[Mlir-commits] [mlir] [mlir][tosa] Update value to values for ConstOp (PR #129943)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Wed Mar 5 14:01:49 PST 2025
https://github.com/Jerry-Ge created https://github.com/llvm/llvm-project/pull/129943
Updated the dialect to match TOSA v1.0 specification (https://www.mlplatform.org/tosa/tosa_spec.html#_const) and the lit tests.
>From 26f1dd8f22423da962a9c4a3f95b9cbf94b4c1df Mon Sep 17 00:00:00 2001
From: Jerry Ge <jerry.ge at arm.com>
Date: Wed, 5 Mar 2025 09:35:12 -0800
Subject: [PATCH] [mlir][tosa] Update value to values for ConstOp
Signed-off-by: Jerry Ge <jerry.ge at arm.com>
Change-Id: I59501284d8612038f819002b250ed4d86a0b05cd
---
mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td | 6 +-
.../Conversion/TosaToArith/TosaToArith.cpp | 2 +-
.../Dialect/Tosa/IR/TosaCanonicalizations.cpp | 2 +-
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp | 4 +-
.../Dialect/Tosa/Transforms/TosaFolders.cpp | 2 +-
.../Tosa/Transforms/TosaReduceTransposes.cpp | 2 +-
.../Conversion/TosaToArith/tosa-to-arith.mlir | 2 +-
.../TosaToLinalg/tosa-to-linalg-named.mlir | 88 ++--
.../TosaToLinalg/tosa-to-linalg.mlir | 8 +-
.../Conversion/TosaToSCF/tosa-to-scf.mlir | 4 +-
.../TosaToTensor/tosa-to-tensor.mlir | 10 +-
mlir/test/Dialect/Tosa/availability.mlir | 30 +-
mlir/test/Dialect/Tosa/canonicalize.mlir | 140 +++---
mlir/test/Dialect/Tosa/constant-op-fold.mlir | 422 +++++++++---------
.../Tosa/constant-reciprocal-fold.mlir | 22 +-
mlir/test/Dialect/Tosa/constant_folding.mlir | 4 +-
mlir/test/Dialect/Tosa/invalid.mlir | 86 ++--
mlir/test/Dialect/Tosa/invalid_extension.mlir | 4 +-
mlir/test/Dialect/Tosa/level_check.mlir | 46 +-
mlir/test/Dialect/Tosa/ops.mlir | 54 +--
mlir/test/Dialect/Tosa/quant-test.mlir | 4 +-
.../Tosa/tosa-decompose-depthwise.mlir | 22 +-
.../Tosa/tosa-decompose-transpose-conv.mlir | 46 +-
mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir | 44 +-
.../Dialect/Tosa/tosa-reduce-transposes.mlir | 72 +--
25 files changed, 563 insertions(+), 563 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index ff83e4072b6c0..d46e992dc36cb 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -2338,7 +2338,7 @@ def Tosa_RescaleOp: Tosa_Op<"rescale", [Pure,
// Operator: const
//===----------------------------------------------------------------------===//
def Tosa_ConstOp : Tosa_Op<"const", [ConstantLike, Pure,
- AllShapesMatch<["value", "output"]>,
+ AllShapesMatch<["values", "output"]>,
FirstAttrDerivedResultType]> {
let summary = "Constant op.";
@@ -2350,12 +2350,12 @@ def Tosa_ConstOp : Tosa_Op<"const", [ConstantLike, Pure,
```mlir
// Generic form
- %out = "tosa.const"() {value = dense<0> : tensor<2x3xi32>} : () -> tensor<2x3xi32>
+ %out = "tosa.const"() {values = dense<0> : tensor<2x3xi32>} : () -> tensor<2x3xi32>
```
}];
let arguments = (ins
- ElementsAttr:$value
+ ElementsAttr:$values
);
let results = (outs
diff --git a/mlir/lib/Conversion/TosaToArith/TosaToArith.cpp b/mlir/lib/Conversion/TosaToArith/TosaToArith.cpp
index 593dbaa6c6545..5c84b4063da2e 100644
--- a/mlir/lib/Conversion/TosaToArith/TosaToArith.cpp
+++ b/mlir/lib/Conversion/TosaToArith/TosaToArith.cpp
@@ -28,7 +28,7 @@ class ConstOpConverter : public OpRewritePattern<tosa::ConstOp> {
LogicalResult matchAndRewrite(tosa::ConstOp op,
PatternRewriter &rewriter) const final {
- rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, op.getValue());
+ rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, op.getValues());
return success();
}
};
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
index 2c0376134b599..ad8dbe312d6cb 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
@@ -882,7 +882,7 @@ OpFoldResult CastOp::fold(FoldAdaptor adaptor) {
return {};
}
-OpFoldResult ConstOp::fold(FoldAdaptor adaptor) { return getValueAttr(); }
+OpFoldResult ConstOp::fold(FoldAdaptor adaptor) { return getValuesAttr(); }
OpFoldResult ConstShapeOp::fold(FoldAdaptor adaptor) { return getValueAttr(); }
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 11d25e657f904..4ad47173348e1 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -352,7 +352,7 @@ static LogicalResult verifyConvOp(T op) {
LogicalResult tosa::ConstOp::verify() {
- auto attrType = llvm::dyn_cast<TensorType>(getValueAttr().getType());
+ auto attrType = llvm::dyn_cast<TensorType>(getValuesAttr().getType());
auto outputType = llvm::dyn_cast<TensorType>(getOutput().getType());
if (!attrType || !outputType) {
@@ -3037,7 +3037,7 @@ LogicalResult tosa::ConstShapeOp::verify() {
auto valuesRank = getValue().getType().getRank();
if (valuesRank != 1)
return emitOpError("expect elements in attribute value with rank 1");
- // check that number of elements in value attr equal to rank of result shape
+ // check that number of elements in values attr equal to rank of result shape
auto count = getValue().getNumElements();
auto rank = (cast<tosa::shapeType>(getResult().getType())).getRank();
if (!(count == rank || (count == 1 && rank == 0))) {
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
index 1f5a906153cd9..9a45d83c61737 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
@@ -363,7 +363,7 @@ struct ReduceConstantOptimization : public OpRewritePattern<OperationType> {
return rewriter.notifyMatchFailure(op, "result type shape is not static");
auto reductionAxis = op.getAxis();
- const auto denseElementsAttr = constOp.getValue();
+ const auto denseElementsAttr = constOp.getValues();
const auto shapedOldElementsValues =
cast<ShapedType>(denseElementsAttr.getType());
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp
index 36f511fa616d8..585e8b1d7f2cd 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp
@@ -418,7 +418,7 @@ std::optional<Value> TosaReduceTransposes::buildMappedToValue(
std::optional<Value> TosaReduceTransposes::buildMappedToValue(
ConstOp constOp, const DenseMap<Value, Value> &valuesMap,
IRRewriter &rewriter, ArrayRef<int32_t> hoistedPerms) {
- auto denseAttr = llvm::dyn_cast<DenseElementsAttr>(constOp.getValue());
+ auto denseAttr = llvm::dyn_cast<DenseElementsAttr>(constOp.getValues());
if (!denseAttr)
return std::nullopt;
auto maybeNewDenseAttr = transposeDenseAttribute(denseAttr, hoistedPerms);
diff --git a/mlir/test/Conversion/TosaToArith/tosa-to-arith.mlir b/mlir/test/Conversion/TosaToArith/tosa-to-arith.mlir
index c4f82d53af982..14f811727c456 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"() {value = dense<3> : tensor<i32>} : () -> tensor<i32>
+ %result = "tosa.const"() {values = dense<3> : tensor<i32>} : () -> tensor<i32>
// CHECK: return [[C3]]
return %result : tensor<i32>
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
index d4afc468eeea4..5bb4a3bddb51b 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -290,8 +290,8 @@ func.func @avg_pool_f32(%arg0: tensor<1x6x34x62xf32>) -> (tensor<1x5x33x62xf32>)
// CHECK: %[[FLT:.+]] = arith.sitofp %[[CAST]]
// CHECK: %[[DIV:.+]] = arith.divf %[[IN]], %[[FLT]]
// CHECK: linalg.yield %[[DIV]]
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %output_zp = "tosa.const"() <{value = 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>
%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>
return %0 : tensor<1x5x33x62xf32>
}
@@ -377,8 +377,8 @@ func.func @avg_pool_f16_f32acc(%arg0: tensor<1x6x34x62xf16>) -> (tensor<1x5x33x6
// CHECK: %[[DIV:.+]] = arith.divf %[[IN]], %[[FLT]]
// CHECK: %[[TRUNC:.+]] = arith.truncf %[[DIV]]
// CHECK: linalg.yield %[[TRUNC]]
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
- %output_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %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>
return %0 : tensor<1x5x33x62xf16>
}
@@ -420,8 +420,8 @@ func.func @avg_pool_i8(%arg0: tensor<1x6x34x62xi8>) -> (tensor<1x5x33x62xi8>) {
// CHECK: %[[CLAMP:.+]] = arith.minsi %[[CMAX]], %[[LOW]]
// CHECK: %[[TRUNC:.+]] = arith.trunci %[[CLAMP]]
// CHECK: linalg.yield %[[TRUNC]]
- %input_zp = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %output_zp = "tosa.const"() <{value = dense<0> : 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>
%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>
return %0 : tensor<1x5x33x62xi8>
}
@@ -445,8 +445,8 @@ func.func @avg_pool_dyn(%arg0: tensor<?x6x34x62xf32>) -> (tensor<?x5x33x62xf32>)
// CHECK-SAME: outs(%[[FILL]] : tensor<?x5x33x62xf32>) -> tensor<?x5x33x62xf32>
// CHECK: %[[EMPTY:.+]] = tensor.empty(%[[BATCH]]) : tensor<?x5x33x62xf32>
// CHECK: %[[GENERIC:.+]] = linalg.generic
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %output_zp = "tosa.const"() <{value = 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>
%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>
return %0 : tensor<?x5x33x62xf32>
}
@@ -460,8 +460,8 @@ func.func @avg_pool_dyn(%arg0: tensor<?x6x34x62xf32>) -> (tensor<?x5x33x62xf32>)
func.func @conv2d_scalar_bias_f32(%input: tensor<1x49x42x27xf32>, %weights: tensor<28x3x3x27xf32>, %bias: tensor<1xf32>) -> () {
// CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x45x40x28xf32>
// 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"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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 %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>
return
}
@@ -482,8 +482,8 @@ func.func @conv2d_i8(%input: tensor<1x49x42x27xi8>, %weights: tensor<28x1x1x27xi
// CHECK: linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x49x42x27xi8>, tensor<28x1x1x27xi8>) outs(%[[BROADCAST]] : tensor<1x49x42x28xi32>) -> tensor<1x49x42x28xi32>
// HWCF: linalg.conv_2d_nhwc_hwcf {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %[[TRANSPOSE]] : tensor<1x49x42x27xi8>, tensor<1x1x27x28xi8>) outs(%{{[a-zA-Z0-9_]*}} : tensor<1x49x42x28xi32>) -> tensor<1x49x42x28xi32>
- %input_zp = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>
return
}
@@ -504,8 +504,8 @@ func.func @conv2d_f32(%input: tensor<1x49x42x27xf32>, %weights: tensor<28x3x3x27
// CHECK: linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>) outs(%[[BROADCAST]] : tensor<1x45x40x28xf32>) -> tensor<1x45x40x28xf32>
// 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"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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 %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>
return
}
@@ -525,8 +525,8 @@ func.func @conv2d_dyn(%input: tensor<?x49x42x27xf32>, %weights: tensor<28x3x3x27
// CHECK: linalg.yield %[[IN]] : f32
// CHECK: } -> tensor<?x45x40x28xf32>
// 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"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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 %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>
return
}
@@ -583,8 +583,8 @@ func.func @conv2d_dyn_w_h(%input: tensor<1x?x?x27xf32>, %weights: tensor<28x3x3x
// CHECK: } -> tensor<1x?x?x28xf32>
// CHECK: linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x?x?x27xf32>, tensor<28x3x3x27xf32>) outs(%[[BROADCAST]] : tensor<1x?x?x28xf32>) -> tensor<1x?x?x28xf32>
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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 %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>
return
}
@@ -608,8 +608,8 @@ func.func @conv2d_dyn_output(%input: tensor<2x6x5x4xf32>, %weights: tensor<4x3x3
// linalg.yield %[[ADD]] : f32
// } -> tensor<?x4x3x4xf32>
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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 %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>
return
}
@@ -622,8 +622,8 @@ func.func @conv2d_padded_f32(%input: tensor<1x47x40x28xf32>, %weights: tensor<28
// CHECK: tensor.pad %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0]
// CHECK: tensor.yield %[[C0]]
// CHECK: linalg.conv_2d_nhwc_fhwc
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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 %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>}
: (tensor<1x47x40x28xf32>, tensor<28x3x3x28xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x45x40x28xf32>
return
@@ -637,8 +637,8 @@ func.func @conv2d_quant(%arg0 : tensor<1x12x12x1xi8>, %arg1 : tensor<1024x3x3x1x
// CHECK: tensor.pad %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0]
// CHECK: tensor.yield %[[C22]]
// CHECK: linalg.conv_2d_nhwc_fhwc_q
- %input_zp = "tosa.const"() <{value = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() <{value = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>}
: (tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, tensor<1024xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x12x12x1024xi32>
return
@@ -662,8 +662,8 @@ func.func @depthwise_conv(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tensor<3x1x3x11xf
// CHECK: [[ADD:%.+]] = arith.addf %[[ARG3]], %[[ARG4]] : f32
// CHECK: linalg.yield [[ADD]] : f32
// CHECK: } -> tensor<1x5x5x33xf32>
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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>
%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>
return
}
@@ -680,8 +680,8 @@ func.func @depthwise_conv_scalar_bias(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tenso
// CHECK: [[ADD:%.+]] = arith.addf %[[ARG3]], %[[ARG4]] : f32
// CHECK: linalg.yield [[ADD]] : f32
// CHECK: } -> tensor<1x5x5x33xf32>
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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>
%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>
return
}
@@ -706,8 +706,8 @@ func.func @depthwise_conv_dyn(%arg0 : tensor<?x7x5x3xf32>, %arg1 : tensor<3x1x3x
// CHECK: %[[ADD:.+]] = arith.addf %[[ARG3]], %[[ARG4]] : f32
// CHECK: linalg.yield %[[ADD]] : f32
// CHECK: } -> tensor<?x5x5x33xf32>
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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>
%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>
return
}
@@ -730,8 +730,8 @@ func.func @depthwise_conv_strides(%arg0 : tensor<1x11x9x3xf32>, %arg1 : tensor<3
// CHECK: [[ADD:%.+]] = arith.addf %[[ARG3]], %[[ARG4]] : f32
// CHECK: linalg.yield [[ADD]] : f32
// CHECK: } -> tensor<1x5x5x33xf32>
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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>
%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>
return
}
@@ -760,8 +760,8 @@ func.func @depthwise_conv_quant(%arg0 : tensor<1x12x12x4xi8>, %arg1 : tensor<3x3
// CHECK: [[ADD:%.+]] = arith.addi %[[ARG3]], %[[ARG4]] : i32
// CHECK: linalg.yield [[ADD]] : i32
// CHECK: } -> tensor<1x12x12x512xi32>
- %input_zp = "tosa.const"() <{value = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() <{value = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>
return
}
@@ -786,8 +786,8 @@ func.func @depthwise_conv_quant_dilations(%arg0 : tensor<1x14x14x4xi8>, %arg1 :
// CHECK: [[ADD:%.+]] = arith.addi %[[ARG3]], %[[ARG4]] : i32
// CHECK: linalg.yield [[ADD]] : i32
// CHECK: } -> tensor<1x10x10x512xi32>
- %input_zp = "tosa.const"() <{value = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() <{value = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>
return
}
@@ -804,8 +804,8 @@ func.func @depthwise_conv2d_dyn_w_h(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<3x
// CHECK: } : tensor<2x?x?x3xf32> to tensor<2x?x?x3xf32>
// CHECK: %[[CONV:.+]] = linalg.depthwise_conv_2d_nhwc_hwcm {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<[1, 2]> : tensor<2xi64>} ins(%[[PADDED]], %arg1 : tensor<2x?x?x3xf32>, tensor<3x6x3x5xf32>) outs(%{{.*}} : tensor<2x?x?x3x5xf32>) -> tensor<2x?x?x3x5xf32>
// CHECK: %[[COLLAPSED:.+]] = tensor.collapse_shape %[[CONV]] {{\[}}[0], [1], [2], [3, 4]]
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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.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>
return
}
@@ -829,8 +829,8 @@ func.func @conv3d_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<28x3x4
// CHECK-SAME: {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
// CHECK-SAME: ins(%arg0, %[[TRANSPOSE]] : tensor<1x49x48x47x27xf32>, tensor<3x4x5x27x28xf32>)
// CHECK-SAME: outs(%[[BROADCAST]] : tensor<1x47x45x43x28xf32>) -> tensor<1x47x45x43x28xf32>
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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.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<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x47x45x43x28xf32>
return
}
@@ -845,8 +845,8 @@ func.func @conv3d_scalar_bias_f32(%input: tensor<1x49x48x47x27xf32>, %weights: t
// CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x28xf32>
// CHECK: %[[BROADCAST:.+]] = linalg.generic
// CHECK-SAME: {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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.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>
return
}
@@ -874,8 +874,8 @@ func.func @conv3d_i8(%input: tensor<1x49x48x47x27xi8>, %weights: tensor<28x3x4x5
// CHECK-SAME: ins(%arg0, %[[TRANSPOSE]], %[[IZP]], %[[FZP]] : tensor<1x49x48x47x27xi8>, tensor<3x4x5x27x28xi8>, i32, i32)
// CHECK-SAME: outs(%[[BROADCAST]] : tensor<1x47x45x43x28xi32>) -> tensor<1x47x45x43x28xi32>
- %input_zp = "tosa.const"() <{value = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() <{value = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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<28x3x4x5x27xi8>, tensor<28xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x47x45x43x28xi32>
return
}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
index 6ca260a5324a9..68e91d7bcb741 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
@@ -472,7 +472,7 @@ func.func @test_simple_f32(%arg0: tensor<1xf32>) -> () {
// CHECK: linalg.generic
// CHECK: arith.mulf
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%4 = tosa.mul %0, %1, %shift : (tensor<1xf32>, tensor<1xf32>, tensor<1xi8>) -> tensor<1xf32>
// CHECK: linalg.generic
@@ -619,7 +619,7 @@ func.func @test_simple_i16(%arg0: tensor<1xi16>) -> () {
// CHECK: arith.extsi
// CHECK: arith.extsi
// CHECK: arith.muli
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%0 = tosa.mul %arg0, %arg0, %shift : (tensor<1xi16>, tensor<1xi16>, tensor<1xi8>) -> tensor<1xi32>
return
@@ -648,13 +648,13 @@ func.func @test_simple_i32(%arg0: tensor<1xi32>, %unsigned: tensor<1xui32>, %uns
// CHECK: linalg.generic
// CHECK: arith.muli
- %shift1 = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift1 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%2 = tosa.mul %arg0, %arg0, %shift1 : (tensor<1xi32>, tensor<1xi32>, tensor<1xi8>) -> tensor<1xi32>
// CHECK: linalg.generic
// CHECK: arith.constant 2
// CHECK: apply_scale
- %shift2 = "tosa.const"() <{value = dense<2> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift2 = "tosa.const"() <{values = dense<2> : tensor<1xi8>}> : () -> tensor<1xi8>
%3 = tosa.mul %arg0, %arg0, %shift2: (tensor<1xi32>, tensor<1xi32>, tensor<1xi8>) -> tensor<1xi32>
// CHECK: linalg.generic
diff --git a/mlir/test/Conversion/TosaToSCF/tosa-to-scf.mlir b/mlir/test/Conversion/TosaToSCF/tosa-to-scf.mlir
index 519f5c9bbe58c..fa7a91cda0a47 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"() {value = 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"() {value = 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/TosaToTensor/tosa-to-tensor.mlir b/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
index c7a689f5a9ae9..a5b9850e99829 100644
--- a/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
+++ b/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
@@ -498,7 +498,7 @@ func.func @slice_dyn(%arg0: tensor<?xf32>) -> (tensor<?xf32>) {
// CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]:
func.func @pad_float(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
%0 = tosa.const_shape {value = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {value = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %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
@@ -514,7 +514,7 @@ func.func @pad_float(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
func.func @pad_int(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) {
%0 = tosa.const_shape {value = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {value = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
+ %pad_const = "tosa.const"() {values = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
// CHECK: [[CST:%.+]] = arith.constant 3 : i32
// CHECK: tensor.pad
// CHECK: tensor.yield [[CST]]
@@ -525,7 +525,7 @@ func.func @pad_int(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) {
func.func @pad_quant(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) {
%0 = tosa.const_shape {value = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %pad_const = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
// CHECK: [[CST:%.+]] = arith.constant 0 : i32
// CHECK: tensor.pad
// CHECK: tensor.yield [[CST]]
@@ -554,7 +554,7 @@ func.func @pad_float_explicit(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
func.func @pad_dyn_input(%arg0 : tensor<?x2xf32>) -> (tensor<?x9xf32>) {
%0 = tosa.const_shape {value = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {value = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %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
@@ -570,7 +570,7 @@ func.func @pad_dyn_input(%arg0 : tensor<?x2xf32>) -> (tensor<?x9xf32>) {
func.func @pad_dyn_padding(%arg0 : tensor<1x2xf32>) -> (tensor<?x9xf32>) {
%0 = tosa.const_shape {value = dense<[-1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {value = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %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
diff --git a/mlir/test/Dialect/Tosa/availability.mlir b/mlir/test/Dialect/Tosa/availability.mlir
index 28fe8c5003d3c..14eca8b4e1824 100644
--- a/mlir/test/Dialect/Tosa/availability.mlir
+++ b/mlir/test/Dialect/Tosa/availability.mlir
@@ -19,8 +19,8 @@ func.func @test_argmax(%arg0: tensor<14x19xf32>) -> tensor<14xi32> {
func.func @test_avg_pool2d(%arg0: tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32> {
// CHECK: profiles: [ [pro_int, pro_fp] ]
// CHECK: extensions: [ [int16, fp8e4m3, fp8e5m2, bf16] ]
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %output_zp = "tosa.const"() <{value = 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>
%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>
return %0 : tensor<1x7x7x9xf32>
}
@@ -30,8 +30,8 @@ func.func @test_avg_pool2d(%arg0: tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32> {
func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
// CHECK: profiles: [ [pro_int, pro_fp] ]
// CHECK: extensions: [ [int4, int16, fp8e4m3, fp8e5m2, bf16] ]
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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, %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<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -41,8 +41,8 @@ func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %
func.func @test_conv3d(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<34xf32>) -> tensor<1x4x8x21x34xf32> {
// CHECK: profiles: [ [pro_int, pro_fp] ]
// CHECK: extensions: [ [int4, int16, fp8e4m3, fp8e5m2, bf16] ]
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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.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<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<34xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
@@ -52,8 +52,8 @@ func.func @test_conv3d(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17
func.func @test_depthwise_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x4x2xf32>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
// CHECK: profiles: [ [pro_int, pro_fp] ]
// CHECK: extensions: [ [int4, int16, fp8e4m3, fp8e5m2, bf16] ]
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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.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<1x4x4x4xf32>, tensor<1x1x4x2xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -99,8 +99,8 @@ func.func @test_rfft2d(%arg0: tensor<13x8x16xf32>) -> (tensor<13x8x9xf32>, tenso
func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// CHECK: profiles: [ [pro_int, pro_fp] ]
// CHECK: extensions: [ [int4, int16, fp8e4m3, fp8e5m2, bf16] ]
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {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>
return %0 : tensor<1x32x32x16xf32>
}
@@ -260,7 +260,7 @@ func.func @test_min(%arg0: tensor<13x21x3xf32>, %arg1: tensor<1x21x3xf32>) -> te
// -----
// CHECK-LABEL: mul
func.func @test_mul(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf32>) -> tensor<13x21x3xf32> {
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: profiles: [ [pro_int, pro_fp] ]
// CHECK: extensions: [ [bf16] ]
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<13x21x3xf32>, tensor<13x1x3xf32>, tensor<1xi8>) -> tensor<13x21x3xf32>
@@ -514,7 +514,7 @@ func.func @test_concat(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>) -
// CHECK-LABEL: pad
func.func @test_pad(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
%padding = tosa.const_shape {value = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
- %pad_const = "tosa.const"() {value = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
// CHECK: profiles: [ [pro_int, pro_fp] ]
// CHECK: extensions: [ [fp8e4m3, fp8e5m2, bf16] ]
%0 = tosa.pad %arg0, %padding, %pad_const : (tensor<13x21x3xf32>, !tosa.shape<6>, tensor<1xf32>) -> tensor<13x21x3xf32>
@@ -623,7 +623,7 @@ func.func @test_rescale(%arg0: tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439
func.func @test_const(%arg0 : index) -> tensor<4xi32> {
// CHECK: profiles: [ [pro_int, pro_fp] ]
// CHECK: extensions: [ [int4, int16, fp8e4m3, fp8e5m2, bf16] ]
- %0 = "tosa.const"() {value = 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>
}
@@ -654,7 +654,7 @@ 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"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
// CHECK: profiles: [ ]
// CHECK: extensions: [ [controlflow] ]
%1:3 = tosa.while_loop (%arg2 = %0, %arg3 = %0, %arg4 = %arg0) : (tensor<i32>, tensor<i32>, tensor<10xi32>) -> (tensor<i32>, tensor<i32>, tensor<10xi32>) {
@@ -663,7 +663,7 @@ 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"() {value = 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 {value = dense<[1]> : tensor<1xindex>} : () -> !tosa.shape<1>
%4 = tosa.reshape %2, %7 : (tensor<i32>, !tosa.shape<1>) -> tensor<1xi32>
diff --git a/mlir/test/Dialect/Tosa/canonicalize.mlir b/mlir/test/Dialect/Tosa/canonicalize.mlir
index 87f766916485a..01650bcbf39bd 100644
--- a/mlir/test/Dialect/Tosa/canonicalize.mlir
+++ b/mlir/test/Dialect/Tosa/canonicalize.mlir
@@ -13,7 +13,7 @@ func.func @argmax_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xi32> {
func.func @add_bcast_zero_int(%arg0: tensor<4x2x3xi32>) -> tensor<4x2x3xi32> {
// CHECK-NOT: tosa.add
// CHECK: return %arg0
- %zeros = "tosa.const"() {value = 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>
}
@@ -24,7 +24,7 @@ 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"() {value = 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>
}
@@ -203,10 +203,10 @@ 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"() {value = dense<[[[[1.0, 1.0]]], [[[1.0, 1.0]]], [[[1.0, 1.0]]]]> : tensor<3x1x1x2xf32>} : ()-> tensor<3x1x1x2xf32>
- %bias = "tosa.const"() {value = dense<0.0> : tensor<3xf32>} : ()-> tensor<3xf32>
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
+ %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>
return %0 : tensor<4x6x6x3xf32>
}
@@ -216,10 +216,10 @@ 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"() {value = dense<[[[[1.0], [1.0]], [[1.0], [1.0]]]]> : tensor<1x2x2x1xf32>} : ()-> tensor<1x2x2x1xf32>
- %bias = "tosa.const"() {value = dense<0.0> : tensor<1xf32>} : ()-> tensor<1xf32>
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = 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>
return %0 : tensor<4x9x9x1xf32>
}
@@ -258,7 +258,7 @@ func.func @max_pool2d_is_noop(%arg0: tensor<10x1x1x3xf32>) -> tensor<10x1x1x3xf3
func.func @pad_noop(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: return %arg0
%0 = tosa.const_shape { value = dense<0> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {value = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %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>
}
@@ -270,7 +270,7 @@ func.func @pad_noop_padding_mismatch_nofold(%arg0: tensor<?x?xf32>) -> tensor<?x
// CHECK: %[[PAD:.+]] = tosa.pad
// CHECK: return %[[PAD]]
%shape = tosa.const_shape { value = dense<[1, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {value = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %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>
}
@@ -282,7 +282,7 @@ func.func @pad_noop_type_mismatch_nofold(%arg0: tensor<10xf32>) -> tensor<?xf32>
// CHECK: %[[PAD:.+]] = tosa.pad
// CHECK: return %[[PAD]]
%shape = tosa.const_shape { value = dense<[1, 2]> : tensor<2xindex>} : () -> !tosa.shape<2>
- %pad_const = "tosa.const"() {value = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %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>
}
@@ -291,10 +291,10 @@ 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"() <{value = dense<0> : tensor<1xi32>}
+ // CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{values = dense<0> : tensor<1xi32>}
// CHECK-DAG: %[[PADDING:.+]] = tosa.const_shape {value = dense<[1, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: tosa.pad %arg0, %[[PADDING]], %[[ZERO]]
- %pad_const = "tosa.const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
+ %pad_const = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
%0 = tosa.const_shape { value = 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>
@@ -304,10 +304,10 @@ 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"() <{value = dense<3.140000e+00> : tensor<1xf32>}
+ // CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{values = dense<3.140000e+00> : tensor<1xf32>}
// CHECK-DAG: %[[PADDING:.+]] = tosa.const_shape {value = dense<[1, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: tosa.pad %arg0, %[[PADDING]], %[[ZERO]]
- %pad_const = "tosa.const"() {value = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
%0 = tosa.const_shape { value = 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>
@@ -317,10 +317,10 @@ 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"() <{value = dense<3> : tensor<1xi32>}
+ // CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{values = dense<3> : tensor<1xi32>}
// CHECK-DAG: %[[PADDING:.+]] = tosa.const_shape {value = dense<[1, 0, 0, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: tosa.pad %arg0, %[[PADDING]], %[[ZERO]]
- %pad_const = "tosa.const"() {value = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
+ %pad_const = "tosa.const"() {values =dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
%0 = tosa.const_shape { value = 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>
@@ -332,8 +332,8 @@ func.func @pad_determine_val_quant(%arg0: tensor<?x?xi32>, %arg1 : tensor<2x2xi3
func.func @mul_one_float(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
// CHECK: return %arg0
// CHECK-NOT: tosa.mul
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %ones = "tosa.const"() {value = dense<1.0> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>
}
@@ -344,8 +344,8 @@ func.func @mul_one_float(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
func.func @mul_bcast_one_float(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
// CHECK: return %arg0
// CHECK-NOT: tosa.mul
- %ones = "tosa.const"() {value = dense<1.0> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>
}
@@ -356,8 +356,8 @@ func.func @mul_bcast_one_float(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
func.func @mul_one_int(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> {
// CHECK: return %arg0
// CHECK-NOT: tosa.mul
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %ones = "tosa.const"() {value = dense<1> : tensor<2x3xi32>} : () -> tensor<2x3xi32>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>
}
@@ -366,11 +366,11 @@ func.func @mul_one_int(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> {
// CHECK-LABEL: @mul_one_int_and_shift
func.func @mul_one_int_and_shift(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> {
- // CHECK-DAG: %[[VAL_1:.*]] = "tosa.const"() <{value = dense<1> : tensor<2x3xi32>}>
- // CHECK-DAG: %[[VAL_2:.*]] = "tosa.const"() <{value = dense<31> : tensor<1xi8>}>
+ // 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"() {value = dense<1> : tensor<2x3xi32>} : () -> tensor<2x3xi32>
- %shift = "tosa.const"() <{value = dense<31> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>
}
@@ -379,10 +379,10 @@ func.func @mul_one_int_and_shift(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> {
// CHECK-LABEL: @mul_zero_broadcast
func.func @mul_zero_broadcast(%arg0: tensor<2x3xf32>) -> (tensor<2x3xf32>, tensor<2x3xf32>) {
- // CHECK: %[[ZERO:.*]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<2x3xf32>}
+ // CHECK: %[[ZERO:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<2x3xf32>}
// CHECK-NOT: tosa.mul
- %zeros = "tosa.const"() {value = dense<0.0> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>
// CHECK-NOT: tosa.mul
@@ -405,7 +405,7 @@ func.func @select_same_value(%arg0: tensor<2x3xi1>, %arg1: tensor<2x3xi32>) -> t
// CHECK-LABEL: @select_true_value
func.func @select_true_value(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>) -> tensor<2x3xi32> {
- %c1 = "tosa.const"() {value = 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
@@ -416,7 +416,7 @@ func.func @select_true_value(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>) ->
// CHECK-LABEL: @select_false_value
func.func @select_false_value(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>) -> tensor<2x3xi32> {
- %c0 = "tosa.const"() {value = 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
@@ -581,9 +581,9 @@ func.func @reshape_canonicalize_double(%arg0: tensor<?x10xf32>) -> tensor<?x5xf3
// CHECK-LABEL: @reshape_canonicalize_const
func.func @reshape_canonicalize_const() -> tensor<1x5xi32> {
- // CHECK: %[[VAR0:.+]] = "tosa.const"() <{value = dense<{{\[\[}}0, 1, 2, 3, 4]]> : tensor<1x5xi32>}
+ // CHECK: %[[VAR0:.+]] = "tosa.const"() <{values = dense<{{\[\[}}0, 1, 2, 3, 4]]> : tensor<1x5xi32>}
// CHECK: return %[[VAR0]]
- %0 = "tosa.const"() {value = dense<[0, 1, 2, 3, 4]> : tensor<5xi32>} : () -> tensor<5xi32>
+ %0 = "tosa.const"() {values = dense<[0, 1, 2, 3, 4]> : tensor<5xi32>} : () -> tensor<5xi32>
%1 = "tosa.const_shape"() {value = dense<[1, 5]> : tensor<2xindex>} : () -> !tosa.shape<2>
%2 = tosa.reshape %0, %1 : (tensor<5xi32>, !tosa.shape<2>) -> tensor<1x5xi32>
return %2 : tensor<1x5xi32>
@@ -594,7 +594,7 @@ 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"() {value = dense<[0, 1, 2, 3, 4]> : tensor<5xi32>} : () -> tensor<5xi32>
+ %0 = "tosa.const"() {values = dense<[0, 1, 2, 3, 4]> : tensor<5xi32>} : () -> tensor<5xi32>
%2 = "tosa.const_shape"() {value = 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>
@@ -604,10 +604,10 @@ func.func @reshape_canonicalize_const_dynamic() -> tensor<1x?xi32> {
// CHECK-LABEL: @reshape_canonicalize_const_splat
func.func @reshape_canonicalize_const_splat() -> (tensor<10xi32>, tensor<1x10xi32>) {
- // CHECK-DAG: %[[VAR0:.+]] = "tosa.const"() <{value = dense<0> : tensor<10xi32>}
- // CHECK-DAG: %[[VAR1:.+]] = "tosa.const"() <{value = dense<0> : tensor<1x10xi32>}
+ // 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"() {value = dense<0> : tensor<10xi32>} : () -> tensor<10xi32>
+ %0 = "tosa.const"() {values = dense<0> : tensor<10xi32>} : () -> tensor<10xi32>
%2 = "tosa.const_shape"() {value = 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>
@@ -618,7 +618,7 @@ 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"() {value = dense<[1, 2, 3]> : tensor<3xi32>} : ()-> tensor<3xi32>
+ %0 = "tosa.const"() {values = dense<[1, 2, 3]> : tensor<3xi32>} : ()-> tensor<3xi32>
%2 = "tosa.const_shape"() {value = 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>
@@ -629,9 +629,9 @@ func.func @reshape_canonicalize_const_sparse() -> (tensor<3xi32>, tensor<1x3xi32
// CHECK-LABEL: @reshape_canonicalize_quant_nofold
func.func @reshape_canonicalize_quant_nofold() -> (tensor<1x3x!quant.uniform<i8:f32, 1.000000e+00>>) {
// disabled folding for quantized element types
- // CHECK{LITERAL}: "tosa.const"() <{value = dense<[1, 2, 3]> : tensor<3xi8>}> : () -> tensor<3x!quant.uniform<i8:f32, 1.000000e+00>>
+ // 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"() {value = dense<[1, 2, 3]> : tensor<3xi8>} : ()-> tensor<3x!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"() {value = 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>>
@@ -642,9 +642,9 @@ 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 {value = dense<[2, 1, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
- // CHECK-DAG: %[[CONST:.*]] = "tosa.const"() <{value = dense<0> : tensor<1x2x3xi8>}> : () -> tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>
+ // 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"() {value = dense<0> : tensor<1x2x3xi8>} : ()-> tensor<1x2x3x!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>>
return %1 : tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
}
@@ -726,7 +726,7 @@ func.func @transpose_is_reshape_unknown_dim(%arg0: tensor<1x4x?x1xf32>) -> tenso
// CHECK-LABEL: @single_bit_reshape
// https://github.com/llvm/llvm-project/issues/55440
func.func @single_bit_reshape() -> tensor<1xi1> {
- // CHECK: "tosa.const"() <{value = dense<true> : tensor<1xi1>}
+ // CHECK: "tosa.const"() <{values = dense<true> : tensor<1xi1>}
%0 = arith.constant dense<true> : tensor<1x1xi1>
%2 = "tosa.const_shape"() <{value = dense<1> : tensor<1xindex>}> : () -> !tosa.shape<1>
%1 = tosa.reshape %0, %2 : (tensor<1x1xi1>, !tosa.shape<1>) -> tensor<1xi1>
@@ -900,7 +900,7 @@ func.func nested @fold_tile_rank_zero() -> tensor<i32> {
// CHECK-LABEL: @reshape_quant_nofold
// check that segfault is fixed
func.func @reshape_quant_nofold() -> tensor<1x1x1x1xi32> {
- %0 = "tosa.const"() {value = 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>>
%cst0 = "tosa.const_shape"() {value = 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>>
%2 = tosa.rescale %1 {double_round = true, input_zp = -128 : i32, multiplier = array<i32: 1073741824>, output_zp = 0 : i32, per_channel = false, scale32 = true, shift = array<i8: 30>, input_unsigned = false, output_unsigned = false} : (tensor<1x1x1x1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<1x1x1x1xi32>
@@ -912,7 +912,7 @@ 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"() {value = 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>>
}
@@ -922,7 +922,7 @@ 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"() {value = 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>>
}
@@ -931,8 +931,8 @@ 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"() {value = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
- // CHECK: "tosa.const"() <{value = dense<false>
+ %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>
}
@@ -941,8 +941,8 @@ func.func @greater_quant_fold() -> tensor<i1> {
// CHECK-LABEL: @greater_equal_quant_fold
func.func @greater_equal_quant_fold() -> tensor<i1> {
- %0 = "tosa.const"() {value = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
- // CHECK: "tosa.const"() <{value = dense<true>
+ %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>
}
@@ -951,8 +951,8 @@ func.func @greater_equal_quant_fold() -> tensor<i1> {
// CHECK-LABEL: @equal_quant_fold
func.func @equal_quant_fold() -> tensor<i1> {
- %0 = "tosa.const"() {value = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
- // CHECK: "tosa.const"() <{value = dense<true>
+ %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>
}
@@ -962,7 +962,7 @@ func.func @equal_quant_fold() -> tensor<i1> {
// CHECK-LABEL: @cast_quant_nofold
func.func @cast_quant_nofold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:3>> {
// CHECK: tosa.cast
- %0 = "tosa.const"() {value = 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>>
%1 = "tosa.cast"(%0) : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:3>>
return %1 : tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:3>>
}
@@ -971,9 +971,9 @@ func.func @cast_quant_nofold() -> tensor<!quant.uniform<i8:f32, 3.07574046018999
// CHECK-LABEL: @reverse_quant_fold
func.func @reverse_quant_fold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
- // CHECK: %[[CST:.*]] = "tosa.const"() <{value = dense<0> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ // CHECK: %[[CST:.*]] = "tosa.const"() <{values = dense<0> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
// CHECK: return %[[CST]]
- %0 = "tosa.const"() {value = 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>>
%1 = "tosa.reverse"(%0) { axis = 0 : i32 } : (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>>
}
@@ -982,11 +982,11 @@ func.func @reverse_quant_fold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899
// CHECK-LABEL: @select_quant_fold
func.func @select_quant_fold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
- // CHECK: %[[CONST_0:.*]] = "tosa.const"() <{value = dense<0> : tensor<i8>}> : () -> 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"() {value = dense<true> : tensor<i1>} : () -> tensor<i1>
- %1 = "tosa.const"() {value = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
- %2 = "tosa.const"() {value = 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>>
}
@@ -996,9 +996,9 @@ 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"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
- %1 = "tosa.const"() {value = dense<1> : tensor<1xi8>} : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>>
}
@@ -1008,10 +1008,10 @@ func.func @mul_quant_nofold() -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899
// CHECK-LABEL: @fold_reciprocal
func.func nested @fold_reciprocal() -> tensor<3x600x1200xf32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<8.620690e-03> : tensor<3x600x1200xf32>}> : () -> 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"(){ value = dense<116.0>: tensor<f32> }: () -> tensor<f32>
+ %0 = "tosa.const"(){ values = dense<116.0>: tensor<f32> }: () -> tensor<f32>
%1 = "tosa.cast"(%0) : (tensor<f32>) -> tensor<3x600x1200xf32>
%2 = "tosa.reciprocal"(%1): (tensor<3x600x1200xf32>) -> tensor<3x600x1200xf32>
return %2 : tensor<3x600x1200xf32>
@@ -1022,7 +1022,7 @@ 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"(){ value = dense<11>: tensor<i32> }: () -> tensor<i32>
+ %0 = "tosa.const"(){ values = dense<11>: tensor<i32> }: () -> tensor<i32>
%1 = "tosa.cast"(%0) : (tensor<i32>) -> tensor<3x600x1200xi32>
%2 = "tosa.reciprocal"(%1): (tensor<3x600x1200xi32>) -> tensor<3x600x1200xi32>
return %2 : tensor<3x600x1200xi32>
@@ -1033,8 +1033,8 @@ func.func nested @do_not_fold_reciprocal_int() -> tensor<3x600x1200xi32> {
// CHECK-LABEL: @do_not_fold_int_div_division_by_0
func.func @do_not_fold_int_div_division_by_0() -> tensor<1x24x2xi32> {
// CHECK: tosa.int_div
- %1 = "tosa.const"() <{value = dense<0> : tensor<1x24x2xi32>}> : () -> tensor<1x24x2xi32>
- %4 = "tosa.const"() <{value = dense<20> : tensor<1x24x2xi32>}> : () -> tensor<1x24x2xi32>
+ %1 = "tosa.const"() <{values = dense<0> : tensor<1x24x2xi32>}> : () -> tensor<1x24x2xi32>
+ %4 = "tosa.const"() <{values = dense<20> : tensor<1x24x2xi32>}> : () -> tensor<1x24x2xi32>
%16 = tosa.int_div %4, %1 : (tensor<1x24x2xi32>, tensor<1x24x2xi32>) -> tensor<1x24x2xi32>
return %16 : tensor<1x24x2xi32>
}
diff --git a/mlir/test/Dialect/Tosa/constant-op-fold.mlir b/mlir/test/Dialect/Tosa/constant-op-fold.mlir
index db76eb54a54cf..014b5dd1140bb 100644
--- a/mlir/test/Dialect/Tosa/constant-op-fold.mlir
+++ b/mlir/test/Dialect/Tosa/constant-op-fold.mlir
@@ -5,7 +5,7 @@
// CHECK-LABEL: @armax_fold_dim_size_1
func.func @armax_fold_dim_size_1(%arg0: tensor<2x1x3xf32>) -> tensor<2x3xi32> {
- // CHECK: "tosa.const"() <{value = dense<0> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ // CHECK: "tosa.const"() <{values = dense<0> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
%0 = tosa.argmax %arg0 {axis = 1 : i32}: (tensor<2x1x3xf32>) -> tensor<2x3xi32>
return %0 : tensor<2x3xi32>
}
@@ -40,9 +40,9 @@ func.func @transpose_nofold_shape(%arg0: tensor<3x4xf32>) -> tensor<?x?xf32> {
// CHECK-LABEL: @transpose_fold_splat
func.func @transpose_fold_splat() -> tensor<3x2xf32> {
- %input = "tosa.const"() {value = 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}: value = dense<4.000000e+00> : tensor<3x2xf32>
+ // CHECK-SAME{LITERAL}: values = dense<4.000000e+00> : tensor<3x2xf32>
%1 = tosa.transpose %input { perms = array<i32: 1, 0> }: (tensor<2x3xf32>) -> tensor<3x2xf32>
// CHECK: return %[[CST]]
return %1 : tensor<3x2xf32>
@@ -50,9 +50,9 @@ func.func @transpose_fold_splat() -> tensor<3x2xf32> {
// CHECK-LABEL: @transpose_fold_2d_float
func.func @transpose_fold_2d_float() -> tensor<3x2xf32> {
- %input = "tosa.const"() {value = 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}: value = dense<[[0.000000e+00, 3.000000e+00], [1.000000e+00, 4.000000e+00], [2.000000e+00, 5.000000e+00]]> : tensor<3x2xf32>
+ // 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>
// CHECK: return %[[CST]]
return %1 : tensor<3x2xf32>
@@ -60,9 +60,9 @@ 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"() {value = 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}: value = dense<[[true, false], [false, false], [false, true]]> : tensor<3x2xi1>
+ // 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>
// CHECK: return %[[CST]]
return %1 : tensor<3x2xi1>
@@ -70,12 +70,12 @@ func.func @transpose_fold_2d_bool() -> tensor<3x2xi1> {
// CHECK-LABEL: @transpose_fold_4d_int
func.func @transpose_fold_4d_int() -> tensor<3x1x4x2xi32> {
- %input = "tosa.const"() {value = dense<[[
+ %input = "tosa.const"() {values = dense<[[
[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]],
[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]
]]> : tensor<1x2x3x4xi32>} : () -> tensor<1x2x3x4xi32>
// CHECK: %[[CST:.+]] = "tosa.const"() <{
- // CHECK-SAME{LITERAL}: value = dense<[
+ // CHECK-SAME{LITERAL}: values = dense<[
// CHECK-SAME{LITERAL}: [[[0, 12], [1, 13], [2, 14], [3, 15]]],
// CHECK-SAME{LITERAL}: [[[4, 16], [5, 17], [6, 18], [7, 19]]],
// CHECK-SAME{LITERAL}: [[[8, 20], [9, 21], [10, 22], [11, 23]]]
@@ -94,7 +94,7 @@ 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"() {value = 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>
return %1, %input : tensor<3x2xf32>, tensor<2x3xf32>
@@ -102,7 +102,7 @@ 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"() {value = 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}>>
return %0: tensor<1x1x2x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>>
@@ -110,7 +110,7 @@ func.func @transpose_nofold_quantized_types() -> tensor<1x1x2x2x!quant.uniform<i
// CHECK-LABEL: @transpose_nofold_dense_resource
func.func @transpose_nofold_dense_resource() -> tensor<2x2xf32> {
- %0 = "tosa.const"() <{value = dense_resource<resource> : tensor<2x2xf32>}> : () -> tensor<2x2xf32>
+ %0 = "tosa.const"() <{values = dense_resource<resource> : tensor<2x2xf32>}> : () -> tensor<2x2xf32>
// CHECK: tosa.transpose
%2 = tosa.transpose %0 { perms = array<i32: 1, 0> }: (tensor<2x2xf32>) -> tensor<2x2xf32>
@@ -128,7 +128,7 @@ func.func @transpose_nofold_dense_resource() -> tensor<2x2xf32> {
// CHECK-LABEL: @fold_add_zero_rhs_f32
func.func @fold_add_zero_rhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
- %zero = "tosa.const"() {value = 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>
@@ -138,7 +138,7 @@ 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"() {value = 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>
@@ -148,7 +148,7 @@ 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"() {value = 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>
@@ -158,7 +158,7 @@ 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"() {value = 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>
@@ -168,10 +168,10 @@ 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"() {value = dense<1> : tensor<10xi32>} : () -> tensor<10xi32>
- %two = "tosa.const"() {value = 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"() <{value = dense<3> : tensor<10xi32>}
+ // CHECK: %[[THREE:.+]] = "tosa.const"() <{values = dense<3> : tensor<10xi32>}
// CHECK: return %[[THREE]]
return %add : tensor<10xi32>
}
@@ -180,10 +180,10 @@ 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"() {value = dense<1.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %two = "tosa.const"() {value = 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"() <{value = dense<3.000000e+00>
+ // CHECK: %[[THREE:.+]] = "tosa.const"() <{values = dense<3.000000e+00>
// CHECK: return %[[THREE]]
return %add : tensor<10xf32>
}
@@ -192,8 +192,8 @@ func.func @fold_add_splat_f32() -> tensor<10xf32> {
// CHECK-LABEL: @fold_div_zero_lhs_i32
func.func @fold_div_zero_lhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
- %zero = "tosa.const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
- // CHECK: %[[ZERO:.+]] = "tosa.const"() <{value = dense<0>
+ %zero = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
+ // CHECK: %[[ZERO:.+]] = "tosa.const"() <{values = dense<0>
%div = tosa.int_div %zero, %arg0 : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK: return %[[ZERO]]
return %div : tensor<i32>
@@ -203,7 +203,7 @@ func.func @fold_div_zero_lhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
// CHECK-LABEL: @fold_div_one_rhs_i32
func.func @fold_div_one_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
- %one = "tosa.const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
+ %one = "tosa.const"() {values = dense<1> : tensor<i32>} : () -> tensor<i32>
%div = tosa.int_div %arg0, %one : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK: return %arg0
return %div : tensor<i32>
@@ -213,9 +213,9 @@ func.func @fold_div_one_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
// CHECK-LABEL: @fold_div_splat_i32
func.func @fold_div_splat_i32() -> tensor<i32> {
- %lhs = "tosa.const"() {value = dense<10> : tensor<i32>} : () -> tensor<i32>
- %rhs = "tosa.const"() {value = dense<-3> : tensor<i32>} : () -> tensor<i32>
- // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{value = dense<-3>
+ %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.int_div %lhs, %rhs : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK: return %[[SPLAT]]
return %div : tensor<i32>
@@ -226,9 +226,9 @@ func.func @fold_div_splat_i32() -> tensor<i32> {
// CHECK-LABEL: @fold_mul_zero_rhs_f32
func.func @fold_mul_zero_rhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
- %zero = "tosa.const"() {value = dense<0.0> : tensor<f32>} : () -> tensor<f32>
- // CHECK: %[[ZERO:.+]] = "tosa.const"() <{value = dense<0.000000e+00>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>
// CHECK: return %[[ZERO]]
return %mul : tensor<f32>
@@ -238,9 +238,9 @@ 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"() {value = dense<0.0> : tensor<f32>} : () -> tensor<f32>
- // CHECK: %[[ZERO:.+]] = "tosa.const"() <{value = dense<0.000000e+00>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>
// CHECK: return %[[ZERO]]
return %mul : tensor<f32>
@@ -250,9 +250,9 @@ 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"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- // CHECK: %[[ZERO:.+]] = "tosa.const"() <{value = dense<0>
+ %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>
// CHECK: return %[[ZERO]]
return %mul : tensor<i32>
@@ -262,9 +262,9 @@ 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"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- // CHECK: %[[ZERO:.+]] = "tosa.const"() <{value = dense<0>
+ %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>
// CHECK: return %[[ZERO]]
return %mul : tensor<i32>
@@ -274,8 +274,8 @@ 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"() {value = dense<1.0> : tensor<f32>} : () -> tensor<f32>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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
return %mul : tensor<f32>
@@ -285,8 +285,8 @@ func.func @fold_mul_one_rhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
// CHECK-LABEL: @fold_mul_one_lhs_f32
func.func @fold_mul_one_lhs_f32(%arg0: tensor<f32>) -> tensor<f32> {
- %one = "tosa.const"() {value = dense<1.0> : tensor<f32>} : () -> tensor<f32>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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
return %mul : tensor<f32>
@@ -296,8 +296,8 @@ 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"() {value = dense<64> : tensor<i32>} : () -> tensor<i32>
- %shift = "tosa.const"() {value = 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>
@@ -307,8 +307,8 @@ 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"() {value = dense<64> : tensor<i32>} : () -> tensor<i32>
- %shift = "tosa.const"() {value = 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>
@@ -318,11 +318,11 @@ func.func @fold_mul_one_lhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
// CHECK-LABEL: @fold_mul_splat_i8
func.func @fold_mul_splat_i8() -> tensor<10xi32> {
- %one = "tosa.const"() {value = dense<17> : tensor<10xi8>} : () -> tensor<10xi8>
- %two = "tosa.const"() {value = dense<32> : tensor<10xi8>} : () -> tensor<10xi8>
- %shift = "tosa.const"() {value = 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: %[[THREE:.+]] = "tosa.const"() <{value = dense<68> : tensor<10xi32>}
+ // CHECK: %[[THREE:.+]] = "tosa.const"() <{values = dense<68> : tensor<10xi32>}
// CHECK: return %[[THREE]]
return %mul : tensor<10xi32>
}
@@ -331,11 +331,11 @@ 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"() {value = dense<3.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %two = "tosa.const"() {value = dense<2.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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: %[[THREE:.+]] = "tosa.const"() <{value = dense<6.000000e+00> : tensor<10xf32>}
+ // CHECK: %[[THREE:.+]] = "tosa.const"() <{values = dense<6.000000e+00> : tensor<10xf32>}
// CHECK: return %[[THREE]]
return %mul : tensor<10xf32>
}
@@ -344,7 +344,7 @@ 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"() {value = 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>
@@ -354,7 +354,7 @@ 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"() {value = 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>
@@ -364,10 +364,10 @@ func.func @fold_sub_zero_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
// CHECK-LABEL: @fold_sub_splat_i32
func.func @fold_sub_splat_i32() -> tensor<10xi32> {
- %one = "tosa.const"() {value = dense<1> : tensor<10xi32>} : () -> tensor<10xi32>
- %two = "tosa.const"() {value = 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: %[[THREE:.+]] = "tosa.const"() <{value = dense<-1> : tensor<10xi32>}
+ // CHECK: %[[THREE:.+]] = "tosa.const"() <{values = dense<-1> : tensor<10xi32>}
// CHECK: return %[[THREE]]
return %sub : tensor<10xi32>
}
@@ -376,10 +376,10 @@ 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"() {value = dense<1.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %two = "tosa.const"() {value = 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: %[[THREE:.+]] = "tosa.const"() <{value = dense<-1.000000e+00> : tensor<10xf32>}
+ // CHECK: %[[THREE:.+]] = "tosa.const"() <{values = dense<-1.000000e+00> : tensor<10xf32>}
// CHECK: return %[[THREE]]
return %sub : tensor<10xf32>
}
@@ -388,14 +388,14 @@ func.func @fold_sub_splat_f32() -> tensor<10xf32> {
// CHECK-LABEL: @fold_greater_splat_f32
func.func @fold_greater_splat_f32() -> (tensor<10xi1>, tensor<10xi1>) {
- %0 = "tosa.const"() {value = dense<4.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %1 = "tosa.const"() {value = dense<2.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %2 = "tosa.const"() {value = dense<1.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %3 = "tosa.const"() {value = 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"() <{value = dense<true> : tensor<10xi1>}
- // CHECK-DAG: %[[FALSE:.+]] = "tosa.const"() <{value = dense<false> : tensor<10xi1>}
+ // CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
+ // CHECK-DAG: %[[FALSE:.+]] = "tosa.const"() <{values = dense<false> : tensor<10xi1>}
// CHECK: return %[[TRUE]], %[[FALSE]]
return %true, %false : tensor<10xi1>, tensor<10xi1>
}
@@ -404,14 +404,14 @@ 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"() {value = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
- %1 = "tosa.const"() {value = dense<8> : tensor<10xi32>} : () -> tensor<10xi32>
- %2 = "tosa.const"() {value = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
- %3 = "tosa.const"() {value = 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"() <{value = dense<false> : tensor<10xi1>}
- // CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{value = dense<true> : tensor<10xi1>}
+ // CHECK-DAG: %[[FALSE:.+]] = "tosa.const"() <{values = dense<false> : tensor<10xi1>}
+ // CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
// CHECK: return %[[FALSE]], %[[TRUE]]
return %false, %true : tensor<10xi1>, tensor<10xi1>
}
@@ -420,14 +420,14 @@ func.func @fold_greater_splat_i32() -> (tensor<10xi1>, tensor<10xi1>) {
// CHECK-LABEL: @fold_greater_eq_splat_f32
func.func @fold_greater_eq_splat_f32() -> (tensor<10xi1>, tensor<10xi1>) {
- %0 = "tosa.const"() {value = dense<4.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %1 = "tosa.const"() {value = dense<4.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %2 = "tosa.const"() {value = dense<1.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %3 = "tosa.const"() {value = 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"() <{value = dense<true> : tensor<10xi1>}
- // CHECK-DAG: %[[FALSE:.+]] = "tosa.const"() <{value = dense<false> : tensor<10xi1>}
+ // CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
+ // CHECK-DAG: %[[FALSE:.+]] = "tosa.const"() <{values = dense<false> : tensor<10xi1>}
// CHECK: return %[[TRUE]], %[[FALSE]]
return %true, %false : tensor<10xi1>, tensor<10xi1>
}
@@ -436,14 +436,14 @@ 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"() {value = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
- %1 = "tosa.const"() {value = dense<8> : tensor<10xi32>} : () -> tensor<10xi32>
- %2 = "tosa.const"() {value = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
- %3 = "tosa.const"() {value = 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"() <{value = dense<true> : tensor<10xi1>}
- // CHECK-DAG: %[[FALSE:.+]] = "tosa.const"() <{value = dense<false> : tensor<10xi1>}
+ // CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
+ // CHECK-DAG: %[[FALSE:.+]] = "tosa.const"() <{values = dense<false> : tensor<10xi1>}
// CHECK: return %[[TRUE]], %[[FALSE]]
return %true, %false : tensor<10xi1>, tensor<10xi1>
}
@@ -452,14 +452,14 @@ func.func @fold_greater_eq_splat_i32() -> (tensor<10xi1>, tensor<10xi1>) {
// CHECK-LABEL: @fold_eq_splat_f32
func.func @fold_eq_splat_f32() -> (tensor<10xi1>, tensor<10xi1>) {
- %0 = "tosa.const"() {value = dense<4.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %1 = "tosa.const"() {value = dense<4.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %2 = "tosa.const"() {value = dense<1.0> : tensor<10xf32>} : () -> tensor<10xf32>
- %3 = "tosa.const"() {value = 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"() <{value = dense<true> : tensor<10xi1>}
- // CHECK-DAG: %[[FALSE:.+]] = "tosa.const"() <{value = dense<false> : tensor<10xi1>}
+ // CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
+ // CHECK-DAG: %[[FALSE:.+]] = "tosa.const"() <{values = dense<false> : tensor<10xi1>}
// CHECK: return %[[TRUE]], %[[FALSE]]
return %true, %false : tensor<10xi1>, tensor<10xi1>
}
@@ -468,14 +468,14 @@ func.func @fold_eq_splat_f32() -> (tensor<10xi1>, tensor<10xi1>) {
// CHECK-LABEL: @fold_eq_splat_i32
func.func @fold_eq_splat_i32() -> (tensor<10xi1>, tensor<10xi1>) {
- %0 = "tosa.const"() {value = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
- %1 = "tosa.const"() {value = dense<8> : tensor<10xi32>} : () -> tensor<10xi32>
- %2 = "tosa.const"() {value = dense<-10> : tensor<10xi32>} : () -> tensor<10xi32>
- %3 = "tosa.const"() {value = 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"() <{value = dense<true> : tensor<10xi1>}
- // CHECK-DAG: %[[FALSE:.+]] = "tosa.const"() <{value = dense<false> : tensor<10xi1>}
+ // CHECK-DAG: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
+ // CHECK-DAG: %[[FALSE:.+]] = "tosa.const"() <{values = dense<false> : tensor<10xi1>}
// CHECK: return %[[TRUE]], %[[FALSE]]
return %true, %false : tensor<10xi1>, tensor<10xi1>
}
@@ -484,7 +484,7 @@ func.func @fold_eq_splat_i32() -> (tensor<10xi1>, tensor<10xi1>) {
// CHECK-LABEL: @fold_eq_i32
func.func @fold_eq_i32(%arg0 : tensor<10xi32>) -> (tensor<10xi1>) {
- // CHECK: %[[TRUE:.+]] = "tosa.const"() <{value = dense<true> : tensor<10xi1>}
+ // CHECK: %[[TRUE:.+]] = "tosa.const"() <{values = dense<true> : tensor<10xi1>}
%0 = tosa.equal %arg0, %arg0 : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi1>
// CHECK: return %[[TRUE]]
return %0 : tensor<10xi1>
@@ -493,8 +493,8 @@ func.func @fold_eq_i32(%arg0 : tensor<10xi32>) -> (tensor<10xi1>) {
// -----
func.func @reshape_splat() -> tensor<6x5x4xi32> {
- // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{value = dense<42> : tensor<6x5x4xi32>}
- %splat = "tosa.const"() {value = dense<42> : tensor<4x5x6xi32>} : () -> tensor<4x5x6xi32>
+ // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<42> : tensor<6x5x4xi32>}
+ %splat = "tosa.const"() {values = dense<42> : tensor<4x5x6xi32>} : () -> tensor<4x5x6xi32>
%const = tosa.const_shape {value = dense<[6, 5, 4]> : tensor<3xindex>} : () -> !tosa.shape<3>
%reshape = tosa.reshape %splat, %const : (tensor<4x5x6xi32>, !tosa.shape<3>) -> tensor<6x5x4xi32>
// CHECK: return %[[SPLAT]]
@@ -505,8 +505,8 @@ func.func @reshape_splat() -> tensor<6x5x4xi32> {
// CHECK-LABEL: @slice_splat
func.func @slice_splat() -> tensor<1x1x1xi32> {
- // CHECK: %[[SLICE:.+]] = "tosa.const"() <{value = dense<42> : tensor<1x1x1xi32>}
- %splat = "tosa.const"() {value = dense<42> : tensor<4x5x6xi32>} : () -> tensor<4x5x6xi32>
+ // CHECK: %[[SLICE:.+]] = "tosa.const"() <{values = dense<42> : tensor<1x1x1xi32>}
+ %splat = "tosa.const"() {values = dense<42> : tensor<4x5x6xi32>} : () -> tensor<4x5x6xi32>
%start = tosa.const_shape {value = dense<[1, 2, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
%size = tosa.const_shape {value = 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>
@@ -519,8 +519,8 @@ func.func @slice_splat() -> tensor<1x1x1xi32> {
// CHECK-LABEL: @slice_singleton
func.func @slice_singleton() -> tensor<1x1xi32> {
- %splat = "tosa.const"() {value = dense<[[0, 1, 2], [3, 4, 5], [6, 7 ,8]]> : tensor<3x3xi32>} : () -> tensor<3x3xi32>
- // CHECK: %[[SLICE:.+]] = "tosa.const"() <{value = dense<4> : tensor<1x1xi32>}
+ %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 {value = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
%size = tosa.const_shape {value = dense<[1, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
%slice= tosa.slice %splat, %start, %size : (tensor<3x3xi32>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x1xi32>
@@ -532,8 +532,8 @@ func.func @slice_singleton() -> tensor<1x1xi32> {
// CHECK: func.func @cast_float_to_float
func.func @cast_float_to_float() -> tensor<f16> {
- %splat = "tosa.const"() {value = dense<42.0> : tensor<f32>} : () -> tensor<f32>
- // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{value = dense<4.200000e+01> : tensor<f16>}
+ %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 : (tensor<f32>) -> tensor<f16>
// CHECK: return %[[SPLAT]]
return %cast : tensor<f16>
@@ -543,8 +543,8 @@ 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"() {value = dense<4> : tensor<i32>} : () -> tensor<i32>
- // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{value = dense<4.000000e+00> : tensor<f16>}
+ %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 : (tensor<i32>) -> tensor<f16>
// CHECK: return %[[SPLAT]]
return %cast : tensor<f16>
@@ -554,8 +554,8 @@ func.func @cast_int_to_float() -> tensor<f16> {
// CHECK: func.func @cast_float_to_int
func.func @cast_float_to_int() -> tensor<i16> {
- %splat = "tosa.const"() {value = dense<-4.0> : tensor<f32>} : () -> tensor<f32>
- // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{value = dense<-4> : tensor<i16>}
+ %splat = "tosa.const"() {values = dense<-4.0> : tensor<f32>} : () -> tensor<f32>
+ // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<-4> : tensor<i16>}
%cast = tosa.cast %splat : (tensor<f32>) -> tensor<i16>
// CHECK: return %[[SPLAT]]
return %cast : tensor<i16>
@@ -565,8 +565,8 @@ 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"() {value = dense<-3.5> : tensor<f32>} : () -> tensor<f32>
- // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{value = dense<-4> : tensor<i16>}
+ %splat = "tosa.const"() {values = dense<-3.5> : tensor<f32>} : () -> tensor<f32>
+ // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<-4> : tensor<i16>}
%cast = tosa.cast %splat : (tensor<f32>) -> tensor<i16>
// CHECK: return %[[SPLAT]]
return %cast : tensor<i16>
@@ -576,8 +576,8 @@ func.func @cast_float_to_int_round() -> tensor<i16> {
// CHECK: func.func @cast_int_to_int_trunc
func.func @cast_int_to_int_trunc() -> tensor<i16> {
- %splat = "tosa.const"() {value = dense<-1> : tensor<i32>} : () -> tensor<i32>
- // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{value = dense<-1> : tensor<i16>}
+ %splat = "tosa.const"() {values = dense<-1> : tensor<i32>} : () -> tensor<i32>
+ // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<-1> : tensor<i16>}
%cast = tosa.cast %splat : (tensor<i32>) -> tensor<i16>
// CHECK: return %[[SPLAT]]
return %cast : tensor<i16>
@@ -587,8 +587,8 @@ 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"() {value = dense<-1> : tensor<i16>} : () -> tensor<i16>
- // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{value = dense<-1> : tensor<i32>}
+ %splat = "tosa.const"() {values = dense<-1> : tensor<i16>} : () -> tensor<i16>
+ // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{values = dense<-1> : tensor<i32>}
%cast = tosa.cast %splat : (tensor<i16>) -> tensor<i32>
// CHECK: return %[[SPLAT]]
return %cast : tensor<i32>
@@ -598,8 +598,8 @@ func.func @cast_int_to_int_sign() -> tensor<i32> {
// CHECK-LABEL: @reverse_splat
func.func @reverse_splat() -> tensor<10xi32> {
- // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{value = dense<42> : tensor<10xi32>}
- %splat = "tosa.const"() {value = dense<42> : tensor<10xi32>} : () -> 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>
// CHECK: return %[[SPLAT]]
return %reverse : tensor<10xi32>
@@ -620,10 +620,10 @@ func.func @reverse_length_one(%arg0 : tensor<10x1xi32>) -> (tensor<10x1xi32>, te
func.func @reduce_sum_constant() -> tensor<1x3xi32> {
// CHECK-LABEL: func.func @reduce_sum_constant() -> tensor<1x3xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}5, 7, 9]]> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}5, 7, 9]]> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
// CHECK: return %[[VAL_0]] : tensor<1x3xi32>
- %const = "tosa.const"() {value = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>} : () -> tensor<2x3xi32>
+ %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>
return %0 : tensor<1x3xi32>
}
@@ -632,10 +632,10 @@ func.func @reverse_length_one(%arg0 : tensor<10x1xi32>) -> (tensor<10x1xi32>, te
func.func @reduce_sum_constant() -> tensor<2x1xi32> {
// CHECK-LABEL: func.func @reduce_sum_constant() -> tensor<2x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}6], [15]]> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}6], [15]]> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
// CHECK: return %[[VAL_0]] : tensor<2x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ %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>
return %0 : tensor<2x1xi32>
}
@@ -645,10 +645,10 @@ func.func @reverse_length_one(%arg0 : tensor<10x1xi32>) -> (tensor<10x1xi32>, te
func.func @reduce_sum_constant() -> tensor<3x1xi32> {
// CHECK-LABEL: func.func @reduce_sum_constant() -> tensor<3x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}6], [15], [24]]> : tensor<3x1xi32>}> : () -> tensor<3x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}6], [15], [24]]> : tensor<3x1xi32>}> : () -> tensor<3x1xi32>
// CHECK: return %[[VAL_0]] : tensor<3x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[1, 2, 3], [4, 5, 6], [7, 8, 9]]> : tensor<3x3xi32>}> : () -> tensor<3x3xi32>
+ %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>
return %0 : tensor<3x1xi32>
}
@@ -657,10 +657,10 @@ func.func @reduce_sum_constant() -> tensor<3x1xi32> {
func.func @reduce_sum_constant() -> tensor<2x1x4xi32> {
// CHECK-LABEL: func.func @reduce_sum_constant() -> tensor<2x1x4xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}[15, 18, 21, 24]], {{\[\[}}51, 54, 57, 60]]]> : tensor<2x1x4xi32>}> : () -> tensor<2x1x4xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}[15, 18, 21, 24]], {{\[\[}}51, 54, 57, 60]]]> : tensor<2x1x4xi32>}> : () -> tensor<2x1x4xi32>
// CHECK: return %[[VAL_0]] : tensor<2x1x4xi32>
// CHECK: }
- %const = "tosa.const"() <{value = 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>
+ %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>
return %0 : tensor<2x1x4xi32>
}
@@ -669,10 +669,10 @@ func.func @reduce_sum_constant() -> tensor<2x1x4xi32> {
func.func @reduce_sum_constant() -> tensor<1x3x3xi32> {
// CHECK-LABEL: func.func @reduce_sum_constant() -> tensor<1x3x3xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}[30, 33, 36], [39, 42, 45], [48, 51, 54]]]> : tensor<1x3x3xi32>}> : () -> tensor<1x3x3xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}[30, 33, 36], [39, 42, 45], [48, 51, 54]]]> : tensor<1x3x3xi32>}> : () -> tensor<1x3x3xi32>
// CHECK: return %[[VAL_0]] : tensor<1x3x3xi32>
// CHECK: }
- %const = "tosa.const"() <{value = 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>
+ %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>
return %0 : tensor<1x3x3xi32>
}
@@ -681,10 +681,10 @@ func.func @reduce_sum_constant() -> tensor<1x3x3xi32> {
func.func @reduce_sum_constant() -> tensor<2x2x2x1xi32> {
// CHECK-LABEL: func.func @reduce_sum_constant() -> tensor<2x2x2x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}{{\[\[}}3], [7]], {{\[\[}}11], [15]]], {{\[\[}}[19], [23]], {{\[\[}}27], [31]]]]> : tensor<2x2x2x1xi32>}> : () -> tensor<2x2x2x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}{{\[\[}}3], [7]], {{\[\[}}11], [15]]], {{\[\[}}[19], [23]], {{\[\[}}27], [31]]]]> : tensor<2x2x2x1xi32>}> : () -> tensor<2x2x2x1xi32>
// CHECK: return %[[VAL_0]] : tensor<2x2x2x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]]]> : tensor<2x2x2x2xi32>}> : () -> tensor<2x2x2x2xi32>
+ %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>
return %0 : tensor<2x2x2x1xi32>
}
@@ -693,10 +693,10 @@ func.func @reduce_sum_constant() -> tensor<2x2x2x1xi32> {
func.func @reduce_sum_constant() -> tensor<1x1x1xi32> {
// CHECK-LABEL: func.func @reduce_sum_constant() -> tensor<1x1x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<42> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<42> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
// CHECK: return %[[VAL_0]] : tensor<1x1x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
+ %const = "tosa.const"() <{values = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
%0 = tosa.reduce_sum %const {axis = 0 : i32} : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
return %0 : tensor<1x1x1xi32>
}
@@ -705,10 +705,10 @@ func.func @reduce_sum_constant() -> tensor<1x1x1xi32> {
func.func @reduce_sum_constant() -> tensor<2x3x1x5xi32> {
// CHECK-LABEL: func.func @reduce_sum_constant() -> tensor<2x3x1x5xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}{{\[\[}}34, 38, 42, 46, 50]], {{\[\[}}114, 118, 122, 126, 130]], {{\[\[}}194, 198, 202, 206, 210]]], {{\[\[}}[274, 278, 282, 286, 290]], {{\[\[}}354, 358, 362, 366, 370]], {{\[\[}}434, 438, 442, 446, 450]]]]> : tensor<2x3x1x5xi32>}> : () -> tensor<2x3x1x5xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}{{\[\[}}34, 38, 42, 46, 50]], {{\[\[}}114, 118, 122, 126, 130]], {{\[\[}}194, 198, 202, 206, 210]]], {{\[\[}}[274, 278, 282, 286, 290]], {{\[\[}}354, 358, 362, 366, 370]], {{\[\[}}434, 438, 442, 446, 450]]]]> : tensor<2x3x1x5xi32>}> : () -> tensor<2x3x1x5xi32>
// CHECK: return %[[VAL_0]] : tensor<2x3x1x5xi32>
// CHECK: }
- %const = "tosa.const"() <{value = 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>
+ %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>
return %0 : tensor<2x3x1x5xi32>
}
@@ -717,10 +717,10 @@ func.func @reduce_sum_constant() -> tensor<2x3x1x5xi32> {
func.func @reduce_prod_constant() -> tensor<1x3xi32> {
// CHECK-LABEL: func.func @reduce_prod_constant() -> tensor<1x3xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}4, 10, 18]]> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}4, 10, 18]]> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
// CHECK: return %[[VAL_0]] : tensor<1x3xi32>
- %const = "tosa.const"() <{value = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ %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>
return %0 : tensor<1x3xi32>
}
@@ -729,11 +729,11 @@ func.func @reduce_sum_constant() -> tensor<2x3x1x5xi32> {
func.func @reduce_prod_constant() -> tensor<2x1xi32> {
// CHECK-LABEL: func.func @reduce_prod_constant() -> tensor<2x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}6], [120]]> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}6], [120]]> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
// CHECK: return %[[VAL_0]] : tensor<2x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ %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>
return %0 : tensor<2x1xi32>
}
@@ -742,10 +742,10 @@ func.func @reduce_sum_constant() -> tensor<2x3x1x5xi32> {
func.func @reduce_prod_constant() -> tensor<3x1xi32> {
// CHECK-LABEL: func.func @reduce_prod_constant() -> tensor<3x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}6], [120], [504]]> : tensor<3x1xi32>}> : () -> tensor<3x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}6], [120], [504]]> : tensor<3x1xi32>}> : () -> tensor<3x1xi32>
// CHECK: return %[[VAL_0]] : tensor<3x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[1, 2, 3], [4, 5, 6], [7, 8, 9]]> : tensor<3x3xi32>}> : () -> tensor<3x3xi32>
+ %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>
return %0 : tensor<3x1xi32>
}
@@ -754,10 +754,10 @@ func.func @reduce_prod_constant() -> tensor<3x1xi32> {
func.func @reduce_prod_constant() -> tensor<2x1x4xi32> {
// CHECK-LABEL: func.func @reduce_prod_constant() -> tensor<2x1x4xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}[45, 120, 231, 384]], {{\[\[}}4641, 5544, 6555, 7680]]]> : tensor<2x1x4xi32>}> : () -> tensor<2x1x4xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}[45, 120, 231, 384]], {{\[\[}}4641, 5544, 6555, 7680]]]> : tensor<2x1x4xi32>}> : () -> tensor<2x1x4xi32>
// CHECK: return %[[VAL_0]] : tensor<2x1x4xi32>
// CHECK: }
- %const = "tosa.const"() <{value = 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>
+ %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>
return %0 : tensor<2x1x4xi32>
}
@@ -766,10 +766,10 @@ func.func @reduce_prod_constant() -> tensor<2x1x4xi32> {
func.func @reduce_prod_constant() -> tensor<1x3x3xi32> {
// CHECK-LABEL: func.func @reduce_prod_constant() -> tensor<1x3x3xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}[190, 440, 756], [1144, 1610, 2160], [2800, 3536, 4374]]]> : tensor<1x3x3xi32>}> : () -> tensor<1x3x3xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}[190, 440, 756], [1144, 1610, 2160], [2800, 3536, 4374]]]> : tensor<1x3x3xi32>}> : () -> tensor<1x3x3xi32>
// CHECK: return %[[VAL_0]] : tensor<1x3x3xi32>
// CHECK: }
- %const = "tosa.const"() <{value = 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>
+ %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>
return %0 : tensor<1x3x3xi32>
}
@@ -778,10 +778,10 @@ func.func @reduce_prod_constant() -> tensor<1x3x3xi32> {
func.func @reduce_prod_constant() -> tensor<2x2x2x1xi32> {
// CHECK-LABEL: func.func @reduce_prod_constant() -> tensor<2x2x2x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}{{\[\[}}2], [12]], {{\[\[}}30], [56]]], {{\[\[}}[90], [132]], {{\[\[}}182], [240]]]]> : tensor<2x2x2x1xi32>}> : () -> tensor<2x2x2x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}{{\[\[}}2], [12]], {{\[\[}}30], [56]]], {{\[\[}}[90], [132]], {{\[\[}}182], [240]]]]> : tensor<2x2x2x1xi32>}> : () -> tensor<2x2x2x1xi32>
// CHECK: return %[[VAL_0]] : tensor<2x2x2x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]]]> : tensor<2x2x2x2xi32>}> : () -> tensor<2x2x2x2xi32>
+ %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>
return %0 : tensor<2x2x2x1xi32>
}
@@ -790,10 +790,10 @@ func.func @reduce_prod_constant() -> tensor<2x2x2x1xi32> {
func.func @reduce_prod_constant() -> tensor<1x1x1xi32> {
// CHECK-LABEL: func.func @reduce_prod_constant() -> tensor<1x1x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<42> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<42> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
// CHECK: return %[[VAL_0]] : tensor<1x1x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
+ %const = "tosa.const"() <{values = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
%0 = tosa.reduce_product %const {axis = 0 : i32} : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
return %0 : tensor<1x1x1xi32>
}
@@ -802,10 +802,10 @@ func.func @reduce_prod_constant() -> tensor<1x1x1xi32> {
func.func @reduce_max_constant() -> tensor<1x3xi32> {
// CHECK-LABEL: func.func @reduce_max_constant() -> tensor<1x3xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}4, 5, 6]]> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}4, 5, 6]]> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
// CHECK: return %[[VAL_0]] : tensor<1x3xi32>
- %const = "tosa.const"() <{value = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ %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>
return %0 : tensor<1x3xi32>
}
@@ -814,11 +814,11 @@ func.func @reduce_prod_constant() -> tensor<1x1x1xi32> {
func.func @reduce_max_constant() -> tensor<2x1xi32> {
// CHECK-LABEL: func.func @reduce_max_constant() -> tensor<2x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}3], [6]]> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}3], [6]]> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
// CHECK: return %[[VAL_0]] : tensor<2x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ %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>
return %0 : tensor<2x1xi32>
}
@@ -827,10 +827,10 @@ func.func @reduce_prod_constant() -> tensor<1x1x1xi32> {
func.func @reduce_max_constant() -> tensor<3x1xi32> {
// CHECK-LABEL: func.func @reduce_max_constant() -> tensor<3x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}3], [6], [9]]> : tensor<3x1xi32>}> : () -> tensor<3x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}3], [6], [9]]> : tensor<3x1xi32>}> : () -> tensor<3x1xi32>
// CHECK: return %[[VAL_0]] : tensor<3x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[1, 2, 3], [4, 5, 6], [7, 8, 9]]> : tensor<3x3xi32>}> : () -> tensor<3x3xi32>
+ %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>
return %0 : tensor<3x1xi32>
}
@@ -839,10 +839,10 @@ func.func @reduce_max_constant() -> tensor<3x1xi32> {
func.func @reduce_max_constant() -> tensor<2x1x4xi32> {
// CHECK-LABEL: func.func @reduce_max_constant() -> tensor<2x1x4xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}[9, 10, 11, 12]], {{\[\[}}21, 22, 23, 24]]]> : tensor<2x1x4xi32>}> : () -> tensor<2x1x4xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}[9, 10, 11, 12]], {{\[\[}}21, 22, 23, 24]]]> : tensor<2x1x4xi32>}> : () -> tensor<2x1x4xi32>
// CHECK: return %[[VAL_0]] : tensor<2x1x4xi32>
// CHECK: }
- %const = "tosa.const"() <{value = 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>
+ %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>
return %0 : tensor<2x1x4xi32>
}
@@ -851,10 +851,10 @@ func.func @reduce_max_constant() -> tensor<2x1x4xi32> {
func.func @reduce_max_constant() -> tensor<1x3x3xi32> {
// CHECK-LABEL: func.func @reduce_max_constant() -> tensor<1x3x3xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}[19, 20, 21], [22, 23, 24], [25, 26, 27]]]> : tensor<1x3x3xi32>}> : () -> tensor<1x3x3xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}[19, 20, 21], [22, 23, 24], [25, 26, 27]]]> : tensor<1x3x3xi32>}> : () -> tensor<1x3x3xi32>
// CHECK: return %[[VAL_0]] : tensor<1x3x3xi32>
// CHECK: }
- %const = "tosa.const"() <{value = 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>
+ %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>
return %0 : tensor<1x3x3xi32>
}
@@ -863,10 +863,10 @@ func.func @reduce_max_constant() -> tensor<1x3x3xi32> {
func.func @reduce_max_constant() -> tensor<2x2x2x1xi32> {
// CHECK-LABEL: func.func @reduce_max_constant() -> tensor<2x2x2x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}{{\[\[}}2], [4]], {{\[\[}}6], [8]]], {{\[\[}}[10], [12]], {{\[\[}}14], [16]]]]> : tensor<2x2x2x1xi32>}> : () -> tensor<2x2x2x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}{{\[\[}}2], [4]], {{\[\[}}6], [8]]], {{\[\[}}[10], [12]], {{\[\[}}14], [16]]]]> : tensor<2x2x2x1xi32>}> : () -> tensor<2x2x2x1xi32>
// CHECK: return %[[VAL_0]] : tensor<2x2x2x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]]]> : tensor<2x2x2x2xi32>}> : () -> tensor<2x2x2x2xi32>
+ %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>
return %0 : tensor<2x2x2x1xi32>
}
@@ -875,10 +875,10 @@ func.func @reduce_max_constant() -> tensor<2x2x2x1xi32> {
func.func @reduce_max_constant() -> tensor<1x1x1xi32> {
// CHECK-LABEL: func.func @reduce_max_constant() -> tensor<1x1x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<42> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<42> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
// CHECK: return %[[VAL_0]] : tensor<1x1x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
+ %const = "tosa.const"() <{values = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
%0 = tosa.reduce_max %const {axis = 0 : i32} : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
return %0 : tensor<1x1x1xi32>
}
@@ -887,9 +887,9 @@ func.func @reduce_max_constant() -> tensor<1x1x1xi32> {
func.func @reduce_min_constant() -> tensor<1x3xi32> {
// CHECK-LABEL: func.func @reduce_min_constant() -> tensor<1x3xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}1, 2, 3]]> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}1, 2, 3]]> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
// CHECK: return %[[VAL_0]] : tensor<1x3xi32>
- %const = "tosa.const"() <{value = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ %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>
return %0 : tensor<1x3xi32>
}
@@ -899,11 +899,11 @@ func.func @reduce_max_constant() -> tensor<1x1x1xi32> {
func.func @reduce_min_constant() -> tensor<2x1xi32> {
// CHECK-LABEL: func.func @reduce_min_constant() -> tensor<2x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}1], [4]]> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}1], [4]]> : tensor<2x1xi32>}> : () -> tensor<2x1xi32>
// CHECK: return %[[VAL_0]] : tensor<2x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ %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>
return %0 : tensor<2x1xi32>
}
@@ -912,10 +912,10 @@ func.func @reduce_max_constant() -> tensor<1x1x1xi32> {
func.func @reduce_min_constant() -> tensor<3x1xi32> {
// CHECK-LABEL: func.func @reduce_min_constant() -> tensor<3x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}1], [4], [7]]> : tensor<3x1xi32>}> : () -> tensor<3x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}1], [4], [7]]> : tensor<3x1xi32>}> : () -> tensor<3x1xi32>
// CHECK: return %[[VAL_0]] : tensor<3x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[1, 2, 3], [4, 5, 6], [7, 8, 9]]> : tensor<3x3xi32>}> : () -> tensor<3x3xi32>
+ %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>
return %0 : tensor<3x1xi32>
}
@@ -924,10 +924,10 @@ func.func @reduce_min_constant() -> tensor<3x1xi32> {
func.func @reduce_min_constant() -> tensor<2x1x4xi32> {
// CHECK-LABEL: func.func @reduce_min_constant() -> tensor<2x1x4xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}[1, 2, 3, 4]], {{\[\[}}13, 14, 15, 16]]]> : tensor<2x1x4xi32>}> : () -> tensor<2x1x4xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}[1, 2, 3, 4]], {{\[\[}}13, 14, 15, 16]]]> : tensor<2x1x4xi32>}> : () -> tensor<2x1x4xi32>
// CHECK: return %[[VAL_0]] : tensor<2x1x4xi32>
// CHECK: }
- %const = "tosa.const"() <{value = 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>
+ %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>
return %0 : tensor<2x1x4xi32>
}
@@ -936,10 +936,10 @@ func.func @reduce_min_constant() -> tensor<2x1x4xi32> {
func.func @reduce_min_constant() -> tensor<1x3x3xi32> {
// CHECK-LABEL: func.func @reduce_min_constant() -> tensor<1x3x3xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}[1, 2, 3], [4, 5, 6], [7, 8, 9]]]> : tensor<1x3x3xi32>}> : () -> tensor<1x3x3xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}[1, 2, 3], [4, 5, 6], [7, 8, 9]]]> : tensor<1x3x3xi32>}> : () -> tensor<1x3x3xi32>
// CHECK: return %[[VAL_0]] : tensor<1x3x3xi32>
// CHECK: }
- %const = "tosa.const"() <{value = 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>
+ %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>
return %0 : tensor<1x3x3xi32>
}
@@ -948,10 +948,10 @@ func.func @reduce_min_constant() -> tensor<1x3x3xi32> {
func.func @reduce_min_constant() -> tensor<2x2x2x1xi32> {
// CHECK-LABEL: func.func @reduce_min_constant() -> tensor<2x2x2x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}{{\[\[}}1], [3]], {{\[\[}}5], [7]]], {{\[\[}}[9], [11]], {{\[\[}}13], [15]]]]> : tensor<2x2x2x1xi32>}> : () -> tensor<2x2x2x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}{{\[\[}}1], [3]], {{\[\[}}5], [7]]], {{\[\[}}[9], [11]], {{\[\[}}13], [15]]]]> : tensor<2x2x2x1xi32>}> : () -> tensor<2x2x2x1xi32>
// CHECK: return %[[VAL_0]] : tensor<2x2x2x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]]]> : tensor<2x2x2x2xi32>}> : () -> tensor<2x2x2x2xi32>
+ %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>
return %0 : tensor<2x2x2x1xi32>
}
@@ -960,10 +960,10 @@ func.func @reduce_min_constant() -> tensor<2x2x2x1xi32> {
func.func @reduce_min_constant() -> tensor<1x1x1xi32> {
// CHECK-LABEL: func.func @reduce_min_constant() -> tensor<1x1x1xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<42> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<42> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
// CHECK: return %[[VAL_0]] : tensor<1x1x1xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
+ %const = "tosa.const"() <{values = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
%0 = tosa.reduce_min %const {axis = 0 : i32} : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
return %0 : tensor<1x1x1xi32>
}
@@ -972,10 +972,10 @@ func.func @reduce_min_constant() -> tensor<1x1x1xi32> {
func.func @reduce_any_constant() -> tensor<1x3xi1> {
// CHECK-LABEL: func.func @reduce_any_constant() -> tensor<1x3xi1> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<true> : tensor<1x3xi1>}> : () -> tensor<1x3xi1>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<true> : tensor<1x3xi1>}> : () -> tensor<1x3xi1>
// CHECK: return %[[VAL_0]] : tensor<1x3xi1>
- %const = "tosa.const"() <{value = dense<[[true,true,true], [true,false,true]]> : tensor<2x3xi1>}> : () -> tensor<2x3xi1>
+ %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>
return %0 : tensor<1x3xi1>
}
@@ -985,11 +985,11 @@ func.func @reduce_any_constant() -> tensor<1x3xi1> {
func.func @reduce_any_constant() -> tensor<2x1xi1> {
// CHECK-LABEL: func.func @reduce_any_constant() -> tensor<2x1xi1> {
-// CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<true> : tensor<2x1xi1>}> : () -> tensor<2x1xi1>
+// CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<true> : tensor<2x1xi1>}> : () -> tensor<2x1xi1>
// CHECK: return %[[VAL_0]] : tensor<2x1xi1>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[true,true,true], [true,false,true]]> : tensor<2x3xi1>}> : () -> tensor<2x3xi1>
+ %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>
return %0 : tensor<2x1xi1>
}
@@ -998,10 +998,10 @@ func.func @reduce_any_constant() -> tensor<2x1xi1> {
func.func @reduce_any_constant() -> tensor<3x1xi1> {
// CHECK-LABEL: func.func @reduce_any_constant() -> tensor<3x1xi1> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}true], [false], [true]]> : tensor<3x1xi1>}> : () -> tensor<3x1xi1>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}true], [false], [true]]> : tensor<3x1xi1>}> : () -> tensor<3x1xi1>
// CHECK: return %[[VAL_0]] : tensor<3x1xi1>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[true, false, false], [false, false, false], [false, false, true]]> : tensor<3x3xi1>}> : () -> tensor<3x3xi1>
+ %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>
return %0 : tensor<3x1xi1>
}
@@ -1010,10 +1010,10 @@ func.func @reduce_any_constant() -> tensor<3x1xi1> {
func.func @reduce_any_constant() -> tensor<2x1x4xi1> {
// CHECK-LABEL: func.func @reduce_any_constant() -> tensor<2x1x4xi1> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}[true, false, true, true]], {{\[\[}}true, false, true, false]]]> : tensor<2x1x4xi1>}> : () -> tensor<2x1x4xi1>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}[true, false, true, true]], {{\[\[}}true, false, true, false]]]> : tensor<2x1x4xi1>}> : () -> tensor<2x1x4xi1>
// CHECK: return %[[VAL_0]] : tensor<2x1x4xi1>
// CHECK: }
- %const = "tosa.const"() <{value = 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>
+ %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>
return %0 : tensor<2x1x4xi1>
}
@@ -1022,10 +1022,10 @@ func.func @reduce_any_constant() -> tensor<2x1x4xi1> {
func.func @reduce_all_constant() -> tensor<1x3xi1> {
// CHECK-LABEL: func.func @reduce_all_constant() -> tensor<1x3xi1> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}true, false, true]]> : tensor<1x3xi1>}> : () -> tensor<1x3xi1>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}true, false, true]]> : tensor<1x3xi1>}> : () -> tensor<1x3xi1>
// CHECK: return %[[VAL_0]] : tensor<1x3xi1>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[true,true,true], [true,false,true]]> : tensor<2x3xi1>}> : () -> tensor<2x3xi1>
+ %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>
return %0 : tensor<1x3xi1>
}
@@ -1034,10 +1034,10 @@ func.func @reduce_any_constant() -> tensor<2x1x4xi1> {
func.func @reduce_all_constant() -> tensor<2x1xi1> {
// CHECK-LABEL: func.func @reduce_all_constant() -> tensor<2x1xi1> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}true], [false]]> : tensor<2x1xi1>}> : () -> tensor<2x1xi1>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<{{\[\[}}true], [false]]> : tensor<2x1xi1>}> : () -> tensor<2x1xi1>
// CHECK: return %[[VAL_0]] : tensor<2x1xi1>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[true,true,true], [true,false,true]]> : tensor<2x3xi1>}> : () -> tensor<2x3xi1>
+ %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>
return %0 : tensor<2x1xi1>
}
@@ -1046,10 +1046,10 @@ func.func @reduce_any_constant() -> tensor<2x1x4xi1> {
func.func @reduce_all_constant() -> tensor<3x1xi1> {
// CHECK-LABEL: func.func @reduce_all_constant() -> tensor<3x1xi1> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<false> : tensor<3x1xi1>}> : () -> tensor<3x1xi1>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<false> : tensor<3x1xi1>}> : () -> tensor<3x1xi1>
// CHECK: return %[[VAL_0]] : tensor<3x1xi1>
// CHECK: }
- %const = "tosa.const"() <{value = dense<[[true, false, false], [false, false, false], [false, false, true]]> : tensor<3x3xi1>}> : () -> tensor<3x3xi1>
+ %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>
return %0 : tensor<3x1xi1>
}
@@ -1058,10 +1058,10 @@ func.func @reduce_all_constant() -> tensor<3x1xi1> {
func.func @reduce_all_constant() -> tensor<2x1x4xi1> {
// CHECK-LABEL: func.func @reduce_all_constant() -> tensor<2x1x4xi1> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<false> : tensor<2x1x4xi1>}> : () -> tensor<2x1x4xi1>
+ // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<false> : tensor<2x1x4xi1>}> : () -> tensor<2x1x4xi1>
// CHECK: return %[[VAL_0]] : tensor<2x1x4xi1>
// CHECK: }
- %const = "tosa.const"() <{value = 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>
+ %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>
return %0 : tensor<2x1x4xi1>
}
@@ -1070,10 +1070,10 @@ func.func @reduce_all_constant() -> tensor<2x1x4xi1> {
func.func @reduce_sum_constant() -> tensor<1x3xi32> {
// CHECK-LABEL: func.func @reduce_sum_constant() -> tensor<1x3xi32> {
-// CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<2> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
+// CHECK: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<2> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
// CHECK: return %[[VAL_0]] : tensor<1x3xi32>
// CHECK: }
- %const = "tosa.const"() <{value = dense<1> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ %const = "tosa.const"() <{values = dense<1> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
%0 = tosa.reduce_sum %const {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
return %0 : tensor<1x3xi32>
}
@@ -1082,13 +1082,13 @@ func.func @reduce_sum_constant() -> tensor<1x3xi32> {
func.func @reduce_sum_constant() -> tensor<1x3xi32> {
// CHECK-LABEL: func.func @reduce_sum_constant() -> tensor<1x3xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<{{\[\[}}1, 2, 3], [4, 5, 6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- // CHECK: %[[VAL_1:.*]] = "tosa.const"() <{value = dense<{{\[\[}}1, 2, 3], [4, 5, 7]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ // 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: return %[[VAL_3]] : tensor<1x3xi32>
- %arg0 = "tosa.const"() <{value = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
- %arg1 = "tosa.const"() <{value = dense<[[1,2,3], [4,5,7]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ %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>
return %0 : tensor<1x3xi32>
@@ -1098,17 +1098,17 @@ func.func @reduce_sum_constant() -> tensor<1x3xi32> {
func.func @reduce_sum_constant_aggressive() -> tensor<1x3xi32> {
// AGGRESIVE-LABEL: func.func @reduce_sum_constant_aggressive() -> tensor<1x3xi32> {
- // AGGRESIVE: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<4> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
+ // AGGRESIVE: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<4> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
// AGGRESIVE: return %[[VAL_0:.*]] : tensor<1x3xi32>
// CHECK-LABEL: func.func @reduce_sum_constant_aggressive() -> tensor<1x3xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<1> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+ // 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_3:.*]] = tosa.add %[[VAL_1]], %[[VAL_2]] : (tensor<1x3xi32>, tensor<1x3xi32>) -> tensor<1x3xi32>
// CHECK: return %[[VAL_3]] : tensor<1x3xi32>
- %const = "tosa.const"() {value = dense<1> : tensor<2x3xi32>} : () -> tensor<2x3xi32>
+ %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>
%res = tosa.add %0, %1 : (tensor<1x3xi32>, tensor<1x3xi32>) -> tensor<1x3xi32>
@@ -1119,9 +1119,9 @@ func.func @reduce_sum_constant_aggressive() -> tensor<1x3xi32> {
func.func @reduce_sum_constant_aggressive() -> tensor<2x3xi32> {
// AGGRESIVE-LABEL: func.func @reduce_sum_constant_aggressive() -> tensor<2x3xi32> {
- // AGGRESIVE-DAG: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<2> : tensor<1x2x3xi32>}> : () -> tensor<1x2x3xi32>
- // AGGRESIVE-DAG: %[[VAL_1:.*]] = "tosa.const"() <{value = dense<1> : tensor<2x2x3xi32>}> : () -> tensor<2x2x3xi32>
- // AGGRESIVE-DAG: %[[VAL_2:.*]] = "tosa.const"() <{value = dense<2> : tensor<2x3xi32>}> : () -> 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_5:.*]] = tosa.add %[[VAL_3]], %[[VAL_2]] : (tensor<1x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
@@ -1129,8 +1129,8 @@ func.func @reduce_sum_constant_aggressive() -> tensor<2x3xi32> {
// AGGRESIVE: return %[[VAL_6]] : tensor<2x3xi32>
// CHECK-LABEL: func.func @reduce_sum_constant_aggressive() -> tensor<2x3xi32> {
- // CHECK: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<1> : tensor<2x2x3xi32>}> : () -> tensor<2x2x3xi32>
- // CHECK: %[[VAL_1:.*]] = "tosa.const"() <{value = dense<2> : tensor<2x3xi32>}> : () -> 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>
@@ -1138,8 +1138,8 @@ func.func @reduce_sum_constant_aggressive() -> 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"() {value = dense<1> : tensor<2x2x3xi32>} : () -> tensor<2x2x3xi32>
- %const1 = "tosa.const"() {value = dense<2> : tensor<2x3xi32>} : () -> 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>
diff --git a/mlir/test/Dialect/Tosa/constant-reciprocal-fold.mlir b/mlir/test/Dialect/Tosa/constant-reciprocal-fold.mlir
index cc71c43d53ce2..12a3aee428ac6 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"() {value = 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"() {value = 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"() {value = 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"() {value = 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"() {value = 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"() {value = 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"() {value = 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"() {value = 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"() {value = 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>
}
@@ -112,7 +112,7 @@ func.func @reciprocal_fold() -> tensor<4x6xf32> {
// CHECK-SAME{LITERAL}: [2.02880907, -1.53280187, 0.552730501, 7.15819644, 0.64495325, -0.973709881]]
// CHECK-NOT: tosa.reciprocal
// CHECK: return [[RES]]
- %0 = "tosa.const"() { value = dense<[
+ %0 = "tosa.const"() { values = dense<[
[ 0.1758, 0.0874, 0.5924, 1.4700, -1.1424, 2.9213],
[-0.2078, 1.4325, 1.5283, -0.0121, -0.2306, -1.3377],
[-0.0804, 0.0761, 0.5277, 1.1292, 0.2446, 0.6946],
@@ -128,7 +128,7 @@ func.func @reciprocal_fold() -> tensor<4x6xf32> {
func.func @reciprocal_of_const_sparse() -> tensor<32xbf16> {
// CHECK: tosa.const
// CHECK: tosa.reciprocal
- %0 = "tosa.const"() { value = sparse<
+ %0 = "tosa.const"() { values = sparse<
[[0], [3], [11], [17], [20], [23], [25], [30], [31]],
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]>
: tensor<32xbf16> } : () -> tensor<32xbf16>
diff --git a/mlir/test/Dialect/Tosa/constant_folding.mlir b/mlir/test/Dialect/Tosa/constant_folding.mlir
index 3ff3121348fca..9b6ccdb54c107 100644
--- a/mlir/test/Dialect/Tosa/constant_folding.mlir
+++ b/mlir/test/Dialect/Tosa/constant_folding.mlir
@@ -3,14 +3,14 @@
// CHECK-LABEL: func @test_const
func.func @test_const(%arg0 : index) -> tensor<4xi32> {
// CHECK: tosa.const
- %0 = "tosa.const"() {value = 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>
}
// CHECK-LABEL: func @test_const_i64
func.func @test_const_i64(%arg0 : index) -> tensor<4xi64> {
// CHECK: tosa.const
- %0 = "tosa.const"() {value = 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>
}
diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
index 53e901621bafe..6c9665b8f1442 100644
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -8,7 +8,7 @@
func.func @test_const() -> tensor<1xf32> {
// expected-error at +1{{'tosa.const' op expected same attr/result element types}}
- %0 = "tosa.const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xf32>
+ %0 = "tosa.const"() {values = dense<1> : tensor<1xi32>} : () -> tensor<1xf32>
return %0 : tensor<1xf32>
}
@@ -16,15 +16,15 @@ 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"() {value = dense<1.0> : vector<f32>} : () -> tensor<f32>
+ %0 = "tosa.const"() {values = dense<1.0> : vector<f32>} : () -> tensor<f32>
return
}
// -----
func.func @test_conv2d(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
- %input_zp = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>}
: (tensor<1x29x29x4xf32>, tensor<16x3x3x4xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
@@ -34,7 +34,7 @@ func.func @test_conv2d(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3x3x4xi8>,
// -----
func.func @test_conv2d(%arg0: tensor<*xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
- %zp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op expect a ranked tensor for input, got <block argument> of type 'tensor<*xi8>' at index: 0}}
%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>}
: (tensor<*xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
@@ -44,7 +44,7 @@ func.func @test_conv2d(%arg0: tensor<*xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: t
// -----
func.func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<*xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
- %zp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op operand #1 must be 4D tensor of 4-bit signless integer or 8-bit signless integer or Quint8 type or Qint4 type or Qint8 type or Qint16 type or Qint32 type or floating-point values, but got 'tensor<*xi8>'}}
%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>}
: (tensor<1x29x29x4xi8>, tensor<*xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
@@ -54,8 +54,8 @@ func.func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<*xi8>, %arg2:
// -----
func.func @test_conv2d_input_zp(%arg0: tensor<1x29x29x4xf16>, %arg1: tensor<16x3x3x4xf16>, %arg2: tensor<16xf16>) -> tensor<1x27x27x16xf16> {
- %input_zp = "tosa.const"() <{value = dense<-1.0> : tensor<1xf16>}> : () -> tensor<1xf16>
- %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %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>}
: (tensor<1x29x29x4xf16>, tensor<16x3x3x4xf16>, tensor<16xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x27x27x16xf16>
@@ -65,8 +65,8 @@ func.func @test_conv2d_input_zp(%arg0: tensor<1x29x29x4xf16>, %arg1: tensor<16x3
// -----
func.func @test_conv2d_weight_zp(%arg0: tensor<1x29x29x4xf16>, %arg1: tensor<16x3x3x4xf16>, %arg2: tensor<16xf16>) -> tensor<1x27x27x16xf16> {
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
- %weight_zp = "tosa.const"() <{value = dense<-1.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %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>}
: (tensor<1x29x29x4xf16>, tensor<16x3x3x4xf16>, tensor<16xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x27x27x16xf16>
@@ -76,7 +76,7 @@ func.func @test_conv2d_weight_zp(%arg0: tensor<1x29x29x4xf16>, %arg1: tensor<16x
// -----
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
- %zp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for i8 tensor is not i32}}
%0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
: (tensor<1x29x29x4xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
@@ -86,8 +86,8 @@ func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<16x3x
// -----
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xi16>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi16>) -> tensor<1x27x27x16xi16> {
- %input_zp = "tosa.const"() {value = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
- %weight_zp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
+ %weight_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for i16 tensor is not i48}}
%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>}
: (tensor<1x29x29x4xi16>, tensor<16x3x3x4xi8>, tensor<16xi16>, tensor<1xi16>, tensor<1xi8>) -> tensor<1x27x27x16xi16>
@@ -97,7 +97,7 @@ func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xi16>, %arg1: tensor<16x3
// -----
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xf8E5M2>, %arg1: tensor<16x3x3x4xf8E5M2>, %arg2: tensor<16xf16>) -> tensor<1x27x27x16xf16> {
- %zp = "tosa.const"() {value = dense<0.0> : tensor<1xf8E5M2>} : () -> tensor<1xf8E5M2>
+ %zp = "tosa.const"() {values = dense<0.0> : tensor<1xf8E5M2>} : () -> tensor<1xf8E5M2>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for f8 tensor is not f16}}
%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>}
: (tensor<1x29x29x4xf8E5M2>, tensor<16x3x3x4xf8E5M2>, tensor<16xf16>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x27x27x16xf16>
@@ -107,7 +107,7 @@ func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xf8E5M2>, %arg1: tensor<1
// -----
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xf8E4M3>, %arg1: tensor<16x3x3x4xf8E4M3>, %arg2: tensor<16xf16>) -> tensor<1x27x27x16xf16> {
- %zp = "tosa.const"() {value = dense<0.0> : tensor<1xf8E4M3>} : () -> tensor<1xf8E4M3>
+ %zp = "tosa.const"() {values = dense<0.0> : tensor<1xf8E4M3>} : () -> tensor<1xf8E4M3>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for f8 tensor is not f16}}
%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>}
: (tensor<1x29x29x4xf8E4M3>, tensor<16x3x3x4xf8E4M3>, tensor<16xf16>, tensor<1xf8E4M3>, tensor<1xf8E4M3>) -> tensor<1x27x27x16xf16>
@@ -117,7 +117,7 @@ func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xf8E4M3>, %arg1: tensor<1
// -----
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xf16>, %arg1: tensor<16x3x3x4xf16>, %arg2: tensor<16xf16>) -> tensor<1x27x27x16xf16> {
- %zp = "tosa.const"() {value = dense<0.0> : tensor<1xf16>} : () -> tensor<1xf16>
+ %zp = "tosa.const"() {values = dense<0.0> : tensor<1xf16>} : () -> tensor<1xf16>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for f16 tensor is not f16/f32}}
%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>}
: (tensor<1x29x29x4xf16>, tensor<16x3x3x4xf16>, tensor<16xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x27x27x16xf16>
@@ -127,7 +127,7 @@ func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xf16>, %arg1: tensor<16x3
// -----
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xbf16>, %arg1: tensor<16x3x3x4xbf16>, %arg2: tensor<16xbf16>) -> tensor<1x27x27x16xbf16> {
- %zp = "tosa.const"() {value = dense<0.0> : tensor<1xbf16>} : () -> tensor<1xbf16>
+ %zp = "tosa.const"() {values = dense<0.0> : tensor<1xbf16>} : () -> tensor<1xbf16>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for bf16 tensor is not f32}}
%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>}
: (tensor<1x29x29x4xbf16>, tensor<16x3x3x4xbf16>, tensor<16xbf16>, tensor<1xbf16>, tensor<1xbf16>) -> tensor<1x27x27x16xbf16>
@@ -137,7 +137,7 @@ func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xbf16>, %arg1: tensor<16x
// -----
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> {
- %zp = "tosa.const"() {value = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
+ %zp = "tosa.const"() {values = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for f32 tensor is not f32}}
%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>}
: (tensor<1x29x29x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x27x27x16xf32>
@@ -147,7 +147,7 @@ func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3
// -----
func.func @test_conv3d_acc_type(%arg0: tensor<1x4x8x21x17xi8>, %arg1: tensor<34x1x1x1x17xi8>, %arg2: tensor<34xi8>) -> tensor<1x4x8x21x34xi8> {
- %zp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv3d' op accumulator type for i8 tensor is not i32}}
%0 = tosa.conv3d %arg0, %arg1, %arg2, %zp, %zp {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<1x4x8x21x17xi8>, tensor<34x1x1x1x17xi8>, tensor<34xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x8x21x34xi8>
@@ -157,7 +157,7 @@ func.func @test_conv3d_acc_type(%arg0: tensor<1x4x8x21x17xi8>, %arg1: tensor<34x
// -----
func.func @test_depthwise_conv2d_acc_type(%arg0: tensor<1x4x4x4xi8>, %arg1: tensor<1x1x4x2xi8>, %arg2: tensor<8xi8>) -> tensor<1x4x4x8xi8> {
- %zp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.depthwise_conv2d' op accumulator type for i8 tensor is not i32}}
%0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xi8>, tensor<1x1x4x2xi8>, tensor<8xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8xi8>
return %0 : tensor<1x4x4x8xi8>
@@ -166,7 +166,7 @@ func.func @test_depthwise_conv2d_acc_type(%arg0: tensor<1x4x4x4xi8>, %arg1: tens
// -----
func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xi8>, %arg1: tensor<16x1x1x8xi8>, %arg2: tensor<16xi8>) -> tensor<1x32x32x16xi8> {
- %zp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.transpose_conv2d' op accumulator type for i8 tensor is not i32}}
%0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f16, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xi8>, tensor<16x1x1x8xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x32x32x16xi8>
return %0 : tensor<1x32x32x16xi8>
@@ -175,7 +175,7 @@ func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xi8>, %arg1: tensor<16x1
// -----
// CHECK-LABEL: conv2d_quant_any_acc
func.func @test_conv2d_quant_any_acc(%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<i8<-8:7>>> {
- %zp = "tosa.const" () { value = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
+ %zp = "tosa.const" () { values = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for i8 tensor is not i32}}
%0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %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<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<i8<-8:7>>>
return %0 : tensor<1x4x4x8x!quant.any<i8<-8:7>>>
@@ -184,7 +184,7 @@ func.func @test_conv2d_quant_any_acc(%arg0: tensor<1x4x4x4x!quant.any<i8<-8:7>>>
// -----
// CHECK-LABEL: conv2d_quant_any_result
func.func @test_conv2d_quant_any_result(%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<i8<-8:7>>> {
- %zp = "tosa.const" () { value = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
+ %zp = "tosa.const" () { values = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op input/output element types are incompatible}}
%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<i8<-8:7>>>
return %0 : tensor<1x4x4x8x!quant.any<i8<-8:7>>>
@@ -232,7 +232,7 @@ func.func @test_concat_axis_out_of_range(%arg0: tensor<1x2xf32>, %arg1: tensor<2
// -----
func.func @test_pad_non_const(%arg0: tensor<13x21x3xf32>, %arg1: !tosa.shape<6>) -> tensor<13x21x3xf32> {
- %pad_const = "tosa.const"() {value = 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>
@@ -251,7 +251,7 @@ func.func @test_pad_non_const(%arg0: tensor<13x21x3xi8>, %arg1: tensor<1xi8>) ->
func.func @test_pad_io_rank_mismatch(%arg0: tensor<13x21xf32>) {
%0 = tosa.const_shape {value = dense<1> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {value = 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 expect same input and output tensor rank.}}
%1 = tosa.pad %arg0, %0, %pad_const : (tensor<13x21xf32>, !tosa.shape<4>, tensor<1xf32>) -> tensor<13x21x3xf32>
}
@@ -268,7 +268,7 @@ func.func @test_concat_input_rank_mismatch(%arg0: tensor<1x2x3xf32>, %arg1: tens
func.func @test_pad_invalid_padding_rank(%arg0: tensor<13x21xf32>) {
%0 = tosa.const_shape {value = dense<1> : tensor<6xindex>} : () -> !tosa.shape<6>
- %pad_const = "tosa.const"() {value = 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 expected padding tensor dim 0 to have size 4 (2*rank(shape1)) but got size 6}}
%1 = tosa.pad %arg0, %0, %pad_const : (tensor<13x21xf32>, !tosa.shape<6>, tensor<1xf32>) -> tensor<13x21xf32>
return
@@ -278,7 +278,7 @@ func.func @test_pad_invalid_padding_rank(%arg0: tensor<13x21xf32>) {
func.func @test_pad_invalid_padConst_rank(%arg0: tensor<13x21xf32>, %arg1: tensor<2x2xi32>) {
%0 = tosa.const_shape {value = dense<1> : tensor<4xindex>} : () -> !tosa.shape<4>
- %1 = "tosa.const"() {value = dense<3.14> : tensor<2xf32>} : () -> tensor<2xf32>
+ %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>
return
@@ -288,7 +288,7 @@ func.func @test_pad_invalid_padConst_rank(%arg0: tensor<13x21xf32>, %arg1: tenso
func.func @test_pad_padding_shape_mismatch(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
%0 = tosa.const_shape {value = dense<1> : tensor<4xindex>} : () -> !tosa.shape<4>
- %pad_const = "tosa.const"() {value = 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 expected padding tensor dim 0 to have size 6 (2*rank(shape1)) but got size 4}}
%1 = tosa.pad %arg0, %0, %pad_const : (tensor<13x21x3xf32>, !tosa.shape<4>, tensor<1xf32>) -> tensor<13x21x3xf32>
return %1 : tensor<13x21x3xf32>
@@ -568,16 +568,16 @@ 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 {value, output} have same shape}}
- %0 = "tosa.const"() {value = dense<0.000000e+00> : tensor<1x1xf32>} : () -> 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>
return %0 : tensor<100x100xf32>
}
// -----
func.func @test_conv2d_static_zero_dim_input(%arg0: tensor<1x29x0x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> {
- %input_zp = "tosa.const"() <{value = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
- %weight_zp = "tosa.const"() <{value = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %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 4-d tosa-conformant tensor, 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>}
: (tensor<1x29x0x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1x27x27x16xf32>
@@ -587,8 +587,8 @@ func.func @test_conv2d_static_zero_dim_input(%arg0: tensor<1x29x0x4xf32>, %arg1:
// -----
func.func @test_conv2d_zero_dim_input(%arg0: tensor<1x?x0x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> {
- %input_zp = "tosa.const"() <{value = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
- %weight_zp = "tosa.const"() <{value = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
+ %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 4-d tosa-conformant tensor, 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>}
: (tensor<1x?x0x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1x27x27x16xf32>
@@ -774,7 +774,7 @@ 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"() {value = 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>
@@ -784,7 +784,7 @@ func.func @test_mul_type_mismatch(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1
// CHECK-LABEL: test_mul_invalid_shift
func.func @test_mul_invalid_shift(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf32>) -> tensor<13x21x3xf32> {
- %shift = "tosa.const"() {value = 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>
@@ -1119,7 +1119,7 @@ func.func @test_sub_with_unequal_result_ranks(%arg0: tensor<1x21x3xf32>, %arg1:
// -----
// CHECK-LABEL: test_mul_non_scalar_shift_2d
func.func @test_mul_non_scalar_shift_2d(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf32>) -> tensor<13x21x3xf32> {
- %shift = "tosa.const"() <{value = dense<0> : tensor<1x1xi8>}> : () -> tensor<1x1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1x1xi8>}> : () -> tensor<1x1xi8>
// expected-error at +1 {{'tosa.mul' op operand #2 must be tosa-conformant scalar tensor of 8-bit signless integer values, but got 'tensor<1x1xi8>'}}
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<13x21x3xf32>, tensor<13x1x3xf32>, tensor<1x1xi8>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
@@ -1128,7 +1128,7 @@ func.func @test_mul_non_scalar_shift_2d(%arg0: tensor<13x21x3xf32>, %arg1: tenso
// -----
// CHECK-LABEL: test_mul_non_scalar_shift_1d
func.func @test_mul_non_scalar_shift_1d(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf32>) -> tensor<13x21x3xf32> {
- %shift = "tosa.const"() <{value = dense<0> : tensor<2xi8>}> : () -> tensor<2xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<2xi8>}> : () -> tensor<2xi8>
// expected-error at +1 {{'tosa.mul' op operand #2 must be tosa-conformant scalar tensor of 8-bit signless integer values, but got 'tensor<2xi8>'}}
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<13x21x3xf32>, tensor<13x1x3xf32>, tensor<2xi8>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
@@ -1137,7 +1137,7 @@ func.func @test_mul_non_scalar_shift_1d(%arg0: tensor<13x21x3xf32>, %arg1: tenso
// -----
// CHECK-LABEL: test_mul_non_broadcast
func.func @test_mul_non_broadcast(%arg0: tensor<13x21x2xf32>, %arg1: tensor<3x1x3xf32>) -> tensor<13x21x3xf32> {
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.mul' op operands don't have broadcast-compatible shapes}}
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<13x21x2xf32>, tensor<3x1x3xf32>, tensor<1xi8>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
@@ -1324,8 +1324,8 @@ 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"() {value = dense<-1.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %output_zp = "tosa.const"() {value = 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>}
: (tensor<1x16x16x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x16x16x8xf32>
@@ -1336,8 +1336,8 @@ 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"() {value = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
- %output_zp = "tosa.const"() {value = 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>}
: (tensor<1x16x16x8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x16x16x8xf32>
diff --git a/mlir/test/Dialect/Tosa/invalid_extension.mlir b/mlir/test/Dialect/Tosa/invalid_extension.mlir
index 684875f231dec..d27703f47f687 100644
--- a/mlir/test/Dialect/Tosa/invalid_extension.mlir
+++ b/mlir/test/Dialect/Tosa/invalid_extension.mlir
@@ -51,7 +51,7 @@ func.func @test_cond_if(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<i1
// -----
func.func @test_while_loop(%arg0: tensor<10xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
// expected-error at +1 {{'tosa.while_loop' op illegal: requires [controlflow]}}
%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>
@@ -59,7 +59,7 @@ 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"() {value = 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 {value = dense<[1]> : tensor<1xindex>} : () -> !tosa.shape<1>
%4 = tosa.reshape %2, %7 : (tensor<i32>, !tosa.shape<1>) -> tensor<1xi32>
diff --git a/mlir/test/Dialect/Tosa/level_check.mlir b/mlir/test/Dialect/Tosa/level_check.mlir
index a83fad1035a6d..1bdb4894c0631 100644
--- a/mlir/test/Dialect/Tosa/level_check.mlir
+++ b/mlir/test/Dialect/Tosa/level_check.mlir
@@ -145,7 +145,7 @@ func.func @test_min_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>, %arg1: tens
// -----
func.func @test_mul_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>, %arg1: tensor<1x1x1x1x13x1x3xf32>) -> tensor<1x1x1x1x13x21x3xf32> {
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.mul' op failed level check: operand rank(shape) <= MAX_RANK}}
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<1x1x1x1x13x21x3xf32>, tensor<1x1x1x1x13x1x3xf32>, tensor<1xi8>) -> tensor<1x1x1x1x13x21x3xf32>
return %0 : tensor<1x1x1x1x13x21x3xf32>
@@ -370,7 +370,7 @@ func.func @test_concat_rank_invalid(%arg0: tensor<1x1x1x13x21x3x8xf32>, %arg1: t
// -----
func.func @test_pad_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32> {
- %pad_const = "tosa.const"() {value = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %pad_const = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
%padding = tosa.const_shape {value = 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>
@@ -440,7 +440,7 @@ func.func @test_rescale_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xi8>) -> tenso
// -----
func.func @test_const(%arg0 : tensor<1x1xi32>) -> tensor<1x1x1x1x1x1x1xi32> {
// expected-error at +1 {{'tosa.const' op failed level check: attribute rank(shape) <= MAX_RANK}}
- %0 = "tosa.const"() {value = dense<0> : tensor<1x1x1x1x1x1x1xi32>} : () -> tensor<1x1x1x1x1x1x1xi32>
+ %0 = "tosa.const"() {values = dense<0> : tensor<1x1x1x1x1x1x1xi32>} : () -> tensor<1x1x1x1x1x1x1xi32>
return %0: tensor<1x1x1x1x1x1x1xi32>
}
@@ -454,7 +454,7 @@ 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"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
+ %0 = "tosa.const"() {values = dense<0> : tensor<i32>} : () -> tensor<i32>
return %0: tensor<i32>
}
@@ -462,7 +462,7 @@ 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"() {value = dense<0> : tensor<1xi2>} : () -> tensor<1xi2>
+ %0 = "tosa.const"() {values = dense<0> : tensor<1xi2>} : () -> tensor<1xi2>
return
}
@@ -470,7 +470,7 @@ 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"() {value = dense<0> : tensor<1xui32>} : () -> tensor<1xui32>
+ %0 = "tosa.const"() {values = dense<0> : tensor<1xui32>} : () -> tensor<1xui32>
return
}
@@ -478,7 +478,7 @@ 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"() {value = dense<0.0> : tensor<1xf64>} : () -> tensor<1xf64>
+ %0 = "tosa.const"() {values = dense<0.0> : tensor<1xf64>} : () -> tensor<1xf64>
return
}
@@ -486,7 +486,7 @@ 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"() {value = dense<0> : tensor<1xui8>} : () -> tensor<1xui8>
+ %0 = "tosa.const"() {values = dense<0> : tensor<1xui8>} : () -> tensor<1xui8>
return
}
@@ -1159,7 +1159,7 @@ func.func @test_variable_read_write_tensor_size_invalid() -> () {
// -----
func.func @test_while_loop_tensor_size_invalid(%arg0: tensor<536870912xi32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {value = 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>
@@ -1167,8 +1167,8 @@ 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"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
- %3 = "tosa.const"() {value = 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>
@@ -1181,7 +1181,7 @@ func.func @test_while_loop_tensor_size_invalid(%arg0: tensor<536870912xi32>, %ar
// -----
func.func @test_const_shape() -> !tosa.shape<4> {
- %cst = tosa.const_shape { value = dense<[1, 1, 536870912, 1]> : tensor<4xindex> } : () -> !tosa.shape<4>
+ %cst = tosa.const_shape {value = dense<[1, 1, 536870912, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
return %cst : !tosa.shape<4>
}
@@ -1228,7 +1228,7 @@ func.func @test_variable_read_write_rank_invalid() -> () {
// CHECK-LABEL: @test_while_loop
func.func @test_while_loop(%arg0: tensor<1x1x1x1x1x1x1xf32>, %arg1: tensor<i32>) {
- %0 = "tosa.const"() {value = 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>
@@ -1236,7 +1236,7 @@ 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"() {value = 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>)
@@ -1268,7 +1268,7 @@ func.func @test_unranked_tensor(%arg0: tensor<*xf32>) {
// CHECK-LABEL: test_concat_tensor_list_size
func.func @test_concat_tensor_list_size() {
- %0 = "tosa.const"() {value = 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 for MAX_TENSOR_LIST_SIZE: input1}}
%1= tosa.concat %0, %0, %0, %0, %0, %0, %0, %0,
%0, %0, %0, %0, %0, %0, %0, %0,
@@ -1297,7 +1297,7 @@ func.func @test_concat_tensor_list_size() {
// CHECK-LABEL: test_custom_tensor_list_size
func.func @test_custom_tensor_list_size() {
- %0 = "tosa.const"() {value = 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 for MAX_TENSOR_LIST_SIZE: input_list}}
%1= tosa.custom %0, %0, %0, %0, %0, %0, %0, %0,
%0, %0, %0, %0, %0, %0, %0, %0,
@@ -1326,7 +1326,7 @@ 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"() {value = 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 for MAX_TENSOR_LIST_SIZE: output_list}}
%r:65 = tosa.custom %0 { domain_name = "tosa_mlir_test", operator_name = "custom_test", implementation_attrs = "" }:
@@ -1349,7 +1349,7 @@ 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"() {value = 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 for MAX_TENSOR_LIST_SIZE: inputs}}
%1 = "tosa.cond_if"(%arg0, // condition
%0, %0, %0, %0, %0, %0, %0, %0,
@@ -1386,7 +1386,7 @@ 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>) {
- %0 = "tosa.const"() {value = 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 for MAX_TENSOR_LIST_SIZE: outputs}}
%r:65 = "tosa.cond_if"(%arg0) ({
@@ -1414,7 +1414,7 @@ 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"() {value = 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 for MAX_TENSOR_LIST_SIZE: inputs}}
%1:2 = "tosa.while_loop"(%0, %arg0,
%0, %0, %0, %0, %0, %0, %0, %0,
@@ -1432,7 +1432,7 @@ func.func @test_while_tensor_list_size(%arg0: tensor<1x1x1x1x1x1x1xf32>, %arg1:
"tosa.yield"(%3) : (tensor<1xi1>) -> ()
}, {
^bb0(%arg3: tensor<i32>, %arg4: tensor<1x1x1x1x1x1x1xf32>):
- %2 = "tosa.const"() {value = 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<1xi32>, tensor<1x1x1x1x1x1x1xf32>,
@@ -1453,7 +1453,7 @@ func.func @test_while_tensor_list_size(%arg0: tensor<1x1x1x1x1x1x1xf32>, %arg1:
// CHECK-LABEL: test_while_tensor_list_size_outputs
func.func @test_while_tensor_list_size_outputs(%arg0: tensor<1x1x1x1x1x1x1xf32>, %arg1: tensor<1xi32>) {
- %0 = "tosa.const"() {value = 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 for MAX_TENSOR_LIST_SIZE: outputs}}
%1:65 = "tosa.while_loop"(%0, %arg0) ({
^bb0(%arg3: tensor<1xi32>, %arg4: tensor<1x1x1x1x1x1x1xf32>):
@@ -1462,7 +1462,7 @@ func.func @test_while_tensor_list_size_outputs(%arg0: tensor<1x1x1x1x1x1x1xf32>,
"tosa.yield"(%3) : (tensor<1xi1>) -> ()
}, {
^bb0(%arg3: tensor<i32>, %arg4: tensor<1x1x1x1x1x1x1xf32>):
- %2 = "tosa.const"() {value = 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<1xi32>, tensor<1x1x1x1x1x1x1xf32>) -> ( tensor<i32>, tensor<1x1x1x1x1x1x1xf32>,
diff --git a/mlir/test/Dialect/Tosa/ops.mlir b/mlir/test/Dialect/Tosa/ops.mlir
index 4c2cda8d9c027..22be702fd8801 100644
--- a/mlir/test/Dialect/Tosa/ops.mlir
+++ b/mlir/test/Dialect/Tosa/ops.mlir
@@ -12,8 +12,8 @@ func.func @test_argmax(%arg0: tensor<14x19xf32>) -> tensor<14xi32> {
// -----
// CHECK-LABEL: avg_pool2d_f32
func.func @test_avg_pool2d_f32(%arg0: tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32> {
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %output_zp = "tosa.const"() <{value = 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>
%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>
return %0 : tensor<1x7x7x9xf32>
}
@@ -21,8 +21,8 @@ func.func @test_avg_pool2d_f32(%arg0: tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32
// -----
// CHECK-LABEL: avg_pool2d_f16
func.func @test_avg_pool2d_f16(%arg0: tensor<1x7x7x9xf16>) -> tensor<1x7x7x9xf16> {
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
- %output_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %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>
return %0 : tensor<1x7x7x9xf16>
}
@@ -30,8 +30,8 @@ func.func @test_avg_pool2d_f16(%arg0: tensor<1x7x7x9xf16>) -> tensor<1x7x7x9xf16
// -----
// CHECK-LABEL: avg_pool2d_f16_accumf32
func.func @test_avg_pool2d_f16_accumf32(%arg0: tensor<1x7x7x9xf16>) -> tensor<1x7x7x9xf16> {
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
- %output_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %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>
return %0 : tensor<1x7x7x9xf16>
}
@@ -39,8 +39,8 @@ func.func @test_avg_pool2d_f16_accumf32(%arg0: tensor<1x7x7x9xf16>) -> tensor<1x
// -----
// CHECK-LABEL: avg_pool2d_i8
func.func @test_avg_pool2d_i8(%arg0: tensor<1x7x7x9xi8>) -> tensor<1x7x7x9xi8> {
- %input_zp = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %output_zp = "tosa.const"() <{value = dense<0> : 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>
%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>
return %0 : tensor<1x7x7x9xi8>
}
@@ -48,8 +48,8 @@ func.func @test_avg_pool2d_i8(%arg0: tensor<1x7x7x9xi8>) -> tensor<1x7x7x9xi8> {
// -----
// CHECK-LABEL: avg_pool2d_i16
func.func @test_avg_pool2d_i16(%arg0: tensor<1x7x7x9xi16>) -> tensor<1x7x7x9xi16> {
- %input_zp = "tosa.const"() <{value = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
- %output_zp = "tosa.const"() <{value = dense<0> : tensor<1xi16>}> : () -> tensor<1xi16>
+ %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>
return %0 : tensor<1x7x7x9xi16>
}
@@ -57,8 +57,8 @@ func.func @test_avg_pool2d_i16(%arg0: tensor<1x7x7x9xi16>) -> tensor<1x7x7x9xi16
// -----
// CHECK-LABEL: avg_pool2d_q8
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"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- %output_zp = "tosa.const"() <{value = dense<0> : 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>
%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>>
return %0 : tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
}
@@ -73,7 +73,7 @@ func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %
// -----
// 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" () { value = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
+ %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>>
return %0 : tensor<1x4x4x8x!quant.uniform<i32:f32, 0.01>>
}
@@ -81,7 +81,7 @@ func.func @test_conv2d_quant_uniform(%arg0: tensor<1x4x4x4x!quant.uniform<i8:f32
// -----
// 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" () { value = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
+ %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>>>
return %0 : tensor<1x4x4x8x!quant.any<i32<-8:7>>>
}
@@ -89,10 +89,10 @@ func.func @test_conv2d_quant_any(%arg0: tensor<1x4x4x4x!quant.any<i8<-8:7>>>, %a
// -----
// CHECK-LABEL: conv2d_q8xi4
func.func @test_conv2d_q8xi4(%arg0: tensor<1x11x11x3xi8>) -> tensor<1x1x1x3xi8> {
- %0 = "tosa.const"() {value = dense<0> : tensor<3x11x11x3xi4>} : () -> tensor<3x11x11x3xi4>
- %1 = "tosa.const"() {value = dense<[12, 23, 55]> : tensor<3xi32>} : () -> tensor<3xi32>
- %izp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %wzp = "tosa.const"() {value = dense<0> : tensor<1xi4>} : () -> tensor<1xi4>
+ %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>
%3 = "tosa.rescale"(%2) {double_round = true, input_zp = 0 : i32, multiplier = array<i32: 2026291432, 1079222024, 1693132724>, output_zp = 27 : i32, per_channel = true, scale32 = true, shift = array<i8: 37, 36, 37>, input_unsigned = false, output_unsigned = false} : (tensor<1x1x1x3xi32>) -> tensor<1x1x1x3xi8>
return %3 : tensor<1x1x1x3xi8>
@@ -353,7 +353,7 @@ func.func @test_min(%arg0: tensor<13x21x3xf32>, %arg1: tensor<1x21x3xf32>) -> te
// -----
// CHECK-LABEL: test_mul_scalar_with_unranked_output
func.func @test_mul_scalar_with_unranked_output(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<*xf32> {
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<f32>, tensor<f32>, tensor<1xi8>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
@@ -361,7 +361,7 @@ func.func @test_mul_scalar_with_unranked_output(%arg0: tensor<f32>, %arg1: tenso
// -----
// CHECK-LABEL: test_mul_scalar
func.func @test_mul_scalar(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<f32>, tensor<f32>, tensor<1xi8>) -> tensor<f32>
return %0 : tensor<f32>
}
@@ -369,7 +369,7 @@ func.func @test_mul_scalar(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32
// -----
// CHECK-LABEL: test_mul
func.func @test_mul(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf32>) -> tensor<13x21x3xf32> {
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<13x21x3xf32>, tensor<13x1x3xf32>, tensor<1xi8>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -377,7 +377,7 @@ func.func @test_mul(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf32>) -> te
// -----
// CHECK-LABEL: i32_mul
func.func @test_i32_mul(%arg0: tensor<13x21x3xi32>, %arg1: tensor<13x1x3xi32>) -> tensor<13x21x3xi32> {
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<13x21x3xi32>, tensor<13x1x3xi32>, tensor<1xi8>) -> tensor<13x21x3xi32>
return %0 : tensor<13x21x3xi32>
}
@@ -385,7 +385,7 @@ func.func @test_i32_mul(%arg0: tensor<13x21x3xi32>, %arg1: tensor<13x1x3xi32>) -
// -----
// CHECK-LABEL: mul
func.func @test_mul_relaxed_result_type(%arg0: tensor<13x21x3xi16>, %arg1: tensor<13x1x3xi16>) -> tensor<13x21x3xi16> {
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<13x21x3xi16>, tensor<13x1x3xi16>, tensor<1xi8>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
@@ -595,7 +595,7 @@ func.func @test_concat(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>) -
// -----
// CHECK-LABEL: pad_explicit_value
func.func @test_pad_explicit_value(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
- %0 = "tosa.const"() {value = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %0 = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
%padding = tosa.const_shape {value = 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>
@@ -718,7 +718,7 @@ func.func @test_rescale(%arg0: tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439
// -----
// CHECK-LABEL: const
func.func @test_const(%arg0 : index) -> tensor<4xi32> {
- %0 = "tosa.const"() {value = 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>
}
@@ -745,14 +745,14 @@ 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"() {value = 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"() {value = 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 {value = dense<[1]> : tensor<1xindex>} : () -> !tosa.shape<1>
%4 = tosa.reshape %2, %7 : (tensor<i32>, !tosa.shape<1>) -> tensor<1xi32>
diff --git a/mlir/test/Dialect/Tosa/quant-test.mlir b/mlir/test/Dialect/Tosa/quant-test.mlir
index 0ed55ce7a1a6b..447a6ef7f9e05 100644
--- a/mlir/test/Dialect/Tosa/quant-test.mlir
+++ b/mlir/test/Dialect/Tosa/quant-test.mlir
@@ -12,8 +12,8 @@ func.func @test_build_qtype(%arg0 : tensor<16x1x1x8x!quant.uniform<u8<1:255>:f32
// 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"() {value = dense<-1> : tensor<1xi8>} : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() {value = dense<1> : tensor<1xi8>} : () -> tensor<1xi8>
+ %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-decompose-depthwise.mlir b/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
index 3eb72c6bcccf6..f97856176cfd7 100644
--- a/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
@@ -22,7 +22,7 @@ func.func @depthwise_conv2d_as_mul(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<1
// CHECK: %[[VAR5:.*]] = tosa.add %[[VAR3]], %[[VAR4]]
// CHECK-SAME: -> tensor<4x10x10x6xf32>
// CHECK: return %[[VAR5]]
- %zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %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>
return %0 : tensor<4x10x10x6xf32>
}
@@ -32,12 +32,12 @@ 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 {value = dense<[4, 10, 10, 2, 1]> : tensor<5xindex>
- // CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{value = dense<7> : tensor<1x1x1x1x1xi32>}
- // CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{value = dense<11> : tensor<1x1x1x1xi32>}
+ // 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 {value = dense<[1, 1, 1, 2, 3]> : tensor<5xindex>
// CHECK-DAG: %[[CONST4:.+]] = tosa.const_shape {value = dense<[4, 10, 10, 6]> : tensor<4xindex>
// CHECK-DAG: %[[CONST5:.+]] = tosa.const_shape {value = dense<[1, 1, 1, 6]> : tensor<4xindex>
- // CHECK-DAG: %[[SHIFT:.*]] = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ // 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>
// CHECK: %[[CAST_W:.+]] = tosa.cast %arg1 : (tensor<1x1x2x3xi8>) -> tensor<1x1x2x3xi32>
@@ -48,8 +48,8 @@ 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"() {value = dense<7> : tensor<1xi8>} : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() {value = dense<11> : tensor<1xi8>} : () -> tensor<1xi8>
+ %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>
return %0 : tensor<4x10x10x6xi32>
}
@@ -60,11 +60,11 @@ func.func @depthwise_conv2d_as_mul_q(%arg0: tensor<4x10x10x2xi8>, %arg1: tensor<
func.func @depthwise_conv2d_as_mul_padded(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<1x1x2x3xf32>, %arg2: tensor<6xf32>) -> tensor<4x12x12x6xf32> {
// CHECK-DAG: %[[CONST0:.+]] = tosa.const_shape {value = dense<[4, 10, 10, 2, 1]> : tensor<5xindex>}
// CHECK-DAG: %[[PAD:.+]] = tosa.const_shape {value = dense<[0, 0, 1, 1, 1, 1, 0, 0, 0, 0]> : tensor<10xindex>} : () -> !tosa.shape<10>
- // CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<1xf32>}
+ // CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}
// CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {value = dense<[1, 1, 1, 2, 3]> : tensor<5xindex>}
// CHECK-DAG: %[[CONST4:.+]] = tosa.const_shape {value = dense<[4, 12, 12, 6]> : tensor<4xindex>}
// CHECK-DAG: %[[CONST5:.+]] = tosa.const_shape {value = dense<[1, 1, 1, 6]> : tensor<4xindex>}
- // CHECK-DAG: %[[SHIFT:.*]] = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ // 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>
// CHECK: %[[RESHAPE_ARG1:.+]] = tosa.reshape %arg1, %[[CONST3]]
@@ -72,7 +72,7 @@ func.func @depthwise_conv2d_as_mul_padded(%arg0: tensor<4x10x10x2xf32>, %arg1: t
// CHECK: %[[RESHAPE_O:.+]] = tosa.reshape %[[MUL]], %[[CONST4]]
// CHECK: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2, %[[CONST5]]
// CHECK: %[[ADD:.+]] = tosa.add %[[RESHAPE_O]], %[[RESHAPE_ARG2]]
- %zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %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>
return %0 : tensor<4x12x12x6xf32>
}
@@ -83,8 +83,8 @@ func.func @depthwise_conv2d_as_mul_padded(%arg0: tensor<4x10x10x2xf32>, %arg1: t
// CHECK-LABEL: @depthwise_conv2d_quant_type
func.func @depthwise_conv2d_quant_type(%arg0: tensor<4x10x10x2x!quant.uniform<i8:f32, 0.015684768557548523>>, %arg1: tensor<1x1x2x3x!quant.uniform<i8<-127:127>:f32, 0.015680249780416489>>, %arg2: tensor<6xi32>) -> tensor<4x10x10x6x!quant.uniform<i32:f32, 0.078431375324726104>> {
- %0 = "tosa.const"() <{value = dense<7> : tensor<1xi8>}> : () -> tensor<1xi8>
- %1 = "tosa.const"() <{value = dense<11> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>>
return %2 : tensor<4x10x10x6x!quant.uniform<i32:f32, 0.078431375324726104>>
diff --git a/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir b/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
index 0a41931b24523..8fae1455e7b33 100644
--- a/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
@@ -2,12 +2,12 @@
// CHECK-LABEL: @transpose_conv2d
func.func @transpose_conv2d(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) -> tensor<2x18x19x5xf32> {
- // CHECK-DAG: %[[ZP:.+]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
+ // 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: 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>
- %zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %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>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x18x19x5xf32>
return %0 : tensor<2x18x19x5xf32>
}
@@ -17,13 +17,13 @@ func.func @transpose_conv2d(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3x
// CHECK-LABEL: @transpose_conv2d_quantized
func.func @transpose_conv2d_quantized(%arg0: tensor<2x16x14x3xi8>, %arg1: tensor<5x3x6x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x18x19x5xi32>) {
- // CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{value = dense<-6> : tensor<1xi8>}
- // CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{value = dense<11> : tensor<1xi8>}
+ // 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"() {value = dense<-6> : tensor<1xi8>} : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() {value = dense<11> : tensor<1xi8>} : () -> tensor<1xi8>
+ %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>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xi8>, tensor<5x3x6x3xi8>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x18x19x5xi32>
return %0 : tensor<2x18x19x5xi32>
}
@@ -32,13 +32,13 @@ func.func @transpose_conv2d_quantized(%arg0: tensor<2x16x14x3xi8>, %arg1: tensor
// CHECK-LABEL: @transpose_conv2d_quantized_padded
func.func @transpose_conv2d_quantized_padded(%arg0: tensor<2x16x14x3xi8>, %arg1: tensor<5x3x6x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x21x26x5xi32>) {
- // CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{value = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
- // CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{value = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
+ // 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>}
- %input_zp = "tosa.const"() <{value = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() <{value = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>,
@@ -69,8 +69,8 @@ func.func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<
// CHECK-DAG: %[[NEWINPUT:.+]] = tosa.pad %arg0, %[[PAD]]
// Manipulate the final shape.
- // CHECK-DAG: %[[ZP:.+]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
- // CHECK-DAG: %[[BIAS:.+]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<30xf32>}
+ // 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 {value = dense<[2, 18, 16, 2, 3, 5]> : tensor<6xindex>}
// CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = tosa.reshape %[[CONV]], %[[CONST6]]
@@ -81,7 +81,7 @@ func.func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<
// CHECK-DAG: %[[CONST9:.+]] = tosa.const_shape {value = 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"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %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>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 2, 3>} : (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>
@@ -93,8 +93,8 @@ func.func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<
func.func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1: tensor<5x3x5x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x35x47x5xi32>) {
// Manipulate the weight matrix to handle striding.
- // CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{value = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
- // CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{value = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
+ // 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 {value = 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 {value = dense<[5, 2, 2, 2, 3, 3]> : tensor<6xindex>}
@@ -110,7 +110,7 @@ func.func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1
// CHECK-DAG: %[[NEWINPUT:.+]] = tosa.pad %arg0, %[[PAD]], %[[INPUT_ZP]]
// Manipulate the final shape.
- // CHECK-DAG: %[[BIAS:.+]] = "tosa.const"() <{value = dense<0> : tensor<30xi32>}
+ // 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 {value = dense<[2, 18, 16, 2, 3, 5]> : tensor<6xindex>}
// CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = tosa.reshape %[[CONV]], %[[CONST6]]
@@ -123,8 +123,8 @@ func.func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1
// CHECK-DAG: %[[CONST9:.+]] = tosa.const_shape {value = 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"() <{value = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() <{value = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>
return %0 : tensor<2x35x47x5xi32>
}
@@ -135,11 +135,11 @@ func.func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1
func.func @transpose_conv2d_strided_overpad(%arg0 : tensor<1x16x1x1xi8>, %arg1 : tensor<1x2x1x1xi8>, %arg2 : tensor<1xi32>) -> (tensor<1x19x2x1xi32>) {
// CHECK-DAG: %[[WEIGHT_PAD:.+]] = tosa.const_shape {value = dense<[0, 0, 0, 0, 0, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
// CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape {value = dense<[1, 2, 1, 1, 2, 1]> : tensor<6xindex>}
- // CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{value = dense<-103> : tensor<1xi8>}> : () -> tensor<1xi8>
- // CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{value = dense<93> : tensor<1xi8>}> : () -> tensor<1xi8>
+ // 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 {value = dense<[2, 2, 1, 1]> : tensor<4xindex>}
// CHECK-DAG: %[[INPUT_PAD:.+]] = tosa.const_shape {value = dense<[0, 0, 1, 1, 0, 0, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
- // CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{value = dense<0> : tensor<2xi32>}
+ // CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{values = dense<0> : tensor<2xi32>}
// CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape {value = dense<[1, 17, 1, 1, 2, 1]> : tensor<6xindex>}
// CHECK-DAG: %[[CONST8:.+]] = tosa.const_shape {value = dense<[1, 17, 2, 1]> : tensor<4xindex>}
// CHECK-DAG: %[[RESULT_PAD:.+]] = tosa.const_shape {value = dense<[0, 0, 2, 0, 0, 0, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
@@ -158,8 +158,8 @@ func.func @transpose_conv2d_strided_overpad(%arg0 : tensor<1x16x1x1xi8>, %arg1 :
// 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"() <{value = dense<-103> : tensor<1xi8>}> : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() <{value = dense<93> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>,
diff --git a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
index 80e55912e131f..ac970d251a1dc 100644
--- a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
@@ -114,7 +114,7 @@ func.func @test_binary_scalar_f32(%arg0 : tensor<4xf32>, %arg1 : tensor<1xf32>)
// CHECK: tosa.minimum %arg0, %arg1 : (tensor<4xf32>, tensor<1xf32>) -> tensor<4xf32>
%2 = tosa.minimum %arg0, %arg1 : (tensor<4xf32>, tensor<1xf32>) -> tensor<*xf32>
- %3 = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %3 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: tosa.mul %arg0, %arg1, %3 : (tensor<4xf32>, tensor<1xf32>, tensor<1xi8>) -> tensor<4xf32>
%4 = tosa.mul %arg0, %arg1, %3 : (tensor<4xf32>, tensor<1xf32>, tensor<1xi8>) -> tensor<*xf32>
@@ -149,7 +149,7 @@ func.func @test_binary_broadcast_f32(%arg0 : tensor<4xf32>, %arg1 : tensor<1xf32
// CHECK: tosa.minimum %arg0, %arg1 : (tensor<4xf32>, tensor<1xf32>) -> tensor<4xf32>
%2 = tosa.minimum %arg0, %arg1 : (tensor<4xf32>, tensor<1xf32>) -> tensor<*xf32>
- %3 = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %3 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: tosa.mul %arg0, %arg1, %3 : (tensor<4xf32>, tensor<1xf32>, tensor<1xi8>) -> tensor<4xf32>
%4 = tosa.mul %arg0, %arg1, %3 : (tensor<4xf32>, tensor<1xf32>, tensor<1xi8>) -> tensor<*xf32>
@@ -209,7 +209,7 @@ func.func @test_binary_i32(%arg0 : tensor<4xi32>, %arg1 : tensor<1xi32>) -> () {
%10 = tosa.minimum %arg0, %arg1 : (tensor<4xi32>, tensor<1xi32>) -> tensor<*xi32>
// CHECK: tosa.mul %arg0, %arg1, %{{.*}} : (tensor<4xi32>, tensor<1xi32>, tensor<1xi8>) -> tensor<4xi32>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%11 = tosa.mul %arg0, %arg1, %shift : (tensor<4xi32>, tensor<1xi32>, tensor<1xi8>) -> tensor<*xi32>
// CHECK: tosa.pow %arg0, %arg1 : (tensor<4xi32>, tensor<1xi32>) -> tensor<4xi32>
@@ -469,7 +469,7 @@ 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 { value = dense<[1, 2, 3, 4]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %1 = "tosa.const"() {value = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %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
@@ -480,7 +480,7 @@ 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 { value = dense<[1, 2, 3, 4]> : tensor<4xindex> } : () -> !tosa.shape<4>
- %1 = "tosa.const"() {value = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
+ %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
@@ -671,8 +671,8 @@ func.func @scatter_minimum_static(%arg0 : tensor<?x4x?xi32>, %arg1 : tensor<3x?x
// CHECK-LABEL: @test_pool_static
func.func @test_pool_static(%arg0: tensor<3x5x6x7xf32>) {
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %output_zp = "tosa.const"() <{value = 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>
// 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>
@@ -704,8 +704,8 @@ func.func @conv2d_dynamic_input(%input: tensor<?x?x?x?xf32>, %weights: tensor<5x
// CHECK-LABEL: @test_pool_dynamic_input
func.func @test_pool_dynamic_input(%arg0: tensor<?x?x?x?xf32>) {
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %output_zp = "tosa.const"() <{value = 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>
// 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>
@@ -719,8 +719,8 @@ func.func @test_pool_dynamic_input(%arg0: tensor<?x?x?x?xf32>) {
// CHECK-LABEL: @test_pool_padded
func.func @test_pool_padded(%arg0: tensor<3x5x6x7xf32>) {
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %output_zp = "tosa.const"() <{value = 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>
// CHECK: -> tensor<3x5x11x7xf32>
%0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, kernel = array<i64: 4, 3>, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>} : (tensor<3x5x6x7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
@@ -752,8 +752,8 @@ func.func @conv2d_dynamic_bias(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3
// CHECK-LABEL: @test_pool_stride
func.func @test_pool_stride(%arg0: tensor<3x11x12x7xf32>) {
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %output_zp = "tosa.const"() <{value = 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>
// CHECK: -> tensor<3x4x4x7xf32>
%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<3x11x12x7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
@@ -1152,7 +1152,7 @@ func.func @while_test(%arg0 : tensor<i32>) -> (tensor<*xi32>) {
// CHECK: tosa.while_loop
// CHECK-SAME: (tensor<i32>) -> tensor<i32>
%1 = tosa.while_loop (%arg1 = %0) : (tensor<*xi32>) -> tensor<*xi32> {
- %2 = "tosa.const"() <{value = dense<3> : tensor<i32>}> : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<3> : tensor<i32>}> : () -> tensor<i32>
// CHECK: tosa.greater_equal
// CHECK-SAME: (tensor<i32>, tensor<i32>) -> tensor<i1>
@@ -1167,7 +1167,7 @@ func.func @while_test(%arg0 : tensor<i32>) -> (tensor<*xi32>) {
// CHECK: ^bb0
// CHECK-SAME: tensor<i32>
^bb0(%arg1: tensor<*xi32>):
- %2 = "tosa.const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
// CHECK: tosa.add
// CHECK-SAME: (tensor<i32>, tensor<i32>) -> tensor<i32>
@@ -1189,7 +1189,7 @@ func.func @while_test(%arg0 : tensor<i32>, %arg1 : tensor<1xi32>) -> () {
// CHECK: tosa.while_loop
// CHECK-SAME: (tensor<i32>, tensor<1xi32>) -> (tensor<i32>, tensor<?xi32>)
%0:2 = tosa.while_loop (%arg2 = %arg0, %arg3 = %arg1) : (tensor<i32>, tensor<1xi32>) -> (tensor<i32>, tensor<?xi32>) {
- %1 = "tosa.const"() <{value = dense<3> : tensor<i32>}> : () -> tensor<i32>
+ %1 = "tosa.const"() <{values = dense<3> : tensor<i32>}> : () -> tensor<i32>
// CHECK: tosa.greater_equal
// CHECK-SAME: (tensor<i32>, tensor<i32>) -> tensor<i1>
%2 = tosa.greater_equal %1, %arg2 : (tensor<i32>, tensor<i32>) -> tensor<i1>
@@ -1203,7 +1203,7 @@ func.func @while_test(%arg0 : tensor<i32>, %arg1 : tensor<1xi32>) -> () {
// CHECK-SAME: tensor<i32>
// CHECK-SAME: tensor<?xi32>
^bb0(%arg2: tensor<i32>, %arg3: tensor<?xi32>):
- %1 = "tosa.const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>
+ %1 = "tosa.const"() <{values = dense<1> : tensor<i32>}> : () -> tensor<i32>
// CHECK: tosa.add
// CHECK-SAME: (tensor<i32>, tensor<i32>) -> tensor<i32>
@@ -1235,7 +1235,7 @@ func.func @while_dont_crash(%arg0 : tensor<i32>) -> (tensor<*xi32>) {
// CHECK: tosa.while_loop
// CHECK-SAME: (tensor<i32>) -> tensor<i32>
%1 = tosa.while_loop (%arg1 = %0) : (tensor<*xi32>) -> tensor<*xi32> {
- %2 = "tosa.const"() <{value = dense<3> : tensor<i32>}> : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<3> : tensor<i32>}> : () -> tensor<i32>
// CHECK: tosa.greater_equal
// CHECK-SAME: (tensor<i32>, tensor<i32>) -> tensor<i1>
%3 = tosa.greater_equal %2, %arg1 : (tensor<i32>, tensor<*xi32>) -> tensor<*xi1>
@@ -1268,7 +1268,7 @@ func.func @while_dont_crash_nested(%arg0 : tensor<i32>) -> (tensor<*xi32>) {
// CHECK: tosa.while_loop
// CHECK-SAME: (tensor<i32>) -> tensor<i32>
%1 = tosa.while_loop (%arg1 = %0) : (tensor<*xi32>) -> tensor<*xi32> {
- %2 = "tosa.const"() <{value = dense<3> : tensor<i32>}> : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<3> : tensor<i32>}> : () -> tensor<i32>
// CHECK: tosa.greater_equal
// CHECK-SAME: (tensor<i32>, tensor<i32>) -> tensor<i1>
%3 = tosa.greater_equal %2, %arg1 : (tensor<i32>, tensor<*xi32>) -> tensor<*xi1>
@@ -1282,7 +1282,7 @@ func.func @while_dont_crash_nested(%arg0 : tensor<i32>) -> (tensor<*xi32>) {
// CHECK: tosa.while_loop
// CHECK-SAME: (tensor<i32>) -> tensor<i32>
%1 = tosa.while_loop (%arg2 = %arg1) : (tensor<*xi32>) -> tensor<*xi32> {
- %2 = "tosa.const"() <{value = dense<3> : tensor<i32>}> : () -> tensor<i32>
+ %2 = "tosa.const"() <{values = dense<3> : tensor<i32>}> : () -> tensor<i32>
// CHECK: tosa.greater_equal
// CHECK-SAME: (tensor<i32>, tensor<i32>) -> tensor<i1>
%4 = tosa.greater_equal %2, %arg2 : (tensor<i32>, tensor<*xi32>) -> tensor<*xi1>
@@ -1478,9 +1478,9 @@ func.func @test_multiple_non_inferrable_consumers(%arg0: tensor<1x2x8xf32>) {
// -----
// CHECK-LABEL: test_mul_scalar
func.func @test_mul_scalar(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<*xf32> {
- // CHECK: %[[SHIFT:.*]] = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ // CHECK: %[[SHIFT:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: tosa.mul %arg0, %arg1, %[[SHIFT]] : (tensor<f32>, tensor<f32>, tensor<1xi8>) -> tensor<f32>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<f32>, tensor<f32>, tensor<1xi8>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir b/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir
index ef557c0d81459..9d9af71bf1780 100644
--- a/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir
@@ -84,16 +84,16 @@ func.func @test_transpose_tracks_to_nullifying__converging_binary(%arg0: tensor<
// CHECK: %[[RES:.*]] = tosa.transpose %[[CONV3]]
// CHECK: return %[[RES]]
func.func @test_torch_conv2d_with_elementwise_in_between(%arg0: tensor<3x3x10x10xf32>) -> tensor<3x3x7x7xf32> {
- %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = "tosa.const"() <{value = dense_resource<torch_tensor_3_torch.float32_2> : tensor<3xf32>}> : () -> tensor<3xf32>
- %1 = "tosa.const"() <{value = dense_resource<torch_tensor_3_3_2_2_torch.float32_2> : tensor<3x3x2x2xf32>}> : () -> tensor<3x3x2x2xf32>
- %2 = "tosa.const"() <{value = dense_resource<torch_tensor_3_torch.float32_1> : tensor<3xf32>}> : () -> tensor<3xf32>
- %3 = "tosa.const"() <{value = dense_resource<torch_tensor_3_3_2_2_torch.float32_1> : tensor<3x3x2x2xf32>}> : () -> tensor<3x3x2x2xf32>
- %4 = "tosa.const"() <{value = dense_resource<torch_tensor_3_3_2_2_torch.float32> : tensor<3x3x2x2xf32>}> : () -> tensor<3x3x2x2xf32>
- %5 = "tosa.const"() <{value = dense_resource<torch_tensor_3_torch.float32> : tensor<3xf32>}> : () -> tensor<3xf32>
- %6 = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32>
- %7 = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
+ %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.const"() <{values = dense_resource<torch_tensor_3_torch.float32_2> : tensor<3xf32>}> : () -> tensor<3xf32>
+ %1 = "tosa.const"() <{values = dense_resource<torch_tensor_3_3_2_2_torch.float32_2> : tensor<3x3x2x2xf32>}> : () -> tensor<3x3x2x2xf32>
+ %2 = "tosa.const"() <{values = dense_resource<torch_tensor_3_torch.float32_1> : tensor<3xf32>}> : () -> tensor<3xf32>
+ %3 = "tosa.const"() <{values = dense_resource<torch_tensor_3_3_2_2_torch.float32_1> : tensor<3x3x2x2xf32>}> : () -> tensor<3x3x2x2xf32>
+ %4 = "tosa.const"() <{values = dense_resource<torch_tensor_3_3_2_2_torch.float32> : tensor<3x3x2x2xf32>}> : () -> tensor<3x3x2x2xf32>
+ %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>
@@ -114,13 +114,13 @@ func.func @test_torch_conv2d_with_elementwise_in_between(%arg0: tensor<3x3x10x10
// -----
// CHECK-LABEL: @test_mulop_conversion
-// CHECK-NEXT: %[[SHIFT:.*]] = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+// CHECK-NEXT: %[[SHIFT:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// 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>
- %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %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>
return %result : tensor<1x2x3x4xi32>
@@ -136,7 +136,7 @@ func.func @test_mulop_conversion(%arg0: tensor<1x2x3x4xi32>, %arg1: tensor<1x2x3
// CHECK: tosa.transpose %[[RESHAPED]] {perms = array<i32: 0, 2, 1>} : (tensor<1x3x2xi32>) -> tensor<1x2x3xi32>
func.func @test_basic_non_broadcasting_reshape(%arg0: tensor<2x3xi32>) -> tensor<1x2x3xi32> {
%shape = tosa.const_shape {value = dense<[1, 3, 2]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %perms = "tosa.const"() {value = dense<[0, 2, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
+ %perms = "tosa.const"() {values = dense<[0, 2, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
%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>
return %2 : tensor<1x2x3xi32>
@@ -150,7 +150,7 @@ func.func @test_basic_non_broadcasting_reshape(%arg0: tensor<2x3xi32>) -> tensor
// CHECK: return %[[RES]]
func.func @test_dynamic_broadcasting_reshape(%arg0: tensor<?xi32>) -> tensor<1x1x?xi32> {
%shape = tosa.const_shape {value = dense<[1, -1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
- %perms = "tosa.const"() {value = dense<[0, 2, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
+ %perms = "tosa.const"() {values = dense<[0, 2, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
%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>
return %2 : tensor<1x1x?xi32>
@@ -159,13 +159,13 @@ func.func @test_dynamic_broadcasting_reshape(%arg0: tensor<?xi32>) -> tensor<1x1
// -----
// CHECK-LABEL: @test_reshape_for_broadcast
-// CHECK-DAG: %[[RESHAPE_INPUT:.*]] = "tosa.const"() <{value = dense<[1, 2, 3, 4]>
+// CHECK-DAG: %[[RESHAPE_INPUT:.*]] = "tosa.const"() <{values = dense<[1, 2, 3, 4]>
// CHECK-DAG: %[[SHAPE:.*]] = tosa.const_shape {value = 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"() {value = dense<[1,2,3,4]> : tensor<4xi32>} : () -> tensor<4xi32>
+ %0 = "tosa.const"() {values = dense<[1,2,3,4]> : tensor<4xi32>} : () -> tensor<4xi32>
%1 = tosa.const_shape {value = 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>
@@ -181,11 +181,11 @@ func.func @test_reshape_for_broadcast(%arg0: tensor<4x3x2xi32>) -> tensor<4x3x2x
// CHECK-LABEL: @test_resnet18_common_case
// COM: note that %74 is now represented by %arg2
-// CHECK-DAG: %[[CONST0:.+]] = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
-// CHECK-DAG: %[[VAL_3:.*]] = "tosa.const"() <{value = dense_resource<torch_tensor_64_torch.float32_1> : tensor<64xf32>}> : () -> tensor<64xf32>
-// CHECK-DAG: %[[VAL_4:.*]] = "tosa.const"() <{value = dense_resource<torch_tensor_64_torch.float32> : tensor<64xf32>}> : () -> tensor<64xf32>
-// CHECK-DAG: %[[VAL_5:.*]] = "tosa.const"() <{value = dense<9.99999974E-6> : tensor<1xf32>}> : () -> tensor<1xf32>
-// CHECK-DAG: %[[VAL_6:.*]] = "tosa.const"() <{value = dense<5.000000e-01> : tensor<1xf32>}> : () -> tensor<1xf32>
+// CHECK-DAG: %[[CONST0:.+]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+// CHECK-DAG: %[[VAL_3:.*]] = "tosa.const"() <{values = dense_resource<torch_tensor_64_torch.float32_1> : tensor<64xf32>}> : () -> tensor<64xf32>
+// CHECK-DAG: %[[VAL_4:.*]] = "tosa.const"() <{values = dense_resource<torch_tensor_64_torch.float32> : tensor<64xf32>}> : () -> tensor<64xf32>
+// CHECK-DAG: %[[VAL_5:.*]] = "tosa.const"() <{values = dense<9.99999974E-6> : tensor<1xf32>}> : () -> tensor<1xf32>
+// CHECK-DAG: %[[VAL_6:.*]] = "tosa.const"() <{values = dense<5.000000e-01> : tensor<1xf32>}> : () -> tensor<1xf32>
// 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>
@@ -203,14 +203,14 @@ func.func @test_reshape_for_broadcast(%arg0: tensor<4x3x2xi32>) -> tensor<4x3x2x
// 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>
func.func @test_resnet18_common_case(%arg0: tensor<64xf32>, %arg1: tensor<64xf32>, %74: tensor<1x112x112x64xf32>) -> tensor<1x112x112x64xf32> {
- %shift = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %shift = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
%58 = tosa.const_shape {value = dense<[1, 64, 1, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
- %59 = "tosa.const"() <{value = dense_resource<torch_tensor_64_torch.float32_1> : tensor<64xf32>}> : () -> tensor<64xf32>
- %60 = "tosa.const"() <{value = dense_resource<torch_tensor_64_torch.float32> : tensor<64xf32>}> : () -> tensor<64xf32>
- %63 = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32>
- %64 = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
- %69 = "tosa.const"() <{value = dense<9.99999974E-6> : tensor<1xf32>}> : () -> tensor<1xf32>
- %70 = "tosa.const"() <{value = dense<5.000000e-01> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %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>
%76 = tosa.add %arg1, %69 : (tensor<64xf32>, tensor<1xf32>) -> tensor<64xf32>
%77 = tosa.pow %76, %70 : (tensor<64xf32>, tensor<1xf32>) -> tensor<64xf32>
@@ -273,7 +273,7 @@ func.func @test_no_transform_if_outside_fan_in_cone(%arg0: tensor<3x3x3x3xi32>)
// CHECK-DAG: %[[CLAMP:.*]] = tosa.clamp %[[RESHAPE]]
// 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"() <{value = dense<[0, 2, 1]> : tensor<3xi32>}> : () -> tensor<3xi32>
+ %0 = "tosa.const"() <{values = dense<[0, 2, 1]> : tensor<3xi32>}> : () -> tensor<3xi32>
%shape = tosa.const_shape {value = 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>
@@ -369,11 +369,11 @@ func.func @test_direct_use_in_other_transpose_with_same_perms(%arg0: tensor<3x3x
// -----
// CHECK-LABEL: @test_const_transpose
-// CHECK: %[[NEW:.*]] = "tosa.const"() <{value = dense<0> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
+// CHECK: %[[NEW:.*]] = "tosa.const"() <{values = dense<0> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
// CHECK-NOT: tosa.transpose
// CHECK: return %[[NEW]]
func.func @test_const_transpose() -> tensor<2x3xi32> {
- %0 = "tosa.const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
+ %0 = "tosa.const"() {values = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
%1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<3x2xi32>) -> tensor<2x3xi32>
return %1 : tensor<2x3xi32>
}
@@ -381,12 +381,12 @@ func.func @test_const_transpose() -> tensor<2x3xi32> {
// -----
// CHECK-LABEL: @test_transpose_tracks_to_const_single_step
-// CHECK: %[[NEW_CONST:.*]] = "tosa.const"() <{value = dense<0> : tensor<1x2x3x4xi32>}> : () -> tensor<1x2x3x4xi32>
+// 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-NOT: tosa.transpose
// CHECK: return %[[NEW_CLAMP]]
func.func @test_transpose_tracks_to_const_single_step() -> tensor<1x2x3x4xi32> {
- %0 = "tosa.const"() {value = dense<0> : tensor<1x3x4x2xi32>} : () -> tensor<1x3x4x2xi32>
+ %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>
return %1 : tensor<1x2x3x4xi32>
@@ -395,13 +395,13 @@ func.func @test_transpose_tracks_to_const_single_step() -> tensor<1x2x3x4xi32> {
// -----
// CHECK-LABEL: @test_static_unary_path_to_const
-// CHECK: %[[NEW_CONST:.*]] = "tosa.const"() <{value = dense<1> : tensor<1x2x3x4xi32>}> : () -> tensor<1x2x3x4xi32>
+// 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_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"() {value = dense<1> : tensor<1x3x4x2xi32>} : () -> tensor<1x3x4x2xi32>
+ %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>
%abs = tosa.abs %clamp : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
%bitwise_not = tosa.bitwise_not %abs : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
@@ -421,7 +421,7 @@ func.func @test_static_unary_path_to_const() -> 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>
- %const = "tosa.const"() {value = dense<[[[[1, 2], [3, 4], [5, 6], [7, 8]],
+ %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>
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