensor<10xi32>
+ %reverse = tosa.reverse %splat axis(0) : (tensor<10xi32>) -> tensor<10xi32>
// CHECK: return %[[SPLAT]]
return %reverse : tensor<10xi32>
}
@@ -1100,9 +1100,9 @@ func.func @reverse_splat() -> tensor<10xi32> {
// CHECK-LABEL: @reverse_length_one
func.func @reverse_length_one(%arg0 : tensor<10x1xi32>) -> (tensor<10x1xi32>, tensor<10x1xi32>) {
- %nofold = tosa.reverse %arg0 { axis = 0 : i32 } : (tensor<10x1xi32>) -> tensor<10x1xi32>
- %fold = tosa.reverse %arg0 { axis = 1 : i32 } : (tensor<10x1xi32>) -> tensor<10x1xi32>
- // CHECK: %[[NOFOLD:.+]] = tosa.reverse %arg0 {axis = 0 : i32}
+ %nofold = tosa.reverse %arg0 axis(0) : (tensor<10x1xi32>) -> tensor<10x1xi32>
+ %fold = tosa.reverse %arg0 axis(1) : (tensor<10x1xi32>) -> tensor<10x1xi32>
+ // CHECK: %[[NOFOLD:.+]] = tosa.reverse %arg0 axis(0)
// CHECK: return %[[NOFOLD]], %arg0
return %nofold, %fold : tensor<10x1xi32>, tensor<10x1xi32>
}
@@ -1113,7 +1113,7 @@ func.func @reverse_length_one(%arg0 : tensor<10x1xi32>) -> (tensor<10x1xi32>, te
// by the folder pass.
// CHECK-LABEL: @no_shift_op_reorder
func.func @no_shift_op_reorder (%arg0 : tensor<44x1xi16>, %arg1 : tensor<1xi8>) -> tensor<44x57xi32> {
- %0 = "tosa.const"() {values = dense<1> : tensor<44x57xi16>} : () -> tensor<44x57xi16>
+ %0 = "tosa.const"() <{values = dense<1> : tensor<44x57xi16>}> : () -> tensor<44x57xi16>
// CHECK: tosa.mul %arg0, %0, %arg1
%1 = tosa.mul %arg0, %0, %arg1 : (tensor<44x1xi16>, tensor<44x57xi16>, tensor<1xi8>) -> tensor<44x57xi32>
return %1 : tensor<44x57xi32>
@@ -1143,7 +1143,7 @@ func.func @fold_select_same_value_dynamic_broadcast(%arg0: tensor<1x1xi1>, %arg1
// CHECK-LABEL: @fold_select_true_value
func.func @fold_select_true_value(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>) -> tensor<2x3xi32> {
- %c1 = "tosa.const"() {values = dense<1> : tensor<2x3xi1>} : () -> tensor<2x3xi1>
+ %c1 = "tosa.const"() <{values = dense<1> : tensor<2x3xi1>}> : () -> tensor<2x3xi1>
%0 = tosa.select %c1, %arg0, %arg1 : (tensor<2x3xi1>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK: return %arg0
// CHECK-NOT: tosa.select
@@ -1154,7 +1154,7 @@ func.func @fold_select_true_value(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>
// CHECK-LABEL: @fold_select_false_value
func.func @fold_select_false_value(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>) -> tensor<2x3xi32> {
- %c0 = "tosa.const"() {values = dense<0> : tensor<2x3xi1>} : () -> tensor<2x3xi1>
+ %c0 = "tosa.const"() <{values = dense<0> : tensor<2x3xi1>}> : () -> tensor<2x3xi1>
%0 = tosa.select %c0, %arg0, %arg1 : (tensor<2x3xi1>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK: return %arg1
// CHECK-NOT: tosa.select
@@ -1175,7 +1175,7 @@ func.func @no_fold_select_broadcast_same_value(%arg0: tensor<2x2xi1>, %arg1: ten
// CHECK-LABEL: @no_fold_select_broadcast_true_value
func.func @no_fold_select_broadcast_true_value(%arg0: tensor<1x1xf32>, %arg1: tensor<2x2xf32>) -> tensor<?x?xf32> {
// CHECK: %[[CONST:.*]] = "tosa.const"
- %0 = "tosa.const"() {values = dense<1> : tensor<2x2xi1>} : () -> tensor<2x2xi1>
+ %0 = "tosa.const"() <{values = dense<1> : tensor<2x2xi1>}> : () -> tensor<2x2xi1>
// CHECK: tosa.select %[[CONST]], %arg0, %arg1
%1 = tosa.select %0, %arg0, %arg1 : (tensor<2x2xi1>, tensor<1x1xf32>, tensor<2x2xf32>) -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
@@ -1186,7 +1186,7 @@ func.func @no_fold_select_broadcast_true_value(%arg0: tensor<1x1xf32>, %arg1: te
// CHECK-LABEL: @no_fold_select_broadcast_false_value
func.func @no_fold_select_broadcast_false_value(%arg0: tensor<2x2xf32>, %arg1: tensor<1x1xf32>) -> tensor<2x2xf32> {
// CHECK: %[[CONST:.*]] = "tosa.const"
- %0 = "tosa.const"() {values = dense<0> : tensor<2x2xi1>} : () -> tensor<2x2xi1>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<2x2xi1>}> : () -> tensor<2x2xi1>
// CHECK: tosa.select %[[CONST]], %arg0, %arg1
%1 = tosa.select %0, %arg0, %arg1 : (tensor<2x2xi1>, tensor<2x2xf32>, tensor<1x1xf32>) -> tensor<2x2xf32>
return %1 : tensor<2x2xf32>
@@ -1197,7 +1197,7 @@ func.func @no_fold_select_broadcast_false_value(%arg0: tensor<2x2xf32>, %arg1: t
// CHECK-LABEL: @no_fold_select_unknown_broadcast_true_value_dynamic_operand
func.func @no_fold_select_unknown_broadcast_true_value_dynamic_operand(%arg0: tensor<2x?xf32>, %arg1: tensor<2x2xf32>) -> tensor<2x?xf32> {
// CHECK: %[[CONST:.*]] = "tosa.const"
- %0 = "tosa.const"() {values = dense<1> : tensor<2x2xi1>} : () -> tensor<2x2xi1>
+ %0 = "tosa.const"() <{values = dense<1> : tensor<2x2xi1>}> : () -> tensor<2x2xi1>
// CHECK: tosa.select %[[CONST]], %arg0, %arg1
%1 = tosa.select %0, %arg0, %arg1 : (tensor<2x2xi1>, tensor<2x?xf32>, tensor<2x2xf32>) -> tensor<2x?xf32>
return %1 : tensor<2x?xf32>
@@ -1208,7 +1208,7 @@ func.func @no_fold_select_unknown_broadcast_true_value_dynamic_operand(%arg0: te
// CHECK-LABEL: @no_fold_select_unknown_broadcast_false_value_dynamic_operand
func.func @no_fold_select_unknown_broadcast_false_value_dynamic_operand(%arg0: tensor<2x?xf32>, %arg1: tensor<2x2xf32>) -> tensor<2x2xf32> {
// CHECK: %[[CONST:.*]] = "tosa.const"
- %0 = "tosa.const"() {values = dense<0> : tensor<2x2xi1>} : () -> tensor<2x2xi1>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<2x2xi1>}> : () -> tensor<2x2xi1>
// CHECK: tosa.select %[[CONST]], %arg0, %arg1
%1 = tosa.select %0, %arg0, %arg1 : (tensor<2x2xi1>, tensor<2x?xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
return %1 : tensor<2x2xf32>
@@ -1218,7 +1218,7 @@ func.func @no_fold_select_unknown_broadcast_false_value_dynamic_operand(%arg0: t
// CHECK-LABEL: @fold_select_dynamic_broadcast_true_value
func.func @fold_select_dynamic_broadcast_true_value(%arg0: tensor<2x?xf32>, %arg1: tensor<2x1xf32>) -> tensor<2x?xf32> {
- %0 = "tosa.const"() {values = dense<1> : tensor<2x1xi1>} : () -> tensor<2x1xi1>
+ %0 = "tosa.const"() <{values = dense<1> : tensor<2x1xi1>}> : () -> tensor<2x1xi1>
%1 = tosa.select %0, %arg0, %arg1 : (tensor<2x1xi1>, tensor<2x?xf32>, tensor<2x1xf32>) -> tensor<2x?xf32>
// CHECK: return %arg0
// CHECK-NOT: tosa.select
@@ -1229,7 +1229,7 @@ func.func @fold_select_dynamic_broadcast_true_value(%arg0: tensor<2x?xf32>, %arg
// CHECK-LABEL: @fold_select_dynamic_broadcast_false_value
func.func @fold_select_dynamic_broadcast_false_value(%arg0: tensor<1x1xf32>, %arg1: tensor<?x2xf32>) -> tensor<?x2xf32> {
- %0 = "tosa.const"() {values = dense<0> : tensor<1x1xi1>} : () -> tensor<1x1xi1>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1x1xi1>}> : () -> tensor<1x1xi1>
%1 = tosa.select %0, %arg0, %arg1 : (tensor<1x1xi1>, tensor<1x1xf32>, tensor<?x2xf32>) -> tensor<?x2xf32>
// CHECK: return %arg1
// CHECK-NOT: tosa.select
@@ -1239,10 +1239,10 @@ func.func @fold_select_dynamic_broadcast_false_value(%arg0: tensor<1x1xf32>, %ar
// -----
// CHECK-LABEL: @test_fold_add_shape
-// CHECK: tosa.const_shape {values = dense<[2, 4, 6, 8, 10, 12]> : tensor<6xindex>} : () -> !tosa.shape<6>
+// CHECK: tosa.const_shape values(dense<[2, 4, 6, 8, 10, 12]> : tensor<6xindex>) : () -> !tosa.shape<6>
func.func @test_fold_add_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 6]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 6]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 6]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 6]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.add_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1252,8 +1252,8 @@ func.func @test_fold_add_shape() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_add_shape_positive_overflow
// CHECK: tosa.add_shape
func.func @test_no_fold_add_shape_positive_overflow() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 9223372036854775807]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 1]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 9223372036854775807]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 1]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.add_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1263,8 +1263,8 @@ func.func @test_no_fold_add_shape_positive_overflow() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_add_shape_negative_overflow
// CHECK: tosa.add_shape
func.func @test_no_fold_add_shape_negative_overflow() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, -9223372036854775808]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, -1]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4, 5, -9223372036854775808]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 3, 4, 5, -1]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.add_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1272,19 +1272,19 @@ func.func @test_no_fold_add_shape_negative_overflow() -> !tosa.shape<6> {
// -----
// CHECK-LABEL: @test_fold_dim
-// CHECK: tosa.const_shape {values = dense<6> : tensor<1xindex>} : () -> !tosa.shape<1>
+// CHECK: tosa.const_shape values(dense<6> : tensor<1xindex>) : () -> !tosa.shape<1>
func.func @test_fold_dim(%arg0: tensor<6xi32>) -> !tosa.shape<1> {
- %dim = tosa.dim %arg0 {axis = 0 : i32} : (tensor<6xi32>) -> !tosa.shape<1>
+ %dim = tosa.dim %arg0 axis(0) : (tensor<6xi32>) -> !tosa.shape<1>
return %dim : !tosa.shape<1>
}
// -----
// CHECK-LABEL: @test_fold_sub_shape
-// CHECK: tosa.const_shape {values = dense<[2, 4, 6, 8, 10, 12]> : tensor<6xindex>} : () -> !tosa.shape<6>
+// CHECK: tosa.const_shape values(dense<[2, 4, 6, 8, 10, 12]> : tensor<6xindex>) : () -> !tosa.shape<6>
func.func @test_fold_sub_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[12, 22, 32, 42, 52, 62]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[10, 18, 26, 34, 42, 50]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[12, 22, 32, 42, 52, 62]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[10, 18, 26, 34, 42, 50]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.sub_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1294,7 +1294,7 @@ func.func @test_fold_sub_shape() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_dim_unranked_input
// CHECK: tosa.dim
func.func @test_no_fold_dim_unranked_input(%arg0: tensor<*xi32>) -> !tosa.shape<1> {
- %dim = tosa.dim %arg0 {axis = 0 : i32} : (tensor<*xi32>) -> !tosa.shape<1>
+ %dim = tosa.dim %arg0 axis(0) : (tensor<*xi32>) -> !tosa.shape<1>
return %dim : !tosa.shape<1>
}
@@ -1303,8 +1303,8 @@ func.func @test_no_fold_dim_unranked_input(%arg0: tensor<*xi32>) -> !tosa.shape<
// CHECK-LABEL: @test_no_fold_sub_shape_positive_overflow
// CHECK: tosa.sub_shape
func.func @test_no_fold_sub_shape_positive_overflow() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 9223372036854775807]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, -1]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 9223372036854775807]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 3, 4, 5, -1]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.sub_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1314,7 +1314,7 @@ func.func @test_no_fold_sub_shape_positive_overflow() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_dim_dynamic
// CHECK: tosa.dim
func.func @test_no_fold_dim_dynamic(%arg0: tensor<4x?xi32>) -> !tosa.shape<1> {
- %dim = tosa.dim %arg0 {axis = 1 : i32} : (tensor<4x?xi32>) -> !tosa.shape<1>
+ %dim = tosa.dim %arg0 axis(1) : (tensor<4x?xi32>) -> !tosa.shape<1>
return %dim : !tosa.shape<1>
}
@@ -1323,8 +1323,8 @@ func.func @test_no_fold_dim_dynamic(%arg0: tensor<4x?xi32>) -> !tosa.shape<1> {
// CHECK-LABEL: @test_no_fold_sub_shape_negative_overflow
// CHECK: tosa.sub_shape
func.func @test_no_fold_sub_shape_negative_overflow() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, -9223372036854775808]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 1]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4, 5, -9223372036854775808]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 1]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.sub_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1332,10 +1332,10 @@ func.func @test_no_fold_sub_shape_negative_overflow() -> !tosa.shape<6> {
// -----
// CHECK-LABEL: @test_fold_mul_shape
-// CHECK: tosa.const_shape {values = dense<[2, 4, 6, 8, 10, 12]> : tensor<6xindex>} : () -> !tosa.shape<6>
+// CHECK: tosa.const_shape values(dense<[2, 4, 6, 8, 10, 12]> : tensor<6xindex>) : () -> !tosa.shape<6>
func.func @test_fold_mul_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[2, 2, 3, 4, 2, 3]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 2, 2, 5, 4]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[2, 2, 3, 4, 2, 3]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 2, 2, 5, 4]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.mul_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1345,8 +1345,8 @@ func.func @test_fold_mul_shape() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_mul_shape_positive_overflow
// CHECK: tosa.mul_shape
func.func @test_no_fold_mul_shape_positive_overflow() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 9223372036854775807]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 2]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 9223372036854775807]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 2]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.mul_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1356,8 +1356,8 @@ func.func @test_no_fold_mul_shape_positive_overflow() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_mul_shape_negative_overflow
// CHECK: tosa.mul_shape
func.func @test_no_fold_mul_shape_negative_overflow() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, -9223372036854775808]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 2]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4, 5, -9223372036854775808]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 2]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.mul_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1365,10 +1365,10 @@ func.func @test_no_fold_mul_shape_negative_overflow() -> !tosa.shape<6> {
// -----
// CHECK-LABEL: @test_fold_div_ceil_shape
-// CHECK: tosa.const_shape {values = dense<[2, 4, 6, 8, 10, 12]> : tensor<6xindex>} : () -> !tosa.shape<6>
+// CHECK: tosa.const_shape values(dense<[2, 4, 6, 8, 10, 12]> : tensor<6xindex>) : () -> !tosa.shape<6>
func.func @test_fold_div_ceil_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[2, 7, 11, 22, 47, 46]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 2, 3, 5, 4]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[2, 7, 11, 22, 47, 46]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 2, 3, 5, 4]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.div_ceil_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1378,8 +1378,8 @@ func.func @test_fold_div_ceil_shape() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_div_ceil_shape_negative_input
// CHECK: tosa.div_ceil_shape
func.func @test_no_fold_div_ceil_shape_negative_input() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[2, 7, 11, 22, 47, -7]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 2, 3, 5, 2]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[2, 7, 11, 22, 47, -7]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 2, 3, 5, 2]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.div_ceil_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1389,8 +1389,8 @@ func.func @test_no_fold_div_ceil_shape_negative_input() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_div_ceil_shape_negative_divisor
// CHECK: tosa.div_ceil_shape
func.func @test_no_fold_div_ceil_shape_negative_divisor() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[2, 7, 11, 22, 47, 7]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 2, 3, 5, -2]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[2, 7, 11, 22, 47, 7]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 2, 3, 5, -2]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.div_ceil_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1400,8 +1400,8 @@ func.func @test_no_fold_div_ceil_shape_negative_divisor() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_div_ceil_shape_positive_overflow
// CHECK: tosa.div_ceil_shape
func.func @test_no_fold_div_ceil_shape_positive_overflow() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[2, 7, 11, 22, 47, 9223372036854775807]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 2, 3, 5, 0]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[2, 7, 11, 22, 47, 9223372036854775807]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 2, 3, 5, 0]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.div_ceil_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1411,8 +1411,8 @@ func.func @test_no_fold_div_ceil_shape_positive_overflow() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_div_ceil_shape_negative_overflow
// CHECK: tosa.div_ceil_shape
func.func @test_no_fold_div_ceil_shape_negative_overflow() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[2, 7, 11, 22, 47, -9223372036854775808]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 2, 3, 5, 0]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[2, 7, 11, 22, 47, -9223372036854775808]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 2, 3, 5, 0]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.div_ceil_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1420,10 +1420,10 @@ func.func @test_no_fold_div_ceil_shape_negative_overflow() -> !tosa.shape<6> {
// -----
// CHECK-LABEL: @test_fold_div_floor_shape
-// CHECK: tosa.const_shape {values = dense<[2, 3, 5, 7, 9, 11]> : tensor<6xindex>} : () -> !tosa.shape<6>
+// CHECK: tosa.const_shape values(dense<[2, 3, 5, 7, 9, 11]> : tensor<6xindex>) : () -> !tosa.shape<6>
func.func @test_fold_div_floor_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[2, 7, 11, 22, 47, 46]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 2, 3, 5, 4]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[2, 7, 11, 22, 47, 46]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 2, 3, 5, 4]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.div_floor_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1433,8 +1433,8 @@ func.func @test_fold_div_floor_shape() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_div_floor_shape_negative_input
// CHECK: tosa.div_floor_shape
func.func @test_no_fold_div_floor_shape_negative_input() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[2, 7, 11, 22, 47, -7]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 2, 3, 5, 2]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[2, 7, 11, 22, 47, -7]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 2, 3, 5, 2]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.div_floor_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1444,8 +1444,8 @@ func.func @test_no_fold_div_floor_shape_negative_input() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_div_floor_shape_negative_divisor
// CHECK: tosa.div_floor_shape
func.func @test_no_fold_div_floor_shape_negative_divisor() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[2, 7, 11, 22, 47, 7]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 2, 3, 5, -2]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[2, 7, 11, 22, 47, 7]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 2, 3, 5, -2]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.div_floor_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1455,8 +1455,8 @@ func.func @test_no_fold_div_floor_shape_negative_divisor() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_div_floor_shape_positive_overflow
// CHECK: tosa.div_floor_shape
func.func @test_no_fold_div_floor_shape_positive_overflow() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[2, 7, 11, 22, 47, 9223372036854775807]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 2, 3, 5, 0]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[2, 7, 11, 22, 47, 9223372036854775807]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 2, 3, 5, 0]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.div_floor_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1466,8 +1466,8 @@ func.func @test_no_fold_div_floor_shape_positive_overflow() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_div_floor_shape_negative_overflow
// CHECK: tosa.div_floor_shape
func.func @test_no_fold_div_floor_shape_negative_overflow() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[2, 7, 11, 22, 47, -9223372036854775808]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 2, 3, 5, 0]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[2, 7, 11, 22, 47, -9223372036854775808]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 2, 3, 5, 0]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.div_floor_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1475,10 +1475,10 @@ func.func @test_no_fold_div_floor_shape_negative_overflow() -> !tosa.shape<6> {
// -----
// CHECK-LABEL: @test_fold_mod_shape
-// CHECK: tosa.const_shape {values = dense<[2, 1, 5, 7, 9, 11]> : tensor<6xindex>} : () -> !tosa.shape<6>
+// CHECK: tosa.const_shape values(dense<[2, 1, 5, 7, 9, 11]> : tensor<6xindex>) : () -> !tosa.shape<6>
func.func @test_fold_mod_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[24, 7, 65, 33, 39, 39]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[11, 2, 12, 13, 15, 14]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[24, 7, 65, 33, 39, 39]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[11, 2, 12, 13, 15, 14]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.mod_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1488,8 +1488,8 @@ func.func @test_fold_mod_shape() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_mod_shape_negative_input
// CHECK: tosa.mod_shape
func.func @test_no_fold_mod_shape_negative_input() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[24, 7, 65, 33, 39, -7]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[11, 2, 12, 13, 15, 2]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[24, 7, 65, 33, 39, -7]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[11, 2, 12, 13, 15, 2]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.mod_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1499,8 +1499,8 @@ func.func @test_no_fold_mod_shape_negative_input() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_mod_shape_negative_divisor
// CHECK: tosa.mod_shape
func.func @test_no_fold_mod_shape_negative_divisor() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[24, 7, 65, 33, 39, 7]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[11, 2, 12, 13, 15, -2]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[24, 7, 65, 33, 39, 7]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[11, 2, 12, 13, 15, -2]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.mod_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1510,8 +1510,8 @@ func.func @test_no_fold_mod_shape_negative_divisor() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_mod_shape_positive_overflow
// CHECK: tosa.mod_shape
func.func @test_no_fold_mod_shape_positive_overflow() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[24, 7, 65, 33, 39, 9223372036854775807]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[11, 2, 12, 13, 15, 0]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[24, 7, 65, 33, 39, 9223372036854775807]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[11, 2, 12, 13, 15, 0]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.mod_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1521,8 +1521,8 @@ func.func @test_no_fold_mod_shape_positive_overflow() -> !tosa.shape<6> {
// CHECK-LABEL: @test_no_fold_mod_shape_negative_overflow
// CHECK: tosa.mod_shape
func.func @test_no_fold_mod_shape_negative_overflow() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[24, 7, 65, 33, 39, -9223372036854775808]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[11, 2, 12, 13, 15, 0]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[24, 7, 65, 33, 39, -9223372036854775808]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[11, 2, 12, 13, 15, 0]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.mod_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1530,10 +1530,10 @@ func.func @test_no_fold_mod_shape_negative_overflow() -> !tosa.shape<6> {
// -----
// CHECK-LABEL: @test_max_shape
-// CHECK: tosa.const_shape {values = dense<[24, 7, 65, 33, 39, 5]> : tensor<6xindex>} : () -> !tosa.shape<6>
+// CHECK: tosa.const_shape values(dense<[24, 7, 65, 33, 39, 5]> : tensor<6xindex>) : () -> !tosa.shape<6>
func.func @test_max_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[24, 7, 65, 33, 39, 1]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[11, 2, 12, 13, 15, 5]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[24, 7, 65, 33, 39, 1]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[11, 2, 12, 13, 15, 5]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.max_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1541,10 +1541,10 @@ func.func @test_max_shape() -> !tosa.shape<6> {
// -----
// CHECK-LABEL: @test_min_shape
-// CHECK: tosa.const_shape {values = dense<[11, 2, 12, 13, 15, 1]> : tensor<6xindex>} : () -> !tosa.shape<6>
+// CHECK: tosa.const_shape values(dense<[11, 2, 12, 13, 15, 1]> : tensor<6xindex>) : () -> !tosa.shape<6>
func.func @test_min_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[24, 7, 65, 33, 39, 1]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[11, 2, 12, 13, 15, 5]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[24, 7, 65, 33, 39, 1]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[11, 2, 12, 13, 15, 5]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.min_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1552,9 +1552,9 @@ func.func @test_min_shape() -> !tosa.shape<6> {
// -----
// CHECK-LABEL: @test_exp2_shape
-// CHECK: tosa.const_shape {values = dense<[4, 8, 2, 16, 64, 32]> : tensor<6xindex>} : () -> !tosa.shape<6>
+// CHECK: tosa.const_shape values(dense<[4, 8, 2, 16, 64, 32]> : tensor<6xindex>) : () -> !tosa.shape<6>
func.func @test_exp2_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[2, 3, 1, 4, 6, 5]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[2, 3, 1, 4, 6, 5]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.exp2_shape %a : (!tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1564,7 +1564,7 @@ func.func @test_exp2_shape() -> !tosa.shape<6> {
// CHECK-LABEL: @test_neg_exp2_shape
// CHECK: tosa.exp2_shape
func.func @test_neg_exp2_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[-10, 3, 1, 4, 6, 5]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[-10, 3, 1, 4, 6, 5]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.exp2_shape %a : (!tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1574,7 +1574,7 @@ func.func @test_neg_exp2_shape() -> !tosa.shape<6> {
// CHECK-LABEL: @test_high_exp2_shape
// CHECK: tosa.exp2_shape
func.func @test_high_exp2_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[32, 64, 1, 4, 6, 5]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[32, 64, 1, 4, 6, 5]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.exp2_shape %a : (!tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1582,9 +1582,9 @@ func.func @test_high_exp2_shape() -> !tosa.shape<6> {
// -----
// CHECK-LABEL: @test_log2_ceil_shape
-// CHECK: tosa.const_shape {values = dense<[2, 4, 4, 0, 3, 5]> : tensor<6xindex>} : () -> !tosa.shape<6>
+// CHECK: tosa.const_shape values(dense<[2, 4, 4, 0, 3, 5]> : tensor<6xindex>) : () -> !tosa.shape<6>
func.func @test_log2_ceil_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[4, 9, 14, 1, 7, 30]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[4, 9, 14, 1, 7, 30]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.log2_ceil_shape %a : (!tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1594,7 +1594,7 @@ func.func @test_log2_ceil_shape() -> !tosa.shape<6> {
// CHECK-LABEL: @test_log2_ceil_shape_zero
// CHECK: tosa.log2_ceil_shape
func.func @test_log2_ceil_shape_zero() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[4, 9, 0, 1, 7, 30]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[4, 9, 0, 1, 7, 30]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.log2_ceil_shape %a : (!tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1604,7 +1604,7 @@ func.func @test_log2_ceil_shape_zero() -> !tosa.shape<6> {
// CHECK-LABEL: @test_log2_ceil_shape_neg
// CHECK: tosa.log2_ceil_shape
func.func @test_log2_ceil_shape_neg() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[4, 9, -123, 1, 7, 30]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[4, 9, -123, 1, 7, 30]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.log2_ceil_shape %a : (!tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1612,9 +1612,9 @@ func.func @test_log2_ceil_shape_neg() -> !tosa.shape<6> {
// -----
// CHECK-LABEL: @test_log2_floor_shape
-// CHECK: tosa.const_shape {values = dense<[2, 3, 3, 0, 2, 4]> : tensor<6xindex>} : () -> !tosa.shape<6>
+// CHECK: tosa.const_shape values(dense<[2, 3, 3, 0, 2, 4]> : tensor<6xindex>) : () -> !tosa.shape<6>
func.func @test_log2_floor_shape() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[4, 9, 14, 1, 7, 30]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[4, 9, 14, 1, 7, 30]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.log2_floor_shape %a : (!tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1624,7 +1624,7 @@ func.func @test_log2_floor_shape() -> !tosa.shape<6> {
// CHECK-LABEL: @test_log2_floor_shape_zero
// CHECK: tosa.log2_floor_shape
func.func @test_log2_floor_shape_zero() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[4, 9, 0, 1, 7, 30]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[4, 9, 0, 1, 7, 30]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.log2_floor_shape %a : (!tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1634,7 +1634,7 @@ func.func @test_log2_floor_shape_zero() -> !tosa.shape<6> {
// CHECK-LABEL: @test_log2_floor_shape_neg
// CHECK: tosa.log2_floor_shape
func.func @test_log2_floor_shape_neg() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[4, 9, -123, 1, 7, 30]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[4, 9, -123, 1, 7, 30]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.log2_floor_shape %a : (!tosa.shape<6>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
}
@@ -1642,10 +1642,10 @@ func.func @test_log2_floor_shape_neg() -> !tosa.shape<6> {
// -----
// CHECK-LABEL: @test_concat_shape
-// CHECK: tosa.const_shape {values = dense<[4, 9, 5, 19]> : tensor<4xindex>} : () -> !tosa.shape<4>
+// CHECK: tosa.const_shape values(dense<[4, 9, 5, 19]> : tensor<4xindex>) : () -> !tosa.shape<4>
func.func @test_concat_shape() -> !tosa.shape<4> {
- %a = tosa.const_shape {values = dense<[4, 9]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %b = tosa.const_shape {values = dense<[5, 19]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %a = tosa.const_shape values(dense<[4, 9]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %b = tosa.const_shape values(dense<[5, 19]> : tensor<2xindex>) : () -> !tosa.shape<2>
%c = tosa.concat_shape %a, %b : (!tosa.shape<2>, !tosa.shape<2>) -> !tosa.shape<4>
return %c : !tosa.shape<4>
}
@@ -1653,10 +1653,10 @@ func.func @test_concat_shape() -> !tosa.shape<4> {
// -----
// CHECK-LABEL: @test_concat_shape_rank6
-// CHECK: tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]> : tensor<12xindex>} : () -> !tosa.shape<12>
+// CHECK: tosa.const_shape values(dense<[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]> : tensor<12xindex>) : () -> !tosa.shape<12>
func.func @test_concat_shape_rank6() -> !tosa.shape<12> {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 6]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[7, 8, 9, 10, 11, 12]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 6]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[7, 8, 9, 10, 11, 12]> : tensor<6xindex>) : () -> !tosa.shape<6>
%c = tosa.concat_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<12>
return %c : !tosa.shape<12>
}
@@ -1664,21 +1664,21 @@ func.func @test_concat_shape_rank6() -> !tosa.shape<12> {
// -----
// CHECK-LABEL: @test_concat_shape_total_rank12_5inputs
-// CHECK-DAG: %[[C:.*]] = tosa.const_shape {values = dense<[6, 7, 8, 9]> : tensor<4xindex>} : () -> !tosa.shape<4>
-// CHECK-DAG: %[[D:.*]] = tosa.const_shape {values = dense<10> : tensor<1xindex>} : () -> !tosa.shape<1>
-// CHECK-DAG: %[[E:.*]] = tosa.const_shape {values = dense<[11, 12]> : tensor<2xindex>} : () -> !tosa.shape<2>
-// CHECK-DAG: %[[AB:.*]] = tosa.const_shape {values = dense<[1, 2, 3, 4, 5]> : tensor<5xindex>} : () -> !tosa.shape<5>
+// CHECK-DAG: %[[C:.*]] = tosa.const_shape values(dense<[6, 7, 8, 9]> : tensor<4xindex>) : () -> !tosa.shape<4>
+// CHECK-DAG: %[[D:.*]] = tosa.const_shape values(dense<10> : tensor<1xindex>) : () -> !tosa.shape<1>
+// CHECK-DAG: %[[E:.*]] = tosa.const_shape values(dense<[11, 12]> : tensor<2xindex>) : () -> !tosa.shape<2>
+// CHECK-DAG: %[[AB:.*]] = tosa.const_shape values(dense<[1, 2, 3, 4, 5]> : tensor<5xindex>) : () -> !tosa.shape<5>
// CHECK: %[[ABC:.*]] = tosa.concat_shape %[[AB]], %[[C]] : (!tosa.shape<5>, !tosa.shape<4>) -> !tosa.shape<9>
// CHECK: %[[ABCD:.*]] = tosa.concat_shape %[[ABC]], %[[D]] : (!tosa.shape<9>, !tosa.shape<1>) -> !tosa.shape<10>
// CHECK: %[[ABCDE:.*]] = tosa.concat_shape %[[ABCD]], %[[E]] : (!tosa.shape<10>, !tosa.shape<2>) -> !tosa.shape<12>
// CHECK: return %[[ABCDE]] : !tosa.shape<12>
func.func @test_concat_shape_total_rank12_5inputs() -> !tosa.shape<12> {
// Ranks: 3 + 2 + 4 + 1 + 2 = 12
- %a = tosa.const_shape {values = dense<[1, 2, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %b = tosa.const_shape {values = dense<[4, 5]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %c = tosa.const_shape {values = dense<[6, 7, 8, 9]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %d = tosa.const_shape {values = dense<[10]> : tensor<1xindex>} : () -> !tosa.shape<1>
- %e = tosa.const_shape {values = dense<[11, 12]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %a = tosa.const_shape values(dense<[1, 2, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %b = tosa.const_shape values(dense<[4, 5]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %c = tosa.const_shape values(dense<[6, 7, 8, 9]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %d = tosa.const_shape values(dense<[10]> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %e = tosa.const_shape values(dense<[11, 12]> : tensor<2xindex>) : () -> !tosa.shape<2>
%ab = tosa.concat_shape %a, %b : (!tosa.shape<3>, !tosa.shape<2>) -> !tosa.shape<5>
%abc = tosa.concat_shape %ab, %c : (!tosa.shape<5>, !tosa.shape<4>) -> !tosa.shape<9>
@@ -1693,10 +1693,10 @@ func.func @test_concat_shape_total_rank12_5inputs() -> !tosa.shape<12> {
// CHECK-LABEL: @test_concat_shape_rank6_4inputs
// CHECK: tosa.concat_shape
func.func @test_concat_shape_rank6_4inputs() -> !tosa.shape<24> {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 6]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[7, 8, 9, 10, 11, 12]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %c = tosa.const_shape {values = dense<[13, 14, 15, 16, 17, 18]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %d = tosa.const_shape {values = dense<[19, 20, 21, 22, 23, 24]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 6]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[7, 8, 9, 10, 11, 12]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %c = tosa.const_shape values(dense<[13, 14, 15, 16, 17, 18]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %d = tosa.const_shape values(dense<[19, 20, 21, 22, 23, 24]> : tensor<6xindex>) : () -> !tosa.shape<6>
%ab = tosa.concat_shape %a, %b : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<12>
%cd = tosa.concat_shape %c, %d : (!tosa.shape<6>, !tosa.shape<6>) -> !tosa.shape<12>
%abcd = tosa.concat_shape %ab, %cd : (!tosa.shape<12>, !tosa.shape<12>) -> !tosa.shape<24>
@@ -1709,11 +1709,11 @@ func.func @test_concat_shape_rank6_4inputs() -> !tosa.shape<24> {
// CHECK: tosa.concat_shape
func.func @test_concat_shape_total_rank13_5inputs() -> !tosa.shape<13> {
// Ranks: 3 + 2 + 4 + 1 + 3 = 13
- %a = tosa.const_shape {values = dense<[1, 2, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %b = tosa.const_shape {values = dense<[4, 5]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %c = tosa.const_shape {values = dense<[6, 7, 8, 9]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %d = tosa.const_shape {values = dense<[10]> : tensor<1xindex>} : () -> !tosa.shape<1>
- %e = tosa.const_shape {values = dense<[11, 12, 13]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %a = tosa.const_shape values(dense<[1, 2, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %b = tosa.const_shape values(dense<[4, 5]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %c = tosa.const_shape values(dense<[6, 7, 8, 9]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %d = tosa.const_shape values(dense<[10]> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %e = tosa.const_shape values(dense<[11, 12, 13]> : tensor<3xindex>) : () -> !tosa.shape<3>
%ab = tosa.concat_shape %a, %b : (!tosa.shape<3>, !tosa.shape<2>) -> !tosa.shape<5>
%abc = tosa.concat_shape %ab, %c : (!tosa.shape<5>, !tosa.shape<4>) -> !tosa.shape<9>
@@ -1728,9 +1728,9 @@ func.func @test_concat_shape_total_rank13_5inputs() -> !tosa.shape<13> {
// CHECK: tosa.const_shape
func.func @test_concat_shape_total_rank9_shapes() -> !tosa.shape<9> {
// Ranks: 3 + 2 + 4 = 9
- %a = tosa.const_shape {values = dense<[1, 2, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %b = tosa.const_shape {values = dense<[4, 5]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %c = tosa.const_shape {values = dense<[6, 7, 8, 9]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[1, 2, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %b = tosa.const_shape values(dense<[4, 5]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %c = tosa.const_shape values(dense<[6, 7, 8, 9]> : tensor<4xindex>) : () -> !tosa.shape<4>
%abc = tosa.concat_shape %a, %b, %c : (!tosa.shape<3>, !tosa.shape<2>, !tosa.shape<4>) -> !tosa.shape<9>
@@ -1740,11 +1740,11 @@ func.func @test_concat_shape_total_rank9_shapes() -> !tosa.shape<9> {
// -----
// CHECK-LABEL: @test_slice_shape
-// CHECK: tosa.const_shape {values = dense<[3, 4, 5, 6]> : tensor<4xindex>} : () -> !tosa.shape<4>
+// CHECK: tosa.const_shape values(dense<[3, 4, 5, 6]> : tensor<4xindex>) : () -> !tosa.shape<4>
func.func @test_slice_shape() -> !tosa.shape<4> {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 6]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
- %c = "tosa.const"() {values = dense<4> : tensor<1xi32>} : () -> tensor<1xi32>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 6]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %c = "tosa.const"() <{values = dense<4> : tensor<1xi32>}> : () -> tensor<1xi32>
%d = tosa.slice_shape %a, %b, %c : (!tosa.shape<6>, tensor<1xi32>, tensor<1xi32>) -> !tosa.shape<4>
return %d : !tosa.shape<4>
}
diff --git a/mlir/test/Dialect/Tosa/dynamic_extension.mlir b/mlir/test/Dialect/Tosa/dynamic_extension.mlir
index bb27f4bbbd8af..850d57c1129bb 100644
--- a/mlir/test/Dialect/Tosa/dynamic_extension.mlir
+++ b/mlir/test/Dialect/Tosa/dynamic_extension.mlir
@@ -21,7 +21,7 @@ func.func @test_table_non_const(%arg0 : tensor<4x5xi8>, %arg1 : tensor<256xi8>)
// -----
func.func @test_pad_non_const(%arg0: tensor<13x21x3xi8>, %arg1: tensor<1xi8>) -> tensor<13x22x4xi8> {
- %0 = tosa.const_shape {values = dense<[0, 0, 0, 1, 0, 1]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %0 = tosa.const_shape values(dense<[0, 0, 0, 1, 0, 1]> : tensor<6xindex>) : () -> !tosa.shape<6>
%1 = tosa.pad %arg0, %0, %arg1 : (tensor<13x21x3xi8>, !tosa.shape<6>, tensor<1xi8>) -> tensor<13x22x4xi8>
return %1 : tensor<13x22x4xi8>
}
@@ -29,38 +29,38 @@ func.func @test_pad_non_const(%arg0: tensor<13x21x3xi8>, %arg1: tensor<1xi8>) ->
// -----
func.func @test_rescale_non_const_multiplier(%arg0: tensor<13x21x3xi32>, %multiplier: tensor<1xi32>) -> tensor<13x21x3xi32> {
- %zps = "tosa.const"() {values = dense<0> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %zps, %zps {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
+ %zps = "tosa.const"() <{values = dense<0> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %zps, %zps scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
// -----
func.func @test_rescale_non_const_shift(%arg0: tensor<13x21x3xi32>, %shift: tensor<1xi8>) -> tensor<13x21x3xi32> {
- %zps = "tosa.const"() {values = dense<0> : tensor<1xi32> } : () -> tensor<1xi32>
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %zps, %zps {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
+ %zps = "tosa.const"() <{values = dense<0> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %zps, %zps scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
// -----
func.func @test_rescale_non_const_input_zp(%arg0: tensor<13x21x3xi32>, %input_zp: tensor<1xi32>) -> tensor<13x21x3xi32> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
%output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = true, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
// -----
func.func @test_rescale_non_const_output_zp(%arg0: tensor<13x21x3xi32>, %output_zp: tensor<1xi32>) -> tensor<13x21x3xi32> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = true, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
@@ -90,14 +90,14 @@ func.func @test_matmul_t_non_const_b_zp(%arg0: tensor<1x14x19xf32>, %arg1: tenso
// -----
func.func @test_negate_non_const_zps(%arg0: tensor<1xf32>, %input1_zp: tensor<1xf32>, %output_zp: tensor<1xf32>) -> tensor<1xf32> {
- %0 = tosa.negate %arg0, %input1_zp, %output_zp {} : (tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
+ %0 = tosa.negate %arg0, %input1_zp, %output_zp : (tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
return %0 : tensor<1xf32>
}
// -----
func.func @test_avg_pool2d_non_const_zps(%arg0: tensor<1x32x32x8xf32>, %input_zp: tensor<1xf32>, %output_zp: tensor<1xf32>) -> tensor<1x32x32x8xf32> {
- %0 = "tosa.avg_pool2d"(%arg0, %input_zp, %output_zp) {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %input_zp, %output_zp) <{kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -105,10 +105,10 @@ func.func @test_avg_pool2d_non_const_zps(%arg0: tensor<1x32x32x8xf32>, %input_zp
// -----
func.func @test_avg_pool2d_adaptive_non_const_zps(%arg0: tensor<1x32x32x8xf32>, %input_zp: tensor<1xf32>, %output_zp: tensor<1xf32>) -> tensor<1x32x32x8xf32> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -116,7 +116,7 @@ func.func @test_avg_pool2d_adaptive_non_const_zps(%arg0: tensor<1x32x32x8xf32>,
// -----
func.func @test_slice_shape_non_const_start_size(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) {
- %0 = tosa.const_shape {values = dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %0 = tosa.const_shape values(dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>) : () -> !tosa.shape<6>
%3 = tosa.slice_shape %0, %arg0, %arg1 : (!tosa.shape<6>, tensor<1xi32>, tensor<1xi32>) -> !tosa.shape<3>
return
}
diff --git a/mlir/test/Dialect/Tosa/error_if_check.mlir b/mlir/test/Dialect/Tosa/error_if_check.mlir
index 5363f3057c5cc..1e5eedd07e621 100644
--- a/mlir/test/Dialect/Tosa/error_if_check.mlir
+++ b/mlir/test/Dialect/Tosa/error_if_check.mlir
@@ -4,11 +4,11 @@
// CHECK-LABEL: test_resize_large_image_size
func.func @test_resize_large_image_size(%arg0: tensor<1x16384x16384x8xf32>) -> tensor<1x32767x32767x8xf32> {
- %scale = tosa.const_shape { values = dense<[2, 1, 2, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[2, 1, 2, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op expect input/output height/width dims to be < 16384, got [OH, OW, IH, IW] = 32767, 32767, 16384, 16384}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x16384x16384x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x32767x32767x8xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x16384x16384x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x32767x32767x8xf32>
return %1 : tensor<1x32767x32767x8xf32>
}
@@ -16,11 +16,11 @@ func.func @test_resize_large_image_size(%arg0: tensor<1x16384x16384x8xf32>) -> t
// CHECK-LABEL: test_resize_invalid_scale_numerator
func.func @test_resize_invalid_scale_numerator(%arg0: tensor<1x9x9x8xf32>) -> tensor<?x?x?x?xf32> {
- %scale = tosa.const_shape { values = dense<[2049, 8, 1, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[2049, 8, 1, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op expect all scale numerator values to be <= (1 << 11), got scale_y_n=2049, scale_x_n=1}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x9x9x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x9x9x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
return %1 : tensor<?x?x?x?xf32>
}
@@ -28,11 +28,11 @@ func.func @test_resize_invalid_scale_numerator(%arg0: tensor<1x9x9x8xf32>) -> te
// CHECK-LABEL: test_resize_invalid_downscale
func.func @test_resize_invalid_downscale(%arg0: tensor<1x37x37x8xf32>) -> tensor<?x?x?x?xf32> {
- %scale = tosa.const_shape { values = dense<[1, 18, 1, 18]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[1, 18, 1, 18]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op expect a downscale ratio larger than 1/16, got y=1/18, x=1/18}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x37x37x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x37x37x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
return %1 : tensor<?x?x?x?xf32>
}
@@ -40,11 +40,11 @@ func.func @test_resize_invalid_downscale(%arg0: tensor<1x37x37x8xf32>) -> tensor
// CHECK-LABEL: test_resize_invalid_offset_y
func.func @test_resize_invalid_offset_y(%arg0: tensor<1x8x8x8xf32>) -> tensor<?x?x?x?xf32> {
- %scale = tosa.const_shape { values = dense<[1, 1, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[17, 0]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[1, 1, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[17, 0]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op expect offsetY / scaleYNumerator to be in range [-1, 16), got 17/1}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
return %1 : tensor<?x?x?x?xf32>
}
@@ -52,11 +52,11 @@ func.func @test_resize_invalid_offset_y(%arg0: tensor<1x8x8x8xf32>) -> tensor<?x
// CHECK-LABEL: test_resize_invalid_offset_x
func.func @test_resize_invalid_offset_x(%arg0: tensor<1x8x8x8xf32>) -> tensor<?x?x?x?xf32> {
- %scale = tosa.const_shape { values = dense<[1, 1, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[0, -2]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[1, 1, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[0, -2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op expect offsetX / scaleXNumerator to be in range [-1, 16), got -2/1}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
return %1 : tensor<?x?x?x?xf32>
}
@@ -64,11 +64,11 @@ func.func @test_resize_invalid_offset_x(%arg0: tensor<1x8x8x8xf32>) -> tensor<?x
// CHECK-LABEL: test_resize_invalid_border_y
func.func @test_resize_invalid_boarder_y(%arg0: tensor<1x8x8x8xf32>) -> tensor<?x?x?x?xf32> {
- %scale = tosa.const_shape { values = dense<[1, 1, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[-17, 0]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[1, 1, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[-17, 0]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op expect borderY / scaleYNumerator to be in range [-16, 1), got -17/1}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
return %1 : tensor<?x?x?x?xf32>
}
@@ -76,11 +76,11 @@ func.func @test_resize_invalid_boarder_y(%arg0: tensor<1x8x8x8xf32>) -> tensor<?
// CHECK-LABEL: test_resize_invalid_border_x
func.func @test_resize_invalid_boarder_x(%arg0: tensor<1x8x8x8xf32>) -> tensor<?x?x?x?xf32> {
- %scale = tosa.const_shape { values = dense<[1, 1, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[0, 2]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[1, 1, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[0, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op expect borderX / scaleXNumerator to be in range [-16, 1), got 2/1}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
return %1 : tensor<?x?x?x?xf32>
}
@@ -88,7 +88,7 @@ func.func @test_resize_invalid_boarder_x(%arg0: tensor<1x8x8x8xf32>) -> tensor<?
// CHECK-LABEL: test_reshape_inferable_dim
func.func @test_reshape_inferable_dim(%arg0: tensor<4xf32>) -> tensor<?x2xf32> {
- %shape = tosa.const_shape { values = dense<[-1, 2]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %shape = tosa.const_shape values(dense<[-1, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op shape input contains inferable dimension (-1) which does not conform to the TOSA specification}}
%0 = tosa.reshape %arg0, %shape : (tensor<4xf32>, !tosa.shape<2>) -> tensor<?x2xf32>
return %0 : tensor<?x2xf32>
@@ -98,8 +98,8 @@ func.func @test_reshape_inferable_dim(%arg0: tensor<4xf32>) -> tensor<?x2xf32> {
// CHECK-LABEL: test_slice_inferable_start
func.func @test_slice_inferable_start(%arg0: tensor<4x4xf32>) -> tensor<2x2xf32> {
- %start = tosa.const_shape { values = dense<[-1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %size = tosa.const_shape { values = dense<[2, 2]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %start = tosa.const_shape values(dense<[-1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %size = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.slice' op start input contains inferable dimension (-1) which does not conform to the TOSA specification}}
%0 = tosa.slice %arg0, %start, %size : (tensor<4x4xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<2x2xf32>
return %0 : tensor<2x2xf32>
@@ -109,8 +109,8 @@ func.func @test_slice_inferable_start(%arg0: tensor<4x4xf32>) -> tensor<2x2xf32>
// CHECK-LABEL: test_slice_inferable_size
func.func @test_slice_inferable_size(%arg0: tensor<4x4xf32>) -> tensor<?x2xf32> {
- %start = tosa.const_shape { values = dense<[0, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %size = tosa.const_shape { values = dense<[-1, 2]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %start = tosa.const_shape values(dense<[0, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %size = tosa.const_shape values(dense<[-1, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.slice' op size input contains inferable dimension (-1) which does not conform to the TOSA specification}}
%0 = tosa.slice %arg0, %start, %size : (tensor<4x4xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x2xf32>
return %0 : tensor<?x2xf32>
@@ -120,7 +120,7 @@ func.func @test_slice_inferable_size(%arg0: tensor<4x4xf32>) -> tensor<?x2xf32>
// CHECK-LABEL: test_mul_negative_shift
func.func @test_mul_negative_shift(%arg0: tensor<1x8x8x8xi32>, %arg1: tensor<1x8x8x8xi32>) -> tensor<1x8x8x8xi32> {
- %shift = "tosa.const" () { values = dense<-1> : tensor<1xi8> } : () -> tensor<1xi8>
+ %shift = "tosa.const" () <{ values = dense<-1> : tensor<1xi8> }> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.mul' op requires 0 <= shift && shift <= 63, but got: -1}}
%mul = tosa.mul %arg0, %arg1, %shift : (tensor<1x8x8x8xi32>, tensor<1x8x8x8xi32>, tensor<1xi8>) -> tensor<1x8x8x8xi32>
return %mul : tensor<1x8x8x8xi32>
@@ -130,7 +130,7 @@ func.func @test_mul_negative_shift(%arg0: tensor<1x8x8x8xi32>, %arg1: tensor<1x8
// CHECK-LABEL: test_mul_too_big_shift
func.func @test_mul_too_big_shift(%arg0: tensor<1x8x8x8xi32>, %arg1: tensor<1x8x8x8xi32>) -> tensor<1x8x8x8xi32> {
- %shift = "tosa.const" () { values = dense<64> : tensor<1xi8> } : () -> tensor<1xi8>
+ %shift = "tosa.const" () <{ values = dense<64> : tensor<1xi8> }> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.mul' op requires 0 <= shift && shift <= 63, but got: 64}}
%mul = tosa.mul %arg0, %arg1, %shift : (tensor<1x8x8x8xi32>, tensor<1x8x8x8xi32>, tensor<1xi8>) -> tensor<1x8x8x8xi32>
return %mul : tensor<1x8x8x8xi32>
@@ -140,7 +140,7 @@ func.func @test_mul_too_big_shift(%arg0: tensor<1x8x8x8xi32>, %arg1: tensor<1x8x
// CHECK-LABEL: test_mul_non_zero_shift
func.func @test_mul_non_zero_shift(%arg0: tensor<1x8x8x8xi16>, %arg1: tensor<1x8x8x8xi16>) -> tensor<1x8x8x8xi32> {
- %shift = "tosa.const" () { values = dense<1> : tensor<1xi8> } : () -> tensor<1xi8>
+ %shift = "tosa.const" () <{ values = dense<1> : tensor<1xi8> }> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.mul' op requires shift = 0 for all input data types that are not int32_t, but got: 1}}
%mul = tosa.mul %arg0, %arg1, %shift : (tensor<1x8x8x8xi16>, tensor<1x8x8x8xi16>, tensor<1xi8>) -> tensor<1x8x8x8xi32>
return %mul : tensor<1x8x8x8xi32>
@@ -165,96 +165,96 @@ func.func @test_i8_table_size(%arg0: tensor<2x64xi8>, %arg1: tensor<513xi8>) ->
// -----
// CHECK-LABEL: test_error_scale32_with_i48
func.func @test_error_scale32_with_i48(%arg0: tensor<1xi48>) -> tensor<1xi8> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi48>} : () -> tensor<1xi48>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi48>}> : () -> tensor<1xi48>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op scale32 is not allowed with 48-bit input}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = true, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<1xi48>, tensor<1xi32>, tensor<1xi8>, tensor<1xi48>, tensor<1xi8>) -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<1xi48>, tensor<1xi32>, tensor<1xi8>, tensor<1xi48>, tensor<1xi8>) -> tensor<1xi8>
return %0 : tensor<1xi8>
}
// -----
// CHECK-LABEL: test_error_input_output_unsigned
func.func @test_error_input_output_unsigned(%arg0: tensor<1xi8>) -> tensor<1xi16> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op input and output cannot be both unsigned}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = true, output_unsigned = true} : (tensor<1xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi16>) -> tensor<1xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(true) : (tensor<1xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi16>) -> tensor<1xi16>
return %0 : tensor<1xi16>
}
// -----
// CHECK-LABEL: test_error_i32_output_unsigned_input
func.func @test_error_i32_output_unsigned_input(%arg0: tensor<1xi8>) -> tensor<1xi32> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.rescale' op i32 output type is not allowed with unsigned input}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = true, output_unsigned = false} : (tensor<1xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi32>) -> tensor<1xi32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<1xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi32>) -> tensor<1xi32>
return %0 : tensor<1xi32>
}
// -----
// CHECK-LABEL: test_error_i32_input_unsigned_output
func.func @test_error_i32_input_unsigned_output(%arg0: tensor<1xi32>) -> tensor<1xi8> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op i32 input type is not allowed with unsigned output}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<1xi32>, tensor<1xi16>, tensor<1xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<1xi32>, tensor<1xi16>, tensor<1xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<1xi8>
return %0 : tensor<1xi8>
}
// -----
// CHECK-LABEL: test_error_i48_input_unsigned_output
func.func @test_error_i48_input_unsigned_output(%arg0: tensor<1xi48>) -> tensor<1xi8> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi48>} : () -> tensor<1xi48>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi48>}> : () -> tensor<1xi48>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op i48 input type is not allowed with unsigned output}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<1xi48>, tensor<1xi16>, tensor<1xi8>, tensor<1xi48>, tensor<1xi8>) -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<1xi48>, tensor<1xi16>, tensor<1xi8>, tensor<1xi48>, tensor<1xi8>) -> tensor<1xi8>
return %0 : tensor<1xi8>
}
// -----
// CHECK-LABEL: test_error_i48_unsigned_input
func.func @test_error_i48_input_unsigned_output(%arg0: tensor<1xi48>) -> tensor<1xi8> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi48>} : () -> tensor<1xi48>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi48>}> : () -> tensor<1xi48>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op i48 input type cannot be unsigned}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = true, output_unsigned = false} : (tensor<1xi48>, tensor<1xi16>, tensor<1xi8>, tensor<1xi48>, tensor<1xi8>) -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<1xi48>, tensor<1xi16>, tensor<1xi8>, tensor<1xi48>, tensor<1xi8>) -> tensor<1xi8>
return %0 : tensor<1xi8>
}
// -----
// CHECK-LABEL: test_error_i32_unsigned_input
func.func @test_error_i32_input_unsigned_output(%arg0: tensor<1xi32>) -> tensor<1xi8> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op i32 input type cannot be unsigned}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = true, output_unsigned = false} : (tensor<1xi32>, tensor<1xi16>, tensor<1xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<1xi32>, tensor<1xi16>, tensor<1xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<1xi8>
return %0 : tensor<1xi8>
}
// -----
// CHECK-LABEL: test_error_i32_unsigned_output
func.func @test_error_i32_unsigned_output(%arg0: tensor<1xi8>) -> tensor<1xi32> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.rescale' op i32 output type cannot be unsigned}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<1xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi32>) -> tensor<1xi32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<1xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi32>) -> tensor<1xi32>
return %0 : tensor<1xi32>
}
@@ -302,7 +302,7 @@ func.func @test_cond_if_simplified_form_not_isolated_from_above(%arg0: tensor<f3
// -----
func.func @test_while_loop_cond_not_isolated_from_above(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<f32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op is not conformant to the TOSA specification. It requires the 'cond' region is isolated from above.}}
%1 = "tosa.while_loop"(%0) ({
^bb0(%arg3: tensor<i32>):
@@ -311,7 +311,7 @@ func.func @test_while_loop_cond_not_isolated_from_above(%arg0: tensor<i32>, %arg
tosa.yield %3 : tensor<i1>
}, {
^bb0(%arg3: tensor<i32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%3 = "tosa.add"(%arg3, %2) : (tensor<i32>, tensor<i32>) -> tensor<i32>
tosa.yield %3 : tensor<i32>
}) : (tensor<i32>) -> (tensor<i32>)
@@ -321,11 +321,11 @@ func.func @test_while_loop_cond_not_isolated_from_above(%arg0: tensor<i32>, %arg
// -----
func.func @test_while_loop_body_not_isolated_from_above(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<f32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op is not conformant to the TOSA specification. It requires the 'body' region is isolated from above.}}
%1 = "tosa.while_loop"(%0) ({
^bb0(%arg3: tensor<i32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%3 = "tosa.greater_equal"(%arg3, %2) : (tensor<i32>, tensor<i32>) -> tensor<i1>
%4 = "tosa.logical_not"(%3) : (tensor<i1>) -> tensor<i1>
tosa.yield %4 : tensor<i1>
diff --git a/mlir/test/Dialect/Tosa/inlining.mlir b/mlir/test/Dialect/Tosa/inlining.mlir
index c97441a501660..72ae29a372a5e 100644
--- a/mlir/test/Dialect/Tosa/inlining.mlir
+++ b/mlir/test/Dialect/Tosa/inlining.mlir
@@ -47,7 +47,7 @@ func.func @inlined_while_fn(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tenso
}
func.func private @while_body_50(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<10xi32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<10xi32>) {
%1 = "tosa.add"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
- %4 = "tosa.const_shape"() {values = dense<1> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %4 = "tosa.const_shape"() <{values = dense<1> : tensor<1xindex>}> : () -> !tosa.shape<1>
%3 = "tosa.reshape"(%1, %4) : (tensor<i32>, !tosa.shape<1>) -> tensor<1xi32>
%2 = "tosa.add"(%arg3, %3) : (tensor<10xi32>, tensor<1xi32>) -> tensor<10xi32>
return %1, %arg1, %arg2, %2: tensor<i32>, tensor<i32>, tensor<i32>, tensor<10xi32>
diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
index 19fb6390365a9..4457505952063 100644
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -14,9 +14,19 @@ func.func @test_cast(%arg0: tensor<i1>) -> tensor<5xi32> {
}
// -----
+
+func.func @test_bool_property_type(%arg0: tensor<1xi32>,
+ %arg1: tensor<1xi32>) {
+ // expected-error at +1 {{invalid kind of attribute specified}}
+ %0 = tosa.arithmetic_right_shift %arg0, %arg1 round("false") : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
+ return
+}
+
+// -----
+
func.func @test_const() -> tensor<1xf32> {
// expected-error at +1{{'tosa.const' op expected same attr/result element types}}
- %0 = "tosa.const"() {values = dense<1> : tensor<1xi32>} : () -> tensor<1xf32>
+ %0 = "tosa.const"() <{values = dense<1> : tensor<1xi32>}> : () -> tensor<1xf32>
return %0 : tensor<1xf32>
}
@@ -24,7 +34,7 @@ func.func @test_const() -> tensor<1xf32> {
func.func @test_const_non_tensor_attr() {
// expected-error at +1{{tosa.const' op expected tensors for attr/result type}}
- %0 = "tosa.const"() {values = dense<1.0> : vector<f32>} : () -> tensor<f32>
+ %0 = "tosa.const"() <{values = dense<1.0> : vector<f32>}> : () -> tensor<f32>
return
}
@@ -34,7 +44,7 @@ func.func @test_conv2d(%arg0: tensor<*xf32>, %arg1: tensor<16x3x3x4xi8>, %arg2:
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op expect both input and weight to be float or not together, got 'f32' and 'i8'}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32)
: (tensor<*xf32>, tensor<16x3x3x4xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
}
@@ -42,9 +52,9 @@ func.func @test_conv2d(%arg0: tensor<*xf32>, %arg1: tensor<16x3x3x4xi8>, %arg2:
// -----
func.func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<*xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
- %zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op illegal: operation operand/result data types did not align with any profile or extension, got (i8,i8,i8,i8,i8,i32,i8), did you mean (i8,i8,i32,i8,i8,i32,i32)?}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32)
: (tensor<1x29x29x4xi8>, tensor<*xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
}
@@ -55,7 +65,7 @@ func.func @test_conv2d_input_zp(%arg0: tensor<1x29x29x4xf16>, %arg1: tensor<16x3
%input_zp = "tosa.const"() <{values = dense<-1.0> : tensor<1xf16>}> : () -> tensor<1xf16>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
// expected-error at +1 {{'tosa.conv2d' op input zero point must be zero for non-int8 integer types}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f16)
: (tensor<1x29x29x4xf16>, tensor<16x3x3x4xf16>, tensor<16xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x27x27x16xf16>
return %0 : tensor<1x27x27x16xf16>
}
@@ -66,7 +76,7 @@ func.func @test_conv2d_weight_zp(%arg0: tensor<1x29x29x4xf16>, %arg1: tensor<16x
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
%weight_zp = "tosa.const"() <{values = dense<-1.0> : tensor<1xf16>}> : () -> tensor<1xf16>
// expected-error at +1 {{'tosa.conv2d' op weight zero point must be zero for non-int8 integer types}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f16)
: (tensor<1x29x29x4xf16>, tensor<16x3x3x4xf16>, tensor<16xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x27x27x16xf16>
return %0 : tensor<1x27x27x16xf16>
}
@@ -75,7 +85,7 @@ func.func @test_conv2d_weight_zp(%arg0: tensor<1x29x29x4xf16>, %arg1: tensor<16x
func.func @test_transpose_conv2d_invalid_padding_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op expected out_pad_top > -KH, but got: out_pad_top=-3 and KH=1}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: -3, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([-3, 0, 0, 0]) stride([1, 1]) acc_type(f32) {out_shape = array<i64: 1, 32, 32, 16>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -83,7 +93,7 @@ func.func @test_transpose_conv2d_invalid_padding_top(%arg0: tensor<1x32x32x8xf32
func.func @test_transpose_conv2d_invalid_padding_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op expected out_pad_bottom > -KH, but got: out_pad_bottom=-1 and KH=1}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, -1, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, -1, 0, 0]) stride([1, 1]) acc_type(f32) {out_shape = array<i64: 1, 32, 32, 16>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -91,7 +101,7 @@ func.func @test_transpose_conv2d_invalid_padding_bottom(%arg0: tensor<1x32x32x8x
func.func @test_transpose_conv2d_invalid_padding_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op expected out_pad_left > -KW, but got: out_pad_left=-8 and KW=1}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, -8, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, -8, 0]) stride([1, 1]) acc_type(f32) {out_shape = array<i64: 1, 32, 32, 16>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -99,7 +109,7 @@ func.func @test_transpose_conv2d_invalid_padding_left(%arg0: tensor<1x32x32x8xf3
func.func @test_transpose_conv2d_invalid_padding_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op expected out_pad_right > -KW, but got: out_pad_right=-9 and KW=1}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, -9>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, -9]) stride([1, 1]) acc_type(f32) {out_shape = array<i64: 1, 32, 32, 16>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -107,7 +117,7 @@ func.func @test_transpose_conv2d_invalid_padding_right(%arg0: tensor<1x32x32x8xf
func.func @test_transpose_conv2d_invalid_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op expect all stride values to be >= 1, got [0, 1]}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 0, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([0, 1]) acc_type(f32) {out_shape = array<i64: 1, 32, 32, 16>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -115,7 +125,7 @@ func.func @test_transpose_conv2d_invalid_stride_y(%arg0: tensor<1x32x32x8xf32>,
func.func @test_transpose_conv2d_invalid_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op expect all stride values to be >= 1, got [1, 0]}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 0>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 0]) acc_type(f32) {out_shape = array<i64: 1, 32, 32, 16>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -123,7 +133,7 @@ func.func @test_transpose_conv2d_invalid_stride_x(%arg0: tensor<1x32x32x8xf32>,
func.func @test_transpose_conv2d_invalid_output_height(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x33x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op dimension mismatch: expected OH == (IH - 1) * stride_y + out_pad_top + out_pad_bottom + KH, but got 33 != (32 - 1) * 1 + 0 + 0 + 1}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 33, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x33x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) {out_shape = array<i64: 1, 33, 32, 16>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x33x32x16xf32>
return %0 : tensor<1x33x32x16xf32>
}
@@ -131,7 +141,7 @@ func.func @test_transpose_conv2d_invalid_output_height(%arg0: tensor<1x32x32x8xf
func.func @test_transpose_conv2d_invalid_output_width(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x40x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op dimension mismatch: expected OW == (IW - 1) * stride_x + out_pad_left + out_pad_right + KW, but got 40 != (32 - 1) * 1 + 0 + 0 + 1}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 40, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x40x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) {out_shape = array<i64: 1, 32, 40, 16>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x40x16xf32>
return %0 : tensor<1x32x40x16xf32>
}
@@ -139,16 +149,16 @@ func.func @test_transpose_conv2d_invalid_output_width(%arg0: tensor<1x32x32x8xf3
func.func @test_transpose_conv2d_invalid_bias(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op bias channels expected to be equal to output channels (16) or 1, got 5}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) {out_shape = array<i64: 1, 32, 32, 16>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
// CHECK-LABEL: conv2d_quant_any
func.func @test_conv2d_quant_any(%arg0: tensor<1x4x4x4x!quant.any<i8<-8:7>>>, %arg1: tensor<8x1x1x4x!quant.any<i8<-8:7>>>, %arg2: tensor<8x!quant.any<i32<-8:7>>>) -> tensor<1x4x4x8x!quant.any<i32<-8:7>>> {
- %zp = "tosa.const" () { values = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
+ %zp = "tosa.const" () <{ values = dense<0> : tensor<1xi8> }> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op is not profile-aligned: element type '!quant.any<i8<-8:7>>'}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4x!quant.any<i8<-8:7>>>, tensor<8x1x1x4x!quant.any<i8<-8:7>>>, tensor<8x!quant.any<i32<-8:7>>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8x!quant.any<i32<-8:7>>>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) local_bound(true) : (tensor<1x4x4x4x!quant.any<i8<-8:7>>>, tensor<8x1x1x4x!quant.any<i8<-8:7>>>, tensor<8x!quant.any<i32<-8:7>>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8x!quant.any<i32<-8:7>>>
return %0 : tensor<1x4x4x8x!quant.any<i32<-8:7>>>
}
@@ -156,14 +166,14 @@ func.func @test_conv2d_quant_any(%arg0: tensor<1x4x4x4x!quant.any<i8<-8:7>>>, %a
func.func @test_concat(%arg0 : tensor<2x1xf32>, %arg1 : tensor<2x2xf32>) -> tensor<?x?xf32> {
// expected-error at +1 {{'tosa.concat' op expect all operand shapes to have the same sizes on non-axis dimensions, but got 2 vs 1 at index 1 on operands 0 and 1}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<2x1xf32>, tensor<2x2xf32>) -> tensor<?x?xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<2x1xf32>, tensor<2x2xf32>) -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
// -----
func.func @test_pad_padding_non_const(%arg0: tensor<13x21x3xf32>, %arg1: !tosa.shape<6>) -> tensor<13x21x3xf32> {
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.pad' op shape operand is not compile time resolvable}}
%0 = tosa.pad %arg0, %arg1, %pad_const : (tensor<13x21x3xf32>, !tosa.shape<6>, tensor<1xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
@@ -172,7 +182,7 @@ func.func @test_pad_padding_non_const(%arg0: tensor<13x21x3xf32>, %arg1: !tosa.s
// -----
func.func @test_pad_const_non_const(%arg0: tensor<13x21x3xi8>, %arg1: tensor<1xi8>) -> tensor<13x22x4xi8> {
- %0 = tosa.const_shape {values = dense<[0, 0, 0, 1, 0, 1]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %0 = tosa.const_shape values(dense<[0, 0, 0, 1, 0, 1]> : tensor<6xindex>) : () -> !tosa.shape<6>
// expected-error at +1 {{'tosa.pad' op expected compile time resolvable constant, but got variable value for operand #2}}
%1 = tosa.pad %arg0, %0, %arg1 : (tensor<13x21x3xi8>, !tosa.shape<6>, tensor<1xi8>) -> tensor<13x22x4xi8>
return %1 : tensor<13x22x4xi8>
@@ -181,8 +191,8 @@ func.func @test_pad_const_non_const(%arg0: tensor<13x21x3xi8>, %arg1: tensor<1xi
// -----
func.func @test_pad_io_rank_mismatch(%arg0: tensor<13x21xf32>) {
- %0 = tosa.const_shape {values = dense<1> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %0 = tosa.const_shape values(dense<1> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.pad' op input rank (2) does not match output rank (3)}}
%1 = tosa.pad %arg0, %0, %pad_const : (tensor<13x21xf32>, !tosa.shape<4>, tensor<1xf32>) -> tensor<13x21x3xf32>
}
@@ -191,7 +201,7 @@ func.func @test_pad_io_rank_mismatch(%arg0: tensor<13x21xf32>) {
func.func @test_concat_input_rank_mismatch(%arg0: tensor<1x2x3xf32>, %arg1: tensor<1x2xf32>) -> tensor<2x2x3xf32> {
// expected-error at +1 {{'tosa.concat' op expect all operands to have the same rank, but got 3 vs 2 on operands 0 and 1}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<1x2x3xf32>, tensor<1x2xf32>) -> tensor<2x2x3xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<1x2x3xf32>, tensor<1x2xf32>) -> tensor<2x2x3xf32>
return %0 : tensor<2x2x3xf32>
}
@@ -199,14 +209,14 @@ func.func @test_concat_input_rank_mismatch(%arg0: tensor<1x2x3xf32>, %arg1: tens
func.func @test_concat_input_output_rank_mismatch(%arg0: tensor<2x2xf32>, %arg1: tensor<2x1xf32>) -> tensor<2xf32> {
// expected-error at +1 {{'tosa.concat' op expect output rank to match inputs rank, got 1 vs 2}}
- %0 = tosa.concat %arg0, %arg1 {axis = 1 : i32} : (tensor<2x2xf32>, tensor<2x1xf32>) -> tensor<2xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(1) : (tensor<2x2xf32>, tensor<2x1xf32>) -> tensor<2xf32>
return %0 : tensor<2xf32>
}
// -----
func.func @test_pad_invalid_padConst_rank(%arg0: tensor<13x21xf32>) {
- %0 = tosa.const_shape {values = dense<1> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %0 = tosa.const_shape values(dense<1> : tensor<4xindex>) : () -> !tosa.shape<4>
%1 = "tosa.const"() {values = dense<3.14> : tensor<2xf32>} : () -> tensor<2xf32>
// expected-error at +1 {{'tosa.pad' op operand #2 must be tosa-conformant scalar tensor of number values, but got 'tensor<2xf32>'}}
%2 = tosa.pad %arg0, %0, %1 : (tensor<13x21xf32>, !tosa.shape<4>, tensor<2xf32>) -> tensor<13x21xf32>
@@ -216,8 +226,8 @@ func.func @test_pad_invalid_padConst_rank(%arg0: tensor<13x21xf32>) {
// -----
func.func @test_pad_invalid_padding_value(%arg0: tensor<10xf32>) {
- %0 = tosa.const_shape {values = dense<[-1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %0 = tosa.const_shape values(dense<[-1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{padding value must all be non-negative, got -1}}
%2 = tosa.pad %arg0, %0, %1 : (tensor<10xf32>, !tosa.shape<2>, tensor<1xf32>) -> tensor<10xf32>
return
@@ -228,7 +238,7 @@ func.func @test_pad_invalid_padding_value(%arg0: tensor<10xf32>) {
func.func @test_reduce_sum_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
// expected-error at +2 {{failed to infer returned types}}
// expected-error at +1 {{'tosa.reduce_sum' op inferred type(s) 'tensor<1x3x4x5xf32>' are incompatible with return type(s) of operation 'tensor<1x3x4x5xi32>'}}
- %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<2x3x4x5xf32>) -> tensor<1x3x4x5xi32>
+ %0 = tosa.reduce_sum %arg0 axis(0) : (tensor<2x3x4x5xf32>) -> tensor<1x3x4x5xi32>
return
}
@@ -237,7 +247,7 @@ func.func @test_reduce_sum_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
func.func @test_reduce_max_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
// expected-error at +2 {{failed to infer returned types}}
// expected-error at +1 {{'tosa.reduce_max' op inferred type(s) 'tensor<2x3x4x1xf32>' are incompatible with return type(s) of operation 'tensor<2x3x4x1xi32>'}}
- %0 = tosa.reduce_max %arg0 {axis = 3 : i32} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x1xi32>
+ %0 = tosa.reduce_max %arg0 axis(3) : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x1xi32>
return
}
@@ -246,7 +256,7 @@ func.func @test_reduce_max_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
func.func @test_reduce_min_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
// expected-error at +2 {{failed to infer returned types}}
// expected-error at +1 {{'tosa.reduce_min' op inferred type(s) 'tensor<2x1x4x5xf32>' are incompatible with return type(s) of operation 'tensor<2x1x4x5xi32>'}}
- %0 = tosa.reduce_min %arg0 {axis = 1 : i32} : (tensor<2x3x4x5xf32>) -> tensor<2x1x4x5xi32>
+ %0 = tosa.reduce_min %arg0 axis(1) : (tensor<2x3x4x5xf32>) -> tensor<2x1x4x5xi32>
return
}
@@ -254,7 +264,7 @@ func.func @test_reduce_min_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
func.func @test_reduce_prod_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
// expected-error at +1 {{'tosa.reduce_product' op expect reduced dimension size to be 1, got 3}}
- %0 = tosa.reduce_product %arg0 {axis = 1 : i32} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32>
+ %0 = tosa.reduce_product %arg0 axis(1) : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32>
return
}
@@ -262,7 +272,7 @@ func.func @test_reduce_prod_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
func.func @test_reduce_all_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
// expected-error at +1 {{'tosa.reduce_all' op expect input tensor rank (3) to be larger than reduce axis (3)}}
- %0 = tosa.reduce_all %arg0 {axis = 3 : i32} : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
+ %0 = tosa.reduce_all %arg0 axis(3) : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
return
}
@@ -270,7 +280,7 @@ func.func @test_reduce_all_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
func.func @test_reduce_any_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
// expected-error at +1 {{'tosa.reduce_any' op expect input tensor rank (3) to be larger than reduce axis (3)}}
- %0 = tosa.reduce_any %arg0 {axis = 3 : i32} : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
+ %0 = tosa.reduce_any %arg0 axis(3) : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
return
}
@@ -278,7 +288,7 @@ func.func @test_reduce_any_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
func.func @test_reduce_max_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
// expected-error at +1 {{'tosa.reduce_max' op expect input tensor rank (3) to be larger than reduce axis (3)}}
- %0 = tosa.reduce_max %arg0 {axis = 3 : i32} : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
+ %0 = tosa.reduce_max %arg0 axis(3) : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
return
}
@@ -286,7 +296,7 @@ func.func @test_reduce_max_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
func.func @test_reduce_min_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
// expected-error at +1 {{'tosa.reduce_min' op expect input tensor rank (3) to be larger than reduce axis (3)}}
- %0 = tosa.reduce_min %arg0 {axis = 3 : i32} : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
+ %0 = tosa.reduce_min %arg0 axis(3) : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
return
}
@@ -294,7 +304,7 @@ func.func @test_reduce_min_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
func.func @test_reduce_prod_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
// expected-error at +1 {{'tosa.reduce_product' op expect input tensor rank (3) to be larger than reduce axis (3)}}
- %0 = tosa.reduce_product %arg0 {axis = 3 : i32} : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
+ %0 = tosa.reduce_product %arg0 axis(3) : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
return
}
@@ -302,7 +312,7 @@ func.func @test_reduce_prod_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
func.func @test_reduce_sum_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
// expected-error at +1 {{'tosa.reduce_sum' op expect input tensor rank (3) to be larger than reduce axis (3)}}
- %0 = tosa.reduce_sum %arg0 {axis = 3 : i32} : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
+ %0 = tosa.reduce_sum %arg0 axis(3) : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
return
}
@@ -310,14 +320,14 @@ func.func @test_reduce_sum_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
func.func @test_reduce_min_invalid_output_rank(%arg0 : tensor<1xi32>) -> () {
// expected-error at +1 {{'tosa.reduce_min' op expect output tensor rank to be equal to input tensor rank}}
- %0 = tosa.reduce_min %arg0 {axis = 0 : i32} : (tensor<1xi32>) -> tensor<1x10xi32>
+ %0 = tosa.reduce_min %arg0 axis(0) : (tensor<1xi32>) -> tensor<1x10xi32>
return
}
// -----
func.func @test_reshape_type_mismatch(%arg0 : tensor<13x21x3xf32>) -> () {
- %1 = tosa.const_shape {values = dense<[13, 21, 3, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %1 = tosa.const_shape values(dense<[13, 21, 3, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.reshape' op expect input and output to have same element type, got 'f32' and 'i32'}}
%0 = tosa.reshape %arg0, %1 : (tensor<13x21x3xf32>, !tosa.shape<4>) -> tensor<13x21x3x1xi32>
return
@@ -326,7 +336,7 @@ func.func @test_reshape_type_mismatch(%arg0 : tensor<13x21x3xf32>) -> () {
// -----
func.func @test_reshape_static_zero_dim_input(%arg0 : tensor<13x0x3xf32>) -> () {
- %s = tosa.const_shape {values = dense<[13, 21, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[13, 21, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.reshape' op operand #0 must be tosa-conformant tensor of number values, but got 'tensor<13x0x3xf32>'}}
%0 = "tosa.reshape"(%arg0, %s) : (tensor<13x0x3xf32>, !tosa.shape<3>) -> tensor<13x0x3xf32>
return
@@ -335,7 +345,7 @@ func.func @test_reshape_static_zero_dim_input(%arg0 : tensor<13x0x3xf32>) -> ()
// -----
func.func @test_reshape_zero_dim_input(%arg0 : tensor<?x0x3xf32>) -> () {
- %s = tosa.const_shape {values = dense<[13, 21, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[13, 21, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.reshape' op operand #0 must be tosa-conformant tensor of number values, but got 'tensor<?x0x3xf32>'}}
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?x0x3xf32>, !tosa.shape<3>) -> tensor<13x0x3xf32>
return
@@ -344,7 +354,7 @@ func.func @test_reshape_zero_dim_input(%arg0 : tensor<?x0x3xf32>) -> () {
// -----
func.func @test_reshape_rank_mismatch(%arg0 : tensor<?xf32>) -> () {
- %s = tosa.const_shape {values = dense<[2, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op new shape does not match result rank}}
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?xf32>, !tosa.shape<2>) -> tensor<?xf32>
return
@@ -353,7 +363,7 @@ func.func @test_reshape_rank_mismatch(%arg0 : tensor<?xf32>) -> () {
// -----
func.func @test_reshape_inconsistent_result_type(%arg0 : tensor<?xf32>) -> () {
- %s = tosa.const_shape {values = dense<[2, 4, -1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[2, 4, -1]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.reshape' op new shape is inconsistent with result shape}}
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?xf32>, !tosa.shape<3>) -> tensor<?x3x5xf32>
return
@@ -362,7 +372,7 @@ func.func @test_reshape_inconsistent_result_type(%arg0 : tensor<?xf32>) -> () {
// -----
func.func @test_reshape_invalid_size(%arg0 : tensor<2x4xf32>) -> () {
- %s = tosa.const_shape {values = dense<[3, 5]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[3, 5]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op cannot reshape 8 elements into 15}}
%0 = "tosa.reshape"(%arg0, %s) : (tensor<2x4xf32>, !tosa.shape<2>) -> tensor<3x5xf32>
return
@@ -371,7 +381,7 @@ func.func @test_reshape_invalid_size(%arg0 : tensor<2x4xf32>) -> () {
// -----
func.func @test_reshape_invalid_newshape(%arg0 : tensor<1xf32>) -> () {
- %s = tosa.const_shape {values = dense<[-1, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[-1, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op cannot reshape 1 elements into 4}}
%0 = "tosa.reshape"(%arg0, %s) : (tensor<1xf32>, !tosa.shape<2>) -> tensor<?x4xf32>
return
@@ -380,7 +390,7 @@ func.func @test_reshape_invalid_newshape(%arg0 : tensor<1xf32>) -> () {
// -----
func.func @test_reshape_invalid_newshape(%arg0 : tensor<8xf32>) -> () {
- %s = tosa.const_shape {values = dense<[1, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[1, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op cannot reshape 8 elements into 4}}
%0 = "tosa.reshape"(%arg0, %s) : (tensor<8xf32>, !tosa.shape<2>) -> tensor<?x4xf32>
return
@@ -389,7 +399,7 @@ func.func @test_reshape_invalid_newshape(%arg0 : tensor<8xf32>) -> () {
// -----
func.func @test_reshape_invalid_placeholders(%arg0 : tensor<?xf32>) -> () {
- %s = tosa.const_shape {values = dense<[2, -1, -1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[2, -1, -1]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.reshape' op expected at most one target dimension to be -1}}
%0 = "tosa.reshape"(%arg0, %s) : (tensor<?xf32>, !tosa.shape<3>) -> tensor<2x?x?xf32>
return
@@ -398,7 +408,7 @@ func.func @test_reshape_invalid_placeholders(%arg0 : tensor<?xf32>) -> () {
// -----
func.func @test_reshape_invalid_tensor_dim(%arg0 : tensor<4x?xf32>) -> () {
- %s = tosa.const_shape {values = dense<[-2, -1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[-2, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op new shape has invalid tensor dimension size -2}}
%0 = "tosa.reshape" (%arg0, %s) : (tensor<4x?xf32>, !tosa.shape<2>) -> tensor<?x4xf32>
return
@@ -408,14 +418,14 @@ func.func @test_reshape_invalid_tensor_dim(%arg0 : tensor<4x?xf32>) -> () {
func.func @test_reverse_axis_out_of_range(%arg0 : tensor<13x21x3xf32>) -> () {
// expected-error at +1 {{'tosa.reverse' op expect input tensor rank (3) to be larger than reverse axis (5)}}
- %0 = tosa.reverse %arg0 {axis = 5 : i32} : (tensor<13x21x3xf32>) -> tensor<?x?x?xf32>
+ %0 = tosa.reverse %arg0 axis(5) : (tensor<13x21x3xf32>) -> tensor<?x?x?xf32>
return
}
// -----
func.func @test_reshape_zero_dim_input(%arg0 : tensor<?x0x3xf32>) -> () {
- %1 = tosa.const_shape {values = dense<[13, 21, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %1 = tosa.const_shape values(dense<[13, 21, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.reshape' op operand #0 must be tosa-conformant tensor of number values, but got 'tensor<?x0x3xf32>'}}
%0 = "tosa.reshape"(%arg0, %1) : (tensor<?x0x3xf32>, !tosa.shape<3>) -> tensor<13x0x3xf32>
return
@@ -425,7 +435,7 @@ func.func @test_reshape_zero_dim_input(%arg0 : tensor<?x0x3xf32>) -> () {
func.func @test_const_attribute_type_mismatch() -> tensor<100x100xf32> {
// expected-error at +1 {{'tosa.const' op failed to verify that all of {values, output} have same shape}}
- %0 = "tosa.const"() {values = dense<0.000000e+00> : tensor<1x1xf32>} : () -> tensor<100x100xf32>
+ %0 = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1x1xf32>}> : () -> tensor<100x100xf32>
return %0 : tensor<100x100xf32>
}
@@ -435,7 +445,7 @@ func.func @test_conv2d_static_zero_dim_input(%arg0: tensor<1x29x0x4xf32>, %arg1:
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
%weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.conv2d' op operand #0 must be 4D tosa-conformant tensor of number values, but got 'tensor<1x29x0x4xf32>'}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32)
: (tensor<1x29x0x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1x27x27x16xf32>
return %0 : tensor<1x27x27x16xf32>
}
@@ -446,7 +456,7 @@ func.func @test_conv2d_zero_dim_input(%arg0: tensor<1x?x0x4xf32>, %arg1: tensor<
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
%weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.conv2d' op operand #0 must be 4D tosa-conformant tensor of number values, but got 'tensor<1x?x0x4xf32>'}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32)
: (tensor<1x?x0x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1x27x27x16xf32>
return %0 : tensor<1x27x27x16xf32>
}
@@ -533,7 +543,7 @@ module {
func.func @test_tile_invalid_multiples() {
%0 = tensor.empty() : tensor<4x31x31xf32>
- %cst = tosa.const_shape { values = dense<1> : tensor<1xindex> } : () -> !tosa.shape<1>
+ %cst = tosa.const_shape values(dense<1> : tensor<1xindex>) : () -> !tosa.shape<1>
// expected-error at +1 {{'tosa.tile' op expect 'multiples' to have rank 3 but got 1.}}
%1 = tosa.tile %0, %cst: (tensor<4x31x31xf32>, !tosa.shape<1>) -> tensor<4x31x31xf32>
return
@@ -543,7 +553,7 @@ func.func @test_tile_invalid_multiples() {
func.func @test_tile_invalid_multiples_value() {
%0 = tensor.empty() : tensor<4x31xf32>
- %multiples = tosa.const_shape { values = dense<[2, -2]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %multiples = tosa.const_shape values(dense<[2, -2]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.tile' op expect element of 'multiples' to be positive integer or -1.}}
%1 = tosa.tile %0, %multiples : (tensor<4x31xf32>, !tosa.shape<2>) -> tensor<4x31xf32>
return
@@ -553,7 +563,7 @@ func.func @test_tile_invalid_multiples_value() {
func.func @test_tile_io_rank_mismatch() {
%0 = tensor.empty() : tensor<4x31xf32>
- %multiples = tosa.const_shape { values = dense<[2, 2]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %multiples = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.tile' op input rank (2) does not match output rank (3)}}
%1 = tosa.tile %0, %multiples : (tensor<4x31xf32>, !tosa.shape<2>) -> tensor<4x31x31xf32>
return
@@ -591,7 +601,7 @@ func.func @test_table_io_shape_mismatch(%arg0: tensor<?x16xi16>, %arg1: tensor<6
// CHECK-LABEL: test_mul_type_mismatch
func.func @test_mul_type_mismatch(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf16>) -> tensor<13x21x3xf32> {
- %shift = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.mul' op requires the same element type for all operands}}
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<13x21x3xf32>, tensor<13x1x3xf16>, tensor<1xi8>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
@@ -611,7 +621,7 @@ func.func @test_mul_int_type_mismatch(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>
// CHECK-LABEL: test_mul_invalid_shift
func.func @test_mul_invalid_shift(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf32>) -> tensor<13x21x3xf32> {
- %shift = "tosa.const"() {values = dense<1> : tensor<1xi8>} : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<1> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.mul' op require shift to be 0 for float type}}
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<13x21x3xf32>, tensor<13x1x3xf32>, tensor<1xi8>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
@@ -631,7 +641,7 @@ func.func @test_mul_missing_shift(%arg0: tensor<13x21x3xi32>, %arg1: tensor<13x1
// CHECK-LABEL: test_mismatch_in_out_data_type_clamp
func.func @test_mismatch_in_out_data_type_clamp(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf16> {
// expected-error at +1 {{'tosa.clamp' op requires the same element type for all operands and results}}
- %0 = tosa.clamp %arg0 {min_val = 0.0 : f32, max_val = 1.0: f32} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf16>
+ %0 = tosa.clamp %arg0 min_val(0.0 : f32) max_val(1.0: f32) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf16>
return %0 : tensor<13x21x3xf16>
}
@@ -640,7 +650,7 @@ func.func @test_mismatch_in_out_data_type_clamp(%arg0: tensor<13x21x3xf32>) -> t
// CHECK-LABEL: test_mismatch_in_out_shape_clamp
func.func @test_mismatch_in_out_shape_clamp(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x1xf32> {
// expected-error at +1 {{'tosa.clamp' op requires the same shape for all operands and results}}
- %0 = tosa.clamp %arg0 {min_val = 0.0 : f32, max_val = 1.0: f32} : (tensor<13x21x3xf32>) -> tensor<13x21x1xf32>
+ %0 = tosa.clamp %arg0 min_val(0.0 : f32) max_val(1.0: f32) : (tensor<13x21x3xf32>) -> tensor<13x21x1xf32>
return %0 : tensor<13x21x1xf32>
}
@@ -649,7 +659,7 @@ func.func @test_mismatch_in_out_shape_clamp(%arg0: tensor<13x21x3xf32>) -> tenso
// CHECK-LABEL: test_unsupported_boolean_type_clamp
func.func @test_unsupported_boolean_type_clamp(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xi1> {
// expected-error at +1 {{'tosa.clamp' op illegal: operation operand/result data types did not align with any profile or extension, got (i1,i1), did you mean (i8,i8)?}}
- %0 = tosa.clamp %arg0 {min_val = false, max_val = true} : (tensor<13x21x3xi1>) -> tensor<13x21x3xi1>
+ %0 = tosa.clamp %arg0 min_val(false) max_val(true) : (tensor<13x21x3xi1>) -> tensor<13x21x3xi1>
return %0 : tensor<13x21x3xi1>
}
@@ -900,7 +910,7 @@ func.func @test_non_tosa_ops() {
// -----
func.func @test_pad_rank0_pad_const(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf8E5M2> {
- %padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %padding = tosa.const_shape values(dense<0> : tensor<6xindex>) : () -> !tosa.shape<6>
%cst = "tosa.const"() { values = dense<-0.0> : tensor<f8E4M3FN> } : () -> tensor<f8E4M3FN>
// expected-error at +1 {{'tosa.pad' op operand #2 must be tosa-conformant scalar tensor of number values, but got 'tensor<f8E4M3FN>'}}
%0 = tosa.pad %arg0, %padding, %cst : (tensor<13x21x3xf8E4M3FN>, !tosa.shape<6>, tensor<f8E4M3FN>) -> tensor<13x21x3xf8E5M2>
@@ -913,7 +923,7 @@ func.func @test_conv2d_rank0_zp(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<16x3x
%input_zp = "tosa.const"() <{values = dense<0> : tensor<i8>}> : () -> tensor<i8>
%weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op operand #3 must be tosa-conformant scalar tensor of unsigned integer or signless integer or floating-point values, but got 'tensor<i8>'}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32)
: (tensor<1x29x29x4xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>, tensor<i8>, tensor<1xi8>) -> tensor<1x27x27x16xi32>
return %0 : tensor<1x27x27x16xi32>
}
@@ -981,7 +991,7 @@ func.func @test_shape_type(%arg0: !tosa.shape<-1>) -> !tosa.shape<-1> {
func.func @test_const_shape() {
// expected-error at +1 {{'tosa.const_shape' op attribute 'values' failed to satisfy constraint: index elements attribute}}
- %cst = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xi32>} : () -> !tosa.shape<4>
+ %cst = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xi32>) : () -> !tosa.shape<4>
return
}
@@ -989,7 +999,7 @@ func.func @test_const_shape() {
func.func @test_const_shape_values() {
// expected-error at +1 {{'tosa.const_shape' op expect number of elements in attribute values (4) to be equal to the rank (5) for the result shape type}}
- %cst = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<5>
+ %cst = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<5>
return
}
@@ -997,7 +1007,7 @@ func.func @test_const_shape_values() {
func.func @test_const_shape_values() {
// expected-error at +1 {{'tosa.const_shape' op expect elements in attribute values with rank 1}}
- %cst = tosa.const_shape {values = dense<[[1, 2], [3, 4]]> : tensor<2x2xindex>} : () -> !tosa.shape<4>
+ %cst = tosa.const_shape values(dense<[[1, 2], [3, 4]]> : tensor<2x2xindex>) : () -> !tosa.shape<4>
return
}
@@ -1074,11 +1084,11 @@ func.func @test_mul_different_b_and_result_ranks(%arg0: tensor<*xf32>, %arg1: te
// -----
// CHECK-LABEL: test_resize_invalid_scale_values
func.func @test_resize_invalid_scale_values(%arg0: tensor<1x8x8x8xf32>) -> tensor<?x?x?x?xf32> {
- %scale = tosa.const_shape { values = dense<[2, 0, -1, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[2, 0, -1, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op expect all scale values to be > 0, got 2, 0, -1, 2}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
return %1 : tensor<?x?x?x?xf32>
}
@@ -1086,11 +1096,11 @@ func.func @test_resize_invalid_scale_values(%arg0: tensor<1x8x8x8xf32>) -> tenso
// CHECK-LABEL: test_resize_invalid_wholly_divisible_height
func.func @test_resize_invalid_wholly_divisible_height(%arg0: tensor<1x8x8x8xf32>) -> tensor<1x8x8x8xf32> {
- %scale = tosa.const_shape { values = dense<[1, 3, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[1, 3, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op expected (input_height - 1) * scale_y_n - offset_y + border_y to be wholly divisible by scale_y_d, got ((8 - 1) * 1 - 0 + 0) / 3}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x8x8x8xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x8x8x8xf32>
return %1 : tensor<1x8x8x8xf32>
}
@@ -1098,11 +1108,11 @@ func.func @test_resize_invalid_wholly_divisible_height(%arg0: tensor<1x8x8x8xf32
// CHECK-LABEL: test_resize_invalid_output_height
func.func @test_resize_invalid_output_height(%arg0: tensor<1x8x8x8xf32>) -> tensor<1x9x8x8xf32> {
- %scale = tosa.const_shape { values = dense<[2, 1, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[2, 1, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op calculated output height did not match expected: calculated=15, expected=9}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x9x8x8xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x9x8x8xf32>
return %1 : tensor<1x9x8x8xf32>
}
@@ -1110,11 +1120,11 @@ func.func @test_resize_invalid_output_height(%arg0: tensor<1x8x8x8xf32>) -> tens
// CHECK-LABEL: test_resize_invalid_wholly_divisible_width
func.func @test_resize_invalid_wholly_divisible_width(%arg0: tensor<1x8x8x8xf32>) -> tensor<1x8x8x8xf32> {
- %scale = tosa.const_shape { values = dense<[1, 1, 1, 3]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[1, 1, 1, 3]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op expected (input_width - 1) * scale_x_n - offset_x + border_x to be wholly divisible by scale_x_d, got ((8 - 1) * 1 - 0 + 0) / 3}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x8x8x8xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x8x8x8xf32>
return %1 : tensor<1x8x8x8xf32>
}
@@ -1122,33 +1132,33 @@ func.func @test_resize_invalid_wholly_divisible_width(%arg0: tensor<1x8x8x8xf32>
// CHECK-LABEL: test_resize_invalid_output_width
func.func @test_resize_invalid_output_width(%arg0: tensor<1x8x8x8xf32>) -> tensor<1x8x9x8xf32> {
- %scale = tosa.const_shape { values = dense<[1, 1, 2, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[1, 1, 2, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op calculated output width did not match expected: calculated=15, expected=9}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x8x9x8xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x8x8x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x8x9x8xf32>
return %1 : tensor<1x8x9x8xf32>
}
// -----
func.func @test_resize_mixed_fp8_types(%arg0: tensor<1x32x32x8xf8E4M3FN>) -> tensor<1x64x64x8xf8E5M2> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op illegal: operation operand/result data types did not align with any profile or extension, got (fp8e4m3,fp8e5m2)}}
- %0 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x32x32x8xf8E4M3FN>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E5M2>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x32x32x8xf8E4M3FN>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E5M2>
return %0 : tensor<1x64x64x8xf8E5M2>
}
// -----
func.func @test_resize_mixed_mxfp_types(%arg0: tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op illegal: operation operand/result data types did not align with any profile or extension, got (bs32_fp8e8m0_fp4e2m1,bs32_fp8e8m0_fp6e2m3)}}
- %0 = tosa.resize %arg0, %scale, %offset, %border { mode = NEAREST_NEIGHBOR } : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
return %0 : tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
}
@@ -1156,11 +1166,11 @@ func.func @test_resize_mixed_mxfp_types(%arg0: tensor<1x32x32x32x!tosa.block_sca
// CHECK-LABEL: broadcast_resize_nearest_f32
func.func @broadcast_resize_nearest_f32(%arg0 : tensor<3x1x1x7xf32>) -> tensor<3x1x5x7xf32> {
- %scale = tosa.const_shape { values = dense<[2, 1, 3, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[2, 1, 3, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op calculated output width did not match expected: calculated=1, expected=5}}
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<3x1x1x7xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x5x7xf32>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<3x1x1x7xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x1x5x7xf32>
return %resize : tensor<3x1x5x7xf32>
}
@@ -1169,11 +1179,11 @@ func.func @broadcast_resize_nearest_f32(%arg0 : tensor<3x1x1x7xf32>) -> tensor<3
// CHECK-LABEL: broadcast_resize_bilinear_i8
func.func @broadcast_resize_bilinear_i8(%arg0 : tensor<3x1x1x7xi8>) -> tensor<3x4x5x7xi32> {
- %scale = tosa.const_shape { values = dense<[2, 1, 3, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[2, 1, 3, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op calculated output height did not match expected: calculated=1, expected=4}}
- %resize = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<3x1x1x7xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x4x5x7xi32>
+ %resize = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<3x1x1x7xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<3x4x5x7xi32>
return %resize : tensor<3x4x5x7xi32>
}
@@ -1182,7 +1192,7 @@ func.func @broadcast_resize_bilinear_i8(%arg0 : tensor<3x1x1x7xi8>) -> tensor<3x
func.func @test_conv2d_invalid_padding(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.conv2d' op expect all padding values to be >= 0, got 0, 0, -1, 0}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, -1, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, -1, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -1191,7 +1201,7 @@ func.func @test_conv2d_invalid_padding(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor
func.func @test_conv2d_invalid_stride(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.conv2d' op expect all stride values to be >= 1, got 0, 1}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 0, 1>, local_bound = true}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([0, 1]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -1200,7 +1210,7 @@ func.func @test_conv2d_invalid_stride(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<
func.func @test_conv2d_invalid_dilation(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.conv2d' op expect all dilation values to be >= 1, got 1, 0}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 0>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 0]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -1209,7 +1219,7 @@ func.func @test_conv2d_invalid_dilation(%arg0: tensor<1x4x4x4xf32>, %arg1: tenso
func.func @test_conv2d_wholly_divisible_height(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.conv2d' op expected input_height - 1 + pad_top + pad_bottom - (kernel_height - 1) * dilation_y to be wholly divisible by stride_y, got (4 - 1 + 0 + 0 - (1 - 1) * 1) / 2}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 1>, local_bound = true}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([2, 1]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -1218,7 +1228,7 @@ func.func @test_conv2d_wholly_divisible_height(%arg0: tensor<1x4x4x4xf32>, %arg1
func.func @test_conv2d_wholly_divisible_width(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.conv2d' op expected input_width - 1 + pad_left + pad_right - (kernel_width - 1) * dilation_x to be wholly divisible by stride_x, got (4 - 1 + 0 + 0 - (1 - 1) * 1) / 2}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 2>, local_bound = true}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 2]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -1227,7 +1237,7 @@ func.func @test_conv2d_wholly_divisible_width(%arg0: tensor<1x4x4x4xf32>, %arg1:
func.func @test_conv2d_unexpected_output_height(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x6x4x8xf32> {
// expected-error at +1 {{'tosa.conv2d' op calculated output height did not match expected: calculated=4, expected=6}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x6x4x8xf32>
return %0 : tensor<1x6x4x8xf32>
}
@@ -1236,7 +1246,7 @@ func.func @test_conv2d_unexpected_output_height(%arg0: tensor<1x4x4x4xf32>, %arg
func.func @test_conv2d_unexpected_output_width(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x6x8xf32> {
// expected-error at +1 {{'tosa.conv2d' op calculated output width did not match expected: calculated=4, expected=6}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x6x8xf32>
return %0 : tensor<1x4x6x8xf32>
}
@@ -1245,7 +1255,7 @@ func.func @test_conv2d_unexpected_output_width(%arg0: tensor<1x4x4x4xf32>, %arg1
func.func @test_conv2d_invalid_bias_size(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<7xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.conv2d' op bias channels expected to be equal to output channels (8) or 1, got 7}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -1255,7 +1265,7 @@ func.func @test_conv2d_invalid_bias_size(%arg0: tensor<1x4x4x4xf32>, %arg1: tens
// CHECK-LABEL: test_avg_pool_input_zp_same_element_type
func.func @test_avg_pool_input_zp_same_element_type(%arg0: tensor<1x16x16x8xf16>, %arg1: tensor<1xi8>, %arg2: tensor<1xf16>) -> tensor<1x16x16x8xf16> {
// expected-error at +1 {{'tosa.avg_pool2d' op expect both input and its zero point are the same element type, got 'f16' and 'i8'}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}>
: (tensor<1x16x16x8xf16>, tensor<1xi8>, tensor<1xf16>) -> tensor<1x16x16x8xf16>
return %0 : tensor<1x16x16x8xf16>
}
@@ -1265,7 +1275,7 @@ func.func @test_avg_pool_input_zp_same_element_type(%arg0: tensor<1x16x16x8xf16>
// CHECK-LABEL: test_avg_pool_output_zp_same_element_type
func.func @test_avg_pool_output_zp_same_element_type(%arg0: tensor<1x16x16x8xi8>, %arg1: tensor<1xi8>, %arg2: tensor<1xf16>) -> tensor<1x16x16x8xi8> {
// expected-error at +1 {{'tosa.avg_pool2d' op expect both output and its zero point are the same element type, got 'i8' and 'f16'}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}>
: (tensor<1x16x16x8xi8>, tensor<1xi8>, tensor<1xf16>) -> tensor<1x16x16x8xi8>
return %0 : tensor<1x16x16x8xi8>
}
@@ -1274,10 +1284,10 @@ func.func @test_avg_pool_output_zp_same_element_type(%arg0: tensor<1x16x16x8xi8>
// CHECK-LABEL: test_avg_pool_input_zp_non_zero
func.func @test_avg_pool_input_zp_non_zero(%arg0: tensor<1x16x16x8xf32>) -> tensor<1x16x16x8xf32> {
- %input_zp = "tosa.const"() {values = dense<-1.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %output_zp = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %input_zp = "tosa.const"() <{values = dense<-1.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.avg_pool2d' op input zero point must be zero for non-int8 integer types}}
- %0 = "tosa.avg_pool2d"(%arg0, %input_zp, %output_zp) {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
+ %0 = "tosa.avg_pool2d"(%arg0, %input_zp, %output_zp) <{acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}>
: (tensor<1x16x16x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x16x16x8xf32>
return %0 : tensor<1x16x16x8xf32>
}
@@ -1286,10 +1296,10 @@ func.func @test_avg_pool_input_zp_non_zero(%arg0: tensor<1x16x16x8xf32>) -> tens
// CHECK-LABEL: test_avg_pool_output_zp_non_zero
func.func @test_avg_pool_output_zp_non_zero(%arg0: tensor<1x16x16x8xf32>) -> tensor<1x16x16x8xf32> {
- %input_zp = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %output_zp = "tosa.const"() {values = dense<-1.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %output_zp = "tosa.const"() <{values = dense<-1.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.avg_pool2d' op output zero point must be zero for non-int8 integer types}}
- %0 = "tosa.avg_pool2d"(%arg0, %input_zp, %output_zp) {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
+ %0 = "tosa.avg_pool2d"(%arg0, %input_zp, %output_zp) <{acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}>
: (tensor<1x16x16x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x16x16x8xf32>
return %0 : tensor<1x16x16x8xf32>
}
@@ -1298,7 +1308,7 @@ func.func @test_avg_pool_output_zp_non_zero(%arg0: tensor<1x16x16x8xf32>) -> ten
func.func @test_fft2d_same_operands_and_result_element_type(%arg0: tensor<1x4x8xf32>, %arg1: tensor<1x4x8xf32>) -> (tensor<1x4x8xf16>, tensor<1x4x8xf16>) {
// expected-error at +1 {{'tosa.fft2d' op requires the same element type for all operands and results}}
- %0, %1 = tosa.fft2d %arg0, %arg1 {inverse = false} : (tensor<1x4x8xf32>, tensor<1x4x8xf32>) -> (tensor<1x4x8xf16>, tensor<1x4x8xf16>)
+ %0, %1 = tosa.fft2d %arg0, %arg1 inverse(false) : (tensor<1x4x8xf32>, tensor<1x4x8xf32>) -> (tensor<1x4x8xf16>, tensor<1x4x8xf16>)
return %0, %1 : tensor<1x4x8xf16>, tensor<1x4x8xf16>
}
@@ -1306,7 +1316,7 @@ func.func @test_fft2d_same_operands_and_result_element_type(%arg0: tensor<1x4x8x
func.func @test_fft2d_same_operands_and_result_shape(%arg0: tensor<1x4x8xf32>, %arg1: tensor<1x4x7xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>) {
// expected-error at +1 {{'tosa.fft2d' op requires the same shape for all operands and results}}
- %0, %1 = tosa.fft2d %arg0, %arg1 {inverse = false} : (tensor<1x4x8xf32>, tensor<1x4x7xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>)
+ %0, %1 = tosa.fft2d %arg0, %arg1 inverse(false) : (tensor<1x4x8xf32>, tensor<1x4x7xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>)
return %0, %1 : tensor<1x4x8xf32>, tensor<1x4x8xf32>
}
@@ -1314,7 +1324,7 @@ func.func @test_fft2d_same_operands_and_result_shape(%arg0: tensor<1x4x8xf32>, %
func.func @test_fft2d_invalid_type(%arg0: tensor<1x4x8xi8>, %arg1: tensor<1x4x8xi8>) -> (tensor<1x4x8xi8>, tensor<1x4x8xi8>) {
// expected-error at +1 {{'tosa.fft2d' op requires a floating point type}}
- %0, %1 = tosa.fft2d %arg0, %arg1 {inverse = false} : (tensor<1x4x8xi8>, tensor<1x4x8xi8>) -> (tensor<1x4x8xi8>, tensor<1x4x8xi8>)
+ %0, %1 = tosa.fft2d %arg0, %arg1 inverse(false) : (tensor<1x4x8xi8>, tensor<1x4x8xi8>) -> (tensor<1x4x8xi8>, tensor<1x4x8xi8>)
return %0, %1 : tensor<1x4x8xi8>, tensor<1x4x8xi8>
}
@@ -1322,7 +1332,7 @@ func.func @test_fft2d_invalid_type(%arg0: tensor<1x4x8xi8>, %arg1: tensor<1x4x8x
func.func @test_fft2d_height_non_power_of_two(%arg0: tensor<1x5x8xf32>, %arg1: tensor<1x5x8xf32>) -> (tensor<1x5x8xf32>, tensor<1x5x8xf32>) {
// expected-error at +1 {{'tosa.fft2d' op expected height to be a power of two, got 5}}
- %0, %1 = tosa.fft2d %arg0, %arg1 {inverse = false} : (tensor<1x5x8xf32>, tensor<1x5x8xf32>) -> (tensor<1x5x8xf32>, tensor<1x5x8xf32>)
+ %0, %1 = tosa.fft2d %arg0, %arg1 inverse(false) : (tensor<1x5x8xf32>, tensor<1x5x8xf32>) -> (tensor<1x5x8xf32>, tensor<1x5x8xf32>)
return %0, %1 : tensor<1x5x8xf32>, tensor<1x5x8xf32>
}
@@ -1378,31 +1388,31 @@ func.func @test_rfft2d_width_input_output_match(%arg0: tensor<1x4x8xf16>) -> (te
func.func @test_argmax_invalid_output_shape(%arg0: tensor<1x2x3xf32>) -> tensor<1x2x3xi32> {
// expected-error at +1 {{'tosa.argmax' op expected output shape '2, 3', got '1, 2, 3'}}
- %0 = tosa.argmax %arg0 {axis = 0 : i32}: (tensor<1x2x3xf32>) -> tensor<1x2x3xi32>
+ %0 = tosa.argmax %arg0 axis(0): (tensor<1x2x3xf32>) -> tensor<1x2x3xi32>
return %0 : tensor<1x2x3xi32>
}
// -----
func.func @test_rescale_invalid_input_type(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xi32> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %output_zp = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.rescale' op expect input to have integer element type, got 'f32'}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xf32>, tensor<1xi32>, tensor<1xi8>, tensor<1xf32>, tensor<1xf32>) -> tensor<13x21x3xi32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xf32>, tensor<1xi32>, tensor<1xi8>, tensor<1xf32>, tensor<1xf32>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
// -----
func.func @test_rescale_invalid_output_type(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xf32> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.rescale' op expect output to have integer element type, got 'f32'}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xf32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -1414,7 +1424,7 @@ func.func @test_rescale_invalid_multiplier_type(%arg0: tensor<13x21x3xi32>) -> t
%input_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
%output_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.rescale' op operand #1 must be 1D tosa-conformant tensor of 16-bit signless integer or 32-bit signless integer values, but got 'tensor<1xi48>'}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi48>, tensor<1xi16>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xf32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi32>, tensor<1xi48>, tensor<1xi16>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -1426,43 +1436,43 @@ func.func @test_rescale_invalid_shift_type(%arg0: tensor<13x21x3xi32>) -> tensor
%input_zp = "tosa.const"() {values = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
%output_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.rescale' op operand #2 must be 1D tosa-conformant tensor of 8-bit signless integer values, but got 'tensor<1xi16>'}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi16>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xf32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi16>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
func.func @test_rescale_invalid_input_zp_i32(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xi32> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.rescale' op expect input_zp of 0, got 1}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
// -----
func.func @test_rescale_invalid_input_zp_s16(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<1> : tensor<1xi16>} : () -> tensor<1xi16>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<1> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op expect input_zp of 0, got 1}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
// -----
func.func @test_rescale_invalid_input_zp_u16(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<1> : tensor<1xi16>} : () -> tensor<1xi16>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<1> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op expect input_zp of 0 or 32768 for unsigned int16 input, got 1}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = true, output_unsigned = false} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
@@ -1470,60 +1480,60 @@ func.func @test_rescale_invalid_input_zp_u16(%arg0: tensor<13x21x3xi16>) -> tens
// -----
func.func @test_rescale_invalid_output_zp_i32(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xi32> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
- %output_zp = "tosa.const"() {values = dense<-1> : tensor<1xi32>} : () -> tensor<1xi32>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %output_zp = "tosa.const"() <{values = dense<-1> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.rescale' op expect output_zp of 0, got -1}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
// -----
func.func @test_rescale_invalid_output_zp_s16(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
- %output_zp = "tosa.const"() {values = dense<-1> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %output_zp = "tosa.const"() <{values = dense<-1> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op expect output_zp of 0, got -1}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = true, output_unsigned = false} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
// -----
func.func @test_rescale_invalid_output_zp_u16(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
- %output_zp = "tosa.const"() {values = dense<-1> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %output_zp = "tosa.const"() <{values = dense<-1> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op expect output_zp of 0 or 32768 for unsigned int16 output, got 65535}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
// -----
func.func @test_rescale_invalid_multiplier_i16(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op expect i32 element type for multiplier for scale32=true, got 'i16'}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi16>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<13x21x3xi16>, tensor<1xi16>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
// -----
func.func @test_rescale_invalid_multiplier_i32(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op expect i16 element type for multiplier for scale32=false, got 'i32'}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = false, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
@@ -1535,7 +1545,7 @@ func.func @test_rescale_invalid_multiplier_rank(%arg0: tensor<13x21x3xi16>) -> t
%input_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
%output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op operand #1 must be 1D tosa-conformant tensor of 16-bit signless integer or 32-bit signless integer values, but got 'tensor<1x1xi32>'}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1x1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<13x21x3xi16>, tensor<1x1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
@@ -1547,67 +1557,67 @@ func.func @test_rescale_invalid_shift_rank(%arg0: tensor<13x21x3xi16>) -> tensor
%input_zp = "tosa.const"() {values = dense<1> : tensor<1xi16>} : () -> tensor<1xi16>
%output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op operand #2 must be 1D tosa-conformant tensor of 8-bit signless integer values, but got 'tensor<1x1xi8>'}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1x1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1x1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
// -----
func.func @test_rescale_invalid_perchannel_multiplier_shape(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<3xi8> } : () -> tensor<3xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<3xi8> }> : () -> tensor<3xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op expect shape of { 3 } for multiplier input, got { 1 }}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = true, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<3xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(true) input_unsigned(false) output_unsigned(true) : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<3xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
// -----
func.func @test_rescale_invalid_non_perchannel_multiplier_shape(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<3xi32> } : () -> tensor<3xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<3xi32> }> : () -> tensor<3xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op expect shape of { 1 } for multiplier input, got { 3 }}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<3xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<13x21x3xi16>, tensor<3xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
// -----
func.func @test_rescale_invalid_perchannel_shift_shape(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<3xi32> } : () -> tensor<3xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<3xi32> }> : () -> tensor<3xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op expect shape of { 3 } for shift input, got { 1 }}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = true, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<3xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(true) input_unsigned(false) output_unsigned(true) : (tensor<13x21x3xi16>, tensor<3xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
// -----
func.func @test_rescale_invalid_non_perchannel_shift_shape(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<3xi8> } : () -> tensor<3xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<3xi8> }> : () -> tensor<3xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op expect shape of { 1 } for shift input, got { 3 }}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<3xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<3xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
// -----
// CHECK-LABEL: test_error_double_round_without_scale32
func.func @test_error_double_round_without_scale32(%arg0: tensor<1xi8>) -> tensor<1xi16> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16> }> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op DOUBLE_ROUND is only allowed with scale32=true}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = DOUBLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<1xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi16>) -> tensor<1xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<DOUBLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<1xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi16>) -> tensor<1xi16>
return %0 : tensor<1xi16>
}
@@ -1655,8 +1665,8 @@ func.func @test_negate_output_zp_same_element_type(%arg0: tensor<1x16x16x8xi8>,
// CHECK-LABEL: test_negate_input_zp_non_zero
func.func @test_negate_input_zp_non_zero(%arg0: tensor<1x16x16x8xf32>) -> tensor<1x16x16x8xf32> {
- %input_zp = "tosa.const"() {values = dense<-1.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %output_zp = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %input_zp = "tosa.const"() <{values = dense<-1.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.negate' op input1 zero point must be zero for non-int8 integer types}}
%0 = tosa.negate %arg0, %input_zp, %output_zp
: (tensor<1x16x16x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x16x16x8xf32>
@@ -1667,8 +1677,8 @@ func.func @test_negate_input_zp_non_zero(%arg0: tensor<1x16x16x8xf32>) -> tensor
// CHECK-LABEL: test_negate_output_zp_non_zero
func.func @test_negate_output_zp_non_zero(%arg0: tensor<1x16x16x8xf32>) -> tensor<1x16x16x8xf32> {
- %input_zp = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %output_zp = "tosa.const"() {values = dense<-1.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %output_zp = "tosa.const"() <{values = dense<-1.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.negate' op output zero point must be zero for non-int8 integer types}}
%0 = tosa.negate %arg0, %input_zp, %output_zp
: (tensor<1x16x16x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x16x16x8xf32>
@@ -1679,7 +1689,7 @@ func.func @test_negate_output_zp_non_zero(%arg0: tensor<1x16x16x8xf32>) -> tenso
func.func @test_avgpool2d_invalid_kernel(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x32x32x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op expect all kernel values to be >= 1, got 0, -1}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 0, -1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 0, -1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1688,7 +1698,7 @@ func.func @test_avgpool2d_invalid_kernel(%arg0: tensor<1x32x32x8xf32>, %arg1: te
func.func @test_avgpool2d_invalid_stride(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x32x32x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op expect all stride values to be >= 1, got 1, 0}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 0>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 0>, acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1697,7 +1707,7 @@ func.func @test_avgpool2d_invalid_stride(%arg0: tensor<1x32x32x8xf32>, %arg1: te
func.func @test_avgpool2d_invalid_padding(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x32x32x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op expect all padding values to be >= 0, got 0, 0, 0, -1}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, -1>, stride = array<i64: 1, 1>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, -1>, stride = array<i64: 1, 1>, acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1706,7 +1716,7 @@ func.func @test_avgpool2d_invalid_padding(%arg0: tensor<1x32x32x8xf32>, %arg1: t
func.func @test_avgpool2d_padding_not_less_than_kernel_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x32x32x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op expected left/right padding to be less than the width of the kernel, got pad_left=0, pad_right=1, kernel_x=1}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 1>, stride = array<i64: 1, 1>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 1>, stride = array<i64: 1, 1>, acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1715,7 +1725,7 @@ func.func @test_avgpool2d_padding_not_less_than_kernel_x(%arg0: tensor<1x32x32x8
func.func @test_avgpool2d_padding_not_less_than_kernel_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x32x32x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op expected top/bottom padding to be less than the height of the kernel, got pad_top=2, pad_bottom=0, kernel_y=1}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 1, 1>, pad = array<i64: 2, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 1, 1>, pad = array<i64: 2, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1724,7 +1734,7 @@ func.func @test_avgpool2d_padding_not_less_than_kernel_y(%arg0: tensor<1x32x32x8
func.func @test_avgpool2d_wholly_divisible_height(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x32x32x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op expected input_height + pad_top + pad_bottom - kernel_y to be wholly divisible by stride_y, got (32 + 0 + 0 - 1) / 2}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 1>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 1>, acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1733,7 +1743,7 @@ func.func @test_avgpool2d_wholly_divisible_height(%arg0: tensor<1x32x32x8xf32>,
func.func @test_avgpool2d_wholly_divisible_width(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x32x32x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op expected input_width + pad_left + pad_right - kernel_x to be wholly divisible by stride_x, got (32 + 0 + 0 - 1) / 2}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 2>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 2>, acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1742,7 +1752,7 @@ func.func @test_avgpool2d_wholly_divisible_width(%arg0: tensor<1x32x32x8xf32>, %
func.func @test_avgpool2d_unexpected_output_height(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x33x32x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op calculated output height did not match expected: calculated=32, expected=33}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x33x32x8xf32>
return %0 : tensor<1x33x32x8xf32>
}
@@ -1751,7 +1761,7 @@ func.func @test_avgpool2d_unexpected_output_height(%arg0: tensor<1x32x32x8xf32>,
func.func @test_avgpool2d_unexpected_output_width(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x?x33x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op calculated output width did not match expected: calculated=32, expected=33}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x?x33x8xf32>
return %0 : tensor<1x?x33x8xf32>
}
@@ -1762,7 +1772,7 @@ func.func @test_avgpool2d_adaptive_non_const_shape_operands(%arg0: tensor<1x32x3
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op shape operand is not compile time resolvable}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1772,11 +1782,11 @@ func.func @test_avgpool2d_adaptive_non_const_shape_operands(%arg0: tensor<1x32x3
func.func @test_avgpool2d_adaptive_invalid_kernel(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[0, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[0, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op expect all kernel values to be >= 1, got 0, 1}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1786,11 +1796,11 @@ func.func @test_avgpool2d_adaptive_invalid_kernel(%arg0: tensor<1x32x32x8xf32>)
func.func @test_avgpool2d_adaptive_invalid_stride(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 0]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 0]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op expect all stride values to be >= 1, got 1, 0}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1800,11 +1810,11 @@ func.func @test_avgpool2d_adaptive_invalid_stride(%arg0: tensor<1x32x32x8xf32>)
func.func @test_avgpool2d_adaptive_invalid_pad(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[2, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 2, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 2, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op expected left/right padding to be less than the width of the kernel}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1814,11 +1824,11 @@ func.func @test_avgpool2d_adaptive_invalid_pad(%arg0: tensor<1x32x32x8xf32>) ->
func.func @test_avgpool2d_adaptive_unexpected_output_height(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x33x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op calculated output height did not match expected: calculated=32, expected=33}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x33x32x8xf32>
return %0 : tensor<1x33x32x8xf32>
}
@@ -1828,11 +1838,11 @@ func.func @test_avgpool2d_adaptive_unexpected_output_height(%arg0: tensor<1x32x3
func.func @test_avgpool2d_adaptive_padding_not_less_than_kernel_x(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op expected left/right padding to be less than the width of the kernel, got pad_left=0, pad_right=1, kernel_x=1}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1842,11 +1852,11 @@ func.func @test_avgpool2d_adaptive_padding_not_less_than_kernel_x(%arg0: tensor<
func.func @test_avgpool2d_adaptive_padding_not_less_than_kernel_y(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[2, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[2, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op expected top/bottom padding to be less than the height of the kernel, got pad_top=2, pad_bottom=0, kernel_y=1}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1856,11 +1866,11 @@ func.func @test_avgpool2d_adaptive_padding_not_less_than_kernel_y(%arg0: tensor<
func.func @test_avgpool2d_adaptive_wholly_divisible_height(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[2, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[2, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op expected input_height + pad_top + pad_bottom - kernel_y to be wholly divisible by stride_y, got (32 + 0 + 0 - 1) / 2}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1870,11 +1880,11 @@ func.func @test_avgpool2d_adaptive_wholly_divisible_height(%arg0: tensor<1x32x32
func.func @test_avgpool2d_adaptive_wholly_divisible_width(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op expected input_width + pad_left + pad_right - kernel_x to be wholly divisible by stride_x, got (32 + 0 + 0 - 1) / 2}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1884,11 +1894,11 @@ func.func @test_avgpool2d_adaptive_wholly_divisible_width(%arg0: tensor<1x32x32x
func.func @test_avgpool2d_adaptive_invalid_acc_type(%arg0: tensor<1x32x32x8xi8>) -> tensor<1x32x32x8xi8> {
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op accumulator type for integer tensor is not i32}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xi8>, tensor<1xi8>, tensor<1xi8>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xi8>
return %0 : tensor<1x32x32x8xi8>
}
@@ -1898,11 +1908,11 @@ func.func @test_avgpool2d_adaptive_invalid_acc_type(%arg0: tensor<1x32x32x8xi8>)
func.func @test_avgpool2d_adaptive_input_zp_type_mismatch(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op expect both input and its zero point are the same element type, got 'f32' and 'i32'}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xi32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1912,11 +1922,11 @@ func.func @test_avgpool2d_adaptive_input_zp_type_mismatch(%arg0: tensor<1x32x32x
func.func @test_avgpool2d_adaptive_output_zp_type_mismatch(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op expect both output and its zero point are the same element type, got 'f32' and 'i32'}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xi32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1926,11 +1936,11 @@ func.func @test_avgpool2d_adaptive_output_zp_type_mismatch(%arg0: tensor<1x32x32
func.func @test_avgpool2d_adaptive_input_zp_non_zero(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<-1.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op input zero point must be zero for non-int8 integer types}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1940,11 +1950,11 @@ func.func @test_avgpool2d_adaptive_input_zp_non_zero(%arg0: tensor<1x32x32x8xf32
func.func @test_avgpool2d_adaptive_output_zp_non_zero(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<-1.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op output zero point must be zero for non-int8 integer types}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -1971,7 +1981,7 @@ func.func @test_maxpool2d_unexpected_output_width(%arg0: tensor<1x32x32x8xf32>)
func.func @test_scalar_argmax(%arg0: tensor<i32>) -> tensor<i32> {
// expected-error at +1 {{'tosa.argmax' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<i32>'}}
- %0 = tosa.argmax %arg0 {axis = 0 : i32} : (tensor<i32>) -> tensor<i32>
+ %0 = tosa.argmax %arg0 axis(0) : (tensor<i32>) -> tensor<i32>
return %0 : tensor<i32>
}
@@ -1979,7 +1989,7 @@ func.func @test_scalar_argmax(%arg0: tensor<i32>) -> tensor<i32> {
func.func @test_scalar_reduce_all(%arg0: tensor<i1>) -> tensor<i1> {
// expected-error at +1 {{'tosa.reduce_all' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<i1>'}}
- %0 = tosa.reduce_all %arg0 {axis = 0 : i32} : (tensor<i1>) -> tensor<i1>
+ %0 = tosa.reduce_all %arg0 axis(0) : (tensor<i1>) -> tensor<i1>
return %0 : tensor<i1>
}
@@ -1987,7 +1997,7 @@ func.func @test_scalar_reduce_all(%arg0: tensor<i1>) -> tensor<i1> {
func.func @test_scalar_inputs_concat(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<2xf32> {
// expected-error at +1 {{'tosa.concat' op operand #0 must be variadic of tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<f32>, tensor<f32>) -> tensor<2xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<f32>, tensor<f32>) -> tensor<2xf32>
return %0 : tensor<2xf32>
}
@@ -1995,7 +2005,7 @@ func.func @test_scalar_inputs_concat(%arg0: tensor<f32>, %arg1: tensor<f32>) ->
func.func @test_scalar_pad(%arg0: tensor<f32>) -> tensor<f32> {
%0 = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
- %padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %padding = tosa.const_shape values(dense<0> : tensor<6xindex>) : () -> !tosa.shape<6>
// expected-error at +1 {{'tosa.pad' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
%1 = tosa.pad %arg0, %padding, %0 : (tensor<f32>, !tosa.shape<6>, tensor<1xf32>) -> tensor<f32>
return %1 : tensor<f32>
@@ -2005,14 +2015,14 @@ func.func @test_scalar_pad(%arg0: tensor<f32>) -> tensor<f32> {
func.func @test_scalar_reverse(%arg0: tensor<f32>) -> tensor<f32> {
// expected-error at +1 {{'tosa.reverse' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
- %0 = tosa.reverse %arg0 {axis = 0: i32} : (tensor<f32>) -> tensor<f32>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<f32>) -> tensor<f32>
return %arg0 : tensor<f32>
}
// -----
func.func @test_scalar_tile(%arg0: tensor<f32>) -> tensor<*xf32> {
- %cst = tosa.const_shape { values = dense<[]> : tensor<0xindex> } : () -> !tosa.shape<0>
+ %cst = tosa.const_shape values(dense<[]> : tensor<0xindex>) : () -> !tosa.shape<0>
// expected-error at +1 {{'tosa.tile' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
%0 = tosa.tile %arg0, %cst: (tensor<f32>, !tosa.shape<0>) -> tensor<*xf32>
return %0 : tensor<*xf32>
@@ -2041,7 +2051,7 @@ func.func @test_mul_out_i16(%arg0: tensor<13x21x3xi8>, %arg1: tensor<13x1x3xi8>,
// CHECK-LABEL: test_clamp_nan_min_val
func.func @test_clamp_nan_min_val(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
// expected-error at +1 {{'tosa.clamp' op min/max attributes should not be 'NaN', got min_val=0xFFFFFFFF : f32, max_val=1.000000e+00 : f32}}
- %0 = tosa.clamp %arg0 {min_val = 0xFFFFFFFF : f32, max_val = 1.0: f32} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.clamp %arg0 min_val(0xFFFFFFFF : f32) max_val(1.0: f32) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -2050,7 +2060,7 @@ func.func @test_clamp_nan_min_val(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3x
// CHECK-LABEL: test_clamp_nan_max_val
func.func @test_clamp_nan_max_val(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
// expected-error at +1 {{'tosa.clamp' op min/max attributes should not be 'NaN', got min_val=2.300000e+00 : f32, max_val=0x7FFFFFFF : f32}}
- %0 = tosa.clamp %arg0 {min_val = 2.3 : f32, max_val = 0x7FFFFFFF: f32} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.clamp %arg0 min_val(2.3 : f32) max_val(0x7FFFFFFF: f32) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -2059,7 +2069,7 @@ func.func @test_clamp_nan_max_val(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3x
// CHECK-LABEL: test_clamp_min_larger_than_max_int8
func.func @test_clamp_min_larger_than_max_int8(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi8> {
// expected-error at +1 {{'tosa.clamp' op expected min_val <= max_val, got min_val=127 : i8, max_val=-128 : i8}}
- %0 = tosa.clamp %arg0 {min_val = 127 : i8, max_val = -128: i8} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi8>
+ %0 = tosa.clamp %arg0 min_val(127 : i8) max_val(-128: i8) : (tensor<13x21x3xi8>) -> tensor<13x21x3xi8>
return %0 : tensor<13x21x3xi8>
}
@@ -2068,7 +2078,7 @@ func.func @test_clamp_min_larger_than_max_int8(%arg0: tensor<13x21x3xi8>) -> ten
// CHECK-LABEL: test_clamp_min_larger_than_max_fp32
func.func @test_clamp_min_larger_than_max_fp32(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
// expected-error at +1 {{'tosa.clamp' op expected min_val <= max_val, got min_val=2.000000e+00 : f32, max_val=-1.100000e+00 : f32}}
- %0 = tosa.clamp %arg0 {min_val = 2.0 : f32, max_val = -1.1: f32} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.clamp %arg0 min_val(2.0 : f32) max_val(-1.1: f32) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -2081,7 +2091,7 @@ func.func @test_rescale_input_unsigned(%arg0: tensor<1x1xui8>) -> (tensor<1x1xi8
%2 = "tosa.const"() <{values = dense<3> : tensor<1xi8>}> : () -> tensor<1xi8>
%3 = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op is not profile-aligned: element type 'ui8' is not legal}}
- %r = tosa.rescale %arg0, %1, %0, %3, %2 {input_unsigned = true, output_unsigned = false, per_channel = false, rounding_mode = SINGLE_ROUND, scale32 = true} : (tensor<1x1xui8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1xi8>
+ %r = tosa.rescale %arg0, %1, %0, %3, %2 scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<1x1xui8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1xi8>
return %r : tensor<1x1xi8>
}
@@ -2094,7 +2104,7 @@ func.func @test_rescale_output_unsigned(%arg0: tensor<1x1xi8>) -> (tensor<1x1xui
%2 = "tosa.const"() <{values = dense<3> : tensor<1xi8>}> : () -> tensor<1xi8>
%3 = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op is not profile-aligned: element type 'ui8' is not legal}}
- %r = tosa.rescale %arg0, %1, %0, %3, %2 {input_unsigned = false, output_unsigned = true, per_channel = false, rounding_mode = SINGLE_ROUND, scale32 = true} : (tensor<1x1xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1xui8>
+ %r = tosa.rescale %arg0, %1, %0, %3, %2 scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<1x1xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1xui8>
return %r : tensor<1x1xui8>
}
@@ -2102,7 +2112,7 @@ func.func @test_rescale_output_unsigned(%arg0: tensor<1x1xi8>) -> (tensor<1x1xui
// CHECK-LABEL: test_scatter_duplicate_indices
func.func @test_scatter_duplicate_indices(%arg0: tensor<2x52x3xf32>, %arg2: tensor<2x12x3xf32>) -> tensor<2x52x3xf32> {
- %indices = "tosa.const"() { values = dense<[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 11, 12]]> : tensor<2x12xi32> } : () -> tensor<2x12xi32>
+ %indices = "tosa.const"() <{ values = dense<[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 11, 12]]> : tensor<2x12xi32> }> : () -> tensor<2x12xi32>
// expected-error at +1 {{'tosa.scatter' op indices values contain duplicates}}
%0 = tosa.scatter %arg0, %indices, %arg2 : (tensor<2x52x3xf32>, tensor<2x12xi32>, tensor<2x12x3xf32>) -> tensor<2x52x3xf32>
return %0 : tensor<2x52x3xf32>
@@ -2112,7 +2122,7 @@ func.func @test_scatter_duplicate_indices(%arg0: tensor<2x52x3xf32>, %arg2: tens
// CHECK-LABEL: test_scatter_duplicate_indices_int64
func.func @test_scatter_duplicate_indices_int64(%arg0: tensor<2x52x3xf32>, %arg2: tensor<2x12x3xf32>) -> tensor<2x52x3xf32> {
- %indices = "tosa.const"() { values = dense<[[1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 11, 12], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]> : tensor<2x12xi64> } : () -> tensor<2x12xi64>
+ %indices = "tosa.const"() <{ values = dense<[[1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 11, 12], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]> : tensor<2x12xi64> }> : () -> tensor<2x12xi64>
// expected-error at +1 {{'tosa.scatter' op indices values contain duplicates}}
%0 = tosa.scatter %arg0, %indices, %arg2 : (tensor<2x52x3xf32>, tensor<2x12xi64>, tensor<2x12x3xf32>) -> tensor<2x52x3xf32>
return %0 : tensor<2x52x3xf32>
@@ -2122,7 +2132,7 @@ func.func @test_scatter_duplicate_indices_int64(%arg0: tensor<2x52x3xf32>, %arg2
func.func @test_reduce_all_unsupported_data_types(%arg0: tensor<2x12x11xf32>) -> tensor<1x12x11xf32> {
// expected-error at +1 {{'tosa.reduce_all' op illegal: operation operand/result data types did not align with any profile or extension, got (f32,f32), did you mean (i1,i1)?}}
- %0 = tosa.reduce_all %arg0 {axis = 0 : i32} : (tensor<2x12x11xf32>) -> tensor<1x12x11xf32>
+ %0 = tosa.reduce_all %arg0 axis(0) : (tensor<2x12x11xf32>) -> tensor<1x12x11xf32>
return %0 : tensor<1x12x11xf32>
}
@@ -2137,8 +2147,8 @@ func.func @test_rfft2d(%arg0: tensor<13x8x16xbf16>) -> (tensor<13x8x9xbf16>, ten
// -----
func.func @test_slice_shape_non_const_start(%arg0: tensor<1xi32>) {
- %0 = tosa.const_shape {values = dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %2 = "tosa.const"() {values = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = tosa.const_shape values(dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %2 = "tosa.const"() <{values = dense<3> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.slice_shape' op expected compile time resolvable constant, but got variable value for operand #1}}
%3 = tosa.slice_shape %0, %arg0, %2 : (!tosa.shape<6>, tensor<1xi32>, tensor<1xi32>) -> !tosa.shape<3>
return
@@ -2147,8 +2157,8 @@ func.func @test_slice_shape_non_const_start(%arg0: tensor<1xi32>) {
// -----
func.func @test_slice_shape_non_const_size(%arg0: tensor<1xi32>) {
- %0 = tosa.const_shape {values = dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %1 = "tosa.const"() {values = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = tosa.const_shape values(dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %1 = "tosa.const"() <{values = dense<3> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.slice_shape' op expected compile time resolvable constant, but got variable value for operand #2}}
%3 = tosa.slice_shape %0, %1, %arg0 : (!tosa.shape<6>, tensor<1xi32>, tensor<1xi32>) -> !tosa.shape<3>
return
@@ -2157,11 +2167,11 @@ func.func @test_slice_shape_non_const_size(%arg0: tensor<1xi32>) {
// -----
func.func @test_conv2d_block_scaled(%arg0: tensor<*xf4E2M1FN>, %arg1: tensor<*xf8E8M0FNU>, %arg2: tensor<*xf4E2M1FN>, %arg3: tensor<*xf8E8M0FNU>, %arg4: tensor<*xf16>) -> tensor<*xf16> {
- %0 = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %1 = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %2 = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %1 = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %2 = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op illegal: operation operand/result data types did not align with any profile or extension, got (fp4e2m1,fp8e8m0,fp4e2m1,fp8e8m0,f16,f16), did you mean (fp4e2m1,fp8e8m0,fp4e2m1,fp8e8m0,f32,f32)? Otherwise, please refer to the 'supported data types' for 'tosa.conv2d_block_scaled' in the specification.}}
- %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %0, %1, %2 {block_size = BLOCK_SIZE_32} : (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<*xf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf16>
+ %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %0, %1, %2 block_size<BLOCK_SIZE_32> : (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<*xf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf16>
return %3 : tensor<*xf16>
}
@@ -2169,7 +2179,7 @@ func.func @test_conv2d_block_scaled(%arg0: tensor<*xf4E2M1FN>, %arg1: tensor<*xf
func.func @test_cast_f32_plain_fp4(%arg0: tensor<4x32xf32>) -> tensor<4x32xf4E2M1FN> {
// expected-error at +1 {{'tosa.cast' op illegal: operation operand/result data types did not align with any profile or extension, got (f32,fp4e2m1)}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32xf32>) -> tensor<4x32xf4E2M1FN>
+ %0 = tosa.cast %arg0 : (tensor<4x32xf32>) -> tensor<4x32xf4E2M1FN>
return %0 : tensor<4x32xf4E2M1FN>
}
@@ -2177,7 +2187,7 @@ func.func @test_cast_f32_plain_fp4(%arg0: tensor<4x32xf32>) -> tensor<4x32xf4E2M
func.func @test_cast_fp4_block_scaled(%arg0: tensor<4x32xf4E2M1FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
// expected-error at +1 {{'tosa.cast' op illegal: operation operand/result data types did not align with any profile or extension, got (fp4e2m1,bs32_fp8e8m0_fp4e2m1), did you mean (fp8e4m3,bs32_fp8e8m0_fp4e2m1)? Otherwise, please refer to the 'supported data types' for 'tosa.cast' in the specification.}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32xf4E2M1FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
+ %0 = tosa.cast %arg0 : (tensor<4x32xf4E2M1FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %0 : tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
}
@@ -2185,7 +2195,7 @@ func.func @test_cast_fp4_block_scaled(%arg0: tensor<4x32xf4E2M1FN>) -> tensor<4x
func.func @test_cast_block_scaled_fp6e2m3(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xf6E2M3FN> {
// expected-error at +1 {{'tosa.cast' op illegal: operation operand/result data types did not align with any profile or extension, got (bs32_fp8e8m0_fp4e2m1,fp6e2m3), did you mean (bs32_fp8e8m0_fp4e2m1,fp8e4m3)? Otherwise, please refer to the 'supported data types' for 'tosa.cast' in the specification.}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xf6E2M3FN>
+ %0 = tosa.cast %arg0 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xf6E2M3FN>
return %0 : tensor<4x32xf6E2M3FN>
}
@@ -2193,6 +2203,6 @@ func.func @test_cast_block_scaled_fp6e2m3(%arg0: tensor<4x32x!tosa.block_scaled<
func.func @test_cast_fp6e3m2_block_scaled(%arg0: tensor<4x32xf6E3M2FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>> {
// expected-error at +1 {{'tosa.cast' op illegal: operation operand/result data types did not align with any profile or extension, got (fp6e3m2,bs32_fp8e8m0_mxint8), did you mean (fp8e4m3,bs32_fp8e8m0_mxint8)? Otherwise, please refer to the 'supported data types' for 'tosa.cast' in the specification.}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32xf6E3M2FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
+ %0 = tosa.cast %arg0 : (tensor<4x32xf6E3M2FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
return %0 : tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
}
diff --git a/mlir/test/Dialect/Tosa/invalid_extension.mlir b/mlir/test/Dialect/Tosa/invalid_extension.mlir
index e7d76459897ea..bed92d985c759 100644
--- a/mlir/test/Dialect/Tosa/invalid_extension.mlir
+++ b/mlir/test/Dialect/Tosa/invalid_extension.mlir
@@ -7,21 +7,21 @@
// -----
func.func @test_argmax(%arg0: tensor<14x19xbf16>) -> tensor<14xi32> {
// expected-error at +1 {{'tosa.argmax' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.argmax %arg0 {axis = 1 : i32} : (tensor<14x19xbf16>) -> tensor<14xi32>
+ %0 = tosa.argmax %arg0 axis(1) : (tensor<14x19xbf16>) -> tensor<14xi32>
return %0 : tensor<14xi32>
}
// -----
func.func @test_avg_pool2d(%arg0: tensor<1x7x7x9xbf16>, %arg1: tensor<1xbf16>, %arg2: tensor<1xbf16>) -> tensor<1x7x7x9xbf16> {
// expected-error at +1 {{'tosa.avg_pool2d' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.avg_pool2d %arg0, %arg1, %arg2 {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xbf16>, tensor<1xbf16>, tensor<1xbf16>) -> tensor<1x7x7x9xbf16>
+ %0 = tosa.avg_pool2d %arg0, %arg1, %arg2 kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(f32) : (tensor<1x7x7x9xbf16>, tensor<1xbf16>, tensor<1xbf16>) -> tensor<1x7x7x9xbf16>
return %0 : tensor<1x7x7x9xbf16>
}
// -----
func.func @test_conv2d(%arg0: tensor<1x4x4x4xi8>, %arg1: tensor<8x1x1x4xi4>, %arg2: tensor<8xi32>, %arg3: tensor<1xi8>, %arg4: tensor<1xi4>) -> tensor<1x4x4x8xi32> {
// expected-error at +1 {{'tosa.conv2d' op illegal: requires any of [int4] profiles/extensions to be specified in the target environment}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4xi8>, tensor<8x1x1x4xi4>, tensor<8xi32>, tensor<1xi8>, tensor<1xi4>) -> tensor<1x4x4x8xi32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) local_bound(true) : (tensor<1x4x4x4xi8>, tensor<8x1x1x4xi4>, tensor<8xi32>, tensor<1xi8>, tensor<1xi4>) -> tensor<1x4x4x8xi32>
return %0 : tensor<1x4x4x8xi32>
}
@@ -30,35 +30,35 @@ func.func @test_conv2d_mxfp(%arg0: tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAP
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.conv2d' op illegal: requires all of [bf16, mx_common, mx_fp4e2m1] profiles/extensions to be specified in the target environment}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = bf16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xbf16>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xbf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(bf16) local_bound(true) : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xbf16>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xbf16>
return %0 : tensor<1x4x4x8xbf16>
}
// -----
func.func @test_conv3d(%arg0: tensor<1x4x8x21x17xi16>, %arg1: tensor<34x1x1x1x17xi8>, %arg2: tensor<34xi48>, %arg3: tensor<1xi16>, %arg4: tensor<1xi8>) -> tensor<1x4x8x21x34xi48> {
// expected-error at +1 {{'tosa.conv3d' op illegal: requires any of [int16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = i48, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<1x4x8x21x17xi16>, tensor<34x1x1x1x17xi8>, tensor<34xi48>, tensor<1xi16>, tensor<1xi8>) -> tensor<1x4x8x21x34xi48>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(i48) : (tensor<1x4x8x21x17xi16>, tensor<34x1x1x1x17xi8>, tensor<34xi48>, tensor<1xi16>, tensor<1xi8>) -> tensor<1x4x8x21x34xi48>
return %0 : tensor<1x4x8x21x34xi48>
}
// -----
func.func @test_depthwise_conv2d(%arg0: tensor<1x4x4x4xbf16>, %arg1: tensor<1x1x4x2xbf16>, %arg2: tensor<8xbf16>, %arg3: tensor<1xbf16>, %arg4: tensor<1xbf16>) -> tensor<1x4x4x8xbf16> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xbf16>, tensor<1x1x4x2xbf16>, tensor<8xbf16>, tensor<1xbf16>, tensor<1xbf16>) -> tensor<1x4x4x8xbf16>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x4x4x4xbf16>, tensor<1x1x4x2xbf16>, tensor<8xbf16>, tensor<1xbf16>, tensor<1xbf16>) -> tensor<1x4x4x8xbf16>
return %0 : tensor<1x4x4x8xbf16>
}
// -----
func.func @test_max_pool2d(%arg0: tensor<1x32x32x8xi16>) -> tensor<1x32x32x8xi16> {
// expected-error at +1 {{'tosa.max_pool2d' op illegal: requires any of [int16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xi16>) -> tensor<1x32x32x8xi16>
+ %0 = tosa.max_pool2d %arg0 kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x32x32x8xi16>) -> tensor<1x32x32x8xi16>
return %0 : tensor<1x32x32x8xi16>
}
// -----
func.func @test_clamp(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
// expected-error at +1 {{'tosa.clamp' op illegal: requires any of [int16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.clamp %arg0 {min_val = 0 : i16, max_val = 1 : i16} : (tensor<13x21x3xi16>) -> tensor<13x21x3xi16>
+ %0 = tosa.clamp %arg0 min_val(0 : i16) max_val(1 : i16) : (tensor<13x21x3xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
@@ -184,29 +184,29 @@ func.func @test_equal(%arg0: tensor<13x21x3xbf16>, %arg1: tensor<13x1x3xbf16>) -
// -----
func.func @test_reduce_max(%arg0: tensor<13x21x3xbf16>) -> tensor<1x21x3xbf16> {
// expected-error at +1 {{'tosa.reduce_max' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.reduce_max %arg0 {axis = 0 : i32} : (tensor<13x21x3xbf16>) -> tensor<1x21x3xbf16>
+ %0 = tosa.reduce_max %arg0 axis(0) : (tensor<13x21x3xbf16>) -> tensor<1x21x3xbf16>
return %0 : tensor<1x21x3xbf16>
}
// -----
func.func @test_concat(%arg0: tensor<13x21x3xbf16>, %arg1: tensor<13x21x3xbf16>) -> tensor<26x21x3xbf16> {
// expected-error at +1 {{'tosa.concat' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x3xbf16>, tensor<13x21x3xbf16>) -> tensor<26x21x3xbf16>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x3xbf16>, tensor<13x21x3xbf16>) -> tensor<26x21x3xbf16>
return %0 : tensor<26x21x3xbf16>
}
// -----
func.func @test_concat(%arg0: tensor<13x21x3xi16>, %arg1: tensor<13x21x3xi16>) -> tensor<26x21x3xi16> {
// expected-error at +1 {{'tosa.concat' op illegal: requires any of [int16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x3xi16>, tensor<13x21x3xi16>) -> tensor<26x21x3xi16>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x3xi16>, tensor<13x21x3xi16>) -> tensor<26x21x3xi16>
return %0 : tensor<26x21x3xi16>
}
// -----
func.func @test_pad(%arg0: tensor<13x21x3xbf16>) -> tensor<13x21x3xbf16> {
- %padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %padding = tosa.const_shape values(dense<0> : tensor<6xindex>) : () -> !tosa.shape<6>
// expected-error at +1 {{'tosa.const' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xbf16>} : () -> tensor<1xbf16>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xbf16>}> : () -> tensor<1xbf16>
// expected-error at +1 {{'tosa.pad' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
%0 = tosa.pad %arg0, %padding, %pad_const : (tensor<13x21x3xbf16>, !tosa.shape<6>, tensor<1xbf16>) -> tensor<13x21x3xbf16>
return %0 : tensor<13x21x3xbf16>
@@ -214,7 +214,7 @@ func.func @test_pad(%arg0: tensor<13x21x3xbf16>) -> tensor<13x21x3xbf16> {
// -----
func.func @test_reshape(%arg0: tensor<13x21x3xbf16>) -> tensor<1x819xbf16> {
- %1 = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
%0 = tosa.reshape %arg0, %1 : (tensor<13x21x3xbf16>, !tosa.shape<2>) -> tensor<1x819xbf16>
return %0 : tensor<1x819xbf16>
@@ -222,7 +222,7 @@ func.func @test_reshape(%arg0: tensor<13x21x3xbf16>) -> tensor<1x819xbf16> {
// -----
func.func @test_reshape_mxfp(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
- %1 = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op illegal: requires all of [mx_common, mx_fp4e2m1] profiles/extensions to be specified in the target environment}}
%0 = tosa.reshape %arg0, %1 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<2>) -> tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %0 : tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
@@ -231,14 +231,14 @@ func.func @test_reshape_mxfp(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_3
// -----
func.func @test_reverse(%arg0: tensor<13x21x3xbf16>) -> tensor<13x21x3xbf16> {
// expected-error at +1 {{'tosa.reverse' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<13x21x3xbf16>) -> tensor<13x21x3xbf16>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<13x21x3xbf16>) -> tensor<13x21x3xbf16>
return %0 : tensor<13x21x3xbf16>
}
// -----
func.func @test_slice(%arg0: tensor<13x21x3xbf16>) -> tensor<4x11x1xbf16> {
- %size = tosa.const_shape {values = dense<[4, 11, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %start = tosa.const_shape {values = dense<[6, 8, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[4, 11, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[6, 8, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.slice' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
%0 = tosa.slice %arg0, %start, %size : (tensor<13x21x3xbf16>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<4x11x1xbf16>
return %0 : tensor<4x11x1xbf16>
@@ -246,7 +246,7 @@ func.func @test_slice(%arg0: tensor<13x21x3xbf16>) -> tensor<4x11x1xbf16> {
// -----
func.func @test_tile(%arg0: tensor<13x21x3xbf16>) -> tensor<39x21x6xbf16> {
- %cst = tosa.const_shape { values = dense<[3, 1, 2]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %cst = tosa.const_shape values(dense<[3, 1, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.tile' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
%0 = tosa.tile %arg0, %cst: (tensor<13x21x3xbf16>, !tosa.shape<3>) -> tensor<39x21x6xbf16>
return %0 : tensor<39x21x6xbf16>
@@ -255,7 +255,7 @@ func.func @test_tile(%arg0: tensor<13x21x3xbf16>) -> tensor<39x21x6xbf16> {
// -----
func.func @test_transpose(%arg0: tensor<13x21x3xbf16>) -> tensor<3x13x21xbf16> {
// expected-error at +1 {{'tosa.transpose' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x21x3xbf16>) -> tensor<3x13x21xbf16>
+ %1 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<13x21x3xbf16>) -> tensor<3x13x21xbf16>
return %1 : tensor<3x13x21xbf16>
}
@@ -284,9 +284,9 @@ func.func @test_row_gather_mxfp(%arg0: tensor<13x21x32x!tosa.block_scaled<BLOCK_
// -----
func.func @test_row_gather_block_scaled(%arg0: tensor<13x21x32xf4E2M1FN>, %arg1: tensor<13x21x1xf8E8M0FNU>, %arg2: tensor<13x26xi32>) -> (tensor<13x52x32xf4E2M1FN>, tensor<13x52x1xf8E8M0FNU>) {
- %row_count = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
+ %row_count = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.row_gather_block_scaled' op illegal: requires any of [mxfp] profiles/extensions to be specified in the target environment}}
- %0:2 = tosa.row_gather_block_scaled %arg0, %arg1, %arg2, %row_count {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<13x21x32xf4E2M1FN>, tensor<13x21x1xf8E8M0FNU>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x32xf4E2M1FN>, tensor<13x52x1xf8E8M0FNU>)
+ %0:2 = tosa.row_gather_block_scaled %arg0, %arg1, %arg2, %row_count block_size<BLOCK_SIZE_32> : (tensor<13x21x32xf4E2M1FN>, tensor<13x21x1xf8E8M0FNU>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x32xf4E2M1FN>, tensor<13x52x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<13x52x32xf4E2M1FN>, tensor<13x52x1xf8E8M0FNU>
}
@@ -299,80 +299,80 @@ func.func @test_scatter(%arg0: tensor<13x26x3xbf16>, %arg1: tensor<13x26xi32>, %
// -----
func.func @test_resize(%arg0: tensor<1x32x32x8xbf16>) -> tensor<1x64x64x8xbf16> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x32x32x8xbf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xbf16>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x32x32x8xbf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xbf16>
return %1 : tensor<1x64x64x8xbf16>
}
// -----
func.func @test_resize_fp8(%arg0: tensor<1x32x32x8xf8E4M3FN>) -> tensor<1x64x64x8xf8E4M3FN> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op illegal: requires any of [fp8e4m3] profiles/extensions to be specified in the target environment}}
- %0 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x32x32x8xf8E4M3FN>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E4M3FN>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x32x32x8xf8E4M3FN>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E4M3FN>
return %0 : tensor<1x64x64x8xf8E4M3FN>
}
// -----
func.func @test_resize_mxfp(%arg0: tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op illegal: requires all of [mx_common, mx_fp4e2m1] profiles/extensions to be specified in the target environment}}
- %0 = tosa.resize %arg0, %scale, %offset, %border { mode = NEAREST_NEIGHBOR } : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %0 : tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
}
// -----
func.func @test_cast_i8_bf16(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xbf16> {
// expected-error at +1 {{'tosa.cast' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi8>) -> tensor<13x21x3xbf16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xbf16>
return %0 : tensor<13x21x3xbf16>
}
// -----
func.func @test_cast_bf16_i8(%arg0: tensor<13x21x3xbf16>) -> tensor<13x21x3xi8> {
// expected-error at +1 {{'tosa.cast' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xbf16>) -> tensor<13x21x3xi8>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xbf16>) -> tensor<13x21x3xi8>
return %0 : tensor<13x21x3xi8>
}
// -----
func.func @test_cast_f32_bf16(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xbf16> {
// expected-error at +1 {{'tosa.cast' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf32>) -> tensor<13x21x3xbf16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf32>) -> tensor<13x21x3xbf16>
return %0 : tensor<13x21x3xbf16>
}
// -----
func.func @test_cast_f32_block_scaled(%arg0: tensor<4x32xf32>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
// expected-error at +1 {{'tosa.cast' op illegal: requires all of [mx_common, mx_fp4e2m1] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32xf32>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
+ %0 = tosa.cast %arg0 : (tensor<4x32xf32>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %0 : tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
}
// -----
func.func @test_cast_block_scaled_f32(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xf32> {
// expected-error at +1 {{'tosa.cast' op illegal: requires all of [mx_common, mx_fp4e2m1] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xf32>
+ %0 = tosa.cast %arg0 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xf32>
return %0 : tensor<4x32xf32>
}
// -----
func.func @test_cast_bf16_block_scaled(%arg0: tensor<4x32xbf16>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
// expected-error at +1 {{'tosa.cast' op illegal: requires all of [bf16, mx_common, mx_fp4e2m1] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32xbf16>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
+ %0 = tosa.cast %arg0 : (tensor<4x32xbf16>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %0 : tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
}
// -----
func.func @test_cast_fp8_block_scaled(%arg0: tensor<4x32xf8E4M3FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>> {
// expected-error at +1 {{'tosa.cast' op illegal: requires all of [fp8e4m3, mx_common, mx_int8] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32xf8E4M3FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
+ %0 = tosa.cast %arg0 : (tensor<4x32xf8E4M3FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
return %0 : tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
}
@@ -390,21 +390,21 @@ func.func @test_matmul_t_mixed_block_scaled_operands(%arg0: tensor<4x8x32xf16>,
func.func @test_fft2d(%arg0: tensor<1x4x8xf32>, %arg1: tensor<1x4x8xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>) {
// expected-error at +1 {{'tosa.fft2d' op illegal: requires any of [fft] profiles/extensions to be specified in the target environment}}
- %0, %1 = tosa.fft2d %arg0, %arg1 {inverse = false} : (tensor<1x4x8xf32>, tensor<1x4x8xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>)
+ %0, %1 = tosa.fft2d %arg0, %arg1 inverse(false) : (tensor<1x4x8xf32>, tensor<1x4x8xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>)
return %0, %1 : tensor<1x4x8xf32>, tensor<1x4x8xf32>
}
// -----
func.func @test_const_i4() -> tensor<3x11x11x3xi4> {
// expected-error at +1 {{'tosa.const' op illegal: requires any of [int4] profiles/extensions to be specified in the target environment}}
- %0 = "tosa.const"() {values = dense<0> : tensor<3x11x11x3xi4>} : () -> tensor<3x11x11x3xi4>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<3x11x11x3xi4>}> : () -> tensor<3x11x11x3xi4>
return %0 : tensor<3x11x11x3xi4>
}
// -----
func.func @test_const_i48() -> tensor<3x11x11x3xi48> {
// expected-error at +1 {{'tosa.const' op illegal: requires any of [int16] profiles/extensions to be specified in the target environment}}
- %0 = "tosa.const"() {values = dense<0> : tensor<3x11x11x3xi48>} : () -> tensor<3x11x11x3xi48>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<3x11x11x3xi48>}> : () -> tensor<3x11x11x3xi48>
return %0 : tensor<3x11x11x3xi48>
}
@@ -443,7 +443,7 @@ module {
// -----
func.func @test_cast_bf16_i32(%arg0: tensor<13x21x3xbf16>) -> tensor<13x21x3xi32> {
// expected-error at +1 {{'tosa.cast' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xbf16>) -> tensor<13x21x3xi32>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xbf16>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
@@ -470,9 +470,9 @@ func.func @test_while_loop(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
tosa.yield %3 : tensor<i1>
} do {
^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<10xi32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%3 = tosa.add %arg3, %2 : (tensor<i32>, tensor<i32>) -> tensor<i32>
- %7 = tosa.const_shape {values = dense<[1]> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %7 = tosa.const_shape values(dense<[1]> : tensor<1xindex>) : () -> !tosa.shape<1>
%4 = tosa.reshape %2, %7 : (tensor<i32>, !tosa.shape<1>) -> tensor<1xi32>
%5 = tosa.add %arg4, %4 : (tensor<10xi32>, tensor<1xi32>) -> tensor<10xi32>
%6 = tosa.add %arg2, %2 : (tensor<i32>, tensor<i32>) -> tensor<i32>
@@ -485,43 +485,43 @@ func.func @test_while_loop(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
// CHECK-LABEL: test_single_round_rescale
func.func @test_single_round_rescale(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi8> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK tosa.rescale
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<13x21x3xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<13x21x3xi8>
return %0 : tensor<13x21x3xi8>
}
// -----
func.func @test_double_round_rescale(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi8> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op failed attribute check: rounding_mode = DOUBLE_ROUND requires extension [doubleround]}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = DOUBLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<13x21x3xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<DOUBLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<13x21x3xi8>
return %0 : tensor<13x21x3xi8>
}
// -----
func.func @test_inexact_round_rescale(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi8> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op failed attribute check: rounding_mode = INEXACT_ROUND requires extension [inexactround]}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = INEXACT_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<13x21x3xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<INEXACT_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<13x21x3xi8>
return %0 : tensor<13x21x3xi8>
}
// -----
func.func @test_pad_non_const(%arg0: tensor<13x21x3xi8>, %arg1: tensor<1xi8>) -> tensor<13x22x4xi8> {
- %0 = tosa.const_shape {values = dense<[0, 0, 0, 1, 0, 1]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %0 = tosa.const_shape values(dense<[0, 0, 0, 1, 0, 1]> : tensor<6xindex>) : () -> !tosa.shape<6>
// expected-error at +1 {{'tosa.pad' op expected compile time resolvable constant, but got variable value for operand #2}}
%1 = tosa.pad %arg0, %0, %arg1 : (tensor<13x21x3xi8>, !tosa.shape<6>, tensor<1xi8>) -> tensor<13x22x4xi8>
return %1 : tensor<13x22x4xi8>
@@ -530,56 +530,56 @@ func.func @test_pad_non_const(%arg0: tensor<13x21x3xi8>, %arg1: tensor<1xi8>) ->
// -----
func.func @test_rescale_non_const_multiplier(%arg0: tensor<13x21x3xi32>, %multiplier: tensor<1xi32>) -> tensor<13x21x3xi32> {
- %zps = "tosa.const"() {values = dense<0> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ %zps = "tosa.const"() <{values = dense<0> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op expected compile time resolvable constant, but got variable value for operand #1}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %zps, %zps {rounding_mode = SINGLE_ROUND, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %zps, %zps scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) {input_zp = 0 : i32, output_zp = 0 : i32} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
// -----
func.func @test_rescale_non_const_shift(%arg0: tensor<13x21x3xi32>, %shift: tensor<1xi8>) -> tensor<13x21x3xi32> {
- %zps = "tosa.const"() {values = dense<0> : tensor<1xi32> } : () -> tensor<1xi32>
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %zps = "tosa.const"() <{values = dense<0> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.rescale' op expected compile time resolvable constant, but got variable value for operand #2}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %zps, %zps {rounding_mode = SINGLE_ROUND, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %zps, %zps scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) {input_zp = 0 : i32, output_zp = 0 : i32} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
// -----
func.func @test_conv2d_non_const_input_zp(%arg0: tensor<1x4x4x4xi8>, %arg1: tensor<8x1x1x4xi8>, %arg2: tensor<8xi32>, %arg3: tensor<1xi8>) -> tensor<1x4x4x8xi32> {
- %weight_zp = "tosa.const"() {values = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
+ %weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8> }> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op expected compile time resolvable constant, but got variable value for operand #3}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %weight_zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4xi8>, tensor<8x1x1x4xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8xi32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) local_bound(true) : (tensor<1x4x4x4xi8>, tensor<8x1x1x4xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8xi32>
return %0 : tensor<1x4x4x8xi32>
}
// -----
func.func @test_conv3d_non_const_weight_zp(%arg0: tensor<1x4x8x21x17xi8>, %arg1: tensor<34x1x1x1x17xi8>, %arg2: tensor<34xi32>, %arg3: tensor<1xi8>) -> tensor<1x4x8x21x34xi32> {
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8> }> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv3d' op expected compile time resolvable constant, but got variable value for operand #4}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %input_zp, %arg3 {acc_type = i32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<1x4x8x21x17xi8>, tensor<34x1x1x1x17xi8>, tensor<34xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x8x21x34xi32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %input_zp, %arg3 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(i32) : (tensor<1x4x8x21x17xi8>, tensor<34x1x1x1x17xi8>, tensor<34xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x8x21x34xi32>
return %0 : tensor<1x4x8x21x34xi32>
}
// -----
func.func @test_depthwise_conv2d_non_const_input_zp(%arg0: tensor<1x4x4x4xi8>, %arg1: tensor<1x1x4x2xi8>, %arg2: tensor<8xi32>, %arg3: tensor<1xi8>) -> tensor<1x4x4x8xi32> {
- %weight_zp = "tosa.const"() {values = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
+ %weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8> }> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.depthwise_conv2d' op expected compile time resolvable constant, but got variable value for operand #3}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %weight_zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xi8>, tensor<1x1x4x2xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8xi32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) : (tensor<1x4x4x4xi8>, tensor<1x1x4x2xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8xi32>
return %0 : tensor<1x4x4x8xi32>
}
// -----
func.func @test_transpose_conv2d_non_const_weight_zp(%arg0: tensor<1x4x4x4xi8>, %arg1: tensor<1x1x4x2xi8>, %arg2: tensor<8xi32>, %arg3: tensor<1xi8>) -> tensor<1x4x7x8xi32> {
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8> }> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.transpose_conv2d' op expected compile time resolvable constant, but got variable value for operand #4}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %arg3 {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xi8>, tensor<1x1x4x2xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x7x8xi32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %arg3 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(i32) : (tensor<1x4x4x4xi8>, tensor<1x1x4x2xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x7x8xi32>
return %0 : tensor<1x4x7x8xi32>
}
@@ -594,7 +594,7 @@ func.func @test_matmul_non_const_a_zp(%arg0: tensor<1x14x19xf32>, %arg1: tensor<
// -----
func.func @test_matmul_non_const_b_zp(%arg0: tensor<1x14x19xf32>, %arg1: tensor<1x19x28xf32>, %b_zp: tensor<1xf32>) -> tensor<1x14x28xf32> {
- %a_zp = "tosa.const"() {values = dense<0.0> : tensor<1xf32> } : () -> tensor<1xf32>
+ %a_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32> }> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.matmul' op expected compile time resolvable constant, but got variable value for operand #3}}
%0 = tosa.matmul %arg0, %arg1, %a_zp, %b_zp : (tensor<1x14x19xf32>, tensor<1x19x28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x14x28xf32>
return %0 : tensor<1x14x28xf32>
@@ -636,18 +636,18 @@ func.func @test_table_non_const(%arg0 : tensor<4x5xi8>, %arg1 : tensor<256xi8>)
// -----
func.func @test_rescale_non_const_input_zp(%arg0: tensor<13x21x3xi32>, %input_zp: tensor<1xi32>) -> tensor<13x21x3xi32> {
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
%output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.rescale' op expected compile time resolvable constant, but got variable value for operand #3}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = true, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
// -----
func.func @test_negate_non_const_input1_zp(%arg0: tensor<1xf32>, %input_zp: tensor<1xf32>) -> tensor<1xf32> {
- %output_zp = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.negate' op expected compile time resolvable constant, but got variable value for operand #1}}
%0 = tosa.negate %arg0, %input_zp, %output_zp : (tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
return %0 : tensor<1xf32>
@@ -656,9 +656,9 @@ func.func @test_negate_non_const_input1_zp(%arg0: tensor<1xf32>, %input_zp: tens
// -----
func.func @test_avg_pool2d_non_const_output_zp(%arg0: tensor<1x32x32x8xf32>, %output_zp: tensor<1xf32>) -> tensor<1x32x32x8xf32> {
- %input_zp = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.avg_pool2d' op expected compile time resolvable constant, but got variable value for operand #2}}
- %0 = "tosa.avg_pool2d"(%arg0, %input_zp, %output_zp) {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %input_zp, %output_zp) <{kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -666,11 +666,11 @@ func.func @test_avg_pool2d_non_const_output_zp(%arg0: tensor<1x32x32x8xf32>, %ou
// -----
func.func @test_avg_pool2d_adaptive_missing_bf16_extension(%arg0: tensor<1x7x7x9xbf16>, %arg1: tensor<1xbf16>, %arg2: tensor<1xbf16>) -> tensor<1x7x7x9xbf16> {
- %kernel = tosa.const_shape {values = dense<[2, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 1, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 1, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op illegal: requires any of [bf16] profiles/extensions to be specified in the target environment}}
- %0 = tosa.avg_pool2d_adaptive %arg0, %arg1, %arg2, %kernel, %stride, %pad {acc_type = f32} :
+ %0 = tosa.avg_pool2d_adaptive %arg0, %arg1, %arg2, %kernel, %stride, %pad acc_type(f32) :
(tensor<1x7x7x9xbf16>, tensor<1xbf16>, tensor<1xbf16>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xbf16>
return %0 : tensor<1x7x7x9xbf16>
}
@@ -678,8 +678,8 @@ func.func @test_avg_pool2d_adaptive_missing_bf16_extension(%arg0: tensor<1x7x7x9
// -----
func.func @test_mul_shape() {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %b = tosa.const_shape {values = dense<[5, 6, 7, 8]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %b = tosa.const_shape values(dense<[5, 6, 7, 8]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.mul_shape' op illegal: requires any of [shape] profiles/extensions to be specified in the target environment}}
%c = tosa.mul_shape %a, %b : (!tosa.shape<4>, !tosa.shape<4>) -> !tosa.shape<4>
return
@@ -688,8 +688,8 @@ func.func @test_mul_shape() {
// -----
func.func @test_max_shape() {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %b = tosa.const_shape {values = dense<[5, 6, 7, 8]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %b = tosa.const_shape values(dense<[5, 6, 7, 8]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_shape' op illegal: requires any of [shape] profiles/extensions to be specified in the target environment}}
%c = tosa.max_shape %a, %b : (!tosa.shape<4>, !tosa.shape<4>) -> !tosa.shape<4>
return
@@ -698,8 +698,8 @@ func.func @test_max_shape() {
// -----
func.func @test_min_shape() {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %b = tosa.const_shape {values = dense<[5, 6, 7, 8]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %b = tosa.const_shape values(dense<[5, 6, 7, 8]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.min_shape' op illegal: requires any of [shape] profiles/extensions to be specified in the target environment}}
%c = tosa.min_shape %a, %b : (!tosa.shape<4>, !tosa.shape<4>) -> !tosa.shape<4>
return
@@ -708,31 +708,31 @@ func.func @test_min_shape() {
// -----
func.func @test_conv2d_block_scaled(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>) -> tensor<1x4x4x2xf32> {
- %0 = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %1 = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %2 = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %1 = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %2 = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op illegal: requires any of [mxfp_conv] profiles/extensions to be specified in the target environment}}
- %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %0, %1, %2 {block_size = BLOCK_SIZE_32} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x4x2xf32>
+ %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %0, %1, %2 block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x4x2xf32>
return %3 : tensor<1x4x4x2xf32>
}
// -----
func.func @test_matmul_t_block_scaled(%arg0: tensor<4x8x32xf6E3M2FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32xf6E3M2FN>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
// expected-error at +1 {{'tosa.matmul_t_block_scaled' op illegal: requires any of [mxfp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x8x32xf6E3M2FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf6E3M2FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32xf6E3M2FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf6E3M2FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
// -----
func.func @test_cast_from_block_scaled(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32> {
// expected-error at +1 {{'tosa.cast_from_block_scaled' op illegal: requires any of [mxfp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32>
return %0 : tensor<4x32xf32>
}
// -----
func.func @test_cast_to_block_scaled(%arg0: tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) {
// expected-error at +1 {{'tosa.cast_to_block_scaled' op illegal: requires any of [mxfp] profiles/extensions to be specified in the target environment}}
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>
}
diff --git a/mlir/test/Dialect/Tosa/level_check.mlir b/mlir/test/Dialect/Tosa/level_check.mlir
index d44d92a5d8e70..1d91bf46fed68 100644
--- a/mlir/test/Dialect/Tosa/level_check.mlir
+++ b/mlir/test/Dialect/Tosa/level_check.mlir
@@ -10,7 +10,7 @@ func.func @test_argmax_rank_invalid(%arg0: tensor<1x1x1x1x29x29x4xf32>) -> tenso
func.func @test_clamp_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32> {
// expected-error at +1 {{'tosa.clamp' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = tosa.clamp %arg0 {min_val = -3.40282347E+38 : f32, max_val = 3.40282347E+38 : f32} : (tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32>
+ %0 = tosa.clamp %arg0 min_val(-3.40282347E+38 : f32) max_val(3.40282347E+38 : f32) : (tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32>
return %0 : tensor<1x1x1x1x13x21x3xf32>
}
@@ -50,7 +50,7 @@ func.func @test_add_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>, %arg1: tens
func.func @test_arithmetic_right_shift_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xi32>, %arg1: tensor<1x1x1x1x13x21x3xi32>) -> tensor<1x1x1x1x13x21x3xi32> {
// expected-error at +1 {{'tosa.arithmetic_right_shift' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = tosa.arithmetic_right_shift %arg0, %arg1 {round = false} : (tensor<1x1x1x1x13x21x3xi32>, tensor<1x1x1x1x13x21x3xi32>) -> tensor<1x1x1x1x13x21x3xi32>
+ %0 = tosa.arithmetic_right_shift %arg0, %arg1 round(false) : (tensor<1x1x1x1x13x21x3xi32>, tensor<1x1x1x1x13x21x3xi32>) -> tensor<1x1x1x1x13x21x3xi32>
return %0 : tensor<1x1x1x1x13x21x3xi32>
}
@@ -324,7 +324,7 @@ func.func @test_greater_equal_rank_invalid(%arg0: tensor<1x1x1x1x13x1x3xf32>, %a
func.func @test_reduce_all_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xi1>) -> tensor<1x1x1x1x1x21x3xi1> {
// expected-error at +1 {{'tosa.reduce_all' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = "tosa.reduce_all"(%arg0) {axis = 4 : i32} : (tensor<1x1x1x1x13x21x3xi1>) -> tensor<1x1x1x1x1x21x3xi1>
+ %0 = "tosa.reduce_all"(%arg0) <{axis = 4 : i32}> : (tensor<1x1x1x1x13x21x3xi1>) -> tensor<1x1x1x1x1x21x3xi1>
return %0 : tensor<1x1x1x1x1x21x3xi1>
}
@@ -332,7 +332,7 @@ func.func @test_reduce_all_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xi1>) -> te
func.func @test_reduce_any_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xi1>) -> tensor<1x1x1x1x13x21x3xi1> {
// expected-error at +1 {{'tosa.reduce_any' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = "tosa.reduce_any"(%arg0) {axis = 0 : i32} : (tensor<1x1x1x1x13x21x3xi1>) -> tensor<1x1x1x1x13x21x3xi1>
+ %0 = "tosa.reduce_any"(%arg0) <{axis = 0 : i32}> : (tensor<1x1x1x1x13x21x3xi1>) -> tensor<1x1x1x1x13x21x3xi1>
return %0 : tensor<1x1x1x1x13x21x3xi1>
}
@@ -356,7 +356,7 @@ func.func @test_reduce_min_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> t
func.func @test_reduce_prod_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32> {
// expected-error at +1 {{'tosa.reduce_product' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = "tosa.reduce_product"(%arg0) {axis = 0 : i32} : (tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32>
+ %0 = "tosa.reduce_product"(%arg0) <{axis = 0 : i32}> : (tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32>
return %0 : tensor<1x1x1x1x13x21x3xf32>
}
@@ -364,7 +364,7 @@ func.func @test_reduce_prod_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) ->
func.func @test_reduce_sum_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32> {
// expected-error at +1 {{'tosa.reduce_sum' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = "tosa.reduce_sum"(%arg0) {axis = 0 : i32} : (tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32>
+ %0 = "tosa.reduce_sum"(%arg0) <{axis = 0 : i32}> : (tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32>
return %0 : tensor<1x1x1x1x13x21x3xf32>
}
@@ -372,15 +372,15 @@ func.func @test_reduce_sum_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> t
func.func @test_concat_rank_invalid(%arg0: tensor<1x1x1x13x21x3x8xf32>, %arg1: tensor<1x1x1x13x21x3x8xf32>) -> tensor<1x1x1x26x21x3x8xf32> {
// expected-error at +1 {{'tosa.concat' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = "tosa.concat"(%arg0, %arg1) {axis = 3 : i32} : (tensor<1x1x1x13x21x3x8xf32>, tensor<1x1x1x13x21x3x8xf32>) -> tensor<1x1x1x26x21x3x8xf32>
+ %0 = "tosa.concat"(%arg0, %arg1) <{axis = 3 : i32}> : (tensor<1x1x1x13x21x3x8xf32>, tensor<1x1x1x13x21x3x8xf32>) -> tensor<1x1x1x26x21x3x8xf32>
return %0 : tensor<1x1x1x26x21x3x8xf32>
}
// -----
func.func @test_pad_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32> {
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
- %padding = tosa.const_shape {values = dense<0> : tensor<14xindex>} : () -> !tosa.shape<14>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %padding = tosa.const_shape values(dense<0> : tensor<14xindex>) : () -> !tosa.shape<14>
// expected-error at +1 {{'tosa.pad' op failed level check: operand rank(shape) <= MAX_RANK}}
%0 = tosa.pad %arg0, %padding, %pad_const : (tensor<1x1x1x1x13x21x3xf32>, !tosa.shape<14>, tensor<1xf32>) -> tensor<1x1x1x1x13x21x3xf32>
return %0 : tensor<1x1x1x1x13x21x3xf32>
@@ -389,7 +389,7 @@ func.func @test_pad_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<1
// -----
func.func @test_reshape_rank_invalid(%arg0: tensor<13x21x3xf32>) -> tensor<1x1x1x1x1x1x819xf32> {
- %1 = tosa.const_shape {values = dense<[1, 1, 1, 1, 1, 1, 819]> : tensor<7xindex>} : () -> !tosa.shape<7>
+ %1 = tosa.const_shape values(dense<[1, 1, 1, 1, 1, 1, 819]> : tensor<7xindex>) : () -> !tosa.shape<7>
// expected-error at +1 {{'tosa.reshape' op failed level check: result rank(shape) <= MAX_RANK}}
%0 = "tosa.reshape"(%arg0, %1) : (tensor<13x21x3xf32>, !tosa.shape<7>) -> tensor<1x1x1x1x1x1x819xf32>
return %0 : tensor<1x1x1x1x1x1x819xf32>
@@ -399,36 +399,36 @@ func.func @test_reshape_rank_invalid(%arg0: tensor<13x21x3xf32>) -> tensor<1x1x1
// -----
func.func @test_reshape_non_block_scaled_output_rank_invalid(%arg0: tensor<13x21x3xf32>) -> tensor<1x1x1x1x1x1x819xf32> {
- %1 = tosa.const_shape {values = dense<[1, 1, 1, 1, 1, 1, 819]> : tensor<7xindex>} : () -> !tosa.shape<7>
+ %1 = tosa.const_shape values(dense<[1, 1, 1, 1, 1, 1, 819]> : tensor<7xindex>) : () -> !tosa.shape<7>
// expected-error at +1 {{'tosa.reshape_block_scaled' op failed level check: result rank(shape) <= MAX_RANK}}
- %0 = tosa.reshape_block_scaled %arg0, %1 {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<13x21x3xf32>, !tosa.shape<7>) -> tensor<1x1x1x1x1x1x819xf32>
+ %0 = tosa.reshape_block_scaled %arg0, %1 block_size<BLOCK_SIZE_1> : (tensor<13x21x3xf32>, !tosa.shape<7>) -> tensor<1x1x1x1x1x1x819xf32>
return %0 : tensor<1x1x1x1x1x1x819xf32>
}
// -----
func.func @test_reshape_non_block_scaled_input_rank_invalid(%arg0: tensor<1x1x1x1x1x1x819xf32>) -> tensor<13x21x3xf32> {
- %1 = tosa.const_shape {values = dense<[13, 21, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %1 = tosa.const_shape values(dense<[13, 21, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.reshape_block_scaled' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = tosa.reshape_block_scaled %arg0, %1 {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<1x1x1x1x1x1x819xf32>, !tosa.shape<3>) -> tensor<13x21x3xf32>
+ %0 = tosa.reshape_block_scaled %arg0, %1 block_size<BLOCK_SIZE_1> : (tensor<1x1x1x1x1x1x819xf32>, !tosa.shape<3>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
func.func @test_reshape_block_scaled_output_rank_invalid(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<4x1xf8E8M0FNU>) -> (tensor<1x1x1x1x1x2x64xf4E2M1FN>, tensor<1x1x1x1x1x2x2xf8E8M0FNU>) {
- %1 = tosa.const_shape {values = dense<[1, 1, 1, 1, 1, 2, 64]> : tensor<7xindex>} : () -> !tosa.shape<7>
+ %1 = tosa.const_shape values(dense<[1, 1, 1, 1, 1, 2, 64]> : tensor<7xindex>) : () -> !tosa.shape<7>
// expected-error at +1 {{'tosa.reshape_block_scaled' op failed level check: result rank(shape) <= MAX_RANK}}
- %0:2 = tosa.reshape_block_scaled %arg0, %arg1, %1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<7>) -> (tensor<1x1x1x1x1x2x64xf4E2M1FN>, tensor<1x1x1x1x1x2x2xf8E8M0FNU>)
+ %0:2 = tosa.reshape_block_scaled %arg0, %arg1, %1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<7>) -> (tensor<1x1x1x1x1x2x64xf4E2M1FN>, tensor<1x1x1x1x1x2x2xf8E8M0FNU>)
return %0#0, %0#1 : tensor<1x1x1x1x1x2x64xf4E2M1FN>, tensor<1x1x1x1x1x2x2xf8E8M0FNU>
}
// -----
func.func @test_reshape_block_scaled_input_rank_invalid(%arg0: tensor<1x1x1x1x1x4x32xf4E2M1FN>, %arg1: tensor<1x1x1x1x1x4x1xf8E8M0FNU>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>) {
- %1 = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0:2 = tosa.reshape_block_scaled %arg0, %arg1, %1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<1x1x1x1x1x4x32xf4E2M1FN>, tensor<1x1x1x1x1x4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
+ %0:2 = tosa.reshape_block_scaled %arg0, %arg1, %1 block_size<BLOCK_SIZE_32> : (tensor<1x1x1x1x1x4x32xf4E2M1FN>, tensor<1x1x1x1x1x4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
return %0#0, %0#1 : tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>
}
@@ -436,15 +436,15 @@ func.func @test_reshape_block_scaled_input_rank_invalid(%arg0: tensor<1x1x1x1x1x
func.func @test_reverse_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32> {
// expected-error at +1 {{'tosa.reverse' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = "tosa.reverse"(%arg0) {axis = 0 : i32} : (tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32>
+ %0 = "tosa.reverse"(%arg0) <{axis = 0 : i32}> : (tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32>
return %0 : tensor<1x1x1x1x13x21x3xf32>
}
// -----
// CHECK-LABEL: slice
func.func @test_slice_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x4x11x1xf32> {
- %0 = tosa.const_shape {values = dense<[0, 0, 0, 0, 6, 8, 0]> : tensor<7xindex>} : () -> !tosa.shape<7>
- %1 = tosa.const_shape {values = dense<[1, 1, 1, 1, 4, 11, 1]> : tensor<7xindex>} : () -> !tosa.shape<7>
+ %0 = tosa.const_shape values(dense<[0, 0, 0, 0, 6, 8, 0]> : tensor<7xindex>) : () -> !tosa.shape<7>
+ %1 = tosa.const_shape values(dense<[1, 1, 1, 1, 4, 11, 1]> : tensor<7xindex>) : () -> !tosa.shape<7>
// expected-error at +1 {{'tosa.slice' op failed level check: operand rank(shape) <= MAX_RANK}}
%2= tosa.slice %arg0, %0, %1 : (tensor<1x1x1x1x13x21x3xf32>, !tosa.shape<7>, !tosa.shape<7>) -> tensor<1x1x1x1x4x11x1xf32>
return %2 : tensor<1x1x1x1x4x11x1xf32>
@@ -453,7 +453,7 @@ func.func @test_slice_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor
// -----
// CHECK-LABEL: tile
func.func @test_tile_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x39x21x6xf32> {
- %cst = tosa.const_shape { values = dense<[1, 1, 1, 1, 3, 1, 2]> : tensor<7xindex> } : () -> !tosa.shape<7>
+ %cst = tosa.const_shape values(dense<[1, 1, 1, 1, 3, 1, 2]> : tensor<7xindex>) : () -> !tosa.shape<7>
// expected-error at +1 {{'tosa.tile' op failed level check: operand rank(shape) <= MAX_RANK}}
%0 = tosa.tile %arg0, %cst : (tensor<1x1x1x1x13x21x3xf32>, !tosa.shape<7>) -> tensor<1x1x1x1x39x21x6xf32>
return %0 : tensor<1x1x1x1x39x21x6xf32>
@@ -463,7 +463,7 @@ func.func @test_tile_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<
func.func @test_transpose_rank_invalid(%arg0: tensor<13x21x3x1x1x1x1xf32>) -> tensor<3x13x21x1x1x1x1xf32> {
// expected-error at +1 {{'tosa.transpose' op failed level check: operand rank(shape) <= MAX_RANK}}
- %1 = "tosa.transpose"(%arg0) {perms = array<i32: 2, 0, 1, 3, 4, 5, 6>} : (tensor<13x21x3x1x1x1x1xf32>) -> tensor<3x13x21x1x1x1x1xf32>
+ %1 = "tosa.transpose"(%arg0) <{perms = array<i32: 2, 0, 1, 3, 4, 5, 6>}> : (tensor<13x21x3x1x1x1x1xf32>) -> tensor<3x13x21x1x1x1x1xf32>
return %1 : tensor<3x13x21x1x1x1x1xf32>
}
@@ -478,19 +478,19 @@ func.func @test_cast_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xi32>) -> tensor<
// -----
func.func @test_rescale_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xi8>) -> tensor<1x1x1x1x13x21x3xi8> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32>} : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8>} : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<127> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<-1> : tensor<1xi8>} : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<127> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<-1> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, input_zp = 127 : i32, output_zp = -1 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<1x1x1x1x13x21x3xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1x1x1x13x21x3xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) {input_zp = 127 : i32, output_zp = -1 : i32} : (tensor<1x1x1x1x13x21x3xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1x1x1x13x21x3xi8>
return %0 : tensor<1x1x1x1x13x21x3xi8>
}
// -----
func.func @test_const(%arg0 : tensor<1x1xi32>) -> tensor<1x1x1x1x1x1x1xi32> {
// expected-error at +1 {{'tosa.const' op failed level check: result rank(shape) <= MAX_RANK}}
- %0 = "tosa.const"() {values = dense<0> : tensor<1x1x1x1x1x1x1xi32>} : () -> tensor<1x1x1x1x1x1x1xi32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1x1x1x1x1x1x1xi32>}> : () -> tensor<1x1x1x1x1x1x1xi32>
return %0: tensor<1x1x1x1x1x1x1xi32>
}
@@ -504,7 +504,7 @@ func.func @test_add_rank_valid(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor
// -----
func.func @test_const_rank_valid(%arg0 : tensor<i32>) -> tensor<i32> {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
return %0: tensor<i32>
}
@@ -512,7 +512,7 @@ func.func @test_const_rank_valid(%arg0 : tensor<i32>) -> tensor<i32> {
func.func @test_const_i2(%arg0 : tensor<1xi2>) {
// expected-error at +1 {{'tosa.const' op is not profile-aligned: element type 'i2' is not legal}}
- %0 = "tosa.const"() {values = dense<0> : tensor<1xi2>} : () -> tensor<1xi2>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1xi2>}> : () -> tensor<1xi2>
return
}
@@ -520,7 +520,7 @@ func.func @test_const_i2(%arg0 : tensor<1xi2>) {
func.func @test_const_ui32(%arg0 : tensor<1xui32>) {
// expected-error at +1 {{'tosa.const' op is not profile-aligned: element type 'ui32' is not legal}}
- %0 = "tosa.const"() {values = dense<0> : tensor<1xui32>} : () -> tensor<1xui32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1xui32>}> : () -> tensor<1xui32>
return
}
@@ -528,7 +528,7 @@ func.func @test_const_ui32(%arg0 : tensor<1xui32>) {
func.func @test_const_f64(%arg0 : tensor<1xf64>) {
// expected-error at +1 {{'tosa.const' op is not profile-aligned: element type 'f64' is not legal}}
- %0 = "tosa.const"() {values = dense<0.0> : tensor<1xf64>} : () -> tensor<1xf64>
+ %0 = "tosa.const"() <{values = dense<0.0> : tensor<1xf64>}> : () -> tensor<1xf64>
return
}
@@ -536,7 +536,7 @@ func.func @test_const_f64(%arg0 : tensor<1xf64>) {
func.func @test_const_ui8(%arg0 : tensor<1xui8>) {
// expected-error at +1 {{'tosa.const' op is not profile-aligned: element type 'ui8' is not legal}}
- %0 = "tosa.const"() {values = dense<0> : tensor<1xui8>} : () -> tensor<1xui8>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1xui8>}> : () -> tensor<1xui8>
return
}
@@ -559,7 +559,7 @@ func.func @test_identity_rank_valid(%arg0: tensor<i32>) -> tensor<i32> {
func.func @test_avgpool2d_kernel_y(%arg0: tensor<1x8194x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x2x32x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op failed level check: kernel <= MAX_KERNEL (8192), got 8193}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 8193, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 8193, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32}> :
(tensor<1x8194x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x2x32x8xf32>
return %0 : tensor<1x2x32x8xf32>
}
@@ -568,7 +568,7 @@ func.func @test_avgpool2d_kernel_y(%arg0: tensor<1x8194x32x8xf32>, %arg1: tensor
func.func @test_avgpool2d_kernel_x(%arg0: tensor<1x32x8194x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x32x2x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op failed level check: kernel <= MAX_KERNEL (8192), got 8193}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 1, 8193>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 1, 8193>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, acc_type = f32}> :
(tensor<1x32x8194x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x2x8xf32>
return %0 : tensor<1x32x2x8xf32>
}
@@ -577,7 +577,7 @@ func.func @test_avgpool2d_kernel_x(%arg0: tensor<1x32x8194x8xf32>, %arg1: tensor
func.func @test_avgpool2d_stride_y(%arg0: tensor<1x8194x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x2x32x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 8193, 1>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 8193, 1>, acc_type = f32}> :
(tensor<1x8194x32x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x2x32x8xf32>
return %0 : tensor<1x2x32x8xf32>
}
@@ -586,7 +586,7 @@ func.func @test_avgpool2d_stride_y(%arg0: tensor<1x8194x32x8xf32>, %arg1: tensor
func.func @test_avgpool2d_stride_x(%arg0: tensor<1x32x8194x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x32x2x8xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 8193>, acc_type = f32} :
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) <{kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 8193>, acc_type = f32}> :
(tensor<1x32x8194x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x2x8xf32>
return %0 : tensor<1x32x2x8xf32>
}
@@ -594,11 +594,11 @@ func.func @test_avgpool2d_stride_x(%arg0: tensor<1x32x8194x8xf32>, %arg1: tensor
// -----
func.func @test_avgpool2d_adaptive_kernel_y(%arg0: tensor<1x8194x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x2x32x8xf32> {
- %kernel = tosa.const_shape {values = dense<[8193, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[8193, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op failed level check: kernel <= MAX_KERNEL (8192), got 8193}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %arg1, %arg2, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %arg1, %arg2, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x8194x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x2x32x8xf32>
return %0 : tensor<1x2x32x8xf32>
}
@@ -606,11 +606,11 @@ func.func @test_avgpool2d_adaptive_kernel_y(%arg0: tensor<1x8194x32x8xf32>, %arg
// -----
func.func @test_avgpool2d_adaptive_kernel_x(%arg0: tensor<1x32x8194x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x32x2x8xf32> {
- %kernel = tosa.const_shape {values = dense<[1, 8193]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 8193]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op failed level check: kernel <= MAX_KERNEL (8192), got 8193}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %arg1, %arg2, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %arg1, %arg2, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x8194x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x2x8xf32>
return %0 : tensor<1x32x2x8xf32>
}
@@ -618,11 +618,11 @@ func.func @test_avgpool2d_adaptive_kernel_x(%arg0: tensor<1x32x8194x8xf32>, %arg
// -----
func.func @test_avgpool2d_adaptive_stride_y(%arg0: tensor<1x8194x32x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x2x32x8xf32> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[8193, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[8193, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %arg1, %arg2, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %arg1, %arg2, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x8194x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x2x32x8xf32>
return %0 : tensor<1x2x32x8xf32>
}
@@ -630,11 +630,11 @@ func.func @test_avgpool2d_adaptive_stride_y(%arg0: tensor<1x8194x32x8xf32>, %arg
// -----
func.func @test_avgpool2d_adaptive_stride_x(%arg0: tensor<1x32x8194x8xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x32x2x8xf32> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 8193]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 8193]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %arg1, %arg2, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %arg1, %arg2, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x8194x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x2x8xf32>
return %0 : tensor<1x32x2x8xf32>
}
@@ -643,7 +643,7 @@ func.func @test_avgpool2d_adaptive_stride_x(%arg0: tensor<1x32x8194x8xf32>, %arg
func.func @test_conv2d_dilation_y(%arg0: tensor<1x8192x8192x1xf32>, %arg1: tensor<16x1025x1024x1xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>) -> tensor<1x1x7170x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: dilation_y * KH <= MAX_KERNEL (8192), got 8200}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 8, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 pad([0, 1, 0, 1]) stride([1, 1]) dilation([8, 1]) acc_type(f32) :
(tensor<1x8192x8192x1xf32>, tensor<16x1025x1024x1xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x7170x16xf32>
return %0 : tensor<1x1x7170x16xf32>
}
@@ -652,7 +652,7 @@ func.func @test_conv2d_dilation_y(%arg0: tensor<1x8192x8192x1xf32>, %arg1: tenso
func.func @test_conv2d_dilation_x(%arg0: tensor<1x8192x8192x1xf32>, %arg1: tensor<16x1024x1025x1xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>) -> tensor<1x7170x1x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: dilation_x * KW <= MAX_KERNEL (8192), got 8200}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 8>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 pad([0, 1, 0, 1]) stride([1, 1]) dilation([1, 8]) acc_type(f32) :
(tensor<1x8192x8192x1xf32>, tensor<16x1024x1025x1xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7170x1x16xf32>
return %0 : tensor<1x7170x1x16xf32>
}
@@ -661,7 +661,7 @@ func.func @test_conv2d_dilation_x(%arg0: tensor<1x8192x8192x1xf32>, %arg1: tenso
func.func @test_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>) -> tensor<1x8225x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 8193, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 pad([8193, 1, 0, 1]) stride([1, 1]) dilation([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8225x32x16xf32>
return %0 : tensor<1x8225x32x16xf32>
}
@@ -670,7 +670,7 @@ func.func @test_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x
func.func @test_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>) -> tensor<1x8224x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 8193, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 pad([0, 8193, 0, 1]) stride([1, 1]) dilation([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8224x32x16xf32>
return %0 : tensor<1x8224x32x16xf32>
}
@@ -679,7 +679,7 @@ func.func @test_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16
func.func @test_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>) -> tensor<1x32x8225x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 8193, 1>, stride = array<i64: 1, 1>} :
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 pad([0, 1, 8193, 1]) stride([1, 1]) dilation([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x8225x16xf32>
return %0 : tensor<1x32x8225x16xf32>
}
@@ -688,7 +688,7 @@ func.func @test_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2
func.func @test_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>) -> tensor<1x32x8224x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 8193>, stride = array<i64: 1, 1>} :
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 pad([0, 1, 0, 8193]) stride([1, 1]) dilation([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x8224x16xf32>
return %0 : tensor<1x32x8224x16xf32>
}
@@ -697,7 +697,7 @@ func.func @test_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x
func.func @test_conv2d_stride_y(%arg0: tensor<1x8194x33x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>) -> tensor<1x2x33x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 8193, 1>} :
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 pad([0, 1, 0, 1]) stride([8193, 1]) dilation([1, 1]) acc_type(f32) :
(tensor<1x8194x33x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x2x33x16xf32>
return %0 : tensor<1x2x33x16xf32>
}
@@ -706,7 +706,7 @@ func.func @test_conv2d_stride_y(%arg0: tensor<1x8194x33x8xf32>, %arg1: tensor<16
func.func @test_conv2d_stride_x(%arg0: tensor<1x33x8194x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>) -> tensor<1x33x2x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 8193>} :
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 pad([0, 1, 0, 1]) stride([1, 8193]) dilation([1, 1]) acc_type(f32) :
(tensor<1x33x8194x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x33x2x16xf32>
return %0 : tensor<1x33x2x16xf32>
}
@@ -715,7 +715,7 @@ func.func @test_conv2d_stride_x(%arg0: tensor<1x33x8194x8xf32>, %arg1: tensor<16
func.func @test_conv3d_dilation_d(%arg0: tensor<1x8192x1x1x8xf32>, %arg1: tensor<16x1025x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x2x2x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: dilation_d * KD <= MAX_KERNEL (8192), got 8200}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 8, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 1, 0, 1]) stride([1, 1, 1]) dilation([8, 1, 1]) acc_type(f32) :
(tensor<1x8192x1x1x8xf32>, tensor<16x1025x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x2x2x16xf32>
return %0 : tensor<1x1x2x2x16xf32>
}
@@ -724,7 +724,7 @@ func.func @test_conv3d_dilation_d(%arg0: tensor<1x8192x1x1x8xf32>, %arg1: tensor
func.func @test_conv3d_dilation_y(%arg0: tensor<1x1x8192x1x8xf32>, %arg1: tensor<16x1x1025x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x2x1x2x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: dilation_y * KH <= MAX_KERNEL (8192), got 8200}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 8, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 1, 0, 1]) stride([1, 1, 1]) dilation([1, 8, 1]) acc_type(f32) :
(tensor<1x1x8192x1x8xf32>, tensor<16x1x1025x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x2x1x2x16xf32>
return %0 : tensor<1x2x1x2x16xf32>
}
@@ -733,7 +733,7 @@ func.func @test_conv3d_dilation_y(%arg0: tensor<1x1x8192x1x8xf32>, %arg1: tensor
func.func @test_conv3d_dilation_x(%arg0: tensor<1x1x1x8192x8xf32>, %arg1: tensor<16x1x1x1025x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x2x2x1x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: dilation_x * KW <= MAX_KERNEL (8192), got 8200}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 8>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 1, 0, 1]) stride([1, 1, 1]) dilation([1, 1, 8]) acc_type(f32) :
(tensor<1x1x1x8192x8xf32>, tensor<16x1x1x1025x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x2x2x1x16xf32>
return %0 : tensor<1x2x2x1x16xf32>
}
@@ -742,7 +742,7 @@ func.func @test_conv3d_dilation_x(%arg0: tensor<1x1x1x8192x8xf32>, %arg1: tensor
func.func @test_conv3d_pad_d0(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x8194x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 8193, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([8193, 1, 0, 1, 0, 1]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8194x32x32x16xf32>
return %0 : tensor<1x8194x32x32x16xf32>
}
@@ -751,7 +751,7 @@ func.func @test_conv3d_pad_d0(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2
func.func @test_conv3d_pad_d1(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x8194x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 1, 8193, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([1, 8193, 0, 1, 0, 1]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8194x32x32x16xf32>
return %0 : tensor<1x8194x32x32x16xf32>
}
@@ -760,7 +760,7 @@ func.func @test_conv3d_pad_d1(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2
func.func @test_conv3d_pad_top(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x8225x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 8193, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 8193, 1, 0, 1]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x8225x32x16xf32>
return %0 : tensor<1x1x8225x32x16xf32>
}
@@ -769,7 +769,7 @@ func.func @test_conv3d_pad_top(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x
func.func @test_conv3d_pad_bottom(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x8224x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 8193, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 8193, 0, 1]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x8224x32x16xf32>
return %0 : tensor<1x1x8224x32x16xf32>
}
@@ -778,7 +778,7 @@ func.func @test_conv3d_pad_bottom(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<
func.func @test_conv3d_pad_left(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x8225x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 8193, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 1, 8193, 1]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x8225x16xf32>
return %0 : tensor<1x1x32x8225x16xf32>
}
@@ -787,7 +787,7 @@ func.func @test_conv3d_pad_left(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16
func.func @test_conv3d_pad_right(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x8224x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 8193>, stride = array<i64: 1, 1, 1>} :
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 1, 0, 8193]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x8224x16xf32>
return %0 : tensor<1x1x32x8224x16xf32>
}
@@ -796,7 +796,7 @@ func.func @test_conv3d_pad_right(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<1
func.func @test_conv3d_stride_d(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32>{
// expected-error at +1 {{'tosa.conv3d' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 8193, 1, 1>} :
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 1, 0, 1]) stride([8193, 1, 1]) dilation([1, 1, 1]) acc_type(f32) :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -805,7 +805,7 @@ func.func @test_conv3d_stride_d(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16
func.func @test_conv3d_stride_y(%arg0: tensor<1x1x8194x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x2x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 8193, 1>} :
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 1, 0, 1]) stride([1, 8193, 1]) dilation([1, 1, 1]) acc_type(f32) :
(tensor<1x1x8194x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x2x32x16xf32>
return %0 : tensor<1x1x2x32x16xf32>
}
@@ -814,7 +814,7 @@ func.func @test_conv3d_stride_y(%arg0: tensor<1x1x8194x32x8xf32>, %arg1: tensor<
func.func @test_conv3d_stride_x(%arg0: tensor<1x1x32x8194x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x2x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 8193>} :
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 1, 0, 1]) stride([1, 1, 8193]) dilation([1, 1, 1]) acc_type(f32) :
(tensor<1x1x32x8194x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x2x16xf32>
return %0 : tensor<1x1x32x2x16xf32>
}
@@ -823,7 +823,7 @@ func.func @test_conv3d_stride_x(%arg0: tensor<1x1x32x8194x8xf32>, %arg1: tensor<
func.func @test_depthwise_conv2d_dilation_y(%arg0: tensor<1x8192x8192x4xf32>, %arg1: tensor<1025x16x4x1xf32>, %arg2: tensor<4xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x8178x4xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: dilation_y * KH <= MAX_KERNEL (8192), got 8200}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 8, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 1]) stride([1, 1]) dilation([8, 1]) acc_type(f32) :
(tensor<1x8192x8192x4xf32>, tensor<1025x16x4x1xf32>, tensor<4xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x8178x4xf32>
return %0 : tensor<1x1x8178x4xf32>
}
@@ -832,7 +832,7 @@ func.func @test_depthwise_conv2d_dilation_y(%arg0: tensor<1x8192x8192x4xf32>, %a
func.func @test_depthwise_conv2d_dilation_x(%arg0: tensor<1x8192x8192x4xf32>, %arg1: tensor<16x1025x4x1xf32>, %arg2: tensor<4xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x8178x1x4xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: dilation_x * KW <= MAX_KERNEL (8192), got 8200}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 8>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 1]) stride([1, 1]) dilation([1, 8]) acc_type(f32) :
(tensor<1x8192x8192x4xf32>, tensor<16x1025x4x1xf32>, tensor<4xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8178x1x4xf32>
return %0 : tensor<1x8178x1x4xf32>
}
@@ -841,7 +841,7 @@ func.func @test_depthwise_conv2d_dilation_x(%arg0: tensor<1x8192x8192x4xf32>, %a
func.func @test_depthwise_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x8225x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 8193, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([8193, 1, 0, 1]) stride([1, 1]) dilation([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8225x32x64xf32>
return %0 : tensor<1x8225x32x64xf32>
}
@@ -850,7 +850,7 @@ func.func @test_depthwise_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: te
func.func @test_depthwise_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x8224x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 8193, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 8193, 0, 1]) stride([1, 1]) dilation([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8224x32x64xf32>
return %0 : tensor<1x8224x32x64xf32>
}
@@ -860,7 +860,7 @@ func.func @test_depthwise_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1:
func.func @test_depthwise_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x8225x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 8193, 1>, stride = array<i64: 1, 1>} :
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 8193, 1]) stride([1, 1]) dilation([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x8225x64xf32>
return %0 : tensor<1x32x8225x64xf32>
}
@@ -869,7 +869,7 @@ func.func @test_depthwise_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: t
func.func @test_depthwise_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x8224x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 8193>, stride = array<i64: 1, 1>} :
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 8193]) stride([1, 1]) dilation([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x8224x64xf32>
return %0 : tensor<1x32x8224x64xf32>
}
@@ -878,7 +878,7 @@ func.func @test_depthwise_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1:
func.func @test_depthwise_conv2d_stride_y(%arg0: tensor<1x8194x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x2x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 8193, 1>} :
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 1]) stride([8193, 1]) dilation([1, 1]) acc_type(f32) :
(tensor<1x8194x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x2x32x64xf32>
return %0 : tensor<1x2x32x64xf32>
}
@@ -887,7 +887,7 @@ func.func @test_depthwise_conv2d_stride_y(%arg0: tensor<1x8194x32x8xf32>, %arg1:
func.func @test_depthwise_conv2d_stride_x(%arg0: tensor<1x32x8194x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x2x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 8193>} :
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 1, 0, 1]) stride([1, 8193]) dilation([1, 1]) acc_type(f32) :
(tensor<1x32x8194x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x2x64xf32>
return %0 : tensor<1x32x2x64xf32>
}
@@ -896,7 +896,7 @@ func.func @test_depthwise_conv2d_stride_x(%arg0: tensor<1x32x8194x8xf32>, %arg1:
func.func @test_fft2d_real_h(%arg0: tensor<32x16384x32xf32>, %arg1: tensor<32x16384x32xf32>) -> (tensor<32x16384x32xf32>, tensor<32x16384x32xf32>) {
// expected-error at +1 {{'tosa.fft2d' op failed level check: H <= MAX_KERNEL (8192), got 16384}}
- %0, %1 = "tosa.fft2d"(%arg0, %arg1) { inverse = false } :
+ %0, %1 = "tosa.fft2d"(%arg0, %arg1) <{ inverse = false }> :
(tensor<32x16384x32xf32>, tensor<32x16384x32xf32>) -> (tensor<32x16384x32xf32>, tensor<32x16384x32xf32>)
return %0, %1 : tensor<32x16384x32xf32>, tensor<32x16384x32xf32>
}
@@ -905,7 +905,7 @@ func.func @test_fft2d_real_h(%arg0: tensor<32x16384x32xf32>, %arg1: tensor<32x16
func.func @test_fft2d_real_w(%arg0: tensor<32x32x16384xf32>, %arg1: tensor<32x32x16384xf32>) -> (tensor<32x32x16384xf32>, tensor<32x32x16384xf32>) {
// expected-error at +1 {{'tosa.fft2d' op failed level check: W <= MAX_KERNEL (8192), got 16384}}
- %0, %1 = "tosa.fft2d"(%arg0, %arg1) { inverse = false } :
+ %0, %1 = "tosa.fft2d"(%arg0, %arg1) <{ inverse = false }> :
(tensor<32x32x16384xf32>, tensor<32x32x16384xf32>) -> (tensor<32x32x16384xf32>, tensor<32x32x16384xf32>)
return %0, %1 : tensor<32x32x16384xf32>, tensor<32x32x16384xf32>
}
@@ -914,7 +914,7 @@ func.func @test_fft2d_real_w(%arg0: tensor<32x32x16384xf32>, %arg1: tensor<32x32
func.func @test_fft2d_imag_h(%arg0: tensor<32x16384x32xf32>, %arg1: tensor<32x16384x32xf32>) -> (tensor<32x16384x32xf32>, tensor<32x16384x32xf32>) {
// expected-error at +1 {{'tosa.fft2d' op failed level check: H <= MAX_KERNEL (8192), got 16384}}
- %0, %1 = "tosa.fft2d"(%arg0, %arg1) { inverse = false } :
+ %0, %1 = "tosa.fft2d"(%arg0, %arg1) <{ inverse = false }> :
(tensor<32x16384x32xf32>, tensor<32x16384x32xf32>) -> (tensor<32x16384x32xf32>, tensor<32x16384x32xf32>)
return %0, %1 : tensor<32x16384x32xf32>, tensor<32x16384x32xf32>
}
@@ -923,7 +923,7 @@ func.func @test_fft2d_imag_h(%arg0: tensor<32x16384x32xf32>, %arg1: tensor<32x16
func.func @test_fft2d_imag_w(%arg0: tensor<32x32x16384xf32>, %arg1: tensor<32x32x16384xf32>) -> (tensor<32x32x16384xf32>, tensor<32x32x16384xf32>) {
// expected-error at +1 {{'tosa.fft2d' op failed level check: W <= MAX_KERNEL (8192), got 16384}}
- %0, %1 = "tosa.fft2d"(%arg0, %arg1) { inverse = false } :
+ %0, %1 = "tosa.fft2d"(%arg0, %arg1) <{ inverse = false }> :
(tensor<32x32x16384xf32>, tensor<32x32x16384xf32>) -> (tensor<32x32x16384xf32>, tensor<32x32x16384xf32>)
return %0, %1 : tensor<32x32x16384xf32>, tensor<32x32x16384xf32>
}
@@ -967,9 +967,9 @@ func.func @test_maxpool2d_stride_x(%arg0: tensor<1x32x8194x8xf32>) -> tensor<1x3
// -----
func.func @test_maxpool2d_adaptive_kernel_y(%arg0: tensor<1x8194x32x8xf32>) -> tensor<1x2x32x8xf32> {
- %kernel = tosa.const_shape {values = dense<[8193, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[8193, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_pool2d_adaptive' op failed level check: kernel <= MAX_KERNEL (8192), got 8193}}
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x8194x32x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x2x32x8xf32>
@@ -979,9 +979,9 @@ func.func @test_maxpool2d_adaptive_kernel_y(%arg0: tensor<1x8194x32x8xf32>) -> t
// -----
func.func @test_maxpool2d_adaptive_kernel_x(%arg0: tensor<1x32x8194x8xf32>) -> tensor<1x32x2x8xf32> {
- %kernel = tosa.const_shape {values = dense<[1, 8193]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 8193]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_pool2d_adaptive' op failed level check: kernel <= MAX_KERNEL (8192), got 8193}}
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x32x8194x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x2x8xf32>
@@ -991,9 +991,9 @@ func.func @test_maxpool2d_adaptive_kernel_x(%arg0: tensor<1x32x8194x8xf32>) -> t
// -----
func.func @test_maxpool2d_adaptive_stride_y(%arg0: tensor<1x8194x32x8xf32>) -> tensor<1x2x32x8xf32> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[8193, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[8193, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_pool2d_adaptive' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x8194x32x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x2x32x8xf32>
@@ -1003,9 +1003,9 @@ func.func @test_maxpool2d_adaptive_stride_y(%arg0: tensor<1x8194x32x8xf32>) -> t
// -----
func.func @test_maxpool2d_adaptive_stride_x(%arg0: tensor<1x32x8194x8xf32>) -> tensor<1x32x2x8xf32> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 8193]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 8193]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_pool2d_adaptive' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x32x8194x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x2x8xf32>
@@ -1017,12 +1017,12 @@ func.func @test_maxpool2d_adaptive_stride_x(%arg0: tensor<1x32x8194x8xf32>) -> t
func.func @test_maxpool2d_adaptive_pad_first(%arg0: tensor<1x32x8194x8xf32>) -> tensor<1x32x2x8xf32> {
// If the source of the kernel passed to max_pool2d_adaptive is a const_shape then pad < kernel check is applied.
// This is a workaround for the above so that we can level check the padding.
- %a = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %b = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %a = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %b = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%kernel = tosa.add_shape %a, %b : (!tosa.shape<2>, !tosa.shape<2>) -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[8193, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[8193, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_pool2d_adaptive' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x32x8194x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x2x8xf32>
@@ -1034,12 +1034,12 @@ func.func @test_maxpool2d_adaptive_pad_first(%arg0: tensor<1x32x8194x8xf32>) ->
func.func @test_maxpool2d_adaptive_pad_second(%arg0: tensor<1x32x8194x8xf32>) -> tensor<1x32x2x8xf32> {
// If the source of the kernel passed to max_pool2d_adaptive is a const_shape then pad < kernel check is applied.
// This is a workaround for the above so that we can level check the padding.
- %a = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %b = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %a = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %b = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%kernel = tosa.add_shape %a, %b : (!tosa.shape<2>, !tosa.shape<2>) -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 8193, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 8193, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_pool2d_adaptive' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x32x8194x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x2x8xf32>
@@ -1051,12 +1051,12 @@ func.func @test_maxpool2d_adaptive_pad_second(%arg0: tensor<1x32x8194x8xf32>) ->
func.func @test_maxpool2d_adaptive_pad_third(%arg0: tensor<1x32x8194x8xf32>) -> tensor<1x32x2x8xf32> {
// If the source of the kernel passed to max_pool2d_adaptive is a const_shape then pad < kernel check is applied.
// This is a workaround for the above so that we can level check the padding.
- %a = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %b = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %a = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %b = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%kernel = tosa.add_shape %a, %b : (!tosa.shape<2>, !tosa.shape<2>) -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 8193, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 8193, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_pool2d_adaptive' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x32x8194x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x2x8xf32>
@@ -1068,12 +1068,12 @@ func.func @test_maxpool2d_adaptive_pad_third(%arg0: tensor<1x32x8194x8xf32>) ->
func.func @test_maxpool2d_adaptive_pad_forth(%arg0: tensor<1x32x8194x8xf32>) -> tensor<1x32x2x8xf32> {
// If the source of the kernel passed to max_pool2d_adaptive is a const_shape then pad < kernel check is applied.
// This is a workaround for the above so that we can level check the padding.
- %a = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %b = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %a = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %b = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%kernel = tosa.add_shape %a, %b : (!tosa.shape<2>, !tosa.shape<2>) -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 8193]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 8193]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_pool2d_adaptive' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x32x8194x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x2x8xf32>
@@ -1084,7 +1084,7 @@ func.func @test_maxpool2d_adaptive_pad_forth(%arg0: tensor<1x32x8194x8xf32>) ->
func.func @test_rfft2d_input_h(%arg0: tensor<13x16384x16xf32>) -> (tensor<13x16384x9xf32>, tensor<13x16384x9xf32>) {
// expected-error at +1 {{'tosa.rfft2d' op failed level check: H <= MAX_KERNEL (8192), got 16384}}
- %0, %1 = "tosa.rfft2d"(%arg0) {} : (tensor<13x16384x16xf32>) -> (tensor<13x16384x9xf32>, tensor<13x16384x9xf32>)
+ %0, %1 = "tosa.rfft2d"(%arg0) <{}> : (tensor<13x16384x16xf32>) -> (tensor<13x16384x9xf32>, tensor<13x16384x9xf32>)
return %0, %1 : tensor<13x16384x9xf32>, tensor<13x16384x9xf32>
}
@@ -1092,7 +1092,7 @@ func.func @test_rfft2d_input_h(%arg0: tensor<13x16384x16xf32>) -> (tensor<13x163
func.func @test_rfft2d_input_w(%arg0: tensor<13x8x16384xf32>) -> (tensor<13x8x8193xf32>, tensor<13x8x8193xf32>) {
// expected-error at +1 {{'tosa.rfft2d' op failed level check: W <= MAX_KERNEL (8192), got 16384}}
- %0, %1 = "tosa.rfft2d"(%arg0) {} : (tensor<13x8x16384xf32>) -> (tensor<13x8x8193xf32>, tensor<13x8x8193xf32>)
+ %0, %1 = "tosa.rfft2d"(%arg0) <{}> : (tensor<13x8x16384xf32>) -> (tensor<13x8x8193xf32>, tensor<13x8x8193xf32>)
return %0, %1 : tensor<13x8x8193xf32>, tensor<13x8x8193xf32>
}
@@ -1100,7 +1100,7 @@ func.func @test_rfft2d_input_w(%arg0: tensor<13x8x16384xf32>) -> (tensor<13x8x81
func.func @test_transpose_conv2d_weight_h(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x8193x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x8224x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: KH <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<16x8193x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8224x32x16xf32>
return %0 : tensor<1x8224x32x16xf32>
}
@@ -1109,7 +1109,7 @@ func.func @test_transpose_conv2d_weight_h(%arg0: tensor<1x32x32x8xf32>, %arg1: t
func.func @test_transpose_conv2d_weight_w(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x8193x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x8224x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: KW <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<16x1x8193x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x8224x16xf32>
return %0 : tensor<1x32x8224x16xf32>
}
@@ -1118,7 +1118,7 @@ func.func @test_transpose_conv2d_weight_w(%arg0: tensor<1x32x32x8xf32>, %arg1: t
func.func @test_transpose_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x8225x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 8193, 0, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([8193, 0, 0, 0]) stride([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8225x32x16xf32>
return %0 : tensor<1x8225x32x16xf32>
}
@@ -1127,7 +1127,7 @@ func.func @test_transpose_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: te
func.func @test_transpose_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x8225x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 8193, 0, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 8193, 0, 0]) stride([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8225x32x16xf32>
return %0 : tensor<1x8225x32x16xf32>
}
@@ -1136,7 +1136,7 @@ func.func @test_transpose_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1:
func.func @test_transpose_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x8225x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 8193, 0>, stride = array<i64: 1, 1>} :
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 8193, 0]) stride([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x8225x16xf32>
return %0 : tensor<1x32x8225x16xf32>
}
@@ -1145,7 +1145,7 @@ func.func @test_transpose_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: t
func.func @test_transpose_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x8225x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL (8192), got 8193}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 8193>, stride = array<i64: 1, 1>} :
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 8193]) stride([1, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x8225x16xf32>
return %0 : tensor<1x32x8225x16xf32>
}
@@ -1154,7 +1154,7 @@ func.func @test_transpose_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1:
func.func @test_transpose_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x253984x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 8193, 1>} :
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([8193, 1]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x253984x32x16xf32>
return %0 : tensor<1x253984x32x16xf32>
}
@@ -1163,7 +1163,7 @@ func.func @test_transpose_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: t
func.func @test_transpose_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x253984x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: stride <= MAX_STRIDE (8192), got 8193}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 8193>} :
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 8193]) acc_type(f32) :
(tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x253984x16xf32>
return %0 : tensor<1x32x253984x16xf32>
}
@@ -1171,11 +1171,11 @@ func.func @test_transpose_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: t
// -----
func.func @test_resize_scale_y(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x7970x64x8xf32> {
- %scale = tosa.const_shape { values = dense<[257, 1, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[257, 1, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op failed level check: scale_y_n/scale_y_d <= MAX_SCALE (256), got 257}}
- %1 = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} :
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> :
(tensor<1x32x32x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x7970x64x8xf32>
return %1 : tensor<1x7970x64x8xf32>
}
@@ -1183,11 +1183,11 @@ func.func @test_resize_scale_y(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x7970x64
// -----
func.func @test_resize_scale_x(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x64x7970x8xf32> {
- %scale = tosa.const_shape { values = dense<[4, 2, 257, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[4, 2, 257, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op failed level check: scale_x_n/scale_x_d <= MAX_SCALE (256), got 257}}
- %1 = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} :
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> :
(tensor<1x32x32x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x7970x8xf32>
return %1 : tensor<1x64x7970x8xf32>
}
@@ -1195,8 +1195,8 @@ func.func @test_resize_scale_x(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x64x7970
// -----
func.func @test_tensor_size_valid(%arg0: tensor<1x536870911xf32>) {
- %0 = tosa.const_shape {values = dense<0> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<1> : tensor<2xindex>) : () -> !tosa.shape<2>
%2= tosa.slice %arg0, %0, %1 : (tensor<1x536870911xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x1xf32>
return
}
@@ -1204,8 +1204,8 @@ func.func @test_tensor_size_valid(%arg0: tensor<1x536870911xf32>) {
// -----
func.func @test_slice_tensor_size_invalid(%arg0: tensor<1x536870912xf32>) {
- %0 = tosa.const_shape {values = dense<0> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.slice' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
%2= tosa.slice %arg0, %0, %1 : (tensor<1x536870912xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x1xf32>
return
@@ -1215,11 +1215,11 @@ func.func @test_slice_tensor_size_invalid(%arg0: tensor<1x536870912xf32>) {
// -----
func.func @test_resize_tensor_size_invalid(%arg0: tensor<1x23178x23178x1xf32>) {
- %scale = tosa.const_shape { values = dense<[127, 49, 12, 49]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[127, 49, 12, 49]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x23178x23178x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x23178x23178x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
return
}
@@ -1227,18 +1227,18 @@ func.func @test_resize_tensor_size_invalid(%arg0: tensor<1x23178x23178x1xf32>) {
func.func @test_avg_pool2d_tensor_size_invalid(%arg0: tensor<1x23178x23178x9xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x23178x23178x9xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
- %0 = tosa.avg_pool2d %arg0, %arg1, %arg2 {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x23178x23178x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x23178x23178x9xf32>
+ %0 = tosa.avg_pool2d %arg0, %arg1, %arg2 kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(f32) : (tensor<1x23178x23178x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x23178x23178x9xf32>
return %0 : tensor<1x23178x23178x9xf32>
}
// -----
func.func @test_avg_pool2d_adaptive_tensor_size_invalid(%arg0: tensor<1x23178x23178x9xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x23178x23178x9xf32> {
- %kernel = tosa.const_shape {values = dense<[2, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 1, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 1, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %arg1, %arg2, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %arg1, %arg2, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x23178x23178x9xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x23178x23178x9xf32>
return %0 : tensor<1x23178x23178x9xf32>
}
@@ -1247,7 +1247,7 @@ func.func @test_avg_pool2d_adaptive_tensor_size_invalid(%arg0: tensor<1x23178x23
func.func @test_conv2d_tensor_size_invalid(%arg0: tensor<1x23178x23178x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x23178x23178x8xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x23178x23178x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x23178x23178x8xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true) : (tensor<1x23178x23178x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x23178x23178x8xf32>
return %0 : tensor<1x23178x23178x8xf32>
}
@@ -1255,7 +1255,7 @@ func.func @test_conv2d_tensor_size_invalid(%arg0: tensor<1x23178x23178x4xf32>, %
func.func @test_fft2d_tensor_size_invalid(%arg0: tensor<123456x8192x8192xf32>, %arg1: tensor<123456x8192x8192xf32>) -> (tensor<123456x8192x8192xf32>, tensor<123456x8192x8192xf32>) {
// expected-error at +1 {{'tosa.fft2d' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
- %0, %1 = tosa.fft2d %arg0, %arg1 {inverse = false} : (tensor<123456x8192x8192xf32>, tensor<123456x8192x8192xf32>) -> (tensor<123456x8192x8192xf32>, tensor<123456x8192x8192xf32>)
+ %0, %1 = tosa.fft2d %arg0, %arg1 inverse(false) : (tensor<123456x8192x8192xf32>, tensor<123456x8192x8192xf32>) -> (tensor<123456x8192x8192xf32>, tensor<123456x8192x8192xf32>)
return %0, %1 : tensor<123456x8192x8192xf32>, tensor<123456x8192x8192xf32>
}
@@ -1270,7 +1270,7 @@ func.func @test_rfft2d_tensor_size_invalid(%arg0: tensor<536870912x8x16xf32>) ->
// -----
func.func @test_matmul_tensor_size_invalid(%arg0: tensor<23178x20000x19xf32>, %arg1: tensor<23178x19x28xf32>) -> tensor<23178x20000x28xf32> {
- %zero = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %zero = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.matmul' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
%0 = tosa.matmul %arg0, %arg1, %zero, %zero : (tensor<23178x20000x19xf32>, tensor<23178x19x28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<23178x20000x28xf32>
return %0 : tensor<23178x20000x28xf32>
@@ -1306,7 +1306,7 @@ func.func @test_row_gather_tensor_size_invalid(%arg0: tensor<536870912x21x3xf32>
func.func @test_custom_tensor_size_invalid(%arg0: tensor<536870912xi32>) -> tensor<536870912xi32> {
// expected-error at +1 {{'tosa.custom' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
- %0 = tosa.custom %arg0 {operator_name="custom_test", domain_name="tosa.mlir_test", implementation_attrs="" } : (tensor<536870912xi32>) -> (tensor<536870912xi32>)
+ %0 = tosa.custom %arg0 operator_name("custom_test") domain_name("tosa.mlir_test") implementation_attrs("") : (tensor<536870912xi32>) -> (tensor<536870912xi32>)
return %0 : tensor<536870912xi32>
}
@@ -1344,7 +1344,7 @@ module {
// -----
func.func @test_while_loop_tensor_size_invalid(%arg0: tensor<536870912xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
%1:3 = tosa.while_loop (%arg2 = %0, %arg3 = %0, %arg4 = %arg0) : (tensor<i32>, tensor<i32>, tensor<536870912xi32>) -> (tensor<i32>, tensor<i32>, tensor<536870912xi32>) {
%2 = tosa.greater_equal %arg3, %arg1 : (tensor<i32>, tensor<i32>) -> tensor<i1>
@@ -1352,8 +1352,8 @@ func.func @test_while_loop_tensor_size_invalid(%arg0: tensor<536870912xi32>, %ar
tosa.yield %2 : tensor<i1>
} do {
^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<536870912xi32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
- %3 = "tosa.const"() {values = dense<4> : tensor<1xi32>} : () -> tensor<1xi32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
+ %3 = "tosa.const"() <{values = dense<4> : tensor<1xi32>}> : () -> tensor<1xi32>
%4 = tosa.add %arg3, %2 : (tensor<i32>, tensor<i32>) -> tensor<i32>
// expected-error at +1 {{'tosa.add' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
%5 = tosa.add %arg4, %3 : (tensor<536870912xi32>, tensor<1xi32>) -> tensor<536870912xi32>
@@ -1366,7 +1366,7 @@ func.func @test_while_loop_tensor_size_invalid(%arg0: tensor<536870912xi32>, %ar
// -----
func.func @test_const_shape() {
- %cst = tosa.const_shape {values = dense<[1, 1, 536870912, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %cst = tosa.const_shape values(dense<[1, 1, 536870912, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
return
}
@@ -1416,7 +1416,7 @@ module {
// CHECK-LABEL: @test_while_loop
func.func @test_while_loop(%arg0: tensor<1x1x1x1x1x1x1xf32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
%1:2 = "tosa.while_loop"(%0, %arg0) ({
^bb0(%arg3: tensor<i32>, %arg4: tensor<1x1x1x1x1x1x1xf32>):
%2 = "tosa.greater_equal"(%arg3, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i1>
@@ -1424,7 +1424,7 @@ func.func @test_while_loop(%arg0: tensor<1x1x1x1x1x1x1xf32>, %arg1: tensor<i32>)
"tosa.yield"(%3) : (tensor<i1>) -> ()
}, {
^bb0(%arg3: tensor<i32>, %arg4: tensor<1x1x1x1x1x1x1xf32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%3 = "tosa.add"(%arg3, %2) : (tensor<i32>, tensor<i32>) -> tensor<i32>
"tosa.yield"(%3, %arg4) : (tensor<i32>, tensor<1x1x1x1x1x1x1xf32>) -> ()
}) : (tensor<i32>, tensor<1x1x1x1x1x1x1xf32>) -> (tensor<i32>, tensor<1x1x1x1x1x1x1xf32>)
@@ -1435,7 +1435,7 @@ func.func @test_while_loop(%arg0: tensor<1x1x1x1x1x1x1xf32>, %arg1: tensor<i32>)
// CHECK-LABEL: @test_custom_rank_valid
func.func @test_custom_rank_valid(%arg0: tensor<1x1x1x1x1x1x10xi32>) -> tensor<1x1x1x1x1x1x10xi32> {
- %0 = "tosa.custom"(%arg0) {operator_name="custom_test", domain_name="tosa_mlir_test", implementation_attrs=""} :
+ %0 = "tosa.custom"(%arg0) <{operator_name="custom_test", domain_name="tosa_mlir_test", implementation_attrs=""}> :
(tensor<1x1x1x1x1x1x10xi32>) -> (tensor<1x1x1x1x1x1x10xi32>)
return %0 : tensor<1x1x1x1x1x1x10xi32>
}
@@ -1444,8 +1444,8 @@ func.func @test_custom_rank_valid(%arg0: tensor<1x1x1x1x1x1x10xi32>) -> tensor<1
// CHECK-LABEL: unranked_tensor
func.func @test_unranked_tensor(%arg0: tensor<*xf32>) {
- %0 = tosa.const_shape {values = dense<[0]> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1 = tosa.const_shape {values = dense<[1]> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %0 = tosa.const_shape values(dense<[0]> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1 = tosa.const_shape values(dense<[1]> : tensor<1xindex>) : () -> !tosa.shape<1>
// expected-error at +1 {{'tosa.slice' op failed level check: unranked tensor}}
%2= tosa.slice %arg0, %0, %1 : (tensor<*xf32>, !tosa.shape<1>, !tosa.shape<1>) -> tensor<*xf32>
@@ -1456,8 +1456,8 @@ func.func @test_unranked_tensor(%arg0: tensor<*xf32>) {
// CHECK-LABEL: tensor_dim
func.func @test_tensor_dim(%arg0: tensor<1x2147483648xf32>) {
- %0 = tosa.const_shape {values = dense<0> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<1> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.slice' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
%2= tosa.slice %arg0, %0, %1 : (tensor<1x2147483648xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x1xf32>
@@ -1468,8 +1468,8 @@ func.func @test_tensor_dim(%arg0: tensor<1x2147483648xf32>) {
// CHECK-LABEL: tensor_size
func.func @test_tensor_size(%arg0: tensor<1x1073741824xf32>) {
- %0 = tosa.const_shape {values = dense<0> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<1> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.slice' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
%2= tosa.slice %arg0, %0, %1 : (tensor<1x1073741824xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x1xf32>
@@ -1480,8 +1480,8 @@ func.func @test_tensor_size(%arg0: tensor<1x1073741824xf32>) {
// CHECK-LABEL: tensor_size
func.func @test_tensor_size_ok(%arg0: tensor<1x536870911xf32>) {
- %0 = tosa.const_shape {values = dense<0> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<1> : tensor<2xindex>) : () -> !tosa.shape<2>
%2= tosa.slice %arg0, %0, %1 : (tensor<1x536870911xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x1xf32>
return
@@ -1491,7 +1491,7 @@ func.func @test_tensor_size_ok(%arg0: tensor<1x536870911xf32>) {
// CHECK-LABEL: test_concat_tensor_list_size
func.func @test_concat_tensor_list_size() {
- %0 = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.concat' op failed level check: length(tensor_list_shape(input1)) <= MAX_TENSOR_LIST_SIZE (64), got 65}}
%1= tosa.concat %0, %0, %0, %0, %0, %0, %0, %0,
%0, %0, %0, %0, %0, %0, %0, %0,
@@ -1501,7 +1501,7 @@ func.func @test_concat_tensor_list_size() {
%0, %0, %0, %0, %0, %0, %0, %0,
%0, %0, %0, %0, %0, %0, %0, %0,
%0, %0, %0, %0, %0, %0, %0, %0,
- %0 { axis = 0 : i32 }:
+ %0 axis(0) :
(
tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>,
tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>,
@@ -1520,7 +1520,7 @@ func.func @test_concat_tensor_list_size() {
// CHECK-LABEL: test_custom_tensor_list_size
func.func @test_custom_tensor_list_size() {
- %0 = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.custom' op failed level check: length(tensor_list_shape(input_list)) <= MAX_TENSOR_LIST_SIZE (64), got 65}}
%1= tosa.custom %0, %0, %0, %0, %0, %0, %0, %0,
%0, %0, %0, %0, %0, %0, %0, %0,
@@ -1530,7 +1530,7 @@ func.func @test_custom_tensor_list_size() {
%0, %0, %0, %0, %0, %0, %0, %0,
%0, %0, %0, %0, %0, %0, %0, %0,
%0, %0, %0, %0, %0, %0, %0, %0,
- %0 { domain_name = "tosa_mlir_test", operator_name = "custom_test", implementation_attrs = "" }:
+ %0 operator_name("custom_test") domain_name("tosa_mlir_test") implementation_attrs("") :
(
tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>,
tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>,
@@ -1549,10 +1549,10 @@ func.func @test_custom_tensor_list_size() {
// CHECK-LABEL: test_custom_tensor_list_size_results
func.func @test_custom_tensor_list_size_results() {
- %0 = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.custom' op failed level check: length(tensor_list_shape(output_list)) <= MAX_TENSOR_LIST_SIZE (64), got 65}}
- %r:65 = tosa.custom %0 { domain_name = "tosa_mlir_test", operator_name = "custom_test", implementation_attrs = "" }:
+ %r:65 = tosa.custom %0 operator_name("custom_test") domain_name("tosa_mlir_test") implementation_attrs("") :
( tensor<1xi32> )
-> (
tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>,
@@ -1572,7 +1572,7 @@ func.func @test_custom_tensor_list_size_results() {
// CHECK-LABEL: test_if_tensor_list_size
func.func @test_if_tensor_list_size(%arg0 : tensor<i1>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.cond_if' op failed level check: length(tensor_list_shape(inputs)) <= MAX_TENSOR_LIST_SIZE (64), got 65}}
%1 = "tosa.cond_if"(%arg0, // condition
%0, %0, %0, %0, %0, %0, %0, %0, %0, %0,
@@ -1618,7 +1618,7 @@ func.func @test_if_tensor_list_size(%arg0 : tensor<i1>) {
// CHECK-LABEL: test_if_tensor_list_size_outputs
func.func @test_if_tensor_list_size_outputs(%arg0 : tensor<i1>) {
- %cst_0 = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %cst_0 = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.cond_if' op failed level check: length(tensor_list_shape(outputs)) <= MAX_TENSOR_LIST_SIZE (64), got 65}}
%r:65 = "tosa.cond_if"(%arg0, %cst_0) ({
@@ -1673,7 +1673,7 @@ func.func @test_if_tensor_list_size_outputs(%arg0 : tensor<i1>) {
// CHECK-LABEL: test_while_tensor_list_size
func.func @test_while_tensor_list_size(%arg0: tensor<1x1x1x1x1x1x1xf32>, %arg1: tensor<1xi32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.while_loop' op failed level check: length(tensor_list_shape(inputs)) <= MAX_TENSOR_LIST_SIZE (64), got 65}}
%1:65 = "tosa.while_loop"(%0, %arg0,
%0, %0, %0, %0, %0, %0, %0, %0, %0, %0,
@@ -1706,7 +1706,7 @@ func.func @test_while_tensor_list_size(%arg0: tensor<1x1x1x1x1x1x1xf32>, %arg1:
%50: tensor<1xi32>, %51: tensor<1xi32>, %52: tensor<1xi32>, %53: tensor<1xi32>, %54: tensor<1xi32>, %55: tensor<1xi32>, %56: tensor<1xi32>, %57: tensor<1xi32>, %58: tensor<1xi32>, %59: tensor<1xi32>,
%60: tensor<1xi32>, %61: tensor<1xi32>, %62: tensor<1xi32>
):
- %2 = "tosa.const"() {values = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
%3 = "tosa.add"(%arg3, %2) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
"tosa.yield"(%3, %arg4,
%0, %0, %0, %0, %0, %0, %0, %0, %0, %0,
@@ -1791,8 +1791,8 @@ func.func @test_cond_if_max_nested_depth(%arg0: tensor<f32>, %arg1: tensor<f32>,
// -----
func.func @test_while_loop_max_nested_depth(%arg0: tensor<i32>) {
- %init_0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
- %cst_1 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %init_0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
+ %cst_1 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%1:2 = tosa.while_loop (%arg2 = %init_0, %arg3 = %arg0) : (tensor<i32>, tensor<i32>) -> (tensor<i32>, tensor<i32>) {
%2 = tosa.greater_equal %arg3, %arg2 : (tensor<i32>, tensor<i32>) -> tensor<i1>
@@ -1850,7 +1850,7 @@ func.func @test_while_loop_max_nested_depth(%arg0: tensor<i32>) {
func.func @test_unranked_weight_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<*xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<*xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: unranked tensor}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4xf32>, tensor<*xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<*xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true) : (tensor<1x4x4x4xf32>, tensor<*xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -1858,7 +1858,7 @@ func.func @test_unranked_weight_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor
func.func @test_matmul_t_block_scaled_invalid_size(%arg0: tensor<4x8x536870912xf4E2M1FN>, %arg1: tensor<4x8x16777216xf8E8M0FNU>, %arg2: tensor<4x16x536870912xf4E2M1FN>, %arg3: tensor<4x16x16777216xf8E8M0FNU>) -> tensor<*xf32> {
// expected-error at +1 {{'tosa.matmul_t_block_scaled' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x8x536870912xf4E2M1FN>, tensor<4x8x16777216xf8E8M0FNU>, tensor<4x16x536870912xf4E2M1FN>, tensor<4x16x16777216xf8E8M0FNU>) -> tensor<*xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x536870912xf4E2M1FN>, tensor<4x8x16777216xf8E8M0FNU>, tensor<4x16x536870912xf4E2M1FN>, tensor<4x16x16777216xf8E8M0FNU>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -1866,7 +1866,7 @@ func.func @test_matmul_t_block_scaled_invalid_size(%arg0: tensor<4x8x536870912xf
func.func @test_cast_from_block_scaled_invalid_size(%arg0: tensor<67108864x32xf6E2M3FN>, %arg1: tensor<67108864x1xf8E8M0FNU>) -> tensor<67108864x32xf32> {
// expected-error at +1 {{'tosa.cast_from_block_scaled' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<67108864x32xf6E2M3FN>, tensor<67108864x1xf8E8M0FNU>) -> tensor<67108864x32xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<67108864x32xf6E2M3FN>, tensor<67108864x1xf8E8M0FNU>) -> tensor<67108864x32xf32>
return %0 : tensor<67108864x32xf32>
}
@@ -1874,7 +1874,7 @@ func.func @test_cast_from_block_scaled_invalid_size(%arg0: tensor<67108864x32xf6
func.func @test_cast_from_block_scaled_invalid_rank(%arg0: tensor<1x2x3x4x5x6x7x32xf6E2M3FN>, %arg1: tensor<1x2x3x4x5x6x7x1xf8E8M0FNU>) -> tensor<1x2x3x4x5x6x7x32xf32> {
// expected-error at +1 {{'tosa.cast_from_block_scaled' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x2x3x4x5x6x7x32xf6E2M3FN>, tensor<1x2x3x4x5x6x7x1xf8E8M0FNU>) -> tensor<1x2x3x4x5x6x7x32xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<1x2x3x4x5x6x7x32xf6E2M3FN>, tensor<1x2x3x4x5x6x7x1xf8E8M0FNU>) -> tensor<1x2x3x4x5x6x7x32xf32>
return %0 : tensor<1x2x3x4x5x6x7x32xf32>
}
@@ -1882,7 +1882,7 @@ func.func @test_cast_from_block_scaled_invalid_rank(%arg0: tensor<1x2x3x4x5x6x7x
func.func @test_cast_to_block_scaled_invalid_size(%arg0: tensor<67108864x32xf32>) -> (tensor<67108864x32xf6E2M3FN>, tensor<67108864x1xf8E8M0FNU>) {
// expected-error at +1 {{'tosa.cast_to_block_scaled' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<67108864x32xf32>) -> (tensor<67108864x32xf6E2M3FN>, tensor<67108864x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<67108864x32xf32>) -> (tensor<67108864x32xf6E2M3FN>, tensor<67108864x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<67108864x32xf6E2M3FN>, tensor<67108864x1xf8E8M0FNU>
}
@@ -1890,15 +1890,15 @@ func.func @test_cast_to_block_scaled_invalid_size(%arg0: tensor<67108864x32xf32>
func.func @test_cast_to_block_scaled_invalid_rank(%arg0: tensor<1x2x3x4x5x6x7x32xf32>) -> (tensor<1x2x3x4x5x6x7x32xf6E2M3FN>, tensor<1x2x3x4x5x6x7x1xf8E8M0FNU>) {
// expected-error at +1 {{'tosa.cast_to_block_scaled' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x2x3x4x5x6x7x32xf32>) -> (tensor<1x2x3x4x5x6x7x32xf6E2M3FN>, tensor<1x2x3x4x5x6x7x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<1x2x3x4x5x6x7x32xf32>) -> (tensor<1x2x3x4x5x6x7x32xf6E2M3FN>, tensor<1x2x3x4x5x6x7x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<1x2x3x4x5x6x7x32xf6E2M3FN>, tensor<1x2x3x4x5x6x7x1xf8E8M0FNU>
}
// -----
func.func @test_add_shape_invalid_rank() {
- %a = tosa.const_shape {values = dense<0> : tensor<17xindex>} : () -> !tosa.shape<17>
- %b = tosa.const_shape {values = dense<0> : tensor<17xindex>} : () -> !tosa.shape<17>
+ %a = tosa.const_shape values(dense<0> : tensor<17xindex>) : () -> !tosa.shape<17>
+ %b = tosa.const_shape values(dense<0> : tensor<17xindex>) : () -> !tosa.shape<17>
// expected-error at +1 {{'tosa.add_shape' op failed shape type level check: '!tosa.shape<17>' exceeds MAX_SHAPE_LEN}}
%c = tosa.add_shape %a, %b : (!tosa.shape<17>, !tosa.shape<17>) -> !tosa.shape<17>
return
@@ -1907,8 +1907,8 @@ func.func @test_add_shape_invalid_rank() {
// -----
func.func @test_div_floor_shape_invalid_rank() {
- %a = tosa.const_shape {values = dense<0> : tensor<17xindex>} : () -> !tosa.shape<17>
- %b = tosa.const_shape {values = dense<0> : tensor<17xindex>} : () -> !tosa.shape<17>
+ %a = tosa.const_shape values(dense<0> : tensor<17xindex>) : () -> !tosa.shape<17>
+ %b = tosa.const_shape values(dense<0> : tensor<17xindex>) : () -> !tosa.shape<17>
// expected-error at +1 {{'tosa.div_floor_shape' op failed shape type level check: '!tosa.shape<17>' exceeds MAX_SHAPE_LEN}}
%c = tosa.div_floor_shape %a, %b : (!tosa.shape<17>, !tosa.shape<17>) -> !tosa.shape<17>
return
@@ -1918,14 +1918,14 @@ func.func @test_div_floor_shape_invalid_rank() {
func.func @test_dim(%arg0: tensor<1x2x3x4x5x6x7x8xi32>) {
// expected-error at +1 {{'tosa.dim' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = tosa.dim %arg0 {axis = 2 : i32} : (tensor<1x2x3x4x5x6x7x8xi32>) -> !tosa.shape<1>
+ %0 = tosa.dim %arg0 axis(2) : (tensor<1x2x3x4x5x6x7x8xi32>) -> !tosa.shape<1>
return
}
// -----
func.func @test_exp2_shape_invalid_rank() {
- %0 = tosa.const_shape {values = dense<0> : tensor<17xindex>} : () -> !tosa.shape<17>
+ %0 = tosa.const_shape values(dense<0> : tensor<17xindex>) : () -> !tosa.shape<17>
// expected-error at +1 {{'tosa.exp2_shape' op failed shape type level check: '!tosa.shape<17>' exceeds MAX_SHAPE_LEN}}
%1 = tosa.exp2_shape %0 : (!tosa.shape<17>) -> !tosa.shape<17>
return
@@ -1934,7 +1934,7 @@ func.func @test_exp2_shape_invalid_rank() {
// -----
func.func @test_log2_floor_shape_invalid_rank() {
- %0 = tosa.const_shape {values = dense<0> : tensor<17xindex>} : () -> !tosa.shape<17>
+ %0 = tosa.const_shape values(dense<0> : tensor<17xindex>) : () -> !tosa.shape<17>
// expected-error at +1 {{'tosa.log2_floor_shape' op failed shape type level check: '!tosa.shape<17>' exceeds MAX_SHAPE_LEN}}
%1 = tosa.log2_floor_shape %0 : (!tosa.shape<17>) -> !tosa.shape<17>
return
@@ -1943,7 +1943,7 @@ func.func @test_log2_floor_shape_invalid_rank() {
// -----
func.func @test_log2_ceil_shape_invalid_rank() {
- %0 = tosa.const_shape {values = dense<0> : tensor<17xindex>} : () -> !tosa.shape<17>
+ %0 = tosa.const_shape values(dense<0> : tensor<17xindex>) : () -> !tosa.shape<17>
// expected-error at +1 {{'tosa.log2_ceil_shape' op failed shape type level check: '!tosa.shape<17>' exceeds MAX_SHAPE_LEN}}
%1 = tosa.log2_ceil_shape %0 : (!tosa.shape<17>) -> !tosa.shape<17>
return
@@ -1952,8 +1952,8 @@ func.func @test_log2_ceil_shape_invalid_rank() {
// -----
func.func @test_mod_shape_invalid_rank() {
- %a = tosa.const_shape {values = dense<0> : tensor<17xindex>} : () -> !tosa.shape<17>
- %b = tosa.const_shape {values = dense<0> : tensor<17xindex>} : () -> !tosa.shape<17>
+ %a = tosa.const_shape values(dense<0> : tensor<17xindex>) : () -> !tosa.shape<17>
+ %b = tosa.const_shape values(dense<0> : tensor<17xindex>) : () -> !tosa.shape<17>
// expected-error at +1 {{'tosa.mod_shape' op failed shape type level check: '!tosa.shape<17>' exceeds MAX_SHAPE_LEN}}
%c = tosa.mod_shape %a, %b : (!tosa.shape<17>, !tosa.shape<17>) -> !tosa.shape<17>
return
@@ -1962,22 +1962,22 @@ func.func @test_mod_shape_invalid_rank() {
// -----
func.func @test_conv2d_block_scaled_invalid_size(%arg0: tensor<67108864x4x4x64xf4E2M1FN>, %arg1: tensor<67108864x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>) -> tensor<67108864x4x4x8xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op failed level check: operand tensor size (in bytes) <= (1 << MAX_LOG2_SIZE - 1)}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = BLOCK_SIZE_32} : (tensor<67108864x4x4x64xf4E2M1FN>, tensor<67108864x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<67108864x4x4x8xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<67108864x4x4x64xf4E2M1FN>, tensor<67108864x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<67108864x4x4x8xf32>
return %0 : tensor<67108864x4x4x8xf32>
}
// -----
func.func @test_conv2d_block_scaled_dilation_y(%arg0: tensor<1x8191x8191x32xf8E4M3FN>, %arg1: tensor<1x8191x8191x1xf8E8M0FNU>, %arg2: tensor<16x1025x1024x32xf8E4M3FN>, %arg3: tensor<16x1025x1024x1xf8E8M0FNU>, %arg4: tensor<16xf32>) -> tensor<1x9x7178x16xf32> {
- %pad = tosa.const_shape {values = dense<[10, 0, 10, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[8, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[10, 0, 10, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[8, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op failed level check: dilation_y * KH <= MAX_KERNEL}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = BLOCK_SIZE_32} :
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> :
(tensor<1x8191x8191x32xf8E4M3FN>, tensor<1x8191x8191x1xf8E8M0FNU>, tensor<16x1025x1024x32xf8E4M3FN>, tensor<16x1025x1024x1xf8E8M0FNU>, tensor<16xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x9x7178x16xf32>
return %0 : tensor<1x9x7178x16xf32>
}
@@ -1985,11 +1985,11 @@ func.func @test_conv2d_block_scaled_dilation_y(%arg0: tensor<1x8191x8191x32xf8E4
// -----
func.func @test_conv2d_block_scaled_dilation_x(%arg0: tensor<1x8191x8191x32xf8E4M3FN>, %arg1: tensor<1x8191x8191x1xf8E8M0FNU>, %arg2: tensor<16x1024x1025x32xf8E4M3FN>, %arg3: tensor<16x1024x1025x1xf8E8M0FNU>, %arg4: tensor<16xf32>) -> tensor<1x7178x9x16xf32> {
- %pad = tosa.const_shape {values = dense<[10, 0, 10, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 8]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[10, 0, 10, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 8]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op failed level check: dilation_x * KW <= MAX_KERNEL}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = BLOCK_SIZE_32} :
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> :
(tensor<1x8191x8191x32xf8E4M3FN>, tensor<1x8191x8191x1xf8E8M0FNU>, tensor<16x1024x1025x32xf8E4M3FN>, tensor<16x1024x1025x1xf8E8M0FNU>, tensor<16xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x7178x9x16xf32>
return %0 : tensor<1x7178x9x16xf32>
}
@@ -1997,11 +1997,11 @@ func.func @test_conv2d_block_scaled_dilation_x(%arg0: tensor<1x8191x8191x32xf8E4
// -----
func.func @test_conv2d_block_scaled_pad_top(%arg0: tensor<1x32x32x32xf8E4M3FN>, %arg1: tensor<1x32x32x1xf8E8M0FNU>, %arg2: tensor<16x2x2x32xf8E4M3FN>, %arg3: tensor<16x2x2x1xf8E8M0FNU>, %arg4: tensor<16xf32>) -> tensor<1x8224x31x16xf32> {
- %pad = tosa.const_shape {values = dense<[8193, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[8193, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op failed level check: pad <= MAX_KERNEL}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} :
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> :
(tensor<1x32x32x32xf8E4M3FN>, tensor<1x32x32x1xf8E8M0FNU>, tensor<16x2x2x32xf8E4M3FN>, tensor<16x2x2x1xf8E8M0FNU>, tensor<16xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x8224x31x16xf32>
return %0 : tensor<1x8224x31x16xf32>
}
@@ -2009,11 +2009,11 @@ func.func @test_conv2d_block_scaled_pad_top(%arg0: tensor<1x32x32x32xf8E4M3FN>,
// -----
func.func @test_conv2d_block_scaled_pad_right(%arg0: tensor<1x32x32x32xf8E4M3FN>, %arg1: tensor<1x32x32x1xf8E8M0FNU>, %arg2: tensor<16x2x2x32xf8E4M3FN>, %arg3: tensor<16x2x2x1xf8E8M0FNU>, %arg4: tensor<16xf32>) -> tensor<1x31x8224x16xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 8193]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 8193]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op failed level check: pad <= MAX_KERNEL}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} :
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> :
(tensor<1x32x32x32xf8E4M3FN>, tensor<1x32x32x1xf8E8M0FNU>, tensor<16x2x2x32xf8E4M3FN>, tensor<16x2x2x1xf8E8M0FNU>, tensor<16xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x31x8224x16xf32>
return %0 : tensor<1x31x8224x16xf32>
}
@@ -2021,11 +2021,11 @@ func.func @test_conv2d_block_scaled_pad_right(%arg0: tensor<1x32x32x32xf8E4M3FN>
// -----
func.func @test_conv2d_block_scaled_stride_y(%arg0: tensor<1x8194x33x32xf8E4M3FN>, %arg1: tensor<1x8194x33x1xf8E8M0FNU>, %arg2: tensor<16x2x2x32xf8E4M3FN>, %arg3: tensor<16x2x2x1xf8E8M0FNU>, %arg4: tensor<16xf32>) -> tensor<1x2x32x16xf32> {
- %pad = tosa.const_shape {values = dense<[1, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[8193, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[1, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[8193, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op failed level check: stride <= MAX_KERNEL}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} :
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> :
(tensor<1x8194x33x32xf8E4M3FN>, tensor<1x8194x33x1xf8E8M0FNU>, tensor<16x2x2x32xf8E4M3FN>, tensor<16x2x2x1xf8E8M0FNU>, tensor<16xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x2x32x16xf32>
return %0 : tensor<1x2x32x16xf32>
}
@@ -2033,9 +2033,9 @@ func.func @test_conv2d_block_scaled_stride_y(%arg0: tensor<1x8194x33x32xf8E4M3FN
// -----
func.func @test_assert_equal_shape_invalid_rank() -> () {
- %a = tosa.const_shape {values = dense<0> : tensor<17xindex>} : () -> !tosa.shape<17>
- %b = tosa.const_shape {values = dense<0> : tensor<17xindex>} : () -> !tosa.shape<17>
+ %a = tosa.const_shape values(dense<0> : tensor<17xindex>) : () -> !tosa.shape<17>
+ %b = tosa.const_shape values(dense<0> : tensor<17xindex>) : () -> !tosa.shape<17>
// expected-error at +1 {{'tosa.assert_equal_shape' op failed shape type level check: '!tosa.shape<17>' exceeds MAX_SHAPE_LEN}}
- tosa.assert_equal_shape %a, %b {allow_broadcast = true} : (!tosa.shape<17>, !tosa.shape<17>) -> ()
+ tosa.assert_equal_shape %a, %b allow_broadcast(true) : (!tosa.shape<17>, !tosa.shape<17>) -> ()
return
}
diff --git a/mlir/test/Dialect/Tosa/ops.mlir b/mlir/test/Dialect/Tosa/ops.mlir
index 5de93b6a01a7e..79d8f8ed74902 100644
--- a/mlir/test/Dialect/Tosa/ops.mlir
+++ b/mlir/test/Dialect/Tosa/ops.mlir
@@ -5,7 +5,7 @@
// -----
// CHECK-LABEL: argmax
func.func @test_argmax(%arg0: tensor<14x19xf32>) -> tensor<14xi32> {
- %0 = tosa.argmax %arg0 {axis = 1 : i32} : (tensor<14x19xf32>) -> tensor<14xi32>
+ %0 = tosa.argmax %arg0 axis(1) : (tensor<14x19xf32>) -> tensor<14xi32>
return %0 : tensor<14xi32>
}
@@ -14,7 +14,7 @@ func.func @test_argmax(%arg0: tensor<14x19xf32>) -> tensor<14xi32> {
func.func @test_avg_pool2d_f32(%arg0: tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf32>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(f32) : (tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf32>
return %0 : tensor<1x7x7x9xf32>
}
@@ -23,7 +23,7 @@ func.func @test_avg_pool2d_f32(%arg0: tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32
func.func @test_avg_pool2d_f16(%arg0: tensor<1x7x7x9xf16>) -> tensor<1x7x7x9xf16> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f16, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x7x7x9xf16>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(f16) : (tensor<1x7x7x9xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x7x7x9xf16>
return %0 : tensor<1x7x7x9xf16>
}
@@ -32,7 +32,7 @@ func.func @test_avg_pool2d_f16(%arg0: tensor<1x7x7x9xf16>) -> tensor<1x7x7x9xf16
func.func @test_avg_pool2d_f16_accumf32(%arg0: tensor<1x7x7x9xf16>) -> tensor<1x7x7x9xf16> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x7x7x9xf16>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(f32) : (tensor<1x7x7x9xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x7x7x9xf16>
return %0 : tensor<1x7x7x9xf16>
}
@@ -41,7 +41,7 @@ func.func @test_avg_pool2d_f16_accumf32(%arg0: tensor<1x7x7x9xf16>) -> tensor<1x
func.func @test_avg_pool2d_i8(%arg0: tensor<1x7x7x9xi8>) -> tensor<1x7x7x9xi8> {
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x7x7x9xi8>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(i32) : (tensor<1x7x7x9xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x7x7x9xi8>
return %0 : tensor<1x7x7x9xi8>
}
@@ -50,7 +50,7 @@ func.func @test_avg_pool2d_i8(%arg0: tensor<1x7x7x9xi8>) -> tensor<1x7x7x9xi8> {
func.func @test_avg_pool2d_i16(%arg0: tensor<1x7x7x9xi16>) -> tensor<1x7x7x9xi16> {
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
%output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xi16>, tensor<1xi16>, tensor<1xi16>) -> tensor<1x7x7x9xi16>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(i32) : (tensor<1x7x7x9xi16>, tensor<1xi16>, tensor<1xi16>) -> tensor<1x7x7x9xi16>
return %0 : tensor<1x7x7x9xi16>
}
@@ -59,7 +59,7 @@ func.func @test_avg_pool2d_i16(%arg0: tensor<1x7x7x9xi16>) -> tensor<1x7x7x9xi16
func.func @test_avg_pool2d_q8(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>> {
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(i32) : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
return %0 : tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
}
@@ -68,10 +68,10 @@ func.func @test_avg_pool2d_q8(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>
func.func @test_avg_pool2d_adaptive_f32(%arg0: tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[2, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 1, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = f32} : (tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xf32>
+ %kernel = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 1, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(f32) : (tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xf32>
return %0 : tensor<1x7x7x9xf32>
}
@@ -80,10 +80,10 @@ func.func @test_avg_pool2d_adaptive_f32(%arg0: tensor<1x7x7x9xf32>) -> tensor<1x
func.func @test_avg_pool2d_adaptive_f16(%arg0: tensor<1x7x7x9xf16>) -> tensor<1x7x7x9xf16> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
- %kernel = tosa.const_shape {values = dense<[2, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 1, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = f16} : (tensor<1x7x7x9xf16>, tensor<1xf16>, tensor<1xf16>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xf16>
+ %kernel = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 1, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(f16) : (tensor<1x7x7x9xf16>, tensor<1xf16>, tensor<1xf16>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xf16>
return %0 : tensor<1x7x7x9xf16>
}
@@ -92,10 +92,10 @@ func.func @test_avg_pool2d_adaptive_f16(%arg0: tensor<1x7x7x9xf16>) -> tensor<1x
func.func @test_avg_pool2d_adaptive_f16_accumf32(%arg0: tensor<1x7x7x9xf16>) -> tensor<1x7x7x9xf16> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
- %kernel = tosa.const_shape {values = dense<[2, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 1, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = f32} : (tensor<1x7x7x9xf16>, tensor<1xf16>, tensor<1xf16>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xf16>
+ %kernel = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 1, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(f32) : (tensor<1x7x7x9xf16>, tensor<1xf16>, tensor<1xf16>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xf16>
return %0 : tensor<1x7x7x9xf16>
}
@@ -104,10 +104,10 @@ func.func @test_avg_pool2d_adaptive_f16_accumf32(%arg0: tensor<1x7x7x9xf16>) ->
func.func @test_avg_pool2d_adaptive_i8(%arg0: tensor<1x7x7x9xi8>) -> tensor<1x7x7x9xi8> {
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %kernel = tosa.const_shape {values = dense<[2, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 1, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = i32} : (tensor<1x7x7x9xi8>, tensor<1xi8>, tensor<1xi8>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xi8>
+ %kernel = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 1, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(i32) : (tensor<1x7x7x9xi8>, tensor<1xi8>, tensor<1xi8>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xi8>
return %0 : tensor<1x7x7x9xi8>
}
@@ -116,10 +116,10 @@ func.func @test_avg_pool2d_adaptive_i8(%arg0: tensor<1x7x7x9xi8>) -> tensor<1x7x
func.func @test_avg_pool2d_adaptive_i16(%arg0: tensor<1x7x7x9xi16>) -> tensor<1x7x7x9xi16> {
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
%output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
- %kernel = tosa.const_shape {values = dense<[2, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 1, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = i32} : (tensor<1x7x7x9xi16>, tensor<1xi16>, tensor<1xi16>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xi16>
+ %kernel = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 1, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(i32) : (tensor<1x7x7x9xi16>, tensor<1xi16>, tensor<1xi16>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xi16>
return %0 : tensor<1x7x7x9xi16>
}
@@ -128,17 +128,17 @@ func.func @test_avg_pool2d_adaptive_i16(%arg0: tensor<1x7x7x9xi16>) -> tensor<1x
func.func @test_avg_pool2d_adaptive_q8(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>> {
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %kernel = tosa.const_shape {values = dense<[2, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 1, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = i32} : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, tensor<1xi8>, tensor<1xi8>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
+ %kernel = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 1, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(i32) : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, tensor<1xi8>, tensor<1xi8>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
return %0 : tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
}
// -----
// CHECK-LABEL: conv2d
func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true) : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -147,88 +147,88 @@ func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %
func.func @test_conv2d_mxfp(%arg0: tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, %arg1: tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, %arg2: tensor<8xf16>) -> tensor<1x4x4x8xf16> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = bf16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xf16>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(bf16) local_bound(true) : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xf16>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf16>
return %0 : tensor<1x4x4x8xf16>
}
// -----
// CHECK-LABEL: conv2d_unranked_input
func.func @test_conv2d_unranked_input(%arg0: tensor<*xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<*xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true) : (tensor<*xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
// -----
// CHECK-LABEL: conv2d_quant_uniform
func.func @test_conv2d_quant_uniform(%arg0: tensor<1x4x4x4x!quant.uniform<i8:f32, 0.01>>, %arg1: tensor<8x1x1x4x!quant.uniform<i8:f32, 0.01>>, %arg2: tensor<8x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x4x4x8x!quant.uniform<i32:f32, 0.01>> {
- %zp = "tosa.const" () { values = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4x!quant.uniform<i8:f32, 0.01>>, tensor<8x1x1x4x!quant.uniform<i8:f32, 0.01>>, tensor<8x!quant.uniform<i8:f32, 0.01>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8x!quant.uniform<i32:f32, 0.01>>
+ %zp = "tosa.const" () <{ values = dense<0> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) local_bound(true) : (tensor<1x4x4x4x!quant.uniform<i8:f32, 0.01>>, tensor<8x1x1x4x!quant.uniform<i8:f32, 0.01>>, tensor<8x!quant.uniform<i8:f32, 0.01>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8x!quant.uniform<i32:f32, 0.01>>
return %0 : tensor<1x4x4x8x!quant.uniform<i32:f32, 0.01>>
}
// -----
// CHECK-LABEL: conv2d_quant_any
func.func @test_conv2d_quant_any(%arg0: tensor<1x4x4x4x!quant.any<i8<-8:7>>>, %arg1: tensor<8x1x1x4x!quant.any<i8<-8:7>>>, %arg2: tensor<8x!quant.any<i8<-8:7>>>) -> tensor<1x4x4x8x!quant.any<i32<-8:7>>> {
- %zp = "tosa.const" () { values = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4x!quant.any<i8<-8:7>>>, tensor<8x1x1x4x!quant.any<i8<-8:7>>>, tensor<8x!quant.any<i8<-8:7>>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8x!quant.any<i32<-8:7>>>
+ %zp = "tosa.const" () <{ values = dense<0> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) local_bound(true) : (tensor<1x4x4x4x!quant.any<i8<-8:7>>>, tensor<8x1x1x4x!quant.any<i8<-8:7>>>, tensor<8x!quant.any<i8<-8:7>>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8x!quant.any<i32<-8:7>>>
return %0 : tensor<1x4x4x8x!quant.any<i32<-8:7>>>
}
// -----
// CHECK-LABEL: conv2d_q8xi4
func.func @test_conv2d_q8xi4(%arg0: tensor<1x11x11x3xi8>) -> tensor<1x1x1x3xi8> {
- %0 = "tosa.const"() {values = dense<0> : tensor<3x11x11x3xi4>} : () -> tensor<3x11x11x3xi4>
- %1 = "tosa.const"() {values = dense<[12, 23, 55]> : tensor<3xi32>} : () -> tensor<3xi32>
- %izp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %wzp = "tosa.const"() {values = dense<0> : tensor<1xi4>} : () -> tensor<1xi4>
- %2 = "tosa.conv2d"(%arg0, %0, %1, %izp, %wzp) {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x11x11x3xi8>, tensor<3x11x11x3xi4>, tensor<3xi32>, tensor<1xi8>, tensor<1xi4>) -> tensor<1x1x1x3xi32>
- %multiplier = "tosa.const"() {values = dense<[2026291432, 1079222024, 1693132724]> : tensor<3xi32>} : () -> tensor<3xi32>
- %shift = "tosa.const"() {values = dense<[37, 36, 37]> : tensor<3xi8>} : () -> tensor<3xi8>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<3x11x11x3xi4>}> : () -> tensor<3x11x11x3xi4>
+ %1 = "tosa.const"() <{values = dense<[12, 23, 55]> : tensor<3xi32>}> : () -> tensor<3xi32>
+ %izp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %wzp = "tosa.const"() <{values = dense<0> : tensor<1xi4>}> : () -> tensor<1xi4>
+ %2 = "tosa.conv2d"(%arg0, %0, %1, %izp, %wzp) <{acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}> : (tensor<1x11x11x3xi8>, tensor<3x11x11x3xi4>, tensor<3xi32>, tensor<1xi8>, tensor<1xi4>) -> tensor<1x1x1x3xi32>
+ %multiplier = "tosa.const"() <{values = dense<[2026291432, 1079222024, 1693132724]> : tensor<3xi32>}> : () -> tensor<3xi32>
+ %shift = "tosa.const"() <{values = dense<[37, 36, 37]> : tensor<3xi8>}> : () -> tensor<3xi8>
%rescale_input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
%rescale_output_zp = "tosa.const"() <{values = dense<27> : tensor<1xi8>}> : () -> tensor<1xi8>
- %3 = tosa.rescale %2, %multiplier, %shift, %rescale_input_zp, %rescale_output_zp {rounding_mode = DOUBLE_ROUND, scale32 = true, per_channel = true, input_unsigned = false, output_unsigned = false} : (tensor<1x1x1x3xi32>, tensor<3xi32>, tensor<3xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<1x1x1x3xi8>
+ %3 = tosa.rescale %2, %multiplier, %shift, %rescale_input_zp, %rescale_output_zp scale32(true) rounding_mode<DOUBLE_ROUND> per_channel(true) input_unsigned(false) output_unsigned(false) : (tensor<1x1x1x3xi32>, tensor<3xi32>, tensor<3xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<1x1x1x3xi8>
return %3 : tensor<1x1x1x3xi8>
}
// -----
// CHECK-LABEL: conv3d
func.func @test_conv3d(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<34xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x8x21x34xf32> {
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<34xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<34xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
// -----
// CHECK-LABEL: conv3d_with_local_bound
func.func @test_conv3d_with_local_bound(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<34xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x8x21x34xf32> {
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, local_bound = true} : (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<34xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) local_bound(true) : (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<34xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
// -----
// CHECK-LABEL: depthwise_conv2d
func.func @test_depthwise_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x4x2xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xf32>, tensor<1x1x4x2xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x4x4x4xf32>, tensor<1x1x4x2xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
// -----
// CHECK-LABEL: depthwise_conv2d_with_local_bound
func.func @test_depthwise_conv2d_with_local_bound(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x4x2xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4xf32>, tensor<1x1x4x2xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true) : (tensor<1x4x4x4xf32>, tensor<1x1x4x2xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
// -----
// CHECK-LABEL: fft2d
func.func @test_fft2d(%arg0: tensor<1x4x8xf32>, %arg1: tensor<1x4x8xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>) {
- %0, %1 = tosa.fft2d %arg0, %arg1 {inverse = false} : (tensor<1x4x8xf32>, tensor<1x4x8xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>)
+ %0, %1 = tosa.fft2d %arg0, %arg1 inverse(false) : (tensor<1x4x8xf32>, tensor<1x4x8xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>)
return %0, %1 : tensor<1x4x8xf32>, tensor<1x4x8xf32>
}
// -----
// CHECK-LABEL: fft2d_with_local_bound
func.func @test_fft2d_with_local_bound(%arg0: tensor<1x4x8xf32>, %arg1: tensor<1x4x8xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>) {
- %0, %1 = tosa.fft2d %arg0, %arg1 {inverse = false, local_bound = true} : (tensor<1x4x8xf32>, tensor<1x4x8xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>)
+ %0, %1 = tosa.fft2d %arg0, %arg1 inverse(false) local_bound(true) : (tensor<1x4x8xf32>, tensor<1x4x8xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>)
return %0, %1 : tensor<1x4x8xf32>, tensor<1x4x8xf32>
}
@@ -352,29 +352,29 @@ func.func @test_matmul_t_unranked_block_scaled(%arg0: tensor<*x!tosa.block_scale
// -----
// CHECK-LABEL: max_pool2d_f32
func.func @test_max_pool2d_f32(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
+ %0 = tosa.max_pool2d %arg0 kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
// -----
// CHECK-LABEL: max_pool2d_bf16
func.func @test_max_pool2d_bf16(%arg0: tensor<1x32x32x8xbf16>) -> tensor<1x32x32x8xbf16> {
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xbf16>) -> tensor<1x32x32x8xbf16>
+ %0 = tosa.max_pool2d %arg0 kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x32x32x8xbf16>) -> tensor<1x32x32x8xbf16>
return %0 : tensor<1x32x32x8xbf16>
}
// -----
// CHECK-LABEL: max_pool2d_f16
func.func @test_max_pool2d_f16(%arg0: tensor<1x32x32x8xf16>) -> tensor<1x32x32x8xf16> {
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf16>) -> tensor<1x32x32x8xf16>
+ %0 = tosa.max_pool2d %arg0 kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x32x32x8xf16>) -> tensor<1x32x32x8xf16>
return %0 : tensor<1x32x32x8xf16>
}
// CHECK-LABEL: max_pool2d_adaptive_f32
func.func @test_max_pool2d_adaptive_f32(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad : (tensor<1x32x32x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -382,9 +382,9 @@ func.func @test_max_pool2d_adaptive_f32(%arg0: tensor<1x32x32x8xf32>) -> tensor<
// -----
// CHECK-LABEL: max_pool2d_adaptive_bf16
func.func @test_max_pool2d_adaptive_bf16(%arg0: tensor<1x32x32x8xbf16>) -> tensor<1x32x32x8xbf16> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad : (tensor<1x32x32x8xbf16>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xbf16>
return %0 : tensor<1x32x32x8xbf16>
}
@@ -392,27 +392,27 @@ func.func @test_max_pool2d_adaptive_bf16(%arg0: tensor<1x32x32x8xbf16>) -> tenso
// -----
// CHECK-LABEL: max_pool2d_adaptive_f16
func.func @test_max_pool2d_adaptive_f16(%arg0: tensor<1x32x32x8xf16>) -> tensor<1x32x32x8xf16> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad : (tensor<1x32x32x8xf16>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf16>
return %0 : tensor<1x32x32x8xf16>
}
// CHECK-LABEL: dynamic_max_pool2d_adaptive_f16
func.func @test_dynamic_max_pool2d_adaptive_f16(%arg0: tensor<1x?x?x8xf16>) -> tensor<1x?x?x8xf16> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad : (tensor<1x?x?x8xf16>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x?x?x8xf16>
return %0 : tensor<1x?x?x8xf16>
}
// CHECK-LABEL: unranked_max_pool2d_adaptive_f16
func.func @test_unranked_max_pool2d_adaptive_f16(%arg0: tensor<*xf16>) -> tensor<*xf16> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad : (tensor<*xf16>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<*xf16>
return %0 : tensor<*xf16>
}
@@ -434,91 +434,91 @@ func.func @test_rfft2d_width1(%arg0: tensor<1x1x1xf32>) -> (tensor<1x1x1xf32>, t
// -----
// CHECK-LABEL: rfft2d_with_local_bound
func.func @test_rfft2d_with_local_bound(%arg0: tensor<13x8x16xf32>) -> (tensor<13x8x9xf32>, tensor<13x8x9xf32>) {
- %0, %1 = tosa.rfft2d %arg0 {local_bound = true} : (tensor<13x8x16xf32>) -> (tensor<13x8x9xf32>, tensor<13x8x9xf32>)
+ %0, %1 = tosa.rfft2d %arg0 local_bound(true) : (tensor<13x8x16xf32>) -> (tensor<13x8x9xf32>, tensor<13x8x9xf32>)
return %0, %1 : tensor<13x8x9xf32>, tensor<13x8x9xf32>
}
// -----
// CHECK-LABEL: transpose_conv2d
func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
// CHECK-LABEL: transpose_conv2d_unranked_input
func.func @test_transpose_conv2d_unranked_input(%arg0: tensor<*xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<*xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) {out_shape = array<i64: 1, 32, 32, 16>} : (tensor<*xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
// CHECK-LABEL: transpose_conv2d_unranked_weight
func.func @test_transpose_conv2d_unranked_weight(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<*xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<*xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) {out_shape = array<i64: 1, 32, 32, 16>} : (tensor<1x32x32x8xf32>, tensor<*xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
// CHECK-LABEL: transpose_conv2d_with_local_bound
func.func @test_transpose_conv2d_with_local_bound(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = false} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) local_bound(false) : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
// CHECK-LABEL: clamp
func.func @test_clamp(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
- %0 = tosa.clamp %arg0 {min_val = 0.0 : f32, max_val = 1.0: f32} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.clamp %arg0 min_val(0.0 : f32) max_val(1.0: f32) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
// CHECK-LABEL: clamp_propagate
func.func @test_clamp_propagate(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
- %0 = tosa.clamp %arg0 {min_val = 0.0 : f32, max_val = 1.0: f32, nan_mode = PROPAGATE} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.clamp %arg0 min_val(0.0 : f32) max_val(1.0: f32) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
// CHECK-LABEL: clamp_ignore
func.func @test_clamp_ignore(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
- %0 = tosa.clamp %arg0 {min_val = 0.0 : f32, max_val = 1.0: f32, nan_mode = IGNORE} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.clamp %arg0 min_val(0.0 : f32) max_val(1.0: f32) nan_mode<IGNORE> : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
// CHECK-LABEL: clamp_ignore_enum
func.func @test_clamp_ignore_enum(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
- %0 = tosa.clamp %arg0 {min_val = 0.0 : f32, max_val = 1.0: f32, nan_mode = #tosa.nan_mode<IGNORE>} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.clamp %arg0 min_val(0.0 : f32) max_val(1.0: f32) nan_mode<IGNORE> : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
// CHECK-LABEL: clamp_f16
func.func @test_clamp_f16(%arg0: tensor<13x21x3xf16>) -> tensor<13x21x3xf16> {
- %0 = tosa.clamp %arg0 {min_val = 0.0 : f16, max_val = 1.0: f16} : (tensor<13x21x3xf16>) -> tensor<13x21x3xf16>
+ %0 = tosa.clamp %arg0 min_val(0.0 : f16) max_val(1.0: f16) : (tensor<13x21x3xf16>) -> tensor<13x21x3xf16>
return %0 : tensor<13x21x3xf16>
}
// -----
// CHECK-LABEL: clamp_bf16
func.func @test_clamp_bf16(%arg0: tensor<13x21x3xbf16>) -> tensor<13x21x3xbf16> {
- %0 = tosa.clamp %arg0 {min_val = 0.0 : bf16, max_val = 1.0: bf16} : (tensor<13x21x3xbf16>) -> tensor<13x21x3xbf16>
+ %0 = tosa.clamp %arg0 min_val(0.0 : bf16) max_val(1.0: bf16) : (tensor<13x21x3xbf16>) -> tensor<13x21x3xbf16>
return %0 : tensor<13x21x3xbf16>
}
// -----
// CHECK-LABEL: clamp_quantized
func.func @test_clamp_quantized(%arg0: tensor<13x21x3x!quant.uniform<i8:f32, 1.000000e-01:-127>>) -> tensor<13x21x3x!quant.uniform<i8:f32, 1.000000e-01:-127>> {
- %0 = tosa.clamp %arg0 {min_val = 0 : i8, max_val = 1 : i8} : (tensor<13x21x3x!quant.uniform<i8:f32, 1.000000e-01:-127>>) -> tensor<13x21x3x!quant.uniform<i8:f32, 1.000000e-01:-127>>
+ %0 = tosa.clamp %arg0 min_val(0 : i8) max_val(1 : i8) : (tensor<13x21x3x!quant.uniform<i8:f32, 1.000000e-01:-127>>) -> tensor<13x21x3x!quant.uniform<i8:f32, 1.000000e-01:-127>>
return %0 : tensor<13x21x3x!quant.uniform<i8:f32, 1.000000e-01:-127>>
}
// -----
// CHECK-LABEL: clamp_quantized_unsigned
func.func @clamp_quantized_unsigned(%arg0:tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) -> (tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) {
- %0 = tosa.clamp %arg0 {max_val = 255 : ui8, min_val = 0 : ui8} : (tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>
+ %0 = tosa.clamp %arg0 min_val(0 : ui8) max_val(255 : ui8) : (tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>
return %0 : tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>
}
@@ -553,7 +553,7 @@ func.func @test_add(%arg0: tensor<13x21x1xf32>, %arg1: tensor<13x21x3xf32>) -> t
// -----
// CHECK-LABEL: arithmetic_right_shift
func.func @test_arithmetic_right_shift(%arg0: tensor<13x21x1xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
- %0 = tosa.arithmetic_right_shift %arg0, %arg1 {round = false} : (tensor<13x21x1xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.arithmetic_right_shift %arg0, %arg1 round(false) : (tensor<13x21x1xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -630,21 +630,21 @@ func.func @test_logical_xor(%arg0: tensor<13x1x3xi1>, %arg1: tensor<13x21x3xi1>)
// -----
// CHECK-LABEL: test_max
func.func @test_max(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x1xf32>) -> tensor<13x21x3xf32> {
- %0 = tosa.maximum %arg0, %arg1 {} : (tensor<13x21x3xf32>, tensor<13x21x1xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.maximum %arg0, %arg1 : (tensor<13x21x3xf32>, tensor<13x21x1xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
// CHECK-LABEL: test_max_ignore
func.func @test_max_ignore(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x1xf32>) -> tensor<13x21x3xf32> {
- %0 = tosa.maximum %arg0, %arg1 { nan_mode = IGNORE } : (tensor<13x21x3xf32>, tensor<13x21x1xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.maximum %arg0, %arg1 nan_mode<IGNORE> : (tensor<13x21x3xf32>, tensor<13x21x1xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
// CHECK-LABEL: test_max_propagate
func.func @test_max_propagate(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x1xf32>) -> tensor<13x21x3xf32> {
- %0 = tosa.maximum %arg0, %arg1 { nan_mode = PROPAGATE } : (tensor<13x21x3xf32>, tensor<13x21x1xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.maximum %arg0, %arg1 : (tensor<13x21x3xf32>, tensor<13x21x1xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -855,8 +855,8 @@ func.func @test_greater_equal(%arg0: tensor<13x1x3xf32>, %arg1: tensor<13x21x3xf
// -----
// CHECK-LABEL: reduce_all
func.func @test_reduce_all(%arg0: tensor<13x21x3xi1>) -> tensor<21x3xi1> {
- %0 = tosa.reduce_all %arg0 {axis = 0 : i32} : (tensor<13x21x3xi1>) -> tensor<1x21x3xi1>
- %2 = tosa.const_shape {values = dense<[21, 3]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.reduce_all %arg0 axis(0) : (tensor<13x21x3xi1>) -> tensor<1x21x3xi1>
+ %2 = tosa.const_shape values(dense<[21, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
%1 = tosa.reshape %0, %2 : (tensor<1x21x3xi1>, !tosa.shape<2>) -> tensor<21x3xi1>
return %1 : tensor<21x3xi1>
}
@@ -864,8 +864,8 @@ func.func @test_reduce_all(%arg0: tensor<13x21x3xi1>) -> tensor<21x3xi1> {
// -----
// CHECK-LABEL: reduce_any
func.func @test_reduce_any(%arg0: tensor<13x21x3xi1>) -> tensor<21x3xi1> {
- %0 = tosa.reduce_any %arg0 {axis = 0 : i32} : (tensor<13x21x3xi1>) -> tensor<1x21x3xi1>
- %2 = tosa.const_shape {values = dense<[21, 3]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.reduce_any %arg0 axis(0) : (tensor<13x21x3xi1>) -> tensor<1x21x3xi1>
+ %2 = tosa.const_shape values(dense<[21, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
%1 = tosa.reshape %0, %2 : (tensor<1x21x3xi1>, !tosa.shape<2>) -> tensor<21x3xi1>
return %1 : tensor<21x3xi1>
}
@@ -873,8 +873,8 @@ func.func @test_reduce_any(%arg0: tensor<13x21x3xi1>) -> tensor<21x3xi1> {
// -----
// CHECK-LABEL: reduce_max
func.func @test_reduce_max(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> {
- %0 = tosa.reduce_max %arg0 {axis = 0 : i32} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
- %2 = tosa.const_shape {values = dense<[21, 3]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.reduce_max %arg0 axis(0) : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
+ %2 = tosa.const_shape values(dense<[21, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
%1 = tosa.reshape %0, %2 : (tensor<1x21x3xf32>, !tosa.shape<2>) -> tensor<21x3xf32>
return %1 : tensor<21x3xf32>
}
@@ -882,8 +882,8 @@ func.func @test_reduce_max(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> {
// -----
// CHECK-LABEL: reduce_min
func.func @test_reduce_min(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> {
- %0 = tosa.reduce_min %arg0 {axis = 0 : i32} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
- %2 = tosa.const_shape {values = dense<[21, 3]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.reduce_min %arg0 axis(0) : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
+ %2 = tosa.const_shape values(dense<[21, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
%1 = tosa.reshape %0, %2 : (tensor<1x21x3xf32>, !tosa.shape<2>) -> tensor<21x3xf32>
return %1 : tensor<21x3xf32>
}
@@ -891,8 +891,8 @@ func.func @test_reduce_min(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> {
// -----
// CHECK-LABEL: reduce_product
func.func @test_reduce_product(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> {
- %0 = tosa.reduce_product %arg0 {axis = 0 : i32} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
- %2 = tosa.const_shape {values = dense<[21, 3]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.reduce_product %arg0 axis(0) : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
+ %2 = tosa.const_shape values(dense<[21, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
%1 = tosa.reshape %0, %2 : (tensor<1x21x3xf32>, !tosa.shape<2>) -> tensor<21x3xf32>
return %1 : tensor<21x3xf32>
}
@@ -900,8 +900,8 @@ func.func @test_reduce_product(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> {
// -----
// CHECK-LABEL: reduce_sum
func.func @test_reduce_sum(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> {
- %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
- %2 = tosa.const_shape {values = dense<[21, 3]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.reduce_sum %arg0 axis(0) : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
+ %2 = tosa.const_shape values(dense<[21, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
%1 = tosa.reshape %0, %2 : (tensor<1x21x3xf32>, !tosa.shape<2>) -> tensor<21x3xf32>
return %1 : tensor<21x3xf32>
}
@@ -909,15 +909,15 @@ func.func @test_reduce_sum(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> {
// -----
// CHECK-LABEL: concat
func.func @test_concat(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<26x21x3xf32> {
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<26x21x3xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<26x21x3xf32>
return %0 : tensor<26x21x3xf32>
}
// -----
// CHECK-LABEL: pad_explicit_value
func.func @test_pad_explicit_value(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
- %0 = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
- %padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %0 = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %padding = tosa.const_shape values(dense<0> : tensor<6xindex>) : () -> !tosa.shape<6>
%1 = tosa.pad %arg0, %padding, %0 : (tensor<13x21x3xf32>, !tosa.shape<6>, tensor<1xf32>) -> tensor<13x21x3xf32>
return %1 : tensor<13x21x3xf32>
}
@@ -925,7 +925,7 @@ func.func @test_pad_explicit_value(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3
// -----
// CHECK-LABEL: test_pad_dynamic_padding
func.func @test_pad_dynamic_padding(%arg0: tensor<1x?x?x576xf32>) -> tensor<1x?x?x576xf32> {
- %0 = tosa.const_shape {values = dense<0> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %0 = tosa.const_shape values(dense<0> : tensor<1xindex>) : () -> !tosa.shape<1>
%1 = tosa.concat_shape %0, %0, %0, %0, %0, %0, %0, %0 : (!tosa.shape<1>, !tosa.shape<1>, !tosa.shape<1>, !tosa.shape<1>, !tosa.shape<1>, !tosa.shape<1>, !tosa.shape<1>, !tosa.shape<1>) -> !tosa.shape<8>
%2 = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
%3 = tosa.pad %arg0, %1, %2 : (tensor<1x?x?x576xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x?x?x576xf32>
@@ -935,7 +935,7 @@ func.func @test_pad_dynamic_padding(%arg0: tensor<1x?x?x576xf32>) -> tensor<1x?x
// -----
// CHECK-LABEL: reshape
func.func @test_reshape(%arg0: tensor<13x21x3xf32>) -> tensor<1x819xf32> {
- %1 = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = tosa.reshape %arg0, %1 : (tensor<13x21x3xf32>, !tosa.shape<2>) -> tensor<1x819xf32>
return %0 : tensor<1x819xf32>
}
@@ -943,7 +943,7 @@ func.func @test_reshape(%arg0: tensor<13x21x3xf32>) -> tensor<1x819xf32> {
// -----
// CHECK-LABEL: reshape_mxfp
func.func @test_reshape_mxfp(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
- %1 = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = tosa.reshape %arg0, %1 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<2>) -> tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %0 : tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
}
@@ -951,7 +951,7 @@ func.func @test_reshape_mxfp(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_3
// -----
// CHECK-LABEL: reshape_unranked_output
func.func @test_reshape_unranked_output(%arg0: tensor<13x21x3xf32>) -> tensor<*xf32> {
- %1 = tosa.const_shape {values = dense<[21, 13, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %1 = tosa.const_shape values(dense<[21, 13, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = tosa.reshape %arg0, %1 : (tensor<13x21x3xf32>, !tosa.shape<3>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -959,70 +959,70 @@ func.func @test_reshape_unranked_output(%arg0: tensor<13x21x3xf32>) -> tensor<*x
// -----
// CHECK-LABEL: reshape_non_block_scaled
func.func @test_reshape_non_block_scaled(%arg0: tensor<13x21x3xf32>) -> tensor<1x819xf32> {
- %1 = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %0 = tosa.reshape_block_scaled %arg0, %1 {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<13x21x3xf32>, !tosa.shape<2>) -> tensor<1x819xf32>
+ %1 = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0 = tosa.reshape_block_scaled %arg0, %1 block_size<BLOCK_SIZE_1> : (tensor<13x21x3xf32>, !tosa.shape<2>) -> tensor<1x819xf32>
return %0 : tensor<1x819xf32>
}
// -----
// CHECK-LABEL: reshape_non_block_scaled_unranked_input
func.func @test_reshape_non_block_scaled_unranked_input(%arg0: tensor<*xf32>) -> tensor<1x819xf32> {
- %1 = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %0 = tosa.reshape_block_scaled %arg0, %1 {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<*xf32>, !tosa.shape<2>) -> tensor<1x819xf32>
+ %1 = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0 = tosa.reshape_block_scaled %arg0, %1 block_size<BLOCK_SIZE_1> : (tensor<*xf32>, !tosa.shape<2>) -> tensor<1x819xf32>
return %0 : tensor<1x819xf32>
}
// -----
// CHECK-LABEL: reshape_non_block_scaled_unranked_output
func.func @test_reshape_non_block_scaled_unranked_output(%arg0: tensor<13x21x3xf32>) -> tensor<*xf32> {
- %1 = tosa.const_shape {values = dense<[21, 13, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %0 = tosa.reshape_block_scaled %arg0, %1 {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<13x21x3xf32>, !tosa.shape<3>) -> tensor<*xf32>
+ %1 = tosa.const_shape values(dense<[21, 13, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %0 = tosa.reshape_block_scaled %arg0, %1 block_size<BLOCK_SIZE_1> : (tensor<13x21x3xf32>, !tosa.shape<3>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
// -----
// CHECK-LABEL: reshape_block_scaled
func.func @test_reshape_block_scaled(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<4x1xf8E8M0FNU>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>) {
- %1 = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %0:2 = tosa.reshape_block_scaled %arg0, %arg1, %1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
+ %1 = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0:2 = tosa.reshape_block_scaled %arg0, %arg1, %1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
return %0#0, %0#1 : tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>
}
// -----
// CHECK-LABEL: reshape_block_scaled_unranked_input
func.func @test_reshape_block_scaled_unranked_input(%arg0: tensor<*xf4E2M1FN>, %arg1: tensor<*xf8E8M0FNU>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>) {
- %1 = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %0:2 = tosa.reshape_block_scaled %arg0, %arg1, %1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
+ %1 = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0:2 = tosa.reshape_block_scaled %arg0, %arg1, %1 block_size<BLOCK_SIZE_32> : (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
return %0#0, %0#1 : tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>
}
// -----
// CHECK-LABEL: reshape_block_scaled_unranked_output
func.func @test_reshape_block_scaled_unranked_output(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<4x1xf8E8M0FNU>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>) {
- %1 = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %0:2 = tosa.reshape_block_scaled %arg0, %arg1, %1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
+ %1 = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0:2 = tosa.reshape_block_scaled %arg0, %arg1, %1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
return %0#0, %0#1 : tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>
}
// -----
// CHECK-LABEL: reverse
func.func @test_reverse(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
// CHECK-LABEL: reverse_unranked
func.func @test_reverse_unranked(%arg0: tensor<*xf32>) -> tensor<*xf32> {
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<*xf32>) -> tensor<*xf32>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<*xf32>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
// -----
// CHECK-LABEL: slice
func.func @test_slice(%arg0: tensor<13x21x3xf32>) -> tensor<4x11x1xf32> {
- %size = tosa.const_shape {values = dense<[4, 11, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %start = tosa.const_shape {values = dense<[6, 8, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[4, 11, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[6, 8, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
%2 = tosa.slice %arg0, %start, %size : (tensor<13x21x3xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<4x11x1xf32>
return %2 : tensor<4x11x1xf32>
}
@@ -1030,8 +1030,8 @@ func.func @test_slice(%arg0: tensor<13x21x3xf32>) -> tensor<4x11x1xf32> {
// -----
// CHECK-LABEL: slice_size
func.func @test_slice_size(%arg0: tensor<13x21x3xf32>) -> tensor<7x11x1xf32> {
- %size = tosa.const_shape {values = dense<[7, 11, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %start = tosa.const_shape {values = dense<[6, 8, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[7, 11, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[6, 8, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
%2 = tosa.slice %arg0, %start, %size : (tensor<13x21x3xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<7x11x1xf32>
return %2 : tensor<7x11x1xf32>
}
@@ -1039,7 +1039,7 @@ func.func @test_slice_size(%arg0: tensor<13x21x3xf32>) -> tensor<7x11x1xf32> {
// -----
// CHECK-LABEL: tile
func.func @test_tile(%arg0: tensor<13x21x3xf32>) -> tensor<39x21x6xf32> {
- %cst = tosa.const_shape { values = dense<[3, 1, 2]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %cst = tosa.const_shape values(dense<[3, 1, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = tosa.tile %arg0, %cst: (tensor<13x21x3xf32>, !tosa.shape<3>) -> tensor<39x21x6xf32>
return %0 : tensor<39x21x6xf32>
}
@@ -1047,21 +1047,21 @@ func.func @test_tile(%arg0: tensor<13x21x3xf32>) -> tensor<39x21x6xf32> {
// -----
// CHECK-LABEL: transpose
func.func @test_transpose(%arg0: tensor<13x21x3xf32>) -> tensor<3x13x21xf32> {
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x21x3xf32>) -> tensor<3x13x21xf32>
+ %1 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<13x21x3xf32>) -> tensor<3x13x21xf32>
return %1 : tensor<3x13x21xf32>
}
// -----
// CHECK-LABEL: transpose_dynamic_dim
func.func @test_transpose_dynamic_dim(%arg0: tensor<13x?x3xf32>) -> tensor<3x13x?xf32> {
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x?x3xf32>) -> tensor<3x13x?xf32>
+ %1 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<13x?x3xf32>) -> tensor<3x13x?xf32>
return %1 : tensor<3x13x?xf32>
}
// -----
// CHECK-LABEL: transpose_half_dynamic_dim
func.func @test_transpose_half_dynamic_dim(%arg0: tensor<13x3x3xf32>) -> tensor<3x13x?xf32> {
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x3x3xf32>) -> tensor<3x13x?xf32>
+ %1 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<13x3x3xf32>) -> tensor<3x13x?xf32>
return %1 : tensor<3x13x?xf32>
}
@@ -1128,16 +1128,16 @@ func.func @test_row_gather_fully_unranked(%arg0: tensor<*xf32>, %arg1: tensor<*x
// -----
// CHECK-LABEL: test_row_gather_block_scaled
func.func @test_row_gather_block_scaled(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x26xi32>) -> tensor<13x52x3xf32> {
- %row_count = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
- %0 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<13x21x3xf32>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x3xf32>)
+ %row_count = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %0 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count block_size<BLOCK_SIZE_1> : (tensor<13x21x3xf32>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x3xf32>)
return %0 : tensor<13x52x3xf32>
}
// -----
// CHECK-LABEL: test_row_gather_block_scaled_mxfp
func.func @test_row_gather_block_scaled_mxfp(%arg0: tensor<13x21x32xf4E2M1FN>, %arg1: tensor<13x21x1xf8E8M0FNU>, %arg2: tensor<13x26xi32>) -> (tensor<13x52x32xf4E2M1FN>, tensor<13x52x1xf8E8M0FNU>) {
- %row_count = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
- %0:2 = tosa.row_gather_block_scaled %arg0, %arg1, %arg2, %row_count {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<13x21x32xf4E2M1FN>, tensor<13x21x1xf8E8M0FNU>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x32xf4E2M1FN>, tensor<13x52x1xf8E8M0FNU>)
+ %row_count = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %0:2 = tosa.row_gather_block_scaled %arg0, %arg1, %arg2, %row_count block_size<BLOCK_SIZE_32> : (tensor<13x21x32xf4E2M1FN>, tensor<13x21x1xf8E8M0FNU>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x32xf4E2M1FN>, tensor<13x52x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<13x52x32xf4E2M1FN>, tensor<13x52x1xf8E8M0FNU>
}
@@ -1165,155 +1165,155 @@ func.func @test_scatter_unranked_indices(%arg0: tensor<13x21x3xf32>, %arg1: tens
// -----
// CHECK-LABEL: test_resize
func.func @test_resize(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x64x64x8xf32> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x32x32x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf32>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x32x32x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf32>
return %1 : tensor<1x64x64x8xf32>
}
// -----
// CHECK-LABEL: test_resize_fp8
func.func @test_resize_fp8(%arg0: tensor<1x32x32x8xf8E4M3FN>, %arg1: tensor<1x32x32x8xf8E5M2>) -> (tensor<1x64x64x8xf8E4M3FN>, tensor<1x64x64x8xf8E5M2>) {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %0 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x32x32x8xf8E4M3FN>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E4M3FN>
- %1 = tosa.resize %arg1, %scale, %offset, %border { mode = NEAREST_NEIGHBOR } : (tensor<1x32x32x8xf8E5M2>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E5M2>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x32x32x8xf8E4M3FN>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E4M3FN>
+ %1 = tosa.resize %arg1, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x32x32x8xf8E5M2>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E5M2>
return %0, %1 : tensor<1x64x64x8xf8E4M3FN>, tensor<1x64x64x8xf8E5M2>
}
// -----
// CHECK-LABEL: test_resize_mxfp
func.func @test_resize_mxfp(%arg0: tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %0 = tosa.resize %arg0, %scale, %offset, %border { mode = NEAREST_NEIGHBOR } : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %0 : tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
}
// -----
// CHECK-LABEL: resize_unranked_output
func.func @test_resize_unranked_output(%arg0: tensor<1x32x32x8xf32>) -> tensor<*xf32> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x32x32x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x32x32x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %1 : tensor<*xf32>
}
// -----
// CHECK-LABEL: resize_unranked_input
func.func @test_resize_unranked_input(%arg0: tensor<*xf32>) -> tensor<1x64x64x8xf32> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<*xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf32>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<*xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf32>
return %1 : tensor<1x64x64x8xf32>
}
// -----
// CHECK-LABEL: test_resize_enum
func.func @test_resize_enum(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x64x64x8xf32> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = #tosa.resize_mode<BILINEAR> } : (tensor<1x32x32x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf32>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x32x32x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf32>
return %1 : tensor<1x64x64x8xf32>
}
// -----
// CHECK-LABEL: test_cast1
func.func @test_cast1(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xf32> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi32>) -> tensor<13x21x3xf32>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
// CHECK-LABEL: test_cast_input_unsigned
func.func @test_cast_input_unsigned(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xf32> {
- %0 = tosa.cast %arg0 {input_unsigned = true} : (tensor<13x21x3xi32>) -> tensor<13x21x3xf32>
+ %0 = tosa.cast %arg0 input_unsigned(true) : (tensor<13x21x3xi32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
// CHECK-LABEL: cast2
func.func @test_cast2(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3x!quant.uniform<u8:f32, 0.078431375324726104:128>> {
- %0 = tosa.cast %arg0 {input_unsigned = false}: (tensor<13x21x3xi32>) -> tensor<13x21x3x!quant.uniform<u8:f32, 0.078431375324726104:128>>
+ %0 = tosa.cast %arg0: (tensor<13x21x3xi32>) -> tensor<13x21x3x!quant.uniform<u8:f32, 0.078431375324726104:128>>
return %0 : tensor<13x21x3x!quant.uniform<u8:f32, 0.078431375324726104:128>>
}
// -----
// CHECK-LABEL: cast3
func.func @test_cast3(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3x!quant.uniform<i16:f32, 0.078431375324726104:128>> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi32>) -> tensor<13x21x3x!quant.uniform<i16:f32, 0.078431375324726104:128>>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi32>) -> tensor<13x21x3x!quant.uniform<i16:f32, 0.078431375324726104:128>>
return %0 : tensor<13x21x3x!quant.uniform<i16:f32, 0.078431375324726104:128>>
}
// -----
// CHECK-LABEL: test_cast_to_block_scaled
func.func @test_cast_to_block_scaled(%arg0: tensor<4x32xf32>, %arg1: tensor<4x32xbf16>, %arg2: tensor<4x32xf8E4M3FN>) -> (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>) {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32xf32>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
- %1 = tosa.cast %arg1 {input_unsigned = false} : (tensor<4x32xbf16>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>
- %2 = tosa.cast %arg2 {input_unsigned = false} : (tensor<4x32xf8E4M3FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
+ %0 = tosa.cast %arg0 : (tensor<4x32xf32>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
+ %1 = tosa.cast %arg1 : (tensor<4x32xbf16>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>
+ %2 = tosa.cast %arg2 : (tensor<4x32xf8E4M3FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
return %0, %1, %2 : tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
}
// -----
// CHECK-LABEL: test_cast_from_block_scaled
func.func @test_cast_from_block_scaled(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>, %arg1: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>, %arg2: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>) -> (tensor<4x32xf32>, tensor<4x32xbf16>, tensor<4x32xf8E5M2>) {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<4x32xf32>
- %1 = tosa.cast %arg1 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>) -> tensor<4x32xbf16>
- %2 = tosa.cast %arg2 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>) -> tensor<4x32xf8E5M2>
+ %0 = tosa.cast %arg0 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<4x32xf32>
+ %1 = tosa.cast %arg1 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>) -> tensor<4x32xbf16>
+ %2 = tosa.cast %arg2 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>) -> tensor<4x32xf8E5M2>
return %0, %1, %2 : tensor<4x32xf32>, tensor<4x32xbf16>, tensor<4x32xf8E5M2>
}
// -----
// CHECK-LABEL: test_cast_block_scaled_dynamic
func.func @test_cast_block_scaled_dynamic(%arg0: tensor<?x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>, %arg1: tensor<4x?x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> (tensor<?x32xf32>, tensor<4x?xf32>) {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<?x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<?x32xf32>
- %1 = tosa.cast %arg1 {input_unsigned = false} : (tensor<4x?x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<4x?xf32>
+ %0 = tosa.cast %arg0 : (tensor<?x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<?x32xf32>
+ %1 = tosa.cast %arg1 : (tensor<4x?x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<4x?xf32>
return %0, %1 : tensor<?x32xf32>, tensor<4x?xf32>
}
// -----
// CHECK-LABEL: rescale
func.func @test_rescale(%arg0: tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8> }> : () -> tensor<1xi8>
%input_zp = "tosa.const"() <{values = dense<127> : tensor<1xi8>}> : () -> tensor<1xi8>
%output_zp = "tosa.const"() <{values = dense<-1> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, scale32 = true, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
return %0 : tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
}
// -----
// CHECK-LABEL: rescale_i16_zp32768
func.func @test_rescale_i16_zp32768(%arg0 : tensor<2xi8>) -> tensor<2xi16> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16>} : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<15> : tensor<1xi8>} : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<17> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<32768> : tensor<1xi16>} : () -> tensor<1xi16>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi16>) -> tensor<2xi16>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<15> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<17> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<32768> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi16>) -> tensor<2xi16>
return %0 : tensor<2xi16>
}
// -----
// CHECK-LABEL: test_rescale_i16_rounding_mode
func.func @test_rescale_i16_rounding_mode(%arg0 : tensor<2xi8>) -> tensor<2xi16> {
- %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16>} : () -> tensor<1xi16>
- %shift = "tosa.const"() {values = dense<15> : tensor<1xi8>} : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<17> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<32768> : tensor<1xi16>} : () -> tensor<1xi16>
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = #tosa.rounding_mode<SINGLE_ROUND>, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi16>) -> tensor<2xi16>
+ %multiplier = "tosa.const"() <{values = dense<19689> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %shift = "tosa.const"() <{values = dense<15> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<17> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<32768> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi16>) -> tensor<2xi16>
return %0 : tensor<2xi16>
}
// -----
// CHECK-LABEL: const
func.func @test_const(%arg0 : index) -> tensor<4xi32> {
- %0 = "tosa.const"() {values = dense<[3, 0, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
+ %0 = "tosa.const"() <{values = dense<[3, 0, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
return %0 : tensor<4xi32>
}
@@ -1340,16 +1340,16 @@ func.func @test_cond_if(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<i1
// -----
// CHECK-LABEL: while_loop
func.func @test_while_loop(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
%1:3 = tosa.while_loop (%arg2 = %0, %arg3 = %0, %arg4 = %arg0) : (tensor<i32>, tensor<i32>, tensor<10xi32>) -> (tensor<i32>, tensor<i32>, tensor<10xi32>) {
%2 = tosa.greater_equal %arg3, %arg1 : (tensor<i32>, tensor<i32>) -> tensor<i1>
%3 = tosa.logical_not %2 : (tensor<i1>) -> tensor<i1>
tosa.yield %3 : tensor<i1>
} do {
^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<10xi32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%3 = tosa.add %arg3, %2 : (tensor<i32>, tensor<i32>) -> tensor<i32>
- %7 = tosa.const_shape {values = dense<[1]> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %7 = tosa.const_shape values(dense<[1]> : tensor<1xindex>) : () -> !tosa.shape<1>
%4 = tosa.reshape %2, %7 : (tensor<i32>, !tosa.shape<1>) -> tensor<1xi32>
%5 = tosa.add %arg4, %4 : (tensor<10xi32>, tensor<1xi32>) -> tensor<10xi32>
%6 = tosa.add %arg2, %2 : (tensor<i32>, tensor<i32>) -> tensor<i32>
@@ -1361,14 +1361,14 @@ func.func @test_while_loop(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
// -----
// CHECK-LABEL: custom
func.func @test_custom(%arg0: tensor<10xi32>) -> tensor<10xi32> {
- %0 = tosa.custom %arg0 {operator_name="custom_test", domain_name="tosa.mlir_test", implementation_attrs="" } : (tensor<10xi32>) -> (tensor<10xi32>)
+ %0 = tosa.custom %arg0 operator_name("custom_test") domain_name("tosa.mlir_test") implementation_attrs("") : (tensor<10xi32>) -> (tensor<10xi32>)
return %0 : tensor<10xi32>
}
// -----
// CHECK-LABEL: const_shape
func.func @test_const_shape() -> !tosa.shape<4> {
- %cst = tosa.const_shape {values = dense<1> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %cst = tosa.const_shape values(dense<1> : tensor<4xindex>) : () -> !tosa.shape<4>
return %cst : !tosa.shape<4>
}
@@ -1377,7 +1377,7 @@ func.func @test_const_shape() -> !tosa.shape<4> {
// -----
// CHECK-LABEL: argmax_f8E5M2
func.func @test_argmax_f8E5M2(%arg0: tensor<12x8x16xf8E5M2>) -> tensor<12x16xi32> {
- %0 = tosa.argmax %arg0 { axis = 1 : i32 } : (tensor<12x8x16xf8E5M2>) -> tensor<12x16xi32>
+ %0 = tosa.argmax %arg0 axis(1) : (tensor<12x8x16xf8E5M2>) -> tensor<12x16xi32>
return %0 : tensor<12x16xi32>
}
@@ -1386,7 +1386,7 @@ func.func @test_argmax_f8E5M2(%arg0: tensor<12x8x16xf8E5M2>) -> tensor<12x16xi32
func.func @test_avg_pool2d_f8E5M2(%arg0: tensor<1x7x7x9xf8E5M2>) -> tensor<1x7x7x9xf8E5M2> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f16, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xf8E5M2>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x7x7x9xf8E5M2>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(f16) : (tensor<1x7x7x9xf8E5M2>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x7x7x9xf8E5M2>
return %0 : tensor<1x7x7x9xf8E5M2>
}
@@ -1395,7 +1395,7 @@ func.func @test_avg_pool2d_f8E5M2(%arg0: tensor<1x7x7x9xf8E5M2>) -> tensor<1x7x7
func.func @test_conv2d_f8E5M2(%arg0: tensor<1x4x4x4xf8E5M2>, %arg1: tensor<8x1x1x4xf8E5M2>, %arg2: tensor<8xf16>) -> tensor<1x4x4x8xf16> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4xf8E5M2>, tensor<8x1x1x4xf8E5M2>, tensor<8xf16>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f16) local_bound(true) : (tensor<1x4x4x4xf8E5M2>, tensor<8x1x1x4xf8E5M2>, tensor<8xf16>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf16>
return %0 : tensor<1x4x4x8xf16>
}
@@ -1404,14 +1404,14 @@ func.func @test_conv2d_f8E5M2(%arg0: tensor<1x4x4x4xf8E5M2>, %arg1: tensor<8x1x1
func.func @test_conv2d_f8E5M2_acc32(%arg0: tensor<1x4x4x4xf8E5M2>, %arg1: tensor<8x1x1x4xf8E5M2>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xf8E5M2>, tensor<8x1x1x4xf8E5M2>, tensor<8xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x4x4x4xf8E5M2>, tensor<8x1x1x4xf8E5M2>, tensor<8xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
// -----
// CHECK-LABEL: conv3d_f8E5M2
func.func @test_conv3d_f8E5M2(%arg0: tensor<1x4x8x21x17xf8E5M2>, %arg1: tensor<34x1x1x1x17xf8E5M2>, %arg2: tensor<34xf16>, %arg3: tensor<1xf8E5M2>, %arg4: tensor<1xf8E5M2>) -> tensor<1x4x8x21x34xf16> {
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f16, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<1x4x8x21x17xf8E5M2>, tensor<34x1x1x1x17xf8E5M2>, tensor<34xf16>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x8x21x34xf16>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f16) : (tensor<1x4x8x21x17xf8E5M2>, tensor<34x1x1x1x17xf8E5M2>, tensor<34xf16>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x8x21x34xf16>
return %0 : tensor<1x4x8x21x34xf16>
}
@@ -1420,14 +1420,14 @@ func.func @test_conv3d_f8E5M2(%arg0: tensor<1x4x8x21x17xf8E5M2>, %arg1: tensor<3
func.func @test_conv3d_f8E5M2_acc32(%arg0: tensor<1x4x8x21x17xf8E5M2>, %arg1: tensor<34x1x1x1x17xf8E5M2>, %arg2: tensor<34xf32>) -> tensor<1x4x8x21x34xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<1x4x8x21x17xf8E5M2>, tensor<34x1x1x1x17xf8E5M2>, tensor<34xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x8x21x34xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<1x4x8x21x17xf8E5M2>, tensor<34x1x1x1x17xf8E5M2>, tensor<34xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
// -----
// CHECK-LABEL: depthwise_conv2d_f8E5M2
func.func @test_depthwise_conv2d_f8E5M2(%arg0: tensor<1x4x4x4xf8E5M2>, %arg1: tensor<1x1x4x2xf8E5M2>, %arg2: tensor<8xf16>, %arg3: tensor<1xf8E5M2>, %arg4: tensor<1xf8E5M2>) -> tensor<1x4x4x8xf16> {
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xf8E5M2>, tensor<1x1x4x2xf8E5M2>, tensor<8xf16>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf16>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f16) : (tensor<1x4x4x4xf8E5M2>, tensor<1x1x4x2xf8E5M2>, tensor<8xf16>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf16>
return %0 : tensor<1x4x4x8xf16>
}
@@ -1436,7 +1436,7 @@ func.func @test_depthwise_conv2d_f8E5M2(%arg0: tensor<1x4x4x4xf8E5M2>, %arg1: te
func.func @test_depthwise_conv2d_f8E5M2_acc32(%arg0: tensor<1x4x4x4xf8E5M2>, %arg1: tensor<1x1x4x2xf8E5M2>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xf8E5M2>, tensor<1x1x4x2xf8E5M2>, tensor<8xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x4x4x4xf8E5M2>, tensor<1x1x4x2xf8E5M2>, tensor<8xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -1461,14 +1461,14 @@ func.func @test_matmul_f8E5M2_f8E4M3(%arg0: tensor<1x14x19xf8E5M2>, %arg1: tenso
// -----
// CHECK-LABEL: max_pool2d_f8E5M2
func.func @test_max_pool2d_f8E5M2(%arg0: tensor<1x32x32x8xf8E5M2>) -> tensor<1x32x32x8xf8E5M2> {
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf8E5M2>) -> tensor<1x32x32x8xf8E5M2>
+ %0 = tosa.max_pool2d %arg0 kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x32x32x8xf8E5M2>) -> tensor<1x32x32x8xf8E5M2>
return %0 : tensor<1x32x32x8xf8E5M2>
}
// -----
// CHECK-LABEL: transpose_conv2d_f8E5M2
func.func @test_transpose_conv2d_f8E5M2(%arg0: tensor<1x32x32x8xf8E5M2>, %arg1: tensor<16x1x1x8xf8E5M2>, %arg2: tensor<16xf16>, %arg3: tensor<1xf8E5M2>, %arg4: tensor<1xf8E5M2>) -> tensor<1x32x32x16xf16> {
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f16, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf8E5M2>, tensor<16x1x1x8xf8E5M2>, tensor<16xf16>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x32x32x16xf16>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f16) : (tensor<1x32x32x8xf8E5M2>, tensor<16x1x1x8xf8E5M2>, tensor<16xf16>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x32x32x16xf16>
return %0 : tensor<1x32x32x16xf16>
}
@@ -1477,28 +1477,28 @@ func.func @test_transpose_conv2d_f8E5M2(%arg0: tensor<1x32x32x8xf8E5M2>, %arg1:
func.func @test_transpose_conv2d_f8E5M2_acc32(%arg0: tensor<1x32x32x8xf8E5M2>, %arg1: tensor<16x1x1x8xf8E5M2>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf8E5M2>, tensor<16x1x1x8xf8E5M2>, tensor<16xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) : (tensor<1x32x32x8xf8E5M2>, tensor<16x1x1x8xf8E5M2>, tensor<16xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
// CHECK-LABEL: const_f8E5M2
func.func @test_const_f8E5M2(%arg0 : index) -> tensor<4xf8E5M2> {
- %0 = "tosa.const"() {values = dense<[3.0, -0.0, -1.0, 2.0]> : tensor<4xf8E5M2>} : () -> tensor<4xf8E5M2>
+ %0 = "tosa.const"() <{values = dense<[3.0, -0.0, -1.0, 2.0]> : tensor<4xf8E5M2>}> : () -> tensor<4xf8E5M2>
return %0 : tensor<4xf8E5M2>
}
// -----
// CHECK-LABEL: cast_f8E5M2
func.func @test_cast_f8E5M2(%arg0: tensor<13x21x3xf8E5M2>) -> tensor<13x21x3xf16> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf8E5M2>) -> tensor<13x21x3xf16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf8E5M2>) -> tensor<13x21x3xf16>
return %0 : tensor<13x21x3xf16>
}
// -----
// CHECK-LABEL: concat_f8E5M2
func.func @test_concat_f8E5M2(%arg0: tensor<13x21x3xf8E5M2>, %arg1: tensor<13x21x3xf8E5M2>) -> tensor<26x21x3xf8E5M2> {
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x3xf8E5M2>, tensor<13x21x3xf8E5M2>) -> tensor<26x21x3xf8E5M2>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x3xf8E5M2>, tensor<13x21x3xf8E5M2>) -> tensor<26x21x3xf8E5M2>
return %0 : tensor<26x21x3xf8E5M2>
}
@@ -1521,8 +1521,8 @@ func.func @test_row_gather_mxfp(%arg0: tensor<13x21x32x!tosa.block_scaled<BLOCK_
// -----
// CHECK-LABEL: pad_f8E5M2
func.func @test_pad_f8E5M2(%arg0: tensor<13x21x3xf8E5M2>) -> tensor<13x21x3xf8E5M2> {
- %padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
- %cst = "tosa.const"() { values = dense<-0.0> : tensor<1xf8E5M2> } : () -> tensor<1xf8E5M2>
+ %padding = tosa.const_shape values(dense<0> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %cst = "tosa.const"() <{ values = dense<-0.0> : tensor<1xf8E5M2> }> : () -> tensor<1xf8E5M2>
%0 = tosa.pad %arg0, %padding, %cst : (tensor<13x21x3xf8E5M2>, !tosa.shape<6>, tensor<1xf8E5M2>) -> tensor<13x21x3xf8E5M2>
return %0 : tensor<13x21x3xf8E5M2>
}
@@ -1530,7 +1530,7 @@ func.func @test_pad_f8E5M2(%arg0: tensor<13x21x3xf8E5M2>) -> tensor<13x21x3xf8E5
// -----
// CHECK-LABEL: reshape_f8E5M2
func.func @test_reshape_f8E5M2(%arg0: tensor<13x21x3xf8E5M2>) -> tensor<1x819xf8E5M2> {
- %1 = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = tosa.reshape %arg0, %1 : (tensor<13x21x3xf8E5M2>, !tosa.shape<2>) -> tensor<1x819xf8E5M2>
return %0 : tensor<1x819xf8E5M2>
}
@@ -1538,15 +1538,15 @@ func.func @test_reshape_f8E5M2(%arg0: tensor<13x21x3xf8E5M2>) -> tensor<1x819xf8
// -----
// CHECK-LABEL: reverse_f8E5M2
func.func @test_reverse_f8E5M2(%arg0: tensor<13x21x3xf8E5M2>) -> tensor<13x21x3xf8E5M2> {
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<13x21x3xf8E5M2>) -> tensor<13x21x3xf8E5M2>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<13x21x3xf8E5M2>) -> tensor<13x21x3xf8E5M2>
return %0 : tensor<13x21x3xf8E5M2>
}
// -----
// CHECK-LABEL: slice_f8E5M2
func.func @test_slice_f8E5M2(%arg0: tensor<13x21x3xf8E5M2>) -> tensor<4x11x1xf8E5M2> {
- %size = tosa.const_shape {values = dense<[4, 11, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %start = tosa.const_shape {values = dense<[6, 8, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[4, 11, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[6, 8, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
%2 = tosa.slice %arg0, %start, %size : (tensor<13x21x3xf8E5M2>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<4x11x1xf8E5M2>
return %2 : tensor<4x11x1xf8E5M2>
}
@@ -1554,7 +1554,7 @@ func.func @test_slice_f8E5M2(%arg0: tensor<13x21x3xf8E5M2>) -> tensor<4x11x1xf8E
// -----
// CHECK-LABEL: tile_f8E5M2
func.func @test_tile_f8E5M2(%arg0: tensor<13x21x3xf8E5M2>) -> tensor<39x21x6xf8E5M2> {
- %cst = tosa.const_shape { values = dense<[3, 1, 2]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %cst = tosa.const_shape values(dense<[3, 1, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = tosa.tile %arg0, %cst: (tensor<13x21x3xf8E5M2>, !tosa.shape<3>) -> tensor<39x21x6xf8E5M2>
return %0 : tensor<39x21x6xf8E5M2>
}
@@ -1562,7 +1562,7 @@ func.func @test_tile_f8E5M2(%arg0: tensor<13x21x3xf8E5M2>) -> tensor<39x21x6xf8E
// -----
// CHECK-LABEL: transpose_f8E5M2
func.func @test_transpose_f8E5M2(%arg0: tensor<13x21x3xf8E5M2>) -> tensor<3x13x21xf8E5M2> {
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x21x3xf8E5M2>) -> tensor<3x13x21xf8E5M2>
+ %1 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<13x21x3xf8E5M2>) -> tensor<3x13x21xf8E5M2>
return %1 : tensor<3x13x21xf8E5M2>
}
@@ -1583,7 +1583,7 @@ func.func @test_scatter_f8E5M2(%arg0: tensor<13x52x3xf8E5M2>, %arg1: tensor<13x2
// -----
// CHECK-LABEL: argmax_f8E4M3FN
func.func @test_argmax_f8E4M3FN(%arg0: tensor<12x8x16xf8E4M3FN>) -> tensor<12x16xi32> {
- %0 = tosa.argmax %arg0 { axis = 1 : i32 } : (tensor<12x8x16xf8E4M3FN>) -> tensor<12x16xi32>
+ %0 = tosa.argmax %arg0 axis(1) : (tensor<12x8x16xf8E4M3FN>) -> tensor<12x16xi32>
return %0 : tensor<12x16xi32>
}
@@ -1592,7 +1592,7 @@ func.func @test_argmax_f8E4M3FN(%arg0: tensor<12x8x16xf8E4M3FN>) -> tensor<12x16
func.func @test_avg_pool2d_f8E4M3FN(%arg0: tensor<1x7x7x9xf8E4M3FN>) -> tensor<1x7x7x9xf8E4M3FN> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E4M3FN>}> : () -> tensor<1xf8E4M3FN>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E4M3FN>}> : () -> tensor<1xf8E4M3FN>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f16, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xf8E4M3FN>, tensor<1xf8E4M3FN>, tensor<1xf8E4M3FN>) -> tensor<1x7x7x9xf8E4M3FN>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(f16) : (tensor<1x7x7x9xf8E4M3FN>, tensor<1xf8E4M3FN>, tensor<1xf8E4M3FN>) -> tensor<1x7x7x9xf8E4M3FN>
return %0 : tensor<1x7x7x9xf8E4M3FN>
}
@@ -1601,21 +1601,21 @@ func.func @test_avg_pool2d_f8E4M3FN(%arg0: tensor<1x7x7x9xf8E4M3FN>) -> tensor<1
func.func @test_conv2d_f8E4M3FN(%arg0: tensor<1x4x4x4xf8E4M3FN>, %arg1: tensor<8x1x1x4xf8E4M3FN>, %arg2: tensor<8xf16>) -> tensor<1x4x4x8xf16> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E4M3FN>}> : () -> tensor<1xf8E4M3FN>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E4M3FN>}> : () -> tensor<1xf8E4M3FN>
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4xf8E4M3FN>, tensor<8x1x1x4xf8E4M3FN>, tensor<8xf16>, tensor<1xf8E4M3FN>, tensor<1xf8E4M3FN>) -> tensor<1x4x4x8xf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f16) local_bound(true) : (tensor<1x4x4x4xf8E4M3FN>, tensor<8x1x1x4xf8E4M3FN>, tensor<8xf16>, tensor<1xf8E4M3FN>, tensor<1xf8E4M3FN>) -> tensor<1x4x4x8xf16>
return %0 : tensor<1x4x4x8xf16>
}
// -----
// CHECK-LABEL: conv3d_f8E4M3FN
func.func @test_conv3d_f8E4M3FN(%arg0: tensor<1x4x8x21x17xf8E4M3FN>, %arg1: tensor<34x1x1x1x17xf8E4M3FN>, %arg2: tensor<34xf16>, %arg3: tensor<1xf8E4M3FN>, %arg4: tensor<1xf8E4M3FN>) -> tensor<1x4x8x21x34xf16> {
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f16, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<1x4x8x21x17xf8E4M3FN>, tensor<34x1x1x1x17xf8E4M3FN>, tensor<34xf16>, tensor<1xf8E4M3FN>, tensor<1xf8E4M3FN>) -> tensor<1x4x8x21x34xf16>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f16) : (tensor<1x4x8x21x17xf8E4M3FN>, tensor<34x1x1x1x17xf8E4M3FN>, tensor<34xf16>, tensor<1xf8E4M3FN>, tensor<1xf8E4M3FN>) -> tensor<1x4x8x21x34xf16>
return %0 : tensor<1x4x8x21x34xf16>
}
// -----
// CHECK-LABEL: depthwise_conv2d_f8E4M3FN
func.func @test_depthwise_conv2d_f8E4M3FN(%arg0: tensor<1x4x4x4xf8E4M3FN>, %arg1: tensor<1x1x4x2xf8E4M3FN>, %arg2: tensor<8xf16>, %arg3: tensor<1xf8E4M3FN>, %arg4: tensor<1xf8E4M3FN>) -> tensor<1x4x4x8xf16> {
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xf8E4M3FN>, tensor<1x1x4x2xf8E4M3FN>, tensor<8xf16>, tensor<1xf8E4M3FN>, tensor<1xf8E4M3FN>) -> tensor<1x4x4x8xf16>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f16) : (tensor<1x4x4x4xf8E4M3FN>, tensor<1x1x4x2xf8E4M3FN>, tensor<8xf16>, tensor<1xf8E4M3FN>, tensor<1xf8E4M3FN>) -> tensor<1x4x4x8xf16>
return %0 : tensor<1x4x4x8xf16>
}
@@ -1631,43 +1631,43 @@ func.func @test_matmul_f8E4M3FN(%arg0: tensor<1x14x19xf8E4M3FN>, %arg1: tensor<1
// -----
// CHECK-LABEL: max_pool2d_f8E4M3FN
func.func @test_max_pool2d_f8E4M3FN(%arg0: tensor<1x32x32x8xf8E4M3FN>) -> tensor<1x32x32x8xf8E4M3FN> {
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf8E4M3FN>) -> tensor<1x32x32x8xf8E4M3FN>
+ %0 = tosa.max_pool2d %arg0 kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x32x32x8xf8E4M3FN>) -> tensor<1x32x32x8xf8E4M3FN>
return %0 : tensor<1x32x32x8xf8E4M3FN>
}
// -----
// CHECK-LABEL: transpose_conv2d_f8E4M3FN
func.func @test_transpose_conv2d_f8E4M3FN(%arg0: tensor<1x32x32x8xf8E4M3FN>, %arg1: tensor<16x1x1x8xf8E4M3FN>, %arg2: tensor<16xf16>, %arg3: tensor<1xf8E4M3FN>, %arg4: tensor<1xf8E4M3FN>) -> tensor<1x32x32x16xf16> {
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f16, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf8E4M3FN>, tensor<16x1x1x8xf8E4M3FN>, tensor<16xf16>, tensor<1xf8E4M3FN>, tensor<1xf8E4M3FN>) -> tensor<1x32x32x16xf16>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f16) : (tensor<1x32x32x8xf8E4M3FN>, tensor<16x1x1x8xf8E4M3FN>, tensor<16xf16>, tensor<1xf8E4M3FN>, tensor<1xf8E4M3FN>) -> tensor<1x32x32x16xf16>
return %0 : tensor<1x32x32x16xf16>
}
// -----
// CHECK-LABEL: const_f8E4M3FN
func.func @test_const_f8E4M3FN(%arg0 : index) -> tensor<4xf8E4M3FN> {
- %0 = "tosa.const"() {values = dense<[3.0, -0.0, -1.0, 2.0]> : tensor<4xf8E4M3FN>} : () -> tensor<4xf8E4M3FN>
+ %0 = "tosa.const"() <{values = dense<[3.0, -0.0, -1.0, 2.0]> : tensor<4xf8E4M3FN>}> : () -> tensor<4xf8E4M3FN>
return %0 : tensor<4xf8E4M3FN>
}
// -----
// CHECK-LABEL: cast_f8E4M3FN
func.func @test_cast_f8E4M3FN(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf16>
return %0 : tensor<13x21x3xf16>
}
// -----
// CHECK-LABEL: concat_f8E4M3FN
func.func @test_concat_f8E4M3FN(%arg0: tensor<13x21x3xf8E4M3FN>, %arg1: tensor<13x21x3xf8E4M3FN>) -> tensor<26x21x3xf8E4M3FN> {
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x3xf8E4M3FN>, tensor<13x21x3xf8E4M3FN>) -> tensor<26x21x3xf8E4M3FN>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x3xf8E4M3FN>, tensor<13x21x3xf8E4M3FN>) -> tensor<26x21x3xf8E4M3FN>
return %0 : tensor<26x21x3xf8E4M3FN>
}
// -----
// CHECK-LABEL: pad_f8E4M3FN
func.func @test_pad_f8E4M3FN(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf8E4M3FN> {
- %padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
- %cst = "tosa.const"() { values = dense<-0.0> : tensor<1xf8E4M3FN> } : () -> tensor<1xf8E4M3FN>
+ %padding = tosa.const_shape values(dense<0> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %cst = "tosa.const"() <{ values = dense<-0.0> : tensor<1xf8E4M3FN> }> : () -> tensor<1xf8E4M3FN>
%0 = tosa.pad %arg0, %padding, %cst : (tensor<13x21x3xf8E4M3FN>, !tosa.shape<6>, tensor<1xf8E4M3FN>) -> tensor<13x21x3xf8E4M3FN>
return %0 : tensor<13x21x3xf8E4M3FN>
}
@@ -1675,7 +1675,7 @@ func.func @test_pad_f8E4M3FN(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3x
// -----
// CHECK-LABEL: reshape_f8E4M3FN
func.func @test_reshape_f8E4M3FN(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<1x819xf8E4M3FN> {
- %1 = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = tosa.reshape %arg0, %1 : (tensor<13x21x3xf8E4M3FN>, !tosa.shape<2>) -> tensor<1x819xf8E4M3FN>
return %0 : tensor<1x819xf8E4M3FN>
}
@@ -1683,15 +1683,15 @@ func.func @test_reshape_f8E4M3FN(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<1x81
// -----
// CHECK-LABEL: reverse_f8E4M3FN
func.func @test_reverse_f8E4M3FN(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf8E4M3FN> {
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf8E4M3FN>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf8E4M3FN>
return %0 : tensor<13x21x3xf8E4M3FN>
}
// -----
// CHECK-LABEL: slice_f8E4M3FN
func.func @test_slice_f8E4M3FN(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<4x11x1xf8E4M3FN> {
- %size = tosa.const_shape {values = dense<[4, 11, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %start = tosa.const_shape {values = dense<[6, 8, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[4, 11, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[6, 8, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
%2 = tosa.slice %arg0, %start, %size : (tensor<13x21x3xf8E4M3FN>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<4x11x1xf8E4M3FN>
return %2 : tensor<4x11x1xf8E4M3FN>
}
@@ -1699,7 +1699,7 @@ func.func @test_slice_f8E4M3FN(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<4x11x1
// -----
// CHECK-LABEL: tile_f8E4M3FN
func.func @test_tile_f8E4M3FN(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<39x21x6xf8E4M3FN> {
- %cst = tosa.const_shape { values = dense<[3, 1, 2]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %cst = tosa.const_shape values(dense<[3, 1, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = tosa.tile %arg0, %cst: (tensor<13x21x3xf8E4M3FN>, !tosa.shape<3>) -> tensor<39x21x6xf8E4M3FN>
return %0 : tensor<39x21x6xf8E4M3FN>
}
@@ -1707,7 +1707,7 @@ func.func @test_tile_f8E4M3FN(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<39x21x6
// -----
// CHECK-LABEL: transpose_f8E4M3FN
func.func @test_transpose_f8E4M3FN(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<3x13x21xf8E4M3FN> {
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x21x3xf8E4M3FN>) -> tensor<3x13x21xf8E4M3FN>
+ %1 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<13x21x3xf8E4M3FN>) -> tensor<3x13x21xf8E4M3FN>
return %1 : tensor<3x13x21xf8E4M3FN>
}
@@ -1728,120 +1728,120 @@ func.func @test_scatter_f8E4M3FN(%arg0: tensor<13x29x3xf8E4M3FN>, %arg1: tensor<
// -----
// CHECK-LABEL: test_matmul_t_block_scaled_static
func.func @test_matmul_t_block_scaled_static(%arg0: tensor<4x8x32xf8E4M3FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32xf8E4M3FN>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf8E4M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf8E4M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
// -----
// CHECK-LABEL: test_matmul_t_block_scaled_unranked
func.func @test_matmul_t_block_scaled_unranked(%arg0: tensor<*xf8E4M3FN>, %arg1: tensor<*xf8E8M0FNU>, %arg2: tensor<*xf8E4M3FN>, %arg3: tensor<*xf8E8M0FNU>) -> tensor<*xf32> {
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>, tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>) -> tensor<*xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>, tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
// -----
// CHECK-LABEL: test_matmul_t_block_scaled_fp6e3m2
func.func @test_matmul_t_block_scaled_fp6e3m2(%arg0: tensor<4x8x32xf6E3M2FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32xf6E3M2FN>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x8x32xf6E3M2FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf6E3M2FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32xf6E3M2FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf6E3M2FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
// -----
// CHECK-LABEL: test_matmul_t_block_scaled_fp6e2m3
func.func @test_matmul_t_block_scaled_fp6e2m3(%arg0: tensor<4x8x32xf6E2M3FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32xf6E2M3FN>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x8x32xf6E2M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf6E2M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32xf6E2M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf6E2M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
// -----
// CHECK-LABEL: test_matmul_t_block_scaled_fp4e2m1
func.func @test_matmul_t_block_scaled_fp4e2m1(%arg0: tensor<4x8x32xf4E2M1FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32xf4E2M1FN>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x8x32xf4E2M1FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf4E2M1FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32xf4E2M1FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf4E2M1FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
// -----
// CHECK-LABEL: test_matmul_t_block_scaled_broadcast
func.func @test_matmul_t_block_scaled_broadcast(%arg0: tensor<?x8x32xf8E4M3FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<?x16x32xf8E4M3FN>, %arg3: tensor<1x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<?x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<?x16x32xf8E4M3FN>, tensor<1x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<?x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<?x16x32xf8E4M3FN>, tensor<1x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
// -----
// CHECK-LABEL: test_matmul_t_block_scaled_mxint8
func.func @test_matmul_t_block_scaled_mxint8(%arg0: tensor<4x8x32x!tosa.mxint8>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32x!tosa.mxint8>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x8x32x!tosa.mxint8>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32x!tosa.mxint8>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32x!tosa.mxint8>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32x!tosa.mxint8>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
// -----
// CHECK-LABEL: test_matmul_t_block_scaled_fp6e3m2_e2e
func.func @test_matmul_t_block_scaled_fp6e3m2_e2e(%arg0: tensor<6x2x32xf32>, %arg1: tensor<6x64x32xf32>) -> tensor<6x2x64xf32> {
- %a, %sa = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<6x2x32xf32>) -> (tensor<6x2x32xf6E3M2FN>, tensor<6x2x1xf8E8M0FNU>)
- %b, %sb = tosa.cast_to_block_scaled %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<6x64x32xf32>) -> (tensor<6x64x32xf6E3M2FN>, tensor<6x64x1xf8E8M0FNU>)
- %res = tosa.matmul_t_block_scaled %a, %sa, %b, %sb {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<6x2x32xf6E3M2FN>, tensor<6x2x1xf8E8M0FNU>, tensor<6x64x32xf6E3M2FN>, tensor<6x64x1xf8E8M0FNU>) -> tensor<6x2x64xf32>
+ %a, %sa = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<6x2x32xf32>) -> (tensor<6x2x32xf6E3M2FN>, tensor<6x2x1xf8E8M0FNU>)
+ %b, %sb = tosa.cast_to_block_scaled %arg1 block_size<BLOCK_SIZE_32> : (tensor<6x64x32xf32>) -> (tensor<6x64x32xf6E3M2FN>, tensor<6x64x1xf8E8M0FNU>)
+ %res = tosa.matmul_t_block_scaled %a, %sa, %b, %sb block_size<BLOCK_SIZE_32> : (tensor<6x2x32xf6E3M2FN>, tensor<6x2x1xf8E8M0FNU>, tensor<6x64x32xf6E3M2FN>, tensor<6x64x1xf8E8M0FNU>) -> tensor<6x2x64xf32>
return %res : tensor<6x2x64xf32>
}
// -----
// CHECK-LABEL: test_matmul_t_block_scaled_fp6e2m3_e2e
func.func @test_matmul_t_block_scaled_fp6e2m3_e2e(%arg0: tensor<6x2x32xf32>, %arg1: tensor<6x64x32xf32>) -> tensor<6x2x64xf32> {
- %a, %sa = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<6x2x32xf32>) -> (tensor<6x2x32xf6E2M3FN>, tensor<6x2x1xf8E8M0FNU>)
- %b, %sb = tosa.cast_to_block_scaled %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<6x64x32xf32>) -> (tensor<6x64x32xf6E2M3FN>, tensor<6x64x1xf8E8M0FNU>)
- %res = tosa.matmul_t_block_scaled %a, %sa, %b, %sb {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<6x2x32xf6E2M3FN>, tensor<6x2x1xf8E8M0FNU>, tensor<6x64x32xf6E2M3FN>, tensor<6x64x1xf8E8M0FNU>) -> tensor<6x2x64xf32>
+ %a, %sa = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<6x2x32xf32>) -> (tensor<6x2x32xf6E2M3FN>, tensor<6x2x1xf8E8M0FNU>)
+ %b, %sb = tosa.cast_to_block_scaled %arg1 block_size<BLOCK_SIZE_32> : (tensor<6x64x32xf32>) -> (tensor<6x64x32xf6E2M3FN>, tensor<6x64x1xf8E8M0FNU>)
+ %res = tosa.matmul_t_block_scaled %a, %sa, %b, %sb block_size<BLOCK_SIZE_32> : (tensor<6x2x32xf6E2M3FN>, tensor<6x2x1xf8E8M0FNU>, tensor<6x64x32xf6E2M3FN>, tensor<6x64x1xf8E8M0FNU>) -> tensor<6x2x64xf32>
return %res : tensor<6x2x64xf32>
}
// -----
// CHECK-LABEL: test_matmul_t_block_scaled_fp4e2m1_e2e
func.func @test_matmul_t_block_scaled_fp4e2m1_e2e(%arg0: tensor<6x2x32xf32>, %arg1: tensor<6x64x32xf32>) -> tensor<6x2x64xf32> {
- %a, %sa = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<6x2x32xf32>) -> (tensor<6x2x32xf4E2M1FN>, tensor<6x2x1xf8E8M0FNU>)
- %b, %sb = tosa.cast_to_block_scaled %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<6x64x32xf32>) -> (tensor<6x64x32xf4E2M1FN>, tensor<6x64x1xf8E8M0FNU>)
- %res = tosa.matmul_t_block_scaled %a, %sa, %b, %sb {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<6x2x32xf4E2M1FN>, tensor<6x2x1xf8E8M0FNU>, tensor<6x64x32xf4E2M1FN>, tensor<6x64x1xf8E8M0FNU>) -> tensor<6x2x64xf32>
+ %a, %sa = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<6x2x32xf32>) -> (tensor<6x2x32xf4E2M1FN>, tensor<6x2x1xf8E8M0FNU>)
+ %b, %sb = tosa.cast_to_block_scaled %arg1 block_size<BLOCK_SIZE_32> : (tensor<6x64x32xf32>) -> (tensor<6x64x32xf4E2M1FN>, tensor<6x64x1xf8E8M0FNU>)
+ %res = tosa.matmul_t_block_scaled %a, %sa, %b, %sb block_size<BLOCK_SIZE_32> : (tensor<6x2x32xf4E2M1FN>, tensor<6x2x1xf8E8M0FNU>, tensor<6x64x32xf4E2M1FN>, tensor<6x64x1xf8E8M0FNU>) -> tensor<6x2x64xf32>
return %res : tensor<6x2x64xf32>
}
// -----
// CHECK-LABEL: test_matmul_t_block_scaled_mxint8_e2e
func.func @test_matmul_t_block_scaled_mxint8_e2e(%arg0: tensor<6x2x32xf32>, %arg1: tensor<6x64x32xf32>) -> tensor<6x2x64xf32> {
- %a, %sa = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<6x2x32xf32>) -> (tensor<6x2x32x!tosa.mxint8>, tensor<6x2x1xf8E8M0FNU>)
- %b, %sb = tosa.cast_to_block_scaled %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<6x64x32xf32>) -> (tensor<6x64x32x!tosa.mxint8>, tensor<6x64x1xf8E8M0FNU>)
- %res = tosa.matmul_t_block_scaled %a, %sa, %b, %sb {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<6x2x32x!tosa.mxint8>, tensor<6x2x1xf8E8M0FNU>, tensor<6x64x32x!tosa.mxint8>, tensor<6x64x1xf8E8M0FNU>) -> tensor<6x2x64xf32>
+ %a, %sa = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<6x2x32xf32>) -> (tensor<6x2x32x!tosa.mxint8>, tensor<6x2x1xf8E8M0FNU>)
+ %b, %sb = tosa.cast_to_block_scaled %arg1 block_size<BLOCK_SIZE_32> : (tensor<6x64x32xf32>) -> (tensor<6x64x32x!tosa.mxint8>, tensor<6x64x1xf8E8M0FNU>)
+ %res = tosa.matmul_t_block_scaled %a, %sa, %b, %sb block_size<BLOCK_SIZE_32> : (tensor<6x2x32x!tosa.mxint8>, tensor<6x2x1xf8E8M0FNU>, tensor<6x64x32x!tosa.mxint8>, tensor<6x64x1xf8E8M0FNU>) -> tensor<6x2x64xf32>
return %res : tensor<6x2x64xf32>
}
// -----
// CHECK-LABEL: test_cast_from_block_scaled_static
func.func @test_cast_from_block_scaled_static(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32> {
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32>
return %0 : tensor<4x32xf32>
}
// -----
// CHECK-LABEL: test_cast_from_block_scaled_unranked
func.func @test_cast_from_block_scaled_unranked(%arg0: tensor<*xf4E2M1FN>, %arg1: tensor<*xf8E8M0FNU>) -> tensor<*xf32> {
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>) -> tensor<*xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
// -----
// CHECK-LABEL: test_cast_to_block_scaled_static
func.func @test_cast_to_block_scaled_static(%arg0: tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) {
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>
}
// -----
// CHECK-LABEL: test_cast_to_block_scaled_unranked
func.func @test_cast_to_block_scaled_unranked(%arg0: tensor<*xf32>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>) {
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<*xf32>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<*xf32>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
return %0#0, %0#1 : tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>
}
// -----
// CHECK-LABEL: test_cast_to_block_scaled_mxint8
func.func @test_cast_to_block_scaled_mxint8(%arg0: tensor<4x32xf32>) -> (tensor<4x32x!tosa.mxint8>, tensor<4x1xf8E8M0FNU>) {
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf32>) -> (tensor<4x32x!tosa.mxint8>, tensor<4x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<4x32xf32>) -> (tensor<4x32x!tosa.mxint8>, tensor<4x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<4x32x!tosa.mxint8>, tensor<4x1xf8E8M0FNU>
}
@@ -1862,8 +1862,8 @@ func.func @test_const_mxint8() -> tensor<2x!tosa.mxint8> {
// -----
// CHECK-LABEL: test_add_shape
func.func @test_add_shape() -> !tosa.shape<4> {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %b = tosa.const_shape {values = dense<[5, 6, 7, 8]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %b = tosa.const_shape values(dense<[5, 6, 7, 8]> : tensor<4xindex>) : () -> !tosa.shape<4>
%c = tosa.add_shape %a, %b : (!tosa.shape<4>, !tosa.shape<4>) -> !tosa.shape<4>
return %c : !tosa.shape<4>
}
@@ -1871,8 +1871,8 @@ func.func @test_add_shape() -> !tosa.shape<4> {
// -----
// CHECK-LABEL: test_sub_shape
func.func @test_sub_shape() -> !tosa.shape<3> {
- %a = tosa.const_shape {values = dense<[10, 5, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %b = tosa.const_shape {values = dense<[2, 1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %a = tosa.const_shape values(dense<[10, 5, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %b = tosa.const_shape values(dense<[2, 1, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
%c = tosa.sub_shape %a, %b : (!tosa.shape<3>, !tosa.shape<3>) -> !tosa.shape<3>
return %c : !tosa.shape<3>
}
@@ -1880,8 +1880,8 @@ func.func @test_sub_shape() -> !tosa.shape<3> {
// -----
// CHECK-LABEL: test_mul_shape
func.func @test_mul_shape() -> !tosa.shape<4> {
- %a = tosa.const_shape {values = dense<[2, 3, 4, 5]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %b = tosa.const_shape {values = dense<[7, 0, 2, 6]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[2, 3, 4, 5]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %b = tosa.const_shape values(dense<[7, 0, 2, 6]> : tensor<4xindex>) : () -> !tosa.shape<4>
%c = tosa.mul_shape %a, %b : (!tosa.shape<4>, !tosa.shape<4>) -> !tosa.shape<4>
return %c : !tosa.shape<4>
}
@@ -1889,8 +1889,8 @@ func.func @test_mul_shape() -> !tosa.shape<4> {
// -----
// CHECK-LABEL: test_mod_shape
func.func @test_mod_shape() -> !tosa.shape<4> {
- %a = tosa.const_shape {values = dense<[9, 12, 10, 5]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %b = tosa.const_shape {values = dense<[2, 5, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[9, 12, 10, 5]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %b = tosa.const_shape values(dense<[2, 5, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
%c = tosa.mod_shape %a, %b : (!tosa.shape<4>, !tosa.shape<4>) -> !tosa.shape<4>
return %c : !tosa.shape<4>
}
@@ -1898,8 +1898,8 @@ func.func @test_mod_shape() -> !tosa.shape<4> {
// -----
// CHECK-LABEL: test_div_ceil_shape
func.func @test_div_ceil_shape() -> !tosa.shape<4> {
- %a = tosa.const_shape {values = dense<[5, 7, 10, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %b = tosa.const_shape {values = dense<[2, 3, 4, 3]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[5, 7, 10, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %b = tosa.const_shape values(dense<[2, 3, 4, 3]> : tensor<4xindex>) : () -> !tosa.shape<4>
%c = tosa.div_ceil_shape %a, %b : (!tosa.shape<4>, !tosa.shape<4>) -> !tosa.shape<4>
return %c : !tosa.shape<4>
}
@@ -1907,8 +1907,8 @@ func.func @test_div_ceil_shape() -> !tosa.shape<4> {
// -----
// CHECK-LABEL: test_div_floor_shape
func.func @test_div_floor_shape() -> !tosa.shape<4> {
- %a = tosa.const_shape {values = dense<[5, 7, 10, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %b = tosa.const_shape {values = dense<[2, 3, 4, 3]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[5, 7, 10, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %b = tosa.const_shape values(dense<[2, 3, 4, 3]> : tensor<4xindex>) : () -> !tosa.shape<4>
%c = tosa.div_floor_shape %a, %b : (!tosa.shape<4>, !tosa.shape<4>) -> !tosa.shape<4>
return %c : !tosa.shape<4>
}
@@ -1916,16 +1916,16 @@ func.func @test_div_floor_shape() -> !tosa.shape<4> {
// -----
// CHECK-LABEL: test_dim
func.func @test_dim(%arg0: tensor<1x2x3x4xi32>) -> !tosa.shape<1> {
- %0 = tosa.dim %arg0 {axis = 2 : i32} : (tensor<1x2x3x4xi32>) -> !tosa.shape<1>
+ %0 = tosa.dim %arg0 axis(2) : (tensor<1x2x3x4xi32>) -> !tosa.shape<1>
return %0 : !tosa.shape<1>
}
// -----
// CHECK-LABEL: test_concat_shape
func.func @test_concat_shape() -> !tosa.shape<5> {
- %0 = tosa.const_shape {values = dense<[10]> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1 = tosa.const_shape {values = dense<[10, 15]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %2 = tosa.const_shape {values = dense<[5, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<[10]> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1 = tosa.const_shape values(dense<[10, 15]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %2 = tosa.const_shape values(dense<[5, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
%3 = tosa.concat_shape %0, %1, %2 : (!tosa.shape<1>, !tosa.shape<2>, !tosa.shape<2>) -> !tosa.shape<5>
return %3 : !tosa.shape<5>
}
@@ -1933,9 +1933,9 @@ func.func @test_concat_shape() -> !tosa.shape<5> {
// -----
// CHECK-LABEL: test_slice_shape
func.func @test_slice_shape() -> !tosa.shape<3> {
- %0 = tosa.const_shape {values = dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %1 = "tosa.const"() {values = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
- %2 = "tosa.const"() {values = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = tosa.const_shape values(dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %1 = "tosa.const"() <{values = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %2 = "tosa.const"() <{values = dense<3> : tensor<1xi32>}> : () -> tensor<1xi32>
%3 = tosa.slice_shape %0, %1, %2 : (!tosa.shape<6>, tensor<1xi32>, tensor<1xi32>) -> !tosa.shape<3>
return %3 : !tosa.shape<3>
}
@@ -1943,7 +1943,7 @@ func.func @test_slice_shape() -> !tosa.shape<3> {
// -----
// CHECK-LABEL: test_slice_shape_dynamic
func.func @test_slice_shape_dynamic(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> !tosa.shape<3> {
- %0 = tosa.const_shape {values = dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %0 = tosa.const_shape values(dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>) : () -> !tosa.shape<6>
%3 = tosa.slice_shape %0, %arg0, %arg1 : (!tosa.shape<6>, tensor<1xi32>, tensor<1xi32>) -> !tosa.shape<3>
return %3 : !tosa.shape<3>
}
@@ -1951,7 +1951,7 @@ func.func @test_slice_shape_dynamic(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>)
// -----
// CHECK-LABEL: test_exp2_shape
func.func @test_exp2_shape() -> !tosa.shape<4> {
- %a = tosa.const_shape {values = dense<[5, 7, 10, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[5, 7, 10, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%b = tosa.exp2_shape %a : (!tosa.shape<4>) -> !tosa.shape<4>
return %b : !tosa.shape<4>
}
@@ -1959,7 +1959,7 @@ func.func @test_exp2_shape() -> !tosa.shape<4> {
// -----
// CHECK-LABEL: test_log2_ceil_shape
func.func @test_log2_ceil_shape() -> !tosa.shape<4> {
- %a = tosa.const_shape {values = dense<[5, 7, 10, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[5, 7, 10, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%b = tosa.log2_ceil_shape %a : (!tosa.shape<4>) -> !tosa.shape<4>
return %b : !tosa.shape<4>
}
@@ -1967,7 +1967,7 @@ func.func @test_log2_ceil_shape() -> !tosa.shape<4> {
// -----
// CHECK-LABEL: test_log2_floor_shape
func.func @test_log2_floor_shape() -> !tosa.shape<4> {
- %a = tosa.const_shape {values = dense<[5, 7, 10, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[5, 7, 10, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%b = tosa.log2_floor_shape %a : (!tosa.shape<4>) -> !tosa.shape<4>
return %b : !tosa.shape<4>
}
@@ -1975,8 +1975,8 @@ func.func @test_log2_floor_shape() -> !tosa.shape<4> {
// -----
// CHECK-LABEL: test_max_shape
func.func @test_max_shape() -> !tosa.shape<4> {
- %a = tosa.const_shape {values = dense<[5, 7, 10, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %b = tosa.const_shape {values = dense<[2, 3, 4, 3]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[5, 7, 10, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %b = tosa.const_shape values(dense<[2, 3, 4, 3]> : tensor<4xindex>) : () -> !tosa.shape<4>
%c = tosa.max_shape %a, %b : (!tosa.shape<4>, !tosa.shape<4>) -> !tosa.shape<4>
return %c : !tosa.shape<4>
}
@@ -1984,8 +1984,8 @@ func.func @test_max_shape() -> !tosa.shape<4> {
// -----
// CHECK-LABEL: test_min_shape
func.func @test_min_shape() -> !tosa.shape<4> {
- %a = tosa.const_shape {values = dense<[5, 7, 10, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %b = tosa.const_shape {values = dense<[2, 3, 4, 3]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[5, 7, 10, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %b = tosa.const_shape values(dense<[2, 3, 4, 3]> : tensor<4xindex>) : () -> !tosa.shape<4>
%c = tosa.min_shape %a, %b : (!tosa.shape<4>, !tosa.shape<4>) -> !tosa.shape<4>
return %c : !tosa.shape<4>
}
@@ -1993,29 +1993,29 @@ func.func @test_min_shape() -> !tosa.shape<4> {
// -----
// CHECK-LABEL: test_conv2d_block_scaled_static
func.func @test_conv2d_block_scaled_static(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>) -> tensor<*xf32> {
- %0 = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %1 = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %2 = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %0, %1, %2 {block_size = BLOCK_SIZE_32} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %1 = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %2 = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %0, %1, %2 block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %3 : tensor<*xf32>
}
// -----
// CHECK-LABEL: test_conv2d_block_scaled_dynamic
func.func @test_conv2d_block_scaled_dynamic(%arg0: tensor<*xf4E2M1FN>, %arg1: tensor<*xf8E8M0FNU>, %arg2: tensor<*xf4E2M1FN>, %arg3: tensor<*xf8E8M0FNU>, %arg4: tensor<*xf32>) -> tensor<*xf32> {
- %0 = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %1 = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %2 = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %0, %1, %2 {block_size = BLOCK_SIZE_32} : (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<*xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %1 = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %2 = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %0, %1, %2 block_size<BLOCK_SIZE_32> : (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<*xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %3 : tensor<*xf32>
}
// -----
// CHECK-LABEL: test_assert_equal_shape
func.func @test_assert_equal_shape() {
- %0 = tosa.const_shape {values = dense<[10, 15]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = tosa.const_shape {values = dense<[5, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- tosa.assert_equal_shape %0, %1 {allow_broadcast = true} : (!tosa.shape<2>, !tosa.shape<2>) -> ()
+ %0 = tosa.const_shape values(dense<[10, 15]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[5, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ tosa.assert_equal_shape %0, %1 allow_broadcast(true) : (!tosa.shape<2>, !tosa.shape<2>) -> ()
return
}
@@ -2087,7 +2087,7 @@ func.func @test_block_scaled_const_scale_values_wide_inner_dim() -> tensor<2x64x
// CHECK-LABEL: test_block_scaled_const_cast_scale_values_no_propagate
func.func @test_block_scaled_const_cast_scale_values_no_propagate() -> tensor<2x32xf32> {
%0 = "tosa.const"() <{values = dense<tensor<2x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN, {2.0, 4.0}>> : 0.0 : f8E4M3FN>}> : () -> tensor<2x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<2x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<2x32xf32>
+ %1 = tosa.cast %0 : (tensor<2x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<2x32xf32>
return %1 : tensor<2x32xf32>
}
@@ -2095,7 +2095,7 @@ func.func @test_block_scaled_const_cast_scale_values_no_propagate() -> tensor<2x
// CHECK-LABEL: test_block_scaled_concat
func.func @test_block_scaled_concat(%arg0: tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, %arg1: tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<26x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>> {
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<26x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<26x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
return %0 : tensor<26x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
}
diff --git a/mlir/test/Dialect/Tosa/profile_all_unsupported.mlir b/mlir/test/Dialect/Tosa/profile_all_unsupported.mlir
index 8cba320230486..676527e512c03 100644
--- a/mlir/test/Dialect/Tosa/profile_all_unsupported.mlir
+++ b/mlir/test/Dialect/Tosa/profile_all_unsupported.mlir
@@ -70,14 +70,14 @@ func.func @test_table(%arg0 : tensor<4x5xi8>, %arg1 : tensor<513xi8>) -> () {
// -----
func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.conv2d' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true) : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
// -----
func.func @test_avg_pool2d(%arg0: tensor<1x7x7x9xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x7x7x9xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.avg_pool2d %arg0, %arg1, %arg2 {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf32>
+ %0 = tosa.avg_pool2d %arg0, %arg1, %arg2 kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(f32) : (tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf32>
return %0 : tensor<1x7x7x9xf32>
}
@@ -105,7 +105,7 @@ func.func @test_sigmoid(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
// -----
func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) {out_shape = array<i64: 1, 32, 32, 16>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -174,30 +174,30 @@ func.func @test_select(%arg0: tensor<1x1x1xi1>, %arg1: tensor<13x21x3xi1>, %arg2
// -----
func.func @test_reduce_all(%arg0: tensor<13x21x3xi1>) -> tensor<1x21x3xi1> {
// expected-error at +1 {{'tosa.reduce_all' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.reduce_all %arg0 {axis = 0 : i32} : (tensor<13x21x3xi1>) -> tensor<1x21x3xi1>
+ %0 = tosa.reduce_all %arg0 axis(0) : (tensor<13x21x3xi1>) -> tensor<1x21x3xi1>
return %0 : tensor<1x21x3xi1>
}
// -----
func.func @test_reduce_any(%arg0: tensor<13x21x3xi1>) -> tensor<1x21x3xi1> {
// expected-error at +1 {{'tosa.reduce_any' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.reduce_any %arg0 {axis = 0 : i32} : (tensor<13x21x3xi1>) -> tensor<1x21x3xi1>
+ %0 = tosa.reduce_any %arg0 axis(0) : (tensor<13x21x3xi1>) -> tensor<1x21x3xi1>
return %0 : tensor<1x21x3xi1>
}
// -----
func.func @test_concat(%arg0: tensor<13x21x3xi1>, %arg1: tensor<13x21x3xi1>) -> tensor<26x21x3xi1> {
// expected-error at +1 {{'tosa.concat' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x3xi1>, tensor<13x21x3xi1>) -> tensor<26x21x3xi1>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x3xi1>, tensor<13x21x3xi1>) -> tensor<26x21x3xi1>
return %0 : tensor<26x21x3xi1>
}
// -----
func.func @test_pad(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xi1> {
// expected-error at +1 {{'tosa.const_shape' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %padding = tosa.const_shape values(dense<0> : tensor<6xindex>) : () -> !tosa.shape<6>
// expected-error at +1 {{'tosa.const' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %pad_const = "tosa.const"() {values = dense<1> : tensor<1xi1>} : () -> tensor<1xi1>
+ %pad_const = "tosa.const"() <{values = dense<1> : tensor<1xi1>}> : () -> tensor<1xi1>
// expected-error at +1 {{'tosa.pad' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
%0 = tosa.pad %arg0, %padding, %pad_const : (tensor<13x21x3xi1>, !tosa.shape<6>, tensor<1xi1>) -> tensor<13x21x3xi1>
return %0 : tensor<13x21x3xi1>
@@ -206,7 +206,7 @@ func.func @test_pad(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xi1> {
// -----
func.func @test_reshape(%arg0: tensor<13x21x3xi1>) -> tensor<1x819xi1> {
// expected-error at +1 {{'tosa.const_shape' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %1 = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
%0 = tosa.reshape %arg0, %1 : (tensor<13x21x3xi1>, !tosa.shape<2>) -> tensor<1x819xi1>
return %0 : tensor<1x819xi1>
@@ -215,16 +215,16 @@ func.func @test_reshape(%arg0: tensor<13x21x3xi1>) -> tensor<1x819xi1> {
// -----
func.func @test_reverse(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xi1> {
// expected-error at +1 {{'tosa.reverse' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<13x21x3xi1>) -> tensor<13x21x3xi1>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<13x21x3xi1>) -> tensor<13x21x3xi1>
return %0 : tensor<13x21x3xi1>
}
// -----
func.func @test_slice(%arg0: tensor<13x21x3xi1>) -> tensor<4x11x1xi1> {
// expected-error at +1 {{'tosa.const_shape' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %size = tosa.const_shape {values = dense<[4, 11, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[4, 11, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.const_shape' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %start = tosa.const_shape {values = dense<[6, 8, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[6, 8, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.slice' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
%2 = tosa.slice %arg0, %start, %size : (tensor<13x21x3xi1>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<4x11x1xi1>
return %2 : tensor<4x11x1xi1>
@@ -233,7 +233,7 @@ func.func @test_slice(%arg0: tensor<13x21x3xi1>) -> tensor<4x11x1xi1> {
// -----
func.func @test_tile(%arg0: tensor<13x21x3xi1>) -> tensor<39x21x6xi1> {
// expected-error at +1 {{'tosa.const_shape' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %cst = tosa.const_shape { values = dense<[3, 1, 2]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %cst = tosa.const_shape values(dense<[3, 1, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.tile' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
%0 = tosa.tile %arg0, %cst: (tensor<13x21x3xi1>, !tosa.shape<3>) -> tensor<39x21x6xi1>
return %0 : tensor<39x21x6xi1>
@@ -242,30 +242,30 @@ func.func @test_tile(%arg0: tensor<13x21x3xi1>) -> tensor<39x21x6xi1> {
// -----
func.func @test_transpose(%arg0: tensor<13x21x3xi1>) -> tensor<3x13x21xi1> {
// expected-error at +1 {{'tosa.transpose' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x21x3xi1>) -> tensor<3x13x21xi1>
+ %1 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<13x21x3xi1>) -> tensor<3x13x21xi1>
return %1 : tensor<3x13x21xi1>
}
// -----
func.func @test_concat(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<26x21x3xf32> {
// expected-error at +1 {{'tosa.concat' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<26x21x3xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<26x21x3xf32>
return %0 : tensor<26x21x3xf32>
}
// -----
func.func @test_concat(%arg0: tensor<13x21x3xi1>, %arg1: tensor<13x21x3xi1>) -> tensor<26x21x3xi1> {
// expected-error at +1 {{'tosa.concat' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x3xi1>, tensor<13x21x3xi1>) -> tensor<26x21x3xi1>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x3xi1>, tensor<13x21x3xi1>) -> tensor<26x21x3xi1>
return %0 : tensor<26x21x3xi1>
}
// -----
func.func @test_pad(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xi1> {
// expected-error at +1 {{'tosa.const_shape' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %padding = tosa.const_shape values(dense<0> : tensor<6xindex>) : () -> !tosa.shape<6>
// expected-error at +1 {{'tosa.const' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %pad_const = "tosa.const"() {values = dense<1> : tensor<1xi1>} : () -> tensor<1xi1>
+ %pad_const = "tosa.const"() <{values = dense<1> : tensor<1xi1>}> : () -> tensor<1xi1>
// expected-error at +1 {{'tosa.pad' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
%0 = tosa.pad %arg0, %padding, %pad_const : (tensor<13x21x3xi1>, !tosa.shape<6>, tensor<1xi1>) -> tensor<13x21x3xi1>
return %0 : tensor<13x21x3xi1>
@@ -274,7 +274,7 @@ func.func @test_pad(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xi1> {
// -----
func.func @test_reshape(%arg0: tensor<13x21x3xi1>) -> tensor<1x819xi1> {
// expected-error at +1 {{'tosa.const_shape' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %1 = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
%0 = tosa.reshape %arg0, %1 : (tensor<13x21x3xi1>, !tosa.shape<2>) -> tensor<1x819xi1>
return %0 : tensor<1x819xi1>
@@ -283,16 +283,16 @@ func.func @test_reshape(%arg0: tensor<13x21x3xi1>) -> tensor<1x819xi1> {
// -----
func.func @test_reverse(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xi1> {
// expected-error at +1 {{'tosa.reverse' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<13x21x3xi1>) -> tensor<13x21x3xi1>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<13x21x3xi1>) -> tensor<13x21x3xi1>
return %0 : tensor<13x21x3xi1>
}
// -----
func.func @test_slice(%arg0: tensor<13x21x3xi1>) -> tensor<4x11x1xi1> {
// expected-error at +1 {{'tosa.const_shape' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %size = tosa.const_shape {values = dense<[4, 11, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[4, 11, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.const_shape' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %start = tosa.const_shape {values = dense<[6, 8, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[6, 8, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.slice' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
%0 = tosa.slice %arg0, %start, %size : (tensor<13x21x3xi1>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<4x11x1xi1>
return %0 : tensor<4x11x1xi1>
@@ -301,7 +301,7 @@ func.func @test_slice(%arg0: tensor<13x21x3xi1>) -> tensor<4x11x1xi1> {
// -----
func.func @test_tile(%arg0: tensor<13x21x3xi1>) -> tensor<39x21x6xi1> {
// expected-error at +1 {{'tosa.const_shape' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %cst = tosa.const_shape { values = dense<[3, 1, 2]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %cst = tosa.const_shape values(dense<[3, 1, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.tile' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
%0 = tosa.tile %arg0, %cst: (tensor<13x21x3xi1>, !tosa.shape<3>) -> tensor<39x21x6xi1>
return %0 : tensor<39x21x6xi1>
@@ -310,19 +310,19 @@ func.func @test_tile(%arg0: tensor<13x21x3xi1>) -> tensor<39x21x6xi1> {
// -----
func.func @test_transpose(%arg0: tensor<13x21x3xi1>) -> tensor<3x13x21xi1> {
// expected-error at +1 {{'tosa.transpose' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x21x3xi1>) -> tensor<3x13x21xi1>
+ %1 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<13x21x3xi1>) -> tensor<3x13x21xi1>
return %1 : tensor<3x13x21xi1>
}
// -----
func.func @test_cast_i32_f32(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xf32> {
// expected-error at +1 {{'tosa.cast' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi32>) -> tensor<13x21x3xf32>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
func.func @test_custom(%arg0: tensor<10xi32>) -> tensor<10xi32> {
// expected-error at +1 {{'tosa.custom' op illegal: requires any of [pro_int, pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.custom %arg0 {operator_name="custom_test", domain_name="tosa.mlir_test", implementation_attrs="" } : (tensor<10xi32>) -> (tensor<10xi32>)
+ %0 = tosa.custom %arg0 operator_name("custom_test") domain_name("tosa.mlir_test") implementation_attrs("") : (tensor<10xi32>) -> (tensor<10xi32>)
return %0 : tensor<10xi32>
}
diff --git a/mlir/test/Dialect/Tosa/profile_pro_fp_unsupported.mlir b/mlir/test/Dialect/Tosa/profile_pro_fp_unsupported.mlir
index 7d8bd3024b940..072e5829aa53e 100644
--- a/mlir/test/Dialect/Tosa/profile_pro_fp_unsupported.mlir
+++ b/mlir/test/Dialect/Tosa/profile_pro_fp_unsupported.mlir
@@ -7,31 +7,31 @@
// -----
func.func @test_const_f16() -> tensor<3x11x11x3xf16> {
// expected-error at +1 {{'tosa.const' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = "tosa.const"() {values = dense<2.0> : tensor<3x11x11x3xf16>} : () -> tensor<3x11x11x3xf16>
+ %0 = "tosa.const"() <{values = dense<2.0> : tensor<3x11x11x3xf16>}> : () -> tensor<3x11x11x3xf16>
return %0 : tensor<3x11x11x3xf16>
}
// -----
func.func @test_const_f32() -> tensor<3x11x11x3xf32> {
// expected-error at +1 {{'tosa.const' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = "tosa.const"() {values = dense<3.0> : tensor<3x11x11x3xf32>} : () -> tensor<3x11x11x3xf32>
+ %0 = "tosa.const"() <{values = dense<3.0> : tensor<3x11x11x3xf32>}> : () -> tensor<3x11x11x3xf32>
return %0 : tensor<3x11x11x3xf32>
}
// -----
func.func @test_avg_pool2d(%arg0: tensor<1x7x7x9xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x7x7x9xf32> {
// expected-error at +1 {{'tosa.avg_pool2d' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.avg_pool2d %arg0, %arg1, %arg2 {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf32>
+ %0 = tosa.avg_pool2d %arg0, %arg1, %arg2 kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(f32) : (tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf32>
return %0 : tensor<1x7x7x9xf32>
}
// -----
func.func @test_avg_pool2d_adaptive(%arg0: tensor<1x7x7x9xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x7x7x9xf32> {
- %kernel = tosa.const_shape {values = dense<[2, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 1, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 1, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.avg_pool2d_adaptive %arg0, %arg1, %arg2, %kernel, %stride, %pad {acc_type = f32} :
+ %0 = tosa.avg_pool2d_adaptive %arg0, %arg1, %arg2, %kernel, %stride, %pad acc_type(f32) :
(tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xf32>
return %0 : tensor<1x7x7x9xf32>
}
@@ -39,21 +39,21 @@ func.func @test_avg_pool2d_adaptive(%arg0: tensor<1x7x7x9xf32>, %arg1: tensor<1x
// -----
func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.conv2d' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true) : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
// -----
func.func @test_conv3d(%arg0: tensor<1x4x8x21x17xf16>, %arg1: tensor<34x1x1x1x17xf16>, %arg2: tensor<34xf16>, %arg3: tensor<1xf16>, %arg4: tensor<1xf16>) -> tensor<1x4x8x21x34xf16> {
// expected-error at +1 {{'tosa.conv3d' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<1x4x8x21x17xf16>, tensor<34x1x1x1x17xf16>, tensor<34xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x4x8x21x34xf16>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<1x4x8x21x17xf16>, tensor<34x1x1x1x17xf16>, tensor<34xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x4x8x21x34xf16>
return %0 : tensor<1x4x8x21x34xf16>
}
// -----
func.func @test_depthwise_conv2d(%arg0: tensor<1x4x4x4xf16>, %arg1: tensor<1x1x4x2xf16>, %arg2: tensor<8xf16>, %arg3: tensor<1xf16>, %arg4: tensor<1xf16>) -> tensor<1x4x4x8xf16> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xf16>, tensor<1x1x4x2xf16>, tensor<8xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x4x4x8xf16>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f16) : (tensor<1x4x4x4xf16>, tensor<1x1x4x2xf16>, tensor<8xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x4x4x8xf16>
return %0 : tensor<1x4x4x8xf16>
}
@@ -78,28 +78,28 @@ func.func @test_sigmoid(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
// -----
func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) {out_shape = array<i64: 1, 32, 32, 16>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
func.func @test_max_pool2d(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
// expected-error at +1 {{'tosa.max_pool2d' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
+ %0 = tosa.max_pool2d %arg0 kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
// -----
func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf16>, %arg1: tensor<16x1x1x8xf16>, %arg2: tensor<16xf16>, %arg3: tensor<1xf16>, %arg4: tensor<1xf16>) -> tensor<1x32x32x16xf16> {
// expected-error at +1 {{'tosa.transpose_conv2d' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f16, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf16>, tensor<16x1x1x8xf16>, tensor<16xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x32x32x16xf16>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f16) : (tensor<1x32x32x8xf16>, tensor<16x1x1x8xf16>, tensor<16xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x32x32x16xf16>
return %0 : tensor<1x32x32x16xf16>
}
// -----
func.func @test_clamp(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
// expected-error at +1 {{'tosa.clamp' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.clamp %arg0 {min_val = 0.0 : f32, max_val = 1.0 : f32} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.clamp %arg0 min_val(0.0 : f32) max_val(1.0 : f32) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -113,14 +113,14 @@ func.func @test_add(%arg0: tensor<13x21x1xf32>, %arg1: tensor<13x21x3xf32>) -> t
// -----
func.func @test_concat(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<26x21x3xf32> {
// expected-error at +1 {{'tosa.concat' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<26x21x3xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<26x21x3xf32>
return %0 : tensor<26x21x3xf32>
}
// -----
func.func @test_cast_i32_f32(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xf32> {
// expected-error at +1 {{'tosa.cast' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi32>) -> tensor<13x21x3xf32>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -246,29 +246,29 @@ func.func @test_greater(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf32>) -
// -----
func.func @test_reduce_max(%arg0: tensor<13x21x3xf16>) -> tensor<1x21x3xf16> {
// expected-error at +1 {{'tosa.reduce_max' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.reduce_max %arg0 {axis = 0 : i32} : (tensor<13x21x3xf16>) -> tensor<1x21x3xf16>
+ %0 = tosa.reduce_max %arg0 axis(0) : (tensor<13x21x3xf16>) -> tensor<1x21x3xf16>
return %0 : tensor<1x21x3xf16>
}
// -----
func.func @test_reduce_sum(%arg0: tensor<13x21x3xf32>) -> tensor<1x21x3xf32> {
// expected-error at +1 {{'tosa.reduce_sum' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
+ %0 = tosa.reduce_sum %arg0 axis(0) : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
return %0 : tensor<1x21x3xf32>
}
// -----
func.func @test_concat(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<26x21x3xf32> {
// expected-error at +1 {{'tosa.concat' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<26x21x3xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<26x21x3xf32>
return %0 : tensor<26x21x3xf32>
}
// -----
func.func @test_pad(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
- %padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %padding = tosa.const_shape values(dense<0> : tensor<6xindex>) : () -> !tosa.shape<6>
// expected-error at +1 {{'tosa.const' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.pad' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
%0 = tosa.pad %arg0, %padding, %pad_const : (tensor<13x21x3xf32>, !tosa.shape<6>, tensor<1xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
@@ -276,7 +276,7 @@ func.func @test_pad(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
// -----
func.func @test_reshape(%arg0: tensor<13x21x3xf32>) -> tensor<1x819xf32> {
- %1 = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
%0 = tosa.reshape %arg0, %1 : (tensor<13x21x3xf32>, !tosa.shape<2>) -> tensor<1x819xf32>
return %0 : tensor<1x819xf32>
@@ -285,14 +285,14 @@ func.func @test_reshape(%arg0: tensor<13x21x3xf32>) -> tensor<1x819xf32> {
// -----
func.func @test_reverse(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
// expected-error at +1 {{'tosa.reverse' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
func.func @test_slice(%arg0: tensor<13x21x3xf32>) -> tensor<4x11x1xf32> {
- %size = tosa.const_shape {values = dense<[4, 11, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %start = tosa.const_shape {values = dense<[6, 8, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[4, 11, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[6, 8, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.slice' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
%2 = tosa.slice %arg0, %start, %size : (tensor<13x21x3xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<4x11x1xf32>
return %2 : tensor<4x11x1xf32>
@@ -300,7 +300,7 @@ func.func @test_slice(%arg0: tensor<13x21x3xf32>) -> tensor<4x11x1xf32> {
// -----
func.func @test_tile(%arg0: tensor<13x21x3xf32>) -> tensor<39x21x6xf32> {
- %cst = tosa.const_shape { values = dense<[3, 1, 2]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %cst = tosa.const_shape values(dense<[3, 1, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.tile' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
%0 = tosa.tile %arg0, %cst: (tensor<13x21x3xf32>, !tosa.shape<3>) -> tensor<39x21x6xf32>
return %0 : tensor<39x21x6xf32>
@@ -309,7 +309,7 @@ func.func @test_tile(%arg0: tensor<13x21x3xf32>) -> tensor<39x21x6xf32> {
// -----
func.func @test_transpose(%arg0: tensor<13x21x3xf32>, %arg1: tensor<3xi32>) -> tensor<3x13x21xf32> {
// expected-error at +1 {{'tosa.transpose' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>}: (tensor<13x21x3xf32>) -> tensor<3x13x21xf32>
+ %1 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<13x21x3xf32>) -> tensor<3x13x21xf32>
return %1 : tensor<3x13x21xf32>
}
@@ -329,10 +329,10 @@ func.func @test_scatter(%arg0: tensor<13x28x3xf32>, %arg1: tensor<13x26xi32>, %a
// -----
func.func @test_resize(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x64x64x8xf32> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op illegal: requires any of [pro_fp] profiles/extensions to be specified in the target environment}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x32x32x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x32x32x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf32>
return %1 : tensor<1x64x64x8xf32>
}
diff --git a/mlir/test/Dialect/Tosa/profile_pro_int_unsupported.mlir b/mlir/test/Dialect/Tosa/profile_pro_int_unsupported.mlir
index f951c302c049a..4243b6f449e6a 100644
--- a/mlir/test/Dialect/Tosa/profile_pro_int_unsupported.mlir
+++ b/mlir/test/Dialect/Tosa/profile_pro_int_unsupported.mlir
@@ -6,30 +6,30 @@
// -----
func.func @test_const_i1() -> tensor<3x11x11x3xi1> {
- %0 = "tosa.const"() {values = dense<0> : tensor<3x11x11x3xi1>} : () -> tensor<3x11x11x3xi1>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<3x11x11x3xi1>}> : () -> tensor<3x11x11x3xi1>
return %0 : tensor<3x11x11x3xi1>
}
// -----
func.func @test_const_i32() -> tensor<3x11x11x3xi32> {
- %0 = "tosa.const"() {values = dense<0> : tensor<3x11x11x3xi32>} : () -> tensor<3x11x11x3xi32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<3x11x11x3xi32>}> : () -> tensor<3x11x11x3xi32>
return %0 : tensor<3x11x11x3xi32>
}
// -----
func.func @test_argmax(%arg0: tensor<14x19xi8>) -> tensor<14xi32> {
// expected-error at +1 {{'tosa.argmax' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.argmax %arg0 {axis = 1 : i32} : (tensor<14x19xi8>) -> tensor<14xi32>
+ %0 = tosa.argmax %arg0 axis(1) : (tensor<14x19xi8>) -> tensor<14xi32>
return %0 : tensor<14xi32>
}
// -----
func.func @test_avg_pool2d_adaptive_missing_pro_int(%arg0: tensor<1x7x7x9xi8>, %arg1: tensor<1xi8>, %arg2: tensor<1xi8>) -> tensor<1x7x7x9xi8> {
- %kernel = tosa.const_shape {values = dense<[2, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 1, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 1, 0, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.avg_pool2d_adaptive %arg0, %arg1, %arg2, %kernel, %stride, %pad {acc_type = i32} :
+ %0 = tosa.avg_pool2d_adaptive %arg0, %arg1, %arg2, %kernel, %stride, %pad acc_type(i32) :
(tensor<1x7x7x9xi8>, tensor<1xi8>, tensor<1xi8>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x7x7x9xi8>
return %0 : tensor<1x7x7x9xi8>
}
@@ -37,28 +37,28 @@ func.func @test_avg_pool2d_adaptive_missing_pro_int(%arg0: tensor<1x7x7x9xi8>, %
// -----
func.func @test_avg_pool2d(%arg0: tensor<1x7x7x9xi8>, %arg1: tensor<1xi8>, %arg2: tensor<1xi8>) -> tensor<1x7x7x9xi8> {
// expected-error at +1 {{'tosa.avg_pool2d' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.avg_pool2d %arg0, %arg1, %arg2 {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x7x7x9xi8>
+ %0 = tosa.avg_pool2d %arg0, %arg1, %arg2 kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(i32) : (tensor<1x7x7x9xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x7x7x9xi8>
return %0 : tensor<1x7x7x9xi8>
}
// -----
func.func @test_conv2d(%arg0: tensor<1x4x4x4xi8>, %arg1: tensor<8x1x1x4xi8>, %arg2: tensor<8xi32>, %arg3: tensor<1xi8>) -> tensor<1x4x4x8xi32> {
// expected-error at +1 {{'tosa.conv2d' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4xi8>, tensor<8x1x1x4xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8xi32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) local_bound(true) : (tensor<1x4x4x4xi8>, tensor<8x1x1x4xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8xi32>
return %0 : tensor<1x4x4x8xi32>
}
// -----
func.func @test_conv3d(%arg0: tensor<1x4x8x21x17xi8>, %arg1: tensor<34x1x1x1x17xi8>, %arg2: tensor<34xi32>, %arg3: tensor<1xi8>, %arg4: tensor<1xi8>) -> tensor<1x4x8x21x34xi32> {
// expected-error at +1 {{'tosa.conv3d' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = i32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<1x4x8x21x17xi8>, tensor<34x1x1x1x17xi8>, tensor<34xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x8x21x34xi32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(i32) : (tensor<1x4x8x21x17xi8>, tensor<34x1x1x1x17xi8>, tensor<34xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x8x21x34xi32>
return %0 : tensor<1x4x8x21x34xi32>
}
// -----
func.func @test_depthwise_conv2d(%arg0: tensor<1x4x4x4xi8>, %arg1: tensor<1x1x4x2xi8>, %arg2: tensor<8xi32>, %arg3: tensor<1xi8>, %arg4: tensor<1xi8>) -> tensor<1x4x4x8xi32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xi8>, tensor<1x1x4x2xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8xi32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) : (tensor<1x4x4x4xi8>, tensor<1x1x4x2xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8xi32>
return %0 : tensor<1x4x4x8xi32>
}
@@ -74,28 +74,28 @@ func.func @test_matmul(%arg0: tensor<1x14x19xi8>, %arg1: tensor<1x19x28xi8>) ->
// -----
func.func @test_max_pool2d(%arg0: tensor<1x32x32x8xi8>) -> tensor<1x32x32x8xi8> {
// expected-error at +1 {{'tosa.max_pool2d' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xi8>) -> tensor<1x32x32x8xi8>
+ %0 = tosa.max_pool2d %arg0 kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<1x32x32x8xi8>) -> tensor<1x32x32x8xi8>
return %0 : tensor<1x32x32x8xi8>
}
// -----
func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xi8>, %arg1: tensor<16x1x1x8xi8>, %arg2: tensor<16xi32>, %arg3: tensor<1xi8>, %arg4: tensor<1xi8>) -> tensor<1x32x32x16xi32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xi8>, tensor<16x1x1x8xi8>, tensor<16xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x32x32x16xi32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(i32) : (tensor<1x32x32x8xi8>, tensor<16x1x1x8xi8>, tensor<16xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x32x32x16xi32>
return %0 : tensor<1x32x32x16xi32>
}
// -----
func.func @test_clamp(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi8> {
// expected-error at +1 {{'tosa.clamp' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.clamp %arg0 {min_val = 0 : i8, max_val = 1: i8} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi8>
+ %0 = tosa.clamp %arg0 min_val(0 : i8) max_val(1: i8) : (tensor<13x21x3xi8>) -> tensor<13x21x3xi8>
return %0 : tensor<13x21x3xi8>
}
// -----
func.func @test_arithmetic_right_shift(%arg0: tensor<13x21x1xi32>, %arg1: tensor<13x21x3xi32>) -> tensor<13x21x3xi32> {
// expected-error at +1 {{'tosa.arithmetic_right_shift' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.arithmetic_right_shift %arg0, %arg1 {round = false} : (tensor<13x21x1xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
+ %0 = tosa.arithmetic_right_shift %arg0, %arg1 round(false) : (tensor<13x21x1xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
@@ -179,21 +179,21 @@ func.func @test_greater(%arg0: tensor<13x21x3xi32>, %arg1: tensor<13x1x3xi32>) -
// -----
func.func @test_reduce_max(%arg0: tensor<13x21x3xi8>) -> tensor<1x21x3xi8> {
// expected-error at +1 {{'tosa.reduce_max' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.reduce_max %arg0 {axis = 0 : i32} : (tensor<13x21x3xi8>) -> tensor<1x21x3xi8>
+ %0 = tosa.reduce_max %arg0 axis(0) : (tensor<13x21x3xi8>) -> tensor<1x21x3xi8>
return %0 : tensor<1x21x3xi8>
}
// -----
func.func @test_reduce_sum(%arg0: tensor<13x21x3xi32>) -> tensor<1x21x3xi32> {
// expected-error at +1 {{'tosa.reduce_sum' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<13x21x3xi32>) -> tensor<1x21x3xi32>
+ %0 = tosa.reduce_sum %arg0 axis(0) : (tensor<13x21x3xi32>) -> tensor<1x21x3xi32>
return %0 : tensor<1x21x3xi32>
}
// -----
func.func @test_pad(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi8> {
- %padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
- %pad_const = "tosa.const"() {values = dense<1> : tensor<1xi8>} : () -> tensor<1xi8>
+ %padding = tosa.const_shape values(dense<0> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %pad_const = "tosa.const"() <{values = dense<1> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.pad' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
%0 = tosa.pad %arg0, %padding, %pad_const : (tensor<13x21x3xi8>, !tosa.shape<6>, tensor<1xi8>) -> tensor<13x21x3xi8>
return %0 : tensor<13x21x3xi8>
@@ -201,7 +201,7 @@ func.func @test_pad(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi8> {
// -----
func.func @test_reshape(%arg0: tensor<13x21x3xi8>) -> tensor<1x819xi8> {
- %1 = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
%0 = tosa.reshape %arg0, %1 : (tensor<13x21x3xi8>, !tosa.shape<2>) -> tensor<1x819xi8>
return %0 : tensor<1x819xi8>
@@ -210,14 +210,14 @@ func.func @test_reshape(%arg0: tensor<13x21x3xi8>) -> tensor<1x819xi8> {
// -----
func.func @test_reverse(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi8> {
// expected-error at +1 {{'tosa.reverse' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi8>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<13x21x3xi8>) -> tensor<13x21x3xi8>
return %0 : tensor<13x21x3xi8>
}
// -----
func.func @test_slice(%arg0: tensor<13x21x3xi8>) -> tensor<4x11x1xi8> {
- %size = tosa.const_shape {values = dense<[4, 11, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %start = tosa.const_shape {values = dense<[6, 8, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[4, 11, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[6, 8, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.slice' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
%2 = tosa.slice %arg0, %start, %size : (tensor<13x21x3xi8>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<4x11x1xi8>
return %2 : tensor<4x11x1xi8>
@@ -225,7 +225,7 @@ func.func @test_slice(%arg0: tensor<13x21x3xi8>) -> tensor<4x11x1xi8> {
// -----
func.func @test_tile(%arg0: tensor<13x21x3xi8>) -> tensor<39x21x6xi8> {
- %cst = tosa.const_shape { values = dense<[3, 1, 2]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %cst = tosa.const_shape values(dense<[3, 1, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.tile' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
%0 = tosa.tile %arg0, %cst: (tensor<13x21x3xi8>, !tosa.shape<3>) -> tensor<39x21x6xi8>
return %0 : tensor<39x21x6xi8>
@@ -234,55 +234,55 @@ func.func @test_tile(%arg0: tensor<13x21x3xi8>) -> tensor<39x21x6xi8> {
// -----
func.func @test_transpose(%arg0: tensor<13x21x3xi8>, %arg1: tensor<3xi32>) -> tensor<3x13x21xi8> {
// expected-error at +1 {{'tosa.transpose' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>}: (tensor<13x21x3xi8>) -> tensor<3x13x21xi8>
+ %1 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<13x21x3xi8>) -> tensor<3x13x21xi8>
return %1 : tensor<3x13x21xi8>
}
// -----
func.func @test_resize(%arg0: tensor<1x32x32x8xi8>) -> tensor<1x64x64x8xi32> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %1 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x32x32x8xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xi32>
+ %1 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x32x32x8xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xi32>
return %1 : tensor<1x64x64x8xi32>
}
// -----
func.func @test_cast_i1_i8(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xi8> {
// expected-error at +1 {{'tosa.cast' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi1>) -> tensor<13x21x3xi8>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi1>) -> tensor<13x21x3xi8>
return %0 : tensor<13x21x3xi8>
}
// -----
func.func @test_cast_i8_i32(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi32> {
// expected-error at +1 {{'tosa.cast' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi32>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi8>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
// -----
func.func @test_cast_i16_i8(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi8> {
// expected-error at +1 {{'tosa.cast' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi16>) -> tensor<13x21x3xi8>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi16>) -> tensor<13x21x3xi8>
return %0 : tensor<13x21x3xi8>
}
// -----
func.func @test_cast_i32_i16(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xi16> {
// expected-error at +1 {{'tosa.cast' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi32>) -> tensor<13x21x3xi16>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi32>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
// -----
func.func @test_rescale(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi32> {
- %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32>} : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<30> : tensor<1xi8>} : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<127> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %multiplier = "tosa.const"() <{values = dense<1073741824> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<30> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<127> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.rescale' op illegal: requires any of [pro_int] profiles/extensions to be specified in the target environment}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, scale32 = true, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi32>) -> tensor<13x21x3xi32>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi32>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
diff --git a/mlir/test/Dialect/Tosa/quant-test.mlir b/mlir/test/Dialect/Tosa/quant-test.mlir
index 88dffe7fdd2e8..548c18be75d4f 100644
--- a/mlir/test/Dialect/Tosa/quant-test.mlir
+++ b/mlir/test/Dialect/Tosa/quant-test.mlir
@@ -20,9 +20,9 @@ func.func @test_build_qtype_signed(%arg0 : tensor<16x1x1x8x!quant.uniform<i8<1:1
// CHECK-LABEL: test_build_mult_and_shift
func.func @test_build_mult_and_shift(%arg0: tensor<1x32x32x8x!quant.uniform<i8:f32, 0.015684768557548523>>, %arg1 : tensor<16x1x1x8x!quant.uniform<i8<-127:127>:f32, 0.015680249780416489>>, %arg2 : tensor<16xi32>) -> tensor<1x34x36x16x!quant.uniform<i32:f32, 0.078431375324726104>> {
// CHECK: tosa.conv2d
- %input_zp = "tosa.const"() {values = dense<-1> : tensor<1xi8>} : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() {values = dense<1> : tensor<1xi8>} : () -> tensor<1xi8>
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2, %input_zp, %weight_zp) {acc_type = i32, pad = array<i64: 1, 1, 2, 2>, dilation = array<i64: 2, 1>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8x!quant.uniform<i8:f32, 0.015684768557548523>>, tensor<16x1x1x8x!quant.uniform<i8<-127:127>:f32, 0.015680249780416489>>, tensor<16xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x34x36x16x!quant.uniform<i32:f32, 0.078431375324726104>>
+ %input_zp = "tosa.const"() <{values = dense<-1> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %weight_zp = "tosa.const"() <{values = dense<1> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %0 = "tosa.conv2d"(%arg0, %arg1, %arg2, %input_zp, %weight_zp) <{acc_type = i32, pad = array<i64: 1, 1, 2, 2>, dilation = array<i64: 2, 1>, stride = array<i64: 1, 1>}> : (tensor<1x32x32x8x!quant.uniform<i8:f32, 0.015684768557548523>>, tensor<16x1x1x8x!quant.uniform<i8<-127:127>:f32, 0.015680249780416489>>, tensor<16xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x34x36x16x!quant.uniform<i32:f32, 0.078431375324726104>>
return %0 : tensor<1x34x36x16x!quant.uniform<i32:f32, 0.078431375324726104>>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-convert-integer-type-to-signless.mlir b/mlir/test/Dialect/Tosa/tosa-convert-integer-type-to-signless.mlir
index e8cfc4562ea13..f89bd577dd82e 100644
--- a/mlir/test/Dialect/Tosa/tosa-convert-integer-type-to-signless.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-convert-integer-type-to-signless.mlir
@@ -9,8 +9,8 @@ func.func @test_rescale_output_unsigned(%arg0: tensor<1x1xi8>) -> (tensor<1x1xui
%1 = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
%2 = "tosa.const"() <{values = dense<3> : tensor<1xi8>}> : () -> tensor<1xi8>
%3 = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
- // CHECK: %[[RESCALE:.*]] = tosa.rescale %arg0, %1, %0, %3, %2 {input_unsigned = false, output_unsigned = true, per_channel = false, rounding_mode = SINGLE_ROUND, scale32 = true} : (tensor<1x1xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1xi8>
- %r = tosa.rescale %arg0, %1, %0, %3, %2 {input_unsigned = false, output_unsigned = true, per_channel = false, rounding_mode = SINGLE_ROUND, scale32 = true} : (tensor<1x1xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1xui8>
+ // CHECK: %[[RESCALE:.*]] = tosa.rescale %arg0, %1, %0, %3, %2 scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<1x1xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1xi8>
+ %r = tosa.rescale %arg0, %1, %0, %3, %2 scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<1x1xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1xui8>
// CHECK: return %[[RESCALE]] : tensor<1x1xi8>
return %r : tensor<1x1xui8>
}
@@ -24,8 +24,8 @@ func.func @test_rescale_input_unsigned(%arg0: tensor<1x1xui16>) -> (tensor<1x1xi
%1 = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
%2 = "tosa.const"() <{values = dense<3> : tensor<1xi8>}> : () -> tensor<1xi8>
%3 = "tosa.const"() <{values = dense<32768> : tensor<1xi16>}> : () -> tensor<1xi16>
- // CHECK: %[[RESCALE:.*]] = tosa.rescale %arg0, %1, %0, %3, %2 {input_unsigned = true, output_unsigned = false, per_channel = false, rounding_mode = SINGLE_ROUND, scale32 = true} : (tensor<1x1xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi8>) -> tensor<1x1xi8>
- %r = tosa.rescale %arg0, %1, %0, %3, %2 {input_unsigned = true, output_unsigned = false, per_channel = false, rounding_mode = SINGLE_ROUND, scale32 = true} : (tensor<1x1xui16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi8>) -> tensor<1x1xi8>
+ // CHECK: %[[RESCALE:.*]] = tosa.rescale %arg0, %1, %0, %3, %2 scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<1x1xi16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi8>) -> tensor<1x1xi8>
+ %r = tosa.rescale %arg0, %1, %0, %3, %2 scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<1x1xui16>, tensor<1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi8>) -> tensor<1x1xi8>
// CHECK: return %[[RESCALE]] : tensor<1x1xi8>
return %r : tensor<1x1xi8>
}
@@ -35,13 +35,13 @@ func.func @test_rescale_input_unsigned(%arg0: tensor<1x1xui16>) -> (tensor<1x1xi
// CHECK-LABEL: test_rescale_unsigned_zp
// CHECK: %[[ZP_IN:.*]] = "tosa.const"() <{values = dense<-2> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[ZP_OUT:.*]] = "tosa.const"() <{values = dense<2> : tensor<1xi8>}> : () -> tensor<1xi8>
-// CHECK: tosa.rescale %arg0, %0, %1, %[[ZP_IN]], %[[ZP_OUT]] {input_unsigned = true, output_unsigned = false, per_channel = false, rounding_mode = SINGLE_ROUND, scale32 = true} : (tensor<1x1xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>)
+// CHECK: tosa.rescale %arg0, %0, %1, %[[ZP_IN]], %[[ZP_OUT]] scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<1x1xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>)
func.func @test_rescale_unsigned_zp(%arg0: tensor<1x1xui8>) -> tensor<1x1xi8> {
%0 = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
%1 = "tosa.const"() <{values = dense<1> : tensor<1xi8>}> : () -> tensor<1xi8>
%2 = "tosa.const"() <{values = dense<254> : tensor<1xui8>}> : () -> tensor<1xui8>
%3 = "tosa.const"() <{values = dense<2> : tensor<1xi8>}> : () -> tensor<1xi8>
- %r = tosa.rescale %arg0, %0, %1, %2, %3 {input_unsigned = true, output_unsigned = false, per_channel = false, rounding_mode = SINGLE_ROUND, scale32 = true} : (tensor<1x1xui8>, tensor<1xi32>, tensor<1xi8>, tensor<1xui8>, tensor<1xi8>) -> tensor<1x1xi8>
+ %r = tosa.rescale %arg0, %0, %1, %2, %3 scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<1x1xui8>, tensor<1xi32>, tensor<1xi8>, tensor<1xui8>, tensor<1xi8>) -> tensor<1x1xi8>
return %r : tensor<1x1xi8>
}
@@ -95,7 +95,7 @@ func.func @test_unsigned_const_data_i48() -> tensor<5xui48> {
// CHECK-LABEL: test_no_change
// CHECK: %arg0: tensor<13x21x3xi8>
func.func @test_no_change(%arg0: tensor<13x21x3xi8>) -> tensor<13x21x3xi8> {
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<13x21x3xi8>) -> tensor<13x21x3xi8>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<13x21x3xi8>) -> tensor<13x21x3xi8>
// CHECK: return %0 : tensor<13x21x3xi8>
return %0 : tensor<13x21x3xi8>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir b/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
index d4c4595e84ee0..60acd8c781152 100644
--- a/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
@@ -5,10 +5,10 @@
// CHECK-LABEL: @depthwise_conv2d_as_mul
func.func @depthwise_conv2d_as_mul(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<1x1x2x3xf32>, %arg2: tensor<6xf32>) -> tensor<4x10x10x6xf32> {
// CHECK-NOT: tosa.depthwise_conv2d
- // CHECK-DAG: %[[CONST0:.+]] = tosa.const_shape {values = dense<[4, 10, 10, 2, 1]> : tensor<5xindex>
- // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape {values = dense<[1, 1, 1, 2, 3]> : tensor<5xindex>
- // CHECK-DAG: %[[CONST2:.+]] = tosa.const_shape {values = dense<[4, 10, 10, 6]> : tensor<4xindex>
- // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {values = dense<[1, 1, 1, 6]> : tensor<4xindex>
+ // CHECK-DAG: %[[CONST0:.+]] = tosa.const_shape values(dense<[4, 10, 10, 2, 1]> : tensor<5xindex>)
+ // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape values(dense<[1, 1, 1, 2, 3]> : tensor<5xindex>)
+ // CHECK-DAG: %[[CONST2:.+]] = tosa.const_shape values(dense<[4, 10, 10, 6]> : tensor<4xindex>)
+ // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape values(dense<[1, 1, 1, 6]> : tensor<4xindex>)
// CHECK-DAG: %[[VAR0:.*]] = tosa.reshape %arg0, %[[CONST0]]
// CHECK-SAME: -> tensor<4x10x10x2x1xf32>
// CHECK: %[[VAR1:.*]] = tosa.reshape %arg1, %[[CONST1]]
@@ -23,7 +23,7 @@ func.func @depthwise_conv2d_as_mul(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<1
// CHECK-SAME: -> tensor<4x10x10x6xf32>
// CHECK: return %[[VAR5]]
%zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x10x10x6xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %zp, %zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x10x10x6xf32>
return %0 : tensor<4x10x10x6xf32>
}
@@ -31,12 +31,12 @@ func.func @depthwise_conv2d_as_mul(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<1
// CHECK-LABEL: @depthwise_conv2d_as_mul_q
func.func @depthwise_conv2d_as_mul_q(%arg0: tensor<4x10x10x2xi8>, %arg1: tensor<1x1x2x3xi8>, %arg2: tensor<6xi32>) -> tensor<4x10x10x6xi32> {
- // CHECK-DAG: %[[CONST0:.+]] = tosa.const_shape {values = dense<[4, 10, 10, 2, 1]> : tensor<5xindex>
+ // CHECK-DAG: %[[CONST0:.+]] = tosa.const_shape values(dense<[4, 10, 10, 2, 1]> : tensor<5xindex>)
// CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{values = dense<7> : tensor<1x1x1x1x1xi32>}
// CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{values = dense<11> : tensor<1x1x1x1xi32>}
- // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {values = dense<[1, 1, 1, 2, 3]> : tensor<5xindex>
- // CHECK-DAG: %[[CONST4:.+]] = tosa.const_shape {values = dense<[4, 10, 10, 6]> : tensor<4xindex>
- // CHECK-DAG: %[[CONST5:.+]] = tosa.const_shape {values = dense<[1, 1, 1, 6]> : tensor<4xindex>
+ // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape values(dense<[1, 1, 1, 2, 3]> : tensor<5xindex>)
+ // CHECK-DAG: %[[CONST4:.+]] = tosa.const_shape values(dense<[4, 10, 10, 6]> : tensor<4xindex>)
+ // CHECK-DAG: %[[CONST5:.+]] = tosa.const_shape values(dense<[1, 1, 1, 6]> : tensor<4xindex>)
// CHECK-DAG: %[[SHIFT:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[RESHAPE_I:.+]] = tosa.reshape %arg0, %[[CONST0]]
// CHECK: %[[CAST_I:.+]] = tosa.cast %[[RESHAPE_I]] : (tensor<4x10x10x2x1xi8>) -> tensor<4x10x10x2x1xi32>
@@ -48,9 +48,9 @@ func.func @depthwise_conv2d_as_mul_q(%arg0: tensor<4x10x10x2xi8>, %arg1: tensor<
// CHECK: %[[RESHAPE_O:.+]] = tosa.reshape %[[MUL]], %[[CONST4]]
// CHECK: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2, %[[CONST5]]
// CHECK: %[[ADD:.+]] = tosa.add %[[RESHAPE_O]], %[[RESHAPE_ARG2]]
- %input_zp = "tosa.const"() {values = dense<7> : tensor<1xi8>} : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() {values = dense<11> : tensor<1xi8>} : () -> tensor<1xi8>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xi8>, tensor<1x1x2x3xi8>, tensor<6xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<4x10x10x6xi32>
+ %input_zp = "tosa.const"() <{values = dense<7> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %weight_zp = "tosa.const"() <{values = dense<11> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) : (tensor<4x10x10x2xi8>, tensor<1x1x2x3xi8>, tensor<6xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<4x10x10x6xi32>
return %0 : tensor<4x10x10x6xi32>
}
@@ -58,12 +58,12 @@ func.func @depthwise_conv2d_as_mul_q(%arg0: tensor<4x10x10x2xi8>, %arg1: tensor<
// CHECK-LABEL: @depthwise_conv2d_as_mul_padded
func.func @depthwise_conv2d_as_mul_padded(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<1x1x2x3xf32>, %arg2: tensor<6xf32>) -> tensor<4x12x12x6xf32> {
- // CHECK-DAG: %[[CONST0:.+]] = tosa.const_shape {values = dense<[4, 10, 10, 2, 1]> : tensor<5xindex>}
- // CHECK-DAG: %[[PAD:.+]] = tosa.const_shape {values = dense<[0, 0, 1, 1, 1, 1, 0, 0, 0, 0]> : tensor<10xindex>} : () -> !tosa.shape<10>
+ // CHECK-DAG: %[[CONST0:.+]] = tosa.const_shape values(dense<[4, 10, 10, 2, 1]> : tensor<5xindex>)
+ // CHECK-DAG: %[[PAD:.+]] = tosa.const_shape values(dense<[0, 0, 1, 1, 1, 1, 0, 0, 0, 0]> : tensor<10xindex>) : () -> !tosa.shape<10>
// CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}
- // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {values = dense<[1, 1, 1, 2, 3]> : tensor<5xindex>}
- // CHECK-DAG: %[[CONST4:.+]] = tosa.const_shape {values = dense<[4, 12, 12, 6]> : tensor<4xindex>}
- // CHECK-DAG: %[[CONST5:.+]] = tosa.const_shape {values = dense<[1, 1, 1, 6]> : tensor<4xindex>}
+ // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape values(dense<[1, 1, 1, 2, 3]> : tensor<5xindex>)
+ // CHECK-DAG: %[[CONST4:.+]] = tosa.const_shape values(dense<[4, 12, 12, 6]> : tensor<4xindex>)
+ // CHECK-DAG: %[[CONST5:.+]] = tosa.const_shape values(dense<[1, 1, 1, 6]> : tensor<4xindex>)
// CHECK-DAG: %[[SHIFT:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[RESHAPE_I:.+]] = tosa.reshape %arg0, %[[CONST0]]
// CHECK: %[[PAD_I:.+]] = tosa.pad %[[RESHAPE_I]], %[[PAD]], %[[ZERO]] : (tensor<4x10x10x2x1xf32>, !tosa.shape<10>, tensor<1xf32>) -> tensor<4x12x12x2x1xf32>
@@ -73,7 +73,7 @@ func.func @depthwise_conv2d_as_mul_padded(%arg0: tensor<4x10x10x2xf32>, %arg1: t
// CHECK: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2, %[[CONST5]]
// CHECK: %[[ADD:.+]] = tosa.add %[[RESHAPE_O]], %[[RESHAPE_ARG2]]
%zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x12x12x6xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %zp, %zp pad([1, 1, 1, 1]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x12x12x6xf32>
return %0 : tensor<4x12x12x6xf32>
}
@@ -86,7 +86,7 @@ func.func @depthwise_conv2d_quant_type(%arg0: tensor<4x10x10x2x!quant.uniform<i8
%0 = "tosa.const"() <{values = dense<7> : tensor<1xi8>}> : () -> tensor<1xi8>
%1 = "tosa.const"() <{values = dense<11> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: tosa.depthwise_conv2d
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %0, %1 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<4x10x10x2x!quant.uniform<i8:f32, 0.015684768557548523>>, tensor<1x1x2x3x!quant.uniform<i8<-127:127>:f32, 0.015680249780416489>>, tensor<6xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<4x10x10x6x!quant.uniform<i32:f32, 0.078431375324726104>>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %0, %1 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) : (tensor<4x10x10x2x!quant.uniform<i8:f32, 0.015684768557548523>>, tensor<1x1x2x3x!quant.uniform<i8<-127:127>:f32, 0.015680249780416489>>, tensor<6xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<4x10x10x6x!quant.uniform<i32:f32, 0.078431375324726104>>
return %2 : tensor<4x10x10x6x!quant.uniform<i32:f32, 0.078431375324726104>>
}
@@ -95,7 +95,7 @@ func.func @depthwise_conv2d_quant_type(%arg0: tensor<4x10x10x2x!quant.uniform<i8
// CHECK-LABEL: @depthwise_conv2d_no_const_zero_point
func.func @depthwise_conv2d_no_const_zero_point(%arg0: tensor<4x10x10x2xi8>, %arg1: tensor<1x1x2x3xi8>, %arg2: tensor<6xi32>, %arg3: tensor<1xi8>, %arg4: tensor<1xi8>) -> tensor<4x10x10x6xi32> {
// CHECK: tosa.depthwise_conv2d
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = i32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xi8>, tensor<1x1x2x3xi8>, tensor<6xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<4x10x10x6xi32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32) : (tensor<4x10x10x2xi8>, tensor<1x1x2x3xi8>, tensor<6xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<4x10x10x6xi32>
return %0 : tensor<4x10x10x6xi32>
}
@@ -104,11 +104,11 @@ func.func @depthwise_conv2d_no_const_zero_point(%arg0: tensor<4x10x10x2xi8>, %ar
// CHECK-SAME: %[[INP:.*]]: tensor<?x10x10x2xf32>,
// CHECK-SAME: %[[WTS:.*]]: tensor<1x1x2x3xf32>,
// CHECK-SAME: %[[BIAS:.*]]: tensor<?xf32>) -> tensor<?x10x10x6xf32> {
-// CHECK: %[[BIAS_EXPANDED_SHAPE:.*]] = tosa.const_shape {values = dense<[1, 1, 1, -1]> : tensor<4xindex>} : () -> !tosa.shape<4>
-// CHECK: %[[RES_EXPANDED_SHAPE:.*]] = tosa.const_shape {values = dense<[-1, 10, 10, 6]> : tensor<4xindex>} : () -> !tosa.shape<4>
+// CHECK: %[[BIAS_EXPANDED_SHAPE:.*]] = tosa.const_shape values(dense<[1, 1, 1, -1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+// CHECK: %[[RES_EXPANDED_SHAPE:.*]] = tosa.const_shape values(dense<[-1, 10, 10, 6]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK: %[[MUL_SHIFT:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
-// CHECK: %[[WTS_EXPANDED_SHAPE:.*]] = tosa.const_shape {values = dense<[1, 1, 1, 2, 3]> : tensor<5xindex>} : () -> !tosa.shape<5>
-// CHECK: %[[INP_EXPANDED_SHAPE:.*]] = tosa.const_shape {values = dense<[-1, 10, 10, 2, 1]> : tensor<5xindex>} : () -> !tosa.shape<5>
+// CHECK: %[[WTS_EXPANDED_SHAPE:.*]] = tosa.const_shape values(dense<[1, 1, 1, 2, 3]> : tensor<5xindex>) : () -> !tosa.shape<5>
+// CHECK: %[[INP_EXPANDED_SHAPE:.*]] = tosa.const_shape values(dense<[-1, 10, 10, 2, 1]> : tensor<5xindex>) : () -> !tosa.shape<5>
// CHECK: %[[INP_RESHAPED:.*]] = tosa.reshape %[[INP]], %[[INP_EXPANDED_SHAPE]] : (tensor<?x10x10x2xf32>, !tosa.shape<5>) -> tensor<?x10x10x2x1xf32>
// CHECK: %[[WTS_RESHAPED:.*]] = tosa.reshape %[[WTS]], %[[WTS_EXPANDED_SHAPE]] : (tensor<1x1x2x3xf32>, !tosa.shape<5>) -> tensor<1x1x1x2x3xf32>
// CHECK: %[[MUL:.*]] = tosa.mul %[[INP_RESHAPED]], %[[WTS_RESHAPED]], %[[MUL_SHIFT]] : (tensor<?x10x10x2x1xf32>, tensor<1x1x1x2x3xf32>, tensor<1xi8>) -> tensor<?x10x10x2x3xf32>
@@ -118,6 +118,6 @@ func.func @depthwise_conv2d_no_const_zero_point(%arg0: tensor<4x10x10x2xi8>, %ar
// CHECK: return %[[RES]]
func.func @depthwise_conv2d_as_mul_dynamic_batch_bias(%arg0: tensor<?x10x10x2xf32>, %arg1: tensor<1x1x2x3xf32>, %arg2: tensor<?xf32>) -> tensor<?x10x10x6xf32> {
%zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<?x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x10x10x6xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %zp, %zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<?x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x10x10x6xf32>
return %0 : tensor<?x10x10x6xf32>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir b/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
index 61ca0aedf6a46..6832d39dda9ca 100644
--- a/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
@@ -3,12 +3,12 @@
// CHECK-LABEL: @transpose_conv2d
func.func @transpose_conv2d(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) -> tensor<2x18x19x5xf32> {
// CHECK-DAG: %[[ZP:.+]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
- // CHECK: %[[REV1:.+]] = tosa.reverse %arg1 {axis = 1 : i32}
- // CHECK: %[[REV2:.+]] = tosa.reverse %[[REV1]] {axis = 2 : i32}
+ // CHECK: %[[REV1:.+]] = tosa.reverse %arg1 axis(1)
+ // CHECK: %[[REV2:.+]] = tosa.reverse %[[REV1]] axis(2)
// CHECK: tosa.conv2d %arg0, %[[REV2]], %arg2, %[[ZP]], %[[ZP]]
- // CHECK-SAME: dilation = array<i64: 1, 1>, pad = array<i64: 2, 2, 5, 5>, stride = array<i64: 1, 1>
+ // CHECK-SAME: pad([2, 2, 5, 5]) stride([1, 1]) dilation([1, 1])
%zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x18x19x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x18x19x5xf32>
return %0 : tensor<2x18x19x5xf32>
}
@@ -19,12 +19,12 @@ func.func @transpose_conv2d(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3x
func.func @transpose_conv2d_quantized(%arg0: tensor<2x16x14x3xi8>, %arg1: tensor<5x3x6x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x18x19x5xi32>) {
// CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{values = dense<-6> : tensor<1xi8>}
// CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{values = dense<11> : tensor<1xi8>}
- // CHECK: %[[REV1:.+]] = tosa.reverse %arg1 {axis = 1 : i32}
- // CHECK: %[[REV2:.+]] = tosa.reverse %[[REV1]] {axis = 2 : i32}
- // CHECK: tosa.conv2d %arg0, %[[REV2]], %arg2, %[[INPUT_ZP]], %[[WEIGHT_ZP]] {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 2, 2, 5, 5>, stride = array<i64: 1, 1>}
- %input_zp = "tosa.const"() {values = dense<-6> : tensor<1xi8>} : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() {values = dense<11> : tensor<1xi8>} : () -> tensor<1xi8>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xi8>, tensor<5x3x6x3xi8>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x18x19x5xi32>
+ // CHECK: %[[REV1:.+]] = tosa.reverse %arg1 axis(1)
+ // CHECK: %[[REV2:.+]] = tosa.reverse %[[REV1]] axis(2)
+ // CHECK: tosa.conv2d %arg0, %[[REV2]], %arg2, %[[INPUT_ZP]], %[[WEIGHT_ZP]] pad([2, 2, 5, 5]) stride([1, 1]) dilation([1, 1]) acc_type(i32)
+ %input_zp = "tosa.const"() <{values = dense<-6> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %weight_zp = "tosa.const"() <{values = dense<11> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(i32) : (tensor<2x16x14x3xi8>, tensor<5x3x6x3xi8>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x18x19x5xi32>
return %0 : tensor<2x18x19x5xi32>
}
@@ -34,15 +34,12 @@ func.func @transpose_conv2d_quantized(%arg0: tensor<2x16x14x3xi8>, %arg1: tensor
func.func @transpose_conv2d_quantized_padded(%arg0: tensor<2x16x14x3xi8>, %arg1: tensor<5x3x6x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x21x26x5xi32>) {
// CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
- // CHECK-DAG: %[[REV0:.+]] = tosa.reverse %arg1 {axis = 1 : i32}
- // CHECK-DAG: %[[REV1:.+]] = tosa.reverse %[[REV0]] {axis = 2 : i32}
- // CHECK: tosa.conv2d %arg0, %[[REV1]], %arg2, %[[INPUT_ZP]], %[[WEIGHT_ZP]] {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 3, 4, 8, 9>, stride = array<i64: 1, 1>}
+ // CHECK-DAG: %[[REV0:.+]] = tosa.reverse %arg1 axis(1)
+ // CHECK-DAG: %[[REV1:.+]] = tosa.reverse %[[REV0]] axis(2)
+ // CHECK: tosa.conv2d %arg0, %[[REV1]], %arg2, %[[INPUT_ZP]], %[[WEIGHT_ZP]] pad([3, 4, 8, 9]) stride([1, 1]) dilation([1, 1]) acc_type(i32)
%input_zp = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {
- acc_type = i32,
- out_pad = array<i64: 1, 2, 3, 4>,
- stride = array<i64: 1, 1>} : (tensor<2x16x14x3xi8>, tensor<5x3x6x3xi8>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x21x26x5xi32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp out_pad([1, 2, 3, 4]) stride([1, 1]) acc_type(i32) : (tensor<2x16x14x3xi8>, tensor<5x3x6x3xi8>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x21x26x5xi32>
return %0 : tensor<2x21x26x5xi32>
}
@@ -52,37 +49,37 @@ func.func @transpose_conv2d_quantized_padded(%arg0: tensor<2x16x14x3xi8>, %arg1:
func.func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<5x3x5x3xf32>, %arg2: tensor<5xf32>) -> tensor<2x?x?x5xf32> {
// Manipulate the weight matrix to handle striding.
- // CHECK-DAG: %[[PADV:.+]] = tosa.const_shape {values = dense<[0, 0, 0, 1, 0, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-DAG: %[[PADV:.+]] = tosa.const_shape values(dense<[0, 0, 0, 1, 0, 1, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
// CHECK-DAG: %[[PADW:.+]] = tosa.pad %arg1, %[[PADV]]
- // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape {values = dense<[5, 2, 2, 2, 3, 3]> : tensor<6xindex>}
+ // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape values(dense<[5, 2, 2, 2, 3, 3]> : tensor<6xindex>)
// CHECK-DAG: %[[RESW1:.+]] = tosa.reshape %[[PADW]], %[[CONST1]]
- // CHECK-DAG: %[[TRANS:.+]] = tosa.transpose %[[RESW1]] {perms = array<i32: 2, 4, 0, 1, 3, 5>}
- // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {values = dense<[30, 2, 2, 3]> : tensor<4xindex>}
+ // CHECK-DAG: %[[TRANS:.+]] = tosa.transpose %[[RESW1]] perms([2, 4, 0, 1, 3, 5])
+ // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape values(dense<[30, 2, 2, 3]> : tensor<4xindex>)
// CHECK-DAG: %[[RESW2:.+]] = tosa.reshape %[[TRANS]], %[[CONST3]]
- // CHECK-DAG: %[[REV1:.+]] = tosa.reverse %[[RESW2]] {axis = 1 : i32}
- // CHECK-DAG: %[[NEWWEIGHT:.+]] = tosa.reverse %[[REV1]] {axis = 2 : i32}
- // CHECK-DAG: %[[SIZE:.*]] = tosa.const_shape {values = dense<[2, 35, 47, 5]> : tensor<4xindex>} : () -> !tosa.shape<4>
- // CHECK-DAG: %[[START:.*]] = tosa.const_shape {values = dense<0> : tensor<4xindex>} : () -> !tosa.shape<4>
+ // CHECK-DAG: %[[REV1:.+]] = tosa.reverse %[[RESW2]] axis(1)
+ // CHECK-DAG: %[[NEWWEIGHT:.+]] = tosa.reverse %[[REV1]] axis(2)
+ // CHECK-DAG: %[[SIZE:.*]] = tosa.const_shape values(dense<[2, 35, 47, 5]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ // CHECK-DAG: %[[START:.*]] = tosa.const_shape values(dense<0> : tensor<4xindex>) : () -> !tosa.shape<4>
// Pad out the input matrix to handle the transpose conv.
- // CHECK-DAG: %[[PAD:.+]] = tosa.const_shape {values = dense<[0, 0, 1, 1, 1, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-DAG: %[[PAD:.+]] = tosa.const_shape values(dense<[0, 0, 1, 1, 1, 1, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
// CHECK-DAG: %[[NEWINPUT:.+]] = tosa.pad %arg0, %[[PAD]]
// Manipulate the final shape.
// CHECK-DAG: %[[ZP:.+]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK-DAG: %[[BIAS:.+]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<30xf32>}
- // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]], %[[ZP]], %[[ZP]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
- // CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape {values = dense<[2, 18, 16, 2, 3, 5]> : tensor<6xindex>}
+ // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]], %[[ZP]], %[[ZP]] pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32)
+ // CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape values(dense<[2, 18, 16, 2, 3, 5]> : tensor<6xindex>)
// CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = tosa.reshape %[[CONV]], %[[CONST6]]
- // CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]] {perms = array<i32: 0, 1, 3, 2, 4, 5>}
- // CHECK-DAG: %[[CONST8:.+]] = tosa.const_shape {values = dense<[2, 36, 48, 5]> : tensor<4xindex>
+ // CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]] perms([0, 1, 3, 2, 4, 5])
+ // CHECK-DAG: %[[CONST8:.+]] = tosa.const_shape values(dense<[2, 36, 48, 5]> : tensor<4xindex>)
// CHECK-DAG: %[[RESHAPE_OUT_2:.+]] = tosa.reshape %[[TRANS_OUT]], %[[CONST8]]
// CHECK-DAG: %[[SLICE:.+]] = tosa.slice %[[RESHAPE_OUT_2]], %[[START]], %[[SIZE]]
- // CHECK-DAG: %[[CONST9:.+]] = tosa.const_shape {values = dense<[1, 1, 1, 5]> : tensor<4xindex>}
+ // CHECK-DAG: %[[CONST9:.+]] = tosa.const_shape values(dense<[1, 1, 1, 5]> : tensor<4xindex>)
// CHECK-DAG: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2, %[[CONST9]]
// CHECK: %[[ADD:.+]] = tosa.add %[[SLICE]], %[[RESHAPE_ARG2]]
%zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<2x17x15x3xf32>, tensor<5x3x5x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x35x47x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp out_pad([0, 0, 0, 0]) stride([2, 3]) acc_type(f32) : (tensor<2x17x15x3xf32>, tensor<5x3x5x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x35x47x5xf32>
%1 = tensor.cast %0 : tensor<2x35x47x5xf32> to tensor<2x?x?x5xf32>
return %1 : tensor<2x?x?x5xf32>
}
@@ -95,37 +92,37 @@ func.func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1
// Manipulate the weight matrix to handle striding.
// CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
- // CHECK-DAG: %[[PADV:.+]] = tosa.const_shape {values = dense<[0, 0, 0, 1, 0, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-DAG: %[[PADV:.+]] = tosa.const_shape values(dense<[0, 0, 0, 1, 0, 1, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
// CHECK-DAG: %[[PADW:.+]] = tosa.pad %arg1, %[[PADV]], %[[WEIGHT_ZP]]
- // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape {values = dense<[5, 2, 2, 2, 3, 3]> : tensor<6xindex>}
+ // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape values(dense<[5, 2, 2, 2, 3, 3]> : tensor<6xindex>)
// CHECK-DAG: %[[RESW1:.+]] = tosa.reshape %[[PADW]], %[[CONST1]]
- // CHECK-DAG: %[[TRANS:.+]] = tosa.transpose %[[RESW1]] {perms = array<i32: 2, 4, 0, 1, 3, 5>}
- // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {values = dense<[30, 2, 2, 3]> : tensor<4xindex>}
+ // CHECK-DAG: %[[TRANS:.+]] = tosa.transpose %[[RESW1]] perms([2, 4, 0, 1, 3, 5])
+ // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape values(dense<[30, 2, 2, 3]> : tensor<4xindex>)
// CHECK-DAG: %[[RESW2:.+]] = tosa.reshape %[[TRANS]], %[[CONST3]]
- // CHECK-DAG: %[[REV1:.+]] = tosa.reverse %[[RESW2]] {axis = 1 : i32}
- // CHECK-DAG: %[[NEWWEIGHT:.+]] = tosa.reverse %[[REV1]] {axis = 2 : i32}
+ // CHECK-DAG: %[[REV1:.+]] = tosa.reverse %[[RESW2]] axis(1)
+ // CHECK-DAG: %[[NEWWEIGHT:.+]] = tosa.reverse %[[REV1]] axis(2)
// Pad out the input matrix to handle the transpose conv.
- // CHECK-DAG: %[[PAD:.+]] = tosa.const_shape {values = dense<[0, 0, 1, 1, 1, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-DAG: %[[PAD:.+]] = tosa.const_shape values(dense<[0, 0, 1, 1, 1, 1, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
// CHECK-DAG: %[[NEWINPUT:.+]] = tosa.pad %arg0, %[[PAD]], %[[INPUT_ZP]]
// Manipulate the final shape.
// CHECK-DAG: %[[BIAS:.+]] = "tosa.const"() <{values = dense<0> : tensor<30xi32>}
- // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]], %[[INPUT_ZP]], %[[WEIGHT_ZP]] {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
- // CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape {values = dense<[2, 18, 16, 2, 3, 5]> : tensor<6xindex>}
+ // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]], %[[INPUT_ZP]], %[[WEIGHT_ZP]] pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(i32)
+ // CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape values(dense<[2, 18, 16, 2, 3, 5]> : tensor<6xindex>)
// CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = tosa.reshape %[[CONV]], %[[CONST6]]
- // CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]] {perms = array<i32: 0, 1, 3, 2, 4, 5>}
- // CHECK-DAG: %[[CONST8:.+]] = tosa.const_shape {values = dense<[2, 36, 48, 5]> : tensor<4xindex>}
+ // CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]] perms([0, 1, 3, 2, 4, 5])
+ // CHECK-DAG: %[[CONST8:.+]] = tosa.const_shape values(dense<[2, 36, 48, 5]> : tensor<4xindex>)
// CHECK-DAG: %[[RESHAPE_OUT_2:.+]] = tosa.reshape %[[TRANS_OUT]], %[[CONST8]]
- // CHECK-DAG: %[[START:.*]] = tosa.const_shape {values = dense<0> : tensor<4xindex>}
- // CHECK-DAG: %[[SIZE:.*]] = tosa.const_shape {values = dense<[2, 35, 47, 5]> : tensor<4xindex>}
+ // CHECK-DAG: %[[START:.*]] = tosa.const_shape values(dense<0> : tensor<4xindex>)
+ // CHECK-DAG: %[[SIZE:.*]] = tosa.const_shape values(dense<[2, 35, 47, 5]> : tensor<4xindex>)
// CHECK-DAG: %[[SLICE:.*]] = tosa.slice %[[RESHAPE_OUT_2]], %[[START]], %[[SIZE]]
- // CHECK-DAG: %[[CONST9:.+]] = tosa.const_shape {values = dense<[1, 1, 1, 5]> : tensor<4xindex>}
+ // CHECK-DAG: %[[CONST9:.+]] = tosa.const_shape values(dense<[1, 1, 1, 5]> : tensor<4xindex>)
// CHECK-DAG: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2, %[[CONST9]]
// CHECK: %[[ADD:.+]] = tosa.add %[[SLICE]], %[[RESHAPE_ARG2]]
%input_zp = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<2x17x15x3xi8>, tensor<5x3x5x3xi8>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x35x47x5xi32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp out_pad([0, 0, 0, 0]) stride([2, 3]) acc_type(i32) : (tensor<2x17x15x3xi8>, tensor<5x3x5x3xi8>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x35x47x5xi32>
return %0 : tensor<2x35x47x5xi32>
}
@@ -134,12 +131,12 @@ func.func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1
// CHECK-LABEL: @transpose_conv2d_strided_quantized_quant_input
func.func @transpose_conv2d_strided_quantized_quant_input(%arg0: tensor<2x17x15x3x!quant.uniform<i8:f32, 0.015684274956583977:-1>>, %arg1: tensor<5x3x5x3x!quant.uniform<i8:f32, 0.015684274956583977:-1>>, %arg2: tensor<5xi32>) -> (tensor<2x35x47x5xi32>) {
// Checks a regression. A typo in `createPadConstTensor` caused the conversion to crash
- // CHECK-DAG: %[[PAD_SHAPE:.+]] = tosa.const_shape {values = dense<[0, 0, 1, 1, 1, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-DAG: %[[PAD_SHAPE:.+]] = tosa.const_shape values(dense<[0, 0, 1, 1, 1, 1, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
// CHECK-DAG: %[[PAD_CONST:.+]] = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1x!quant.uniform<i8:f32, 0.015684274956583977:-1>>
// CHECK: %[[PAD:.+]] = tosa.pad %arg0, %[[PAD_SHAPE]], %[[PAD_CONST]] : (tensor<2x17x15x3x!quant.uniform<i8:f32, 0.015684274956583977:-1>>, !tosa.shape<8>, tensor<1x!quant.uniform<i8:f32, 0.015684274956583977:-1>>)
%input_zp = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<2x17x15x3x!quant.uniform<i8:f32, 0.015684274956583977:-1>>, tensor<5x3x5x3x!quant.uniform<i8:f32, 0.015684274956583977:-1>>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x35x47x5xi32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp out_pad([0, 0, 0, 0]) stride([2, 3]) acc_type(i32) : (tensor<2x17x15x3x!quant.uniform<i8:f32, 0.015684274956583977:-1>>, tensor<5x3x5x3x!quant.uniform<i8:f32, 0.015684274956583977:-1>>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x35x47x5xi32>
return %0 : tensor<2x35x47x5xi32>
}
@@ -147,37 +144,34 @@ func.func @transpose_conv2d_strided_quantized_quant_input(%arg0: tensor<2x17x15x
// CHECK-LABEL: @transpose_conv2d_strided_overpad
func.func @transpose_conv2d_strided_overpad(%arg0 : tensor<1x16x1x1xi8>, %arg1 : tensor<1x2x1x1xi8>, %arg2 : tensor<1xi32>) -> (tensor<1x19x2x1xi32>) {
- // CHECK-DAG: %[[WEIGHT_PAD:.+]] = tosa.const_shape {values = dense<[0, 0, 0, 0, 0, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
- // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape {values = dense<[1, 2, 1, 1, 2, 1]> : tensor<6xindex>}
+ // CHECK-DAG: %[[WEIGHT_PAD:.+]] = tosa.const_shape values(dense<[0, 0, 0, 0, 0, 1, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
+ // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape values(dense<[1, 2, 1, 1, 2, 1]> : tensor<6xindex>)
// CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{values = dense<-103> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{values = dense<93> : tensor<1xi8>}> : () -> tensor<1xi8>
- // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {values = dense<[2, 2, 1, 1]> : tensor<4xindex>}
- // CHECK-DAG: %[[INPUT_PAD:.+]] = tosa.const_shape {values = dense<[0, 0, 1, 1, 0, 0, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
+ // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape values(dense<[2, 2, 1, 1]> : tensor<4xindex>)
+ // CHECK-DAG: %[[INPUT_PAD:.+]] = tosa.const_shape values(dense<[0, 0, 1, 1, 0, 0, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
// CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{values = dense<0> : tensor<2xi32>}
- // CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape {values = dense<[1, 17, 1, 1, 2, 1]> : tensor<6xindex>}
- // CHECK-DAG: %[[CONST8:.+]] = tosa.const_shape {values = dense<[1, 17, 2, 1]> : tensor<4xindex>}
- // CHECK-DAG: %[[RESULT_PAD:.+]] = tosa.const_shape {values = dense<[0, 0, 2, 0, 0, 0, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
- // CHECK-DAG: %[[CONST10:.+]] = tosa.const_shape {values = dense<1> : tensor<4xindex>}
+ // CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape values(dense<[1, 17, 1, 1, 2, 1]> : tensor<6xindex>)
+ // CHECK-DAG: %[[CONST8:.+]] = tosa.const_shape values(dense<[1, 17, 2, 1]> : tensor<4xindex>)
+ // CHECK-DAG: %[[RESULT_PAD:.+]] = tosa.const_shape values(dense<[0, 0, 2, 0, 0, 0, 0, 0]> : tensor<8xindex>) : () -> !tosa.shape<8>
+ // CHECK-DAG: %[[CONST10:.+]] = tosa.const_shape values(dense<1> : tensor<4xindex>)
// CHECK: %[[PAD_WEIGHT:.+]] = tosa.pad %arg1, %[[WEIGHT_PAD]], %[[WEIGHT_ZP]]
// CHECK: %[[RESHAPE_WEIGHT_0:.+]] = tosa.reshape %[[PAD_WEIGHT]], %[[CONST1]]
- // CHECK: %[[TRANSPOSE_WEIGHT:.+]] = tosa.transpose %[[RESHAPE_WEIGHT_0]] {perms = array<i32: 2, 4, 0, 1, 3, 5>}
+ // CHECK: %[[TRANSPOSE_WEIGHT:.+]] = tosa.transpose %[[RESHAPE_WEIGHT_0]] perms([2, 4, 0, 1, 3, 5])
// CHECK: %[[RESHAPE_WEIGHT_1:.+]] = tosa.reshape %[[TRANSPOSE_WEIGHT]], %[[CONST3]]
- // CHECK: %[[REVERSE:.+]] = tosa.reverse %[[RESHAPE_WEIGHT_1]] {axis = 1 : i32}
+ // CHECK: %[[REVERSE:.+]] = tosa.reverse %[[RESHAPE_WEIGHT_1]] axis(1)
// CHECK: %[[PAD_INPUT:.+]] = tosa.pad %arg0, %[[INPUT_PAD]], %[[INPUT_ZP]]
// CHECK: %[[CONV:.+]] = tosa.conv2d %[[PAD_INPUT]], %[[REVERSE]], %[[ZERO]], %[[INPUT_ZP]], %[[WEIGHT_ZP]]
- // CHECK-SAME{literal}: dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]}
+ // CHECK-SAME: pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1])
// CHECK: %[[RESHAPE_RESULT_0:.+]] = tosa.reshape %[[CONV]], %[[CONST6]]
- // CHECK: %[[TRANSPOSE_RESULT:.+]] = tosa.transpose %[[RESHAPE_RESULT_0]] {perms = array<i32: 0, 1, 3, 2, 4, 5>}
+ // CHECK: %[[TRANSPOSE_RESULT:.+]] = tosa.transpose %[[RESHAPE_RESULT_0]] perms([0, 1, 3, 2, 4, 5])
// CHECK: %[[RESHAPE_RESULT_1:.+]] = tosa.reshape %[[TRANSPOSE_RESULT]], %[[CONST8]]
// CHECK: %[[PAD_RESULT:.+]] = tosa.pad %[[RESHAPE_RESULT_1]], %[[RESULT_PAD]]
// CHECK: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2, %[[CONST10]]
// CHECK: %[[ADD:.+]] = tosa.add %[[PAD_RESULT]], %[[RESHAPE_ARG2]]
%input_zp = "tosa.const"() <{values = dense<-103> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<93> : tensor<1xi8>}> : () -> tensor<1xi8>
- %2 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {
- acc_type = i32,
- out_pad = array<i64: 2, 0, 0, 1>,
- stride = array<i64: 1, 2>} :
+ %2 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp out_pad([2, 0, 0, 1]) stride([1, 2]) acc_type(i32) :
(tensor<1x16x1x1xi8>, tensor<1x2x1x1xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x19x2x1xi32>
"func.return" (%2) : (tensor<1x19x2x1xi32>) -> ()
}
@@ -189,7 +183,7 @@ func.func @transpose_conv2d_strided_overpad(%arg0 : tensor<1x16x1x1xi8>, %arg1 :
// CHECK-NOT: tosa.transpose_conv2d
func.func @transpose_conv2d_non_strided_dynamic_batch(%arg0: tensor<?x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) -> tensor<?x18x19x5xf32> {
%zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<?x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x18x19x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) : (tensor<?x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x18x19x5xf32>
return %0 : tensor<?x18x19x5xf32>
}
@@ -199,6 +193,6 @@ func.func @transpose_conv2d_non_strided_dynamic_batch(%arg0: tensor<?x16x14x3xf3
// CHECK-NOT: tosa.transpose_conv2d
func.func @transpose_conv2d_strided_dynamic_batch(%arg0: tensor<?x17x15x3xf32>, %arg1: tensor<5x3x5x3xf32>, %arg2: tensor<5xf32>) -> tensor<?x35x47x5xf32> {
%zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<?x17x15x3xf32>, tensor<5x3x5x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x35x47x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp out_pad([0, 0, 0, 0]) stride([2, 3]) acc_type(f32) : (tensor<?x17x15x3xf32>, tensor<5x3x5x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x35x47x5xf32>
return %0 : tensor<?x35x47x5xf32>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-downgrade-1-1-to-1-0.mlir b/mlir/test/Dialect/Tosa/tosa-downgrade-1-1-to-1-0.mlir
index 1dd67777dee10..c1af136959fa4 100644
--- a/mlir/test/Dialect/Tosa/tosa-downgrade-1-1-to-1-0.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-downgrade-1-1-to-1-0.mlir
@@ -5,7 +5,7 @@
// CHECK: %[[I8_TO_F32:.+]] = tosa.cast %[[BOOL_TO_I8]] : (tensor<13x21x3xi8>) -> tensor<13x21x3xf32>
// CHECK: return %[[I8_TO_F32]]
func.func @test_bool_to_fp32(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xf32> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi1>) -> tensor<13x21x3xf32>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi1>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -16,7 +16,7 @@ func.func @test_bool_to_fp32(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xf32> {
// CHECK: %[[I8_TO_F32:.+]] = tosa.cast %[[BOOL_TO_I8]] : (tensor<*xi8>) -> tensor<*xf32>
// CHECK: return %[[I8_TO_F32]]
func.func @test_bool_to_fp32_unranked(%arg0: tensor<*xi1>) -> tensor<*xf32> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<*xi1>) -> tensor<*xf32>
+ %0 = tosa.cast %arg0 : (tensor<*xi1>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -27,7 +27,7 @@ func.func @test_bool_to_fp32_unranked(%arg0: tensor<*xi1>) -> tensor<*xf32> {
// CHECK: %[[I8_TO_BOOL:.+]] = tosa.cast %[[FP32_TO_I8]] : (tensor<13x?x3xi8>) -> tensor<13x?x3xi1>
// CHECK: return %[[I8_TO_BOOL]]
func.func @test_fp32_to_bool_ranked_dynamic(%arg0: tensor<13x?x3xf32>) -> tensor<13x?x3xi1> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x?x3xf32>) -> tensor<13x?x3xi1>
+ %0 = tosa.cast %arg0 : (tensor<13x?x3xf32>) -> tensor<13x?x3xi1>
return %0 : tensor<13x?x3xi1>
}
@@ -38,7 +38,7 @@ func.func @test_fp32_to_bool_ranked_dynamic(%arg0: tensor<13x?x3xf32>) -> tensor
// CHECK: %[[I8_TO_BOOL:.+]] = tosa.cast %[[FP32_TO_I8]] : (tensor<*xi8>) -> tensor<*xi1>
// CHECK: return %[[I8_TO_BOOL]]
func.func @test_unranked_fp32_to_bool(%arg0: tensor<*xf32>) -> tensor<*xi1> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<*xf32>) -> tensor<*xi1>
+ %0 = tosa.cast %arg0 : (tensor<*xf32>) -> tensor<*xi1>
return %0 : tensor<*xi1>
}
@@ -48,7 +48,7 @@ func.func @test_unranked_fp32_to_bool(%arg0: tensor<*xf32>) -> tensor<*xi1> {
// CHECK: %[[CAST:.+]] = tosa.cast %arg0 : (tensor<13x21x3xi1>) -> tensor<13x21x3xi8>
// CHECK: return %[[CAST]]
func.func @test_preserve_bool_to_i8(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xi8> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi1>) -> tensor<13x21x3xi8>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi1>) -> tensor<13x21x3xi8>
return %0 : tensor<13x21x3xi8>
}
@@ -120,7 +120,7 @@ func.func @test_preserve_scatter_i8_i32(%arg0: tensor<13x52x3xi8>, %arg1: tensor
// -----
// CHECK-LABEL: @test_matmul_t_static_batch
-// CHECK: %[[TRANSPOSE:.+]] = tosa.transpose %arg1 {perms = array<i32: 0, 2, 1>} : (tensor<4x28x19xf32>) -> tensor<4x19x28xf32>
+// CHECK: %[[TRANSPOSE:.+]] = tosa.transpose %arg1 perms([0, 2, 1]) : (tensor<4x28x19xf32>) -> tensor<4x19x28xf32>
// CHECK: %[[MATMUL:.+]] = tosa.matmul %arg0, %[[TRANSPOSE]], %arg2, %arg3 : (tensor<4x14x19xf32>, tensor<4x19x28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x14x28xf32>
// CHECK: return %[[MATMUL]]
func.func @test_matmul_t_static_batch(%arg0: tensor<4x14x19xf32>, %arg1: tensor<4x28x19xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<4x14x28xf32> {
@@ -131,8 +131,8 @@ func.func @test_matmul_t_static_batch(%arg0: tensor<4x14x19xf32>, %arg1: tensor<
// -----
// CHECK-LABEL: @test_matmul_t_static_broadcast
-// CHECK: %[[MULTIPLES:.+]] = tosa.const_shape {values = dense<[4, 1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
-// CHECK: %[[TRANSPOSE:.+]] = tosa.transpose %arg1 {perms = array<i32: 0, 2, 1>} : (tensor<1x28x19xf32>) -> tensor<1x19x28xf32>
+// CHECK: %[[MULTIPLES:.+]] = tosa.const_shape values(dense<[4, 1, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
+// CHECK: %[[TRANSPOSE:.+]] = tosa.transpose %arg1 perms([0, 2, 1]) : (tensor<1x28x19xf32>) -> tensor<1x19x28xf32>
// CHECK: %[[TILE:.+]] = tosa.tile %[[TRANSPOSE]], %[[MULTIPLES]] : (tensor<1x19x28xf32>, !tosa.shape<3>) -> tensor<4x19x28xf32>
// CHECK: %[[MATMUL:.+]] = tosa.matmul %arg0, %[[TILE]], %arg2, %arg3 : (tensor<4x14x19xf32>, tensor<4x19x28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x14x28xf32>
// CHECK: return %[[MATMUL]]
diff --git a/mlir/test/Dialect/Tosa/tosa-infer-shapes-fold-shape-expressions.mlir b/mlir/test/Dialect/Tosa/tosa-infer-shapes-fold-shape-expressions.mlir
index 067f0efba9c86..4d66ed539e811 100644
--- a/mlir/test/Dialect/Tosa/tosa-infer-shapes-fold-shape-expressions.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-infer-shapes-fold-shape-expressions.mlir
@@ -7,20 +7,20 @@
func.func @test_simple_shape_expression(%arg0: tensor<7x12xi32>, %arg1: tensor<80xi32>, %arg2: tensor<4xi32>) -> tensor<?xi32> {
// CHECK-NOT: tosa.dim
// CHECK-NOT: tosa.add_shape
- // CHECK: %[[SHAPE:.+]] = tosa.const_shape {values = dense<84> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-NOT: tosa.const_shape {values = dense<4> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-NOT: tosa.const_shape {values = dense<80> : tensor<1xindex>} : () -> !tosa.shape<1>
+ // CHECK: %[[SHAPE:.+]] = tosa.const_shape values(dense<84> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-NOT: tosa.const_shape values(dense<4> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-NOT: tosa.const_shape values(dense<80> : tensor<1xindex>) : () -> !tosa.shape<1>
// CHECK: %[[RESHAPE:.+]] = tosa.reshape %arg0, %[[SHAPE]] : (tensor<7x12xi32>, !tosa.shape<1>) -> tensor<84xi32>
// CHECK: %[[TILE:.+]] = tosa.tile %[[RESHAPE]], %[[SHAPE]] : (tensor<84xi32>, !tosa.shape<1>) -> tensor<7056xi32>
// DEFAULT: %[[CAST:.+]] = tensor.cast %[[TILE]] : tensor<7056xi32> to tensor<?xi32>
// DEFAULT: return %[[CAST]] : tensor<?xi32>
// FUNCBOUND-NOT: tensor.cast
// FUNCBOUND: return %[[TILE]] : tensor<7056xi32>
- %a = tosa.dim %arg1 {axis = 0: i32} : (tensor<80xi32>) -> !tosa.shape<1>
- %b = tosa.dim %arg2 {axis = 0: i32} : (tensor<4xi32>) -> !tosa.shape<1>
+ %a = tosa.dim %arg1 axis(0) : (tensor<80xi32>) -> !tosa.shape<1>
+ %b = tosa.dim %arg2 axis(0) : (tensor<4xi32>) -> !tosa.shape<1>
%c = tosa.add_shape %a, %b : (!tosa.shape<1>, !tosa.shape<1>) -> !tosa.shape<1>
%d = tosa.reshape %arg0, %c : (tensor<7x12xi32>, !tosa.shape<1>) -> tensor<?xi32>
- %e = tosa.dim %d {axis = 0: i32} : (tensor<?xi32>) -> !tosa.shape<1>
+ %e = tosa.dim %d axis(0) : (tensor<?xi32>) -> !tosa.shape<1>
%f = tosa.tile %d, %e : (tensor<?xi32>, !tosa.shape<1>) -> tensor<?xi32>
return %f : tensor<?xi32>
}
@@ -29,13 +29,13 @@ func.func @test_simple_shape_expression(%arg0: tensor<7x12xi32>, %arg1: tensor<8
// CHECK-LABEL: test_cond_if_with_shape_expressions
func.func @test_cond_if_with_shape_expressions(%arg0 : tensor<3xf32>, %arg1 : tensor<3xf32>, %arg2 : tensor<i1>) -> () {
- // CHECK: %[[CONST_SHAPE:.*]] = tosa.const_shape {values = dense<3> : tensor<1xindex>} : () -> !tosa.shape<1>
+ // CHECK: %[[CONST_SHAPE:.*]] = tosa.const_shape values(dense<3> : tensor<1xindex>) : () -> !tosa.shape<1>
// CHECK: tosa.cond_if %arg2 (%arg3 = %arg0, %arg4 = %arg1) : tensor<i1> (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32> {
%0 = tosa.cond_if %arg2 (%arg3 = %arg0, %arg4 = %arg1) : tensor<i1> (tensor<3xf32>, tensor<3xf32>) -> tensor<?xf32> {
// CHECK: ^bb0(%arg3: tensor<3xf32>, %arg4: tensor<3xf32>)
^bb0(%arg3 : tensor<?xf32>, %arg4 : tensor<?xf32>):
// CHECK-NOT: tosa.dim
- %0 = tosa.dim %arg3 {axis = 0 : i32} : (tensor<?xf32>) -> !tosa.shape<1>
+ %0 = tosa.dim %arg3 axis(0) : (tensor<?xf32>) -> !tosa.shape<1>
// CHECK: %[[RESHAPE:.*]] = tosa.reshape %arg3, %[[CONST_SHAPE]] : (tensor<3xf32>, !tosa.shape<1>) -> tensor<3xf32>
%1 = tosa.reshape %arg3, %0 : (tensor<?xf32>, !tosa.shape<1>) -> tensor<?xf32>
// CHECK: tosa.yield %[[RESHAPE]] : tensor<3xf32>
@@ -54,7 +54,7 @@ func.func @test_cond_if_with_shape_expressions(%arg0 : tensor<3xf32>, %arg1 : te
// CHECK-LABEL: test_no_fold_shape_expression
func.func @test_no_fold_shape_expression(%arg0: tensor<1x?x3xf32>, %arg1: tensor<?xf32>) -> tensor<?xf32> {
// CHECK: tosa.dim
- %0 = tosa.dim %arg0 {axis = 1: i32} : (tensor<1x?x3xf32>) -> !tosa.shape<1>
+ %0 = tosa.dim %arg0 axis(1) : (tensor<1x?x3xf32>) -> !tosa.shape<1>
// CHECK: tosa.tile
%1 = tosa.tile %arg1, %0 : (tensor<?xf32>, !tosa.shape<1>) -> tensor<?xf32>
// CHECK: return %{{.*}} : tensor<?xf32>
diff --git a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
index c0b5ec23abee2..2f0d995237790 100644
--- a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
@@ -37,7 +37,7 @@ func.func @test_unary_f32(%arg0 : tensor<4xf32>) -> () {
%1 = tosa.ceil %arg0 : (tensor<4xf32>) -> tensor<*xf32>
// CHECK: tosa.clamp %arg0 {{.+}} : (tensor<4xf32>) -> tensor<4xf32>
- %2 = tosa.clamp %arg0 { min_val = 0.0 : f32, max_val = 10.0 : f32 } : (tensor<4xf32>) -> tensor<*xf32>
+ %2 = tosa.clamp %arg0 min_val(0.0 : f32) max_val(10.0 : f32) : (tensor<4xf32>) -> tensor<*xf32>
// CHECK: tosa.exp %arg0 : (tensor<4xf32>) -> tensor<4xf32>
%3 = tosa.exp %arg0 : (tensor<4xf32>) -> tensor<*xf32>
@@ -56,8 +56,8 @@ func.func @test_unary_f32(%arg0 : tensor<4xf32>) -> () {
// CHECK: tosa.reciprocal %arg0 : (tensor<4xf32>) -> tensor<4xf32>
%7 = tosa.reciprocal %arg0 : (tensor<4xf32>) -> tensor<*xf32>
- // CHECK: tosa.reverse %arg0 {axis = 0 : i32} : (tensor<4xf32>) -> tensor<4xf32>
- %8 = tosa.reverse %arg0 { axis = 0 : i32 } : (tensor<4xf32>) -> tensor<?xf32>
+ // CHECK: tosa.reverse %arg0 axis(0) : (tensor<4xf32>) -> tensor<4xf32>
+ %8 = tosa.reverse %arg0 axis(0) : (tensor<4xf32>) -> tensor<?xf32>
// CHECK: tosa.rsqrt %arg0 : (tensor<4xf32>) -> tensor<4xf32>
%9 = tosa.rsqrt %arg0 : (tensor<4xf32>) -> tensor<*xf32>
@@ -69,7 +69,7 @@ func.func @test_unary_f32(%arg0 : tensor<4xf32>) -> () {
%11 = tosa.sigmoid %arg0 : (tensor<4xf32>) -> tensor<*xf32>
// CHECK: tosa.cast %arg0 : (tensor<4xf32>) -> tensor<4xi32>
- %12 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4xf32>) -> tensor<*xi32>
+ %12 = tosa.cast %arg0 : (tensor<4xf32>) -> tensor<*xi32>
// CHECK: tosa.erf %arg0 : (tensor<4xf32>) -> tensor<4xf32>
%13 = tosa.erf %arg0 : (tensor<4xf32>) -> tensor<*xf32>
@@ -87,7 +87,7 @@ func.func @test_unary_i32(%arg0 : tensor<4xi32>, %arg1 : tensor<2xi8>) -> () {
%1 = tosa.bitwise_not %arg0 : (tensor<4xi32>) -> tensor<*xi32>
// CHECK: tosa.clamp %arg0 {{.+}} : (tensor<4xi32>) -> tensor<4xi32>
- %2 = tosa.clamp %arg0 { max_val = 10 : i32, min_val = 0 : i32} : (tensor<4xi32>) -> tensor<*xi32>
+ %2 = tosa.clamp %arg0 min_val(0 : i32) max_val(10 : i32) : (tensor<4xi32>) -> tensor<*xi32>
// CHECK: tosa.clz %arg0 : (tensor<4xi32>) -> tensor<4xi32>
%3 = tosa.clz %arg0 : (tensor<4xi32>) -> tensor<*xi32>
@@ -97,19 +97,19 @@ func.func @test_unary_i32(%arg0 : tensor<4xi32>, %arg1 : tensor<2xi8>) -> () {
// CHECK: tosa.negate %arg0, {{.+}} : (tensor<4xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<4xi32>
%4 = tosa.negate %arg0, %in_zp, %out_zp : (tensor<4xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<*xi32>
- // CHECK: tosa.reverse %arg0 {axis = 0 : i32} : (tensor<4xi32>) -> tensor<4xi32>
- %5 = tosa.reverse %arg0 { axis = 0 : i32 } : (tensor<4xi32>) -> tensor<?xi32>
+ // CHECK: tosa.reverse %arg0 axis(0) : (tensor<4xi32>) -> tensor<4xi32>
+ %5 = tosa.reverse %arg0 axis(0) : (tensor<4xi32>) -> tensor<?xi32>
// CHECK-DAG: %[[MULT:.+]] = "tosa.const"() <{values = dense<[42, 43]> : tensor<2xi16>}> : () -> tensor<2xi16>
// CHECK-DAG: %[[SHIFT:.+]] = "tosa.const"() <{values = dense<[14, 15]> : tensor<2xi8>}> : () -> tensor<2xi8>
// CHECK-DAG: %[[INPUTZP:.+]] = "tosa.const"() <{values = dense<43> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK-DAG: %[[OUTPUTZP:.+]] = "tosa.const"() <{values = dense<52> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: tosa.rescale %arg1, %[[MULT]], %[[SHIFT]], %[[INPUTZP]], %[[OUTPUTZP]] {{.+}} : (tensor<2xi8>, tensor<2xi16>, tensor<2xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
- %multiplier = "tosa.const"() {values = dense<[42, 43]> : tensor<2xi16>} : () -> tensor<2xi16>
- %shift = "tosa.const"() {values = dense<[14, 15]> : tensor<2xi8>} : () -> tensor<2xi8>
- %input_zp = "tosa.const"() {values = dense<43> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<52> : tensor<1xi8>} : () -> tensor<1xi8>
- %6 = tosa.rescale %arg1, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = true, input_unsigned = true, output_unsigned = true} : (tensor<2xi8>, tensor<2xi16>, tensor<2xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
+ %multiplier = "tosa.const"() <{values = dense<[42, 43]> : tensor<2xi16>}> : () -> tensor<2xi16>
+ %shift = "tosa.const"() <{values = dense<[14, 15]> : tensor<2xi8>}> : () -> tensor<2xi8>
+ %input_zp = "tosa.const"() <{values = dense<43> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<52> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %6 = tosa.rescale %arg1, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(true) input_unsigned(true) output_unsigned(true) : (tensor<2xi8>, tensor<2xi16>, tensor<2xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8>
// CHECK: tosa.identity %arg0 : (tensor<4xi32>) -> tensor<4xi32>
%7 = tosa.identity %arg0 : (tensor<4xi32>) -> tensor<?xi32>
@@ -212,10 +212,10 @@ func.func @test_binary_i32(%arg0 : tensor<4xi32>, %arg1 : tensor<1xi32>) -> () {
%6 = tosa.greater_equal %arg0, %arg1 : (tensor<4xi32>, tensor<1xi32>) -> tensor<*xi1>
// CHECK: tosa.logical_left_shift %arg0, %arg1 {shift = 0 : i32} : (tensor<4xi32>, tensor<1xi32>) -> tensor<4xi32>
- %7 = tosa.logical_left_shift %arg0, %arg1 { shift = 0 : i32 }: (tensor<4xi32>, tensor<1xi32>) -> tensor<*xi32>
+ %7 = tosa.logical_left_shift %arg0, %arg1 {shift = 0 : i32}: (tensor<4xi32>, tensor<1xi32>) -> tensor<*xi32>
// CHECK: tosa.logical_right_shift %arg0, %arg1 {shift = 0 : i32} : (tensor<4xi32>, tensor<1xi32>) -> tensor<4xi32>
- %8 = tosa.logical_right_shift %arg0, %arg1 { shift = 0 : i32 }: (tensor<4xi32>, tensor<1xi32>) -> tensor<*xi32>
+ %8 = tosa.logical_right_shift %arg0, %arg1 {shift = 0 : i32}: (tensor<4xi32>, tensor<1xi32>) -> tensor<*xi32>
// CHECK: tosa.maximum %arg0, %arg1 : (tensor<4xi32>, tensor<1xi32>) -> tensor<4xi32>
%9 = tosa.maximum %arg0, %arg1 : (tensor<4xi32>, tensor<1xi32>) -> tensor<*xi32>
@@ -284,11 +284,11 @@ func.func @test_select_i32(%arg0 : tensor<4xi1>, %arg1 : tensor<1xi32>, %arg2 :
// CHECK-LABEL: @test_static_argmax
func.func @test_static_argmax(%arg0 : tensor<2x3xi32>) -> () {
- // CHECK: tosa.argmax %arg0 {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<3xi32>
- %0 = tosa.argmax %arg0 {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<?xi32>
+ // CHECK: tosa.argmax %arg0 axis(0) : (tensor<2x3xi32>) -> tensor<3xi32>
+ %0 = tosa.argmax %arg0 axis(0) : (tensor<2x3xi32>) -> tensor<?xi32>
- // CHECK: tosa.argmax %arg0 {axis = 1 : i32} : (tensor<2x3xi32>) -> tensor<2xi32>
- %1 = tosa.argmax %arg0 {axis = 1 : i32} : (tensor<2x3xi32>) -> tensor<?xi32>
+ // CHECK: tosa.argmax %arg0 axis(1) : (tensor<2x3xi32>) -> tensor<2xi32>
+ %1 = tosa.argmax %arg0 axis(1) : (tensor<2x3xi32>) -> tensor<?xi32>
return
}
@@ -296,11 +296,11 @@ func.func @test_static_argmax(%arg0 : tensor<2x3xi32>) -> () {
// CHECK-LABEL: @test_dynamic_argmax
func.func @test_dynamic_argmax(%arg0 : tensor<2x?xi32>) -> () {
- // CHECK: tosa.argmax %arg0 {axis = 0 : i32} : (tensor<2x?xi32>) -> tensor<?xi32>
- %0 = tosa.argmax %arg0 {axis = 0 : i32} : (tensor<2x?xi32>) -> tensor<?xi32>
+ // CHECK: tosa.argmax %arg0 axis(0) : (tensor<2x?xi32>) -> tensor<?xi32>
+ %0 = tosa.argmax %arg0 axis(0) : (tensor<2x?xi32>) -> tensor<?xi32>
- // CHECK: tosa.argmax %arg0 {axis = 1 : i32} : (tensor<2x?xi32>) -> tensor<2xi32>
- %1 = tosa.argmax %arg0 {axis = 1 : i32} : (tensor<2x?xi32>) -> tensor<?xi32>
+ // CHECK: tosa.argmax %arg0 axis(1) : (tensor<2x?xi32>) -> tensor<2xi32>
+ %1 = tosa.argmax %arg0 axis(1) : (tensor<2x?xi32>) -> tensor<?xi32>
return
}
@@ -433,10 +433,10 @@ func.func @test_unranked_matmul_t(%arg0 : tensor<*xi32>, %arg1 : tensor<*xi32>)
// CHECK-LABEL: @test_accepts_unranked_scalar_tensor
func.func @test_accepts_unranked_scalar_tensor(%arg0: tensor<1x2x2xf32>, %arg1: tensor<1xf32>) -> tensor<*xf32> {
- // CHECK-DAG: %[[SHAPE:.*]] = tosa.const_shape {values = dense<[0, 0, 0, 1, 0, 1]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ // CHECK-DAG: %[[SHAPE:.*]] = tosa.const_shape values(dense<[0, 0, 0, 1, 0, 1]> : tensor<6xindex>) : () -> !tosa.shape<6>
// CHECK-DAG: %[[ZP:.*]] = tosa.cast %arg1 : (tensor<1xf32>) -> tensor<1xf32>
- %0 = tosa.cast %arg1 {input_unsigned = false} : (tensor<1xf32>) -> tensor<*xf32>
- %1 = tosa.const_shape {values = dense<[0, 0, 0, 1, 0, 1]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %0 = tosa.cast %arg1 : (tensor<1xf32>) -> tensor<*xf32>
+ %1 = tosa.const_shape values(dense<[0, 0, 0, 1, 0, 1]> : tensor<6xindex>) : () -> !tosa.shape<6>
// CHECK: %[[PAD:.*]] = tosa.pad %arg0, %[[SHAPE]], %[[ZP]] : (tensor<1x2x2xf32>, !tosa.shape<6>, tensor<1xf32>) -> tensor<1x3x3xf32>
%2 = tosa.pad %arg0, %1, %0 : (tensor<1x2x2xf32>, !tosa.shape<6>, tensor<*xf32>) -> tensor<*xf32>
// DEFAULT: %[[CAST:.*]] = tensor.cast %[[PAD]] : tensor<1x3x3xf32> to tensor<*xf32>
@@ -450,7 +450,7 @@ func.func @test_accepts_unranked_scalar_tensor(%arg0: tensor<1x2x2xf32>, %arg1:
// CHECK-LABEL: @test_unranked_scalar_i8_tensor
func.func @test_unranked_scalar_i8_tensor(%arg0: tensor<4xi32>, %arg1: tensor<4xi32>, %arg2: tensor<1xi8>) -> tensor<4xi32> {
// CHECK: %[[SHIFT:.*]] = tosa.cast %arg2 : (tensor<1xi8>) -> tensor<1xi8>
- %shift = tosa.cast %arg2 {input_unsigned = false} : (tensor<1xi8>) -> tensor<*xi8>
+ %shift = tosa.cast %arg2 : (tensor<1xi8>) -> tensor<*xi8>
// CHECK: tosa.mul %arg0, %arg1, %[[SHIFT]] : (tensor<4xi32>, tensor<4xi32>, tensor<1xi8>) -> tensor<4xi32>
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<4xi32>, tensor<4xi32>, tensor<*xi8>) -> tensor<4xi32>
return %0 : tensor<4xi32>
@@ -478,17 +478,17 @@ func.func @test_table_dynamic(%arg0 : tensor<4x?xi16>, %arg1 : tensor<513xi16>)
// CHECK-LABEL: @test_static_reshape
func.func @test_static_reshape(%arg0 : tensor<4x4xi32>) -> () {
- // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape {values = dense<16> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape {values = dense<-1> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-DAG: %[[CONSTSHAPE3:.+]] = tosa.const_shape {values = dense<[2, -1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape values(dense<16> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape values(dense<-1> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-DAG: %[[CONSTSHAPE3:.+]] = tosa.const_shape values(dense<[2, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK-DAG: tosa.reshape %arg0, %[[CONSTSHAPE1]] : (tensor<4x4xi32>, !tosa.shape<1>) -> tensor<16xi32>
// CHECK-DAG: tosa.reshape %arg0, %[[CONSTSHAPE2]] : (tensor<4x4xi32>, !tosa.shape<1>) -> tensor<16xi32>
// CHECK-DAG: tosa.reshape %arg0, %[[CONSTSHAPE3]] : (tensor<4x4xi32>, !tosa.shape<2>) -> tensor<2x8xi32>
- %3 = tosa.const_shape {values = dense<16> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %3 = tosa.const_shape values(dense<16> : tensor<1xindex>) : () -> !tosa.shape<1>
%0 = tosa.reshape %arg0, %3 : (tensor<4x4xi32>, !tosa.shape<1>) -> tensor<16xi32>
- %4 = tosa.const_shape {values = dense<-1> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %4 = tosa.const_shape values(dense<-1> : tensor<1xindex>) : () -> !tosa.shape<1>
%1 = tosa.reshape %arg0, %4 : (tensor<4x4xi32>, !tosa.shape<1>) -> tensor<16xi32>
- %5 = tosa.const_shape {values = dense<[2, -1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %5 = tosa.const_shape values(dense<[2, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%2 = tosa.reshape %arg0, %5 : (tensor<4x4xi32>, !tosa.shape<2>) -> tensor<2x8xi32>
return
@@ -498,17 +498,17 @@ func.func @test_static_reshape(%arg0 : tensor<4x4xi32>) -> () {
// CHECK-LABEL: @test_dynamic_reshape
func.func @test_dynamic_reshape(%arg0 : tensor<4x?xi32>) -> () {
- // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape {values = dense<16> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape {values = dense<-1> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-DAG: %[[CONSTSHAPE3:.+]] = tosa.const_shape {values = dense<[2, -1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape values(dense<16> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape values(dense<-1> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-DAG: %[[CONSTSHAPE3:.+]] = tosa.const_shape values(dense<[2, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK-DAG: tosa.reshape %arg0, %[[CONSTSHAPE1]] : (tensor<4x?xi32>, !tosa.shape<1>) -> tensor<16xi32>
// CHECK-DAG: tosa.reshape %arg0, %[[CONSTSHAPE2]] : (tensor<4x?xi32>, !tosa.shape<1>) -> tensor<?xi32>
// CHECK-DAG: tosa.reshape %arg0, %[[CONSTSHAPE3]] : (tensor<4x?xi32>, !tosa.shape<2>) -> tensor<2x?xi32>
- %0 = tosa.const_shape {values = dense<16> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %0 = tosa.const_shape values(dense<16> : tensor<1xindex>) : () -> !tosa.shape<1>
%1 = tosa.reshape %arg0, %0 : (tensor<4x?xi32>, !tosa.shape<1>) -> tensor<?xi32>
- %2 = tosa.const_shape {values = dense<-1> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %2 = tosa.const_shape values(dense<-1> : tensor<1xindex>) : () -> !tosa.shape<1>
%3 = tosa.reshape %arg0, %2 : (tensor<4x?xi32>, !tosa.shape<1>) -> tensor<?xi32>
- %4 = tosa.const_shape {values = dense<[2, -1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %4 = tosa.const_shape values(dense<[2, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
%5 = tosa.reshape %arg0, %4 : (tensor<4x?xi32>, !tosa.shape<2>) -> tensor<?x?xi32>
return
@@ -518,14 +518,14 @@ func.func @test_dynamic_reshape(%arg0 : tensor<4x?xi32>) -> () {
// CHECK-LABEL: @test_static_reshape_non_block_scaled
func.func @test_static_reshape_non_block_scaled(%arg0 : tensor<4x4xi32>) -> () {
- // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape {values = dense<16> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape {values = dense<[2, 8]> : tensor<2xindex>} : () -> !tosa.shape<2>
- // CHECK-DAG: tosa.reshape_block_scaled %arg0, %[[CONSTSHAPE1]] {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<4x4xi32>, !tosa.shape<1>) -> tensor<16xi32>
- // CHECK-DAG: tosa.reshape_block_scaled %arg0, %[[CONSTSHAPE2]] {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<4x4xi32>, !tosa.shape<2>) -> tensor<2x8xi32>
- %0 = tosa.const_shape {values = dense<16> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1 = tosa.reshape_block_scaled %arg0, %0 {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<4x4xi32>, !tosa.shape<1>) -> tensor<16xi32>
- %2 = tosa.const_shape {values = dense<[2, 8]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %3 = tosa.reshape_block_scaled %arg0, %2 {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<4x4xi32>, !tosa.shape<2>) -> tensor<2x8xi32>
+ // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape values(dense<16> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape values(dense<[2, 8]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ // CHECK-DAG: tosa.reshape_block_scaled %arg0, %[[CONSTSHAPE1]] block_size<BLOCK_SIZE_1> : (tensor<4x4xi32>, !tosa.shape<1>) -> tensor<16xi32>
+ // CHECK-DAG: tosa.reshape_block_scaled %arg0, %[[CONSTSHAPE2]] block_size<BLOCK_SIZE_1> : (tensor<4x4xi32>, !tosa.shape<2>) -> tensor<2x8xi32>
+ %0 = tosa.const_shape values(dense<16> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1 = tosa.reshape_block_scaled %arg0, %0 block_size<BLOCK_SIZE_1> : (tensor<4x4xi32>, !tosa.shape<1>) -> tensor<16xi32>
+ %2 = tosa.const_shape values(dense<[2, 8]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %3 = tosa.reshape_block_scaled %arg0, %2 block_size<BLOCK_SIZE_1> : (tensor<4x4xi32>, !tosa.shape<2>) -> tensor<2x8xi32>
return
}
@@ -534,14 +534,14 @@ func.func @test_static_reshape_non_block_scaled(%arg0 : tensor<4x4xi32>) -> () {
// CHECK-LABEL: @test_dynamic_reshape_non_block_scaled
func.func @test_dynamic_reshape_non_block_scaled(%arg0 : tensor<4x?xi32>) -> () {
- // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape {values = dense<16> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape {values = dense<[2, 8]> : tensor<2xindex>} : () -> !tosa.shape<2>
- // CHECK-DAG: tosa.reshape_block_scaled %arg0, %[[CONSTSHAPE1]] {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<4x?xi32>, !tosa.shape<1>) -> tensor<16xi32>
- // CHECK-DAG: tosa.reshape_block_scaled %arg0, %[[CONSTSHAPE2]] {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<4x?xi32>, !tosa.shape<2>) -> tensor<2x8xi32>
- %0 = tosa.const_shape {values = dense<16> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1 = tosa.reshape_block_scaled %arg0, %0 {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<4x?xi32>, !tosa.shape<1>) -> tensor<?xi32>
- %2 = tosa.const_shape {values = dense<[2, 8]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %3 = tosa.reshape_block_scaled %arg0, %2 {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<4x?xi32>, !tosa.shape<2>) -> tensor<?x?xi32>
+ // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape values(dense<16> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape values(dense<[2, 8]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ // CHECK-DAG: tosa.reshape_block_scaled %arg0, %[[CONSTSHAPE1]] block_size<BLOCK_SIZE_1> : (tensor<4x?xi32>, !tosa.shape<1>) -> tensor<16xi32>
+ // CHECK-DAG: tosa.reshape_block_scaled %arg0, %[[CONSTSHAPE2]] block_size<BLOCK_SIZE_1> : (tensor<4x?xi32>, !tosa.shape<2>) -> tensor<2x8xi32>
+ %0 = tosa.const_shape values(dense<16> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1 = tosa.reshape_block_scaled %arg0, %0 block_size<BLOCK_SIZE_1> : (tensor<4x?xi32>, !tosa.shape<1>) -> tensor<?xi32>
+ %2 = tosa.const_shape values(dense<[2, 8]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %3 = tosa.reshape_block_scaled %arg0, %2 block_size<BLOCK_SIZE_1> : (tensor<4x?xi32>, !tosa.shape<2>) -> tensor<?x?xi32>
return
}
@@ -550,14 +550,14 @@ func.func @test_dynamic_reshape_non_block_scaled(%arg0 : tensor<4x?xi32>) -> ()
// CHECK-LABEL: @test_unranked_reshape_non_block_scaled
func.func @test_unranked_reshape_non_block_scaled(%arg0 : tensor<4x4xi32>) -> () {
- // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape {values = dense<16> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape {values = dense<[2, 8]> : tensor<2xindex>} : () -> !tosa.shape<2>
- // CHECK-DAG: tosa.reshape_block_scaled %arg0, %[[CONSTSHAPE1]] {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<4x4xi32>, !tosa.shape<1>) -> tensor<16xi32>
- // CHECK-DAG: tosa.reshape_block_scaled %arg0, %[[CONSTSHAPE2]] {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<4x4xi32>, !tosa.shape<2>) -> tensor<2x8xi32>
- %0 = tosa.const_shape {values = dense<16> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1 = tosa.reshape_block_scaled %arg0, %0 {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<4x4xi32>, !tosa.shape<1>) -> tensor<*xi32>
- %2 = tosa.const_shape {values = dense<[2, 8]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %3 = tosa.reshape_block_scaled %arg0, %2 {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<4x4xi32>, !tosa.shape<2>) -> tensor<*xi32>
+ // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape values(dense<16> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape values(dense<[2, 8]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ // CHECK-DAG: tosa.reshape_block_scaled %arg0, %[[CONSTSHAPE1]] block_size<BLOCK_SIZE_1> : (tensor<4x4xi32>, !tosa.shape<1>) -> tensor<16xi32>
+ // CHECK-DAG: tosa.reshape_block_scaled %arg0, %[[CONSTSHAPE2]] block_size<BLOCK_SIZE_1> : (tensor<4x4xi32>, !tosa.shape<2>) -> tensor<2x8xi32>
+ %0 = tosa.const_shape values(dense<16> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1 = tosa.reshape_block_scaled %arg0, %0 block_size<BLOCK_SIZE_1> : (tensor<4x4xi32>, !tosa.shape<1>) -> tensor<*xi32>
+ %2 = tosa.const_shape values(dense<[2, 8]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %3 = tosa.reshape_block_scaled %arg0, %2 block_size<BLOCK_SIZE_1> : (tensor<4x4xi32>, !tosa.shape<2>) -> tensor<*xi32>
return
}
@@ -567,14 +567,14 @@ func.func @test_unranked_reshape_non_block_scaled(%arg0 : tensor<4x4xi32>) -> ()
// CHECK-LABEL: @test_static_reshape_block_scaled
func.func @test_static_reshape_block_scaled(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<4x1xf8E8M0FNU>) -> () {
- // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape {values = dense<128> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
- // CHECK-DAG: tosa.reshape_block_scaled %arg0, %arg1, %[[CONSTSHAPE1]] {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<1>) -> (tensor<128xf4E2M1FN>, tensor<4xf8E8M0FNU>)
- // CHECK-DAG: tosa.reshape_block_scaled %arg0, %arg1, %[[CONSTSHAPE2]] {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
- %0 = tosa.const_shape {values = dense<128> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1:2 = tosa.reshape_block_scaled %arg0, %arg1, %0 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<1>) -> (tensor<128xf4E2M1FN>, tensor<4xf8E8M0FNU>)
- %2 = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %3:2 = tosa.reshape_block_scaled %arg0, %arg1, %2 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
+ // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape values(dense<128> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ // CHECK-DAG: tosa.reshape_block_scaled %arg0, %arg1, %[[CONSTSHAPE1]] block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<1>) -> (tensor<128xf4E2M1FN>, tensor<4xf8E8M0FNU>)
+ // CHECK-DAG: tosa.reshape_block_scaled %arg0, %arg1, %[[CONSTSHAPE2]] block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
+ %0 = tosa.const_shape values(dense<128> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1:2 = tosa.reshape_block_scaled %arg0, %arg1, %0 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<1>) -> (tensor<128xf4E2M1FN>, tensor<4xf8E8M0FNU>)
+ %2 = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %3:2 = tosa.reshape_block_scaled %arg0, %arg1, %2 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
return
}
@@ -583,14 +583,14 @@ func.func @test_static_reshape_block_scaled(%arg0: tensor<4x32xf4E2M1FN>, %arg1:
// CHECK-LABEL: @test_dynamic_reshape_block_scaled
func.func @test_dynamic_reshape_block_scaled(%arg0: tensor<4x?xf4E2M1FN>, %arg1: tensor<?x1xf8E8M0FNU>) -> () {
- // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape {values = dense<128> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
- // CHECK-DAG: tosa.reshape_block_scaled %arg0, %arg1, %[[CONSTSHAPE1]] {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x?xf4E2M1FN>, tensor<?x1xf8E8M0FNU>, !tosa.shape<1>) -> (tensor<128xf4E2M1FN>, tensor<4xf8E8M0FNU>)
- // CHECK-DAG: tosa.reshape_block_scaled %arg0, %arg1, %[[CONSTSHAPE2]] {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x?xf4E2M1FN>, tensor<?x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
- %0 = tosa.const_shape {values = dense<128> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1:2 = tosa.reshape_block_scaled %arg0, %arg1, %0 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x?xf4E2M1FN>, tensor<?x1xf8E8M0FNU>, !tosa.shape<1>) -> (tensor<?xf4E2M1FN>, tensor<?xf8E8M0FNU>)
- %2 = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %3:2 = tosa.reshape_block_scaled %arg0, %arg1, %2 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x?xf4E2M1FN>, tensor<?x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x?xf4E2M1FN>, tensor<?x2xf8E8M0FNU>)
+ // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape values(dense<128> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ // CHECK-DAG: tosa.reshape_block_scaled %arg0, %arg1, %[[CONSTSHAPE1]] block_size<BLOCK_SIZE_32> : (tensor<4x?xf4E2M1FN>, tensor<?x1xf8E8M0FNU>, !tosa.shape<1>) -> (tensor<128xf4E2M1FN>, tensor<4xf8E8M0FNU>)
+ // CHECK-DAG: tosa.reshape_block_scaled %arg0, %arg1, %[[CONSTSHAPE2]] block_size<BLOCK_SIZE_32> : (tensor<4x?xf4E2M1FN>, tensor<?x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
+ %0 = tosa.const_shape values(dense<128> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1:2 = tosa.reshape_block_scaled %arg0, %arg1, %0 block_size<BLOCK_SIZE_32> : (tensor<4x?xf4E2M1FN>, tensor<?x1xf8E8M0FNU>, !tosa.shape<1>) -> (tensor<?xf4E2M1FN>, tensor<?xf8E8M0FNU>)
+ %2 = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %3:2 = tosa.reshape_block_scaled %arg0, %arg1, %2 block_size<BLOCK_SIZE_32> : (tensor<4x?xf4E2M1FN>, tensor<?x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x?xf4E2M1FN>, tensor<?x2xf8E8M0FNU>)
return
}
@@ -599,14 +599,14 @@ func.func @test_dynamic_reshape_block_scaled(%arg0: tensor<4x?xf4E2M1FN>, %arg1:
// CHECK-LABEL: @test_unranked_reshape_block_scaled
func.func @test_unranked_reshape_block_scaled(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<4x1xf8E8M0FNU>) -> () {
- // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape {values = dense<128> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
- // CHECK-DAG: tosa.reshape_block_scaled %arg0, %arg1, %[[CONSTSHAPE1]] {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<1>) -> (tensor<128xf4E2M1FN>, tensor<4xf8E8M0FNU>)
- // CHECK-DAG: tosa.reshape_block_scaled %arg0, %arg1, %[[CONSTSHAPE2]] {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
- %0 = tosa.const_shape {values = dense<128> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1:2 = tosa.reshape_block_scaled %arg0, %arg1, %0 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<1>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
- %2 = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %3:2 = tosa.reshape_block_scaled %arg0, %arg1, %2 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
+ // CHECK-DAG: %[[CONSTSHAPE1:.+]] = tosa.const_shape values(dense<128> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-DAG: %[[CONSTSHAPE2:.+]] = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ // CHECK-DAG: tosa.reshape_block_scaled %arg0, %arg1, %[[CONSTSHAPE1]] block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<1>) -> (tensor<128xf4E2M1FN>, tensor<4xf8E8M0FNU>)
+ // CHECK-DAG: tosa.reshape_block_scaled %arg0, %arg1, %[[CONSTSHAPE2]] block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
+ %0 = tosa.const_shape values(dense<128> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1:2 = tosa.reshape_block_scaled %arg0, %arg1, %0 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<1>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
+ %2 = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %3:2 = tosa.reshape_block_scaled %arg0, %arg1, %2 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
return
}
@@ -615,20 +615,20 @@ func.func @test_unranked_reshape_block_scaled(%arg0: tensor<4x32xf4E2M1FN>, %arg
// CHECK: @test_reduce_binary
func.func @test_reduce_binary(%arg0 : tensor<2x3x?x?xi1>) -> () {
- // CHECK: tosa.reduce_all %arg0 {axis = 0 : i32} : (tensor<2x3x?x?xi1>) -> tensor<1x3x?x?xi1>
- %0 = tosa.reduce_all %arg0 {axis = 0 : i32} : (tensor<2x3x?x?xi1>) -> tensor<?x?x?x?xi1>
+ // CHECK: tosa.reduce_all %arg0 axis(0) : (tensor<2x3x?x?xi1>) -> tensor<1x3x?x?xi1>
+ %0 = tosa.reduce_all %arg0 axis(0) : (tensor<2x3x?x?xi1>) -> tensor<?x?x?x?xi1>
- // CHECK: tosa.reduce_all %arg0 {axis = 1 : i32} : (tensor<2x3x?x?xi1>) -> tensor<2x1x?x?xi1>
- %1 = tosa.reduce_all %arg0 {axis = 1 : i32} : (tensor<2x3x?x?xi1>) -> tensor<?x?x?x?xi1>
+ // CHECK: tosa.reduce_all %arg0 axis(1) : (tensor<2x3x?x?xi1>) -> tensor<2x1x?x?xi1>
+ %1 = tosa.reduce_all %arg0 axis(1) : (tensor<2x3x?x?xi1>) -> tensor<?x?x?x?xi1>
- // CHECK: tosa.reduce_all %arg0 {axis = 2 : i32} : (tensor<2x3x?x?xi1>) -> tensor<2x3x1x?xi1>
- %2 = tosa.reduce_all %arg0 {axis = 2 : i32} : (tensor<2x3x?x?xi1>) -> tensor<?x?x?x?xi1>
+ // CHECK: tosa.reduce_all %arg0 axis(2) : (tensor<2x3x?x?xi1>) -> tensor<2x3x1x?xi1>
+ %2 = tosa.reduce_all %arg0 axis(2) : (tensor<2x3x?x?xi1>) -> tensor<?x?x?x?xi1>
- // CHECK: tosa.reduce_all %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xi1>) -> tensor<2x3x?x1xi1>
- %3 = tosa.reduce_all %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xi1>) -> tensor<?x?x?x?xi1>
+ // CHECK: tosa.reduce_all %arg0 axis(3) : (tensor<2x3x?x?xi1>) -> tensor<2x3x?x1xi1>
+ %3 = tosa.reduce_all %arg0 axis(3) : (tensor<2x3x?x?xi1>) -> tensor<?x?x?x?xi1>
- // CHECK: tosa.reduce_any %arg0 {axis = 0 : i32} : (tensor<2x3x?x?xi1>) -> tensor<1x3x?x?xi1>
- %4 = tosa.reduce_any %arg0 {axis = 0 : i32} : (tensor<2x3x?x?xi1>) -> tensor<?x?x?x?xi1>
+ // CHECK: tosa.reduce_any %arg0 axis(0) : (tensor<2x3x?x?xi1>) -> tensor<1x3x?x?xi1>
+ %4 = tosa.reduce_any %arg0 axis(0) : (tensor<2x3x?x?xi1>) -> tensor<?x?x?x?xi1>
return
}
@@ -637,26 +637,26 @@ func.func @test_reduce_binary(%arg0 : tensor<2x3x?x?xi1>) -> () {
// CHECK: @test_reduce_float
func.func @test_reduce_float(%arg0 : tensor<2x3x?x?xf32>) -> () {
- // CHECK: tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<2x3x?x?xf32>) -> tensor<1x3x?x?xf32>
- %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
+ // CHECK: tosa.reduce_sum %arg0 axis(0) : (tensor<2x3x?x?xf32>) -> tensor<1x3x?x?xf32>
+ %0 = tosa.reduce_sum %arg0 axis(0) : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
- // CHECK: tosa.reduce_sum %arg0 {axis = 1 : i32} : (tensor<2x3x?x?xf32>) -> tensor<2x1x?x?xf32>
- %1 = tosa.reduce_sum %arg0 {axis = 1 : i32} : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
+ // CHECK: tosa.reduce_sum %arg0 axis(1) : (tensor<2x3x?x?xf32>) -> tensor<2x1x?x?xf32>
+ %1 = tosa.reduce_sum %arg0 axis(1) : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
- // CHECK: tosa.reduce_sum %arg0 {axis = 2 : i32} : (tensor<2x3x?x?xf32>) -> tensor<2x3x1x?xf32>
- %2 = tosa.reduce_sum %arg0 {axis = 2 : i32} : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
+ // CHECK: tosa.reduce_sum %arg0 axis(2) : (tensor<2x3x?x?xf32>) -> tensor<2x3x1x?xf32>
+ %2 = tosa.reduce_sum %arg0 axis(2) : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
- // CHECK: tosa.reduce_sum %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<2x3x?x1xf32>
- %3 = tosa.reduce_sum %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
+ // CHECK: tosa.reduce_sum %arg0 axis(3) : (tensor<2x3x?x?xf32>) -> tensor<2x3x?x1xf32>
+ %3 = tosa.reduce_sum %arg0 axis(3) : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
- // CHECK: tosa.reduce_max %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<2x3x?x1xf32>
- %4 = tosa.reduce_max %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
+ // CHECK: tosa.reduce_max %arg0 axis(3) : (tensor<2x3x?x?xf32>) -> tensor<2x3x?x1xf32>
+ %4 = tosa.reduce_max %arg0 axis(3) : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
- // CHECK: tosa.reduce_min %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<2x3x?x1xf32>
- %5 = tosa.reduce_min %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
+ // CHECK: tosa.reduce_min %arg0 axis(3) : (tensor<2x3x?x?xf32>) -> tensor<2x3x?x1xf32>
+ %5 = tosa.reduce_min %arg0 axis(3) : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
- // CHECK: tosa.reduce_product %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<2x3x?x1xf32>
- %6 = tosa.reduce_product %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
+ // CHECK: tosa.reduce_product %arg0 axis(3) : (tensor<2x3x?x?xf32>) -> tensor<2x3x?x1xf32>
+ %6 = tosa.reduce_product %arg0 axis(3) : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -665,8 +665,8 @@ func.func @test_reduce_float(%arg0 : tensor<2x3x?x?xf32>) -> () {
// CHECK-LABEL: @test_concat
func.func @test_concat(%arg0 : tensor<1x2xf32>, %arg1 : tensor<2x2xf32>) -> () {
- // CHECK: tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<1x2xf32>, tensor<2x2xf32>) -> tensor<3x2xf32>
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<1x2xf32>, tensor<2x2xf32>) -> tensor<?x?xf32>
+ // CHECK: tosa.concat %arg0, %arg1 axis(0) : (tensor<1x2xf32>, tensor<2x2xf32>) -> tensor<3x2xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<1x2xf32>, tensor<2x2xf32>) -> tensor<?x?xf32>
return
}
@@ -675,8 +675,8 @@ func.func @test_concat(%arg0 : tensor<1x2xf32>, %arg1 : tensor<2x2xf32>) -> () {
// CHECK-LABEL: @test_concat_dynamic
func.func @test_concat_dynamic(%arg0 : tensor<1x2xf32>, %arg1 : tensor<2x?xf32>) -> () {
- // CHECK: tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<1x2xf32>, tensor<2x?xf32>) -> tensor<3x2xf32>
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<1x2xf32>, tensor<2x?xf32>) -> tensor<?x?xf32>
+ // CHECK: tosa.concat %arg0, %arg1 axis(0) : (tensor<1x2xf32>, tensor<2x?xf32>) -> tensor<3x2xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<1x2xf32>, tensor<2x?xf32>) -> tensor<?x?xf32>
return
}
@@ -685,8 +685,8 @@ func.func @test_concat_dynamic(%arg0 : tensor<1x2xf32>, %arg1 : tensor<2x?xf32>)
// CHECK-LABEL: @test_concat_dynamic_axis
func.func @test_concat_dynamic_axis(%arg0 : tensor<?x2xf32>, %arg1 : tensor<2x2xf32>) -> () {
- // CHECK: tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<?x2xf32>, tensor<2x2xf32>) -> tensor<?x2xf32>
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<?x2xf32>, tensor<2x2xf32>) -> tensor<?x?xf32>
+ // CHECK: tosa.concat %arg0, %arg1 axis(0) : (tensor<?x2xf32>, tensor<2x2xf32>) -> tensor<?x2xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<?x2xf32>, tensor<2x2xf32>) -> tensor<?x?xf32>
return
}
@@ -695,8 +695,8 @@ func.func @test_concat_dynamic_axis(%arg0 : tensor<?x2xf32>, %arg1 : tensor<2x2x
// CHECK-LABEL: @test_concat_axis_1
func.func @test_concat_axis_1(%arg0 : tensor<2x1xf32>, %arg1 : tensor<2x2xf32>) -> () {
- // CHECK: tosa.concat %arg0, %arg1 {axis = 1 : i32} : (tensor<2x1xf32>, tensor<2x2xf32>) -> tensor<2x3xf32>
- %0 = tosa.concat %arg0, %arg1 {axis = 1 : i32} : (tensor<2x1xf32>, tensor<2x2xf32>) -> tensor<?x?xf32>
+ // CHECK: tosa.concat %arg0, %arg1 axis(1) : (tensor<2x1xf32>, tensor<2x2xf32>) -> tensor<2x3xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(1) : (tensor<2x1xf32>, tensor<2x2xf32>) -> tensor<?x?xf32>
return
}
@@ -706,8 +706,8 @@ func.func @test_concat_axis_1(%arg0 : tensor<2x1xf32>, %arg1 : tensor<2x2xf32>)
// CHECK-LABEL:@test_padding_dynamic_input
func.func @test_padding_dynamic_input(%arg0 : tensor<1x?xf32>) -> () {
- %0 = tosa.const_shape { values = dense<[1, 2, 3, 4]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %1 = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %0 = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %1 = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: tosa.pad %arg0, %0, %1 : (tensor<1x?xf32>, !tosa.shape<4>, tensor<1xf32>) -> tensor<4x?xf32>
%2 = tosa.pad %arg0, %0, %1 : (tensor<1x?xf32>, !tosa.shape<4>, tensor<1xf32>) -> tensor<?x?xf32>
return
@@ -717,8 +717,8 @@ func.func @test_padding_dynamic_input(%arg0 : tensor<1x?xf32>) -> () {
// CHECK-LABEL: @test_padding_simple
func.func @test_padding_simple(%arg0 : tensor<1x2xf32>) -> () {
- %0 = tosa.const_shape { values = dense<[1, 2, 3, 4]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %1 = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %0 = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %1 = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: tosa.pad %arg0, %0, %1 : (tensor<1x2xf32>, !tosa.shape<4>, tensor<1xf32>) -> tensor<4x9xf32>
%2 = tosa.pad %arg0, %0, %1 : (tensor<1x2xf32>, !tosa.shape<4>, tensor<1xf32>) -> tensor<?x?xf32>
return
@@ -728,11 +728,11 @@ func.func @test_padding_simple(%arg0 : tensor<1x2xf32>) -> () {
// CHECK-LABEL: @test_slice
func.func @test_slice(%arg0 : tensor<?xi32>) -> () {
- // CHECK-DAG: %[[SIZE:.+]] = tosa.const_shape {values = dense<2> : tensor<1xindex>} : () -> !tosa.shape<1>
- // CHECK-DAG: %[[START:.+]] = tosa.const_shape {values = dense<1> : tensor<1xindex>} : () -> !tosa.shape<1>
+ // CHECK-DAG: %[[SIZE:.+]] = tosa.const_shape values(dense<2> : tensor<1xindex>) : () -> !tosa.shape<1>
+ // CHECK-DAG: %[[START:.+]] = tosa.const_shape values(dense<1> : tensor<1xindex>) : () -> !tosa.shape<1>
// CHECK: %[[SLICE:.+]] = tosa.slice %arg0, %[[START]], %[[SIZE]] : (tensor<?xi32>, !tosa.shape<1>, !tosa.shape<1>) -> tensor<2xi32>
- %0 = tosa.const_shape {values = dense<1> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1 = tosa.const_shape {values = dense<2> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %0 = tosa.const_shape values(dense<1> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1 = tosa.const_shape values(dense<2> : tensor<1xindex>) : () -> !tosa.shape<1>
%2= tosa.slice %arg0, %0, %1 : (tensor<?xi32>, !tosa.shape<1>, !tosa.shape<1>) -> tensor<?xi32>
return
}
@@ -741,16 +741,16 @@ func.func @test_slice(%arg0 : tensor<?xi32>) -> () {
// CHECK-LABEL: @test_slice_size_minus_one
func.func @test_slice_size_minus_one(%arg0 : tensor<?x8x8x8xi32>) -> () {
- // CHECK-DAG: %[[SIZE:.+]] = tosa.const_shape {values = dense<-1> : tensor<4xindex>} : () -> !tosa.shape<4>
- // CHECK-DAG: %[[START:.+]] = tosa.const_shape {values = dense<[0, 1, -1, 8]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ // CHECK-DAG: %[[SIZE:.+]] = tosa.const_shape values(dense<-1> : tensor<4xindex>) : () -> !tosa.shape<4>
+ // CHECK-DAG: %[[START:.+]] = tosa.const_shape values(dense<[0, 1, -1, 8]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK: %[[VAL:.+]] = tosa.slice %arg0, %[[START]], %[[SIZE]] : (tensor<?x8x8x8xi32>, !tosa.shape<4>, !tosa.shape<4>) -> tensor<?x7x?x?xi32>
// this checks following
// dim 0: size=-1, input dim=? => inferred output dim is ?
// dim 1: size=-1 => inferred output dim is input_dim - start
// dim 2: size=-1, start=-1 => inferred output dim is ?
// dim 3: size=-1, start=8 => inferred output dim is ? because start is out of bound
- %start = tosa.const_shape {values = dense<[0, 1, -1, 8]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %size = tosa.const_shape {values = dense<[-1, -1, -1, -1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %start = tosa.const_shape values(dense<[0, 1, -1, 8]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %size = tosa.const_shape values(dense<[-1, -1, -1, -1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%2= tosa.slice %arg0, %start, %size : (tensor<?x8x8x8xi32>, !tosa.shape<4>, !tosa.shape<4>) -> tensor<?x?x?x?xi32>
return
}
@@ -759,11 +759,11 @@ func.func @test_slice_size_minus_one(%arg0 : tensor<?x8x8x8xi32>) -> () {
// CHECK-LABEL: @test_slice_dynamic
func.func @test_slice_dynamic(%arg0 : tensor<10x?x2xf32>) -> () {
- // CHECK-DAG: %[[SIZE:.+]] = tosa.const_shape {values = dense<[7, -1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
- // CHECK-DAG: %[[START:.+]] = tosa.const_shape {values = dense<[1, 0, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ // CHECK-DAG: %[[SIZE:.+]] = tosa.const_shape values(dense<[7, -1, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ // CHECK-DAG: %[[START:.+]] = tosa.const_shape values(dense<[1, 0, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
// CHECK: %[[SLICE:.+]] = tosa.slice %arg0, %[[START]], %[[SIZE]] : (tensor<10x?x2xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<7x?x1xf32>
- %0 = tosa.const_shape {values = dense<[1, 0, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %1 = tosa.const_shape {values = dense<[7, -1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %0 = tosa.const_shape values(dense<[1, 0, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %1 = tosa.const_shape values(dense<[7, -1, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
%2= tosa.slice %arg0, %0, %1 : (tensor<10x?x2xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<?x?x?xf32>
return
}
@@ -772,9 +772,9 @@ func.func @test_slice_dynamic(%arg0 : tensor<10x?x2xf32>) -> () {
// CHECK-LABEL: @test_tile
func.func @test_tile(%arg0 : tensor<2x3x?xi32>) -> () {
- // CHECK: %[[CST:.*]] = tosa.const_shape {values = dense<[2, 1, 5]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ // CHECK: %[[CST:.*]] = tosa.const_shape values(dense<[2, 1, 5]> : tensor<3xindex>) : () -> !tosa.shape<3>
// CHECK: tosa.tile %arg0, %[[CST]] : (tensor<2x3x?xi32>, !tosa.shape<3>) -> tensor<4x3x?xi32>
- %cst = tosa.const_shape {values = dense<[2, 1, 5]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %cst = tosa.const_shape values(dense<[2, 1, 5]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = tosa.tile %arg0, %cst : (tensor<2x3x?xi32>, !tosa.shape<3>) -> tensor<?x?x?xi32>
return
}
@@ -783,9 +783,9 @@ func.func @test_tile(%arg0 : tensor<2x3x?xi32>) -> () {
// CHECK-LABEL: @test_tile_unknown_multiples
func.func @test_tile_unknown_multiples(%arg0 : tensor<2x3x?xi32>) -> () {
- // CHECK: %[[CST:.*]] = tosa.const_shape {values = dense<[2, -1, 5]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ // CHECK: %[[CST:.*]] = tosa.const_shape values(dense<[2, -1, 5]> : tensor<3xindex>) : () -> !tosa.shape<3>
// CHECK: tosa.tile %arg0, %[[CST]] : (tensor<2x3x?xi32>, !tosa.shape<3>) -> tensor<4x?x?xi32>
- %cst = tosa.const_shape {values = dense<[2, -1, 5]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %cst = tosa.const_shape values(dense<[2, -1, 5]> : tensor<3xindex>) : () -> !tosa.shape<3>
%0 = tosa.tile %arg0, %cst : (tensor<2x3x?xi32>, !tosa.shape<3>) -> tensor<?x?x?xi32>
return
}
@@ -794,8 +794,8 @@ func.func @test_tile_unknown_multiples(%arg0 : tensor<2x3x?xi32>) -> () {
// CHECK-LABEL: @test_transpose_static
func.func @test_transpose_static(%arg0 : tensor<3x4x5xi32>) -> () {
- // CHECK: tosa.transpose %arg0 {perms = array<i32: 2, 1, 0>} : (tensor<3x4x5xi32>) -> tensor<5x4x3xi32>
- %1 = tosa.transpose %arg0 { perms = array<i32: 2, 1, 0> }: (tensor<3x4x5xi32>) -> tensor<?x?x?xi32>
+ // CHECK: tosa.transpose %arg0 perms([2, 1, 0]) : (tensor<3x4x5xi32>) -> tensor<5x4x3xi32>
+ %1 = tosa.transpose %arg0 perms([2, 1, 0]) : (tensor<3x4x5xi32>) -> tensor<?x?x?xi32>
return
}
@@ -888,9 +888,9 @@ func.func @row_gather_unranked(%arg0 : tensor<*xi32>, %arg1 : tensor<*xi32>) {
// CHECK-LABEL: @row_gather_block_scaled_static
func.func @row_gather_block_scaled_static(%arg0 : tensor<3x4x5xi32>, %arg1 : tensor<3x6xi32>) {
- %row_count = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
- // CHECK: tosa.row_gather_block_scaled %arg0, %arg1, %[[ROW_COUNT:.+]] {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<3x4x5xi32>, tensor<3x6xi32>, tensor<1xi32>) -> tensor<3x12x5xi32>
- %0 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<3x4x5xi32>, tensor<3x6xi32>, tensor<1xi32>) -> (tensor<?x?x?xi32>)
+ %row_count = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
+ // CHECK: tosa.row_gather_block_scaled %arg0, %arg1, %[[ROW_COUNT:.+]] block_size<BLOCK_SIZE_1> : (tensor<3x4x5xi32>, tensor<3x6xi32>, tensor<1xi32>) -> tensor<3x12x5xi32>
+ %0 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count block_size<BLOCK_SIZE_1> : (tensor<3x4x5xi32>, tensor<3x6xi32>, tensor<1xi32>) -> (tensor<?x?x?xi32>)
return
}
@@ -898,9 +898,9 @@ func.func @row_gather_block_scaled_static(%arg0 : tensor<3x4x5xi32>, %arg1 : ten
// CHECK-LABEL: @row_gather_block_scaled_mxfp_static
func.func @row_gather_block_scaled_mxfp_static(%arg0 : tensor<3x4x32xf4E2M1FN>, %arg1 : tensor<3x4x1xf8E8M0FNU>, %arg2 : tensor<3x6xi32>) {
- %row_count = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
- // CHECK: tosa.row_gather_block_scaled %arg0, %arg1, %arg2, %[[ROW_COUNT:.+]] {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<3x4x32xf4E2M1FN>, tensor<3x4x1xf8E8M0FNU>, tensor<3x6xi32>, tensor<1xi32>) -> (tensor<3x12x32xf4E2M1FN>, tensor<3x12x1xf8E8M0FNU>)
- %0:2 = tosa.row_gather_block_scaled %arg0, %arg1, %arg2, %row_count {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<3x4x32xf4E2M1FN>, tensor<3x4x1xf8E8M0FNU>, tensor<3x6xi32>, tensor<1xi32>) -> (tensor<?x?x?xf4E2M1FN>, tensor<?x?x?xf8E8M0FNU>)
+ %row_count = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
+ // CHECK: tosa.row_gather_block_scaled %arg0, %arg1, %arg2, %[[ROW_COUNT:.+]] block_size<BLOCK_SIZE_32> : (tensor<3x4x32xf4E2M1FN>, tensor<3x4x1xf8E8M0FNU>, tensor<3x6xi32>, tensor<1xi32>) -> (tensor<3x12x32xf4E2M1FN>, tensor<3x12x1xf8E8M0FNU>)
+ %0:2 = tosa.row_gather_block_scaled %arg0, %arg1, %arg2, %row_count block_size<BLOCK_SIZE_32> : (tensor<3x4x32xf4E2M1FN>, tensor<3x4x1xf8E8M0FNU>, tensor<3x6xi32>, tensor<1xi32>) -> (tensor<?x?x?xf4E2M1FN>, tensor<?x?x?xf8E8M0FNU>)
return
}
@@ -957,10 +957,10 @@ func.func @test_pool_static(%arg0: tensor<3x5x6x7xf32>) {
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: -> tensor<3x2x4x7xf32>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x5x6x7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([4, 3]) stride([1, 1]) pad([0, 0, 0, 0]) acc_type(f32) : (tensor<3x5x6x7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
// CHECK: -> tensor<3x2x4x7xf32>
- %1 = tosa.max_pool2d %arg0 {kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
+ %1 = tosa.max_pool2d %arg0 kernel([4, 3]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -969,7 +969,7 @@ func.func @test_pool_static(%arg0: tensor<3x5x6x7xf32>) {
// CHECK-LABEL: @conv2d_static
func.func @conv2d_static(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x6x4x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -978,7 +978,7 @@ func.func @conv2d_static(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf
// CHECK-LABEL: @conv2d_dynamic_input
func.func @conv2d_dynamic_input(%input: tensor<?x?x?x?xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<?x?x?x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -990,10 +990,10 @@ func.func @test_pool_dynamic_input(%arg0: tensor<?x?x?x?xf32>) {
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: -> tensor<?x?x?x?xf32>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([4, 3]) stride([1, 1]) pad([0, 0, 0, 0]) acc_type(f32) : (tensor<?x?x?x?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
// CHECK: -> tensor<?x?x?x?xf32>
- %1 = tosa.max_pool2d %arg0 {kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+ %1 = tosa.max_pool2d %arg0 kernel([4, 3]) stride([1, 1]) pad([0, 0, 0, 0]) : (tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -1005,10 +1005,10 @@ func.func @test_pool_padded(%arg0: tensor<3x5x6x7xf32>) {
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: -> tensor<3x7x5x7xf32>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 4, 3>, pad = array<i64: 3, 2, 1, 0>, stride = array<i64: 1, 1>} : (tensor<3x5x6x7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([4, 3]) stride([1, 1]) pad([3, 2, 1, 0]) acc_type(f32) : (tensor<3x5x6x7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
// CHECK: -> tensor<3x7x5x7xf32>
- %1 = tosa.max_pool2d %arg0 {kernel = array<i64: 4, 3>, pad = array<i64: 3, 2, 1, 0>, stride = array<i64: 1, 1>} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
+ %1 = tosa.max_pool2d %arg0 kernel([4, 3]) stride([1, 1]) pad([3, 2, 1, 0]) : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -1017,7 +1017,7 @@ func.func @test_pool_padded(%arg0: tensor<3x5x6x7xf32>) {
// CHECK-LABEL: @conv2d_dynamic_weight
func.func @conv2d_dynamic_weight(%input: tensor<2x8x9x3xf32>, %weights: tensor<?x?x?x?xf32>, %bias: tensor<5xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x?x?x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -1026,7 +1026,7 @@ func.func @conv2d_dynamic_weight(%input: tensor<2x8x9x3xf32>, %weights: tensor<?
// CHECK-LABEL: @conv2d_dynamic_bias
func.func @conv2d_dynamic_bias(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<?xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x6x4x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -1038,10 +1038,10 @@ func.func @test_pool_stride(%arg0: tensor<3x14x12x7xf32>) {
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: -> tensor<3x6x4x7xf32>
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<3x14x12x7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([4, 3]) stride([2, 3]) pad([0, 0, 0, 0]) acc_type(f32) : (tensor<3x14x12x7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
// CHECK: -> tensor<3x6x4x7xf32>
- %1 = tosa.max_pool2d %arg0 {kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<3x14x12x7xf32>) -> tensor<?x?x?x?xf32>
+ %1 = tosa.max_pool2d %arg0 kernel([4, 3]) stride([2, 3]) pad([0, 0, 0, 0]) : (tensor<3x14x12x7xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -1051,12 +1051,12 @@ func.func @test_pool_stride(%arg0: tensor<3x14x12x7xf32>) {
func.func @test_avg_pool2d_adaptive_static(%arg0: tensor<3x5x6x7xf32>) {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape { values = dense<[4, 3]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %stride = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %pad = tosa.const_shape { values = dense<[0, 0, 0, 0]> : tensor<4xindex> } : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[4, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK: -> tensor<3x2x4x7xf32>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = f32} : (tensor<3x5x6x7xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(f32) : (tensor<3x5x6x7xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<?x?x?x?xf32>
return
}
@@ -1066,12 +1066,12 @@ func.func @test_avg_pool2d_adaptive_static(%arg0: tensor<3x5x6x7xf32>) {
func.func @test_avg_pool2d_adaptive_dynamic_input(%arg0: tensor<?x?x?x?xf32>) {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape { values = dense<[4, 3]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %stride = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %pad = tosa.const_shape { values = dense<[0, 0, 0, 0]> : tensor<4xindex> } : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[4, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK: -> tensor<?x?x?x?xf32>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = f32} : (tensor<?x?x?x?xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(f32) : (tensor<?x?x?x?xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<?x?x?x?xf32>
return
}
@@ -1081,12 +1081,12 @@ func.func @test_avg_pool2d_adaptive_dynamic_input(%arg0: tensor<?x?x?x?xf32>) {
func.func @test_avg_pool2d_adaptive_padded(%arg0: tensor<3x5x6x7xf32>) {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape { values = dense<[4, 3]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %stride = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %pad = tosa.const_shape { values = dense<[3, 2, 1, 0]> : tensor<4xindex> } : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[4, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[3, 2, 1, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK: -> tensor<3x7x5x7xf32>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = f32} : (tensor<3x5x6x7xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(f32) : (tensor<3x5x6x7xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<?x?x?x?xf32>
return
}
@@ -1096,12 +1096,12 @@ func.func @test_avg_pool2d_adaptive_padded(%arg0: tensor<3x5x6x7xf32>) {
func.func @test_avg_pool2d_adaptive_stride(%arg0: tensor<3x14x12x7xf32>) {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %kernel = tosa.const_shape { values = dense<[4, 3]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %stride = tosa.const_shape { values = dense<[2, 3]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %pad = tosa.const_shape { values = dense<[0, 0, 0, 0]> : tensor<4xindex> } : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[4, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[2, 3]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK: -> tensor<3x6x4x7xf32>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = f32} : (tensor<3x14x12x7xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(f32) : (tensor<3x14x12x7xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<?x?x?x?xf32>
return
}
@@ -1111,20 +1111,20 @@ func.func @test_avg_pool2d_adaptive_stride(%arg0: tensor<3x14x12x7xf32>) {
func.func @test_avg_pool2d_adaptive_non_constshape_operands(%arg0: tensor<3x5x6x7xf32>) {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %k0 = tosa.const_shape { values = dense<[4]> : tensor<1xindex> } : () -> !tosa.shape<1>
- %k1 = tosa.const_shape { values = dense<[3]> : tensor<1xindex> } : () -> !tosa.shape<1>
+ %k0 = tosa.const_shape values(dense<[4]> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %k1 = tosa.const_shape values(dense<[3]> : tensor<1xindex>) : () -> !tosa.shape<1>
%kernel = tosa.concat_shape %k0, %k1 : (!tosa.shape<1>, !tosa.shape<1>) -> !tosa.shape<2>
- %s0 = tosa.const_shape { values = dense<[2]> : tensor<1xindex> } : () -> !tosa.shape<1>
- %s1 = tosa.const_shape { values = dense<[3]> : tensor<1xindex> } : () -> !tosa.shape<1>
+ %s0 = tosa.const_shape values(dense<[2]> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %s1 = tosa.const_shape values(dense<[3]> : tensor<1xindex>) : () -> !tosa.shape<1>
%stride = tosa.concat_shape %s0, %s1 : (!tosa.shape<1>, !tosa.shape<1>) -> !tosa.shape<2>
- %p0 = tosa.const_shape { values = dense<[0]> : tensor<1xindex> } : () -> !tosa.shape<1>
+ %p0 = tosa.const_shape values(dense<[0]> : tensor<1xindex>) : () -> !tosa.shape<1>
%pad = tosa.concat_shape %p0, %p0, %p0, %p0 : (!tosa.shape<1>, !tosa.shape<1>, !tosa.shape<1>, !tosa.shape<1>) -> !tosa.shape<4>
// Use concat_shape to build resolvable shape operands that are not direct
// const_shape producers. This exercises the adaptive pooling fallback path
// where only N and C are inferred, while H and W remain dynamic.
// CHECK: -> tensor<3x?x?x7xf32>
- %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad {acc_type = f32} : (tensor<3x5x6x7xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.avg_pool2d_adaptive %arg0, %input_zp, %output_zp, %kernel, %stride, %pad acc_type(f32) : (tensor<3x5x6x7xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<?x?x?x?xf32>
return
}
@@ -1133,7 +1133,7 @@ func.func @test_avg_pool2d_adaptive_non_constshape_operands(%arg0: tensor<3x5x6x
// CHECK-LABEL: @conv2d_padded
func.func @conv2d_padded(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x9x11x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([1, 2, 3, 4]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -1142,7 +1142,7 @@ func.func @conv2d_padded(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf
// CHECK-LABEL: @conv2d_dilated
func.func @conv2d_dilated(%input: tensor<2x12x14x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x6x4x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 3, 2>} : (tensor<2x12x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([3, 2]) acc_type(f32) : (tensor<2x12x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -1151,7 +1151,7 @@ func.func @conv2d_dilated(%input: tensor<2x12x14x3xf32>, %weights: tensor<5x3x6x
// CHECK-LABEL: @conv2d_strided
func.func @conv2d_strided(%input: tensor<1x13x15x1xf32>, %weights: tensor<1x1x1x1xf32>, %bias: tensor<1xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<1x5x8x1xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 3, 2>, dilation = array<i64: 1, 1>} : (tensor<1x13x15x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([3, 2]) dilation([1, 1]) acc_type(f32) : (tensor<1x13x15x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -1160,7 +1160,7 @@ func.func @conv2d_strided(%input: tensor<1x13x15x1xf32>, %weights: tensor<1x1x1x
// CHECK-LABEL: @conv3d_static
func.func @conv3d_static(%input: tensor<2x8x9x10x3xf32>, %weights: tensor<5x3x6x4x3xf32>, %bias: tensor<5xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x6x4x7x5xf32>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -1169,7 +1169,7 @@ func.func @conv3d_static(%input: tensor<2x8x9x10x3xf32>, %weights: tensor<5x3x6x
// CHECK-LABEL: @conv3d_dynamic_input
func.func @conv3d_dynamic_input(%arg0: tensor<?x?x?x?x?xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<?x?x?x?x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<?x?x?x?x?xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<?x?x?x?x?xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -1178,7 +1178,7 @@ func.func @conv3d_dynamic_input(%arg0: tensor<?x?x?x?x?xf32>, %arg1: tensor<5x3x
// CHECK-LABEL: @conv3d_dynamic_weight
func.func @conv3d_dynamic_weight(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<?x?x?x?x?xf32>, %arg2: tensor<7xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x?x?x?x7xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<?x?x?x?x?xf32>, tensor<7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<2x8x9x10x3xf32>, tensor<?x?x?x?x?xf32>, tensor<7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -1187,7 +1187,7 @@ func.func @conv3d_dynamic_weight(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<?x
// CHECK-LABEL: @conv3d_dynamic_bias
func.func @conv3d_dynamic_bias(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<?xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x6x4x7x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -1196,7 +1196,7 @@ func.func @conv3d_dynamic_bias(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x
// CHECK-LABEL: @conv3d_padded
func.func @conv3d_padded(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x9x11x18x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 1, 2, 3, 4, 5, 6>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([1, 2, 3, 4, 5, 6]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -1205,7 +1205,7 @@ func.func @conv3d_padded(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x6x4x3x
// CHECK-LABEL: @conv3d_dilated
func.func @conv3d_dilated(%arg0: tensor<2x12x14x16x3xf32>, %arg1: tensor<5x3x6x2x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x6x4x12x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 3, 2, 4>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x12x14x16x3xf32>, tensor<5x3x6x2x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([3, 2, 4]) acc_type(f32) : (tensor<2x12x14x16x3xf32>, tensor<5x3x6x2x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -1214,7 +1214,7 @@ func.func @conv3d_dilated(%arg0: tensor<2x12x14x16x3xf32>, %arg1: tensor<5x3x6x2
// CHECK-LABEL: @conv3d_strided
func.func @conv3d_strided(%arg0: tensor<1x13x17x17x1xf32>, %arg1: tensor<1x1x1x1x1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<1x5x9x5x1xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 3, 2, 4>} : (tensor<1x13x17x17x1xf32>, tensor<1x1x1x1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([3, 2, 4]) dilation([1, 1, 1]) acc_type(f32) : (tensor<1x13x17x17x1xf32>, tensor<1x1x1x1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -1223,7 +1223,7 @@ func.func @conv3d_strided(%arg0: tensor<1x13x17x17x1xf32>, %arg1: tensor<1x1x1x1
// CHECK-LABEL: @depthwise_conv2d_static
func.func @depthwise_conv2d_static(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x6x4x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x6x4x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x6x4x15xf32>
return
}
@@ -1232,7 +1232,7 @@ func.func @depthwise_conv2d_static(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6
// CHECK-LABEL: @depthwise_conv2d_dynamic_input
func.func @depthwise_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<?x?x?x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<?x?x?x?xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x15xf32>
return
}
@@ -1241,7 +1241,7 @@ func.func @depthwise_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: ten
// CHECK-LABEL: @depthwise_conv2d_dynamic_weight
func.func @depthwise_conv2d_dynamic_weight(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<15xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x?x?x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x15xf32>
return
}
@@ -1250,7 +1250,7 @@ func.func @depthwise_conv2d_dynamic_weight(%arg0: tensor<2x8x9x3xf32>, %arg1: te
// CHECK-LABEL: @depthwise_conv2d_dynamic_bias
func.func @depthwise_conv2d_dynamic_bias(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<?xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x6x4x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x6x4x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x6x4x15xf32>
return
}
@@ -1259,7 +1259,7 @@ func.func @depthwise_conv2d_dynamic_bias(%arg0: tensor<2x8x9x3xf32>, %arg1: tens
// CHECK-LABEL: @depthwise_conv2d_padded
func.func @depthwise_conv2d_padded(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x9x11x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x9x11x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([1, 2, 3, 4]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x9x11x15xf32>
return
}
@@ -1268,7 +1268,7 @@ func.func @depthwise_conv2d_padded(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6
// CHECK-LABEL: @depthwise_conv2d_dilated
func.func @depthwise_conv2d_dilated(%arg0: tensor<2x12x14x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x6x4x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 3, 2>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x12x14x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x6x4x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([3, 2]) acc_type(f32) : (tensor<2x12x14x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x6x4x15xf32>
return
}
@@ -1277,7 +1277,7 @@ func.func @depthwise_conv2d_dilated(%arg0: tensor<2x12x14x3xf32>, %arg1: tensor<
// CHECK-LABEL: @depthwise_conv2d_strided
func.func @depthwise_conv2d_strided(%arg0: tensor<1x13x15x1xf32>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<1x5x8x1xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 3, 2>} : (tensor<1x13x15x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x8x1xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([3, 2]) dilation([1, 1]) acc_type(f32) : (tensor<1x13x15x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x8x1xf32>
return
}
@@ -1286,7 +1286,7 @@ func.func @depthwise_conv2d_strided(%arg0: tensor<1x13x15x1xf32>, %arg1: tensor<
// CHECK-LABEL: @transpose_conv2d_out_shape
func.func @transpose_conv2d_out_shape(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x8x9x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x?x?x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x8x9x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) : (tensor<2x?x?x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x8x9x5xf32>
return
}
@@ -1295,7 +1295,7 @@ func.func @transpose_conv2d_out_shape(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<
// CHECK-LABEL: @transpose_conv2d_static
func.func @transpose_conv2d_static(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x18x19x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x5xf32>
return
}
@@ -1304,7 +1304,7 @@ func.func @transpose_conv2d_static(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5
// CHECK-LABEL: @transpose_conv2d_static_strided
func.func @transpose_conv2d_static_strided(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x33x45x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([2, 3]) acc_type(f32) : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x5xf32>
return
}
@@ -1313,7 +1313,7 @@ func.func @transpose_conv2d_static_strided(%arg0: tensor<2x16x14x3xf32>, %arg1:
// CHECK-LABEL: @transpose_conv2d_dynamic_input
func.func @transpose_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<?x?x?x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x5xf32>
return
}
@@ -1322,7 +1322,7 @@ func.func @transpose_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: ten
// CHECK-LABEL: @transpose_conv2d_dynamic_weights
func.func @transpose_conv2d_dynamic_weights(%arg0: tensor<2x6x4x3xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x?x?x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x6x4x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) : (tensor<2x6x4x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x5xf32>
return
}
@@ -1331,7 +1331,7 @@ func.func @transpose_conv2d_dynamic_weights(%arg0: tensor<2x6x4x3xf32>, %arg1: t
// CHECK-LABEL: @transpose_conv2d_dynamic_bias
func.func @transpose_conv2d_dynamic_bias(%arg0: tensor<2x6x4x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<?xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x8x9x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x6x4x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x8x9x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) : (tensor<2x6x4x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x8x9x5xf32>
return
}
@@ -1340,7 +1340,7 @@ func.func @transpose_conv2d_dynamic_bias(%arg0: tensor<2x6x4x3xf32>, %arg1: tens
// CHECK-LABEL: @transpose_conv2d_padded
func.func @transpose_conv2d_padded(%arg0: tensor<2x9x11x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x12x19x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 1, 0, 3, 0>, stride = array<i64: 1, 1>} : (tensor<2x9x11x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x12x19x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([1, 0, 3, 0]) stride([1, 1]) acc_type(f32) : (tensor<2x9x11x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x12x19x5xf32>
return
}
@@ -1349,7 +1349,7 @@ func.func @transpose_conv2d_padded(%arg0: tensor<2x9x11x3xf32>, %arg1: tensor<5x
// CHECK-LABEL: @transpose_conv2d_strided
func.func @transpose_conv2d_strided(%arg0: tensor<1x5x7x1xf32>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<1x13x13x1xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 3, 2>} : (tensor<1x5x7x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x13x13x1xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([3, 2]) acc_type(f32) : (tensor<1x5x7x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x13x13x1xf32>
return
}
@@ -1358,7 +1358,7 @@ func.func @transpose_conv2d_strided(%arg0: tensor<1x5x7x1xf32>, %arg1: tensor<1x
// CHECK-LABEL: @transpose_conv2d_dynamic_out_channels
func.func @transpose_conv2d_dynamic_out_channels(%arg0: tensor<2x1x1x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x3x6x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x1x1x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x3x6x?xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) : (tensor<2x1x1x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x3x6x?xf32>
return
}
@@ -1366,11 +1366,11 @@ func.func @transpose_conv2d_dynamic_out_channels(%arg0: tensor<2x1x1x3xf32>, %ar
// CHECK-LABEL: @resize_int_horizontal
func.func @resize_int_horizontal(%arg0: tensor<1x15x13x1xi8>) {
- %scale = tosa.const_shape { values = dense<[11, 7, 89, 6]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[11, 7, 89, 6]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<1x23x179x1xi8>
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x15x13x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xi8>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x15x13x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xi8>
return
}
@@ -1378,11 +1378,11 @@ func.func @resize_int_horizontal(%arg0: tensor<1x15x13x1xi8>) {
// CHECK-LABEL: @resize_int_vertical
func.func @resize_int_vertical(%arg0: tensor<1x49x42x1xi16>) {
- %scale = tosa.const_shape { values = dense<[37, 16, 219, 41]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[37, 16, 219, 41]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<1x112x220x1xi16>
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x49x42x1xi16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xi16>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x49x42x1xi16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xi16>
return
}
@@ -1390,11 +1390,11 @@ func.func @resize_int_vertical(%arg0: tensor<1x49x42x1xi16>) {
// CHECK-LABEL: @resize_int_power_of_two_upscale
func.func @resize_int_power_of_two_upscale(%arg0: tensor<1x23x19x1xi8>) {
- %scale = tosa.const_shape { values = dense<[16, 1, 16, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[16, 1, 16, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<1x353x289x1xi32>
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<1x23x19x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xi32>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x23x19x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xi32>
return
}
@@ -1402,33 +1402,33 @@ func.func @resize_int_power_of_two_upscale(%arg0: tensor<1x23x19x1xi8>) {
// CHECK-LABEL: @resize_int_power_of_two_upscale_offsetted
func.func @resize_int_power_of_two_upscale_offsetted(%arg0: tensor<1x41x26x1xi16>) {
- %scale = tosa.const_shape { values = dense<[16, 2, 16, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-7, -7]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[7, 7]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[16, 2, 16, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-7, -7]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[7, 7]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<1x328x208x1xi48>
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<1x41x26x1xi16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xi48>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x41x26x1xi16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xi48>
return
}
// -----
// CHECK-LABEL: @resize_fp_horizontal
func.func @resize_fp_horizontal(%arg0: tensor<1x50x48x1xf32>) {
- %scale = tosa.const_shape { values = dense<[15, 7, 84, 47]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[15, 7, 84, 47]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<1x106x85x1xf32>
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<1x50x48x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x50x48x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @resize_fp_vertical
func.func @resize_fp_vertical(%arg0: tensor<1x50x48x1xf32>) {
- %scale = tosa.const_shape { values = dense<[127, 49, 12, 47]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[127, 49, 12, 47]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<1x128x13x1xf32>
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x50x48x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x50x48x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
return
}
@@ -1436,11 +1436,11 @@ func.func @resize_fp_vertical(%arg0: tensor<1x50x48x1xf32>) {
// CHECK-LABEL: @resize_fp_power_of_two_upscale
func.func @resize_fp_power_of_two_upscale(%arg0: tensor<1x23x23x1xf32>) {
- %scale = tosa.const_shape { values = dense<[4, 1, 4, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<0> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[4, 1, 4, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<0> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<1x89x89x1xf32>
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = BILINEAR} : (tensor<1x23x23x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x23x23x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
return
}
@@ -1448,11 +1448,11 @@ func.func @resize_fp_power_of_two_upscale(%arg0: tensor<1x23x23x1xf32>) {
// CHECK-LABEL: @resize_fp_power_of_two_upscale_offsetted
func.func @resize_fp_power_of_two_upscale_offsetted(%arg0: tensor<1x50x48x1xf32>) {
- %scale = tosa.const_shape { values = dense<[64, 2, 64, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-31, -31]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[31, 31]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[64, 2, 64, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-31, -31]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[31, 31]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<1x1600x1536x1xf32>
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x50x48x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x50x48x1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x?x?x?xf32>
return
}
@@ -1460,11 +1460,11 @@ func.func @resize_fp_power_of_two_upscale_offsetted(%arg0: tensor<1x50x48x1xf32>
// CHECK-LABEL: @resize_negative_output_dim
func.func @resize_negative_output_dim(%arg0: tensor<1x3x1x1xi8>) {
- %scale = tosa.const_shape { values = dense<[1, 3, 1, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[6, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[-15, 0]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[1, 3, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[6, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[-15, 0]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at below {{calculated output height and width must be non-negative, got height = -5, width = 0}}
- %0 = tosa.resize %arg0, %scale, %offset, %border {mode = NEAREST_NEIGHBOR} : (tensor<1x3x1x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xi8>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x3x1x1xi8>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xi8>
return
}
@@ -1620,7 +1620,7 @@ func.func @while_test(%arg0 : tensor<i32>, %arg1 : tensor<1xi32>) -> () {
// CHECK: tosa.concat
// CHECK-SAME: (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>
- %3 = tosa.concat %arg3, %arg3 {axis = 0 : i32} : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>
+ %3 = tosa.concat %arg3, %arg3 axis(0) : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>
// CHECK: tosa.yield
// CHECK-SAME: tensor<i32>
@@ -1757,7 +1757,7 @@ func.func @test_dynamic_width_rfft2d(%arg0 : tensor<5x2x?xf32>) -> () {
// CHECK-LABEL: @test_static_fft2d
func.func @test_static_fft2d(%arg0: tensor<1x4x8xf32>, %arg1: tensor<1x4x8xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>) {
// CHECK: -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>)
- %output_real, %output_imag = tosa.fft2d %arg0, %arg1 {inverse = false} : (tensor<1x4x8xf32>, tensor<1x4x8xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>)
+ %output_real, %output_imag = tosa.fft2d %arg0, %arg1 inverse(false) : (tensor<1x4x8xf32>, tensor<1x4x8xf32>) -> (tensor<1x4x8xf32>, tensor<1x4x8xf32>)
return %output_real, %output_imag : tensor<1x4x8xf32>, tensor<1x4x8xf32>
}
@@ -1766,7 +1766,7 @@ func.func @test_static_fft2d(%arg0: tensor<1x4x8xf32>, %arg1: tensor<1x4x8xf32>)
// CHECK-LABEL: @test_dynamic_batch_fft2d
func.func @test_dynamic_batch_fft2d(%arg0: tensor<?x4x8xf32>, %arg1: tensor<?x4x8xf32>) -> (tensor<?x4x8xf32>, tensor<?x4x8xf32>) {
// CHECK: -> (tensor<?x4x8xf32>, tensor<?x4x8xf32>)
- %output_real, %output_imag = tosa.fft2d %arg0, %arg1 {inverse = false} : (tensor<?x4x8xf32>, tensor<?x4x8xf32>) -> (tensor<?x4x8xf32>, tensor<?x4x8xf32>)
+ %output_real, %output_imag = tosa.fft2d %arg0, %arg1 inverse(false) : (tensor<?x4x8xf32>, tensor<?x4x8xf32>) -> (tensor<?x4x8xf32>, tensor<?x4x8xf32>)
return %output_real, %output_imag : tensor<?x4x8xf32>, tensor<?x4x8xf32>
}
@@ -1828,10 +1828,10 @@ func.func @test_non_tosa_consumer_still_propagates(%arg0: tensor<1x1x8xf32>, %ar
func.func @test_tosa_use_def_chain(%arg0: tensor<1x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<?x16x16x16xf32> {
// CHECK: [[CONV:%.+]] = tosa.conv2d %arg0, %arg1, %arg2
// CHECK: (tensor<1x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>} : (tensor<1x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x32x32x16xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([1, 1, 1, 1]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x32x32x16xf32>
// CHECK: tosa.max_pool2d [[CONV]]
// CHECK: (tensor<1x32x32x16xf32>) -> tensor<1x16x16x16xf32>
- %1 = tosa.max_pool2d %0 {kernel = array<i64: 2, 2>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>} : (tensor<?x32x32x16xf32>) -> tensor<?x16x16x16xf32>
+ %1 = tosa.max_pool2d %0 kernel([2, 2]) stride([2, 2]) pad([0, 0, 0, 0]) : (tensor<?x32x32x16xf32>) -> tensor<?x16x16x16xf32>
return %1 : tensor<?x16x16x16xf32>
}
@@ -1877,7 +1877,7 @@ func.func @test_tosa_use_def_chain(%arg0: tensor<1x32x32x3xf32>, %arg1: tensor<1
func.func @test_multiple_non_inferrable_consumers(%arg0: tensor<1x2x8xf32>) {
// CHECK: %[[TOSA_CAST:.*]] = tosa.cast %[[ARG]] : (tensor<1x2x8xf32>) -> tensor<1x2x8xf32>
// CHECK: %[[TENSOR_CAST:.*]] = tensor.cast %[[TOSA_CAST]] : tensor<1x2x8xf32> to tensor<?x2x8xf32>
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<1x2x8xf32>) -> tensor<?x2x8xf32>
+ %0 = tosa.cast %arg0 : (tensor<1x2x8xf32>) -> tensor<?x2x8xf32>
%c0 = arith.constant 0 : index
%dim = tensor.dim %0, %c0 : tensor<?x2x8xf32>
@@ -1904,7 +1904,7 @@ func.func @test_mul_scalar(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<*xf
// CHECK-LABEL: test_matmul_t_block_scaled_static
func.func @test_matmul_t_block_scaled_static(%arg0: tensor<4x8x32xf8E4M3FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<1x16x32xf8E4M3FN>, %arg3: tensor<1x16x1xf8E8M0FNU>) -> tensor<?x?x?xf32> {
// CHECK: -> tensor<4x8x16xf32>
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<1x16x32xf8E4M3FN>, tensor<1x16x1xf8E8M0FNU>) -> tensor<?x?x?xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<1x16x32xf8E4M3FN>, tensor<1x16x1xf8E8M0FNU>) -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
@@ -1913,7 +1913,7 @@ func.func @test_matmul_t_block_scaled_static(%arg0: tensor<4x8x32xf8E4M3FN>, %ar
// CHECK-LABEL: test_matmul_t_block_scaled_unranked_a_data
func.func @test_matmul_t_block_scaled_unranked_a_data(%arg0: tensor<*xf8E4M3FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32xf8E4M3FN>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<?x?x?xf32> {
// CHECK: -> tensor<4x8x16xf32>
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<*xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf8E4M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<?x?x?xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<*xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf8E4M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
@@ -1922,7 +1922,7 @@ func.func @test_matmul_t_block_scaled_unranked_a_data(%arg0: tensor<*xf8E4M3FN>,
// CHECK-LABEL: test_matmul_t_block_scaled_unranked_b_data_and_scale
func.func @test_matmul_t_block_scaled_unranked_b_data_and_scale(%arg0: tensor<4x8x32xf8E4M3FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<*xf8E4M3FN>, %arg3: tensor<*xf8E8M0FNU>) -> tensor<?x?x?xf32> {
// CHECK: -> tensor<4x8x?xf32>
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>) -> tensor<?x?x?xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>) -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
@@ -1931,7 +1931,7 @@ func.func @test_matmul_t_block_scaled_unranked_b_data_and_scale(%arg0: tensor<4x
// CHECK-LABEL: test_matmul_t_block_scaled_unranked_all
func.func @test_matmul_t_block_scaled_unranked_all(%arg0: tensor<*xf8E4M3FN>, %arg1: tensor<*xf8E8M0FNU>, %arg2: tensor<*xf8E4M3FN>, %arg3: tensor<*xf8E8M0FNU>) -> tensor<?x?x?xf32> {
// CHECK: -> tensor<?x?x?xf32>
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>, tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>) -> tensor<?x?x?xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>, tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>) -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
@@ -1940,7 +1940,7 @@ func.func @test_matmul_t_block_scaled_unranked_all(%arg0: tensor<*xf8E4M3FN>, %a
// CHECK-LABEL: test_matmul_t_block_scaled_broadcast_b_data
func.func @test_matmul_t_block_scaled_broadcast_b_data(%arg0: tensor<*xf8E4M3FN>, %arg1: tensor<*xf8E8M0FNU>, %arg2: tensor<1x4x32xf8E4M3FN>, %arg3: tensor<1x4x1xf8E8M0FNU>) -> tensor<?x?x?xf32> {
// CHECK: -> tensor<?x?x4xf32>
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>, tensor<1x4x32xf8E4M3FN>, tensor<1x4x1xf8E8M0FNU>) -> tensor<?x?x?xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>, tensor<1x4x32xf8E4M3FN>, tensor<1x4x1xf8E8M0FNU>) -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
@@ -1949,7 +1949,7 @@ func.func @test_matmul_t_block_scaled_broadcast_b_data(%arg0: tensor<*xf8E4M3FN>
// CHECK-LABEL: test_matmul_t_block_scaled_broadcast_b_scale
func.func @test_matmul_t_block_scaled_broadcast_b_scale(%arg0: tensor<*xf8E4M3FN>, %arg1: tensor<*xf8E8M0FNU>, %arg2: tensor<*xf8E4M3FN>, %arg3: tensor<1x4x1xf8E8M0FNU>) -> tensor<?x?x?xf32> {
// CHECK: -> tensor<?x?x4xf32>
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>, tensor<*xf8E4M3FN>, tensor<1x4x1xf8E8M0FNU>) -> tensor<?x?x?xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<*xf8E4M3FN>, tensor<*xf8E8M0FNU>, tensor<*xf8E4M3FN>, tensor<1x4x1xf8E8M0FNU>) -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
@@ -1958,7 +1958,7 @@ func.func @test_matmul_t_block_scaled_broadcast_b_scale(%arg0: tensor<*xf8E4M3FN
// CHECK-LABEL: test_cast_from_block_scaled_static
func.func @test_cast_from_block_scaled_static(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<4x1xf8E8M0FNU>) -> tensor<*xf32> {
// CHECK: -> tensor<4x32xf32>
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) -> tensor<*xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -1967,7 +1967,7 @@ func.func @test_cast_from_block_scaled_static(%arg0: tensor<4x32xf4E2M1FN>, %arg
// CHECK-LABEL: test_cast_from_block_scaled_unranked_input_scale
func.func @test_cast_from_block_scaled_unranked_input_scale(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<*xf8E8M0FNU>) -> tensor<*xf32> {
// CHECK: -> tensor<4x32xf32>
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<*xf8E8M0FNU>) -> tensor<*xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<*xf8E8M0FNU>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -1976,7 +1976,7 @@ func.func @test_cast_from_block_scaled_unranked_input_scale(%arg0: tensor<4x32xf
// CHECK-LABEL: test_cast_to_block_scaled_static
func.func @test_cast_to_block_scaled_static(%arg0: tensor<4x32xf32>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>) {
// CHECK: -> (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>)
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf32>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<4x32xf32>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
return %0#0, %0#1 : tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>
}
@@ -1985,7 +1985,7 @@ func.func @test_cast_to_block_scaled_static(%arg0: tensor<4x32xf32>) -> (tensor<
// CHECK-LABEL: test_cast_to_block_scaled_unranked
func.func @test_cast_to_block_scaled_unranked(%arg0: tensor<*xf32>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>) {
// CHECK: -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<*xf32>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<*xf32>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
return %0#0, %0#1 : tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>
}
@@ -1994,7 +1994,7 @@ func.func @test_cast_to_block_scaled_unranked(%arg0: tensor<*xf32>) -> (tensor<*
// CHECK-LABEL: test_cast_to_block_scaled_dynamic_scales
func.func @test_cast_to_block_scaled_dynamic_scales(%arg0: tensor<4x?xf32>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>) {
// CHECK: -> (tensor<4x?xf4E2M1FN>, tensor<4x?xf8E8M0FNU>)
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x?xf32>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<4x?xf32>) -> (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>)
return %0#0, %0#1 : tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>
}
@@ -2002,11 +2002,11 @@ func.func @test_cast_to_block_scaled_dynamic_scales(%arg0: tensor<4x?xf32>) -> (
// CHECK-LABEL: test_conv2d_block_scaled_static
func.func @test_conv2d_block_scaled_static(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<1x4x4x8xf32>
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -2014,11 +2014,11 @@ func.func @test_conv2d_block_scaled_static(%arg0: tensor<1x4x4x64xf4E2M1FN>, %ar
// CHECK-LABEL: test_conv2d_block_scaled_dynamic_scales
func.func @test_conv2d_block_scaled_dynamic_scales(%arg0: tensor<?x4x4x64xf4E2M1FN>, %arg1: tensor<*xf8E8M0FNU>, %arg2: tensor<?x1x1x64xf4E2M1FN>, %arg3: tensor<*xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<?x4x4x?xf32>
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<?x4x4x64xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<?x1x1x64xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<?x4x4x64xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<?x1x1x64xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -2026,11 +2026,11 @@ func.func @test_conv2d_block_scaled_dynamic_scales(%arg0: tensor<?x4x4x64xf4E2M1
// CHECK-LABEL: test_conv2d_block_scaled_dynamic_data
func.func @test_conv2d_block_scaled_dynamic_data(%arg0: tensor<*xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<*xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<1x4x4x8xf32>
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<*xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<*xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<*xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<*xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -2038,11 +2038,11 @@ func.func @test_conv2d_block_scaled_dynamic_data(%arg0: tensor<*xf4E2M1FN>, %arg
// CHECK-LABEL: test_conv2d_block_scaled_dynamic_unranked
func.func @test_conv2d_block_scaled_dynamic_unranked(%arg0: tensor<*xf4E2M1FN>, %arg1: tensor<*xf8E8M0FNU>, %arg2: tensor<*xf4E2M1FN>, %arg3: tensor<*xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<?x?x?x?xf32>
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -2052,7 +2052,7 @@ func.func @test_conv2d_block_scaled_dynamic_unranked(%arg0: tensor<*xf4E2M1FN>,
func.func @test_dwconv2d_bias_broadcast(%input: tensor<2x8x9x?xf32>, %weight: tensor<3x3x?x?xf32>, %bias: tensor<1xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) {
// CHECK: -> tensor<2x6x7x?xf32>
%0 = tosa.depthwise_conv2d %input, %weight, %bias, %input_zp, %weight_zp
- { acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> }
+ pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32)
: (tensor<2x8x9x?xf32>, tensor<3x3x?x?xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -2063,7 +2063,7 @@ func.func @test_dwconv2d_bias_broadcast(%input: tensor<2x8x9x?xf32>, %weight: te
func.func @test_tconv2d_bias_broadcast(%input: tensor<2x6x7x3xf32>, %weight: tensor<?x3x3x3xf32>, %bias: tensor<1xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) {
// CHECK: -> tensor<2x8x9x?xf32>
%0 = tosa.transpose_conv2d %input, %weight, %bias, %input_zp, %weight_zp
- { acc_type = f32, pad = array<i64: 0, 0, 0, 0>, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> }
+ out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) { pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, dilation = array<i64: 1, 1> }
: (tensor<2x6x7x3xf32>, tensor<?x3x3x3xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -2073,7 +2073,7 @@ func.func @test_tconv2d_bias_broadcast(%input: tensor<2x6x7x3xf32>, %weight: ten
// CHECK-LABEL: @test_conv2d_bias_broadcast
func.func @test_conv2d_bias_broadcast(%input: tensor<2x8x9x3xf32>, %weights: tensor<?x3x6x3xf32>, %bias: tensor<1xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x6x4x?xf32>
- %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<?x3x6x3xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<2x8x9x3xf32>, tensor<?x3x6x3xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -2082,7 +2082,7 @@ func.func @test_conv2d_bias_broadcast(%input: tensor<2x8x9x3xf32>, %weights: ten
// CHECK-LABEL: @test_conv3d_bias_broadcast
func.func @test_conv3d_bias_broadcast(%input: tensor<2x8x9x10x3xf32>, %weights: tensor<?x3x6x4x3xf32>, %bias: tensor<1xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x6x4x7x?xf32>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<?x3x6x4x3xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<2x8x9x10x3xf32>, tensor<?x3x6x4x3xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -2091,7 +2091,7 @@ func.func @test_conv3d_bias_broadcast(%input: tensor<2x8x9x10x3xf32>, %weights:
// CHECK-LABEL: test_avg_pool2d_unranked_input
func.func @test_avg_pool2d_unranked_input(%input: tensor<*xi32>, %zp: tensor<1xi32>) {
// CHECK: -> tensor<?x?x?x?xi32>
- %0 = tosa.avg_pool2d %input, %zp, %zp { acc_type = i32, kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1> } : (tensor<*xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<*xi32>
+ %0 = tosa.avg_pool2d %input, %zp, %zp kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) acc_type(i32) : (tensor<*xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<*xi32>
return
}
@@ -2106,11 +2106,11 @@ func.func @test_simple_shape_expression(%arg0: tensor<7x12xi32>, %arg1: tensor<8
// CHECK: %[[DIM3:.+]] = tosa.dim %[[RESHAPE]]
// CHECK: %[[TILE:.+]] = tosa.tile %[[RESHAPE]], %[[DIM3]] : (tensor<?xi32>, !tosa.shape<1>) -> tensor<?xi32>
// CHECK: return %[[TILE]] : tensor<?xi32>
- %a = tosa.dim %arg1 {axis = 0: i32} : (tensor<80xi32>) -> !tosa.shape<1>
- %b = tosa.dim %arg2 {axis = 0: i32} : (tensor<4xi32>) -> !tosa.shape<1>
+ %a = tosa.dim %arg1 axis(0) : (tensor<80xi32>) -> !tosa.shape<1>
+ %b = tosa.dim %arg2 axis(0) : (tensor<4xi32>) -> !tosa.shape<1>
%c = tosa.add_shape %a, %b : (!tosa.shape<1>, !tosa.shape<1>) -> !tosa.shape<1>
%d = tosa.reshape %arg0, %c : (tensor<7x12xi32>, !tosa.shape<1>) -> tensor<?xi32>
- %e = tosa.dim %d {axis = 0: i32} : (tensor<?xi32>) -> !tosa.shape<1>
+ %e = tosa.dim %d axis(0) : (tensor<?xi32>) -> !tosa.shape<1>
%f = tosa.tile %d, %e : (tensor<?xi32>, !tosa.shape<1>) -> tensor<?xi32>
return %f : tensor<?xi32>
}
@@ -2119,11 +2119,11 @@ func.func @test_simple_shape_expression(%arg0: tensor<7x12xi32>, %arg1: tensor<8
// CHECK-LABEL: test_conv2d_block_scaled_static
func.func @test_conv2d_block_scaled_static(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<1x4x4x8xf32>
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -2131,11 +2131,11 @@ func.func @test_conv2d_block_scaled_static(%arg0: tensor<1x4x4x64xf4E2M1FN>, %ar
// CHECK-LABEL: test_conv2d_block_scaled_dynamic_scales
func.func @test_conv2d_block_scaled_dynamic_scales(%arg0: tensor<?x4x4x64xf4E2M1FN>, %arg1: tensor<*xf8E8M0FNU>, %arg2: tensor<?x1x1x64xf4E2M1FN>, %arg3: tensor<*xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<?x4x4x?xf32>
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<?x4x4x64xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<?x1x1x64xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<?x4x4x64xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<?x1x1x64xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -2143,11 +2143,11 @@ func.func @test_conv2d_block_scaled_dynamic_scales(%arg0: tensor<?x4x4x64xf4E2M1
// CHECK-LABEL: test_conv2d_block_scaled_dynamic_data
func.func @test_conv2d_block_scaled_dynamic_data(%arg0: tensor<*xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<*xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<1x4x4x8xf32>
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<*xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<*xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<*xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<*xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -2155,11 +2155,11 @@ func.func @test_conv2d_block_scaled_dynamic_data(%arg0: tensor<*xf4E2M1FN>, %arg
// CHECK-LABEL: test_conv2d_block_scaled_dynamic_unranked
func.func @test_conv2d_block_scaled_dynamic_unranked(%arg0: tensor<*xf4E2M1FN>, %arg1: tensor<*xf8E8M0FNU>, %arg2: tensor<*xf4E2M1FN>, %arg3: tensor<*xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// CHECK: -> tensor<?x?x?x?xf32>
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<*xf4E2M1FN>, tensor<*xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -2169,7 +2169,7 @@ func.func @test_conv2d_block_scaled_dynamic_unranked(%arg0: tensor<*xf4E2M1FN>,
func.func @test_dwconv2d_bias_broadcast(%input: tensor<2x8x9x?xf32>, %weight: tensor<3x3x?x?xf32>, %bias: tensor<1xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) {
// CHECK: -> tensor<2x6x7x?xf32>
%0 = tosa.depthwise_conv2d %input, %weight, %bias, %input_zp, %weight_zp
- { acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> }
+ pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32)
: (tensor<2x8x9x?xf32>, tensor<3x3x?x?xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -2180,7 +2180,7 @@ func.func @test_dwconv2d_bias_broadcast(%input: tensor<2x8x9x?xf32>, %weight: te
func.func @test_tconv2d_bias_broadcast(%input: tensor<2x6x7x3xf32>, %weight: tensor<?x3x3x3xf32>, %bias: tensor<1xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) {
// CHECK: -> tensor<2x8x9x?xf32>
%0 = tosa.transpose_conv2d %input, %weight, %bias, %input_zp, %weight_zp
- { acc_type = f32, pad = array<i64: 0, 0, 0, 0>, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> }
+ out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) { pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, dilation = array<i64: 1, 1> }
: (tensor<2x6x7x3xf32>, tensor<?x3x3x3xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -2190,7 +2190,7 @@ func.func @test_tconv2d_bias_broadcast(%input: tensor<2x6x7x3xf32>, %weight: ten
// CHECK-LABEL: test_avg_pool2d_unranked_input
func.func @test_avg_pool2d_unranked_input(%input: tensor<*xi32>, %zp: tensor<1xi32>) {
// CHECK: -> tensor<?x?x?x?xi32>
- %0 = tosa.avg_pool2d %input, %zp, %zp { acc_type = i32, kernel = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1> } : (tensor<*xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<*xi32>
+ %0 = tosa.avg_pool2d %input, %zp, %zp kernel([1, 1]) stride([1, 1]) pad([0, 0, 0, 0]) acc_type(i32) : (tensor<*xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<*xi32>
return
}
diff --git a/mlir/test/Dialect/Tosa/tosa-layerwise-constant-fold-dense-resource.mlir b/mlir/test/Dialect/Tosa/tosa-layerwise-constant-fold-dense-resource.mlir
index 86c8f64adbc6b..8b07e8da4a016 100644
--- a/mlir/test/Dialect/Tosa/tosa-layerwise-constant-fold-dense-resource.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-layerwise-constant-fold-dense-resource.mlir
@@ -9,7 +9,7 @@ func.func @transpose_fold_dense_resource() -> tensor<2x2xf32> {
%0 = "tosa.const"() <{values = dense_resource<resource> : tensor<2x2xf32>}> : () -> tensor<2x2xf32>
// CHECK-NOT: tosa.transpose
- %2 = tosa.transpose %0 { perms = array<i32: 1, 0> }: (tensor<2x2xf32>) -> tensor<2x2xf32>
+ %2 = tosa.transpose %0 perms([1, 0]): (tensor<2x2xf32>) -> tensor<2x2xf32>
return %2 : tensor<2x2xf32>
}
{-#
@@ -28,7 +28,7 @@ func.func @transpose_fold_dense_resource_f8e4m3fn() -> tensor<2x2xf8E4M3FN> {
// CHECK: %[[CST:.+]] = "tosa.const"() <{
// CHECK-SAME{LITERAL}: values = dense<[[1.000000e+00, 3.000000e+00], [2.000000e+00, 4.000000e+00]]> : tensor<2x2xf8E4M3FN>
- %1 = tosa.transpose %0 { perms = array<i32: 1, 0> }: (tensor<2x2xf8E4M3FN>) -> tensor<2x2xf8E4M3FN>
+ %1 = tosa.transpose %0 perms([1, 0]): (tensor<2x2xf8E4M3FN>) -> tensor<2x2xf8E4M3FN>
// CHECK: return %[[CST]]
return %1 : tensor<2x2xf8E4M3FN>
}
@@ -48,7 +48,7 @@ func.func @transpose_fold_dense_resource_f8e5m2() -> tensor<2x2xf8E5M2> {
// CHECK: %[[CST:.+]] = "tosa.const"() <{
// CHECK-SAME{LITERAL}: values = dense<[[1.000000e+00, 3.000000e+00], [2.000000e+00, 4.000000e+00]]> : tensor<2x2xf8E5M2>
- %1 = tosa.transpose %0 { perms = array<i32: 1, 0> }: (tensor<2x2xf8E5M2>) -> tensor<2x2xf8E5M2>
+ %1 = tosa.transpose %0 perms([1, 0]): (tensor<2x2xf8E5M2>) -> tensor<2x2xf8E5M2>
// CHECK: return %[[CST]]
return %1 : tensor<2x2xf8E5M2>
}
@@ -68,7 +68,7 @@ func.func @transpose_fold_dense_resource_f4e2m1fn() -> tensor<2x2xf4E2M1FN> {
// CHECK: %[[CST:.+]] = "tosa.const"() <{
// CHECK-SAME{LITERAL}: values = dense<[[1.000000e+00, 3.000000e+00], [2.000000e+00, 4.000000e+00]]> : tensor<2x2xf4E2M1FN>
- %1 = tosa.transpose %0 { perms = array<i32: 1, 0> }: (tensor<2x2xf4E2M1FN>) -> tensor<2x2xf4E2M1FN>
+ %1 = tosa.transpose %0 perms([1, 0]): (tensor<2x2xf4E2M1FN>) -> tensor<2x2xf4E2M1FN>
// CHECK: return %[[CST]]
return %1 : tensor<2x2xf4E2M1FN>
}
@@ -88,7 +88,7 @@ func.func @transpose_fold_dense_resource_f16() -> tensor<2x2xf16> {
// CHECK: %[[CST:.+]] = "tosa.const"() <{
// CHECK-SAME{LITERAL}: values = dense<[[1.000000e+00, 3.000000e+00], [2.000000e+00, 4.000000e+00]]> : tensor<2x2xf16>
- %1 = tosa.transpose %0 { perms = array<i32: 1, 0> }: (tensor<2x2xf16>) -> tensor<2x2xf16>
+ %1 = tosa.transpose %0 perms([1, 0]): (tensor<2x2xf16>) -> tensor<2x2xf16>
// CHECK: return %[[CST]]
return %1 : tensor<2x2xf16>
}
@@ -108,7 +108,7 @@ func.func @transpose_fold_dense_resource_bf16() -> tensor<2x2xbf16> {
// CHECK: %[[CST:.+]] = "tosa.const"() <{
// CHECK-SAME{LITERAL}: values = dense<[[1.000000e+00, 3.000000e+00], [2.000000e+00, 4.000000e+00]]> : tensor<2x2xbf16>
- %1 = tosa.transpose %0 { perms = array<i32: 1, 0> }: (tensor<2x2xbf16>) -> tensor<2x2xbf16>
+ %1 = tosa.transpose %0 perms([1, 0]): (tensor<2x2xbf16>) -> tensor<2x2xbf16>
// CHECK: return %[[CST]]
return %1 : tensor<2x2xbf16>
}
@@ -128,7 +128,7 @@ func.func @transpose_fold_dense_resource_f64() -> tensor<2x2xf64> {
// CHECK: %[[CST:.+]] = "tosa.const"() <{
// CHECK-SAME{LITERAL}: values = dense<[[1.000000e+00, 3.000000e+00], [2.000000e+00, 4.000000e+00]]> : tensor<2x2xf64>
- %1 = tosa.transpose %0 { perms = array<i32: 1, 0> }: (tensor<2x2xf64>) -> tensor<2x2xf64>
+ %1 = tosa.transpose %0 perms([1, 0]): (tensor<2x2xf64>) -> tensor<2x2xf64>
// CHECK: return %[[CST]]
return %1 : tensor<2x2xf64>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-layerwise-constant-fold.mlir b/mlir/test/Dialect/Tosa/tosa-layerwise-constant-fold.mlir
index 9f745d4f57640..ca449cc0a1704 100644
--- a/mlir/test/Dialect/Tosa/tosa-layerwise-constant-fold.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-layerwise-constant-fold.mlir
@@ -4,7 +4,7 @@
// CHECK-LABEL: @armax_fold_dim_size_1
func.func @armax_fold_dim_size_1(%arg0: tensor<2x1x3xf32>) -> tensor<2x3xi32> {
// CHECK: "tosa.const"() <{values = dense<0> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- %0 = tosa.argmax %arg0 {axis = 1 : i32}: (tensor<2x1x3xf32>) -> tensor<2x3xi32>
+ %0 = tosa.argmax %arg0 axis(1): (tensor<2x1x3xf32>) -> tensor<2x3xi32>
return %0 : tensor<2x3xi32>
}
@@ -13,7 +13,7 @@ func.func @armax_fold_dim_size_1(%arg0: tensor<2x1x3xf32>) -> tensor<2x3xi32> {
// CHECK-LABEL: @argmax_dynamic_shape_no_fold_dim_size_1
func.func @argmax_dynamic_shape_no_fold_dim_size_1(%arg0: tensor<?x1x3xf32>) -> tensor<?x3xi32> {
// CHECK: tosa.argmax
- %0 = tosa.argmax %arg0 {axis = 1 : i32}: (tensor<?x1x3xf32>) -> tensor<?x3xi32>
+ %0 = tosa.argmax %arg0 axis(1): (tensor<?x1x3xf32>) -> tensor<?x3xi32>
return %0 : tensor<?x3xi32>
}
@@ -22,7 +22,7 @@ func.func @argmax_dynamic_shape_no_fold_dim_size_1(%arg0: tensor<?x1x3xf32>) ->
// CHECK-LABEL: @transpose_fold
func.func @transpose_fold(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
// CHECK: return %arg0
- %1 = tosa.transpose %arg0 { perms = array<i32: 0, 1> }: (tensor<3x4xf32>) -> tensor<3x4xf32>
+ %1 = tosa.transpose %arg0 perms([0, 1]) : (tensor<3x4xf32>) -> tensor<3x4xf32>
return %1 : tensor<3x4xf32>
}
@@ -31,7 +31,7 @@ func.func @transpose_fold(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
// CHECK-LABEL: @transpose_nofold
func.func @transpose_nofold(%arg0: tensor<3x3xf32>) -> tensor<3x3xf32> {
// CHECK: tosa.transpose
- %1 = tosa.transpose %arg0 { perms = array<i32: 1, 0> }: (tensor<3x3xf32>) -> tensor<3x3xf32>
+ %1 = tosa.transpose %arg0 perms([1, 0]) : (tensor<3x3xf32>) -> tensor<3x3xf32>
return %1 : tensor<3x3xf32>
}
@@ -40,7 +40,7 @@ func.func @transpose_nofold(%arg0: tensor<3x3xf32>) -> tensor<3x3xf32> {
// CHECK-LABEL: @transpose_nofold_shape
func.func @transpose_nofold_shape(%arg0: tensor<3x4xf32>) -> tensor<?x?xf32> {
// CHECK: tosa.transpose
- %1 = tosa.transpose %arg0 { perms = array<i32: 1, 0> }: (tensor<3x4xf32>) -> tensor<?x?xf32>
+ %1 = tosa.transpose %arg0 perms([1, 0]) : (tensor<3x4xf32>) -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}
@@ -49,7 +49,7 @@ func.func @transpose_nofold_shape(%arg0: tensor<3x4xf32>) -> tensor<?x?xf32> {
// CHECK-LABEL: @transpose_nofold_unranked_result_not_reshape
func.func @transpose_nofold_unranked_result_not_reshape(%arg0: tensor<6x7xf32>) -> tensor<*xf32> {
// CHECK: tosa.transpose
- %1 = tosa.transpose %arg0 { perms = array<i32: 1, 0> }: (tensor<6x7xf32>) -> tensor<*xf32>
+ %1 = tosa.transpose %arg0 perms([1, 0]) : (tensor<6x7xf32>) -> tensor<*xf32>
return %1 : tensor<*xf32>
}
@@ -57,7 +57,7 @@ func.func @transpose_nofold_unranked_result_not_reshape(%arg0: tensor<6x7xf32>)
// CHECK-LABEL: @reciprocal_nofold_unranked_result
func.func @reciprocal_nofold_unranked_result() -> tensor<*xf32> {
- %input = "tosa.const"() {values = dense<2.0> : tensor<6x7xf32>} : () -> tensor<6x7xf32>
+ %input = "tosa.const"() <{values = dense<2.0> : tensor<6x7xf32>}> : () -> tensor<6x7xf32>
// CHECK: tosa.reciprocal
%1 = tosa.reciprocal %input : (tensor<6x7xf32>) -> tensor<*xf32>
return %1 : tensor<*xf32>
@@ -67,10 +67,10 @@ func.func @reciprocal_nofold_unranked_result() -> tensor<*xf32> {
// CHECK-LABEL: @transpose_fold_splat
func.func @transpose_fold_splat() -> tensor<3x2xf32> {
- %input = "tosa.const"() {values = dense<4.0> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
+ %input = "tosa.const"() <{values = dense<4.0> : tensor<2x3xf32>}> : () -> tensor<2x3xf32>
// CHECK: %[[CST:.+]] = "tosa.const"() <{
// CHECK-SAME{LITERAL}: values = dense<4.000000e+00> : tensor<3x2xf32>
- %1 = tosa.transpose %input { perms = array<i32: 1, 0> }: (tensor<2x3xf32>) -> tensor<3x2xf32>
+ %1 = tosa.transpose %input perms([1, 0]) : (tensor<2x3xf32>) -> tensor<3x2xf32>
// CHECK: return %[[CST]]
return %1 : tensor<3x2xf32>
}
@@ -79,10 +79,10 @@ func.func @transpose_fold_splat() -> tensor<3x2xf32> {
// CHECK-LABEL: @transpose_fold_2d_float
func.func @transpose_fold_2d_float() -> tensor<3x2xf32> {
- %input = "tosa.const"() {values = dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
+ %input = "tosa.const"() <{values = dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32>}> : () -> tensor<2x3xf32>
// CHECK: %[[CST:.+]] = "tosa.const"() <{
// CHECK-SAME{LITERAL}: values = dense<[[0.000000e+00, 3.000000e+00], [1.000000e+00, 4.000000e+00], [2.000000e+00, 5.000000e+00]]> : tensor<3x2xf32>
- %1 = tosa.transpose %input { perms = array<i32: 1, 0> }: (tensor<2x3xf32>) -> tensor<3x2xf32>
+ %1 = tosa.transpose %input perms([1, 0]) : (tensor<2x3xf32>) -> tensor<3x2xf32>
// CHECK: return %[[CST]]
return %1 : tensor<3x2xf32>
}
@@ -91,10 +91,10 @@ func.func @transpose_fold_2d_float() -> tensor<3x2xf32> {
// CHECK-LABEL: @transpose_fold_2d_bool
func.func @transpose_fold_2d_bool() -> tensor<3x2xi1> {
- %input = "tosa.const"() {values = dense<[[true, false, false], [false, false, true]]> : tensor<2x3xi1>} : () -> tensor<2x3xi1>
+ %input = "tosa.const"() <{values = dense<[[true, false, false], [false, false, true]]> : tensor<2x3xi1>}> : () -> tensor<2x3xi1>
// CHECK: %[[CST:.+]] = "tosa.const"() <{
// CHECK-SAME{LITERAL}: values = dense<[[true, false], [false, false], [false, true]]> : tensor<3x2xi1>
- %1 = tosa.transpose %input { perms = array<i32: 1, 0> }: (tensor<2x3xi1>) -> tensor<3x2xi1>
+ %1 = tosa.transpose %input perms([1, 0]) : (tensor<2x3xi1>) -> tensor<3x2xi1>
// CHECK: return %[[CST]]
return %1 : tensor<3x2xi1>
}
@@ -113,7 +113,7 @@ func.func @transpose_fold_4d_int() -> tensor<3x1x4x2xi32> {
// CHECK-SAME{LITERAL}: [[[4, 16], [5, 17], [6, 18], [7, 19]]],
// CHECK-SAME{LITERAL}: [[[8, 20], [9, 21], [10, 22], [11, 23]]]
// CHECK-SAME{LITERAL}: ]>
- %1 = tosa.transpose %input { perms = array<i32: 2, 0, 3, 1> }: (tensor<1x2x3x4xi32>) -> tensor<3x1x4x2xi32>
+ %1 = tosa.transpose %input perms([2, 0, 3, 1]) : (tensor<1x2x3x4xi32>) -> tensor<3x1x4x2xi32>
// CHECK: return %[[CST]]
return %1 : tensor<3x1x4x2xi32>
}
@@ -123,7 +123,7 @@ func.func @transpose_fold_4d_int() -> tensor<3x1x4x2xi32> {
// CHECK-LABEL: @transpose_nofold_non_cst_input
func.func @transpose_nofold_non_cst_input(%input: tensor<2x3xf32>) -> tensor<3x2xf32> {
// CHECK: tosa.transpose
- %1 = tosa.transpose %input { perms = array<i32: 1, 0> }: (tensor<2x3xf32>) -> tensor<3x2xf32>
+ %1 = tosa.transpose %input perms([1, 0]) : (tensor<2x3xf32>) -> tensor<3x2xf32>
return %1 : tensor<3x2xf32>
}
@@ -131,9 +131,9 @@ func.func @transpose_nofold_non_cst_input(%input: tensor<2x3xf32>) -> tensor<3x2
// CHECK-LABEL: @transpose_nofold_multi_users
func.func @transpose_nofold_multi_users() -> (tensor<3x2xf32>, tensor<2x3xf32>) {
- %input = "tosa.const"() {values = dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
+ %input = "tosa.const"() <{values = dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32>}> : () -> tensor<2x3xf32>
// CHECK: tosa.transpose
- %1 = tosa.transpose %input { perms = array<i32: 1, 0> }: (tensor<2x3xf32>) -> tensor<3x2xf32>
+ %1 = tosa.transpose %input perms([1, 0]) : (tensor<2x3xf32>) -> tensor<3x2xf32>
return %1, %input : tensor<3x2xf32>, tensor<2x3xf32>
}
@@ -141,9 +141,9 @@ func.func @transpose_nofold_multi_users() -> (tensor<3x2xf32>, tensor<2x3xf32>)
// CHECK-LABEL: @transpose_nofold_quantized_types
func.func @transpose_nofold_quantized_types() -> tensor<1x1x2x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>> {
- %input = "tosa.const"() {values = dense<-127> : tensor<2x1x1x2xi8>} : () -> tensor<2x1x1x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>>
+ %input = "tosa.const"() <{values = dense<-127> : tensor<2x1x1x2xi8>}> : () -> tensor<2x1x1x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>>
// CHECK: tosa.transpose
- %0 = tosa.transpose %input { perms = array<i32: 1, 2, 3, 0> }: (tensor<2x1x1x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>>) -> tensor<1x1x2x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>>
+ %0 = tosa.transpose %input perms([1, 2, 3, 0]) : (tensor<2x1x1x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>>) -> tensor<1x1x2x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>>
return %0: tensor<1x1x2x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>>
}
@@ -154,8 +154,8 @@ func.func @transpose_nofold_quantized_types() -> tensor<1x1x2x2x!quant.uniform<i
// CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}5, 7, 9]]> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
// CHECK: return %[[VAL_0]] : tensor<1x3xi32>
- %const = "tosa.const"() {values = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>} : () -> tensor<2x3xi32>
- %0 = tosa.reduce_sum %const {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
+ %const = "tosa.const"() <{values = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ %0 = tosa.reduce_sum %const axis(0) : (tensor<2x3xi32>) -> tensor<1x3xi32>
return %0 : tensor<1x3xi32>
}
@@ -167,7 +167,7 @@ func.func @transpose_nofold_quantized_types() -> tensor<1x1x2x2x!quant.uniform<i
// CHECK: return %[[VAL_0]] : tensor<2x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- %0 = tosa.reduce_sum %const {axis = 1 : i32} : (tensor<2x3xi32>) -> tensor<2x1xi32>
+ %0 = tosa.reduce_sum %const axis(1) : (tensor<2x3xi32>) -> tensor<2x1xi32>
return %0 : tensor<2x1xi32>
}
@@ -180,7 +180,7 @@ func.func @reduce_sum_constant() -> tensor<3x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<3x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[1, 2, 3], [4, 5, 6], [7, 8, 9]]> : tensor<3x3xi32>}> : () -> tensor<3x3xi32>
- %0 = tosa.reduce_sum %const {axis = 1 : i32} : (tensor<3x3xi32>) -> tensor<3x1xi32>
+ %0 = tosa.reduce_sum %const axis(1) : (tensor<3x3xi32>) -> tensor<3x1xi32>
return %0 : tensor<3x1xi32>
}
@@ -192,7 +192,7 @@ func.func @reduce_sum_constant() -> tensor<2x1x4xi32> {
// CHECK: return %[[VAL_0]] : tensor<2x1x4xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]> : tensor<2x3x4xi32>}> : () -> tensor<2x3x4xi32>
- %0 = tosa.reduce_sum %const {axis = 1 : i32} : (tensor<2x3x4xi32>) -> tensor<2x1x4xi32>
+ %0 = tosa.reduce_sum %const axis(1) : (tensor<2x3x4xi32>) -> tensor<2x1x4xi32>
return %0 : tensor<2x1x4xi32>
}
@@ -204,7 +204,7 @@ func.func @reduce_sum_constant() -> tensor<1x3x3xi32> {
// CHECK: return %[[VAL_0]] : tensor<1x3x3xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]]]> : tensor<3x3x3xi32>}> : () -> tensor<3x3x3xi32>
- %0 = tosa.reduce_sum %const {axis = 0 : i32} : (tensor<3x3x3xi32>) -> tensor<1x3x3xi32>
+ %0 = tosa.reduce_sum %const axis(0) : (tensor<3x3x3xi32>) -> tensor<1x3x3xi32>
return %0 : tensor<1x3x3xi32>
}
@@ -216,7 +216,7 @@ func.func @reduce_sum_constant() -> tensor<2x2x2x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<2x2x2x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]]]> : tensor<2x2x2x2xi32>}> : () -> tensor<2x2x2x2xi32>
- %0 = tosa.reduce_sum %const {axis = 3 : i32} : (tensor<2x2x2x2xi32>) -> tensor<2x2x2x1xi32>
+ %0 = tosa.reduce_sum %const axis(3) : (tensor<2x2x2x2xi32>) -> tensor<2x2x2x1xi32>
return %0 : tensor<2x2x2x1xi32>
}
@@ -228,7 +228,7 @@ func.func @reduce_sum_constant() -> tensor<1x1x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<1x1x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
- %0 = tosa.reduce_sum %const {axis = 0 : i32} : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
+ %0 = tosa.reduce_sum %const axis(0) : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
return %0 : tensor<1x1x1xi32>
}
@@ -240,7 +240,7 @@ func.func @reduce_sum_constant() -> tensor<2x3x1x5xi32> {
// CHECK: return %[[VAL_0]] : tensor<2x3x1x5xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20]], [[21, 22, 23, 24, 25], [26, 27, 28, 29, 30], [31, 32, 33, 34, 35], [36, 37, 38, 39, 40]], [[41, 42, 43, 44, 45], [46, 47, 48, 49, 50], [51, 52, 53, 54, 55], [56, 57, 58, 59, 60]]], [[[61, 62, 63, 64, 65], [66, 67, 68, 69, 70], [71, 72, 73, 74, 75], [76, 77, 78, 79, 80]], [[81, 82, 83, 84, 85], [86, 87, 88, 89, 90], [91, 92, 93, 94, 95], [96, 97, 98, 99, 100]], [[101, 102, 103, 104, 105], [106, 107, 108, 109, 110], [111, 112, 113, 114, 115], [116, 117, 118, 119, 120]]]]> : tensor<2x3x4x5xi32>}> : () -> tensor<2x3x4x5xi32>
- %0 = tosa.reduce_sum %const {axis = 2 : i32} : (tensor<2x3x4x5xi32>) -> tensor<2x3x1x5xi32>
+ %0 = tosa.reduce_sum %const axis(2) : (tensor<2x3x4x5xi32>) -> tensor<2x3x1x5xi32>
return %0 : tensor<2x3x1x5xi32>
}
@@ -252,7 +252,7 @@ func.func @reduce_sum_constant() -> tensor<2x3x1x5xi32> {
// CHECK: return %[[VAL_0]] : tensor<1x3xi32>
%const = "tosa.const"() <{values = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- %0 = tosa.reduce_product %const {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
+ %0 = tosa.reduce_product %const axis(0) : (tensor<2x3xi32>) -> tensor<1x3xi32>
return %0 : tensor<1x3xi32>
}
@@ -265,7 +265,7 @@ func.func @reduce_sum_constant() -> tensor<2x3x1x5xi32> {
// CHECK: }
%const = "tosa.const"() <{values = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- %0 = tosa.reduce_product %const {axis = 1 : i32} : (tensor<2x3xi32>) -> tensor<2x1xi32>
+ %0 = tosa.reduce_product %const axis(1) : (tensor<2x3xi32>) -> tensor<2x1xi32>
return %0 : tensor<2x1xi32>
}
@@ -277,7 +277,7 @@ func.func @reduce_prod_constant() -> tensor<3x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<3x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[1, 2, 3], [4, 5, 6], [7, 8, 9]]> : tensor<3x3xi32>}> : () -> tensor<3x3xi32>
- %0 = tosa.reduce_product %const {axis = 1 : i32} : (tensor<3x3xi32>) -> tensor<3x1xi32>
+ %0 = tosa.reduce_product %const axis(1) : (tensor<3x3xi32>) -> tensor<3x1xi32>
return %0 : tensor<3x1xi32>
}
@@ -289,7 +289,7 @@ func.func @reduce_prod_constant() -> tensor<2x1x4xi32> {
// CHECK: return %[[VAL_0]] : tensor<2x1x4xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]> : tensor<2x3x4xi32>}> : () -> tensor<2x3x4xi32>
- %0 = tosa.reduce_product %const {axis = 1 : i32} : (tensor<2x3x4xi32>) -> tensor<2x1x4xi32>
+ %0 = tosa.reduce_product %const axis(1) : (tensor<2x3x4xi32>) -> tensor<2x1x4xi32>
return %0 : tensor<2x1x4xi32>
}
@@ -301,7 +301,7 @@ func.func @reduce_prod_constant() -> tensor<1x3x3xi32> {
// CHECK: return %[[VAL_0]] : tensor<1x3x3xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]]]> : tensor<3x3x3xi32>}> : () -> tensor<3x3x3xi32>
- %0 = tosa.reduce_product %const {axis = 0 : i32} : (tensor<3x3x3xi32>) -> tensor<1x3x3xi32>
+ %0 = tosa.reduce_product %const axis(0) : (tensor<3x3x3xi32>) -> tensor<1x3x3xi32>
return %0 : tensor<1x3x3xi32>
}
@@ -313,7 +313,7 @@ func.func @reduce_prod_constant() -> tensor<2x2x2x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<2x2x2x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]]]> : tensor<2x2x2x2xi32>}> : () -> tensor<2x2x2x2xi32>
- %0 = tosa.reduce_product %const {axis = 3 : i32} : (tensor<2x2x2x2xi32>) -> tensor<2x2x2x1xi32>
+ %0 = tosa.reduce_product %const axis(3) : (tensor<2x2x2x2xi32>) -> tensor<2x2x2x1xi32>
return %0 : tensor<2x2x2x1xi32>
}
@@ -325,7 +325,7 @@ func.func @reduce_prod_constant() -> tensor<1x1x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<1x1x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
- %0 = tosa.reduce_product %const {axis = 0 : i32} : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
+ %0 = tosa.reduce_product %const axis(0) : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
return %0 : tensor<1x1x1xi32>
}
@@ -337,7 +337,7 @@ func.func @reduce_prod_constant() -> tensor<1x1x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<1x3xi32>
%const = "tosa.const"() <{values = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- %0 = tosa.reduce_max %const {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
+ %0 = tosa.reduce_max %const axis(0) : (tensor<2x3xi32>) -> tensor<1x3xi32>
return %0 : tensor<1x3xi32>
}
@@ -350,7 +350,7 @@ func.func @reduce_prod_constant() -> tensor<1x1x1xi32> {
// CHECK: }
%const = "tosa.const"() <{values = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- %0 = tosa.reduce_max %const {axis = 1 : i32} : (tensor<2x3xi32>) -> tensor<2x1xi32>
+ %0 = tosa.reduce_max %const axis(1) : (tensor<2x3xi32>) -> tensor<2x1xi32>
return %0 : tensor<2x1xi32>
}
@@ -362,7 +362,7 @@ func.func @reduce_max_constant() -> tensor<3x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<3x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[1, 2, 3], [4, 5, 6], [7, 8, 9]]> : tensor<3x3xi32>}> : () -> tensor<3x3xi32>
- %0 = tosa.reduce_max %const {axis = 1 : i32} : (tensor<3x3xi32>) -> tensor<3x1xi32>
+ %0 = tosa.reduce_max %const axis(1) : (tensor<3x3xi32>) -> tensor<3x1xi32>
return %0 : tensor<3x1xi32>
}
@@ -374,7 +374,7 @@ func.func @reduce_max_constant() -> tensor<2x1x4xi32> {
// CHECK: return %[[VAL_0]] : tensor<2x1x4xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]> : tensor<2x3x4xi32>}> : () -> tensor<2x3x4xi32>
- %0 = tosa.reduce_max %const {axis = 1 : i32} : (tensor<2x3x4xi32>) -> tensor<2x1x4xi32>
+ %0 = tosa.reduce_max %const axis(1) : (tensor<2x3x4xi32>) -> tensor<2x1x4xi32>
return %0 : tensor<2x1x4xi32>
}
@@ -386,7 +386,7 @@ func.func @reduce_max_constant() -> tensor<1x3x3xi32> {
// CHECK: return %[[VAL_0]] : tensor<1x3x3xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]]]> : tensor<3x3x3xi32>}> : () -> tensor<3x3x3xi32>
- %0 = tosa.reduce_max %const {axis = 0 : i32} : (tensor<3x3x3xi32>) -> tensor<1x3x3xi32>
+ %0 = tosa.reduce_max %const axis(0) : (tensor<3x3x3xi32>) -> tensor<1x3x3xi32>
return %0 : tensor<1x3x3xi32>
}
@@ -398,7 +398,7 @@ func.func @reduce_max_constant() -> tensor<2x2x2x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<2x2x2x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]]]> : tensor<2x2x2x2xi32>}> : () -> tensor<2x2x2x2xi32>
- %0 = tosa.reduce_max %const {axis = 3 : i32} : (tensor<2x2x2x2xi32>) -> tensor<2x2x2x1xi32>
+ %0 = tosa.reduce_max %const axis(3) : (tensor<2x2x2x2xi32>) -> tensor<2x2x2x1xi32>
return %0 : tensor<2x2x2x1xi32>
}
@@ -410,7 +410,7 @@ func.func @reduce_max_constant() -> tensor<1x1x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<1x1x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
- %0 = tosa.reduce_max %const {axis = 0 : i32} : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
+ %0 = tosa.reduce_max %const axis(0) : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
return %0 : tensor<1x1x1xi32>
}
@@ -422,7 +422,7 @@ func.func @reduce_max_constant_no_overflow() -> tensor<1xi8> {
// CHECK: return %[[VAL_0]] : tensor<1xi8>
// CHECK: }
%const = "tosa.const"() <{values = dense<[-127, 120, -126]> : tensor<3xi8>}> : () -> tensor<3xi8>
- %0 = tosa.reduce_max %const {axis = 0 : i32} : (tensor<3xi8>) -> tensor<1xi8>
+ %0 = tosa.reduce_max %const axis(0) : (tensor<3xi8>) -> tensor<1xi8>
return %0 : tensor<1xi8>
}
@@ -433,7 +433,7 @@ func.func @reduce_max_constant_no_overflow() -> tensor<1xi8> {
// CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}1, 2, 3]]> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
// CHECK: return %[[VAL_0]] : tensor<1x3xi32>
%const = "tosa.const"() <{values = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- %0 = tosa.reduce_min %const {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
+ %0 = tosa.reduce_min %const axis(0) : (tensor<2x3xi32>) -> tensor<1x3xi32>
return %0 : tensor<1x3xi32>
}
@@ -447,7 +447,7 @@ func.func @reduce_max_constant_no_overflow() -> tensor<1xi8> {
// CHECK: }
%const = "tosa.const"() <{values = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- %0 = tosa.reduce_min %const {axis = 1 : i32} : (tensor<2x3xi32>) -> tensor<2x1xi32>
+ %0 = tosa.reduce_min %const axis(1) : (tensor<2x3xi32>) -> tensor<2x1xi32>
return %0 : tensor<2x1xi32>
}
@@ -459,7 +459,7 @@ func.func @reduce_min_constant() -> tensor<3x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<3x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[1, 2, 3], [4, 5, 6], [7, 8, 9]]> : tensor<3x3xi32>}> : () -> tensor<3x3xi32>
- %0 = tosa.reduce_min %const {axis = 1 : i32} : (tensor<3x3xi32>) -> tensor<3x1xi32>
+ %0 = tosa.reduce_min %const axis(1) : (tensor<3x3xi32>) -> tensor<3x1xi32>
return %0 : tensor<3x1xi32>
}
@@ -471,7 +471,7 @@ func.func @reduce_min_constant() -> tensor<2x1x4xi32> {
// CHECK: return %[[VAL_0]] : tensor<2x1x4xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]> : tensor<2x3x4xi32>}> : () -> tensor<2x3x4xi32>
- %0 = tosa.reduce_min %const {axis = 1 : i32} : (tensor<2x3x4xi32>) -> tensor<2x1x4xi32>
+ %0 = tosa.reduce_min %const axis(1) : (tensor<2x3x4xi32>) -> tensor<2x1x4xi32>
return %0 : tensor<2x1x4xi32>
}
@@ -483,7 +483,7 @@ func.func @reduce_min_constant() -> tensor<1x3x3xi32> {
// CHECK: return %[[VAL_0]] : tensor<1x3x3xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]]]> : tensor<3x3x3xi32>}> : () -> tensor<3x3x3xi32>
- %0 = tosa.reduce_min %const {axis = 0 : i32} : (tensor<3x3x3xi32>) -> tensor<1x3x3xi32>
+ %0 = tosa.reduce_min %const axis(0) : (tensor<3x3x3xi32>) -> tensor<1x3x3xi32>
return %0 : tensor<1x3x3xi32>
}
@@ -495,7 +495,7 @@ func.func @reduce_min_constant() -> tensor<2x2x2x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<2x2x2x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]]]> : tensor<2x2x2x2xi32>}> : () -> tensor<2x2x2x2xi32>
- %0 = tosa.reduce_min %const {axis = 3 : i32} : (tensor<2x2x2x2xi32>) -> tensor<2x2x2x1xi32>
+ %0 = tosa.reduce_min %const axis(3) : (tensor<2x2x2x2xi32>) -> tensor<2x2x2x1xi32>
return %0 : tensor<2x2x2x1xi32>
}
@@ -507,7 +507,7 @@ func.func @reduce_min_constant() -> tensor<1x1x1xi32> {
// CHECK: return %[[VAL_0]] : tensor<1x1x1xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
- %0 = tosa.reduce_min %const {axis = 0 : i32} : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
+ %0 = tosa.reduce_min %const axis(0) : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
return %0 : tensor<1x1x1xi32>
}
@@ -519,7 +519,7 @@ func.func @reduce_min_constant_no_overflow() -> tensor<1xi8> {
// CHECK: return %[[VAL_0]] : tensor<1xi8>
// CHECK: }
%const = "tosa.const"() <{values = dense<[-127, 120, -126]> : tensor<3xi8>}> : () -> tensor<3xi8>
- %0 = tosa.reduce_min %const {axis = 0 : i32} : (tensor<3xi8>) -> tensor<1xi8>
+ %0 = tosa.reduce_min %const axis(0) : (tensor<3xi8>) -> tensor<1xi8>
return %0 : tensor<1xi8>
}
@@ -532,7 +532,7 @@ func.func @reduce_any_constant() -> tensor<1x3xi1> {
// CHECK: return %[[VAL_0]] : tensor<1x3xi1>
%const = "tosa.const"() <{values = dense<[[true,true,true], [true,false,true]]> : tensor<2x3xi1>}> : () -> tensor<2x3xi1>
- %0 = tosa.reduce_any %const {axis = 0 : i32} : (tensor<2x3xi1>) -> tensor<1x3xi1>
+ %0 = tosa.reduce_any %const axis(0) : (tensor<2x3xi1>) -> tensor<1x3xi1>
return %0 : tensor<1x3xi1>
}
@@ -546,7 +546,7 @@ func.func @reduce_any_constant() -> tensor<2x1xi1> {
// CHECK: }
%const = "tosa.const"() <{values = dense<[[true,true,true], [true,false,true]]> : tensor<2x3xi1>}> : () -> tensor<2x3xi1>
- %0 = tosa.reduce_any %const {axis = 1 : i32} : (tensor<2x3xi1>) -> tensor<2x1xi1>
+ %0 = tosa.reduce_any %const axis(1) : (tensor<2x3xi1>) -> tensor<2x1xi1>
return %0 : tensor<2x1xi1>
}
@@ -558,7 +558,7 @@ func.func @reduce_any_constant() -> tensor<3x1xi1> {
// CHECK: return %[[VAL_0]] : tensor<3x1xi1>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[true, false, false], [false, false, false], [false, false, true]]> : tensor<3x3xi1>}> : () -> tensor<3x3xi1>
- %0 = tosa.reduce_any %const {axis = 1 : i32} : (tensor<3x3xi1>) -> tensor<3x1xi1>
+ %0 = tosa.reduce_any %const axis(1) : (tensor<3x3xi1>) -> tensor<3x1xi1>
return %0 : tensor<3x1xi1>
}
@@ -570,7 +570,7 @@ func.func @reduce_any_constant() -> tensor<2x1x4xi1> {
// CHECK: return %[[VAL_0]] : tensor<2x1x4xi1>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[true, false, false, true], [false, false, true, false], [true, false, true, true]], [[false, false, false, false], [false, false, true, false], [true, false, true, false]]]> : tensor<2x3x4xi1>}> : () -> tensor<2x3x4xi1>
- %0 = tosa.reduce_any %const {axis = 1 : i32} : (tensor<2x3x4xi1>) -> tensor<2x1x4xi1>
+ %0 = tosa.reduce_any %const axis(1) : (tensor<2x3x4xi1>) -> tensor<2x1x4xi1>
return %0 : tensor<2x1x4xi1>
}
@@ -582,7 +582,7 @@ func.func @reduce_any_constant() -> tensor<2x1x4xi1> {
// CHECK: return %[[VAL_0]] : tensor<1x3xi1>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[true,true,true], [true,false,true]]> : tensor<2x3xi1>}> : () -> tensor<2x3xi1>
- %0 = tosa.reduce_all %const {axis = 0 : i32} : (tensor<2x3xi1>) -> tensor<1x3xi1>
+ %0 = tosa.reduce_all %const axis(0) : (tensor<2x3xi1>) -> tensor<1x3xi1>
return %0 : tensor<1x3xi1>
}
@@ -594,7 +594,7 @@ func.func @reduce_any_constant() -> tensor<2x1x4xi1> {
// CHECK: return %[[VAL_0]] : tensor<2x1xi1>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[true,true,true], [true,false,true]]> : tensor<2x3xi1>}> : () -> tensor<2x3xi1>
- %0 = tosa.reduce_all %const {axis = 1 : i32} : (tensor<2x3xi1>) -> tensor<2x1xi1>
+ %0 = tosa.reduce_all %const axis(1) : (tensor<2x3xi1>) -> tensor<2x1xi1>
return %0 : tensor<2x1xi1>
}
@@ -606,7 +606,7 @@ func.func @reduce_all_constant() -> tensor<3x1xi1> {
// CHECK: return %[[VAL_0]] : tensor<3x1xi1>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[true, false, false], [false, false, false], [false, false, true]]> : tensor<3x3xi1>}> : () -> tensor<3x3xi1>
- %0 = tosa.reduce_all %const {axis = 1 : i32} : (tensor<3x3xi1>) -> tensor<3x1xi1>
+ %0 = tosa.reduce_all %const axis(1) : (tensor<3x3xi1>) -> tensor<3x1xi1>
return %0 : tensor<3x1xi1>
}
@@ -618,7 +618,7 @@ func.func @reduce_all_constant() -> tensor<2x1x4xi1> {
// CHECK: return %[[VAL_0]] : tensor<2x1x4xi1>
// CHECK: }
%const = "tosa.const"() <{values = dense<[[[true, false, false, true], [false, false, true, false], [true, false, true, true]], [[false, false, false, false], [false, false, true, false], [true, false, true, false]]]> : tensor<2x3x4xi1>}> : () -> tensor<2x3x4xi1>
- %0 = tosa.reduce_all %const {axis = 1 : i32} : (tensor<2x3x4xi1>) -> tensor<2x1x4xi1>
+ %0 = tosa.reduce_all %const axis(1) : (tensor<2x3x4xi1>) -> tensor<2x1x4xi1>
return %0 : tensor<2x1x4xi1>
}
@@ -630,7 +630,7 @@ func.func @reduce_sum_constant() -> tensor<1x3xi32> {
// CHECK: return %[[VAL_0]] : tensor<1x3xi32>
// CHECK: }
%const = "tosa.const"() <{values = dense<1> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- %0 = tosa.reduce_sum %const {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
+ %0 = tosa.reduce_sum %const axis(0) : (tensor<2x3xi32>) -> tensor<1x3xi32>
return %0 : tensor<1x3xi32>
}
@@ -641,12 +641,12 @@ func.func @reduce_sum_constant() -> tensor<1x3xi32> {
// CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}1, 2, 3], [4, 5, 6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
// CHECK: %[[VAL_1:.*]] = "tosa.const"() <{values = dense<{{\[\[}}1, 2, 3], [4, 5, 7]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
// CHECK: %[[VAL_2:.*]] = tosa.add %[[VAL_0]], %[[VAL_1]] : (tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
- // CHECK: %[[VAL_3:.*]] = tosa.reduce_sum %[[VAL_2]] {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
+ // CHECK: %[[VAL_3:.*]] = tosa.reduce_sum %[[VAL_2]] axis(0) : (tensor<2x3xi32>) -> tensor<1x3xi32>
// CHECK: return %[[VAL_3]] : tensor<1x3xi32>
%arg0 = "tosa.const"() <{values = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
%arg1 = "tosa.const"() <{values = dense<[[1,2,3], [4,5,7]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
%arg2 = tosa.add %arg0, %arg1 : (tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
- %0 = tosa.reduce_sum %arg2 {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
+ %0 = tosa.reduce_sum %arg2 axis(0) : (tensor<2x3xi32>) -> tensor<1x3xi32>
return %0 : tensor<1x3xi32>
}
@@ -659,14 +659,14 @@ func.func @reduce_sum_constant_aggressive() -> tensor<1x3xi32> {
// CHECK-LABEL: func.func @reduce_sum_constant_aggressive() -> tensor<1x3xi32> {
// CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<1> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- // CHECK: %[[VAL_1:.*]] = tosa.reduce_sum %[[VAL_0]] {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
- // CHECK: %[[VAL_2:.*]] = tosa.reduce_sum %[[VAL_0]] {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
+ // CHECK: %[[VAL_1:.*]] = tosa.reduce_sum %[[VAL_0]] axis(0) : (tensor<2x3xi32>) -> tensor<1x3xi32>
+ // CHECK: %[[VAL_2:.*]] = tosa.reduce_sum %[[VAL_0]] axis(0) : (tensor<2x3xi32>) -> tensor<1x3xi32>
// CHECK: %[[VAL_3:.*]] = tosa.add %[[VAL_1]], %[[VAL_2]] : (tensor<1x3xi32>, tensor<1x3xi32>) -> tensor<1x3xi32>
// CHECK: return %[[VAL_3]] : tensor<1x3xi32>
- %const = "tosa.const"() {values = dense<1> : tensor<2x3xi32>} : () -> tensor<2x3xi32>
- %0 = tosa.reduce_sum %const {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
- %1 = tosa.reduce_sum %const {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
+ %const = "tosa.const"() <{values = dense<1> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ %0 = tosa.reduce_sum %const axis(0) : (tensor<2x3xi32>) -> tensor<1x3xi32>
+ %1 = tosa.reduce_sum %const axis(0) : (tensor<2x3xi32>) -> tensor<1x3xi32>
%res = tosa.add %0, %1 : (tensor<1x3xi32>, tensor<1x3xi32>) -> tensor<1x3xi32>
return %res : tensor<1x3xi32>
}
@@ -678,8 +678,8 @@ func.func @reduce_sum_constant_aggressive() -> tensor<2x3xi32> {
// AGGRESIVE-DAG: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<2> : tensor<1x2x3xi32>}> : () -> tensor<1x2x3xi32>
// AGGRESIVE-DAG: %[[VAL_1:.*]] = "tosa.const"() <{values = dense<1> : tensor<2x2x3xi32>}> : () -> tensor<2x2x3xi32>
// AGGRESIVE-DAG: %[[VAL_2:.*]] = "tosa.const"() <{values = dense<2> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- // AGGRESIVE: %[[VAL_3:.*]] = tosa.argmax %[[VAL_0]] {axis = 1 : i32} : (tensor<1x2x3xi32>) -> tensor<1x3xi32>
- // AGGRESIVE: %[[VAL_4:.*]] = tosa.argmax %[[VAL_1]] {axis = 0 : i32} : (tensor<2x2x3xi32>) -> tensor<2x3xi32>
+ // AGGRESIVE: %[[VAL_3:.*]] = tosa.argmax %[[VAL_0]] axis(1) : (tensor<1x2x3xi32>) -> tensor<1x3xi32>
+ // AGGRESIVE: %[[VAL_4:.*]] = tosa.argmax %[[VAL_1]] axis(0) : (tensor<2x2x3xi32>) -> tensor<2x3xi32>
// AGGRESIVE: %[[VAL_5:.*]] = tosa.add %[[VAL_3]], %[[VAL_2]] : (tensor<1x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// AGGRESIVE: %[[VAL_6:.*]] = tosa.add %[[VAL_5]], %[[VAL_4]] : (tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// AGGRESIVE: return %[[VAL_6]] : tensor<2x3xi32>
@@ -687,18 +687,18 @@ func.func @reduce_sum_constant_aggressive() -> tensor<2x3xi32> {
// CHECK-LABEL: func.func @reduce_sum_constant_aggressive() -> tensor<2x3xi32> {
// CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<1> : tensor<2x2x3xi32>}> : () -> tensor<2x2x3xi32>
// CHECK: %[[VAL_1:.*]] = "tosa.const"() <{values = dense<2> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- // CHECK: %[[VAL_2:.*]] = tosa.reduce_sum %[[VAL_0]] {axis = 0 : i32} : (tensor<2x2x3xi32>) -> tensor<1x2x3xi32>
- // CHECK: %[[VAL_3:.*]] = tosa.argmax %[[VAL_2]] {axis = 1 : i32} : (tensor<1x2x3xi32>) -> tensor<1x3xi32>
- // CHECK: %[[VAL_4:.*]] = tosa.argmax %[[VAL_0]] {axis = 0 : i32} : (tensor<2x2x3xi32>) -> tensor<2x3xi32>
+ // CHECK: %[[VAL_2:.*]] = tosa.reduce_sum %[[VAL_0]] axis(0) : (tensor<2x2x3xi32>) -> tensor<1x2x3xi32>
+ // CHECK: %[[VAL_3:.*]] = tosa.argmax %[[VAL_2]] axis(1) : (tensor<1x2x3xi32>) -> tensor<1x3xi32>
+ // CHECK: %[[VAL_4:.*]] = tosa.argmax %[[VAL_0]] axis(0) : (tensor<2x2x3xi32>) -> tensor<2x3xi32>
// CHECK: %[[VAL_5:.*]] = tosa.add %[[VAL_3]], %[[VAL_1]] : (tensor<1x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK: %[[VAL_6:.*]] = tosa.add %[[VAL_5]], %[[VAL_4]] : (tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK: return %[[VAL_6]] : tensor<2x3xi32>
- %const0 = "tosa.const"() {values = dense<1> : tensor<2x2x3xi32>} : () -> tensor<2x2x3xi32>
- %const1 = "tosa.const"() {values = dense<2> : tensor<2x3xi32>} : () -> tensor<2x3xi32>
- %reduce0 = tosa.reduce_sum %const0 {axis = 0 : i32} : (tensor<2x2x3xi32>) -> tensor<1x2x3xi32>
- %argmax0 = tosa.argmax %reduce0 {axis = 1 : i32} : (tensor<1x2x3xi32>) -> tensor<1x3xi32>
- %argmax1 = tosa.argmax %const0 {axis = 0 : i32} : (tensor<2x2x3xi32>) -> tensor<2x3xi32>
+ %const0 = "tosa.const"() <{values = dense<1> : tensor<2x2x3xi32>}> : () -> tensor<2x2x3xi32>
+ %const1 = "tosa.const"() <{values = dense<2> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ %reduce0 = tosa.reduce_sum %const0 axis(0) : (tensor<2x2x3xi32>) -> tensor<1x2x3xi32>
+ %argmax0 = tosa.argmax %reduce0 axis(1) : (tensor<1x2x3xi32>) -> tensor<1x3xi32>
+ %argmax1 = tosa.argmax %const0 axis(0) : (tensor<2x2x3xi32>) -> tensor<2x3xi32>
%res0 = tosa.add %argmax0, %const1 : (tensor<1x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
%res1 = tosa.add %res0, %argmax1 : (tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
return %res1 : tensor<2x3xi32>
diff --git a/mlir/test/Dialect/Tosa/tosa-narrow-f64-to-f32-aggressive.mlir b/mlir/test/Dialect/Tosa/tosa-narrow-f64-to-f32-aggressive.mlir
index 40e9a7b2dba5f..2f02e3e9e7609 100644
--- a/mlir/test/Dialect/Tosa/tosa-narrow-f64-to-f32-aggressive.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-narrow-f64-to-f32-aggressive.mlir
@@ -51,7 +51,7 @@ func.func @test_convert_input_parameters(%arg0: tensor<1x3xf64>) -> tensor<1x3xf
// FUNCBOUND: %[[IDENTITY:.*]] = tosa.identity %[[IN]] : (tensor<1x3xf32>) -> tensor<1x3xf32>
%0 = tosa.identity %arg0 : (tensor<1x3xf64>) -> tensor<1x3xf64>
// COMMON: %[[TO_F32:.*]] = tosa.cast %[[IDENTITY]] : (tensor<1x3xf32>) -> tensor<1x3xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<1x3xf64>) -> tensor<1x3xf32>
+ %1 = tosa.cast %0 : (tensor<1x3xf64>) -> tensor<1x3xf32>
// DEFAULT: return %[[TO_F32]] : tensor<1x3xf32>
// FUNCBOUND: return %[[TO_F32]] : tensor<1x3xf32>
return %1 : tensor<1x3xf32>
diff --git a/mlir/test/Dialect/Tosa/tosa-narrow-f64-to-f32.mlir b/mlir/test/Dialect/Tosa/tosa-narrow-f64-to-f32.mlir
index 7c78f0067ffca..69c1c0e4eaf04 100644
--- a/mlir/test/Dialect/Tosa/tosa-narrow-f64-to-f32.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-narrow-f64-to-f32.mlir
@@ -57,8 +57,8 @@ func.func @test_f64_const_precision_loss_small() -> tensor<1xf64> {
func.func @test_f64_concat(%arg0: tensor<13x21x3xf64>, %arg1: tensor<13x21x3xf64>) -> tensor<26x21x3xf64> {
// DEFAULT-DAG: %[[CAST0:.*]] = tosa.cast %[[A0]] : (tensor<13x21x3xf64>) -> tensor<13x21x3xf32>
// DEFAULT-DAG: %[[CAST1:.*]] = tosa.cast %[[A1]] : (tensor<13x21x3xf64>) -> tensor<13x21x3xf32>
- // COMMON: %[[CONCAT:.*]] = tosa.concat %{{.*}}, %{{.*}} {axis = 0 : i32} : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<26x21x3xf32>
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x3xf64>, tensor<13x21x3xf64>) -> tensor<26x21x3xf64>
+ // COMMON: %[[CONCAT:.*]] = tosa.concat %{{.*}}, %{{.*}} axis(0) : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<26x21x3xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x3xf64>, tensor<13x21x3xf64>) -> tensor<26x21x3xf64>
// DEFAULT: %[[CAST_OUT:.*]] = tosa.cast %[[CONCAT]] : (tensor<26x21x3xf32>) -> tensor<26x21x3xf64>
// DEFAULT: return %[[CAST_OUT]] : tensor<26x21x3xf64>
// FUNCBOUND: return %[[CONCAT]] : tensor<26x21x3xf32>
@@ -69,7 +69,7 @@ func.func @test_f64_concat(%arg0: tensor<13x21x3xf64>, %arg1: tensor<13x21x3xf64
// CHECK-LABEL: test_f64_pad
func.func @test_f64_pad(%arg0: tensor<13x21x3xf64>, %arg1: tensor<1xf64>) -> tensor<15x23x5xf64> {
- %padding = tosa.const_shape {values = dense<1> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %padding = tosa.const_shape values(dense<1> : tensor<6xindex>) : () -> !tosa.shape<6>
// DEFAULT-DAG: %[[IN_CAST:.*]] = tosa.cast %arg0 : (tensor<13x21x3xf64>) -> tensor<13x21x3xf32>
// DEFAULT-DAG: %[[PAD_CAST:.*]] = tosa.cast %arg1 : (tensor<1xf64>) -> tensor<1xf32>
// COMMON: %[[PAD:.*]] = tosa.pad %{{.*}}, %{{.*}}, %{{.*}} : (tensor<13x21x3xf32>, !tosa.shape<6>, tensor<1xf32>) -> tensor<15x23x5xf32>
@@ -84,7 +84,7 @@ func.func @test_f64_pad(%arg0: tensor<13x21x3xf64>, %arg1: tensor<1xf64>) -> ten
// CHECK-LABEL: test_f64_reshape
func.func @test_f64_reshape(%arg0: tensor<13x21x3xf64>) -> tensor<1x819xf64> {
- %shape = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %shape = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
// COMMON: %[[RESHAPE:.*]] = tosa.reshape %{{.*}}, %{{.*}} : (tensor<13x21x3xf32>, !tosa.shape<2>) -> tensor<1x819xf32>
%0 = tosa.reshape %arg0, %shape : (tensor<13x21x3xf64>, !tosa.shape<2>) -> tensor<1x819xf64>
// DEFAULT: %[[OUT_CAST:.*]] = tosa.cast %[[RESHAPE]] : (tensor<1x819xf32>) -> tensor<1x819xf64>
@@ -97,8 +97,8 @@ func.func @test_f64_reshape(%arg0: tensor<13x21x3xf64>) -> tensor<1x819xf64> {
// CHECK-LABEL: test_f64_reverse
func.func @test_f64_reverse(%arg0: tensor<13x21x3xf64>) -> tensor<13x21x3xf64> {
- // COMMON: %[[REV:.*]] = tosa.reverse %{{.*}} {axis = 0 : i32} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<13x21x3xf64>) -> tensor<13x21x3xf64>
+ // COMMON: %[[REV:.*]] = tosa.reverse %{{.*}} axis(0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<13x21x3xf64>) -> tensor<13x21x3xf64>
// DEFAULT: %[[OUT:.*]] = tosa.cast %[[REV]] : (tensor<13x21x3xf32>) -> tensor<13x21x3xf64>
// DEFAULT: return %[[OUT]] : tensor<13x21x3xf64>
// FUNCBOUND: return %[[REV]] : tensor<13x21x3xf32>
@@ -109,8 +109,8 @@ func.func @test_f64_reverse(%arg0: tensor<13x21x3xf64>) -> tensor<13x21x3xf64> {
// CHECK-LABEL: test_f64_slice
func.func @test_f64_slice(%arg0: tensor<13x21x3xf64>) -> tensor<4x11x1xf64> {
- %size = tosa.const_shape {values = dense<[4, 11, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %start = tosa.const_shape {values = dense<[6, 8, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[4, 11, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[6, 8, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
// COMMON: %[[SLICE:.*]] = tosa.slice %{{.*}}, %{{.*}}, %{{.*}} : (tensor<13x21x3xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<4x11x1xf32>
%0 = tosa.slice %arg0, %start, %size : (tensor<13x21x3xf64>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<4x11x1xf64>
// DEFAULT: %[[OUT:.*]] = tosa.cast %[[SLICE]] : (tensor<4x11x1xf32>) -> tensor<4x11x1xf64>
@@ -123,7 +123,7 @@ func.func @test_f64_slice(%arg0: tensor<13x21x3xf64>) -> tensor<4x11x1xf64> {
// CHECK-LABEL: test_f64_tile
func.func @test_f64_tile(%arg0: tensor<13x21x3xf64>) -> tensor<39x21x6xf64> {
- %multipliers = tosa.const_shape { values = dense<[3, 1, 2]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %multipliers = tosa.const_shape values(dense<[3, 1, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
// COMMON: %[[TILE:.*]] = tosa.tile %{{.*}}, %{{.*}} : (tensor<13x21x3xf32>, !tosa.shape<3>) -> tensor<39x21x6xf32>
%0 = tosa.tile %arg0, %multipliers : (tensor<13x21x3xf64>, !tosa.shape<3>) -> tensor<39x21x6xf64>
// DEFAULT: %[[OUT:.*]] = tosa.cast %[[TILE]] : (tensor<39x21x6xf32>) -> tensor<39x21x6xf64>
@@ -136,8 +136,8 @@ func.func @test_f64_tile(%arg0: tensor<13x21x3xf64>) -> tensor<39x21x6xf64> {
// CHECK-LABEL: test_f64_transpose
func.func @test_f64_transpose(%arg0: tensor<13x21x3xf64>) -> tensor<3x13x21xf64> {
- // COMMON: %[[TRANSPOSE:.*]] = tosa.transpose %{{.*}} {perms = array<i32: 2, 0, 1>} : (tensor<13x21x3xf32>) -> tensor<3x13x21xf32>
- %0 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x21x3xf64>) -> tensor<3x13x21xf64>
+ // COMMON: %[[TRANSPOSE:.*]] = tosa.transpose %{{.*}} perms([2, 0, 1]) : (tensor<13x21x3xf32>) -> tensor<3x13x21xf32>
+ %0 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<13x21x3xf64>) -> tensor<3x13x21xf64>
// DEFAULT: %[[OUT:.*]] = tosa.cast %[[TRANSPOSE]] : (tensor<3x13x21xf32>) -> tensor<3x13x21xf64>
// DEFAULT: return %[[OUT]] : tensor<3x13x21xf64>
// FUNCBOUND: return %[[TRANSPOSE]] : tensor<3x13x21xf32>
diff --git a/mlir/test/Dialect/Tosa/tosa-narrow-i64-to-i32-aggressive.mlir b/mlir/test/Dialect/Tosa/tosa-narrow-i64-to-i32-aggressive.mlir
index 5d8ca4dcd5ad5..48dc00f73755e 100644
--- a/mlir/test/Dialect/Tosa/tosa-narrow-i64-to-i32-aggressive.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-narrow-i64-to-i32-aggressive.mlir
@@ -3,8 +3,8 @@
// CHECK-LABEL: test_i64_argmax_large_axis_dim
func.func @test_i64_argmax_large_axis_dim(%arg0: tensor<1x513x513x2147483650xi8>) -> tensor<1x513x513xi64> {
- // DEFAULT: tosa.argmax %arg0 {axis = 3 : i32} : (tensor<1x513x513x2147483650xi8>) -> tensor<1x513x513xi32>
- %0 = tosa.argmax %arg0 {axis = 3 : i32} : (tensor<1x513x513x2147483650xi8>) -> tensor<1x513x513xi64>
+ // DEFAULT: tosa.argmax %arg0 axis(3) : (tensor<1x513x513x2147483650xi8>) -> tensor<1x513x513xi32>
+ %0 = tosa.argmax %arg0 axis(3) : (tensor<1x513x513x2147483650xi8>) -> tensor<1x513x513xi64>
return %0 : tensor<1x513x513xi64>
}
@@ -17,10 +17,10 @@ func.func @test_convert_input_parameters(%arg0: tensor<1x513x513x3xi64>) -> tens
// DEFAULT: %[[FUNC_BOUND_CAST:.*]] = tosa.cast %[[IN]] : (tensor<1x513x513x3xi64>) -> tensor<1x513x513x3xi32>
// DEFAULT: %[[CAST1:.*]] = tosa.cast %[[FUNC_BOUND_CAST]] : (tensor<1x513x513x3xi32>) -> tensor<1x513x513x3xi32>
// FUNCBOUND: %[[CAST1:.*]] = tosa.cast %[[IN]] : (tensor<1x513x513x3xi32>) -> tensor<1x513x513x3xi32>
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<1x513x513x3xi64>) -> tensor<1x513x513x3xi32>
+ %0 = tosa.cast %arg0 : (tensor<1x513x513x3xi64>) -> tensor<1x513x513x3xi32>
// COMMON: %[[CAST2:.*]] = tosa.cast %[[CAST1]] : (tensor<1x513x513x3xi32>) -> tensor<1x513x513x3xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<1x513x513x3xi32>) -> tensor<1x513x513x3xf32>
+ %1 = tosa.cast %0 : (tensor<1x513x513x3xi32>) -> tensor<1x513x513x3xf32>
return %1 : tensor<1x513x513x3xf32>
}
@@ -84,8 +84,8 @@ func.func @test_const() -> tensor<2xi64> {
// CHECK-LABEL: test_clamp_trunc
func.func @test_clamp_trunc(%arg0: tensor<100xi64>) -> tensor<100xi64> {
- // COMMON: tosa.clamp %{{.*}} {max_val = 2147483647 : i32, min_val = -2147483648 : i32} : (tensor<100xi32>) -> tensor<100xi32>
- %1 = tosa.clamp %arg0 {max_val = 3000000000 : i64, min_val = -2147483648 : i64} : (tensor<100xi64>) -> tensor<100xi64>
+ // COMMON: tosa.clamp %{{.*}} min_val(-2147483648 : i32) max_val(2147483647 : i32) : (tensor<100xi32>) -> tensor<100xi32>
+ %1 = tosa.clamp %arg0 min_val(-2147483648 : i64) max_val(3000000000 : i64) : (tensor<100xi64>) -> tensor<100xi64>
return %1 : tensor<100xi64>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-narrow-i64-to-i32.mlir b/mlir/test/Dialect/Tosa/tosa-narrow-i64-to-i32.mlir
index 4ff8a38548c81..dff4445d83806 100644
--- a/mlir/test/Dialect/Tosa/tosa-narrow-i64-to-i32.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-narrow-i64-to-i32.mlir
@@ -5,8 +5,8 @@
// CHECK-LABEL: test_i64_argmax
func.func @test_i64_argmax(%arg0: tensor<1x513x513x19xi8>) -> tensor<1x513x513xi64> {
- // COMMON: %[[ARGMAX:.*]] = tosa.argmax %arg0 {axis = 3 : i32} : (tensor<1x513x513x19xi8>) -> tensor<1x513x513xi32>
- %0 = tosa.argmax %arg0 {axis = 3 : i32} : (tensor<1x513x513x19xi8>) -> tensor<1x513x513xi64>
+ // COMMON: %[[ARGMAX:.*]] = tosa.argmax %arg0 axis(3) : (tensor<1x513x513x19xi8>) -> tensor<1x513x513xi32>
+ %0 = tosa.argmax %arg0 axis(3) : (tensor<1x513x513x19xi8>) -> tensor<1x513x513xi64>
// DEFAULT: %[[CAST:.*]] = tosa.cast %[[ARGMAX]] : (tensor<1x513x513xi32>) -> tensor<1x513x513xi64>
// FUNCBOUND: return %[[ARGMAX]] : tensor<1x513x513xi32>
@@ -29,10 +29,10 @@ func.func @test_i64_const() -> tensor<2xi64> {
// CHECK-LABEL: test_i64_argmax_cast
func.func @test_i64_argmax_cast(%arg0: tensor<1x513x513x19xi8>) -> tensor<1x513x513xf32> {
- // COMMON: %[[ARGMAX:.*]] = tosa.argmax %arg0 {axis = 3 : i32} : (tensor<1x513x513x19xi8>) -> tensor<1x513x513xi32>
- %0 = tosa.argmax %arg0 {axis = 3 : i32} : (tensor<1x513x513x19xi8>) -> tensor<1x513x513xi64>
+ // COMMON: %[[ARGMAX:.*]] = tosa.argmax %arg0 axis(3) : (tensor<1x513x513x19xi8>) -> tensor<1x513x513xi32>
+ %0 = tosa.argmax %arg0 axis(3) : (tensor<1x513x513x19xi8>) -> tensor<1x513x513xi64>
// COMMON: tosa.cast %[[ARGMAX]] : (tensor<1x513x513xi32>) -> tensor<1x513x513xf32>
- %1 = tosa.cast %0 {input_unsigned = false} : (tensor<1x513x513xi64>) -> tensor<1x513x513xf32>
+ %1 = tosa.cast %0 : (tensor<1x513x513xi64>) -> tensor<1x513x513xf32>
return %1 : tensor<1x513x513xf32>
}
@@ -41,7 +41,7 @@ func.func @test_i64_argmax_cast(%arg0: tensor<1x513x513x19xi8>) -> tensor<1x513x
// CHECK-LABEL: test_i64_argmax_large_axis_dim
func.func @test_i64_argmax_large_axis_dim(%arg0: tensor<1x513x513x2147483650xi8>) -> tensor<1x513x513xi64> {
// expected-error @+1 {{failed to legalize operation 'tosa.argmax'}}
- %0 = tosa.argmax %arg0 {axis = 3 : i32} : (tensor<1x513x513x2147483650xi8>) -> tensor<1x513x513xi64>
+ %0 = tosa.argmax %arg0 axis(3) : (tensor<1x513x513x2147483650xi8>) -> tensor<1x513x513xi64>
return %0 : tensor<1x513x513xi64>
}
@@ -60,10 +60,10 @@ func.func @test_add(%arg0: tensor<13x21x1xi64>, %arg1: tensor<13x21x3xi64>) -> t
func.func @test_regions(%arg0: tensor<1x2xi32>, %arg1: tensor<1xi32>, %arg2: tensor<i1>) -> tensor<1xi32> {
// COMMON: %[[IF_RESULT:.*]] = tosa.cond_if %arg2 : tensor<i1> -> tensor<1xi32>
%0 = tosa.cond_if %arg2 : tensor<i1> -> tensor<1xi32> {
- // COMMON: %[[ARGMAX:.*]] = tosa.argmax %arg0 {axis = 1 : i32} : (tensor<1x2xi32>) -> tensor<1xi32>
- %1 = tosa.argmax %arg0 {axis = 1 : i32} : (tensor<1x2xi32>) -> tensor<1xi64>
+ // COMMON: %[[ARGMAX:.*]] = tosa.argmax %arg0 axis(1) : (tensor<1x2xi32>) -> tensor<1xi32>
+ %1 = tosa.argmax %arg0 axis(1) : (tensor<1x2xi32>) -> tensor<1xi64>
// COMMON: %[[CAST:.*]] = tosa.cast %[[ARGMAX]] : (tensor<1xi32>) -> tensor<1xi32>
- %2 = tosa.cast %1 {input_unsigned = false} : (tensor<1xi64>) -> tensor<1xi32>
+ %2 = tosa.cast %1 : (tensor<1xi64>) -> tensor<1xi32>
// COMMON: tosa.yield %[[CAST]] : tensor<1xi32>
tosa.yield %2 : tensor<1xi32>
} else {
@@ -98,8 +98,8 @@ module {
// CHECK-LABEL: test_concat
func.func @test_concat(%arg0: tensor<13x21x3xi64>, %arg1: tensor<13x21x3xi64>) -> tensor<26x21x3xi64> {
- // COMMON: tosa.concat %{{.*}}, %{{.*}} {axis = 0 : i32} : (tensor<13x21x3xi32>, tensor<13x21x3xi32>) -> tensor<26x21x3xi32>
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x3xi64>, tensor<13x21x3xi64>) -> tensor<26x21x3xi64>
+ // COMMON: tosa.concat %{{.*}}, %{{.*}} axis(0) : (tensor<13x21x3xi32>, tensor<13x21x3xi32>) -> tensor<26x21x3xi32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x3xi64>, tensor<13x21x3xi64>) -> tensor<26x21x3xi64>
return %0 : tensor<26x21x3xi64>
}
@@ -107,7 +107,7 @@ func.func @test_concat(%arg0: tensor<13x21x3xi64>, %arg1: tensor<13x21x3xi64>) -
// CHECK-LABEL: test_pad
func.func @test_pad(%arg0: tensor<13x21x3xi64>, %arg1: tensor<1xi64>) -> tensor<15x23x5xi64> {
- %padding = tosa.const_shape {values = dense<1> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %padding = tosa.const_shape values(dense<1> : tensor<6xindex>) : () -> !tosa.shape<6>
// COMMON: tosa.pad %{{.*}}, %{{.*}}, %{{.*}} : (tensor<13x21x3xi32>, !tosa.shape<6>, tensor<1xi32>) -> tensor<15x23x5xi32>
%1 = tosa.pad %arg0, %padding, %arg1 : (tensor<13x21x3xi64>, !tosa.shape<6>, tensor<1xi64>) -> tensor<15x23x5xi64>
return %1 : tensor<15x23x5xi64>
@@ -117,7 +117,7 @@ func.func @test_pad(%arg0: tensor<13x21x3xi64>, %arg1: tensor<1xi64>) -> tensor<
// CHECK-LABEL: test_reshape
func.func @test_reshape(%arg0: tensor<13x21x3xi64>) -> tensor<1x819xi64> {
- %1 = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
// COMMON: tosa.reshape %{{.*}}, %{{.*}} : (tensor<13x21x3xi32>, !tosa.shape<2>) -> tensor<1x819xi32>
%0 = tosa.reshape %arg0, %1 : (tensor<13x21x3xi64>, !tosa.shape<2>) -> tensor<1x819xi64>
return %0 : tensor<1x819xi64>
@@ -127,8 +127,8 @@ func.func @test_reshape(%arg0: tensor<13x21x3xi64>) -> tensor<1x819xi64> {
// CHECK-LABEL: test_reverse
func.func @test_reverse(%arg0: tensor<13x21x3xi64>) -> tensor<13x21x3xi64> {
- // COMMON: tosa.reverse %{{.*}} {axis = 0 : i32} : (tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
- %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<13x21x3xi64>) -> tensor<13x21x3xi64>
+ // COMMON: tosa.reverse %{{.*}} axis(0) : (tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
+ %0 = tosa.reverse %arg0 axis(0) : (tensor<13x21x3xi64>) -> tensor<13x21x3xi64>
return %0 : tensor<13x21x3xi64>
}
@@ -136,8 +136,8 @@ func.func @test_reverse(%arg0: tensor<13x21x3xi64>) -> tensor<13x21x3xi64> {
// CHECK-LABEL: test_slice
func.func @test_slice(%arg0: tensor<13x21x3xi64>) -> tensor<4x11x1xi64> {
- %size = tosa.const_shape {values = dense<[4, 11, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %start = tosa.const_shape {values = dense<[6, 8, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[4, 11, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[6, 8, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
// COMMON: tosa.slice %{{.*}}, %{{.*}}, %{{.*}} : (tensor<13x21x3xi32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<4x11x1xi32>
%2 = tosa.slice %arg0, %start, %size : (tensor<13x21x3xi64>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<4x11x1xi64>
return %2 : tensor<4x11x1xi64>
@@ -147,7 +147,7 @@ func.func @test_slice(%arg0: tensor<13x21x3xi64>) -> tensor<4x11x1xi64> {
// CHECK-LABEL: test_tile
func.func @test_tile(%arg0: tensor<13x21x3xi64>) -> tensor<39x21x6xi64> {
- %cst = tosa.const_shape { values = dense<[3, 1, 2]> : tensor<3xindex> } : () -> !tosa.shape<3>
+ %cst = tosa.const_shape values(dense<[3, 1, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
// COMMON: tosa.tile %{{.*}}, %{{.*}} : (tensor<13x21x3xi32>, !tosa.shape<3>) -> tensor<39x21x6xi32>
%0 = tosa.tile %arg0, %cst: (tensor<13x21x3xi64>, !tosa.shape<3>) -> tensor<39x21x6xi64>
return %0 : tensor<39x21x6xi64>
@@ -157,8 +157,8 @@ func.func @test_tile(%arg0: tensor<13x21x3xi64>) -> tensor<39x21x6xi64> {
// CHECK-LABEL: transpose
func.func @test_transpose(%arg0: tensor<13x21x3xi64>) -> tensor<3x13x21xi64> {
- // COMMON: tosa.transpose %{{.*}} {perms = array<i32: 2, 0, 1>} : (tensor<13x21x3xi32>) -> tensor<3x13x21xi32>
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x21x3xi64>) -> tensor<3x13x21xi64>
+ // COMMON: tosa.transpose %{{.*}} perms([2, 0, 1]) : (tensor<13x21x3xi32>) -> tensor<3x13x21xi32>
+ %1 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<13x21x3xi64>) -> tensor<3x13x21xi64>
return %1 : tensor<3x13x21xi64>
}
@@ -167,7 +167,7 @@ func.func @test_transpose(%arg0: tensor<13x21x3xi64>) -> tensor<3x13x21xi64> {
// CHECK-LABEL: test_transition_to_i64
func.func @test_transition_to_i64(%arg0: tensor<1xi32>) -> tensor<1xi64> {
// COMMON: %[[CAST:.*]] = tosa.cast %arg0 : (tensor<1xi32>) -> tensor<1xi32>
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<1xi32>) -> tensor<1xi64>
+ %0 = tosa.cast %arg0 : (tensor<1xi32>) -> tensor<1xi64>
// COMMON: %[[IDENTITY1:.*]] = tosa.identity %[[CAST]] : (tensor<1xi32>) -> tensor<1xi32>
%1 = tosa.identity %0 : (tensor<1xi64>) -> tensor<1xi64>
// COMMON: %[[IDENTITY2:.*]] = tosa.identity %[[IDENTITY1]] : (tensor<1xi32>) -> tensor<1xi32>
@@ -189,7 +189,7 @@ func.func @test_transition_from_i64(%arg0: tensor<1xi64>) -> tensor<1xi32> {
// COMMON: %[[IDENTITY2:.*]] = tosa.identity %[[IDENTITY1]] : (tensor<1xi32>) -> tensor<1xi32>
%1 = tosa.identity %0 : (tensor<1xi64>) -> tensor<1xi64>
// COMMON: %[[OUT_CAST:.*]] = tosa.cast %[[IDENTITY2]] : (tensor<1xi32>) -> tensor<1xi32>
- %2 = tosa.cast %1 {input_unsigned = false} : (tensor<1xi64>) -> tensor<1xi32>
+ %2 = tosa.cast %1 : (tensor<1xi64>) -> tensor<1xi32>
// COMMON: return %[[OUT_CAST]] : tensor<1xi32>
return %2 : tensor<1xi32>
}
@@ -198,7 +198,7 @@ func.func @test_transition_from_i64(%arg0: tensor<1xi64>) -> tensor<1xi32> {
// CHECK-LABEL test_transition_from_i64_input_unsigned
func.func @test_transition_from_i64_input_unsigned(%arg0: tensor<1xui64>) -> tensor<1xui64> {
- // DEFAULT: %[[CAST:.*]] = tosa.cast %arg0 {input_unsigned = true} : (tensor<1xui64>) -> tensor<1xi32>
+ // DEFAULT: %[[CAST:.*]] = tosa.cast %arg0 input_unsigned(true) : (tensor<1xui64>) -> tensor<1xi32>
// FUNCBOUND: %[[IDENTITY:.*]] = tosa.identity %arg0 : (tensor<1xi32>) -> tensor<1xi32>
// DEFAULT: %[[IDENTITY:.*]] = tosa.identity %[[CAST]] : (tensor<1xi32>) -> tensor<1xi32>
%0 = tosa.identity %arg0 : (tensor<1xui64>) -> tensor<1xui64>
@@ -212,8 +212,8 @@ func.func @test_transition_from_i64_input_unsigned(%arg0: tensor<1xui64>) -> ten
// CHECK-LABEL: test_clamp
func.func @test_clamp(%arg0: tensor<100xi64>) -> tensor<100xi64> {
- // COMMON: tosa.clamp %{{.*}} {max_val = 2147483647 : i32, min_val = -2147483648 : i32} : (tensor<100xi32>) -> tensor<100xi32>
- %1 = tosa.clamp %arg0 {max_val = 2147483647 : i64, min_val = -2147483648 : i64} : (tensor<100xi64>) -> tensor<100xi64>
+ // COMMON: tosa.clamp %{{.*}} min_val(-2147483648 : i32) max_val(2147483647 : i32) : (tensor<100xi32>) -> tensor<100xi32>
+ %1 = tosa.clamp %arg0 min_val(-2147483648 : i64) max_val(2147483647 : i64) : (tensor<100xi64>) -> tensor<100xi64>
return %1 : tensor<100xi64>
}
@@ -222,7 +222,7 @@ func.func @test_clamp(%arg0: tensor<100xi64>) -> tensor<100xi64> {
// CHECK-LABEL: test_clamp_max_outside_i32_range
func.func @test_clamp_max_outside_i32_range(%arg0: tensor<100xi64>) -> tensor<100xi64> {
// expected-error at +1 {{failed to legalize operation 'tosa.clamp'}}
- %1 = tosa.clamp %arg0 {max_val = 2147483648 : i64, min_val = -2147483648 : i64} : (tensor<100xi64>) -> tensor<100xi64>
+ %1 = tosa.clamp %arg0 min_val(-2147483648 : i64) max_val(2147483648 : i64) : (tensor<100xi64>) -> tensor<100xi64>
return %1 : tensor<100xi64>
}
@@ -231,7 +231,7 @@ func.func @test_clamp_max_outside_i32_range(%arg0: tensor<100xi64>) -> tensor<10
// CHECK-LABEL: test_clamp_min_outside_i32_range
func.func @test_clamp_min_outside_i32_range(%arg0: tensor<100xi64>) -> tensor<100xi64> {
// expected-error at +1 {{failed to legalize operation 'tosa.clamp'}}
- %1 = tosa.clamp %arg0 {max_val = 2147483647 : i64, min_val = -2147483649 : i64} : (tensor<100xi64>) -> tensor<100xi64>
+ %1 = tosa.clamp %arg0 min_val(-2147483649 : i64) max_val(2147483647 : i64) : (tensor<100xi64>) -> tensor<100xi64>
return %1 : tensor<100xi64>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir b/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir
index 46e4f504af0ec..4f186ee03752a 100644
--- a/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir
@@ -4,9 +4,9 @@
// CHECK-NEXT: %[[RESULT:.*]] = tosa.ceil %arg0
// CHECK-NEXT: return %[[RESULT]]
func.func @test_transpose_tracks_to_nullifying_single_step(%arg0: tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32> {
- %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+ %0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
%ceil = tosa.ceil %0 : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
- %1 = tosa.transpose %ceil {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
+ %1 = tosa.transpose %ceil perms([0, 3, 1, 2]) : (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
return %1 : tensor<1x2x3x4xi32>
}
@@ -18,11 +18,11 @@ func.func @test_transpose_tracks_to_nullifying_single_step(%arg0: tensor<1x2x3x4
// CHECK-NEXT: %[[NOT:.*]] = tosa.bitwise_not %[[ABS]]
// CHECK-NEXT: return %[[NOT]]
func.func @test_transpose_tracks_to_nullifying_multi_unary_step(%arg0: tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32> {
- %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
- %clamp = tosa.clamp %0 {max_val = 1 : i32, min_val = 0 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
+ %0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+ %clamp = tosa.clamp %0 min_val(0 : i32) max_val(1 : i32) : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
%abs = tosa.abs %clamp : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
%bitwise_not = tosa.bitwise_not %abs : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
- %1 = tosa.transpose %bitwise_not {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
+ %1 = tosa.transpose %bitwise_not perms([0, 3, 1, 2]) : (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
return %1 : tensor<1x2x3x4xi32>
}
@@ -34,12 +34,12 @@ func.func @test_transpose_tracks_to_nullifying_multi_unary_step(%arg0: tensor<1x
// CHECK-NEXT: %[[ADD:.*]] = tosa.add %[[CLAMP]], %[[ABS]]
// CHECK-NEXT: return %[[ADD]]
func.func @test_transpose_tracks_to_nullifying_diverging_binary(%arg0: tensor<1x2x3x4xi32>, %arg1: tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32> {
- %transpose0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
- %transpose1 = tosa.transpose %arg1 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
- %clamp = tosa.clamp %transpose0 {max_val = 1 : i32, min_val = 0 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
+ %transpose0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+ %transpose1 = tosa.transpose %arg1 perms([0, 2, 3, 1]) : (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+ %clamp = tosa.clamp %transpose0 min_val(0 : i32) max_val(1 : i32) : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
%abs = tosa.abs %transpose1 : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
%add = tosa.add %clamp, %abs : (tensor<1x3x4x2xi32>, tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
- %result = tosa.transpose %add {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
+ %result = tosa.transpose %add perms([0, 3, 1, 2]) : (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
return %result : tensor<1x2x3x4xi32>
}
@@ -52,12 +52,12 @@ func.func @test_transpose_tracks_to_nullifying_diverging_binary(%arg0: tensor<1x
// CHECK-NEXT: %[[ADD:.*]] = tosa.add %[[CLAMP]], %[[ABS]]
// CHECK-NEXT: return %[[ADD]]
func.func @test_transpose_tracks_to_nullifying_diverging_binary_with_broadcasting(%arg0: tensor<1x2x3x4xi32>, %arg1: tensor<1x2x1x4xi32>) -> tensor<1x2x3x4xi32> {
- %transpose0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
- %transpose1 = tosa.transpose %arg1 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x1x4xi32>) -> tensor<1x1x4x2xi32>
- %clamp = tosa.clamp %transpose0 {max_val = 1 : i32, min_val = 0 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
+ %transpose0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+ %transpose1 = tosa.transpose %arg1 perms([0, 2, 3, 1]) : (tensor<1x2x1x4xi32>) -> tensor<1x1x4x2xi32>
+ %clamp = tosa.clamp %transpose0 min_val(0 : i32) max_val(1 : i32) : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
%abs = tosa.abs %transpose1 : (tensor<1x1x4x2xi32>) -> tensor<1x1x4x2xi32>
%add = tosa.add %clamp, %abs : (tensor<1x3x4x2xi32>, tensor<1x1x4x2xi32>) -> tensor<1x3x4x2xi32>
- %result = tosa.transpose %add {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
+ %result = tosa.transpose %add perms([0, 3, 1, 2]) : (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
return %result : tensor<1x2x3x4xi32>
}
@@ -67,9 +67,9 @@ func.func @test_transpose_tracks_to_nullifying_diverging_binary_with_broadcastin
// CHECK-NEXT: %[[RESULT:.*]] = tosa.add %arg0, %arg0
// CHECK-NEXT: return %[[RESULT]]
func.func @test_transpose_tracks_to_nullifying__converging_binary(%arg0: tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32> {
- %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+ %0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
%clamp = tosa.add %0, %0 : (tensor<1x3x4x2xi32>, tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
- %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
+ %1 = tosa.transpose %clamp perms([0, 3, 1, 2]) : (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
return %1 : tensor<1x2x3x4xi32>
}
@@ -94,20 +94,20 @@ func.func @test_torch_conv2d_with_elementwise_in_between(%arg0: tensor<3x3x10x10
%5 = "tosa.const"() <{values = dense_resource<torch_tensor_3_torch.float32> : tensor<3xf32>}> : () -> tensor<3xf32>
%6 = "tosa.const"() <{values = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32>
%7 = "tosa.const"() <{values = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
- %8 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x10x10xf32>) -> tensor<3x10x10x3xf32>
- %9 = tosa.transpose %4 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x2x2xf32>) -> tensor<3x2x2x3xf32>
- %10 = tosa.conv2d %8, %9, %5, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x10x10x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<3x9x9x3xf32>
- %11 = tosa.transpose %10 {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x9x9x3xf32>) -> tensor<3x3x9x9xf32>
+ %8 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<3x3x10x10xf32>) -> tensor<3x10x10x3xf32>
+ %9 = tosa.transpose %4 perms([0, 2, 3, 1]) : (tensor<3x3x2x2xf32>) -> tensor<3x2x2x3xf32>
+ %10 = tosa.conv2d %8, %9, %5, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<3x10x10x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<3x9x9x3xf32>
+ %11 = tosa.transpose %10 perms([0, 3, 1, 2]) : (tensor<3x9x9x3xf32>) -> tensor<3x3x9x9xf32>
%12 = tosa.ceil %11 : (tensor<3x3x9x9xf32>) -> tensor<3x3x9x9xf32>
- %13 = tosa.transpose %12 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x9x9xf32>) -> tensor<3x9x9x3xf32>
- %14 = tosa.transpose %3 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x2x2xf32>) -> tensor<3x2x2x3xf32>
- %15 = tosa.conv2d %13, %14, %2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x9x9x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<3x8x8x3xf32>
- %16 = tosa.transpose %15 {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x8x8x3xf32>) -> tensor<3x3x8x8xf32>
+ %13 = tosa.transpose %12 perms([0, 2, 3, 1]) : (tensor<3x3x9x9xf32>) -> tensor<3x9x9x3xf32>
+ %14 = tosa.transpose %3 perms([0, 2, 3, 1]) : (tensor<3x3x2x2xf32>) -> tensor<3x2x2x3xf32>
+ %15 = tosa.conv2d %13, %14, %2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<3x9x9x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<3x8x8x3xf32>
+ %16 = tosa.transpose %15 perms([0, 3, 1, 2]) : (tensor<3x8x8x3xf32>) -> tensor<3x3x8x8xf32>
%17 = tosa.floor %16 : (tensor<3x3x8x8xf32>) -> tensor<3x3x8x8xf32>
- %18 = tosa.transpose %17 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x8x8xf32>) -> tensor<3x8x8x3xf32>
- %19 = tosa.transpose %1 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x2x2xf32>) -> tensor<3x2x2x3xf32>
- %20 = tosa.conv2d %18, %19, %0, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x8x8x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<3x7x7x3xf32>
- %21 = tosa.transpose %20 {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x7x7x3xf32>) -> tensor<3x3x7x7xf32>
+ %18 = tosa.transpose %17 perms([0, 2, 3, 1]) : (tensor<3x3x8x8xf32>) -> tensor<3x8x8x3xf32>
+ %19 = tosa.transpose %1 perms([0, 2, 3, 1]) : (tensor<3x3x2x2xf32>) -> tensor<3x2x2x3xf32>
+ %20 = tosa.conv2d %18, %19, %0, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<3x8x8x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<3x7x7x3xf32>
+ %21 = tosa.transpose %20 perms([0, 3, 1, 2]) : (tensor<3x7x7x3xf32>) -> tensor<3x3x7x7xf32>
return %21 : tensor<3x3x7x7xf32>
}
@@ -118,11 +118,11 @@ func.func @test_torch_conv2d_with_elementwise_in_between(%arg0: tensor<3x3x10x10
// CHECK-NEXT: %[[RES:.*]] = tosa.mul %arg0, %arg1, %[[SHIFT]]
// CHECK-NEXT: return %[[RES]]
func.func @test_mulop_conversion(%arg0: tensor<1x2x3x4xi32>, %arg1: tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32> {
- %transpose0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
- %transpose1 = tosa.transpose %arg1 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+ %transpose0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+ %transpose1 = tosa.transpose %arg1 perms([0, 2, 3, 1]) : (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
%shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%mul = tosa.mul %transpose0, %transpose1, %shift : (tensor<1x3x4x2xi32>, tensor<1x3x4x2xi32>, tensor<1xi8>) -> tensor<1x3x4x2xi32>
- %result = tosa.transpose %mul {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
+ %result = tosa.transpose %mul perms([0, 3, 1, 2]) : (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
return %result : tensor<1x2x3x4xi32>
}
@@ -131,26 +131,26 @@ func.func @test_mulop_conversion(%arg0: tensor<1x2x3x4xi32>, %arg1: tensor<1x2x3
// COM: this case is a reshape we don't convert, since can't fold the transpose into it.
// COM: a transform actually occurs underneath the hood, but it results in identical IR.
// CHECK-LABEL: @test_basic_non_broadcasting_reshape
-// CHECK: %[[SHAPE:.+]] = tosa.const_shape {values = dense<[1, 3, 2]> : tensor<3xindex>}
+// CHECK: %[[SHAPE:.+]] = tosa.const_shape values(dense<[1, 3, 2]> : tensor<3xindex>)
// CHECK: %[[RESHAPED:.+]] = tosa.reshape %arg0, %[[SHAPE]] : (tensor<2x3xi32>, !tosa.shape<3>) -> tensor<1x3x2xi32>
-// CHECK: tosa.transpose %[[RESHAPED]] {perms = array<i32: 0, 2, 1>} : (tensor<1x3x2xi32>) -> tensor<1x2x3xi32>
+// CHECK: tosa.transpose %[[RESHAPED]] perms([0, 2, 1]) : (tensor<1x3x2xi32>) -> tensor<1x2x3xi32>
func.func @test_basic_non_broadcasting_reshape(%arg0: tensor<2x3xi32>) -> tensor<1x2x3xi32> {
- %shape = tosa.const_shape {values = dense<[1, 3, 2]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %shape = tosa.const_shape values(dense<[1, 3, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
%1 = tosa.reshape %arg0, %shape : (tensor<2x3xi32>, !tosa.shape<3>) -> tensor<1x3x2xi32>
- %2 = tosa.transpose %1 {perms = array<i32: 0, 2, 1>}: (tensor<1x3x2xi32>) -> tensor<1x2x3xi32>
+ %2 = tosa.transpose %1 perms([0, 2, 1]) : (tensor<1x3x2xi32>) -> tensor<1x2x3xi32>
return %2 : tensor<1x2x3xi32>
}
// -----
// CHECK-LABEL: @test_dynamic_broadcasting_reshape
-// CHECK-DAG: %[[SHAPE:.*]] = tosa.const_shape {values = dense<[1, 1, -1]> : tensor<3xindex>}
+// CHECK-DAG: %[[SHAPE:.*]] = tosa.const_shape values(dense<[1, 1, -1]> : tensor<3xindex>)
// CHECK: %[[RES:.*]] = tosa.reshape %arg0, %[[SHAPE]] : (tensor<?xi32>, !tosa.shape<3>) -> tensor<1x1x?xi32>
// CHECK: return %[[RES]]
func.func @test_dynamic_broadcasting_reshape(%arg0: tensor<?xi32>) -> tensor<1x1x?xi32> {
- %shape = tosa.const_shape {values = dense<[1, -1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %shape = tosa.const_shape values(dense<[1, -1, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
%1 = tosa.reshape %arg0, %shape : (tensor<?xi32>, !tosa.shape<3>) -> tensor<1x?x1xi32>
- %2 = tosa.transpose %1 {perms = array<i32: 0, 2, 1>}: (tensor<1x?x1xi32>) -> tensor<1x1x?xi32>
+ %2 = tosa.transpose %1 perms([0, 2, 1]) : (tensor<1x?x1xi32>) -> tensor<1x1x?xi32>
return %2 : tensor<1x1x?xi32>
}
@@ -158,33 +158,33 @@ func.func @test_dynamic_broadcasting_reshape(%arg0: tensor<?xi32>) -> tensor<1x1
// CHECK-LABEL: @test_reshape_for_broadcast
// CHECK-DAG: %[[RESHAPE_INPUT:.*]] = "tosa.const"() <{values = dense<[1, 2, 3, 4]>
-// CHECK-DAG: %[[SHAPE:.*]] = tosa.const_shape {values = dense<[4, 1, 1]> : tensor<3xindex>}
+// CHECK-DAG: %[[SHAPE:.*]] = tosa.const_shape values(dense<[4, 1, 1]> : tensor<3xindex>)
// CHECK: %[[RESHAPE:.*]] = tosa.reshape %[[RESHAPE_INPUT]], %[[SHAPE]] : (tensor<4xi32>, !tosa.shape<3>) -> tensor<4x1x1xi32>
// CHECK: %[[ADD:.*]] = tosa.add %arg0, %[[RESHAPE]]
// CHECK: return %[[ADD]]
func.func @test_reshape_for_broadcast(%arg0: tensor<4x3x2xi32>) -> tensor<4x3x2xi32> {
- %0 = "tosa.const"() {values = dense<[1,2,3,4]> : tensor<4xi32>} : () -> tensor<4xi32>
- %1 = tosa.const_shape {values = dense<[1, 1, 4]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %0 = "tosa.const"() <{values = dense<[1,2,3,4]> : tensor<4xi32>}> : () -> tensor<4xi32>
+ %1 = tosa.const_shape values(dense<[1, 1, 4]> : tensor<3xindex>) : () -> !tosa.shape<3>
%reshape = tosa.reshape %0, %1 : (tensor<4xi32>, !tosa.shape<3>) -> tensor<1x1x4xi32>
- %transpose0 = tosa.transpose %arg0 {perms = array<i32: 2, 1, 0>}: (tensor<4x3x2xi32>) -> tensor<2x3x4xi32>
+ %transpose0 = tosa.transpose %arg0 perms([2, 1, 0]) : (tensor<4x3x2xi32>) -> tensor<2x3x4xi32>
%add = tosa.add %transpose0, %reshape : (tensor<2x3x4xi32>, tensor<1x1x4xi32>) -> tensor<2x3x4xi32>
- %transpose1 = tosa.transpose %add {perms = array<i32: 2, 1, 0>}: (tensor<2x3x4xi32>) -> tensor<4x3x2xi32>
+ %transpose1 = tosa.transpose %add perms([2, 1, 0]) : (tensor<2x3x4xi32>) -> tensor<4x3x2xi32>
return %transpose1 : tensor<4x3x2xi32>
}
// -----
// CHECK-LABEL: @test_multi_dim_reshape_for_broadcast
-// CHECK-DAG: %[[SHAPE:.*]] = tosa.const_shape {values = dense<[4, 1, 1]> : tensor<3xindex>}
+// CHECK-DAG: %[[SHAPE:.*]] = tosa.const_shape values(dense<[4, 1, 1]> : tensor<3xindex>)
// CHECK: %[[RESHAPE:.*]] = tosa.reshape %arg0, %[[SHAPE]] : (tensor<1x4x1xi32>, !tosa.shape<3>) -> tensor<4x1x1xi32>
// CHECK: %[[ADD:.*]] = tosa.add %arg1, %[[RESHAPE]]
// CHECK: return %[[ADD]]
func.func @test_multi_dim_reshape_for_broadcast(%arg0: tensor<1x4x1xi32>, %arg1: tensor<4x3x2xi32>) -> tensor<4x3x2xi32> {
- %shape = tosa.const_shape {values = dense<[1, 1, 4]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %shape = tosa.const_shape values(dense<[1, 1, 4]> : tensor<3xindex>) : () -> !tosa.shape<3>
%reshape = tosa.reshape %arg0, %shape : (tensor<1x4x1xi32>, !tosa.shape<3>) -> tensor<1x1x4xi32>
- %transpose0 = tosa.transpose %arg1 {perms = array<i32: 2, 1, 0>}: (tensor<4x3x2xi32>) -> tensor<2x3x4xi32>
+ %transpose0 = tosa.transpose %arg1 perms([2, 1, 0]): (tensor<4x3x2xi32>) -> tensor<2x3x4xi32>
%add = tosa.add %transpose0, %reshape : (tensor<2x3x4xi32>, tensor<1x1x4xi32>) -> tensor<2x3x4xi32>
- %transpose1 = tosa.transpose %add {perms = array<i32: 2, 1, 0>}: (tensor<2x3x4xi32>) -> tensor<4x3x2xi32>
+ %transpose1 = tosa.transpose %add perms([2, 1, 0]): (tensor<2x3x4xi32>) -> tensor<4x3x2xi32>
return %transpose1 : tensor<4x3x2xi32>
}
@@ -203,29 +203,29 @@ func.func @test_multi_dim_reshape_for_broadcast(%arg0: tensor<1x4x1xi32>, %arg1:
// CHECK-DAG: %[[VAL_7:.*]] = tosa.add %arg1, %[[VAL_5]] : (tensor<64xf32>, tensor<1xf32>) -> tensor<64xf32>
// CHECK-DAG: %[[VAL_8:.*]] = tosa.pow %[[VAL_7]], %[[VAL_6]] : (tensor<64xf32>, tensor<1xf32>) -> tensor<64xf32>
// CHECK-DAG: %[[VAL_9:.*]] = tosa.reciprocal %[[VAL_8]] : (tensor<64xf32>) -> tensor<64xf32>
-// CHECK-DAG: %[[VAL_10:.*]] = tosa.const_shape {values = dense<[1, 1, 1, 64]> : tensor<4xindex>} : () -> !tosa.shape<4>
+// CHECK-DAG: %[[VAL_10:.*]] = tosa.const_shape values(dense<[1, 1, 1, 64]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK-DAG: %[[VAL_11:.*]] = tosa.reshape %arg0, %[[VAL_10]] : (tensor<64xf32>, !tosa.shape<4>) -> tensor<1x1x1x64xf32>
// CHECK-DAG: %[[VAL_12:.*]] = tosa.sub %arg2, %[[VAL_11]] : (tensor<1x112x112x64xf32>, tensor<1x1x1x64xf32>) -> tensor<1x112x112x64xf32>
-// CHECK-DAG: %[[VAL_13:.*]] = tosa.const_shape {values = dense<[1, 1, 1, 64]> : tensor<4xindex>} : () -> !tosa.shape<4>
+// CHECK-DAG: %[[VAL_13:.*]] = tosa.const_shape values(dense<[1, 1, 1, 64]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK-DAG: %[[VAL_14:.*]] = tosa.reshape %[[VAL_9]], %[[VAL_13]] : (tensor<64xf32>, !tosa.shape<4>) -> tensor<1x1x1x64xf32>
// CHECK-DAG: %[[VAL_15:.*]] = tosa.mul %[[VAL_12]], %[[VAL_14]], %[[CONST0]] : (tensor<1x112x112x64xf32>, tensor<1x1x1x64xf32>, tensor<1xi8>) -> tensor<1x112x112x64xf32>
-// CHECK-DAG: %[[VAL_16:.*]] = tosa.const_shape {values = dense<[1, 1, 1, 64]> : tensor<4xindex>} : () -> !tosa.shape<4>
+// CHECK-DAG: %[[VAL_16:.*]] = tosa.const_shape values(dense<[1, 1, 1, 64]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK-DAG: %[[VAL_17:.*]] = tosa.reshape %[[VAL_4]], %[[VAL_16]] : (tensor<64xf32>, !tosa.shape<4>) -> tensor<1x1x1x64xf32>
// CHECK-DAG: %[[VAL_18:.*]] = tosa.mul %[[VAL_15]], %[[VAL_17]], %[[CONST0]] : (tensor<1x112x112x64xf32>, tensor<1x1x1x64xf32>, tensor<1xi8>) -> tensor<1x112x112x64xf32>
-// CHECK-DAG: %[[VAL_19:.*]] = tosa.const_shape {values = dense<[1, 1, 1, 64]> : tensor<4xindex>} : () -> !tosa.shape<4>
+// CHECK-DAG: %[[VAL_19:.*]] = tosa.const_shape values(dense<[1, 1, 1, 64]> : tensor<4xindex>) : () -> !tosa.shape<4>
// CHECK-DAG: %[[VAL_20:.*]] = tosa.reshape %[[VAL_3]], %[[VAL_19]] : (tensor<64xf32>, !tosa.shape<4>) -> tensor<1x1x1x64xf32>
// CHECK-DAG: %[[VAL_21:.*]] = tosa.add %[[VAL_18]], %[[VAL_20]] : (tensor<1x112x112x64xf32>, tensor<1x1x1x64xf32>) -> tensor<1x112x112x64xf32>
-// CHECK-DAG: %[[VAL_22:.*]] = tosa.clamp %[[VAL_21]] {max_val = 3.40282347E+38 : f32, min_val = 0.000000e+00 : f32} : (tensor<1x112x112x64xf32>) -> tensor<1x112x112x64xf32>
+// CHECK-DAG: %[[VAL_22:.*]] = tosa.clamp %[[VAL_21]] min_val(0.000000e+00 : f32) max_val(3.40282347E+38 : f32) : (tensor<1x112x112x64xf32>) -> tensor<1x112x112x64xf32>
func.func @test_resnet18_common_case(%arg0: tensor<64xf32>, %arg1: tensor<64xf32>, %74: tensor<1x112x112x64xf32>) -> tensor<1x112x112x64xf32> {
- %shift = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %58 = tosa.const_shape {values = dense<[1, 64, 1, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %58 = tosa.const_shape values(dense<[1, 64, 1, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%59 = "tosa.const"() <{values = dense_resource<torch_tensor_64_torch.float32_1> : tensor<64xf32>}> : () -> tensor<64xf32>
%60 = "tosa.const"() <{values = dense_resource<torch_tensor_64_torch.float32> : tensor<64xf32>}> : () -> tensor<64xf32>
%63 = "tosa.const"() <{values = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32>
%64 = "tosa.const"() <{values = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
%69 = "tosa.const"() <{values = dense<9.99999974E-6> : tensor<1xf32>}> : () -> tensor<1xf32>
%70 = "tosa.const"() <{values = dense<5.000000e-01> : tensor<1xf32>}> : () -> tensor<1xf32>
- %75 = tosa.transpose %74 {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x112x112x64xf32>) -> tensor<1x64x112x112xf32>
+ %75 = tosa.transpose %74 perms([0, 3, 1, 2]) : (tensor<1x112x112x64xf32>) -> tensor<1x64x112x112xf32>
%76 = tosa.add %arg1, %69 : (tensor<64xf32>, tensor<1xf32>) -> tensor<64xf32>
%77 = tosa.pow %76, %70 : (tensor<64xf32>, tensor<1xf32>) -> tensor<64xf32>
%78 = tosa.reciprocal %77 : (tensor<64xf32>) -> tensor<64xf32>
@@ -237,32 +237,32 @@ func.func @test_resnet18_common_case(%arg0: tensor<64xf32>, %arg1: tensor<64xf32
%84 = tosa.mul %82, %83, %shift : (tensor<1x64x112x112xf32>, tensor<1x64x1x1xf32>, tensor<1xi8>) -> tensor<1x64x112x112xf32>
%85 = tosa.reshape %59, %58 : (tensor<64xf32>, !tosa.shape<4>) -> tensor<1x64x1x1xf32>
%86 = tosa.add %84, %85 : (tensor<1x64x112x112xf32>, tensor<1x64x1x1xf32>) -> tensor<1x64x112x112xf32>
- %87 = tosa.clamp %86 {max_val = 3.40282347E+38 : f32, min_val = 0.000000e+00 : f32} : (tensor<1x64x112x112xf32>) -> tensor<1x64x112x112xf32>
- %88 = tosa.transpose %87 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x64x112x112xf32>) -> tensor<1x112x112x64xf32>
+ %87 = tosa.clamp %86 min_val(0.000000e+00 : f32) max_val(3.40282347E+38 : f32) : (tensor<1x64x112x112xf32>) -> tensor<1x64x112x112xf32>
+ %88 = tosa.transpose %87 perms([0, 2, 3, 1]) : (tensor<1x64x112x112xf32>) -> tensor<1x112x112x64xf32>
return %88 : tensor<1x112x112x64xf32>
}
// -----
// CHECK-LABEL: @test_back_to_back_nullifiers
-// CHECK: %[[RES:.*]] = tosa.transpose %arg0 {perms = array<i32: 1, 0>}
+// CHECK: %[[RES:.*]] = tosa.transpose %arg0 perms([1, 0])
// CHECK: return %[[RES]]
func.func @test_back_to_back_nullifiers(%arg0: tensor<2x3xi32>) -> tensor<3x2xi32> {
- %0 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<2x3xi32>) -> tensor<3x2xi32>
- %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<3x2xi32>) -> tensor<2x3xi32>
- %2 = tosa.transpose %1 {perms = array<i32: 1, 0>}: (tensor<2x3xi32>) -> tensor<3x2xi32>
+ %0 = tosa.transpose %arg0 perms([1, 0]) : (tensor<2x3xi32>) -> tensor<3x2xi32>
+ %1 = tosa.transpose %0 perms([1, 0]) : (tensor<3x2xi32>) -> tensor<2x3xi32>
+ %2 = tosa.transpose %1 perms([1, 0]) : (tensor<2x3xi32>) -> tensor<3x2xi32>
return %2 : tensor<3x2xi32>
}
// -----
// CHECK-LABEL: @test_back_to_back_nullifiers_different_transposes
-// CHECK: %[[RES:.*]] = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}
+// CHECK: %[[RES:.*]] = tosa.transpose %arg0 perms([0, 2, 3, 1])
// CHECK: return %[[RES]]
func.func @test_back_to_back_nullifiers_different_transposes(%arg0: tensor<2x3x4x5xi32>) -> tensor<2x4x5x3xi32> {
- %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<2x3x4x5xi32>) -> tensor<2x4x5x3xi32>
- %1 = tosa.transpose %0 {perms = array<i32: 0, 3, 1, 2>}: (tensor<2x4x5x3xi32>) -> tensor<2x3x4x5xi32>
- %2 = tosa.transpose %1 {perms = array<i32: 0, 2, 3, 1>}: (tensor<2x3x4x5xi32>) -> tensor<2x4x5x3xi32>
+ %0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<2x3x4x5xi32>) -> tensor<2x4x5x3xi32>
+ %1 = tosa.transpose %0 perms([0, 3, 1, 2]) : (tensor<2x4x5x3xi32>) -> tensor<2x3x4x5xi32>
+ %2 = tosa.transpose %1 perms([0, 2, 3, 1]) : (tensor<2x3x4x5xi32>) -> tensor<2x4x5x3xi32>
return %2 : tensor<2x4x5x3xi32>
}
@@ -274,9 +274,9 @@ func.func @test_back_to_back_nullifiers_different_transposes(%arg0: tensor<2x3x4
// CHECK: %[[RES1:.*]] = tosa.transpose
// CHECK: return %[[RES1]], %[[RES2]]
func.func @test_no_transform_if_outside_fan_in_cone(%arg0: tensor<3x3x3x3xi32>) -> (tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>) {
- %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>} : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
- %clamp = tosa.clamp %0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
- %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>} : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %clamp = tosa.clamp %0 min_val(0 : i32) max_val(2147483647 : i32) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %1 = tosa.transpose %clamp perms([0, 3, 1, 2]) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
return %1, %clamp : tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>
}
@@ -288,11 +288,11 @@ func.func @test_no_transform_if_outside_fan_in_cone(%arg0: tensor<3x3x3x3xi32>)
// CHECK: return %[[RESHAPE]], %[[CLAMP]]
func.func @test_two_different_downstream_converge_to_reshape_same_perms(%arg0: tensor<64xf32>) -> (tensor<1x1x64xf32>, tensor<1x1x64xf32>) {
%0 = "tosa.const"() <{values = dense<[0, 2, 1]> : tensor<3xi32>}> : () -> tensor<3xi32>
- %shape = tosa.const_shape {values = dense<[1, 64, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %shape = tosa.const_shape values(dense<[1, 64, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
%1 = tosa.reshape %arg0, %shape : (tensor<64xf32>, !tosa.shape<3>) -> tensor<1x64x1xf32>
- %2 = tosa.clamp %1 {max_val = 3.40282347E+38 : f32, min_val = 0.000000e+00 : f32} : (tensor<1x64x1xf32>) -> tensor<1x64x1xf32>
- %3 = tosa.transpose %1 {perms = array<i32: 0, 2, 1>}: (tensor<1x64x1xf32>) -> tensor<1x1x64xf32>
- %4 = tosa.transpose %2 {perms = array<i32: 0, 2, 1>}: (tensor<1x64x1xf32>) -> tensor<1x1x64xf32>
+ %2 = tosa.clamp %1 min_val(0.000000e+00 : f32) max_val(3.40282347E+38 : f32) : (tensor<1x64x1xf32>) -> tensor<1x64x1xf32>
+ %3 = tosa.transpose %1 perms([0, 2, 1]) : (tensor<1x64x1xf32>) -> tensor<1x1x64xf32>
+ %4 = tosa.transpose %2 perms([0, 2, 1]) : (tensor<1x64x1xf32>) -> tensor<1x1x64xf32>
return %3, %4 : tensor<1x1x64xf32>, tensor<1x1x64xf32>
}
@@ -305,11 +305,11 @@ func.func @test_two_different_downstream_converge_to_reshape_same_perms(%arg0: t
// CHECK-DAG: %[[RET2:.*]] = tosa.transpose
// CHECK-DAG: return %[[RET1]], %[[RET2]]
func.func @test_two_different_downstream_converge_to_reshape_different_perms(%arg0: tensor<64xf32>) -> (tensor<1x1x64xf32>, tensor<64x1x1xf32>) {
- %shape = tosa.const_shape {values = dense<[1, 64, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %shape = tosa.const_shape values(dense<[1, 64, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
%2 = tosa.reshape %arg0, %shape : (tensor<64xf32>, !tosa.shape<3>) -> tensor<1x64x1xf32>
- %3 = tosa.clamp %2 {max_val = 3.40282347E+38 : f32, min_val = 0.000000e+00 : f32} : (tensor<1x64x1xf32>) -> tensor<1x64x1xf32>
- %4 = tosa.transpose %2 {perms = array<i32: 0, 2, 1>}: (tensor<1x64x1xf32>) -> tensor<1x1x64xf32>
- %5 = tosa.transpose %3 {perms = array<i32: 1, 2, 0>}: (tensor<1x64x1xf32>) -> tensor<64x1x1xf32>
+ %3 = tosa.clamp %2 min_val(0.000000e+00 : f32) max_val(3.40282347E+38 : f32) : (tensor<1x64x1xf32>) -> tensor<1x64x1xf32>
+ %4 = tosa.transpose %2 perms([0, 2, 1]) : (tensor<1x64x1xf32>) -> tensor<1x1x64xf32>
+ %5 = tosa.transpose %3 perms([1, 2, 0]) : (tensor<1x64x1xf32>) -> tensor<64x1x1xf32>
return %4, %5 : tensor<1x1x64xf32>, tensor<64x1x1xf32>
}
@@ -323,9 +323,9 @@ func.func @test_two_different_downstream_converge_to_reshape_different_perms(%ar
// CHECK: %[[RES2:.*]] = tosa.add
// CHECK: return %[[RES1]], %[[RES2]]
func.func @test_outside_perms_usage_of_fan_in(%arg0: tensor<2x3xf32>, %arg1: tensor<3x2xf32>) -> (tensor<2x3xf32>, tensor<3x2xf32>) {
- %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<2x3xf32>) -> tensor<3x2xf32>
- %2 = tosa.clamp %1 {max_val = 3.40282347E+38 : f32, min_val = 0.000000e+00 : f32} : (tensor<3x2xf32>) -> tensor<3x2xf32>
- %3 = tosa.transpose %2 {perms = array<i32: 1, 0>}: (tensor<3x2xf32>) -> tensor<2x3xf32>
+ %1 = tosa.transpose %arg0 perms([1, 0]) : (tensor<2x3xf32>) -> tensor<3x2xf32>
+ %2 = tosa.clamp %1 min_val(0.000000e+00 : f32) max_val(3.40282347E+38 : f32) : (tensor<3x2xf32>) -> tensor<3x2xf32>
+ %3 = tosa.transpose %2 perms([1, 0]) : (tensor<3x2xf32>) -> tensor<2x3xf32>
%4 = tosa.add %arg1, %2 : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf32>
return %3, %4: tensor<2x3xf32>, tensor<3x2xf32>
}
@@ -339,12 +339,12 @@ func.func @test_outside_perms_usage_of_fan_in(%arg0: tensor<2x3xf32>, %arg1: ten
// CHECK-DAG: %[[NEW_ADD:.*]] = tosa.add %arg1, %[[NEW_CLAMP]]
// CHECK: return %[[NEW_CLAMP]], %[[NEW_ADD]]
func.func @test_use_present_in_another_valid_perms_fan_in(%arg0: tensor<2x3xf32>, %arg1: tensor<2x3xf32>) -> (tensor<2x3xf32>, tensor<2x3xf32>) {
- %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<2x3xf32>) -> tensor<3x2xf32>
- %2 = tosa.clamp %1 {max_val = 3.40282347E+38 : f32, min_val = 0.000000e+00 : f32} : (tensor<3x2xf32>) -> tensor<3x2xf32>
- %3 = tosa.transpose %2 {perms = array<i32: 1, 0>}: (tensor<3x2xf32>) -> tensor<2x3xf32>
- %4 = tosa.transpose %arg1 {perms = array<i32: 1, 0>}: (tensor<2x3xf32>) -> tensor<3x2xf32>
+ %1 = tosa.transpose %arg0 perms([1, 0]) : (tensor<2x3xf32>) -> tensor<3x2xf32>
+ %2 = tosa.clamp %1 min_val(0.000000e+00 : f32) max_val(3.40282347E+38 : f32) : (tensor<3x2xf32>) -> tensor<3x2xf32>
+ %3 = tosa.transpose %2 perms([1, 0]) : (tensor<3x2xf32>) -> tensor<2x3xf32>
+ %4 = tosa.transpose %arg1 perms([1, 0]) : (tensor<2x3xf32>) -> tensor<3x2xf32>
%5 = tosa.add %4, %2 : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf32>
- %6 = tosa.transpose %5 {perms = array<i32: 1, 0>}: (tensor<3x2xf32>) -> tensor<2x3xf32>
+ %6 = tosa.transpose %5 perms([1, 0]) : (tensor<3x2xf32>) -> tensor<2x3xf32>
return %3, %6: tensor<2x3xf32>, tensor<2x3xf32>
}
@@ -359,11 +359,11 @@ func.func @test_use_present_in_another_valid_perms_fan_in(%arg0: tensor<2x3xf32>
// CHECK: %[[RES2:.*]] = tosa.transpose %[[ADD]]
// CHECK: return %[[RES1]], %[[RES2]]
func.func @test_two_same_perms_fan_in_but_one_doesnt_convert_dependents(%arg0: tensor<2x3xi32>, %arg1: tensor<3x2xi32>) -> (tensor<2x3xi32>, tensor<2x3xi32>) {
- %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<2x3xi32>) -> tensor<3x2xi32>
+ %1 = tosa.transpose %arg0 perms([1, 0]) : (tensor<2x3xi32>) -> tensor<3x2xi32>
%2 = tosa.ceil %1 : (tensor<3x2xi32>) -> tensor<3x2xi32>
%3 = tosa.add %2, %arg1 : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
- %4 = tosa.transpose %2 {perms = array<i32: 1, 0>}: (tensor<3x2xi32>) -> tensor<2x3xi32>
- %5 = tosa.transpose %3 {perms = array<i32: 1, 0>}: (tensor<3x2xi32>) -> tensor<2x3xi32>
+ %4 = tosa.transpose %2 perms([1, 0]) : (tensor<3x2xi32>) -> tensor<2x3xi32>
+ %5 = tosa.transpose %3 perms([1, 0]) : (tensor<3x2xi32>) -> tensor<2x3xi32>
return %4, %5 : tensor<2x3xi32>, tensor<2x3xi32>
}
@@ -373,10 +373,10 @@ func.func @test_two_same_perms_fan_in_but_one_doesnt_convert_dependents(%arg0: t
// CHECK-NEXT: %[[RES:.*]] = tosa.clamp %arg0
// CHECK-NEXT: return %[[RES]], %[[RES]]
func.func @test_direct_use_in_other_transpose_with_same_perms(%arg0: tensor<3x3x3x3xi32>) -> (tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>) {
- %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
- %clamp = tosa.clamp %0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
- %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
- %2 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %clamp = tosa.clamp %0 min_val(0 : i32) max_val(2147483647 : i32) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %1 = tosa.transpose %clamp perms([0, 3, 1, 2]) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %2 = tosa.transpose %clamp perms([0, 3, 1, 2]) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
return %1, %2 : tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>
}
@@ -387,8 +387,8 @@ func.func @test_direct_use_in_other_transpose_with_same_perms(%arg0: tensor<3x3x
// CHECK-NOT: tosa.transpose
// CHECK: return %[[NEW]]
func.func @test_const_transpose() -> tensor<2x3xi32> {
- %0 = "tosa.const"() {values = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
- %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<3x2xi32>) -> tensor<2x3xi32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<3x2xi32>}> : () -> tensor<3x2xi32>
+ %1 = tosa.transpose %0 perms([1, 0]) : (tensor<3x2xi32>) -> tensor<2x3xi32>
return %1 : tensor<2x3xi32>
}
@@ -396,13 +396,13 @@ func.func @test_const_transpose() -> tensor<2x3xi32> {
// CHECK-LABEL: @test_transpose_tracks_to_const_single_step
// CHECK: %[[NEW_CONST:.*]] = "tosa.const"() <{values = dense<0> : tensor<1x2x3x4xi32>}> : () -> tensor<1x2x3x4xi32>
-// CHECK: %[[NEW_CLAMP:.*]] = tosa.clamp %[[NEW_CONST]] {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32>
+// CHECK: %[[NEW_CLAMP:.*]] = tosa.clamp %[[NEW_CONST]] min_val(0 : i32) max_val(2147483647 : i32) : (tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32>
// CHECK-NOT: tosa.transpose
// CHECK: return %[[NEW_CLAMP]]
func.func @test_transpose_tracks_to_const_single_step() -> tensor<1x2x3x4xi32> {
- %0 = "tosa.const"() {values = dense<0> : tensor<1x3x4x2xi32>} : () -> tensor<1x3x4x2xi32>
- %clamp = tosa.clamp %0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
- %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<1x3x4x2xi32>}> : () -> tensor<1x3x4x2xi32>
+ %clamp = tosa.clamp %0 min_val(0 : i32) max_val(2147483647 : i32) : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
+ %1 = tosa.transpose %clamp perms([0, 3, 1, 2]) : (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
return %1 : tensor<1x2x3x4xi32>
}
@@ -410,16 +410,16 @@ func.func @test_transpose_tracks_to_const_single_step() -> tensor<1x2x3x4xi32> {
// CHECK-LABEL: @test_static_unary_path_to_const
// CHECK: %[[NEW_CONST:.*]] = "tosa.const"() <{values = dense<1> : tensor<1x2x3x4xi32>}> : () -> tensor<1x2x3x4xi32>
-// CHECK: %[[NEW_CLAMP:.*]] = tosa.clamp %[[NEW_CONST]] {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32>
+// CHECK: %[[NEW_CLAMP:.*]] = tosa.clamp %[[NEW_CONST]] min_val(0 : i32) max_val(2147483647 : i32) : (tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32>
// CHECK: %[[NEW_ABS:.*]] = tosa.abs %[[NEW_CLAMP]] : (tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32>
// CHECK: %[[NEW_NOT:.*]] = tosa.bitwise_not %[[NEW_ABS]] : (tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32>
// CHECK: return %[[NEW_NOT]]
func.func @test_static_unary_path_to_const() -> tensor<1x2x3x4xi32> {
- %0 = "tosa.const"() {values = dense<1> : tensor<1x3x4x2xi32>} : () -> tensor<1x3x4x2xi32>
- %clamp = tosa.clamp %0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
+ %0 = "tosa.const"() <{values = dense<1> : tensor<1x3x4x2xi32>}> : () -> tensor<1x3x4x2xi32>
+ %clamp = tosa.clamp %0 min_val(0 : i32) max_val(2147483647 : i32) : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
%abs = tosa.abs %clamp : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
%bitwise_not = tosa.bitwise_not %abs : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
- %1 = tosa.transpose %bitwise_not {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
+ %1 = tosa.transpose %bitwise_not perms([0, 3, 1, 2]) : (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
return %1 : tensor<1x2x3x4xi32>
}
@@ -429,19 +429,19 @@ func.func @test_static_unary_path_to_const() -> tensor<1x2x3x4xi32> {
// CHECK: %[[NEW_CONST:.*]] = "tosa.const"()
// CHECK-SAME{LITERAL}: dense<[[[[1, 3, 5, 7], [9, 11, 13, 15], [17, 19, 21, 23]], [[2, 4, 6, 8], [10, 12, 14, 16], [18, 20, 22, 24]]]]>
// CHECK: tensor<1x2x3x4xi32>}> : () -> tensor<1x2x3x4xi32>
-// CHECK: %[[NEW_CLAMP:.*]] = tosa.clamp %arg0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32>
+// CHECK: %[[NEW_CLAMP:.*]] = tosa.clamp %arg0 min_val(0 : i32) max_val(2147483647 : i32) : (tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32>
// CHECK: %[[NEW_ABS:.*]] = tosa.abs %[[NEW_CONST]] : (tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32>
// CHECK: %[[NEW_ADD:.*]] = tosa.add %[[NEW_ABS]], %[[NEW_CLAMP]] : (tensor<1x2x3x4xi32>, tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32>
// CHECK: return %[[NEW_ADD]]
func.func @test_static_diverges_to_non_splat_const_and_nullifying(%arg0: tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32> {
- %transpose0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+ %transpose0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
%const = "tosa.const"() {values = dense<[[[[1, 2], [3, 4], [5, 6], [7, 8]],
[[9, 10], [11, 12], [13, 14], [15, 16]],
[[17, 18], [19, 20], [21, 22], [23, 24]]]]> : tensor<1x3x4x2xi32>} : () -> tensor<1x3x4x2xi32>
- %clamp = tosa.clamp %transpose0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
+ %clamp = tosa.clamp %transpose0 min_val(0 : i32) max_val(2147483647 : i32) : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
%abs = tosa.abs %const : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
%add = tosa.add %abs, %clamp : (tensor<1x3x4x2xi32>, tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
- %result = tosa.transpose %add {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
+ %result = tosa.transpose %add perms([0, 3, 1, 2]) : (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
return %result : tensor<1x2x3x4xi32>
}
@@ -451,10 +451,10 @@ func.func @test_static_diverges_to_non_splat_const_and_nullifying(%arg0: tensor<
// CHECK-NEXT: %[[RES:.*]] = tosa.clamp %arg0
// CHECK-NEXT: return %[[RES]], %[[RES]]
func.func @test_multi_downstream_both_nullify(%arg0: tensor<3x3x3x3xi32>) -> (tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>) {
- %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
- %clamp = tosa.clamp %0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
- %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
- %2 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %clamp = tosa.clamp %0 min_val(0 : i32) max_val(2147483647 : i32) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %1 = tosa.transpose %clamp perms([0, 3, 1, 2]) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %2 = tosa.transpose %clamp perms([0, 3, 1, 2]) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
return %1, %2 : tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>
}
@@ -467,10 +467,10 @@ func.func @test_multi_downstream_both_nullify(%arg0: tensor<3x3x3x3xi32>) -> (te
// CHECK: tosa.transpose
// CHECK: tosa.transpose
func.func @test_multi_downstream_one_nullifies_upstream_other_does_not(%arg0: tensor<3x3x3x3xi32>) -> (tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>) {
- %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
- %clamp = tosa.clamp %0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
- %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
- %2 = tosa.transpose %clamp {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %clamp = tosa.clamp %0 min_val(0 : i32) max_val(2147483647 : i32) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %1 = tosa.transpose %clamp perms([0, 3, 1, 2]) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+ %2 = tosa.transpose %clamp perms([0, 2, 3, 1]) : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
return %1, %2 : tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>
}
@@ -479,8 +479,8 @@ func.func @test_multi_downstream_one_nullifies_upstream_other_does_not(%arg0: te
// CHECK-LABEL: @test_unknown_dim_inner_replacement_matches
// CHECK-NEXT: return %arg0
func.func @test_unknown_dim_inner_replacement_matches(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
- %0 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<3x2xi32>) -> tensor<?x3xi32>
- %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<?x3xi32>) -> tensor<3x2xi32>
+ %0 = tosa.transpose %arg0 perms([1, 0]) : (tensor<3x2xi32>) -> tensor<?x3xi32>
+ %1 = tosa.transpose %0 perms([1, 0]) : (tensor<?x3xi32>) -> tensor<3x2xi32>
return %1 : tensor<3x2xi32>
}
@@ -490,8 +490,8 @@ func.func @test_unknown_dim_inner_replacement_matches(%arg0: tensor<3x2xi32>) ->
// CHECK-LABEL: @test_unknown_dim_outer_replacement_matches
// CHECK-NEXT: return %arg0
func.func @test_unknown_dim_outer_replacement_matches(%arg0: tensor<3x?xi32>) -> tensor<3x?xi32> {
- %0 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<3x?xi32>) -> tensor<2x3xi32>
- %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<2x3xi32>) -> tensor<3x?xi32>
+ %0 = tosa.transpose %arg0 perms([1, 0]) : (tensor<3x?xi32>) -> tensor<2x3xi32>
+ %1 = tosa.transpose %0 perms([1, 0]) : (tensor<2x3xi32>) -> tensor<3x?xi32>
return %1 : tensor<3x?xi32>
}
@@ -503,12 +503,12 @@ func.func @test_unknown_dim_outer_replacement_matches(%arg0: tensor<3x?xi32>) ->
// CHECK-NEXT: %[[ADD:.*]] = tosa.add %[[CLAMP]], %[[ABS]]
// CHECK-NEXT: return %[[ADD]]
func.func @test_transpose_tracks_to_nullifying_diverging_binary_unknown_dim_replacements_match(%arg0: tensor<1x?x3x4xi32>, %arg1: tensor<1x2x?x4xi32>) -> tensor<1x2x3x4xi32> {
- %transpose0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x?x3x4xi32>) -> tensor<?x3x4x?xi32>
- %transpose1 = tosa.transpose %arg1 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x?x4xi32>) -> tensor<1x?x?x2xi32>
- %clamp = tosa.clamp %transpose0 {min_val = 0 : i32, max_val = 1 : i32} : (tensor<?x3x4x?xi32>) -> tensor<?x3x4x?xi32>
+ %transpose0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<1x?x3x4xi32>) -> tensor<?x3x4x?xi32>
+ %transpose1 = tosa.transpose %arg1 perms([0, 2, 3, 1]) : (tensor<1x2x?x4xi32>) -> tensor<1x?x?x2xi32>
+ %clamp = tosa.clamp %transpose0 min_val(0 : i32) max_val(1 : i32) : (tensor<?x3x4x?xi32>) -> tensor<?x3x4x?xi32>
%abs = tosa.abs %transpose1 : (tensor<1x?x?x2xi32>) -> tensor<1x?x?x2xi32>
%add = tosa.add %clamp, %abs : (tensor<?x3x4x?xi32>, tensor<1x?x?x2xi32>) -> tensor<1x3x4x2xi32>
- %result = tosa.transpose %add {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
+ %result = tosa.transpose %add perms([0, 3, 1, 2]) : (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
return %result : tensor<1x2x3x4xi32>
}
@@ -522,9 +522,9 @@ func.func @test_transpose_tracks_to_nullifying_diverging_binary_unknown_dim_repl
// CHECK-NEXT: tosa.transpose
// CHECK-NEXT: return
func.func @test_unimplemented_transpose_tracks_to_non_nullifying_transpose_single_step(%arg0: tensor<1x2x3x4xi32>) -> tensor<1x2x4x3xi32> {
- %0 = tosa.transpose %arg0 {perms = array<i32: 0, 3, 2, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x4x3x2xi32>
- %clamp = tosa.clamp %0 {min_val = 0 : i32, max_val = 1 : i32} : (tensor<1x4x3x2xi32>) -> tensor<1x4x3x2xi32>
- %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>} : (tensor<1x4x3x2xi32>) -> tensor<1x2x4x3xi32>
+ %0 = tosa.transpose %arg0 perms([0, 3, 2, 1]) : (tensor<1x2x3x4xi32>) -> tensor<1x4x3x2xi32>
+ %clamp = tosa.clamp %0 min_val(0 : i32) max_val(1 : i32) : (tensor<1x4x3x2xi32>) -> tensor<1x4x3x2xi32>
+ %1 = tosa.transpose %clamp perms([0, 3, 1, 2]) : (tensor<1x4x3x2xi32>) -> tensor<1x2x4x3xi32>
return %1 : tensor<1x2x4x3xi32>
}
@@ -536,8 +536,8 @@ func.func @test_unimplemented_transpose_tracks_to_non_nullifying_transpose_singl
// CHECK-NEXT: tosa.transpose
// CHECK-NEXT: return
func.func @test_unimplemented_unknown_dim_input_nullifying_pair(%arg0: tensor<3x?xi32>) -> tensor<3x2xi32> {
- %0 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<3x?xi32>) -> tensor<2x3xi32>
- %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<2x3xi32>) -> tensor<3x2xi32>
+ %0 = tosa.transpose %arg0 perms([1, 0]) : (tensor<3x?xi32>) -> tensor<2x3xi32>
+ %1 = tosa.transpose %0 perms([1, 0]) : (tensor<2x3xi32>) -> tensor<3x2xi32>
return %1 : tensor<3x2xi32>
}
@@ -548,8 +548,8 @@ func.func @test_unimplemented_unknown_dim_input_nullifying_pair(%arg0: tensor<3x
// CHECK-NEXT: tosa.transpose
// CHECK-NEXT: return
func.func @test_unimplemented_unknown_dim_replacement_does_not_match(%arg0: tensor<3x?xi32>) -> tensor<?x?xi32> {
- %0 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<3x?xi32>) -> tensor<?x3xi32>
- %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<?x3xi32>) -> tensor<?x?xi32>
+ %0 = tosa.transpose %arg0 perms([1, 0]) : (tensor<3x?xi32>) -> tensor<?x3xi32>
+ %1 = tosa.transpose %0 perms([1, 0]) : (tensor<?x3xi32>) -> tensor<?x?xi32>
return %1 : tensor<?x?xi32>
}
@@ -561,8 +561,8 @@ func.func @test_unimplemented_unknown_dim_replacement_does_not_match(%arg0: tens
// CHECK-NEXT: tosa.transpose
// CHECK-NEXT: return
func.func @test_unimplemented_unranked_tensors_present(%arg0: tensor<3x2xi32>) -> tensor<*xi32> {
- %0 = tosa.transpose %arg0 {perms = array<i32: 0, 1>}: (tensor<3x2xi32>) -> tensor<*xi32>
- %1 = tosa.transpose %0 {perms = array<i32: 0, 1>}: (tensor<*xi32>) -> tensor<*xi32>
+ %0 = tosa.transpose %arg0 perms([0, 1]) : (tensor<3x2xi32>) -> tensor<*xi32>
+ %1 = tosa.transpose %0 perms([0, 1]) : (tensor<*xi32>) -> tensor<*xi32>
return %1 : tensor<*xi32>
}
@@ -573,8 +573,8 @@ func.func @test_unimplemented_unranked_tensors_present(%arg0: tensor<3x2xi32>) -
// CHECK-NEXT: tosa.transpose
// CHECK-NEXT: return
func.func @test_unimplemented_unranked_everything(%arg0: tensor<*xi32>) -> tensor<*xi32> {
- %0 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<*xi32>) -> tensor<*xi32>
- %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<*xi32>) -> tensor<*xi32>
+ %0 = tosa.transpose %arg0 perms([1, 0]) : (tensor<*xi32>) -> tensor<*xi32>
+ %1 = tosa.transpose %0 perms([1, 0]) : (tensor<*xi32>) -> tensor<*xi32>
return %1 : tensor<*xi32>
}
@@ -589,12 +589,12 @@ func.func @test_unimplemented_unranked_everything(%arg0: tensor<*xi32>) -> tenso
// CHECK-NEXT: tosa.transpose
// CHECK-NEXT: return
func.func @test_unimplemented_static_diverges_to_one_nullifying_one_non_nullifying(%arg0: tensor<1x2x3x4xi32>, %arg1: tensor<1x2x4x3xi32>) -> tensor<1x2x3x4xi32> {
- %transpose0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
- %transpose1 = tosa.transpose %arg1 {perms = array<i32: 0, 3, 2, 1>}: (tensor<1x2x4x3xi32>) -> tensor<1x3x4x2xi32>
- %clamp = tosa.clamp %transpose0 {min_val = 0 : i32, max_val = 1 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
+ %transpose0 = tosa.transpose %arg0 perms([0, 2, 3, 1]) : (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+ %transpose1 = tosa.transpose %arg1 perms([0, 3, 2, 1]) : (tensor<1x2x4x3xi32>) -> tensor<1x3x4x2xi32>
+ %clamp = tosa.clamp %transpose0 min_val(0 : i32) max_val(1 : i32) : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
%abs = tosa.abs %transpose1 : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
%add = tosa.add %clamp, %abs : (tensor<1x3x4x2xi32>, tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
- %result = tosa.transpose %add {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
+ %result = tosa.transpose %add perms([0, 3, 1, 2]) : (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
return %result : tensor<1x2x3x4xi32>
}
@@ -603,7 +603,7 @@ func.func @test_unimplemented_static_diverges_to_one_nullifying_one_non_nullifyi
// CHECK-SAME{LITERAL}: dense<[[true, false], [false, false], [false, true]]>
func.func @test_transpose_bool() -> tensor<3x2xi1> {
%0 = "tosa.const"() <{values = dense<[[true, false, false], [false, false, true]]> : tensor<2x3xi1>}> : () -> tensor<2x3xi1>
- %1 = tosa.transpose %0 {perms = array<i32: 1, 0>} : (tensor<2x3xi1>) -> tensor<3x2xi1>
+ %1 = tosa.transpose %0 perms([1, 0]) : (tensor<2x3xi1>) -> tensor<3x2xi1>
return %1 : tensor<3x2xi1>
}
@@ -612,6 +612,6 @@ func.func @test_transpose_bool() -> tensor<3x2xi1> {
// CHECK-SAME{LITERAL}: dense<[[1, 4], [2, 5], [3, 6]]>
func.func @test_transpose_i4() -> tensor<3x2xi4> {
%0 = "tosa.const"() <{values = dense<[[1, 2, 3], [4, 5, 6]]> : tensor<2x3xi4>}> : () -> tensor<2x3xi4>
- %1 = tosa.transpose %0 {perms = array<i32: 1, 0>} : (tensor<2x3xi4>) -> tensor<3x2xi4>
+ %1 = tosa.transpose %0 perms([1, 0]) : (tensor<2x3xi4>) -> tensor<3x2xi4>
return %1 : tensor<3x2xi4>
}
\ No newline at end of file
diff --git a/mlir/test/Dialect/Tosa/tosa-validation-valid-strict.mlir b/mlir/test/Dialect/Tosa/tosa-validation-valid-strict.mlir
index 8e0ad0a5e46a7..6186f94a6749a 100644
--- a/mlir/test/Dialect/Tosa/tosa-validation-valid-strict.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-validation-valid-strict.mlir
@@ -18,7 +18,7 @@ func.func @test_cond_if_isolated_from_above(%arg0: tensor<f32>, %arg1: tensor<f3
// CHECK-LABEL: test_while_loop_isolated_from_above
func.func @test_while_loop_isolated_from_above(%arg0: tensor<f32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
%1:3 = "tosa.while_loop"(%0, %arg0, %arg1) ({
^bb0(%arg3: tensor<i32>, %arg4: tensor<f32>, %arg5: tensor<i32>):
%2 = "tosa.greater_equal"(%arg3, %arg5) : (tensor<i32>, tensor<i32>) -> tensor<i1>
@@ -26,7 +26,7 @@ func.func @test_while_loop_isolated_from_above(%arg0: tensor<f32>, %arg1: tensor
"tosa.yield"(%3) : (tensor<i1>) -> ()
}, {
^bb0(%arg3: tensor<i32>, %arg4: tensor<f32>, %arg5: tensor<i32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%3 = "tosa.add"(%arg3, %2) : (tensor<i32>, tensor<i32>) -> tensor<i32>
"tosa.yield"(%3, %arg4, %arg5) : (tensor<i32>, tensor<f32>, tensor<i32>) -> ()
}) : (tensor<i32>, tensor<f32>, tensor<i32>) -> (tensor<i32>, tensor<f32>, tensor<i32>)
diff --git a/mlir/test/Dialect/Tosa/tosa-validation-valid.mlir b/mlir/test/Dialect/Tosa/tosa-validation-valid.mlir
index a5276248eecc4..bef396c5bc86c 100644
--- a/mlir/test/Dialect/Tosa/tosa-validation-valid.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-validation-valid.mlir
@@ -14,7 +14,7 @@ func.func @test_rescale_input_unsigned(%arg0: tensor<1x1xui8>) -> (tensor<1x1xi8
%1 = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
%2 = "tosa.const"() <{values = dense<3> : tensor<1xi8>}> : () -> tensor<1xi8>
%3 = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
- %r = tosa.rescale %arg0, %1, %0, %3, %2 {input_unsigned = true, output_unsigned = false, per_channel = false, rounding_mode = SINGLE_ROUND, scale32 = true} : (tensor<1x1xui8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1xi8>
+ %r = tosa.rescale %arg0, %1, %0, %3, %2 scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(true) output_unsigned(false) : (tensor<1x1xui8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1xi8>
return %r : tensor<1x1xi8>
}
@@ -26,7 +26,7 @@ func.func @test_rescale_output_unsigned(%arg0: tensor<1x1xi8>) -> (tensor<1x1xui
%1 = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
%2 = "tosa.const"() <{values = dense<3> : tensor<1xi8>}> : () -> tensor<1xi8>
%3 = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
- %r = tosa.rescale %arg0, %1, %0, %3, %2 {input_unsigned = false, output_unsigned = true, per_channel = false, rounding_mode = SINGLE_ROUND, scale32 = true} : (tensor<1x1xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1xui8>
+ %r = tosa.rescale %arg0, %1, %0, %3, %2 scale32(true) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(true) : (tensor<1x1xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1xui8>
return %r : tensor<1x1xui8>
}
@@ -42,8 +42,8 @@ func.func @test_validate_without_tosa(%arg0: tensor<f32>) -> tensor<f32> {
// CHECK-LABEL: test_pad_large_input_rank
func.func @test_pad_large_input_rank(%arg0: tensor<13x21x3x1x1x1xf32>) -> tensor<13x21x3x1x1x1xf32> {
- %0 = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
- %padding = tosa.const_shape {values = dense<0> : tensor<12xindex>} : () -> !tosa.shape<12>
+ %0 = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %padding = tosa.const_shape values(dense<0> : tensor<12xindex>) : () -> !tosa.shape<12>
%1 = tosa.pad %arg0, %padding, %0 : (tensor<13x21x3x1x1x1xf32>, !tosa.shape<12>, tensor<1xf32>) -> tensor<13x21x3x1x1x1xf32>
return %1 : tensor<13x21x3x1x1x1xf32>
}
@@ -52,6 +52,6 @@ func.func @test_pad_large_input_rank(%arg0: tensor<13x21x3x1x1x1xf32>) -> tensor
// CHECK-LABEL: test_cast_input_unsigned_false
func.func @test_cast_input_unsigned_false(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xf32> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi32>) -> tensor<13x21x3xf32>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-validation-validate-func-signature-invalid.mlir b/mlir/test/Dialect/Tosa/tosa-validation-validate-func-signature-invalid.mlir
index ea16422b1ca73..8b1950c800ac2 100644
--- a/mlir/test/Dialect/Tosa/tosa-validation-validate-func-signature-invalid.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-validation-validate-func-signature-invalid.mlir
@@ -9,7 +9,7 @@ func.func @test_shape_func_input(%arg0: !tosa.shape<1>) {
// expected-error at +1 {{'func.func' op Function return types must be a tensor type to be TOSA compliant, got !tosa.shape type}}
func.func @test_shape_func_output() -> !tosa.shape<4> {
- %cst = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %cst = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
return %cst : !tosa.shape<4>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-validation-version-1p0-invalid.mlir b/mlir/test/Dialect/Tosa/tosa-validation-version-1p0-invalid.mlir
index f97ecb8ff0b43..8fff309564ec3 100644
--- a/mlir/test/Dialect/Tosa/tosa-validation-version-1p0-invalid.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-validation-version-1p0-invalid.mlir
@@ -36,7 +36,7 @@ func.func @test_conv2d_fp8_acc32(%arg0: tensor<1x4x4x4xf8E5M2>, %arg1: tensor<8x
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
// expected-error at +1 {{'tosa.conv2d' op illegal: requires specification version compatible with 1.1.draft (got 1.0) to be specified in the target environment}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xf8E5M2>, tensor<8x1x1x4xf8E5M2>, tensor<8xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x4x4x4xf8E5M2>, tensor<8x1x1x4xf8E5M2>, tensor<8xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -46,7 +46,7 @@ func.func @test_conv2d_mxfp(%arg0: tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAP
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.conv2d' op illegal: requires specification version compatible with 1.1.draft (got 1.0) and requires all of [bf16, mx_common, mx_fp4e2m1] profiles/extensions to be specified in the target environment}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = bf16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xbf16>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xbf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(bf16) local_bound(true) : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xbf16>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xbf16>
return %0 : tensor<1x4x4x8xbf16>
}
@@ -56,7 +56,7 @@ func.func @test_conv3d_fp8_acc32(%arg0: tensor<1x4x8x21x17xf8E5M2>, %arg1: tenso
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
// expected-error at +1 {{'tosa.conv3d' op illegal: requires specification version compatible with 1.1.draft (got 1.0) to be specified in the target environment}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<1x4x8x21x17xf8E5M2>, tensor<34x1x1x1x17xf8E5M2>, tensor<34xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x8x21x34xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<1x4x8x21x17xf8E5M2>, tensor<34x1x1x1x17xf8E5M2>, tensor<34xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
@@ -66,7 +66,7 @@ func.func @test_depthwise_conv2d_fp8_acc32(%arg0: tensor<1x4x4x4xf8E5M2>, %arg1:
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
// expected-error at +1 {{'tosa.depthwise_conv2d' op illegal: requires specification version compatible with 1.1.draft (got 1.0) to be specified in the target environment}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xf8E5M2>, tensor<1x1x4x2xf8E5M2>, tensor<8xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x4x4x4xf8E5M2>, tensor<1x1x4x2xf8E5M2>, tensor<8xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -76,7 +76,7 @@ func.func @test_transpose_conv2d_fp8_acc32(%arg0: tensor<1x32x32x8xf8E5M2>, %arg
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
// expected-error at +1 {{'tosa.transpose_conv2d' op illegal: requires specification version compatible with 1.1.draft (got 1.0) to be specified in the target environment}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf8E5M2>, tensor<16x1x1x8xf8E5M2>, tensor<16xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) : (tensor<1x32x32x8xf8E5M2>, tensor<16x1x1x8xf8E5M2>, tensor<16xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -117,7 +117,7 @@ func.func @test_row_gather_mxfp(%arg0: tensor<13x21x32x!tosa.block_scaled<BLOCK_
// -----
func.func @test_reshape_mxfp(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
- %1 = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape' op illegal: requires specification version compatible with 1.1.draft (got 1.0) and requires all of [mx_common, mx_fp4e2m1] profiles/extensions to be specified in the target environment}}
%0 = tosa.reshape %arg0, %1 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<2>) -> tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %0 : tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
@@ -126,9 +126,9 @@ func.func @test_reshape_mxfp(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_3
// -----
func.func @test_row_gather_block_scaled_i8_i32(%arg0: tensor<13x21x3xi8>, %arg1: tensor<13x26xi32>) -> tensor<13x52x3xi8> {
- %row_count = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
+ %row_count = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.row_gather_block_scaled' op illegal: requires specification version compatible with 1.1.draft (got 1.0) OR requires specification version compatible with 1.1.draft (got 1.0) to be specified in the target environment}}
- %0 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<13x21x3xi8>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x3xi8>)
+ %0 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count block_size<BLOCK_SIZE_1> : (tensor<13x21x3xi8>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x3xi8>)
return %0 : tensor<13x52x3xi8>
}
@@ -160,7 +160,7 @@ func.func @test_cast_bool_fp32(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xf32>
func.func @test_cast_input_unsigned_true_wrong_version(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xf32> {
// expected-error at +1 {{'tosa.cast' op failed attribute check: CAST attribute input_unsigned requires version 1.1.draft (got 1.0)}}
- %0 = tosa.cast %arg0 {input_unsigned = true} : (tensor<13x21x3xi32>) -> tensor<13x21x3xf32>
+ %0 = tosa.cast %arg0 input_unsigned(true) : (tensor<13x21x3xi32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -202,7 +202,7 @@ func.func @test_dyanmic_dims(%arg0: tensor<1x8x16xi8>) {
// expected-error at +1 {{'tosa.identity' op failed level check: result shape dimension cannot be dynamic when targeting TOSA specification version 1.0 or below}}
%0 = tosa.identity %arg0 : (tensor<1x8x16xi8>) -> tensor<?x8x16xi8>
// expected-error at +1 {{'tosa.argmax' op failed level check: operand shape dimension cannot be dynamic when targeting TOSA specification version 1.0 or below}}
- %1 = tosa.argmax %0 { axis = 1 : i32 } : (tensor<?x8x16xi8>) -> tensor<?x16xi32>
+ %1 = tosa.argmax %0 axis(1) : (tensor<?x8x16xi8>) -> tensor<?x16xi32>
return
}
@@ -210,7 +210,7 @@ func.func @test_dyanmic_dims(%arg0: tensor<1x8x16xi8>) {
func.func @test_matmul_t_block_scaled(%arg0: tensor<4x8x32xf8E4M3FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32xf8E4M3FN>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
// expected-error at +1 {{'tosa.matmul_t_block_scaled' op illegal: requires specification version compatible with 1.1.draft (got 1.0) and requires any of [mxfp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf8E4M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf8E4M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
@@ -218,14 +218,14 @@ func.func @test_matmul_t_block_scaled(%arg0: tensor<4x8x32xf8E4M3FN>, %arg1: ten
func.func @test_argmax_int64(%arg0: tensor<1x13x13x5xf32>) -> tensor<1x13x13xi64> {
// expected-error at +1 {{'tosa.argmax' op illegal: requires specification version compatible with 1.1.draft (got 1.0) and requires any of [int64] profiles/extensions to be specified in the target environment}}
- %0 = tosa.argmax %arg0 {axis = 3 : i32} : (tensor<1x13x13x5xf32>) -> tensor<1x13x13xi64>
+ %0 = tosa.argmax %arg0 axis(3) : (tensor<1x13x13x5xf32>) -> tensor<1x13x13xi64>
return %0 : tensor<1x13x13xi64>
}
// -----
func.func @test_const_fp6e3m2() -> tensor<4xf6E3M2FN> {
// expected-error at +1 {{'tosa.const' op illegal: requires specification version compatible with 1.1.draft (got 1.0) and requires any of [mxfp] profiles/extensions to be specified in the target environment}}
- %0 = "tosa.const"() {values = dense<[0.0, 0.0, 0.0, 0.0]> : tensor<4xf6E3M2FN>} : () -> tensor<4xf6E3M2FN>
+ %0 = "tosa.const"() <{values = dense<[0.0, 0.0, 0.0, 0.0]> : tensor<4xf6E3M2FN>}> : () -> tensor<4xf6E3M2FN>
return %0 : tensor<4xf6E3M2FN>
}
@@ -251,7 +251,7 @@ func.func @test_const_block_scaled_types() -> (tensor<1x32x!tosa.block_scaled<BL
func.func @test_cast_from_block_scaled(%arg0: tensor<4x32xf8E5M2>, %arg1: tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32> {
// expected-error at +1 {{'tosa.cast_from_block_scaled' op illegal: requires specification version compatible with 1.1.draft (got 1.0) and requires any of [mxfp] profiles/extensions to be specified in the target environment}}
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf8E5M2>, tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf8E5M2>, tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32>
return %0 : tensor<4x32xf32>
}
@@ -259,26 +259,26 @@ func.func @test_cast_from_block_scaled(%arg0: tensor<4x32xf8E5M2>, %arg1: tensor
func.func @test_cast_to_block_scaled(%arg0: tensor<4x32xf32>) -> (tensor<4x32xf6E3M2FN>, tensor<4x1xf8E8M0FNU>) {
// expected-error at +1 {{'tosa.cast_to_block_scaled' op illegal: requires specification version compatible with 1.1.draft (got 1.0) and requires any of [mxfp] profiles/extensions to be specified in the target environment}}
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf32>) -> (tensor<4x32xf6E3M2FN>, tensor<4x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<4x32xf32>) -> (tensor<4x32xf6E3M2FN>, tensor<4x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<4x32xf6E3M2FN>, tensor<4x1xf8E8M0FNU>
}
// -----
func.func @test_conv2d_block_scaled(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>) -> tensor<1x4x4x2xf32> {
- %0 = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %1 = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %2 = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %1 = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %2 = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op illegal: requires specification version compatible with 1.1.draft (got 1.0) and requires any of [mxfp_conv] profiles/extensions to be specified in the target environment}}
- %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %0, %1, %2 {block_size = BLOCK_SIZE_32} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x4x2xf32>
+ %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %0, %1, %2 block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x4x2xf32>
return %3 : tensor<1x4x4x2xf32>
}
// -----
func.func @test_maxpool2d_adaptive(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_pool2d_adaptive' op illegal: requires specification version compatible with 1.1.draft (got 1.0) to be specified in the target environment}}
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x32x32x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
@@ -288,7 +288,7 @@ func.func @test_maxpool2d_adaptive(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x
// -----
func.func @test_block_scaled_concat(%arg0: tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, %arg1: tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<26x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>> {
// expected-error at +1 {{'tosa.concat' op illegal: requires specification version compatible with 1.1.draft (got 1.0) and requires all of [mx_common, mx_fp8e4m3] profiles/extensions to be specified in the target environment}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<26x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<26x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
return %0 : tensor<26x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
}
@@ -302,21 +302,21 @@ func.func @test_block_scaled_dim(%arg0: tensor<13x21x32x!tosa.block_scaled<BLOCK
// -----
func.func @test_resize_fp8(%arg0: tensor<1x32x32x8xf8E4M3FN>) -> tensor<1x64x64x8xf8E4M3FN> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op illegal: requires specification version compatible with 1.1.draft (got 1.0) to be specified in the target environment}}
- %0 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x32x32x8xf8E4M3FN>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E4M3FN>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x32x32x8xf8E4M3FN>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E4M3FN>
return %0 : tensor<1x64x64x8xf8E4M3FN>
}
// -----
func.func @test_resize_mxfp(%arg0: tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op illegal: requires specification version compatible with 1.1.draft (got 1.0) and requires all of [mx_common, mx_fp4e2m1] profiles/extensions to be specified in the target environment}}
- %0 = tosa.resize %arg0, %scale, %offset, %border { mode = NEAREST_NEIGHBOR } : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %0 : tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-invalid.mlir b/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-invalid.mlir
index 20e004077d291..a68807fa7bb2f 100644
--- a/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-invalid.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-invalid.mlir
@@ -3,12 +3,12 @@
// -----
func.func @test_avg_pool2d_adaptive_non_const_input_zp(%arg0: tensor<1x32x32x8xf32>, %input_zp: tensor<1xf32>) -> tensor<1x32x32x8xf32> {
- %output_zp = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op expected compile time resolvable constant, but got variable value for operand #1}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
@@ -16,12 +16,12 @@ func.func @test_avg_pool2d_adaptive_non_const_input_zp(%arg0: tensor<1x32x32x8xf
// -----
func.func @test_avg_pool2d_adaptive_non_const_output_zp(%arg0: tensor<1x32x32x8xf32>, %output_zp: tensor<1xf32>) -> tensor<1x32x32x8xf32> {
- %input_zp = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.avg_pool2d_adaptive' op expected compile time resolvable constant, but got variable value for operand #2}}
- %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) {acc_type = f32} :
+ %0 = "tosa.avg_pool2d_adaptive"(%arg0, %input_zp, %output_zp, %kernel, %stride, %pad) <{acc_type = f32}> :
(tensor<1x32x32x8xf32>, tensor<1xf32>, tensor<1xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-pro-fp-valid.mlir b/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-pro-fp-valid.mlir
index 408ee86690154..4a14fc8ece2c8 100644
--- a/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-pro-fp-valid.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-pro-fp-valid.mlir
@@ -27,7 +27,7 @@ func.func @test_row_gather_i8_i32(%input: tensor<13x27x3xi8>, %indices: tensor<1
// CHECK-LABEL: test_row_gather_block_scaled_i8_i32
func.func @test_row_gather_block_scaled_i8_i32(%input: tensor<13x27x3xi8>, %indices: tensor<13x26xi32>) -> tensor<13x52x3xi8> {
- %row_count = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
- %gather = tosa.row_gather_block_scaled %input, %indices, %row_count {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<13x27x3xi8>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x3xi8>)
+ %row_count = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %gather = tosa.row_gather_block_scaled %input, %indices, %row_count block_size<BLOCK_SIZE_1> : (tensor<13x27x3xi8>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x3xi8>)
return %gather : tensor<13x52x3xi8>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-valid.mlir b/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-valid.mlir
index 4ffb4dc412d9c..f0a6b41f1c7e2 100644
--- a/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-valid.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-valid.mlir
@@ -66,7 +66,7 @@ func.func @test_matmul_t_f16_with_block_scaled_inputs_type_fp6e3m2(%arg0: tensor
func.func @test_conv2d_fp8_acc32(%arg0: tensor<1x4x4x4xf8E5M2>, %arg1: tensor<8x1x1x4xf8E5M2>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xf8E5M2>, tensor<8x1x1x4xf8E5M2>, tensor<8xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x4x4x4xf8E5M2>, tensor<8x1x1x4xf8E5M2>, tensor<8xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -76,7 +76,7 @@ func.func @test_conv2d_fp8_acc32(%arg0: tensor<1x4x4x4xf8E5M2>, %arg1: tensor<8x
func.func @test_conv2d_mxfp(%arg0: tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, %arg1: tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, %arg2: tensor<8xf16>) -> tensor<1x4x4x8xf16> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xf16>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f16) local_bound(true) : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xf16>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf16>
return %0 : tensor<1x4x4x8xf16>
}
@@ -84,7 +84,7 @@ func.func @test_conv2d_mxfp(%arg0: tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAP
func.func @test_conv2d_mxfp_acc32(%arg0: tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, %arg1: tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, %arg2: tensor<8xf16>) -> tensor<1x4x4x8xf16> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xf16>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true) : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xf16>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf16>
return %0 : tensor<1x4x4x8xf16>
}
@@ -94,7 +94,7 @@ func.func @test_conv2d_mxfp_acc32(%arg0: tensor<1x4x4x32x!tosa.block_scaled<BLOC
func.func @test_conv3d_fp8_acc32(%arg0: tensor<1x4x8x21x17xf8E5M2>, %arg1: tensor<34x1x1x1x17xf8E5M2>, %arg2: tensor<34xf32>) -> tensor<1x4x8x21x34xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<1x4x8x21x17xf8E5M2>, tensor<34x1x1x1x17xf8E5M2>, tensor<34xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x8x21x34xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32) : (tensor<1x4x8x21x17xf8E5M2>, tensor<34x1x1x1x17xf8E5M2>, tensor<34xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
@@ -104,7 +104,7 @@ func.func @test_conv3d_fp8_acc32(%arg0: tensor<1x4x8x21x17xf8E5M2>, %arg1: tenso
func.func @test_depthwise_conv2d_fp8_acc32(%arg0: tensor<1x4x4x4xf8E5M2>, %arg1: tensor<1x1x4x2xf8E5M2>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xf8E5M2>, tensor<1x1x4x2xf8E5M2>, tensor<8xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) : (tensor<1x4x4x4xf8E5M2>, tensor<1x1x4x2xf8E5M2>, tensor<8xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -114,7 +114,7 @@ func.func @test_depthwise_conv2d_fp8_acc32(%arg0: tensor<1x4x4x4xf8E5M2>, %arg1:
func.func @test_transpose_conv2d_fp8_acc32(%arg0: tensor<1x32x32x8xf8E5M2>, %arg1: tensor<16x1x1x8xf8E5M2>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf8E5M2>, tensor<16x1x1x8xf8E5M2>, tensor<16xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp out_pad([0, 0, 0, 0]) stride([1, 1]) acc_type(f32) : (tensor<1x32x32x8xf8E5M2>, tensor<16x1x1x8xf8E5M2>, tensor<16xf32>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -122,7 +122,7 @@ func.func @test_transpose_conv2d_fp8_acc32(%arg0: tensor<1x32x32x8xf8E5M2>, %arg
// CHECK-LABEL: test_matmul_t_block_scaled_fp6e2m3
func.func @test_matmul_t_block_scaled_fp6e2m3(%arg0: tensor<4x8x32xf6E2M3FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32xf6E2M3FN>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = BLOCK_SIZE_32} : (tensor<4x8x32xf6E2M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf6E2M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32xf6E2M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf6E2M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
@@ -130,7 +130,7 @@ func.func @test_matmul_t_block_scaled_fp6e2m3(%arg0: tensor<4x8x32xf6E2M3FN>, %a
// CHECK-LABEL: test_argmax_int64
func.func @test_argmax_int64(%arg0: tensor<1x13x13x5xf32>) -> tensor<1x13x13xi64> {
- %0 = tosa.argmax %arg0 {axis = 3 : i32} : (tensor<1x13x13x5xf32>) -> tensor<1x13x13xi64>
+ %0 = tosa.argmax %arg0 axis(3) : (tensor<1x13x13x5xf32>) -> tensor<1x13x13xi64>
return %0 : tensor<1x13x13xi64>
}
@@ -138,7 +138,7 @@ func.func @test_argmax_int64(%arg0: tensor<1x13x13x5xf32>) -> tensor<1x13x13xi64
// CHECK-LABEL: test_const_i64
func.func @test_const_i64() -> tensor<4xi64> {
- %0 = "tosa.const"() {values = dense<[3, 0, 1, 2]> : tensor<4xi64>} : () -> tensor<4xi64>
+ %0 = "tosa.const"() <{values = dense<[3, 0, 1, 2]> : tensor<4xi64>}> : () -> tensor<4xi64>
return %0 : tensor<4xi64>
}
@@ -146,7 +146,7 @@ func.func @test_const_i64() -> tensor<4xi64> {
// CHECK-LABEL: test_const_fp6e3m2
func.func @test_const_fp6e3m2() -> tensor<4xf6E3M2FN> {
- %0 = "tosa.const"() {values = dense<[0.0, 0.0, 0.0, 0.0]> : tensor<4xf6E3M2FN>} : () -> tensor<4xf6E3M2FN>
+ %0 = "tosa.const"() <{values = dense<[0.0, 0.0, 0.0, 0.0]> : tensor<4xf6E3M2FN>}> : () -> tensor<4xf6E3M2FN>
return %0 : tensor<4xf6E3M2FN>
}
@@ -154,7 +154,7 @@ func.func @test_const_fp6e3m2() -> tensor<4xf6E3M2FN> {
// CHECK-LABEL: test_cast_from_block_scaled_fp8e5m2_fp32
func.func @test_cast_from_block_scaled_fp8e5m2_fp32(%arg0: tensor<4x32xf8E5M2>, %arg1: tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32> {
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf8E5M2>, tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf8E5M2>, tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32>
return %0 : tensor<4x32xf32>
}
@@ -162,7 +162,7 @@ func.func @test_cast_from_block_scaled_fp8e5m2_fp32(%arg0: tensor<4x32xf8E5M2>,
// CHECK-LABEL: test_cast_from_block_scaled_fp8e5m2_bf16
func.func @test_cast_from_block_scaled_fp8e5m2_bf16(%arg0: tensor<4x32xf8E5M2>, %arg1: tensor<4x1xf8E8M0FNU>) -> tensor<4x32xbf16> {
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf8E5M2>, tensor<4x1xf8E8M0FNU>) -> tensor<4x32xbf16>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf8E5M2>, tensor<4x1xf8E8M0FNU>) -> tensor<4x32xbf16>
return %0 : tensor<4x32xbf16>
}
@@ -170,7 +170,7 @@ func.func @test_cast_from_block_scaled_fp8e5m2_bf16(%arg0: tensor<4x32xf8E5M2>,
// CHECK-LABEL: test_cast_to_block_scaled_static
func.func @test_cast_to_block_scaled_static(%arg0: tensor<4x32xf32>) -> (tensor<4x32xf6E3M2FN>, tensor<4x1xf8E8M0FNU>) {
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf32>) -> (tensor<4x32xf6E3M2FN>, tensor<4x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<4x32xf32>) -> (tensor<4x32xf6E3M2FN>, tensor<4x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<4x32xf6E3M2FN>, tensor<4x1xf8E8M0FNU>
}
@@ -178,7 +178,7 @@ func.func @test_cast_to_block_scaled_static(%arg0: tensor<4x32xf32>) -> (tensor<
// CHECK-LABEL: test_cast_to_block_scaled_mxint8
func.func @test_cast_to_block_scaled_mxint8(%arg0: tensor<4x32xf32>) -> (tensor<4x32x!tosa.mxint8>, tensor<4x1xf8E8M0FNU>) {
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32, stochastic_round = false} : (tensor<4x32xf32>) -> (tensor<4x32x!tosa.mxint8>, tensor<4x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> {stochastic_round = false} : (tensor<4x32xf32>) -> (tensor<4x32x!tosa.mxint8>, tensor<4x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<4x32x!tosa.mxint8>, tensor<4x1xf8E8M0FNU>
}
@@ -186,7 +186,7 @@ func.func @test_cast_to_block_scaled_mxint8(%arg0: tensor<4x32xf32>) -> (tensor<
// CHECK-LABEL: test_const_fp6e3m2
func.func @test_const_fp6e3m2() -> tensor<4xf6E3M2FN> {
- %0 = "tosa.const"() {values = dense<[0.0, 0.0, 0.0, 0.0]> : tensor<4xf6E3M2FN>} : () -> tensor<4xf6E3M2FN>
+ %0 = "tosa.const"() <{values = dense<[0.0, 0.0, 0.0, 0.0]> : tensor<4xf6E3M2FN>}> : () -> tensor<4xf6E3M2FN>
return %0 : tensor<4xf6E3M2FN>
}
@@ -194,7 +194,7 @@ func.func @test_const_fp6e3m2() -> tensor<4xf6E3M2FN> {
// CHECK-LABEL: test_const_mxint8
func.func @test_const_mxint8() -> tensor<2x!tosa.mxint8> {
- %0 = "tosa.const"() {values = dense<["0x00", "0x7F"]> : tensor<2x!tosa.mxint8>} : () -> tensor<2x!tosa.mxint8>
+ %0 = "tosa.const"() <{values = dense<["0x00", "0x7F"]> : tensor<2x!tosa.mxint8>}> : () -> tensor<2x!tosa.mxint8>
return %0 : tensor<2x!tosa.mxint8>
}
@@ -215,7 +215,7 @@ func.func @test_const_block_scaled_types() -> (tensor<1x32x!tosa.block_scaled<BL
// CHECK-LABEL: test_matmul_t_block_scaled_mxint8
func.func @test_matmul_t_block_scaled_mxint8(%arg0: tensor<4x8x32x!tosa.mxint8>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32x!tosa.mxint8>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x8x32x!tosa.mxint8>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32x!tosa.mxint8>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32x!tosa.mxint8>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32x!tosa.mxint8>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
@@ -223,7 +223,7 @@ func.func @test_matmul_t_block_scaled_mxint8(%arg0: tensor<4x8x32x!tosa.mxint8>,
// CHECK-LABEL: test_cast_to_block_scaled_mxint8
func.func @test_cast_to_block_scaled_mxint8(%arg0: tensor<4x32xf32>) -> (tensor<4x32x!tosa.mxint8>, tensor<4x1xf8E8M0FNU>) {
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32, stochastic_round = false} : (tensor<4x32xf32>) -> (tensor<4x32x!tosa.mxint8>, tensor<4x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> {stochastic_round = false} : (tensor<4x32xf32>) -> (tensor<4x32x!tosa.mxint8>, tensor<4x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<4x32x!tosa.mxint8>, tensor<4x1xf8E8M0FNU>
}
@@ -231,7 +231,7 @@ func.func @test_cast_to_block_scaled_mxint8(%arg0: tensor<4x32xf32>) -> (tensor<
// CHECK-LABEL: test_argmax_fp8_i64
func.func @test_argmax_fp8_i64(%arg0: tensor<12x8x16xf8E5M2>) -> tensor<12x16xi64> {
- %0 = tosa.argmax %arg0 { axis = 1 : i32 } : (tensor<12x8x16xf8E5M2>) -> tensor<12x16xi64>
+ %0 = tosa.argmax %arg0 axis(1) : (tensor<12x8x16xf8E5M2>) -> tensor<12x16xi64>
return %0 : tensor<12x16xi64>
}
@@ -239,7 +239,7 @@ func.func @test_argmax_fp8_i64(%arg0: tensor<12x8x16xf8E5M2>) -> tensor<12x16xi6
// CHECK-LABEL: test_argmax_bf16_i64
func.func @test_argmax_bf16_i64(%arg0: tensor<12x8x16xbf16>) -> tensor<12x16xi64> {
- %0 = tosa.argmax %arg0 { axis = 1 : i32 } : (tensor<12x8x16xbf16>) -> tensor<12x16xi64>
+ %0 = tosa.argmax %arg0 axis(1) : (tensor<12x8x16xbf16>) -> tensor<12x16xi64>
return %0 : tensor<12x16xi64>
}
@@ -247,7 +247,7 @@ func.func @test_argmax_bf16_i64(%arg0: tensor<12x8x16xbf16>) -> tensor<12x16xi64
// CHECK-LABEL: test_scatter_const_indices_int64
func.func @test_scatter_const_indices_int64(%arg0: tensor<2x52x3xf32>, %arg2: tensor<2x12x3xf32>) -> tensor<2x52x3xf32> {
- %indices = "tosa.const"() { values = dense<[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]> : tensor<2x12xi64> } : () -> tensor<2x12xi64>
+ %indices = "tosa.const"() <{ values = dense<[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]> : tensor<2x12xi64> }> : () -> tensor<2x12xi64>
%0 = tosa.scatter %arg0, %indices, %arg2 : (tensor<2x52x3xf32>, tensor<2x12xi64>, tensor<2x12x3xf32>) -> tensor<2x52x3xf32>
return %0 : tensor<2x52x3xf32>
}
@@ -340,7 +340,7 @@ func.func @test_row_gather_mxfp_i64_indices(%arg0: tensor<13x21x32x!tosa.block_s
// CHECK-LABEL: test_reshape_mxfp
func.func @test_reshape_mxfp(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
- %1 = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = tosa.reshape %arg0, %1 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<2>) -> tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %0 : tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
}
@@ -349,7 +349,7 @@ func.func @test_reshape_mxfp(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_3
// CHECK-LABEL: test_reshape_mxfp_mxint8
func.func @test_reshape_mxfp_mxint8(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>) -> tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>> {
- %1 = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
%0 = tosa.reshape %arg0, %1 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>, !tosa.shape<2>) -> tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
return %0 : tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
}
@@ -358,8 +358,8 @@ func.func @test_reshape_mxfp_mxint8(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_
// CHECK-LABEL: test_row_gather_block_scaled_i8_i32_indices
func.func @test_row_gather_block_scaled_i8_i32_indices(%arg0: tensor<13x21x3xi8>, %arg1: tensor<13x26xi32>) -> tensor<13x52x3xi8> {
- %row_count = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
- %0 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<13x21x3xi8>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x3xi8>)
+ %row_count = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %0 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count block_size<BLOCK_SIZE_1> : (tensor<13x21x3xi8>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x3xi8>)
return %0 : tensor<13x52x3xi8>
}
@@ -455,7 +455,7 @@ func.func @test_scatter_i32_i64_indices(%arg0: tensor<13x27x3xi32>, %arg1: tenso
// CHECK-LABEL: test_cast_bool_fp32
func.func @test_cast_bool_fp32(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xf32> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi1>) -> tensor<13x21x3xf32>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi1>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -463,7 +463,7 @@ func.func @test_cast_bool_fp32(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xf32>
// CHECK-LABEL: test_cast_bool_i64
func.func @test_cast_bool_i64(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xi64> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi1>) -> tensor<13x21x3xi64>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi1>) -> tensor<13x21x3xi64>
return %0 : tensor<13x21x3xi64>
}
@@ -471,7 +471,7 @@ func.func @test_cast_bool_i64(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xi64>
// CHECK-LABEL: test_cast_fp32_bool
func.func @test_cast_fp32_bool(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xi1> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xf32>) -> tensor<13x21x3xi1>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xf32>) -> tensor<13x21x3xi1>
return %0 : tensor<13x21x3xi1>
}
@@ -479,7 +479,7 @@ func.func @test_cast_fp32_bool(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xi1>
// CHECK-LABEL: test_cast_i64_bool
func.func @test_cast_i64_bool(%arg0: tensor<13x21x3xi64>) -> tensor<13x21x3xi1> {
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<13x21x3xi64>) -> tensor<13x21x3xi1>
+ %0 = tosa.cast %arg0 : (tensor<13x21x3xi64>) -> tensor<13x21x3xi1>
return %0 : tensor<13x21x3xi1>
}
@@ -487,13 +487,13 @@ func.func @test_cast_i64_bool(%arg0: tensor<13x21x3xi64>) -> tensor<13x21x3xi1>
// CHECK-LABEL: test_cast_to_block_scaled_types
func.func @test_cast_to_block_scaled_types(%fp16: tensor<4x32xf16>, %fp32: tensor<4x32xf32>, %bf16: tensor<4x32xbf16>, %fp8e4m3: tensor<4x32xf8E4M3FN>, %fp8e5m2: tensor<4x32xf8E5M2>) -> (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>) {
- %0 = tosa.cast %fp32 {input_unsigned = false} : (tensor<4x32xf32>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
- %1 = tosa.cast %fp32 {input_unsigned = false} : (tensor<4x32xf32>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
- %2 = tosa.cast %fp32 {input_unsigned = false} : (tensor<4x32xf32>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>
- %3 = tosa.cast %fp16 {input_unsigned = false} : (tensor<4x32xf16>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
- %4 = tosa.cast %bf16 {input_unsigned = false} : (tensor<4x32xbf16>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>
- %5 = tosa.cast %fp8e4m3 {input_unsigned = false} : (tensor<4x32xf8E4M3FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
- %6 = tosa.cast %fp8e5m2 {input_unsigned = false} : (tensor<4x32xf8E5M2>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
+ %0 = tosa.cast %fp32 : (tensor<4x32xf32>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
+ %1 = tosa.cast %fp32 : (tensor<4x32xf32>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
+ %2 = tosa.cast %fp32 : (tensor<4x32xf32>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>
+ %3 = tosa.cast %fp16 : (tensor<4x32xf16>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
+ %4 = tosa.cast %bf16 : (tensor<4x32xbf16>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>
+ %5 = tosa.cast %fp8e4m3 : (tensor<4x32xf8E4M3FN>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
+ %6 = tosa.cast %fp8e5m2 : (tensor<4x32xf8E5M2>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
return %0, %1, %2, %3, %4, %5 : tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>, tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
}
@@ -501,13 +501,13 @@ func.func @test_cast_to_block_scaled_types(%fp16: tensor<4x32xf16>, %fp32: tenso
// CHECK-LABEL: test_cast_from_block_scaled_types
func.func @test_cast_from_block_scaled_types(%fp4: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, %fp6e2m3: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>, %fp6e3m2: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>, %fp8e4m3: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, %fp8e5m2: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>, %mxint8: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>) -> (tensor<4x32xf32>, tensor<4x32xf16>, tensor<4x32xbf16>, tensor<4x32xf8E4M3FN>, tensor<4x32xf8E5M2>) {
- %0 = tosa.cast %fp4 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xf32>
- %1 = tosa.cast %fp6e2m3 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>) -> tensor<4x32xf32>
- %2 = tosa.cast %fp6e3m2 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>) -> tensor<4x32xf32>
- %3 = tosa.cast %fp8e4m3 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<4x32xf16>
- %4 = tosa.cast %fp8e5m2 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<4x32xbf16>
- %5 = tosa.cast %mxint8 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>) -> tensor<4x32xf8E4M3FN>
- %6 = tosa.cast %fp4 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xf8E5M2>
+ %0 = tosa.cast %fp4 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xf32>
+ %1 = tosa.cast %fp6e2m3 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>) -> tensor<4x32xf32>
+ %2 = tosa.cast %fp6e3m2 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>) -> tensor<4x32xf32>
+ %3 = tosa.cast %fp8e4m3 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<4x32xf16>
+ %4 = tosa.cast %fp8e5m2 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>) -> tensor<4x32xbf16>
+ %5 = tosa.cast %mxint8 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>) -> tensor<4x32xf8E4M3FN>
+ %6 = tosa.cast %fp4 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xf8E5M2>
return %0, %3, %4, %5, %6 : tensor<4x32xf32>, tensor<4x32xf16>, tensor<4x32xbf16>, tensor<4x32xf8E4M3FN>, tensor<4x32xf8E5M2>
}
@@ -515,7 +515,7 @@ func.func @test_cast_from_block_scaled_types(%fp4: tensor<4x32x!tosa.block_scale
// CHECK-LABEL: test_dynamic_dims
func.func @test_dynamic_dims(%arg0: tensor<?x8x16xi8>) -> tensor<?x16xi32> {
- %0 = tosa.argmax %arg0 { axis = 1 : i32 } : (tensor<?x8x16xi8>) -> tensor<?x16xi32>
+ %0 = tosa.argmax %arg0 axis(1) : (tensor<?x8x16xi8>) -> tensor<?x16xi32>
return %0 : tensor<?x16xi32>
}
@@ -523,8 +523,8 @@ func.func @test_dynamic_dims(%arg0: tensor<?x8x16xi8>) -> tensor<?x16xi32> {
// CHECK-LABEL: test_add_shape
func.func @test_add_shape() {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %b = tosa.const_shape {values = dense<[5, 6, 7, 8]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %b = tosa.const_shape values(dense<[5, 6, 7, 8]> : tensor<4xindex>) : () -> !tosa.shape<4>
%c = tosa.add_shape %a, %b : (!tosa.shape<4>, !tosa.shape<4>) -> !tosa.shape<4>
return
}
@@ -533,7 +533,7 @@ func.func @test_add_shape() {
// CHECK-LABEL: test_dim
func.func @test_dim(%arg0: tensor<1x2x3x4xi32>) {
- %0 = tosa.dim %arg0 {axis = 2 : i32} : (tensor<1x2x3x4xi32>) -> !tosa.shape<1>
+ %0 = tosa.dim %arg0 axis(2) : (tensor<1x2x3x4xi32>) -> !tosa.shape<1>
return
}
@@ -541,14 +541,14 @@ func.func @test_dim(%arg0: tensor<1x2x3x4xi32>) {
// CHECK-LABEL: test_dim_bf16
func.func @test_dim_bf16(%0: tensor<6x4x6x9xbf16>) {
- %1 = tosa.dim %0 {axis = 1 : i32} : (tensor<6x4x6x9xbf16>) -> !tosa.shape<1>
+ %1 = tosa.dim %0 axis(1) : (tensor<6x4x6x9xbf16>) -> !tosa.shape<1>
return
}
// -----
// CHECK-LABEL: test_exp2_shape
func.func @test_exp2_shape() {
- %a = tosa.const_shape {values = dense<[5, 7, 10, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[5, 7, 10, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%b = tosa.exp2_shape %a : (!tosa.shape<4>) -> !tosa.shape<4>
return
}
@@ -556,7 +556,7 @@ func.func @test_exp2_shape() {
// -----
// CHECK-LABEL: test_log2_ceil_shape
func.func @test_log2_ceil_shape() {
- %a = tosa.const_shape {values = dense<[5, 7, 10, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %a = tosa.const_shape values(dense<[5, 7, 10, 1]> : tensor<4xindex>) : () -> !tosa.shape<4>
%b = tosa.log2_ceil_shape %a : (!tosa.shape<4>) -> !tosa.shape<4>
return
}
@@ -565,8 +565,8 @@ func.func @test_log2_ceil_shape() {
// CHECK-LABEL: test_mod_shape
func.func @test_mod_shape() {
- %a = tosa.const_shape {values = dense<[10, 11, 12]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %b = tosa.const_shape {values = dense<[3, 5, 2]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %a = tosa.const_shape values(dense<[10, 11, 12]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %b = tosa.const_shape values(dense<[3, 5, 2]> : tensor<3xindex>) : () -> !tosa.shape<3>
%c = tosa.mod_shape %a, %b : (!tosa.shape<3>, !tosa.shape<3>) -> !tosa.shape<3>
return
}
@@ -575,10 +575,10 @@ func.func @test_mod_shape() {
// CHECK-LABEL: test_conv2d_block_scaled
func.func @test_conv2d_block_scaled(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = BLOCK_SIZE_32} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x4x8xf32>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -586,17 +586,17 @@ func.func @test_conv2d_block_scaled(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: ten
// CHECK-LABEL: test_assert_equal_shape
func.func @test_assert_equal_shape() {
- %0 = tosa.const_shape {values = dense<[10, 15]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %1 = tosa.const_shape {values = dense<[5, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- tosa.assert_equal_shape %0, %1 {allow_broadcast = true} : (!tosa.shape<2>, !tosa.shape<2>) -> ()
+ %0 = tosa.const_shape values(dense<[10, 15]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %1 = tosa.const_shape values(dense<[5, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ tosa.assert_equal_shape %0, %1 allow_broadcast(true) : (!tosa.shape<2>, !tosa.shape<2>) -> ()
return
}
// -----
func.func @test_maxpool2d_adaptive(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x32x32x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf32>
return %0 : tensor<1x32x32x8xf32>
@@ -606,7 +606,7 @@ func.func @test_maxpool2d_adaptive(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x
// CHECK-LABEL: test_block_scaled_concat
func.func @test_block_scaled_concat(%arg0: tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, %arg1: tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<26x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>> {
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<26x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, tensor<13x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<26x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
return %0 : tensor<26x21x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
}
@@ -622,11 +622,11 @@ func.func @test_block_scaled_dim(%arg0: tensor<13x21x32x!tosa.block_scaled<BLOCK
// CHECK-LABEL: test_resize_fp8
func.func @test_resize_fp8(%arg0: tensor<1x32x32x8xf8E4M3FN>, %arg1: tensor<1x32x32x8xf8E5M2>) {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %0 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x32x32x8xf8E4M3FN>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E4M3FN>
- %1 = tosa.resize %arg1, %scale, %offset, %border { mode = NEAREST_NEIGHBOR } : (tensor<1x32x32x8xf8E5M2>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E5M2>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x32x32x8xf8E4M3FN>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E4M3FN>
+ %1 = tosa.resize %arg1, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x32x32x8xf8E5M2>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf8E5M2>
return
}
@@ -640,14 +640,14 @@ func.func @test_resize_mxfp_types(
%fp8e4m3: tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>,
%fp8e5m2: tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>,
%mxint8: tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>) {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %0 = tosa.resize %fp4, %scale, %offset, %border { mode = NEAREST_NEIGHBOR } : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
- %1 = tosa.resize %fp6e2m3, %scale, %offset, %border { mode = NEAREST_NEIGHBOR } : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
- %2 = tosa.resize %fp6e3m2, %scale, %offset, %border { mode = NEAREST_NEIGHBOR } : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>
- %3 = tosa.resize %fp8e4m3, %scale, %offset, %border { mode = NEAREST_NEIGHBOR } : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
- %4 = tosa.resize %fp8e5m2, %scale, %offset, %border { mode = NEAREST_NEIGHBOR } : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>
- %5 = tosa.resize %mxint8, %scale, %offset, %border { mode = NEAREST_NEIGHBOR } : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %0 = tosa.resize %fp4, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
+ %1 = tosa.resize %fp6e2m3, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E2M3FN>>
+ %2 = tosa.resize %fp6e3m2, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f6E3M2FN>>
+ %3 = tosa.resize %fp8e4m3, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
+ %4 = tosa.resize %fp8e5m2, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E5M2>>
+ %5 = tosa.resize %mxint8, %scale, %offset, %border mode<NEAREST_NEIGHBOR> : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
return
}
diff --git a/mlir/test/Dialect/Tosa/transpose-fold.mlir b/mlir/test/Dialect/Tosa/transpose-fold.mlir
index d29cb54eb2117..f2b38f935bab9 100644
--- a/mlir/test/Dialect/Tosa/transpose-fold.mlir
+++ b/mlir/test/Dialect/Tosa/transpose-fold.mlir
@@ -6,8 +6,8 @@
// CHECK: }
func.func @test_cancel_transpose_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<1x2x3xi32>) {
- %1 = tosa.transpose %arg0 { perms = array<i32: 1, 2, 0> }: (tensor<1x2x3xi32>) -> tensor<2x3x1xi32>
- %3 = tosa.transpose %1 { perms = array<i32: 2, 0, 1> }: (tensor<2x3x1xi32>) -> tensor<1x2x3xi32>
+ %1 = tosa.transpose %arg0 perms([1, 2, 0]) : (tensor<1x2x3xi32>) -> tensor<2x3x1xi32>
+ %3 = tosa.transpose %1 perms([2, 0, 1]) : (tensor<2x3x1xi32>) -> tensor<1x2x3xi32>
return %3 : tensor<1x2x3xi32>
}
@@ -19,7 +19,7 @@ func.func @test_cancel_transpose_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<
// CHECK: }
func.func @test_remove_identity_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<1x2x3xi32>) {
- %1 = tosa.transpose %arg0 { perms = array<i32: 0, 1, 2> }: (tensor<1x2x3xi32>) -> tensor<1x2x3xi32>
+ %1 = tosa.transpose %arg0 perms([0, 1, 2]) : (tensor<1x2x3xi32>) -> tensor<1x2x3xi32>
return %1 : tensor<1x2x3xi32>
}
@@ -27,13 +27,13 @@ func.func @test_remove_identity_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<1
// CHECK-LABEL: func.func @test_do_not_cancel_different_transpose(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<2x3x4x5xi32>) -> tensor<5x4x3x2xi32> {
-// CHECK: %[[VAL_2:.*]] = tosa.transpose %[[VAL_0]] {perms = array<i32: 3, 2, 1, 0>} : (tensor<2x3x4x5xi32>) -> tensor<5x4x3x2xi32>
+// CHECK: %[[VAL_2:.*]] = tosa.transpose %[[VAL_0]] perms([3, 2, 1, 0]) : (tensor<2x3x4x5xi32>) -> tensor<5x4x3x2xi32>
// CHECK: return %[[VAL_2]] : tensor<5x4x3x2xi32>
// CHECK: }
func.func @test_do_not_cancel_different_transpose(%arg0: tensor<2x3x4x5xi32>) -> (tensor<5x4x3x2xi32>) {
- %1 = tosa.transpose %arg0 { perms = array<i32: 1, 2, 0, 3> }: (tensor<2x3x4x5xi32>) -> tensor<3x4x2x5xi32>
- %3 = tosa.transpose %1 { perms = array<i32: 3, 1, 0, 2> }: (tensor<3x4x2x5xi32>) -> tensor<5x4x3x2xi32>
+ %1 = tosa.transpose %arg0 perms([1, 2, 0, 3]) : (tensor<2x3x4x5xi32>) -> tensor<3x4x2x5xi32>
+ %3 = tosa.transpose %1 perms([3, 1, 0, 2]) : (tensor<3x4x2x5xi32>) -> tensor<5x4x3x2xi32>
return %3 : tensor<5x4x3x2xi32>
}
@@ -41,12 +41,12 @@ func.func @test_do_not_cancel_different_transpose(%arg0: tensor<2x3x4x5xi32>) ->
// CHECK-LABEL: func.func @test_prefer_compose_transpose(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x2x3x4xi32>) -> tensor<4x3x2x1xi32> {
-// CHECK: %[[VAL_2:.*]] = tosa.transpose %[[VAL_0]] {perms = array<i32: 3, 2, 1, 0>} : (tensor<1x2x3x4xi32>) -> tensor<4x3x2x1xi32>
+// CHECK: %[[VAL_2:.*]] = tosa.transpose %[[VAL_0]] perms([3, 2, 1, 0]) : (tensor<1x2x3x4xi32>) -> tensor<4x3x2x1xi32>
// CHECK: return %[[VAL_2]] : tensor<4x3x2x1xi32>
// CHECK: }
func.func @test_prefer_compose_transpose(%arg0: tensor<1x2x3x4xi32>) -> (tensor<4x3x2x1xi32>) {
- %1 = tosa.transpose %arg0 { perms = array<i32: 1, 2, 0, 3> }: (tensor<1x2x3x4xi32>) -> tensor<2x3x1x4xi32>
- %3 = tosa.transpose %1 { perms = array<i32: 3, 1, 0, 2> }: (tensor<2x3x1x4xi32>) -> tensor<4x3x2x1xi32>
+ %1 = tosa.transpose %arg0 perms([1, 2, 0, 3]) : (tensor<1x2x3x4xi32>) -> tensor<2x3x1x4xi32>
+ %3 = tosa.transpose %1 perms([3, 1, 0, 2]) : (tensor<2x3x1x4xi32>) -> tensor<4x3x2x1xi32>
return %3 : tensor<4x3x2x1xi32>
}
diff --git a/mlir/test/Dialect/Tosa/verifier.mlir b/mlir/test/Dialect/Tosa/verifier.mlir
index 7c690276e2eb2..7b7162f087ace 100644
--- a/mlir/test/Dialect/Tosa/verifier.mlir
+++ b/mlir/test/Dialect/Tosa/verifier.mlir
@@ -7,11 +7,11 @@
// -----
func.func @test_resize_mxfp_requires_nearest_neighbor(%arg0: tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
- %scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
- %border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
+ %scale = tosa.const_shape values(dense<[4, 2, 4, 2]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %offset = tosa.const_shape values(dense<[-1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %border = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.resize' op requires NEAREST_NEIGHBOR mode for block scaled input}}
- %0 = tosa.resize %arg0, %scale, %offset, %border { mode = BILINEAR } : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
+ %0 = tosa.resize %arg0, %scale, %offset, %border mode<BILINEAR> : (tensor<1x32x32x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %0 : tensor<1x64x64x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
}
@@ -19,7 +19,7 @@ func.func @test_resize_mxfp_requires_nearest_neighbor(%arg0: tensor<1x32x32x32x!
func.func @test_transpose_io_rank_mismatch(%arg0: tensor<13x21x3xf32>, %arg1: tensor<3xi32>) -> tensor<3x13x21x1xf32> {
// expected-error at +1 {{'tosa.transpose' op expected input tensor rank to equal result tensor rank}}
- %0 = tosa.transpose %arg0 {perms = array<i32: 2, 1, 0>}: (tensor<13x21x3xf32>) -> tensor<3x13x21x1xf32>
+ %0 = tosa.transpose %arg0 perms([2, 1, 0]) : (tensor<13x21x3xf32>) -> tensor<3x13x21x1xf32>
return %0 : tensor<3x13x21x1xf32>
}
@@ -28,7 +28,7 @@ func.func @test_transpose_io_rank_mismatch(%arg0: tensor<13x21x3xf32>, %arg1: te
func.func @test_transpose_rank0_perms() {
%14 = tensor.empty() : tensor<5x27xi64>
// expected-error at +1 {{'tosa.transpose' op expected perms attribute to have size 2 (input rank) but got size 0}}
- %72 = tosa.transpose %14 {perms = array<i32> }: (tensor<5x27xi64>) -> tensor<?x?xi64>
+ %72 = tosa.transpose %14 perms([]) : (tensor<5x27xi64>) -> tensor<?x?xi64>
return
}
@@ -36,7 +36,7 @@ func.func @test_transpose_rank0_perms() {
func.func @test_transpose_invalid_perms_size(%arg0: tensor<13x21x3xf32>) -> tensor<3x13x21xf32> {
// expected-error at +1 {{'tosa.transpose' op expected perms attribute to have size 3 (input rank) but got size 7}}
- %0 = tosa.transpose %arg0 {perms = array<i32: 6, 5, 4, 3, 2, 1, 0> }: (tensor<13x21x3xf32>) -> tensor<3x13x21xf32>
+ %0 = tosa.transpose %arg0 perms([6, 5, 4, 3, 2, 1, 0]) : (tensor<13x21x3xf32>) -> tensor<3x13x21xf32>
return %0 : tensor<3x13x21xf32>
}
@@ -44,7 +44,7 @@ func.func @test_transpose_invalid_perms_size(%arg0: tensor<13x21x3xf32>) -> tens
func.func @test_transpose_invalid_permutation_tensor(%arg0: tensor<13x21x3xf32>) -> tensor<?x?x?xf32> {
// expected-error at +1 {{'tosa.transpose' op expected valid permutation indices}}
- %0 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 0> }: (tensor<13x21x3xf32>) -> tensor<?x?x?xf32>
+ %0 = tosa.transpose %arg0 perms([2, 0, 0]) : (tensor<13x21x3xf32>) -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
@@ -52,7 +52,7 @@ func.func @test_transpose_invalid_permutation_tensor(%arg0: tensor<13x21x3xf32>)
func.func @test_transpose_invalid_permutation_negative(%arg0: tensor<3x2xi32>) -> tensor<*xi32> {
// expected-error at +1 {{'tosa.transpose' op expected valid permutation indices}}
- %1 = tosa.transpose %arg0 {perms = array<i32: -1, 0> }: (tensor<3x2xi32>) -> tensor<*xi32>
+ %1 = tosa.transpose %arg0 perms([-1, 0]) : (tensor<3x2xi32>) -> tensor<*xi32>
return %1 : tensor<*xi32>
}
@@ -60,7 +60,7 @@ func.func @test_transpose_invalid_permutation_negative(%arg0: tensor<3x2xi32>) -
func.func @test_transpose_invalid_permutation_tensor_above_range(%arg0: tensor<3x2xi32>) -> tensor<*xi32> {
// expected-error at +1 {{'tosa.transpose' op expected valid permutation indices}}
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0> }: (tensor<3x2xi32>) -> tensor<*xi32>
+ %1 = tosa.transpose %arg0 perms([2, 0]) : (tensor<3x2xi32>) -> tensor<*xi32>
return %1 : tensor<*xi32>
}
@@ -68,7 +68,7 @@ func.func @test_transpose_invalid_permutation_tensor_above_range(%arg0: tensor<3
func.func @test_transpose_invalid_num_elements(%arg0: tensor<3x2xi32>) -> tensor<3x4xi32> {
// expected-error at +1 {{'tosa.transpose' op expected input1 and output to have same numbers of elements, got 6 and 12}}
- %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0> }: (tensor<3x2xi32>) -> tensor<3x4xi32>
+ %1 = tosa.transpose %arg0 perms([1, 0]) : (tensor<3x2xi32>) -> tensor<3x4xi32>
return %1 : tensor<3x4xi32>
}
@@ -76,7 +76,7 @@ func.func @test_transpose_invalid_num_elements(%arg0: tensor<3x2xi32>) -> tensor
func.func @test_transpose_invalid_permutation_types(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
// expected-error at +1 {{'tosa.transpose' op expected output tensor dim 0 to match input dim 1 with value of 2}}
- %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0> }: (tensor<3x2xi32>) -> tensor<3x2xi32>
+ %1 = tosa.transpose %arg0 perms([1, 0]) : (tensor<3x2xi32>) -> tensor<3x2xi32>
return %1 : tensor<3x2xi32>
}
@@ -84,7 +84,7 @@ func.func @test_transpose_invalid_permutation_types(%arg0: tensor<3x2xi32>) -> t
func.func @test_transpose_invalid_permutation_types_dynamic_dim_ok(%arg0: tensor<2x?xi32>) -> tensor<3x4xi32> {
// expected-error at +1 {{'tosa.transpose' op expected output tensor dim 1 to match input dim 0 with value of 2}}
- %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0> }: (tensor<2x?xi32>) -> tensor<3x4xi32>
+ %1 = tosa.transpose %arg0 perms([1, 0]) : (tensor<2x?xi32>) -> tensor<3x4xi32>
return %1 : tensor<3x4xi32>
}
@@ -92,7 +92,7 @@ func.func @test_transpose_invalid_permutation_types_dynamic_dim_ok(%arg0: tensor
func.func @test_transpose_element_type_mismatch(%arg0: tensor<2x3xi32>) -> tensor<3x2xf32> {
// expected-error at +1 {{'tosa.transpose' op failed to verify that all of {input1, output} have same element type}}
- %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0>} : (tensor<2x3xi32>) -> tensor<3x2xf32>
+ %1 = tosa.transpose %arg0 perms([1, 0]) : (tensor<2x3xi32>) -> tensor<3x2xf32>
return %1 : tensor<3x2xf32>
}
@@ -102,7 +102,7 @@ func.func @test_transpose_element_type_mismatch(%arg0: tensor<2x3xi32>) -> tenso
func.func @test_invalid_constant_permutation() {
%0 = tensor.empty() : tensor<3x4x5xi32>
// expected-error at +1 {{'tosa.transpose' op expected valid permutation indices}}
- %2 = tosa.transpose %0 {perms = array<i32: 3, 0, 1>}: (tensor<3x4x5xi32>) -> tensor<3x4x5xi32>
+ %2 = tosa.transpose %0 perms([3, 0, 1]) : (tensor<3x4x5xi32>) -> tensor<3x4x5xi32>
return
}
@@ -113,7 +113,7 @@ func.func @test_rank_size_constant_permutation() {
%0 = arith.constant 6 : index
%2 = tensor.empty(%0) : tensor<?x27xi64>
// expected-error at +1 {{'tosa.transpose' op expected valid permutation indices}}
- %3 = tosa.transpose %2 {perms = array<i32: 0, 2>}: (tensor<?x27xi64>) -> tensor<?x27xi64>
+ %3 = tosa.transpose %2 perms([0, 2]) : (tensor<?x27xi64>) -> tensor<?x27xi64>
return
}
@@ -124,7 +124,7 @@ func.func @test_large_constant_permutation() {
%0 = arith.constant 6 : index
%2 = tensor.empty(%0) : tensor<?x27xi64>
// expected-error at +1 {{'tosa.transpose' op expected valid permutation indices}}
- %3 = tosa.transpose %2 {perms = array<i32: 1185677355, 332462212>}: (tensor<?x27xi64>) -> tensor<?x27xi64>
+ %3 = tosa.transpose %2 perms([1185677355, 332462212]) : (tensor<?x27xi64>) -> tensor<?x27xi64>
return
}
@@ -132,7 +132,7 @@ func.func @test_large_constant_permutation() {
func.func @test_scalar_output_transpose(%arg0: tensor<*xf32>) -> tensor<f32> {
// expected-error at +1 {{'tosa.transpose' op result #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
- %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<*xf32>) -> tensor<f32>
+ %1 = tosa.transpose %arg0 perms([2, 0, 1]) : (tensor<*xf32>) -> tensor<f32>
return %1 : tensor<f32>
}
@@ -140,7 +140,7 @@ func.func @test_scalar_output_transpose(%arg0: tensor<*xf32>) -> tensor<f32> {
func.func @test_reverse_element_type_mismatch(%arg0: tensor<2x3xi32>) -> tensor<2x3xf32> {
// expected-error at +1 {{'tosa.reverse' op requires the same element type for all operands and results}}
- %0 = tosa.reverse %arg0 {axis = 1 : i32} : (tensor<2x3xi32>) -> tensor<2x3xf32>
+ %0 = tosa.reverse %arg0 axis(1) : (tensor<2x3xi32>) -> tensor<2x3xf32>
return %0 : tensor<2x3xf32>
}
@@ -148,7 +148,7 @@ func.func @test_reverse_element_type_mismatch(%arg0: tensor<2x3xi32>) -> tensor<
func.func @test_reverse_shape_mismatch(%arg0: tensor<2x3xi32>) -> tensor<2x4xi32> {
// expected-error at +1 {{'tosa.reverse' op requires the same shape for all operands and results}}
- %0 = tosa.reverse %arg0 {axis = 1 : i32} : (tensor<2x3xi32>) -> tensor<2x4xi32>
+ %0 = tosa.reverse %arg0 axis(1) : (tensor<2x3xi32>) -> tensor<2x4xi32>
return %0 : tensor<2x4xi32>
}
@@ -156,7 +156,7 @@ func.func @test_reverse_shape_mismatch(%arg0: tensor<2x3xi32>) -> tensor<2x4xi32
func.func @test_reverse_rank_mismatch(%arg0: tensor<2x3xi32>) -> tensor<1x2x3xi32> {
// expected-error at +1 {{'tosa.reverse' op result type has different rank than operands}}
- %0 = tosa.reverse %arg0 {axis = 1 : i32} : (tensor<2x3xi32>) -> tensor<1x2x3xi32>
+ %0 = tosa.reverse %arg0 axis(1) : (tensor<2x3xi32>) -> tensor<1x2x3xi32>
return %0 : tensor<1x2x3xi32>
}
@@ -164,8 +164,8 @@ func.func @test_reverse_rank_mismatch(%arg0: tensor<2x3xi32>) -> tensor<1x2x3xi3
func.func @test_slice_invalid_output_rank() {
%0 = tensor.empty() : tensor<4x31x31xf32>
- %start = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %size = tosa.const_shape {values = dense<[1, 1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %size = tosa.const_shape values(dense<[1, 1, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.slice' op expect input1 and output to have the same ranks, got 3 and 4}}
%3 = tosa.slice %0, %start, %size : (tensor<4x31x31xf32>, !tosa.shape<2>, !tosa.shape<3>) -> tensor<?x?x?x?xf32>
return
@@ -175,8 +175,8 @@ func.func @test_slice_invalid_output_rank() {
func.func @test_slice_invalid_start() {
%0 = tensor.empty() : tensor<4x31x31xf32>
- %start = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %size = tosa.const_shape {values = dense<[1, 1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %size = tosa.const_shape values(dense<[1, 1, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.slice' op length of start is not equal to rank of input shape}}
%3 = tosa.slice %0, %start, %size : (tensor<4x31x31xf32>, !tosa.shape<2>, !tosa.shape<3>) -> tensor<*xf32>
return
@@ -186,8 +186,8 @@ func.func @test_slice_invalid_start() {
func.func @test_slice_invalid_size() {
%0 = tensor.empty() : tensor<4x31x31xf32>
- %start = tosa.const_shape {values = dense<[1, 1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %size = tosa.const_shape {values = dense<[1]> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %start = tosa.const_shape values(dense<[1, 1, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[1]> : tensor<1xindex>) : () -> !tosa.shape<1>
// expected-error at +1 {{'tosa.slice' op length of size is not equal to rank of input shape}}
%3 = tosa.slice %0, %start, %size : (tensor<4x31x31xf32>, !tosa.shape<3>, !tosa.shape<1>) -> tensor<*xf32>
return
@@ -196,8 +196,8 @@ func.func @test_slice_invalid_size() {
// -----
func.func @test_scalar_slice(%arg0: tensor<f32>) -> tensor<f32> {
- %0 = tosa.const_shape {values = dense<[]> : tensor<0xindex>} : () -> !tosa.shape<0>
- %1 = tosa.const_shape {values = dense<[]> : tensor<0xindex>} : () -> !tosa.shape<0>
+ %0 = tosa.const_shape values(dense<[]> : tensor<0xindex>) : () -> !tosa.shape<0>
+ %1 = tosa.const_shape values(dense<[]> : tensor<0xindex>) : () -> !tosa.shape<0>
// expected-error at +1 {{'tosa.slice' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
%2 = tosa.slice %arg0, %0, %1 : (tensor<f32>, !tosa.shape<0>, !tosa.shape<0>) -> tensor<f32>
return %2 : tensor<f32>
@@ -206,8 +206,8 @@ func.func @test_scalar_slice(%arg0: tensor<f32>) -> tensor<f32> {
// -----
func.func @test_slice_invalid_start_values(%arg0: tensor<4x31x31xf32>) {
- %start = tosa.const_shape {values = dense<[-2, 0, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %size = tosa.const_shape {values = dense<[1, 1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[-2, 0, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[1, 1, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.slice' op start values must be non-negative, got [-2, 0, 0]}}
%1 = tosa.slice %arg0, %start, %size : (tensor<4x31x31xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<1x1x1xf32>
return
@@ -216,8 +216,8 @@ func.func @test_slice_invalid_start_values(%arg0: tensor<4x31x31xf32>) {
// -----
func.func @test_slice_invalid_size_values(%arg0: tensor<4x31x31xf32>) {
- %start = tosa.const_shape {values = dense<[0, 0, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %size = tosa.const_shape {values = dense<[1, 0, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[0, 0, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[1, 0, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.slice' op size values must be > 0, got [1, 0, 1]}}
%1 = tosa.slice %arg0, %start, %size : (tensor<4x31x31xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<?x?x?xf32>
return
@@ -226,8 +226,8 @@ func.func @test_slice_invalid_size_values(%arg0: tensor<4x31x31xf32>) {
// -----
func.func @test_slice_output_shape_mismatch(%arg0: tensor<4x5x6xf32>) {
- %start = tosa.const_shape {values = dense<[0, 0, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %size = tosa.const_shape {values = dense<[1, 2, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[0, 0, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[1, 2, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.slice' op expected output shape to match size values, got 'tensor<1x2x4xf32>' vs [1, 2, 3]}}
%1 = tosa.slice %arg0, %start, %size : (tensor<4x5x6xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<1x2x4xf32>
return
@@ -236,8 +236,8 @@ func.func @test_slice_output_shape_mismatch(%arg0: tensor<4x5x6xf32>) {
// -----
func.func @test_slice_invalid_start_plus_size(%arg0: tensor<?x2xf32>) {
- %start = tosa.const_shape {values = dense<[-1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %size = tosa.const_shape {values = dense<[3, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %start = tosa.const_shape values(dense<[-1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %size = tosa.const_shape values(dense<[3, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.slice' op start + size must be less than or equal to input dimension size, got start=1, size=2 vs input dim size=2 at dimension 1}}
%1 = tosa.slice %arg0, %start, %size : (tensor<?x2xf32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<?x2xf32>
return
@@ -246,8 +246,8 @@ func.func @test_slice_invalid_start_plus_size(%arg0: tensor<?x2xf32>) {
// -----
func.func @test_slice_output_shape_mismatch_dynamic(%arg0: tensor<?x5x6xf32>) {
- %start = tosa.const_shape {values = dense<[0, 0, 0]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %size = tosa.const_shape {values = dense<[1, 2, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %start = tosa.const_shape values(dense<[0, 0, 0]> : tensor<3xindex>) : () -> !tosa.shape<3>
+ %size = tosa.const_shape values(dense<[1, 2, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.slice' op expected output shape to match size values, got 'tensor<?x2x4xf32>' vs [1, 2, 3]}}
%1 = tosa.slice %arg0, %start, %size : (tensor<?x5x6xf32>, !tosa.shape<3>, !tosa.shape<3>) -> tensor<?x2x4xf32>
return
@@ -259,7 +259,7 @@ func.func @test_conv2d_mxfp_invalid_weight_zp(%arg0: tensor<1x4x4x32x!tosa.block
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
// expected-error at +1 {{'tosa.conv2d' op expect block scaled weight to have fp32 zero point}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = bf16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xf16>, tensor<1xf32>, tensor<1xf16>) -> tensor<1x4x4x8xf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(bf16) local_bound(true) : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xf16>, tensor<1xf32>, tensor<1xf16>) -> tensor<1x4x4x8xf16>
return %0 : tensor<1x4x4x8xf16>
}
@@ -269,7 +269,7 @@ func.func @test_conv2d_mxfp_invalid_input_zp(%arg0: tensor<1x4x4x32x!tosa.block_
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.conv2d' op expect block scaled input to have fp32 zero point}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = bf16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xf16>, tensor<1xf16>, tensor<1xf32>) -> tensor<1x4x4x8xf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(bf16) local_bound(true) : (tensor<1x4x4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8x1x1x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>, tensor<8xf16>, tensor<1xf16>, tensor<1xf32>) -> tensor<1x4x4x8xf16>
return %0 : tensor<1x4x4x8xf16>
}
@@ -277,7 +277,7 @@ func.func @test_conv2d_mxfp_invalid_input_zp(%arg0: tensor<1x4x4x32x!tosa.block_
func.func @test_depthwise_conv2d_invalid_padding(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x8x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op expect all padding values to be >= 0, got 0, 0, -1, 0}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, -1, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, -1, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<1x1x8x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -286,7 +286,7 @@ func.func @test_depthwise_conv2d_invalid_padding(%arg0: tensor<1x4x4x4xf32>, %ar
func.func @test_depthwise_conv2d_invalid_stride(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x8x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op expect all stride values to be >= 1, got 0, 1}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 0, 1>, local_bound = true}
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([0, 1]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<1x1x8x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -295,7 +295,7 @@ func.func @test_depthwise_conv2d_invalid_stride(%arg0: tensor<1x4x4x4xf32>, %arg
func.func @test_depthwise_conv2d_invalid_dilation(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x8x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op expect all dilation values to be >= 1, got 1, 0}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 0>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 0]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<1x1x8x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -304,7 +304,7 @@ func.func @test_depthwise_conv2d_invalid_dilation(%arg0: tensor<1x4x4x4xf32>, %a
func.func @test_depthwise_conv2d_wholly_divisible_height(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x8x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op expected input_height - 1 + pad_top + pad_bottom - (kernel_height - 1) * dilation_y to be wholly divisible by stride_y, got (4 - 1 + 0 + 0 - (1 - 1) * 1) / 2}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 1>, local_bound = true}
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([2, 1]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<1x1x8x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -313,7 +313,7 @@ func.func @test_depthwise_conv2d_wholly_divisible_height(%arg0: tensor<1x4x4x4xf
func.func @test_depthwise_conv2d_wholly_divisible_width(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x8x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op expected input_width - 1 + pad_left + pad_right - (kernel_width - 1) * dilation_x to be wholly divisible by stride_x, got (4 - 1 + 0 + 0 - (1 - 1) * 1) / 2}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 2>, local_bound = true}
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 2]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<1x1x8x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -322,7 +322,7 @@ func.func @test_depthwise_conv2d_wholly_divisible_width(%arg0: tensor<1x4x4x4xf3
func.func @test_depthwise_conv2d_unexpected_output_height(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x8x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x6x4x8xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op calculated output height did not match expected: calculated=4, expected=6}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<1x1x8x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x6x4x8xf32>
return %0 : tensor<1x6x4x8xf32>
}
@@ -331,7 +331,7 @@ func.func @test_depthwise_conv2d_unexpected_output_height(%arg0: tensor<1x4x4x4x
func.func @test_depthwise_conv2d_unexpected_output_width(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x8x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x6x8xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op calculated output width did not match expected: calculated=4, expected=6}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<1x1x8x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x6x8xf32>
return %0 : tensor<1x4x6x8xf32>
}
@@ -340,7 +340,7 @@ func.func @test_depthwise_conv2d_unexpected_output_width(%arg0: tensor<1x4x4x4xf
func.func @test_depthwise_conv2d_invalid_bias_size(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x8x4xf32>, %arg2: tensor<7xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op bias channels expected to be equal to output channels (8) or 1, got 7}}
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0]) stride([1, 1]) dilation([1, 1]) acc_type(f32) local_bound(true)
: (tensor<1x4x4x4xf32>, tensor<1x1x8x4xf32>, tensor<7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -349,7 +349,7 @@ func.func @test_depthwise_conv2d_invalid_bias_size(%arg0: tensor<1x4x4x4xf32>, %
func.func @test_conv3d_invalid_padding(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<21xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x8x21x34xf32> {
// expected-error at +1 {{'tosa.conv3d' op expect all padding values to be >= 0, got 0, -1, 0, -1, 0, 0}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 2, 1>, pad = array<i64: 0, -1, 0, -1, 0, 0>, stride = array<i64: 1, 1, 1>}
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, -1, 0, -1, 0, 0]) stride([1, 1, 1]) dilation([1, 2, 1]) acc_type(f32)
: (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<21xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
@@ -357,7 +357,7 @@ func.func @test_conv3d_invalid_padding(%arg0: tensor<1x4x8x21x17xf32>, %arg1: te
func.func @test_conv3d_invalid_stride(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<21xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x8x21x34xf32> {
// expected-error at +1 {{'tosa.conv3d' op expect all stride values to be >= 1, got 0, 1, 1}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 0, 1, 1>}
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([0, 1, 1]) dilation([1, 1, 1]) acc_type(f32)
: (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<21xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
@@ -366,7 +366,7 @@ func.func @test_conv3d_invalid_stride(%arg0: tensor<1x4x8x21x17xf32>, %arg1: ten
func.func @test_conv3d_invalid_dilation(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<21xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x8x21x34xf32> {
// expected-error at +1 {{'tosa.conv3d' op expect all dilation values to be >= 1, got 1, 0, 1}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 0, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>}
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 0, 1]) acc_type(f32)
: (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<21xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
@@ -375,7 +375,7 @@ func.func @test_conv3d_invalid_dilation(%arg0: tensor<1x4x8x21x17xf32>, %arg1: t
func.func @test_conv3d_wholly_divisible_input_depth(%arg0: tensor<1x4x16x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<21xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x8x21x34xf32> {
// expected-error at +1 {{'tosa.conv3d' op expected input_depth - 1 + pad_front + pad_back - (kernel_depth - 1) * dilation_d to be wholly divisible by stride_d, got (4 - 1 + 0 + 0 - (1 - 1) * 1) / 2}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 2, 1, 1>}
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([2, 1, 1]) dilation([1, 1, 1]) acc_type(f32)
: (tensor<1x4x16x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<21xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
@@ -384,7 +384,7 @@ func.func @test_conv3d_wholly_divisible_input_depth(%arg0: tensor<1x4x16x21x17xf
func.func @test_conv3d_wholly_divisible_input_height(%arg0: tensor<1x4x10x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<21xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x8x21x34xf32> {
// expected-error at +1 {{'tosa.conv3d' op expected input_height - 1 + pad_top + pad_bottom - (kernel_height - 1) * dilation_y to be wholly divisible by stride_y, got (10 - 1 + 0 + 0 - (1 - 1) * 1) / 4}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 4, 1>}
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 4, 1]) dilation([1, 1, 1]) acc_type(f32)
: (tensor<1x4x10x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<21xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
@@ -393,7 +393,7 @@ func.func @test_conv3d_wholly_divisible_input_height(%arg0: tensor<1x4x10x21x17x
func.func @test_conv3d_wholly_divisible_input_width(%arg0: tensor<1x4x8x21x19xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<21xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x8x21x34xf32> {
// expected-error at +1 {{'tosa.conv3d' op expected input_width - 1 + pad_left + pad_right - (kernel_width - 1) * dilation_x to be wholly divisible by stride_x, got (21 - 1 + 0 + 0 - (1 - 1) * 1) / 8}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 8>}
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 8]) dilation([1, 1, 1]) acc_type(f32)
: (tensor<1x4x8x21x19xf32>, tensor<34x1x1x1x17xf32>, tensor<21xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
@@ -402,7 +402,7 @@ func.func @test_conv3d_wholly_divisible_input_width(%arg0: tensor<1x4x8x21x19xf3
func.func @test_conv3d_wholly_divisible_output_depth(%arg0: tensor<1x4x10x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<21xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x3x10x21x34xf32> {
// expected-error at +1 {{'tosa.conv3d' op calculated output depth did not match expected: calculated=4, expected=3}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>}
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32)
: (tensor<1x4x10x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<21xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x3x10x21x34xf32>
return %0 : tensor<1x3x10x21x34xf32>
}
@@ -411,7 +411,7 @@ func.func @test_conv3d_wholly_divisible_output_depth(%arg0: tensor<1x4x10x21x17x
func.func @test_conv3d_wholly_divisible_output_height(%arg0: tensor<1x4x16x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<21xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x8x21x34xf32> {
// expected-error at +1 {{'tosa.conv3d' op calculated output height did not match expected: calculated=16, expected=8}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>}
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32)
: (tensor<1x4x16x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<21xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
@@ -420,7 +420,7 @@ func.func @test_conv3d_wholly_divisible_output_height(%arg0: tensor<1x4x16x21x17
func.func @test_conv3d_wholly_divisible_output_width(%arg0: tensor<1x4x8x21x19xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<21xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x8x19x34xf32> {
// expected-error at +1 {{'tosa.conv3d' op calculated output width did not match expected: calculated=21, expected=19}}
- %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>}
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 pad([0, 0, 0, 0, 0, 0]) stride([1, 1, 1]) dilation([1, 1, 1]) acc_type(f32)
: (tensor<1x4x8x21x19xf32>, tensor<34x1x1x1x17xf32>, tensor<21xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x19x34xf32>
return %0 : tensor<1x4x8x19x34xf32>
}
@@ -429,7 +429,7 @@ func.func @test_conv3d_wholly_divisible_output_width(%arg0: tensor<1x4x8x21x19xf
func.func @test_concat_element_type_mismatch(%arg0 : tensor<1x2xf32>, %arg1 : tensor<2x2xf32>) -> tensor<?x?xi8> {
// expected-error at +1 {{'tosa.concat' op expect input and output to have same element type, got 'f32' and 'i8'}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<1x2xf32>, tensor<2x2xf32>) -> tensor<?x?xi8>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<1x2xf32>, tensor<2x2xf32>) -> tensor<?x?xi8>
return %0 : tensor<?x?xi8>
}
@@ -437,14 +437,14 @@ func.func @test_concat_element_type_mismatch(%arg0 : tensor<1x2xf32>, %arg1 : te
func.func @test_concat_zero_inputs() {
// expected-error at +1 {{'tosa.concat' op expect at least one input}}
- %0 = tosa.concat {axis = 0 : i32} : () -> tensor<*xf32>
+ %0 = tosa.concat axis(0) : () -> tensor<*xf32>
}
// -----
func.func @test_concat_axis_negative(%arg0: tensor<1x2xf32>, %arg1: tensor<2x2xf32>) -> tensor<2x2xf32> {
// expected-error at +1 {{'tosa.concat' op expect axis to be within range 0 < axis < rank(input1[firstRankedTensorIdx]), got -1}}
- %0 = tosa.concat %arg0, %arg1 {axis = -1 : i32} : (tensor<1x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(-1) : (tensor<1x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
return %0 : tensor<2x2xf32>
}
@@ -452,7 +452,7 @@ func.func @test_concat_axis_negative(%arg0: tensor<1x2xf32>, %arg1: tensor<2x2xf
func.func @test_concat_axis_out_of_range(%arg0: tensor<1x2xf32>, %arg1: tensor<2x2xf32>) -> tensor<2x2xf32> {
// expected-error at +1 {{'tosa.concat' op expect axis to be within range 0 < axis < rank(input1[firstRankedTensorIdx]), got 3}}
- %0 = tosa.concat %arg0, %arg1 {axis = 3 : i32} : (tensor<1x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(3) : (tensor<1x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
return %0 : tensor<2x2xf32>
}
@@ -460,19 +460,19 @@ func.func @test_concat_axis_out_of_range(%arg0: tensor<1x2xf32>, %arg1: tensor<2
func.func @test_concat_axis_sum_error(%arg0: tensor<1x2xf32>, %arg1: tensor<2x?xf32>) -> tensor<2x?xf32> {
// expected-error at +1 {{'tosa.concat' op requires sum of axis dimensions of input1 equal to output axis dimension, got 3 and 2}}
- %0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<1x2xf32>, tensor<2x?xf32>) -> tensor<2x?xf32>
+ %0 = tosa.concat %arg0, %arg1 axis(0) : (tensor<1x2xf32>, tensor<2x?xf32>) -> tensor<2x?xf32>
return %0 : tensor<2x?xf32>
}
// -----
func.func @test_error_scalar_input_with_per_channel(%arg0: tensor<i8>) -> tensor<i16> {
- %multiplier = "tosa.const"() {values = dense<4> : tensor<1xi32> } : () -> tensor<1xi32>
- %shift = "tosa.const"() {values = dense<2> : tensor<1xi8> } : () -> tensor<1xi8>
- %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %multiplier = "tosa.const"() <{values = dense<4> : tensor<1xi32> }> : () -> tensor<1xi32>
+ %shift = "tosa.const"() <{values = dense<2> : tensor<1xi8> }> : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.rescale' op requires input to be at least rank 1 when per_channel is true, but got rank 0}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = true, rounding_mode = SINGLE_ROUND, per_channel = true, input_unsigned = false, output_unsigned = false} : (tensor<i8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi16>) -> tensor<i16>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(true) rounding_mode<SINGLE_ROUND> per_channel(true) input_unsigned(false) output_unsigned(false) : (tensor<i8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi16>) -> tensor<i16>
return %0 : tensor<i16>
}
@@ -484,7 +484,7 @@ func.func @test_rescale_invalid_static_output_shape(%arg0: tensor<13x21x3xi8>) -
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%output_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op expected output shape 13, 21, 4 to be compatible with inferred shape 13, 21, 3}}
- %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = SINGLE_ROUND, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<13x21x4xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp scale32(false) rounding_mode<SINGLE_ROUND> per_channel(false) input_unsigned(false) output_unsigned(false) : (tensor<13x21x3xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<13x21x4xi8>
return %0 : tensor<13x21x4xi8>
}
@@ -596,43 +596,43 @@ func.func @test_row_gather_input_output_mismatch(%arg0: tensor<13x21x3xf32>, %ar
// -----
// CHECK-LABEL: test_row_gather_block_scaled_output_count_mismatch
func.func @test_row_gather_block_scaled_output_count_mismatch(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x26xi32>) -> (tensor<13x52x3xf32>, tensor<13x52x3xf32>) {
- %row_count = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
+ %row_count = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.row_gather_block_scaled' op expects output tensor list length to match values tensor list length, got 2 results for 1 input tensors}}
- %0:2 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<13x21x3xf32>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x3xf32>, tensor<13x52x3xf32>)
+ %0:2 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count block_size<BLOCK_SIZE_1> : (tensor<13x21x3xf32>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x3xf32>, tensor<13x52x3xf32>)
return %0#0, %0#1 : tensor<13x52x3xf32>, tensor<13x52x3xf32>
}
// -----
// CHECK-LABEL: test_row_gather_block_scaled_block_size_one_required
func.func @test_row_gather_block_scaled_block_size_one_required(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x26xi32>) -> tensor<13x52x3xf32> {
- %row_count = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
+ %row_count = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.row_gather_block_scaled' op requires block_size to be BLOCK_SIZE_1 when values tensor list length is 1}}
- %0 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<13x21x3xf32>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x3xf32>)
+ %0 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count block_size<BLOCK_SIZE_32> : (tensor<13x21x3xf32>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x3xf32>)
return %0 : tensor<13x52x3xf32>
}
// -----
// CHECK-LABEL: test_row_gather_block_scaled_output_rows_mismatch
func.func @test_row_gather_block_scaled_output_rows_mismatch(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x26xi32>) -> tensor<13x53x3xf32> {
- %row_count = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
+ %row_count = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.row_gather_block_scaled' op requires output[0] dimension 1 to have size 52, got 53}}
- %0 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<13x21x3xf32>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x53x3xf32>)
+ %0 = tosa.row_gather_block_scaled %arg0, %arg1, %row_count block_size<BLOCK_SIZE_1> : (tensor<13x21x3xf32>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x53x3xf32>)
return %0 : tensor<13x53x3xf32>
}
// -----
// CHECK-LABEL: test_row_gather_block_scaled_scale_channel_mismatch
func.func @test_row_gather_block_scaled_scale_channel_mismatch(%arg0: tensor<13x21x32xf4E2M1FN>, %arg1: tensor<13x21x2xf8E8M0FNU>, %arg2: tensor<13x26xi32>) -> (tensor<13x52x32xf4E2M1FN>, tensor<13x52x2xf8E8M0FNU>) {
- %row_count = "tosa.const"() {values = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
+ %row_count = "tosa.const"() <{values = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.row_gather_block_scaled' op expects channels of scale tensors to equal C/block_size (32/32), got 2}}
- %0:2 = tosa.row_gather_block_scaled %arg0, %arg1, %arg2, %row_count {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<13x21x32xf4E2M1FN>, tensor<13x21x2xf8E8M0FNU>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x32xf4E2M1FN>, tensor<13x52x2xf8E8M0FNU>)
+ %0:2 = tosa.row_gather_block_scaled %arg0, %arg1, %arg2, %row_count block_size<BLOCK_SIZE_32> : (tensor<13x21x32xf4E2M1FN>, tensor<13x21x2xf8E8M0FNU>, tensor<13x26xi32>, tensor<1xi32>) -> (tensor<13x52x32xf4E2M1FN>, tensor<13x52x2xf8E8M0FNU>)
return %0#0, %0#1 : tensor<13x52x32xf4E2M1FN>, tensor<13x52x2xf8E8M0FNU>
}
// -----
func.func @test_pad_padding_shape_mismatch(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
- %0 = tosa.const_shape {values = dense<1> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %0 = tosa.const_shape values(dense<1> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.pad' op padding tensor must have 3 * 2 = 6 elements, but got 4}}
%1 = tosa.pad %arg0, %0, %pad_const : (tensor<13x21x3xf32>, !tosa.shape<4>, tensor<1xf32>) -> tensor<13x21x3xf32>
return %1 : tensor<13x21x3xf32>
@@ -640,8 +640,8 @@ func.func @test_pad_padding_shape_mismatch(%arg0: tensor<13x21x3xf32>) -> tensor
// -----
func.func @test_pad_invalid_padding_rank(%arg0: tensor<13x21xf32>) {
- %0 = tosa.const_shape {values = dense<1> : tensor<6xindex>} : () -> !tosa.shape<6>
- %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %0 = tosa.const_shape values(dense<1> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %pad_const = "tosa.const"() <{values = dense<3.14> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.pad' op padding tensor must have 2 * 2 = 4 elements, but got 6}}
%1 = tosa.pad %arg0, %0, %pad_const : (tensor<13x21xf32>, !tosa.shape<6>, tensor<1xf32>) -> tensor<13x21xf32>
return
@@ -649,7 +649,7 @@ func.func @test_pad_invalid_padding_rank(%arg0: tensor<13x21xf32>) {
// -----
func.func @test_pad_output_mismatch(%arg0: tensor<13x21x3xi8>, %arg1: tensor<1xi8>) -> tensor<13x21x3xi8> {
- %0 = tosa.const_shape {values = dense<[0, 0, 0, 1, 0, 1]> : tensor<6xindex>} : () -> !tosa.shape<6>
+ %0 = tosa.const_shape values(dense<[0, 0, 0, 1, 0, 1]> : tensor<6xindex>) : () -> !tosa.shape<6>
// expected-error at +1 {{mismatch in output shape at dimension 1: expected 21 + 0 + 1 = 22, but got 21}}
%1 = tosa.pad %arg0, %0, %arg1 : (tensor<13x21x3xi8>, !tosa.shape<6>, tensor<1xi8>) -> tensor<13x21x3xi8>
return %1 : tensor<13x21x3xi8>
@@ -657,7 +657,7 @@ func.func @test_pad_output_mismatch(%arg0: tensor<13x21x3xi8>, %arg1: tensor<1xi
// -----
func.func @test_pad_invalid_padding_value(%arg0: tensor<10xi8>, %arg1: tensor<1xi8>) -> tensor<10xi8> {
- %0 = tosa.const_shape {values = dense<[-2, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<[-2, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{invalid padding values at dimension 0: values must be non-negative or -1 for dynamic padding, got [-2, 2]}}
%1 = tosa.pad %arg0, %0, %arg1 : (tensor<10xi8>, !tosa.shape<2>, tensor<1xi8>) -> tensor<10xi8>
return %1 : tensor<10xi8>
@@ -956,14 +956,14 @@ func.func @test_while_loop_missing_body_terminator(%arg0: tensor<i32>, %arg1: te
// -----
func.func @test_while_loop_input_list_mismatch_body_block_in(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op require same number of values in 'body_graph' arguments (3) and 'input_list' (2)}}
%1:2 = tosa.while_loop (%arg2 = %0, %arg3 = %arg0) : (tensor<i32>, tensor<10xi32>) -> (tensor<i32>, tensor<10xi32>) {
%2 = tosa.greater_equal %arg2, %arg1 : (tensor<i32>, tensor<i32>) -> tensor<i1>
tosa.yield %2 : tensor<i1>
} do {
^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<10xi32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%3 = tosa.add %arg2, %2 : (tensor<i32>, tensor<i32>) -> tensor<i32>
tosa.yield %3, %arg4 : tensor<i32>, tensor<10xi32>
}
@@ -973,7 +973,7 @@ func.func @test_while_loop_input_list_mismatch_body_block_in(%arg0: tensor<10xi3
// -----
func.func @test_while_loop_input_list_mismatch_body_block_in_2(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op require same number of values in 'body_graph' arguments (2) and 'input_list' (3)}}
%1:3 = tosa.while_loop (%arg2 = %0, %arg3 = %arg0, %arg4 = %arg0)
: (tensor<i32>, tensor<10xi32>, tensor<10xi32>) -> (tensor<i32>, tensor<10xi32>, tensor<10xi32>) {
@@ -981,7 +981,7 @@ func.func @test_while_loop_input_list_mismatch_body_block_in_2(%arg0: tensor<10x
tosa.yield %2 : tensor<i1>
} do {
^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%3 = tosa.add %arg2, %2 : (tensor<i32>, tensor<i32>) -> tensor<i32>
tosa.yield %3, %arg3 : tensor<i32>, tensor<i32>
}
@@ -991,7 +991,7 @@ func.func @test_while_loop_input_list_mismatch_body_block_in_2(%arg0: tensor<10x
// -----
func.func @test_while_loop_input_list_mismatch_output_list(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op require same number of values in 'input_list' (3) and 'output_list' (2)}}
%1:2 = tosa.while_loop (%arg2 = %0, %arg3 = %arg0, %arg4 = %arg0)
: (tensor<i32>, tensor<10xi32>, tensor<10xi32>) -> (tensor<i32>, tensor<10xi32>) {
@@ -999,7 +999,7 @@ func.func @test_while_loop_input_list_mismatch_output_list(%arg0: tensor<10xi32>
tosa.yield %2 : tensor<i1>
} do {
^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%3 = tosa.add %arg2, %2 : (tensor<i32>, tensor<i32>) -> tensor<i32>
tosa.yield %3, %arg3 : tensor<i32>, tensor<i32>
}
@@ -1009,7 +1009,7 @@ func.func @test_while_loop_input_list_mismatch_output_list(%arg0: tensor<10xi32>
// -----
func.func @test_while_loop_input_list_mismatch_output_list_2(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op require same number of values in 'input_list' (2) and 'output_list' (3)}}
%1:3 = tosa.while_loop (%arg2 = %0, %arg3 = %arg0)
: (tensor<i32>, tensor<10xi32>) -> (tensor<i32>, tensor<10xi32>, tensor<10xi32>) {
@@ -1017,7 +1017,7 @@ func.func @test_while_loop_input_list_mismatch_output_list_2(%arg0: tensor<10xi3
tosa.yield %2 : tensor<i1>
} do {
^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%3 = tosa.add %arg2, %2 : (tensor<i32>, tensor<i32>) -> tensor<i32>
tosa.yield %3, %arg3 : tensor<i32>, tensor<i32>
}
@@ -1027,7 +1027,7 @@ func.func @test_while_loop_input_list_mismatch_output_list_2(%arg0: tensor<10xi3
// -----
func.func @test_while_loop_input_list_mismatch_cond_block(%arg0: tensor<2xf32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op require same number of values in 'cond_graph' arguments (3) and 'input_list' (2)}}
%1:2 = "tosa.while_loop"(%0, %arg0) ({
^bb0(%arg3: tensor<i32>, %arg4: tensor<2xf32>, %arg5: tensor<2xf32>):
@@ -1035,8 +1035,8 @@ func.func @test_while_loop_input_list_mismatch_cond_block(%arg0: tensor<2xf32>,
"tosa.yield"(%2) : (tensor<i1>) -> ()
}, {
^bb0(%arg3: tensor<i32>, %arg4: tensor<2xf32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
- %3 = "tosa.const"() {values = dense<2> : tensor<1xi8>} : () -> tensor<1xi8>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
+ %3 = "tosa.const"() <{values = dense<2> : tensor<1xi8>}> : () -> tensor<1xi8>
%4 = "tosa.mul"(%arg3, %2, %3) : (tensor<i32>, tensor<i32>, tensor<1xi8>) -> tensor<i32>
"tosa.yield"(%4, %arg4) : (tensor<i32>, tensor<2xf32>) -> ()
}) : (tensor<i32>, tensor<2xf32>) -> (tensor<i32>, tensor<2xf32>)
@@ -1046,7 +1046,7 @@ func.func @test_while_loop_input_list_mismatch_cond_block(%arg0: tensor<2xf32>,
// -----
func.func @test_while_loop_input_list_mismatch_cond_block_2(%arg0: tensor<2xf32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op require same number of values in 'cond_graph' arguments (1) and 'input_list' (3)}}
%1:3 = "tosa.while_loop"(%0, %arg0, %arg1) ({
^bb0(%arg3: tensor<i32>):
@@ -1054,8 +1054,8 @@ func.func @test_while_loop_input_list_mismatch_cond_block_2(%arg0: tensor<2xf32>
"tosa.yield"(%2) : (tensor<i1>) -> ()
}, {
^bb0(%arg3: tensor<i32>, %arg4: tensor<2xf32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
- %3 = "tosa.const"() {values = dense<2> : tensor<1xi8>} : () -> tensor<1xi8>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
+ %3 = "tosa.const"() <{values = dense<2> : tensor<1xi8>}> : () -> tensor<1xi8>
%4 = "tosa.mul"(%arg3, %2, %3) : (tensor<i32>, tensor<i32>, tensor<1xi8>) -> tensor<i32>
"tosa.yield"(%4, %arg4) : (tensor<i32>, tensor<2xf32>) -> ()
}) : (tensor<i32>, tensor<2xf32>, tensor<i32>) -> (tensor<i32>, tensor<2xf32>, tensor<i32>)
@@ -1065,14 +1065,14 @@ func.func @test_while_loop_input_list_mismatch_cond_block_2(%arg0: tensor<2xf32>
// -----
func.func @test_while_loop_input_list_mismatch_body_block_out(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op require same number of values in 'body_graph' results (3) and 'input_list' (2)}}
%1:2 = tosa.while_loop (%arg2 = %0, %arg3 = %arg0) : (tensor<i32>, tensor<10xi32>) -> (tensor<i32>, tensor<10xi32>) {
%2 = tosa.greater_equal %arg2, %arg1 : (tensor<i32>, tensor<i32>) -> tensor<i1>
tosa.yield %2 : tensor<i1>
} do {
^bb0(%arg2: tensor<i32>, %arg4: tensor<10xi32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%3 = tosa.add %arg2, %2 : (tensor<i32>, tensor<i32>) -> tensor<i32>
tosa.yield %2, %3, %arg4 : tensor<i32>, tensor<i32>, tensor<10xi32>
}
@@ -1082,14 +1082,14 @@ func.func @test_while_loop_input_list_mismatch_body_block_out(%arg0: tensor<10xi
// -----
func.func @test_while_loop_input_list_mismatch_body_block_out_2(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op require same number of values in 'body_graph' results (1) and 'input_list' (2)}}
%1:2 = tosa.while_loop (%arg2 = %0, %arg3 = %arg0) : (tensor<i32>, tensor<10xi32>) -> (tensor<i32>, tensor<10xi32>) {
%2 = tosa.greater_equal %arg2, %arg1 : (tensor<i32>, tensor<i32>) -> tensor<i1>
tosa.yield %2 : tensor<i1>
} do {
^bb0(%arg2: tensor<i32>, %arg4: tensor<10xi32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%3 = tosa.add %arg2, %2 : (tensor<i32>, tensor<i32>) -> tensor<i32>
tosa.yield %3 : tensor<i32>
}
@@ -1099,7 +1099,7 @@ func.func @test_while_loop_input_list_mismatch_body_block_out_2(%arg0: tensor<10
// -----
func.func @test_while_loop_type_mismatch(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op require same element type for 'body_graph' arguments ('f32') and 'input_list' ('i32')}}
%1:3 = tosa.while_loop (%arg2 = %0, %arg3 = %0, %arg4 = %arg0) : (tensor<i32>, tensor<i32>, tensor<10xi32>) -> (tensor<i32>, tensor<i32>, tensor<10xi32>) {
%2 = tosa.greater_equal %arg3, %arg1 : (tensor<i32>, tensor<i32>) -> tensor<i1>
@@ -1107,7 +1107,7 @@ func.func @test_while_loop_type_mismatch(%arg0: tensor<10xi32>, %arg1: tensor<i3
tosa.yield %3 : tensor<i1>
} do {
^bb0(%arg2: tensor<i32>, %arg3: tensor<f32>, %arg4: tensor<10xi32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%6 = tosa.add %arg2, %2 : (tensor<i32>, tensor<i32>) -> tensor<i32>
tosa.yield %6, %2, %arg4 : tensor<i32>, tensor<i32>, tensor<10xi32>
}
@@ -1117,7 +1117,7 @@ func.func @test_while_loop_type_mismatch(%arg0: tensor<10xi32>, %arg1: tensor<i3
// -----
func.func @test_while_loop_type_mismatch_2(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op require same shapes for 'body_graph' arguments ('tensor<10xi32>') and 'input_list' ('tensor<i32>')}}
%1:3 = tosa.while_loop (%arg2 = %0, %arg3 = %0, %arg4 = %arg0) : (tensor<i32>, tensor<i32>, tensor<10xi32>) -> (tensor<i32>, tensor<i32>, tensor<10xi32>) {
%2 = tosa.greater_equal %arg3, %arg1 : (tensor<i32>, tensor<i32>) -> tensor<i1>
@@ -1125,7 +1125,7 @@ func.func @test_while_loop_type_mismatch_2(%arg0: tensor<10xi32>, %arg1: tensor<
tosa.yield %3 : tensor<i1>
} do {
^bb0(%arg2: tensor<10xi32>, %arg3: tensor<i32>, %arg4: tensor<10xi32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%6 = tosa.add %arg2, %2 : (tensor<10xi32>, tensor<i32>) -> tensor<i32>
tosa.yield %6, %2, %arg4 : tensor<i32>, tensor<i32>, tensor<10xi32>
}
@@ -1135,15 +1135,15 @@ func.func @test_while_loop_type_mismatch_2(%arg0: tensor<10xi32>, %arg1: tensor<
// -----
func.func @test_while_loop_cond_output_not_size_one(%arg0: tensor<10xi32>, %arg1: tensor<2xi32>) {
- %0 = "tosa.const"() {values = dense<[4, 1]> : tensor<2xi32>} : () -> tensor<2xi32>
+ %0 = "tosa.const"() <{values = dense<[4, 1]> : tensor<2xi32>}> : () -> tensor<2xi32>
// expected-error at +1 {{'tosa.while_loop' op 'cond_graph' result must be a size 1 tensor, got 'tensor<2xi1>'}}
%1:3 = tosa.while_loop (%arg2 = %arg0, %arg3 = %0, %arg4 = %arg0) : (tensor<10xi32>, tensor<2xi32>, tensor<10xi32>) -> (tensor<10xi32>, tensor<2xi32>, tensor<10xi32>) {
%2 = tosa.greater_equal %arg3, %arg1 : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi1>
tosa.yield %2 : tensor<2xi1>
} do {
^bb0(%arg2: tensor<10xi32>, %arg3: tensor<2xi32>, %arg4: tensor<10xi32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
- %3 = "tosa.const"() {values = dense<[3, 5]> : tensor<2xi32>} : () -> tensor<2xi32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
+ %3 = "tosa.const"() <{values = dense<[3, 5]> : tensor<2xi32>}> : () -> tensor<2xi32>
%4 = tosa.add %arg2, %2 : (tensor<10xi32>, tensor<i32>) -> tensor<10xi32>
tosa.yield %4, %3, %arg4 : tensor<10xi32>, tensor<2xi32>, tensor<10xi32>
}
@@ -1153,14 +1153,14 @@ func.func @test_while_loop_cond_output_not_size_one(%arg0: tensor<10xi32>, %arg1
// -----
func.func @test_while_loop_cond_output_not_bool(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {values = dense<9> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() <{values = dense<9> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op 'cond_graph' result must be a boolean tensor, got 'tensor<i32>'}}
%1:3 = tosa.while_loop (%arg2 = %arg0, %arg3 = %0, %arg4 = %arg0) : (tensor<10xi32>, tensor<i32>, tensor<10xi32>) -> (tensor<10xi32>, tensor<i32>, tensor<10xi32>) {
%2 = tosa.add %arg3, %arg1 : (tensor<i32>, tensor<i32>) -> tensor<i32>
tosa.yield %2 : tensor<i32>
} do {
^bb0(%arg2: tensor<10xi32>, %arg3: tensor<i32>, %arg4: tensor<10xi32>):
- %2 = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
%4 = tosa.add %arg2, %2 : (tensor<10xi32>, tensor<i32>) -> tensor<10xi32>
tosa.yield %4, %2, %arg4 : tensor<10xi32>, tensor<i32>, tensor<10xi32>
}
@@ -1323,8 +1323,8 @@ func.func @scatter_invalid_K_W(%arg0 : tensor<2x4x5xi32>, %arg1 : tensor<2x6xi32
// -----
func.func @test_matmul_output_batch_mismatch(%arg0: tensor<2x3x4xf32>, %arg1: tensor<5x4x6xf32>) -> tensor<2x3x6xf32> {
- %azp0 = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %bzp0 = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %azp0 = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %bzp0 = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.matmul' op expected batch of b to match size 2, got 5}}
%0 = tosa.matmul %arg0, %arg1, %azp0, %bzp0 : (tensor<2x3x4xf32>, tensor<5x4x6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x3x6xf32>
return %0 : tensor<2x3x6xf32>
@@ -1333,8 +1333,8 @@ func.func @test_matmul_output_batch_mismatch(%arg0: tensor<2x3x4xf32>, %arg1: te
// -----
func.func @test_matmul_output_channel_mismatch(%arg0: tensor<2x3x4xf32>, %arg1: tensor<2x7x6xf32>) -> tensor<2x3x6xf32> {
- %azp0 = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %bzp0 = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %azp0 = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %bzp0 = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.matmul' op expected channels of b to match size 4, got 7}}
%0 = tosa.matmul %arg0, %arg1, %azp0, %bzp0 : (tensor<2x3x4xf32>, tensor<2x7x6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x3x6xf32>
return %0 : tensor<2x3x6xf32>
@@ -1354,8 +1354,8 @@ func.func @test_matmul_output_shape_mismatch(%arg0: tensor<2x3x4xf32>, %arg1: te
func.func @test_matmul_dynamic_batch_mismatch(%arg0: tensor<2x?x4xf32>, %arg1: tensor<5x4x6xf32>) -> tensor<2x?x6xf32> {
- %azp0 = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %bzp0 = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %azp0 = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %bzp0 = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.matmul' op expected batch of b to match size 2, got 5}}
%0 = tosa.matmul %arg0, %arg1, %azp0, %bzp0 : (tensor<2x?x4xf32>, tensor<5x4x6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x6xf32>
return %0 : tensor<2x?x6xf32>
@@ -1364,8 +1364,8 @@ func.func @test_matmul_dynamic_batch_mismatch(%arg0: tensor<2x?x4xf32>, %arg1: t
// -----
func.func @test_matmul_dynamic_channel_mismatch(%arg0: tensor<?x3x4xf32>, %arg1: tensor<?x7x6xf32>) -> tensor<?x3x6xf32> {
- %azp0 = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %bzp0 = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %azp0 = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %bzp0 = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.matmul' op expected channels of b to match size 4, got 7}}
%0 = tosa.matmul %arg0, %arg1, %azp0, %bzp0 : (tensor<?x3x4xf32>, tensor<?x7x6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x3x6xf32>
return %0 : tensor<?x3x6xf32>
@@ -1396,8 +1396,8 @@ func.func @test_matmul_unranked_b_output_shape_mismatch(%arg0: tensor<2x3x4xf32>
func.func @test_matmul_quantized_mixed_operands(%arg0: tensor<2x3x4x!quant.uniform<i8:f32, 0.125>>, %arg1: tensor<2x4x6xf32>) -> tensor<2x3x6xi32> {
- %azp0 = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %bzp0 = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %azp0 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %bzp0 = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.matmul' op expect operands to be both quantized or both not quantized, got '!quant.uniform<i8:f32, 1.250000e-01>' and 'f32'}}
%0 = tosa.matmul %arg0, %arg1, %azp0, %bzp0 : (tensor<2x3x4x!quant.uniform<i8:f32, 0.125>>, tensor<2x4x6xf32>, tensor<1xi8>, tensor<1xf32>) -> tensor<2x3x6xi32>
return %0 : tensor<2x3x6xi32>
@@ -1406,8 +1406,8 @@ func.func @test_matmul_quantized_mixed_operands(%arg0: tensor<2x3x4x!quant.unifo
// -----
func.func @test_matmul_quantized_width_mismatch(%arg0: tensor<2x3x4x!quant.uniform<i8:f32, 0.125>>, %arg1: tensor<2x4x6x!quant.uniform<i16:f32, 0.125>>) -> tensor<2x3x6xi32> {
- %azp0 = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %bzp0 = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %azp0 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %bzp0 = "tosa.const"() <{values = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
// expected-error at +1 {{'tosa.matmul' op expect quantized operands to have same widths, got 8 and 16}}
%0 = tosa.matmul %arg0, %arg1, %azp0, %bzp0 : (tensor<2x3x4x!quant.uniform<i8:f32, 0.125>>, tensor<2x4x6x!quant.uniform<i16:f32, 0.125>>, tensor<1xi8>, tensor<1xi16>) -> tensor<2x3x6xi32>
return %0 : tensor<2x3x6xi32>
@@ -1541,7 +1541,7 @@ func.func @test_matmul_t_a_zp_non_zero(%arg0: tensor<1x14x19xf32>, %arg1: tensor
func.func @test_matmul_t_block_scaled_data_mismatch(%arg0: tensor<4x8x32xf8E4M3FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32xf8E5M2>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
// expected-error at +1 {{'tosa.matmul_t_block_scaled' op expect A_data and B_data to have same element type, got 'f8E4M3FN' and 'f8E5M2'}}
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf8E5M2>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf8E5M2>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
@@ -1549,7 +1549,7 @@ func.func @test_matmul_t_block_scaled_data_mismatch(%arg0: tensor<4x8x32xf8E4M3F
func.func @test_matmul_t_block_scaled_output_batch_mismatch(%arg0: tensor<*xf8E4M3FN>, %arg1: tensor<?x8x1xf8E8M0FNU>, %arg2: tensor<*xf8E4M3FN>, %arg3: tensor<4x?x?xf8E8M0FNU>) -> tensor<5x?x?xf32> {
// expected-error at +1 {{'tosa.matmul_t_block_scaled' op expected output shape 5, ?, ? to be compatible with expected output shape 4, 8, ?}}
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<*xf8E4M3FN>, tensor<?x8x1xf8E8M0FNU>, tensor<*xf8E4M3FN>, tensor<4x?x?xf8E8M0FNU>) -> tensor<5x?x?xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<*xf8E4M3FN>, tensor<?x8x1xf8E8M0FNU>, tensor<*xf8E4M3FN>, tensor<4x?x?xf8E8M0FNU>) -> tensor<5x?x?xf32>
return %0 : tensor<5x?x?xf32>
}
@@ -1557,7 +1557,7 @@ func.func @test_matmul_t_block_scaled_output_batch_mismatch(%arg0: tensor<*xf8E4
func.func @test_matmul_t_block_scaled_output_height_mismatch(%arg0: tensor<*xf8E4M3FN>, %arg1: tensor<?x9x1xf8E8M0FNU>, %arg2: tensor<*xf8E4M3FN>, %arg3: tensor<4x?x?xf8E8M0FNU>) -> tensor<4x8x?xf32> {
// expected-error at +1 {{'tosa.matmul_t_block_scaled' op expected output shape 4, 8, ? to be compatible with expected output shape 4, 9, ?}}
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<*xf8E4M3FN>, tensor<?x9x1xf8E8M0FNU>, tensor<*xf8E4M3FN>, tensor<4x?x?xf8E8M0FNU>) -> tensor<4x8x?xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<*xf8E4M3FN>, tensor<?x9x1xf8E8M0FNU>, tensor<*xf8E4M3FN>, tensor<4x?x?xf8E8M0FNU>) -> tensor<4x8x?xf32>
return %0 : tensor<4x8x?xf32>
}
@@ -1565,7 +1565,7 @@ func.func @test_matmul_t_block_scaled_output_height_mismatch(%arg0: tensor<*xf8E
func.func @test_matmul_t_block_scaled_output_width_mismatch(%arg0: tensor<*xf8E4M3FN>, %arg1: tensor<?x?x1xf8E8M0FNU>, %arg2: tensor<?x1x?xf8E4M3FN>, %arg3: tensor<*xf8E8M0FNU>) -> tensor<?x?x10xf32> {
// expected-error at +1 {{'tosa.matmul_t_block_scaled' op expected output shape ?, ?, 10 to be compatible with expected output shape ?, ?, 1}}
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<*xf8E4M3FN>, tensor<?x?x1xf8E8M0FNU>, tensor<?x1x?xf8E4M3FN>, tensor<*xf8E8M0FNU>) -> tensor<?x?x10xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<*xf8E4M3FN>, tensor<?x?x1xf8E8M0FNU>, tensor<?x1x?xf8E4M3FN>, tensor<*xf8E8M0FNU>) -> tensor<?x?x10xf32>
return %0 : tensor<?x?x10xf32>
}
@@ -1573,7 +1573,7 @@ func.func @test_matmul_t_block_scaled_output_width_mismatch(%arg0: tensor<*xf8E4
func.func @test_matmul_t_block_scaled_channel_not_multiple_of_block_size(%arg0: tensor<4x8x55xf8E4M3FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32xf8E4M3FN>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
// expected-error at +1 {{'tosa.matmul_t_block_scaled' op expected channels of b_data to match size 55, got 32}}
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x8x55xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf8E4M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x55xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf8E4M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
@@ -1581,7 +1581,7 @@ func.func @test_matmul_t_block_scaled_channel_not_multiple_of_block_size(%arg0:
func.func @test_matmul_t_block_scaled_batch_mismatch(%arg0: tensor<4x8x32xf8E4M3FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<2x16x32xf8E4M3FN>, %arg3: tensor<2x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
// expected-error at +1 {{'tosa.matmul_t_block_scaled' op expect B matrix batch size to be broadcast compatible with A, got D=2 vs N=4}}
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<2x16x32xf8E4M3FN>, tensor<2x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_32> : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<2x16x32xf8E4M3FN>, tensor<2x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
@@ -1589,7 +1589,7 @@ func.func @test_matmul_t_block_scaled_batch_mismatch(%arg0: tensor<4x8x32xf8E4M3
func.func @test_matmul_t_block_scaled_block_size_mismatch(%arg0: tensor<4x8x32xf8E4M3FN>, %arg1: tensor<4x8x1xf8E8M0FNU>, %arg2: tensor<4x16x32xf8E4M3FN>, %arg3: tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32> {
// expected-error at +1 {{'tosa.matmul_t_block_scaled' op expect block size to be 32, got 1}}
- %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf8E4M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
+ %0 = tosa.matmul_t_block_scaled %arg0, %arg1, %arg2, %arg3 block_size<BLOCK_SIZE_1> : (tensor<4x8x32xf8E4M3FN>, tensor<4x8x1xf8E8M0FNU>, tensor<4x16x32xf8E4M3FN>, tensor<4x16x1xf8E8M0FNU>) -> tensor<4x8x16xf32>
return %0 : tensor<4x8x16xf32>
}
@@ -1597,7 +1597,7 @@ func.func @test_matmul_t_block_scaled_block_size_mismatch(%arg0: tensor<4x8x32xf
func.func @cast_from_block_scaled_incompatible_input_output_shape(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<4x1xf8E8M0FNU>) -> tensor<5x32xf32> {
// expected-error at +1 {{'tosa.cast_from_block_scaled' op require compatible shapes for input_data ('tensor<4x32xf4E2M1FN>') and output_data ('tensor<5x32xf32>')}}
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) -> tensor<5x32xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) -> tensor<5x32xf32>
return %0 : tensor<5x32xf32>
}
@@ -1605,7 +1605,7 @@ func.func @cast_from_block_scaled_incompatible_input_output_shape(%arg0: tensor<
func.func @cast_from_block_scaled_not_scalar(%arg0: tensor<f4E2M1FN>, %arg1: tensor<f8E8M0FNU>) -> tensor<f32> {
// expected-error at +1 {{'tosa.cast_from_block_scaled' op operand #0 must be tosa-conformant tensor of at least rank 1 of micro-scaling format number values, but got 'tensor<f4E2M1FN>'}}
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<f4E2M1FN>, tensor<f8E8M0FNU>) -> tensor<f32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<f4E2M1FN>, tensor<f8E8M0FNU>) -> tensor<f32>
return %0 : tensor<f32>
}
@@ -1613,7 +1613,7 @@ func.func @cast_from_block_scaled_not_scalar(%arg0: tensor<f4E2M1FN>, %arg1: ten
func.func @cast_from_block_scaled_not_divisible_by_block_size(%arg0: tensor<4x33xf4E2M1FN>, %arg1: tensor<4x1xf8E8M0FNU>) -> tensor<4x33xf32> {
// expected-error at +1 {{'tosa.cast_from_block_scaled' op expect last dimension of input_data (33) to be divisible by block_size (32)}}
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x33xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) -> tensor<4x33xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<4x33xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) -> tensor<4x33xf32>
return %0 : tensor<4x33xf32>
}
@@ -1621,7 +1621,7 @@ func.func @cast_from_block_scaled_not_divisible_by_block_size(%arg0: tensor<4x33
func.func @cast_from_block_scaled_data_scale_mismatch(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<5x1xf8E8M0FNU>) -> tensor<4x32xf32> {
// expected-error at +1 {{'tosa.cast_from_block_scaled' op require compatible shapes for input_data ('tensor<4x32xf4E2M1FN>') and input_scale ('tensor<5x1xf8E8M0FNU>') except for the last dimension}}
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<4x32xf4E2M1FN>, tensor<5x1xf8E8M0FNU>) -> tensor<4x32xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<5x1xf8E8M0FNU>) -> tensor<4x32xf32>
return %0 : tensor<4x32xf32>
}
@@ -1629,7 +1629,7 @@ func.func @cast_from_block_scaled_data_scale_mismatch(%arg0: tensor<4x32xf4E2M1F
func.func @cast_from_block_scaled_data_scale_channel_mismatch(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<4x2xf8E8M0FNU>) -> tensor<4x32xf32> {
// expected-error at +1 {{'tosa.cast_from_block_scaled' op expect last dimension of input_scale (2) to be equal to last dimension of input_data / block_size (1)}}
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf4E2M1FN>, tensor<4x2xf8E8M0FNU>) -> tensor<4x32xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_32> : (tensor<4x32xf4E2M1FN>, tensor<4x2xf8E8M0FNU>) -> tensor<4x32xf32>
return %0 : tensor<4x32xf32>
}
@@ -1637,7 +1637,7 @@ func.func @cast_from_block_scaled_data_scale_channel_mismatch(%arg0: tensor<4x32
func.func @test_cast_invalid_input_unsigned_f32(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf16> {
// expected-error at +1{{'tosa.cast' op attribute input_unsigned requires integer type inputs. Got: 'f32'}}
- %0 = tosa.cast %arg0 {input_unsigned = true} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf16>
+ %0 = tosa.cast %arg0 input_unsigned(true) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf16>
return %0 : tensor<13x21x3xf16>
}
@@ -1645,7 +1645,7 @@ func.func @test_cast_invalid_input_unsigned_f32(%arg0: tensor<13x21x3xf32>) -> t
func.func @test_cast_invalid_input_unsigned_bool(%arg0: tensor<13x21x3xi1>) -> tensor<13x21x3xf16> {
// expected-error at +1{{'tosa.cast' op attribute input_unsigned requires integer type inputs. Got: 'i1'}}
- %0 = tosa.cast %arg0 {input_unsigned = true} : (tensor<13x21x3xi1>) -> tensor<13x21x3xf16>
+ %0 = tosa.cast %arg0 input_unsigned(true) : (tensor<13x21x3xi1>) -> tensor<13x21x3xf16>
return %0 : tensor<13x21x3xf16>
}
@@ -1653,7 +1653,7 @@ func.func @test_cast_invalid_input_unsigned_bool(%arg0: tensor<13x21x3xi1>) -> t
func.func @test_cast_from_block_scaled_block_size_mismatch(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32> {
// expected-error at +1 {{'tosa.cast_from_block_scaled' op expect block size to be 32, got 1}}
- %0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32>
+ %0 = tosa.cast_from_block_scaled %arg0, %arg1 block_size<BLOCK_SIZE_1> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) -> tensor<4x32xf32>
return %0 : tensor<4x32xf32>
}
@@ -1661,7 +1661,7 @@ func.func @test_cast_from_block_scaled_block_size_mismatch(%arg0: tensor<4x32xf4
func.func @test_cast_to_block_scaled_incompatible_input_output_shape(%arg0: tensor<4x32xf32>) -> (tensor<5x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) {
// expected-error at +1 {{'tosa.cast_to_block_scaled' op require compatible shapes for input_data ('tensor<4x32xf32>') and output_data ('tensor<5x32xf4E2M1FN>')}}
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf32>) -> (tensor<5x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<4x32xf32>) -> (tensor<5x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<5x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>
}
@@ -1669,7 +1669,7 @@ func.func @test_cast_to_block_scaled_incompatible_input_output_shape(%arg0: tens
func.func @test_cast_to_block_scaled_not_scalar(%arg0: tensor<f32>) -> (tensor<f4E2M1FN>, tensor<f8E8M0FNU>) {
// expected-error at +1 {{'tosa.cast_to_block_scaled' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<f32>) -> (tensor<f4E2M1FN>, tensor<f8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<f32>) -> (tensor<f4E2M1FN>, tensor<f8E8M0FNU>)
return %0#0, %0#1 : tensor<f4E2M1FN>, tensor<f8E8M0FNU>
}
@@ -1677,7 +1677,7 @@ func.func @test_cast_to_block_scaled_not_scalar(%arg0: tensor<f32>) -> (tensor<f
func.func @test_cast_to_block_scaled_not_divisible_by_block_size(%arg0: tensor<4x33xf32>) -> (tensor<4x33xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) {
// expected-error at +1 {{'tosa.cast_to_block_scaled' op expect last dimension of input_data (33) to be divisible by block_size (32)}}
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x33xf32>) -> (tensor<4x33xf4E2M1FN>, tensor<4x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<4x33xf32>) -> (tensor<4x33xf4E2M1FN>, tensor<4x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<4x33xf4E2M1FN>, tensor<4x1xf8E8M0FNU>
}
@@ -1685,7 +1685,7 @@ func.func @test_cast_to_block_scaled_not_divisible_by_block_size(%arg0: tensor<4
func.func @test_cast_to_block_scaled_data_scale_mismatch(%arg0: tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<5x1xf8E8M0FNU>) {
// expected-error at +1 {{'tosa.cast_to_block_scaled' op require compatible shapes for output_data ('tensor<4x32xf4E2M1FN>') and output_scale ('tensor<5x1xf8E8M0FNU>') except for the last dimension}}
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<5x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<5x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<4x32xf4E2M1FN>, tensor<5x1xf8E8M0FNU>
}
@@ -1693,7 +1693,7 @@ func.func @test_cast_to_block_scaled_data_scale_mismatch(%arg0: tensor<4x32xf32>
func.func @test_cast_to_block_scaled_data_scale_channel_mismatch(%arg0: tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<4x2xf8E8M0FNU>) {
// expected-error at +1 {{'tosa.cast_to_block_scaled' op expect last dimension of output_scale (2) to be equal to last dimension of output_data / block_size (1)}}
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<4x2xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_32> : (tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<4x2xf8E8M0FNU>)
return %0#0, %0#1 : tensor<4x32xf4E2M1FN>, tensor<4x2xf8E8M0FNU>
}
@@ -1701,7 +1701,7 @@ func.func @test_cast_to_block_scaled_data_scale_channel_mismatch(%arg0: tensor<4
func.func @test_cast_to_block_scaled_block_size_mismatch(%arg0: tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>) {
// expected-error at +1 {{'tosa.cast_to_block_scaled' op expect block size to be 32, got 1}}
- %0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>)
+ %0:2 = tosa.cast_to_block_scaled %arg0 block_size<BLOCK_SIZE_1> : (tensor<4x32xf32>) -> (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>)
return %0#0, %0#1 : tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>
}
@@ -1709,7 +1709,7 @@ func.func @test_cast_to_block_scaled_block_size_mismatch(%arg0: tensor<4x32xf32>
func.func @test_cast_i8_block_scaled(%arg0: tensor<4x32xi8>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>> {
// expected-error at +1 {{'tosa.cast' op requires non-block-scaled element type to be floating-point when casting to or from block scaled element type, got 'i8'}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32xi8>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
+ %0 = tosa.cast %arg0 : (tensor<4x32xi8>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
return %0 : tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>
}
@@ -1717,7 +1717,7 @@ func.func @test_cast_i8_block_scaled(%arg0: tensor<4x32xi8>) -> tensor<4x32x!tos
func.func @test_cast_block_scaled_i32(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xi32> {
// expected-error at +1 {{'tosa.cast' op requires non-block-scaled element type to be floating-point when casting to or from block scaled element type, got 'i32'}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xi32>
+ %0 = tosa.cast %arg0 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32xi32>
return %0 : tensor<4x32xi32>
}
@@ -1725,7 +1725,7 @@ func.func @test_cast_block_scaled_i32(%arg0: tensor<4x32x!tosa.block_scaled<BLOC
func.func @test_cast_between_block_scaled(%arg0: tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>> {
// expected-error at +1 {{'tosa.cast' op requires exactly one of input or output to have block scaled element type}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
+ %0 = tosa.cast %arg0 : (tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f4E2M1FN>>) -> tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
return %0 : tensor<4x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>
}
@@ -1733,7 +1733,7 @@ func.func @test_cast_between_block_scaled(%arg0: tensor<4x32x!tosa.block_scaled<
func.func @test_block_scaled_cast_invalid_block_shape(%arg0: tensor<1x16x31x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<1x16x31xf32> {
// expected-error at +1 {{'tosa.cast' op operand #0 must be tosa-conformant tensor of number values: last dimension of block scaled tensor type (31) must be divisible by block size (32), but got 'tensor<1x16x31x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>'}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<1x16x31x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<1x16x31xf32>
+ %0 = tosa.cast %arg0 : (tensor<1x16x31x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<1x16x31xf32>
return %0 : tensor<1x16x31xf32>
}
@@ -1741,7 +1741,7 @@ func.func @test_block_scaled_cast_invalid_block_shape(%arg0: tensor<1x16x31x!tos
func.func @test_block_scaled_cast_scalar(%arg0: tensor<!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<f32> {
// expected-error at +1 {{'tosa.cast' op operand #0 must be tosa-conformant tensor of number values: block scaled tensor type must have rank greater than zero, but got 'tensor<!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>'}}
- %0 = tosa.cast %arg0 {input_unsigned = false} : (tensor<!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<f32>
+ %0 = tosa.cast %arg0 : (tensor<!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<f32>
return %0 : tensor<f32>
}
@@ -1749,7 +1749,7 @@ func.func @test_block_scaled_cast_scalar(%arg0: tensor<!tosa.block_scaled<BLOCK_
func.func @test_clamp_quantized(%arg0:tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) -> (tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) {
// expected-error at +1 {{'tosa.clamp' op min/max attributes types are incompatible with input/output element types.}}
- %0 = tosa.clamp %arg0 {max_val = 127 : i8, min_val = -128 : i8} : (tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>
+ %0 = tosa.clamp %arg0 min_val(-128 : i8) max_val(127 : i8) : (tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>) -> tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>
return %0 : tensor<?x112x112x32x!quant.uniform<u8:f32, 0.023529412224888802:-128>>
}
@@ -1773,7 +1773,7 @@ func.func @test_elementwise_shape_op_same_input_output_rank(%arg0: !tosa.shape<4
func.func @test_dim_invalid_output_rank(%arg0: tensor<1x2x3xi32>) -> !tosa.shape<2> {
// expected-error at +1 {{'tosa.dim' op expect output shape type to contain one element, got '!tosa.shape<2>'}}
- %0 = tosa.dim %arg0 {axis = 2 : i32} : (tensor<1x2x3xi32>) -> !tosa.shape<2>
+ %0 = tosa.dim %arg0 axis(2) : (tensor<1x2x3xi32>) -> !tosa.shape<2>
return %0 : !tosa.shape<2>
}
@@ -1781,7 +1781,7 @@ func.func @test_dim_invalid_output_rank(%arg0: tensor<1x2x3xi32>) -> !tosa.shape
func.func @test_dim_invalid_axis(%arg0: tensor<1x2x3xi32>) -> !tosa.shape<1> {
// expected-error at +1 {{'tosa.dim' op expect axis to be in the range [0, 3), got 4}}
- %0 = tosa.dim %arg0 {axis = 4 : i32} : (tensor<1x2x3xi32>) -> !tosa.shape<1>
+ %0 = tosa.dim %arg0 axis(4) : (tensor<1x2x3xi32>) -> !tosa.shape<1>
return %0 : !tosa.shape<1>
}
@@ -1789,16 +1789,16 @@ func.func @test_dim_invalid_axis(%arg0: tensor<1x2x3xi32>) -> !tosa.shape<1> {
func.func @test_dim_scalar(%arg0: tensor<i32>) -> !tosa.shape<1> {
// expected-error at +1 {{'tosa.dim' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<i32>'}}
- %0 = tosa.dim %arg0 {axis = 4 : i32} : (tensor<i32>) -> !tosa.shape<1>
+ %0 = tosa.dim %arg0 axis(4) : (tensor<i32>) -> !tosa.shape<1>
return %0 : !tosa.shape<1>
}
// -----
func.func @test_concat_shape_rank_mismatch() -> !tosa.shape<4> {
- %0 = tosa.const_shape {values = dense<[10]> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1 = tosa.const_shape {values = dense<[10, 15]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %2 = tosa.const_shape {values = dense<[5, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<[10]> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1 = tosa.const_shape values(dense<[10, 15]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %2 = tosa.const_shape values(dense<[5, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.concat_shape' op requires output shape rank to be equal to the sum of the input shape ranks (5), got 4}}
%3 = tosa.concat_shape %0, %1, %2 : (!tosa.shape<1>, !tosa.shape<2>, !tosa.shape<2>) -> !tosa.shape<4>
return %3 : !tosa.shape<4>
@@ -1808,16 +1808,16 @@ func.func @test_concat_shape_rank_mismatch() -> !tosa.shape<4> {
func.func @test_concat_shape_no_inputs() -> !tosa.shape<0> {
// expected-error at +1 {{'tosa.concat_shape' op requires at least one input shape}}
- %0 = tosa.concat_shape {} : () -> !tosa.shape<0>
+ %0 = tosa.concat_shape : () -> !tosa.shape<0>
return %0 : !tosa.shape<0>
}
// -----
func.func @test_concat_shape_rank_0() -> !tosa.shape<0> {
- %0 = tosa.const_shape {values = dense<[]> : tensor<0xindex>} : () -> !tosa.shape<0>
- %1 = tosa.const_shape {values = dense<[]> : tensor<0xindex>} : () -> !tosa.shape<0>
- %2 = tosa.const_shape {values = dense<[]> : tensor<0xindex>} : () -> !tosa.shape<0>
+ %0 = tosa.const_shape values(dense<[]> : tensor<0xindex>) : () -> !tosa.shape<0>
+ %1 = tosa.const_shape values(dense<[]> : tensor<0xindex>) : () -> !tosa.shape<0>
+ %2 = tosa.const_shape values(dense<[]> : tensor<0xindex>) : () -> !tosa.shape<0>
// expected-error at +1 {{'tosa.concat_shape' op requires all inputs shapes have a rank greater than 0}}
%3 = tosa.concat_shape %0, %1, %2 : (!tosa.shape<0>, !tosa.shape<0>, !tosa.shape<0>) -> !tosa.shape<0>
return %3 : !tosa.shape<0>
@@ -1826,9 +1826,9 @@ func.func @test_concat_shape_rank_0() -> !tosa.shape<0> {
// -----
func.func @test_slice_shape_negative_start() -> !tosa.shape<3> {
- %0 = tosa.const_shape {values = dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %1 = "tosa.const"() {values = dense<-1> : tensor<1xi32>} : () -> tensor<1xi32>
- %2 = "tosa.const"() {values = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = tosa.const_shape values(dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %1 = "tosa.const"() <{values = dense<-1> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %2 = "tosa.const"() <{values = dense<3> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.slice_shape' op expected non-negative start index, got -1}}
%3 = tosa.slice_shape %0, %1, %2 : (!tosa.shape<6>, tensor<1xi32>, tensor<1xi32>) -> !tosa.shape<3>
return %3 : !tosa.shape<3>
@@ -1837,9 +1837,9 @@ func.func @test_slice_shape_negative_start() -> !tosa.shape<3> {
// -----
func.func @test_slice_shape_non_positive_size() -> !tosa.shape<3> {
- %0 = tosa.const_shape {values = dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %1 = "tosa.const"() {values = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
- %2 = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = tosa.const_shape values(dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %1 = "tosa.const"() <{values = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %2 = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.slice_shape' op expected positive size, got 0}}
%3 = tosa.slice_shape %0, %1, %2 : (!tosa.shape<6>, tensor<1xi32>, tensor<1xi32>) -> !tosa.shape<3>
return %3 : !tosa.shape<3>
@@ -1848,9 +1848,9 @@ func.func @test_slice_shape_non_positive_size() -> !tosa.shape<3> {
// -----
func.func @test_slice_out_of_range() -> !tosa.shape<3> {
- %0 = tosa.const_shape {values = dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %1 = "tosa.const"() {values = dense<5> : tensor<1xi32>} : () -> tensor<1xi32>
- %2 = "tosa.const"() {values = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
+ %0 = tosa.const_shape values(dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %1 = "tosa.const"() <{values = dense<5> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %2 = "tosa.const"() <{values = dense<3> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.slice_shape' op expected start + size to be less than or equal to input shape rank (6), got 8}}
%3 = tosa.slice_shape %0, %1, %2 : (!tosa.shape<6>, tensor<1xi32>, tensor<1xi32>) -> !tosa.shape<3>
return %3 : !tosa.shape<3>
@@ -1859,9 +1859,9 @@ func.func @test_slice_out_of_range() -> !tosa.shape<3> {
// -----
func.func @test_slice_shape_incorrect_output_size() -> !tosa.shape<4> {
- %shape = tosa.const_shape {values = dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %start = "tosa.const"() {values = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
- %size = "tosa.const"() {values = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
+ %shape = tosa.const_shape values(dense<[4, 5, 6, 7, 8, 9]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %start = "tosa.const"() <{values = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %size = "tosa.const"() <{values = dense<3> : tensor<1xi32>}> : () -> tensor<1xi32>
// expected-error at +1 {{'tosa.slice_shape' op expected output type size to be equal to size attribute, got 4 vs 3}}
%slice = tosa.slice_shape %shape, %start, %size : (!tosa.shape<6>, tensor<1xi32>, tensor<1xi32>) -> !tosa.shape<4>
return %slice : !tosa.shape<4>
@@ -1870,8 +1870,8 @@ func.func @test_slice_shape_incorrect_output_size() -> !tosa.shape<4> {
// -----
func.func @test_mod_shape_input1_input2_rank_mismatch() -> !tosa.shape<6> {
- %a = tosa.const_shape {values = dense<[1, 2, 3, 4, 5, 6]> : tensor<6xindex>} : () -> !tosa.shape<6>
- %b = tosa.const_shape {values = dense<[1, 2, 3, 4, 5]> : tensor<5xindex>} : () -> !tosa.shape<5>
+ %a = tosa.const_shape values(dense<[1, 2, 3, 4, 5, 6]> : tensor<6xindex>) : () -> !tosa.shape<6>
+ %b = tosa.const_shape values(dense<[1, 2, 3, 4, 5]> : tensor<5xindex>) : () -> !tosa.shape<5>
// expected-error at +1 {{'tosa.mod_shape' op operands don't have matching ranks}}
%c = tosa.mod_shape %a, %b : (!tosa.shape<6>, !tosa.shape<5>) -> !tosa.shape<6>
return %c : !tosa.shape<6>
@@ -1880,396 +1880,396 @@ func.func @test_mod_shape_input1_input2_rank_mismatch() -> !tosa.shape<6> {
// -----
func.func @test_conv2d_block_scaled_data_type_mismatch(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf8E4M3FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op expect input_data and weight_data to have same element type, got 'f4E2M1FN' and 'f8E4M3FN'}}
- %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = BLOCK_SIZE_32} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf8E4M3FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf8E4M3FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %3 : tensor<*xf32>
}
// -----
func.func @test_conv2d_block_scaled_bias_output_type_mismatch(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf16>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op expect bias and output to have same element type, got 'f16' and 'f32'}}
- %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = BLOCK_SIZE_32} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf16>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %3 : tensor<*xf32>
}
// -----
func.func @test_conv2d_block_scaled_invalid_padding(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, -1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, -1]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op expect all padding values to be >= 0, got 0, 0, 0, -1}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
// -----
func.func @test_conv2d_block_scaled_invalid_stride(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[0, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[0, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op expect all stride values to be >= 1, got 0, 1}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
// -----
func.func @test_conv2d_block_scaled_invalid_dilation(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 0]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 0]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op expect all dilation values to be >= 1, got 1, 0}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
// -----
func.func @test_conv2d_block_scaled_input_width_mismatch(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x5x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<1x4x4x8xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op expected input width of input_scale to match size 4, got 5}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x5x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x5x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
// -----
func.func @test_conv2d_block_scaled_kernel_height_mismatch(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x2x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<1x4x4x8xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op expected kernel height of weight_scale to match size 2, got 1}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x2x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x2x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
// -----
func.func @test_conv2d_block_scaled_output_shape_indivisible(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<1x4x5x8xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 2]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op expected input_width - 1 + pad_left + pad_right - (kernel_width - 1) * dilation_x to be wholly divisible by stride_x, got (4 - 1 + 0 + 0 - (1 - 1) * 1) / 2}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x5x8xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x5x8xf32>
return %0 : tensor<1x4x5x8xf32>
}
// -----
func.func @test_conv2d_block_scaled_output_shape_mismatch(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<1x4x5x8xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op calculated output width did not match expected: calculated=4, expected=5}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x5x8xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x5x8xf32>
return %0 : tensor<1x4x5x8xf32>
}
// -----
func.func @test_conv2d_block_scaled_invalid_ic(%arg0: tensor<1x4x4x63xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x63xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<1x4x5x8xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op expect IC to be a multiple of block size, got IC=63, block_size=32}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x4x4x63xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x63xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x5x8xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x63xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x63xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x5x8xf32>
return %0 : tensor<1x4x5x8xf32>
}
// -----
func.func @test_conv2d_block_scaled_invalid_ic_mutiple(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x3xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x3xf8E8M0FNU>, %arg4: tensor<1xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<1x4x5x8xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op expect scale operands dimension 2 to equal IC/block_size (64/32), got 3}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x3xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x3xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x5x8xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x3xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x3xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x5x8xf32>
return %0 : tensor<1x4x5x8xf32>
}
// -----
func.func @test_conv2d_block_scaled_invalid_bias_size(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<6xf32>, %arg5: tensor<4xi32>, %arg6: tensor<2xi32>, %arg7: tensor<2xi32>) -> tensor<1x4x4x8xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op bias channels expected to be equal to output channels (8) or 1, got 6}}
- %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<6xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_32> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<6xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
// -----
func.func @test_conv2d_block_scaled_block_size_mismatch(%arg0: tensor<1x4x4x64xf4E2M1FN>, %arg1: tensor<1x4x4x2xf8E8M0FNU>, %arg2: tensor<8x1x1x64xf4E2M1FN>, %arg3: tensor<8x1x1x2xf8E8M0FNU>, %arg4: tensor<1xf32>) -> tensor<*xf32> {
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %dilation = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %dilation = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.conv2d_block_scaled' op expect block size to be 32, got 1}}
- %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation {block_size = #tosa.block_size<BLOCK_SIZE_1>} : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
+ %3 = tosa.conv2d_block_scaled %arg0, %arg1, %arg2, %arg3, %arg4, %pad, %stride, %dilation block_size<BLOCK_SIZE_1> : (tensor<1x4x4x64xf4E2M1FN>, tensor<1x4x4x2xf8E8M0FNU>, tensor<8x1x1x64xf4E2M1FN>, tensor<8x1x1x2xf8E8M0FNU>, tensor<1xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %3 : tensor<*xf32>
}
// -----
func.func @test_missmatched_ranks() {
- %0 = tosa.const_shape {values = dense<[10]> : tensor<1xindex>} : () -> !tosa.shape<1>
- %1 = tosa.const_shape {values = dense<[10, 15]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %0 = tosa.const_shape values(dense<[10]> : tensor<1xindex>) : () -> !tosa.shape<1>
+ %1 = tosa.const_shape values(dense<[10, 15]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.assert_equal_shape' op operands don't have matching ranks}}
- tosa.assert_equal_shape %0, %1 {allow_broadcast = true} : (!tosa.shape<1>, !tosa.shape<2>) -> ()
+ tosa.assert_equal_shape %0, %1 allow_broadcast(true) : (!tosa.shape<1>, !tosa.shape<2>) -> ()
return
}
// -----
func.func @test_reshape_block_scaled_static_zero_dim_input(%arg0 : tensor<13x0x3xf32>) -> () {
- %s = tosa.const_shape {values = dense<[13, 21, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[13, 21, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.reshape_block_scaled' op operand #0 must be variadic of tosa-conformant tensor of number values, but got 'tensor<13x0x3xf32>'}}
- %0 = "tosa.reshape_block_scaled"(%arg0, %s) {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<13x0x3xf32>, !tosa.shape<3>) -> tensor<13x0x3xf32>
+ %0 = "tosa.reshape_block_scaled"(%arg0, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_1> : i32}> : (tensor<13x0x3xf32>, !tosa.shape<3>) -> tensor<13x0x3xf32>
return
}
// -----
func.func @test_reshape_block_scaled_zero_dim_input(%arg0 : tensor<?x0x3xf32>) -> () {
- %s = tosa.const_shape {values = dense<[13, 21, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[13, 21, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.reshape_block_scaled' op operand #0 must be variadic of tosa-conformant tensor of number values, but got 'tensor<?x0x3xf32>'}}
- %0 = "tosa.reshape_block_scaled"(%arg0, %s) {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<?x0x3xf32>, !tosa.shape<3>) -> tensor<13x0x3xf32>
+ %0 = "tosa.reshape_block_scaled"(%arg0, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_1> : i32}> : (tensor<?x0x3xf32>, !tosa.shape<3>) -> tensor<13x0x3xf32>
return
}
// -----
func.func @test_reshape_block_scaled_zero_dim_input(%arg0 : tensor<?x0x3xf32>) -> () {
- %1 = tosa.const_shape {values = dense<[13, 21, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %1 = tosa.const_shape values(dense<[13, 21, 3]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.reshape_block_scaled' op operand #0 must be variadic of tosa-conformant tensor of number values, but got 'tensor<?x0x3xf32>'}}
- %0 = "tosa.reshape_block_scaled"(%arg0, %1) {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<?x0x3xf32>, !tosa.shape<3>) -> tensor<13x0x3xf32>
+ %0 = "tosa.reshape_block_scaled"(%arg0, %1) <{block_size = #tosa.block_size<BLOCK_SIZE_1> : i32}> : (tensor<?x0x3xf32>, !tosa.shape<3>) -> tensor<13x0x3xf32>
return
}
// -----
func.func @test_reshape_block_scaled_invalid_tensor_dim(%arg0 : tensor<4x?xf32>) -> () {
- %s = tosa.const_shape {values = dense<[1, -1]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[1, -1]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op result shape is inconsistent with new shape}}
- %0 = "tosa.reshape_block_scaled" (%arg0, %s) {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<4x?xf32>, !tosa.shape<2>) -> tensor<?x4xf32>
+ %0 = "tosa.reshape_block_scaled" (%arg0, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_1> : i32}> : (tensor<4x?xf32>, !tosa.shape<2>) -> tensor<?x4xf32>
return
}
// -----
func.func @test_reshape_block_scaled_invalid_size(%arg0 : tensor<2x4xf32>) -> () {
- %s = tosa.const_shape {values = dense<[3, 5]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[3, 5]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op cannot reshape 8 elements into 15}}
- %0 = "tosa.reshape_block_scaled"(%arg0, %s) {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<2x4xf32>, !tosa.shape<2>) -> tensor<3x5xf32>
+ %0 = "tosa.reshape_block_scaled"(%arg0, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_1> : i32}> : (tensor<2x4xf32>, !tosa.shape<2>) -> tensor<3x5xf32>
return
}
// -----
func.func @test_reshape_block_scaled_invalid_newshape(%arg0 : tensor<1xf32>) -> () {
- %s = tosa.const_shape {values = dense<[1, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[1, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op cannot reshape 1 elements into 4}}
- %0 = "tosa.reshape_block_scaled"(%arg0, %s) {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<1xf32>, !tosa.shape<2>) -> tensor<?x4xf32>
+ %0 = "tosa.reshape_block_scaled"(%arg0, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_1> : i32}> : (tensor<1xf32>, !tosa.shape<2>) -> tensor<?x4xf32>
return
}
// -----
func.func @test_reshape_block_scaled_inconsistent_dynamic_result(%arg0 : tensor<?xf32>) -> () {
- %s = tosa.const_shape {values = dense<[2, 4, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+ %s = tosa.const_shape values(dense<[2, 4, 1]> : tensor<3xindex>) : () -> !tosa.shape<3>
// expected-error at +1 {{'tosa.reshape_block_scaled' op result shape is inconsistent with new shape}}
- %0 = "tosa.reshape_block_scaled"(%arg0, %s) {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<?xf32>, !tosa.shape<3>) -> tensor<?x3x5xf32>
+ %0 = "tosa.reshape_block_scaled"(%arg0, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_1> : i32}> : (tensor<?xf32>, !tosa.shape<3>) -> tensor<?x3x5xf32>
return
}
// -----
func.func @test_reshape_block_scaled_rank_mismatch(%arg0 : tensor<?xf32>) -> () {
- %s = tosa.const_shape {values = dense<[2, 4]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 4]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op result does not match new shape rank}}
- %0 = "tosa.reshape_block_scaled"(%arg0, %s) {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<?xf32>, !tosa.shape<2>) -> tensor<?xf32>
+ %0 = "tosa.reshape_block_scaled"(%arg0, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_1> : i32}> : (tensor<?xf32>, !tosa.shape<2>) -> tensor<?xf32>
return
}
// -----
func.func @test_reshape_block_scaled_types_mismatch(%arg0 : tensor<2x4xf32>) -> () {
- %s = tosa.const_shape {values = dense<[8]> : tensor<1xindex>} : () -> !tosa.shape<1>
+ %s = tosa.const_shape values(dense<[8]> : tensor<1xindex>) : () -> !tosa.shape<1>
// expected-error at +1 {{'tosa.reshape_block_scaled' op expect input and output to have same element type, got 'f32' and 'i32'}}
- %0 = "tosa.reshape_block_scaled"(%arg0, %s) {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<2x4xf32>, !tosa.shape<1>) -> tensor<8xi32>
+ %0 = "tosa.reshape_block_scaled"(%arg0, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_1> : i32}> : (tensor<2x4xf32>, !tosa.shape<1>) -> tensor<8xi32>
return
}
// -----
func.func @test_reshape_block_scaled_outputs_mismatch_inputs(%arg0 : tensor<64xf8E4M3FN>) -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op requires number of results to match inputs}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %s) {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<64xf8E4M3FN>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x1xf8E8M0FNU>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_1> : i32}> : (tensor<64xf8E4M3FN>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x1xf8E8M0FNU>)
return
}
// -----
func.func @test_reshape_block_scaled_inputs_mismatch_outputs(%arg0 : tensor<64xf8E4M3FN>, %arg1 : tensor<2xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op requires number of results to match inputs}}
- %0 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>)
+ %0 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>)
return
}
// -----
func.func @test_reshape_block_scaled_no_inputs() -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op requires at least one input}}
- %0 = "tosa.reshape_block_scaled"(%s) {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (!tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>)
+ %0 = "tosa.reshape_block_scaled"(%s) <{block_size = #tosa.block_size<BLOCK_SIZE_1> : i32}> : (!tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>)
return
}
// -----
func.func @test_reshape_block_scaled_too_many_inputs(%arg0 : tensor<64xf8E4M3FN>, %arg1 : tensor<2xf8E8M0FNU>, %arg2 : tensor<2xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op requires at most two inputs}}
- %0 = "tosa.reshape_block_scaled"(%arg0, %arg1, %arg2, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>)
+ %0 = "tosa.reshape_block_scaled"(%arg0, %arg1, %arg2, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>)
return
}
// -----
func.func @test_reshape_block_scaled_scale_types_mismatch(%arg0 : tensor<64xf8E4M3FN>, %arg1 : tensor<2xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op expect input and output to have same element type, got 'f8E8M0FNU' and 'f8E4M3FN'}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x1xf8E4M3FN>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x1xf8E4M3FN>)
return
}
// -----
func.func @test_reshape_block_scaled_input_ranks_mismatch(%arg0 : tensor<64xf8E4M3FN>, %arg1 : tensor<2x1xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op input shapes do not have same rank}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<64xf8E4M3FN>, tensor<2x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x1xf8E8M0FNU>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<64xf8E4M3FN>, tensor<2x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x1xf8E8M0FNU>)
return
}
// -----
func.func @test_reshape_block_scaled_scale_dims_mismatch(%arg0 : tensor<1x64xf8E4M3FN>, %arg1 : tensor<2x2xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op input shapes for data and scale do not match on dimension 0}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<1x64xf8E4M3FN>, tensor<2x2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x1xf8E8M0FNU>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<1x64xf8E4M3FN>, tensor<2x2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x1xf8E8M0FNU>)
return
}
// -----
func.func @test_reshape_block_scaled_block_size_dim_mismatch(%arg0 : tensor<60xf8E4M3FN>, %arg1 : tensor<2xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op expect last dimension of input_data (60) to be divisible by block_size (32)}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<60xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x1xf8E8M0FNU>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<60xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x1xf8E8M0FNU>)
return
}
// -----
func.func @test_reshape_block_scaled_scale_blocks_mismatch(%arg0 : tensor<64xf8E4M3FN>, %arg1 : tensor<3xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op expect last dimension of scale_data (3) to be 64/32}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<64xf8E4M3FN>, tensor<3xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x1xf8E8M0FNU>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<64xf8E4M3FN>, tensor<3xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x1xf8E8M0FNU>)
return
}
// -----
func.func @test_reshape_block_scaled_new_shape_block_size_dim_mismatch(%arg0 : tensor<64xf8E4M3FN>, %arg1 : tensor<2xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<[2, 30]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 30]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op expect last dimension of new shape (30) to be divisible by block_size (32)}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x30xf8E4M3FN>, tensor<2x1xf8E8M0FNU>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x30xf8E4M3FN>, tensor<2x1xf8E8M0FNU>)
return
}
// -----
func.func @test_reshape_block_scaled_inconsistent_scale_output_rank(%arg0 : tensor<64xf8E4M3FN>, %arg1 : tensor<2xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op result scale does not match new shape rank}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2xf8E8M0FNU>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2xf8E8M0FNU>)
return
}
// -----
func.func @test_reshape_block_scaled_inconsistent_scale(%arg0 : tensor<64xf8E4M3FN>, %arg1 : tensor<2xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op result scale shape is inconsistent with new shape}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<3x?xf8E8M0FNU>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<3x?xf8E8M0FNU>)
return
}
// -----
func.func @test_reshape_block_scaled_rank0_input(%arg0 : tensor<f8E4M3FN>, %arg1 : tensor<2xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op requires all input shapes have a rank greater than 0}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<f8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x2xf8E8M0FNU>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<f8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<2x2xf8E8M0FNU>)
return
}
// -----
func.func @test_reshape_block_scaled_rank0_scale_output(%arg0 : tensor<64xf8E4M3FN>, %arg1 : tensor<2xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<[2, 32]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 32]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op requires all result shapes have a rank greater than 0}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<f8E8M0FNU>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x32xf8E4M3FN>, tensor<f8E8M0FNU>)
return
}
// -----
func.func @test_reshape_block_scaled_rank0_scale_input(%arg0 : tensor<64xf8E4M3FN>, %arg1 : tensor<2xf8E8M0FNU>) -> () {
- %s = tosa.const_shape {values = dense<> : tensor<0xindex>} : () -> !tosa.shape<0>
+ %s = tosa.const_shape values(dense<> : tensor<0xindex>) : () -> !tosa.shape<0>
// expected-error at +1 {{'tosa.reshape_block_scaled' op requires new shape to have a rank greater than 0}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<0>) -> (tensor<2x32xf8E4M3FN>, tensor<2x2xf8E8M0FNU>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<64xf8E4M3FN>, tensor<2xf8E8M0FNU>, !tosa.shape<0>) -> (tensor<2x32xf8E4M3FN>, tensor<2x2xf8E8M0FNU>)
return
}
// -----
func.func @test_reshape_non_block_scaled_block_size_mismatch(%arg0: tensor<13x21x3xf32>) -> tensor<1x819xf32> {
- %s = tosa.const_shape {values = dense<[1, 819]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[1, 819]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op expect block size to be 1, got 32}}
- %0 = "tosa.reshape_block_scaled"(%arg0, %s) {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<13x21x3xf32>, !tosa.shape<2>) -> tensor<1x819xf32>
+ %0 = "tosa.reshape_block_scaled"(%arg0, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_32> : i32}> : (tensor<13x21x3xf32>, !tosa.shape<2>) -> tensor<1x819xf32>
return %0 : tensor<1x819xf32>
}
// -----
func.func @test_reshape_block_scaled_block_size_mismatch(%arg0: tensor<4x32xf4E2M1FN>, %arg1: tensor<4x1xf8E8M0FNU>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>) {
- %s = tosa.const_shape {values = dense<[2, 64]> : tensor<2xindex>} : () -> !tosa.shape<2>
+ %s = tosa.const_shape values(dense<[2, 64]> : tensor<2xindex>) : () -> !tosa.shape<2>
// expected-error at +1 {{'tosa.reshape_block_scaled' op expect block size to be 32, got 1}}
- %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) {block_size = #tosa.block_size<BLOCK_SIZE_1> : i32} : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
+ %0:2 = "tosa.reshape_block_scaled"(%arg0, %arg1, %s) <{block_size = #tosa.block_size<BLOCK_SIZE_1> : i32}> : (tensor<4x32xf4E2M1FN>, tensor<4x1xf8E8M0FNU>, !tosa.shape<2>) -> (tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>)
return %0#0, %0#1 : tensor<2x64xf4E2M1FN>, tensor<2x2xf8E8M0FNU>
}
// -----
func.func @test_maxpool2d_adaptive_invalid_kernel(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x2x32x8xf32> {
- %kernel = tosa.const_shape {values = dense<[0, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[0, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_pool2d_adaptive' op expect all kernel values to be >= 1, got 0, 1}}
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x32x32x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x2x32x8xf32>
@@ -2279,9 +2279,9 @@ func.func @test_maxpool2d_adaptive_invalid_kernel(%arg0: tensor<1x32x32x8xf32>)
// -----
func.func @test_maxpool2d_adaptive_unexpected_output_width(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x2x8xf32> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_pool2d_adaptive' op calculated output width did not match expected: calculated=32, expected=2}}
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x32x32x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x2x8xf32>
@@ -2294,16 +2294,16 @@ func.func @test_avg_pool2d_same_input_output_type(%arg0: tensor<1x7x7x9xf32>) ->
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%output_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.avg_pool2d' op expect input and output to have same element type, got 'f32' and 'f16'}}
- %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf16>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp kernel([2, 2]) stride([1, 1]) pad([0, 1, 0, 1]) acc_type(f32) : (tensor<1x7x7x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf16>
return %0 : tensor<1x7x7x9xf16>
}
// -----
func.func @test_maxpool2d_adaptive_unexpected_output_width(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf16> {
- %kernel = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %stride = tosa.const_shape {values = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad = tosa.const_shape {values = dense<[0, 0, 0, 0]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ %kernel = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %stride = tosa.const_shape values(dense<[1, 1]> : tensor<2xindex>) : () -> !tosa.shape<2>
+ %pad = tosa.const_shape values(dense<[0, 0, 0, 0]> : tensor<4xindex>) : () -> !tosa.shape<4>
// expected-error at +1 {{'tosa.max_pool2d_adaptive' op expect input and output to have same element type, got 'f32' and 'f16'}}
%0 = tosa.max_pool2d_adaptive %arg0, %kernel, %stride, %pad :
(tensor<1x32x32x8xf32>, !tosa.shape<2>, !tosa.shape<2>, !tosa.shape<4>) -> tensor<1x32x32x8xf16>
diff --git a/mlir/test/IR/invalid-custom-print-parse.mlir b/mlir/test/IR/invalid-custom-print-parse.mlir
index 774af05943817..37c0d73d8ad60 100644
--- a/mlir/test/IR/invalid-custom-print-parse.mlir
+++ b/mlir/test/IR/invalid-custom-print-parse.mlir
@@ -29,7 +29,6 @@ test.op_with_enum_prop_attr_form <{value = 0 : i32, foo}>
// Test that an integer literal cannot be used where an APFloat is expected.
// parseOptionalAttribute(FloatAttr &) should reject a non-float attribute.
-// expected-error at +2 {{expected attribute of type 'builtin.float', but found attribute '5 : i64'}}
+// expected-error at +2 {{invalid kind of attribute specified: expected builtin.float, but found 5 : i64}}
// expected-error at +1 {{failed to parse TestTypeAPFloat parameter}}
func.func private @test_ap_float_wrong_attr_type() -> !test.ap_float<5U0>
-
diff --git a/mlir/test/Integration/Dialect/EmitC/regalloc-eviction-test-model-tosa.mlir b/mlir/test/Integration/Dialect/EmitC/regalloc-eviction-test-model-tosa.mlir
index f9828f67121e5..2531d1a991b87 100644
--- a/mlir/test/Integration/Dialect/EmitC/regalloc-eviction-test-model-tosa.mlir
+++ b/mlir/test/Integration/Dialect/EmitC/regalloc-eviction-test-model-tosa.mlir
@@ -66,7 +66,7 @@
module attributes {tf_saved_model.semantics, tfl.description = "MLIR Converted.", tfl.schema_version = 3 : i32} {
func.func @mock_model(%arg0: tensor<33xi64> {tf_saved_model.index_path = ["mask"]}) -> (tensor<i64> {tf_saved_model.index_path = ["index_to_evict"]}) attributes {tf.entry_function = {inputs = "action_mask:0", outputs = "StatefulPartitionedCall:0"}, tf_saved_model.exported_names = ["action"]} {
%0 = tosa.cast %arg0 : (tensor<33xi64>) -> tensor<33xi32>
- %1 = tosa.argmax %0 {axis = 0 : i32} : (tensor<33xi32>) -> tensor<i64>
+ %1 = tosa.argmax %0 axis(0) : (tensor<33xi32>) -> tensor<i64>
return %1 : tensor<i64>
}
}
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