[Mlir-commits] [mlir] [mlir][tosa] Add acc_type to Tosa-v1.0 Conv Ops (PR #121466)
Jack Frankland
llvmlistbot at llvm.org
Thu Jan 2 02:17:07 PST 2025
https://github.com/FranklandJack created https://github.com/llvm/llvm-project/pull/121466
Tosa v1.0 adds accumulator type attributes to the various convolution operations defined in the spec. Update the dialect and any lit tests to include these attributes.
>From b367a8a73da51195b208ebf6e7db6e757a4a1327 Mon Sep 17 00:00:00 2001
From: Tai Ly <tai.ly at arm.com>
Date: Wed, 13 Mar 2024 17:36:11 +0000
Subject: [PATCH] [mlir][tosa] Add acc_type to Tosa-v1.0 Conv Ops
Tosa v1.0 adds accumulator type attributes to the various convolution
operations defined in the spec. Update the dialect and any lit tests to
include these attributes.
Signed-off-by: Tai Ly <tai.ly at arm.com>
---
.../mlir/Dialect/Tosa/IR/TosaOpBase.td | 10 ++-
mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td | 6 +-
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp | 74 +++++++++++++++++--
.../Transforms/TosaDecomposeTransposeConv.cpp | 11 ++-
.../TosaToLinalg/tosa-to-linalg-named.mlir | 36 ++++-----
mlir/test/Dialect/Tosa/canonicalize.mlir | 8 +-
mlir/test/Dialect/Tosa/invalid.mlir | 10 +--
mlir/test/Dialect/Tosa/level_check.mlir | 72 +++++++++---------
mlir/test/Dialect/Tosa/ops.mlir | 16 ++--
mlir/test/Dialect/Tosa/quant-test.mlir | 6 +-
.../Dialect/Tosa/tosa-decompose-conv2d.mlir | 8 +-
.../Tosa/tosa-decompose-depthwise.mlir | 6 +-
.../Tosa/tosa-decompose-transpose-conv.mlir | 16 ++--
mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir | 60 +++++++--------
mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp | 2 +-
15 files changed, 208 insertions(+), 133 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
index f5536927dc251d..d3f12c34421b06 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
@@ -126,11 +126,12 @@ def Tosa_ConvOpQuantInfoBuilder : OpBuilder<
(ins "::mlir::Type":$outputType, "::mlir::Value":$input,
"::mlir::Value":$weight, "::mlir::Value":$bias,
"::mlir::DenseI64ArrayAttr":$pad, "::mlir::DenseI64ArrayAttr":$stride,
- "::mlir::DenseI64ArrayAttr":$dilation),
+ "::mlir::DenseI64ArrayAttr":$dilation,
+ "::mlir::TypeAttr":$acc_type),
[{
buildConvOpWithQuantInfo($_builder, $_state, outputType,
input, weight, bias,
- pad, stride, dilation);
+ pad, stride, dilation, acc_type);
}]>;
// Handles tosa.transpose_conv2d which has an outpad and output shape attribute.
@@ -139,12 +140,13 @@ def Tosa_TransConvOpQuantInfoBuilder : OpBuilder<
"::mlir::Value":$weight, "mlir::Value":$bias,
"::mlir::DenseI64ArrayAttr":$outpad,
"::mlir::DenseI64ArrayAttr":$stride,
- "::mlir::DenseI64ArrayAttr":$outputShape),
+ "::mlir::DenseI64ArrayAttr":$outputShape,
+ "::mlir::TypeAttr":$acc_type),
[{
buildTransConvOpWithQuantInfo($_builder, $_state, outputType,
input, weight, bias,
outpad, stride,
- outputShape);
+ outputShape, acc_type);
}]>;
// The tosa.fully_connected op has its own builder as it does not have
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index 8ae5d3ab417b69..ec7fcd7749848b 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -57,7 +57,7 @@ def Tosa_ArgMaxOp : Tosa_InferShapedTypeOp<"argmax"> {
// Accumulator types.
//===----------------------------------------------------------------------===//
-def Tosa_AccType : AnyTypeOf<[I<32>, SI<32>, F16, F32]>;
+def Tosa_AccType : AnyTypeOf<[I<32>, I<48>, F16, F32]>;
//===----------------------------------------------------------------------===//
// Operator: avg_pool2d
@@ -106,6 +106,7 @@ def Tosa_Conv2DOp : Tosa_InferShapedTypeOp<"conv2d"> {
Tosa_IntArrayAttr4:$pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr2:$dilation,
+ TypeAttrOf<Tosa_AccType>:$acc_type,
OptionalAttr<Tosa_ConvOpQuantizationAttr>:$quantization_info,
DefaultValuedOptionalAttr<BoolAttr, "false">:$local_bound
);
@@ -135,6 +136,7 @@ def Tosa_Conv3DOp : Tosa_InferShapedTypeOp<"conv3d"> {
Tosa_IntArrayAttr6:$pad,
Tosa_IntArrayAttr3:$stride,
Tosa_IntArrayAttr3:$dilation,
+ TypeAttrOf<Tosa_AccType>:$acc_type,
OptionalAttr<Tosa_ConvOpQuantizationAttr>:$quantization_info,
DefaultValuedOptionalAttr<BoolAttr, "false">:$local_bound
);
@@ -165,6 +167,7 @@ def Tosa_DepthwiseConv2DOp : Tosa_InferShapedTypeOp<"depthwise_conv2d"> {
Tosa_IntArrayAttr4:$pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr2:$dilation,
+ TypeAttrOf<Tosa_AccType>:$acc_type,
OptionalAttr<Tosa_ConvOpQuantizationAttr>:$quantization_info,
DefaultValuedOptionalAttr<BoolAttr, "false">:$local_bound
);
@@ -348,6 +351,7 @@ def Tosa_TransposeConv2DOp : Tosa_InferShapedTypeOp<"transpose_conv2d"> {
Tosa_IntArrayAttr4:$out_pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr4:$out_shape,
+ TypeAttrOf<Tosa_AccType>:$acc_type,
OptionalAttr<Tosa_ConvOpQuantizationAttr>:$quantization_info,
DefaultValuedOptionalAttr<BoolAttr, "false">:$local_bound
);
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 631d3c48f2df02..cb844117508a45 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -271,6 +271,55 @@ LogicalResult tosa::ConstOp::verify() {
return success();
}
+template <typename T>
+static LogicalResult verifyConvOpModes(T op) {
+ auto inputEType =
+ llvm::cast<ShapedType>(op.getInput().getType()).getElementType();
+
+ if (auto quantType =
+ llvm::dyn_cast<mlir::quant::UniformQuantizedType>(inputEType))
+ inputEType = quantType.getStorageType();
+
+ auto accType = op.getAccType();
+ if (inputEType.isInteger(8) && !accType.isInteger(32))
+ return op.emitOpError("accumulator type for i8 tensor is not i32");
+
+ if (inputEType.isInteger(16) && !accType.isInteger(48))
+ return op.emitOpError("accumulator type for i16 tensor is not i48");
+
+ if ((inputEType.isFloat8E5M2() || inputEType.isFloat8E4M3FN()) &&
+ !accType.isF16())
+ return op.emitOpError("accumulator type for f8 tensor is not f16");
+
+ if (inputEType.isF16() && !(accType.isF16() || accType.isF32()))
+ return op.emitOpError("accumulator type for f16 tensor is not f16/f32");
+
+ if (inputEType.isBF16() && !accType.isF32())
+ return op.emitOpError("accumulator type for bf16 tensor is not f32");
+
+ if (inputEType.isF32() && !accType.isF32())
+ return op.emitOpError("accumulator type for f32 tensor is not f32");
+
+ auto resultEType =
+ llvm::cast<ShapedType>(op.getResult().getType()).getElementType();
+
+ if (auto quantType =
+ llvm::dyn_cast<mlir::quant::UniformQuantizedType>(resultEType))
+ resultEType = quantType.getStorageType();
+
+ // check allowed input/result element types combinations
+ if ((inputEType.isInteger(8) && resultEType.isInteger(32)) ||
+ (inputEType.isInteger(16) && resultEType.isInteger(48)) ||
+ (inputEType.isFloat8E5M2() && resultEType.isF16()) ||
+ (inputEType.isFloat8E4M3FN() && resultEType.isF16()) ||
+ (inputEType.isF16() && resultEType.isF16()) ||
+ (inputEType.isBF16() && resultEType.isBF16()) ||
+ (inputEType.isF32() && resultEType.isF32()))
+ return success();
+
+ return op.emitOpError("input/output element types are incompatible.");
+}
+
LogicalResult tosa::ArgMaxOp::verify() {
// Ensure output is of 32-bit integer
const auto resultETy = llvm::cast<ShapedType>(getType()).getElementType();
@@ -368,12 +417,14 @@ static void buildConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
Type outputType, Value input, Value weight,
Value bias, DenseI64ArrayAttr pad,
DenseI64ArrayAttr stride,
- DenseI64ArrayAttr dilation) {
+ DenseI64ArrayAttr dilation,
+ TypeAttr accType) {
result.addOperands({input, weight, bias});
result.addAttribute("pad", pad);
result.addAttribute("stride", stride);
result.addAttribute("dilation", dilation);
+ result.addAttribute("acc_type", accType);
auto quantAttr = buildConvOpQuantizationAttr(builder, input, weight);
if (quantAttr) {
@@ -390,11 +441,12 @@ static void buildConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
static void buildTransConvOpWithQuantInfo(
OpBuilder &builder, OperationState &result, Type outputType, Value input,
Value weight, Value bias, DenseI64ArrayAttr outpad,
- DenseI64ArrayAttr stride, DenseI64ArrayAttr outputShape) {
+ DenseI64ArrayAttr stride, DenseI64ArrayAttr outputShape, TypeAttr accType) {
result.addOperands({input, weight, bias});
result.addAttribute("out_pad", outpad);
result.addAttribute("stride", stride);
result.addAttribute("out_shape", outputShape);
+ result.addAttribute("acc_type", accType);
auto quantAttr = ::buildConvOpQuantizationAttr(builder, input, weight);
if (quantAttr) {
@@ -1595,7 +1647,11 @@ LogicalResult Conv2DOp::inferReturnTypeComponents(
return success();
}
-LogicalResult Conv2DOp::verify() { return verifyConvOp(*this); }
+LogicalResult Conv2DOp::verify() {
+ if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
+ return failure();
+ return success();
+}
LogicalResult Conv3DOp::inferReturnTypeComponents(
MLIRContext *context, ::std::optional<Location> location,
@@ -1667,7 +1723,11 @@ LogicalResult Conv3DOp::inferReturnTypeComponents(
return success();
}
-LogicalResult Conv3DOp::verify() { return verifyConvOp(*this); }
+LogicalResult Conv3DOp::verify() {
+ if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
+ return failure();
+ return success();
+}
LogicalResult AvgPool2dOp::inferReturnTypeComponents(
MLIRContext *context, ::std::optional<Location> location,
@@ -1762,7 +1822,11 @@ LogicalResult DepthwiseConv2DOp::inferReturnTypeComponents(
return success();
}
-LogicalResult DepthwiseConv2DOp::verify() { return verifyConvOp(*this); }
+LogicalResult DepthwiseConv2DOp::verify() {
+ if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
+ return failure();
+ return success();
+}
LogicalResult TransposeConv2DOp::inferReturnTypeComponents(
MLIRContext *context, ::std::optional<Location> location,
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
index 0779cdb9667a1a..275c2f80f7f49d 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
@@ -75,13 +75,15 @@ class TransposeConvNonStridedConverter
loc, resultTy, input, reverse2, bias,
rewriter.getDenseI64ArrayAttr(convPad),
rewriter.getDenseI64ArrayAttr(stride),
- rewriter.getDenseI64ArrayAttr({1, 1}), *op.getQuantizationInfo());
+ rewriter.getDenseI64ArrayAttr({1, 1}),
+ /* acc_type = */ op.getAccType(), *op.getQuantizationInfo());
} else {
conv2d = rewriter.create<tosa::Conv2DOp>(
loc, resultTy, input, reverse2, bias,
rewriter.getDenseI64ArrayAttr(convPad),
rewriter.getDenseI64ArrayAttr(stride),
- rewriter.getDenseI64ArrayAttr({1, 1}));
+ rewriter.getDenseI64ArrayAttr({1, 1}),
+ /* acc_type = */ op.getAccTypeAttr());
}
rewriter.replaceOp(op, conv2d);
@@ -238,7 +240,7 @@ class TransposeConvStridedConverter
/*pad=*/rewriter.getDenseI64ArrayAttr({0, 0, 0, 0}),
/*stride=*/rewriter.getDenseI64ArrayAttr({1, 1}),
/*dilation=*/rewriter.getDenseI64ArrayAttr({1, 1}),
- *op.getQuantizationInfo())
+ /* acc_type = */ op.getAccType(), *op.getQuantizationInfo())
.getResult();
} else {
conv2d = CreateOpAndInferShape<tosa::Conv2DOp>(
@@ -246,7 +248,8 @@ class TransposeConvStridedConverter
weight, zeroBias,
/*pad=*/rewriter.getDenseI64ArrayAttr({0, 0, 0, 0}),
/*stride=*/rewriter.getDenseI64ArrayAttr({1, 1}),
- /*dilation=*/rewriter.getDenseI64ArrayAttr({1, 1}))
+ /*dilation=*/rewriter.getDenseI64ArrayAttr({1, 1}),
+ /* acc_type = */ op.getAccTypeAttr())
.getResult();
}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
index bfdc72ee07e97f..453a8610e7169a 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -510,7 +510,7 @@ 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>) {
- %0 = tosa.conv2d %input, %weights, %bias {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<1xf32>) -> tensor<1x45x40x28xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {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<1x45x40x28xf32>
return
}
@@ -531,7 +531,7 @@ func.func @conv2d_i8(%input: tensor<1x49x42x27xi8>, %weights: tensor<28x1x1x27xi
// CHECK: linalg.conv_2d_nhwc_fhwc_q {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, %c0_i32, %c0_i32_0 : tensor<1x49x42x27xi8>, tensor<28x1x1x27xi8>, i32, i32) outs(%[[BROADCAST]] : tensor<1x45x40x28xi32>) -> tensor<1x45x40x28xi32>
// HWCF: linalg.conv_2d_nhwc_hwcf_q {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %[[TRANSPOSE]], %c0_i32, %c0_i32_0 : tensor<1x49x42x27xi8>, tensor<1x1x27x28xi8>, i32, i32) outs(%{{[a-zA-Z0-9_]*}} : tensor<1x45x40x28xi32>) -> tensor<1x45x40x28xi32>
- %0 = tosa.conv2d %input, %weights, %bias {dilation = array<i64: 2, 1>, pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = 0, weight_zp = 0>, stride = array<i64: 1, 1>} : (tensor<1x49x42x27xi8>, tensor<28x1x1x27xi8>, tensor<28xi8>) -> tensor<1x45x40x28xi32>
+ %0 = tosa.conv2d %input, %weights, %bias {acc_type = i32, dilation = array<i64: 2, 1>, pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = 0, weight_zp = 0>, stride = array<i64: 1, 1>} : (tensor<1x49x42x27xi8>, tensor<28x1x1x27xi8>, tensor<28xi8>) -> tensor<1x45x40x28xi32>
return
}
@@ -552,7 +552,7 @@ 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(%1 : 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>
- %0 = tosa.conv2d %input, %weights, %bias {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>) -> tensor<1x45x40x28xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {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<1x45x40x28xf32>
return
}
@@ -571,7 +571,7 @@ func.func @conv2d_dyn(%input: tensor<?x49x42x27xf32>, %weights: tensor<28x3x3x27
// CHECK: linalg.yield %[[IN]] : f32
// CHECK: } -> tensor<?x45x40x28xf32>
// CHECK: %2 = 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>
- %0 = tosa.conv2d %input, %weights, %bias {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<?x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>) -> tensor<?x45x40x28xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {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<?x45x40x28xf32>
return
}
@@ -627,7 +627,7 @@ 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(%17 : tensor<1x?x?x28xf32>) -> tensor<1x?x?x28xf32>
- %0 = tosa.conv2d %input, %weights, %bias {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<1x?x?x28xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {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<1x?x?x28xf32>
return
}
@@ -650,7 +650,7 @@ func.func @conv2d_dyn_output(%input: tensor<2x6x5x4xf32>, %weights: tensor<4x3x3
// linalg.yield %[[ADD]] : f32
// } -> tensor<?x4x3x4xf32>
- %0 = tosa.conv2d %input, %weights, %bias {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x6x5x4xf32 >, tensor<4x3x3x4xf32>, tensor<4xf32>) -> tensor<?x4x3x4xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {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<?x4x3x4xf32>
return
}
@@ -662,7 +662,7 @@ 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
- %0 = tosa.conv2d %input, %weights, %bias {pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x47x40x28xf32>, tensor<28x3x3x28xf32>, tensor<28xf32>) -> tensor<1x45x40x28xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {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<1x45x40x28xf32>
return
}
@@ -674,7 +674,7 @@ 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
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, stride = array<i64: 1, 1>} : (tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, tensor<1024xi32>) -> tensor<1x12x12x1024xi32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, stride = array<i64: 1, 1>} : (tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, tensor<1024xi32>) -> tensor<1x12x12x1024xi32>
return
}
@@ -696,7 +696,7 @@ 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>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 { pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>) -> tensor<1x5x5x33xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 { 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<1x5x5x33xf32>
return
}
@@ -712,7 +712,7 @@ 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>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 { pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {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<1x5x5x33xf32>
return
}
@@ -736,7 +736,7 @@ 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>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 { pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<?x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>) -> tensor<?x5x5x33xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 { 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<?x5x5x33xf32>
return
}
@@ -758,7 +758,7 @@ 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>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 { pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1> } : (tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>) -> tensor<1x5x5x33xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 { 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<1x5x5x33xf32>
return
}
@@ -786,7 +786,7 @@ 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>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {pad = array<i64: 1, 1, 1, 1>, quantization_info = #tosa.conv_quant<input_zp = -128, weight_zp = 42>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x12x12x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>) -> tensor<1x12x12x512xi32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = i32, pad = array<i64: 1, 1, 1, 1>, quantization_info = #tosa.conv_quant<input_zp = -128, weight_zp = 42>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x12x12x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>) -> tensor<1x12x12x512xi32>
return
}
@@ -810,7 +810,7 @@ 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>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -128, weight_zp = 42>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 2> } : (tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>) -> tensor<1x10x10x512xi32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = i32, pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -128, weight_zp = 42>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 2> } : (tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>) -> tensor<1x10x10x512xi32>
return
}
@@ -826,7 +826,7 @@ 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]]
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {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<2x?x?x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {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<2x?x?x15xf32>
return
}
@@ -850,7 +850,7 @@ 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>
- %0 = tosa.conv3d %input, %weights, %bias {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<1x47x45x43x28xf32>
+ %0 = tosa.conv3d %input, %weights, %bias {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<1x47x45x43x28xf32>
return
}
@@ -864,7 +864,7 @@ 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"]}
- %0 = tosa.conv3d %input, %weights, %bias {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<1x47x45x43x28xf32>
+ %0 = tosa.conv3d %input, %weights, %bias {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<1x47x45x43x28xf32>
return
}
@@ -892,7 +892,7 @@ 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>
- %0 = tosa.conv3d %input, %weights, %bias {pad = array<i64: 0, 0, 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -128, weight_zp = 42>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xi8>, tensor<28x3x4x5x27xi8>, tensor<28xi32>) -> tensor<1x47x45x43x28xi32>
+ %0 = tosa.conv3d %input, %weights, %bias {acc_type = i32, pad = array<i64: 0, 0, 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -128, weight_zp = 42>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xi8>, tensor<28x3x4x5x27xi8>, tensor<28xi32>) -> tensor<1x47x45x43x28xi32>
return
}
diff --git a/mlir/test/Dialect/Tosa/canonicalize.mlir b/mlir/test/Dialect/Tosa/canonicalize.mlir
index 67cd01f62f0bdf..8fdc03e5b1aa18 100644
--- a/mlir/test/Dialect/Tosa/canonicalize.mlir
+++ b/mlir/test/Dialect/Tosa/canonicalize.mlir
@@ -162,7 +162,7 @@ func.func @conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>) -> tensor<4x10x10x3xf32
// 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>
- %0 = tosa.conv2d %arg0, %weight, %bias {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<4x10x10x3xf32>
+ %0 = tosa.conv2d %arg0, %weight, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<4x10x10x3xf32>
return %0 : tensor<4x10x10x3xf32>
}
@@ -173,7 +173,7 @@ func.func @conv2d_weight_2x2(%arg0: tensor<4x10x10x1xf32>) -> tensor<4x10x10x1xf
// 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>
- %0 = tosa.conv2d %arg0, %weight, %bias {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x1xf32>, tensor<1x2x2x1xf32>, tensor<1xf32>) -> tensor<4x10x10x1xf32>
+ %0 = tosa.conv2d %arg0, %weight, %bias {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<4x10x10x1xf32>
return %0 : tensor<4x10x10x1xf32>
}
@@ -182,7 +182,7 @@ func.func @conv2d_weight_2x2(%arg0: tensor<4x10x10x1xf32>) -> tensor<4x10x10x1xf
// CHECK-LABEL: @depthwise_conv2d_stride_2
func.func @depthwise_conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<1x1x2x3xf32>, %arg2: tensor<6xf32>) -> tensor<4x10x10x6xf32> {
// CHECK: tosa.depthwise_conv2d
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
return %0 : tensor<4x10x10x6xf32>
}
@@ -191,7 +191,7 @@ func.func @depthwise_conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor
// CHECK-LABEL: @depthwise_conv2d_weight_2x2
func.func @depthwise_conv2d_weight_2x2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<2x2x2x3xf32>, %arg2: tensor<6xf32>) -> tensor<4x10x10x6xf32> {
// CHECK: tosa.depthwise_conv2d
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<2x2x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<2x2x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
return %0 : tensor<4x10x10x6xf32>
}
diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
index b796a6343e5ed1..917f9fbfb2b43b 100644
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -25,7 +25,7 @@ func.func @test_const_non_tensor_attr() {
func.func @test_conv2d(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
// expected-error at +1 {{expect both input and weight to be float or not together, got 'f32' and 'i8'}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2 {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<1x27x27x16xi8>
return %0 : 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> {
// expected-error at +1 {{expect a ranked tensor for input, got <block argument> of type 'tensor<*xi8>' at index: 0}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2 {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<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
}
@@ -43,7 +43,7 @@ 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> {
// 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 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2 {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<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
}
@@ -52,7 +52,7 @@ func.func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<*xi8>, %arg2:
func.func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
// expected-error at +1 {{'tosa.conv2d' op quantizationattr is required for quantized type, and not allowed for float type}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2 {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<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
}
@@ -425,7 +425,7 @@ func.func @test_const_attribute_type_mismatch() -> tensor<100x100xf32> {
func.func @test_conv2d_static_zero_dim_input(%arg0: tensor<1x29x0x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> {
// 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) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {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<1x27x27x16xf32>
return %0 : tensor<1x27x27x16xf32>
}
diff --git a/mlir/test/Dialect/Tosa/level_check.mlir b/mlir/test/Dialect/Tosa/level_check.mlir
index 529a16ca48c7eb..ba8ed8a1e5f50f 100644
--- a/mlir/test/Dialect/Tosa/level_check.mlir
+++ b/mlir/test/Dialect/Tosa/level_check.mlir
@@ -226,7 +226,7 @@ func.func @test_avgpool2d_pad_right(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32
func.func @test_conv2d_dilation_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: dilation_y * KH <= MAX_KERNEL}}
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 4097, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 4097, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -235,7 +235,7 @@ func.func @test_conv2d_dilation_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16
func.func @test_conv2d_dilation_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: dilation_x * KW <= MAX_KERNEL}}
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 4097>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 4097>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -244,7 +244,7 @@ func.func @test_conv2d_dilation_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16
func.func @test_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 8193, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 8193, 1, 0, 1>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -253,7 +253,7 @@ func.func @test_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x
func.func @test_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 8193, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 8193, 0, 1>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -262,7 +262,7 @@ func.func @test_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16
func.func @test_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 8193, 1>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 8193, 1>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -271,7 +271,7 @@ func.func @test_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2
func.func @test_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 8193>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 8193>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -280,7 +280,7 @@ func.func @test_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x
func.func @test_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 8193, 1>} :
+ %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 8193, 1>} :
(tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -289,7 +289,7 @@ func.func @test_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2
func.func @test_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 8193>} :
+ %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 8193>} :
(tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -298,7 +298,7 @@ func.func @test_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2
func.func @test_conv3d_dilation_d(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: dilation_d * KD <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = array<i64: 4097, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 4097, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -307,7 +307,7 @@ func.func @test_conv3d_dilation_d(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<
func.func @test_conv3d_dilation_y(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: dilation_y * KH <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 4097, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 4097, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -316,7 +316,7 @@ func.func @test_conv3d_dilation_y(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<
func.func @test_conv3d_dilation_x(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: dilation_x * KW <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1, 4097>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 4097>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -325,7 +325,7 @@ func.func @test_conv3d_dilation_x(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<
func.func @test_conv3d_pad_d0(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1, 1>, pad = array<i64: 8193, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 8193, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -334,7 +334,7 @@ func.func @test_conv3d_pad_d0(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2
func.func @test_conv3d_pad_d1(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1, 1>, pad = array<i64: 1, 8193, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 1, 8193, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -343,7 +343,7 @@ func.func @test_conv3d_pad_d1(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2
func.func @test_conv3d_pad_top(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 8193, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 8193, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -352,7 +352,7 @@ func.func @test_conv3d_pad_top(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x
func.func @test_conv3d_pad_bottom(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 8193, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 8193, 0, 1>, stride = array<i64: 1, 1, 1>} :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -361,7 +361,7 @@ func.func @test_conv3d_pad_bottom(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<
func.func @test_conv3d_pad_left(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 8193, 1>, stride = array<i64: 1, 1, 1>} :
+ %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 8193, 1>, stride = array<i64: 1, 1, 1>} :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -370,7 +370,7 @@ func.func @test_conv3d_pad_left(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16
func.func @test_conv3d_pad_right(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 8193>, stride = array<i64: 1, 1, 1>} :
+ %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 8193>, stride = array<i64: 1, 1, 1>} :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -379,7 +379,7 @@ func.func @test_conv3d_pad_right(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<1
func.func @test_conv3d_stride_d(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 8193, 1, 1>} :
+ %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 8193, 1, 1>} :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -388,7 +388,7 @@ func.func @test_conv3d_stride_d(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16
func.func @test_conv3d_stride_y(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 8193, 1>} :
+ %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 8193, 1>} :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -397,7 +397,7 @@ func.func @test_conv3d_stride_y(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16
func.func @test_conv3d_stride_x(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 8193>} :
+ %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 8193>} :
(tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
@@ -406,7 +406,7 @@ func.func @test_conv3d_stride_x(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16
func.func @test_depthwise_conv2d_dilation_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: dilation_y * KH <= MAX_KERNEL}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 4097, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 4097, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
@@ -415,7 +415,7 @@ func.func @test_depthwise_conv2d_dilation_y(%arg0: tensor<1x32x32x8xf32>, %arg1:
func.func @test_depthwise_conv2d_dilation_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: dilation_x * KW <= MAX_KERNEL}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 4097>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 4097>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
@@ -424,7 +424,7 @@ func.func @test_depthwise_conv2d_dilation_x(%arg0: tensor<1x32x32x8xf32>, %arg1:
func.func @test_depthwise_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 8193, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 8193, 1, 0, 1>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
@@ -433,7 +433,7 @@ func.func @test_depthwise_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: te
func.func @test_depthwise_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 8193, 0, 1>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 8193, 0, 1>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
@@ -442,7 +442,7 @@ func.func @test_depthwise_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1:
func.func @test_depthwise_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 8193, 1>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 8193, 1>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
@@ -451,7 +451,7 @@ func.func @test_depthwise_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: t
func.func @test_depthwise_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 8193>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 8193>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
@@ -460,7 +460,7 @@ func.func @test_depthwise_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1:
func.func @test_depthwise_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 8193, 1>} :
+ %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 8193, 1>} :
(tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
@@ -469,7 +469,7 @@ func.func @test_depthwise_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: t
func.func @test_depthwise_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 8193>} :
+ %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 8193>} :
(tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
@@ -603,7 +603,7 @@ func.func @test_rfft2d_input_w(%arg0: tensor<13x8x8193xf32>) -> (tensor<13x8x9xf
func.func @test_transpose_conv2d_weight_h(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x8193x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: KH <= MAX_KERNEL}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {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<16x8193x1x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -612,7 +612,7 @@ func.func @test_transpose_conv2d_weight_h(%arg0: tensor<1x32x32x8xf32>, %arg1: t
func.func @test_transpose_conv2d_weight_w(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x8193x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: KW <= MAX_KERNEL}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {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<16x1x8193x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -621,7 +621,7 @@ func.func @test_transpose_conv2d_weight_w(%arg0: tensor<1x32x32x8xf32>, %arg1: t
func.func @test_transpose_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = array<i64: 8193, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, out_pad = array<i64: 8193, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -630,7 +630,7 @@ func.func @test_transpose_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: te
func.func @test_transpose_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = array<i64: 0, 8193, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, out_pad = array<i64: 0, 8193, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -639,7 +639,7 @@ func.func @test_transpose_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1:
func.func @test_transpose_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = array<i64: 0, 0, 8193, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, out_pad = array<i64: 0, 0, 8193, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -648,7 +648,7 @@ func.func @test_transpose_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: t
func.func @test_transpose_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = array<i64: 0, 0, 0, 8193>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
+ %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, out_pad = array<i64: 0, 0, 0, 8193>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
(tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -657,7 +657,7 @@ func.func @test_transpose_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1:
func.func @test_transpose_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 8193, 1>} :
+ %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 8193, 1>} :
(tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
@@ -666,7 +666,7 @@ func.func @test_transpose_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: t
func.func @test_transpose_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 8193>} :
+ %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 8193>} :
(tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
diff --git a/mlir/test/Dialect/Tosa/ops.mlir b/mlir/test/Dialect/Tosa/ops.mlir
index 88fa94ae90db69..2a066eb9f27707 100644
--- a/mlir/test/Dialect/Tosa/ops.mlir
+++ b/mlir/test/Dialect/Tosa/ops.mlir
@@ -54,7 +54,7 @@ func.func @test_avg_pool2d_q8(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>
// -----
// CHECK-LABEL: conv2d
func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {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<1x4x4x8xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2 {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<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -63,7 +63,7 @@ func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %
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>
- %2 = "tosa.conv2d"(%arg0, %0, %1) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = 0, weight_zp = 0>, stride = array<i64: 1, 1>} : (tensor<1x11x11x3xi8>, tensor<3x11x11x3xi4>, tensor<3xi32>) -> tensor<1x1x1x3xi32>
+ %2 = "tosa.conv2d"(%arg0, %0, %1) {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = 0, weight_zp = 0>, stride = array<i64: 1, 1>} : (tensor<1x11x11x3xi8>, tensor<3x11x11x3xi4>, tensor<3xi32>) -> 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>} : (tensor<1x1x1x3xi32>) -> tensor<1x1x1x3xi8>
return %3 : tensor<1x1x1x3xi8>
}
@@ -71,28 +71,28 @@ func.func @test_conv2d_q8xi4(%arg0: tensor<1x11x11x3xi8>) -> tensor<1x1x1x3xi8>
// -----
// CHECK-LABEL: conv3d
func.func @test_conv3d(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<34xf32>) -> tensor<1x4x8x21x34xf32> {
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {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<1x4x8x21x34xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2 {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<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
// -----
// CHECK-LABEL: conv3d_with_local_bound
func.func @test_conv3d_with_local_bound(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<34xf32>) -> tensor<1x4x8x21x34xf32> {
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, local_bound = true} : (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<34xf32>) -> tensor<1x4x8x21x34xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, local_bound = true} : (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<34xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
// -----
// CHECK-LABEL: depthwise_conv2d
func.func @test_depthwise_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x4x2xf32>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xf32>, tensor<1x1x4x2xf32>, tensor<8xf32>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {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<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
// -----
// CHECK-LABEL: depthwise_conv2d_with_local_bound
func.func @test_depthwise_conv2d_with_local_bound(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x4x2xf32>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4xf32>, tensor<1x1x4x2xf32>, tensor<8xf32>) -> tensor<1x4x4x8xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4xf32>, tensor<1x1x4x2xf32>, tensor<8xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -162,14 +162,14 @@ func.func @test_rfft2d_with_local_bound(%arg0: tensor<13x8x16xf32>) -> (tensor<1
// -----
// CHECK-LABEL: transpose_conv2d
func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
// CHECK-LABEL: transpose_conv2d_with_local_bound
func.func @test_transpose_conv2d_with_local_bound(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>, local_bound = false} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>, local_bound = false} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
diff --git a/mlir/test/Dialect/Tosa/quant-test.mlir b/mlir/test/Dialect/Tosa/quant-test.mlir
index 82a87dbc2494ce..6437f12e3ff85e 100644
--- a/mlir/test/Dialect/Tosa/quant-test.mlir
+++ b/mlir/test/Dialect/Tosa/quant-test.mlir
@@ -10,9 +10,9 @@ 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<1x32x32x16x!quant.uniform<i8:f32, 0.078431375324726104>> {
+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<1x32x32x16x!quant.uniform<i32:f32, 0.078431375324726104>> {
// CHECK: tosa.conv2d
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {pad = array<i64: 1, 1, 2, 2>, dilation = array<i64: 2, 1>, stride = array<i64: 1, 1>, quantization_info = #tosa.conv_quant<input_zp = -1, weight_zp = 0>} : (tensor<1x32x32x8x!quant.uniform<i8:f32, 0.015684768557548523>>, tensor<16x1x1x8x!quant.uniform<i8<-127:127>:f32, 0.015680249780416489>>, tensor<16xi32>) -> tensor<1x32x32x16x!quant.uniform<i8:f32, 0.078431375324726104>>
- return %0 : tensor<1x32x32x16x!quant.uniform<i8:f32, 0.078431375324726104>>
+ %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = i32, pad = array<i64: 1, 1, 2, 2>, dilation = array<i64: 2, 1>, stride = array<i64: 1, 1>, quantization_info = #tosa.conv_quant<input_zp = -1, weight_zp = 0>} : (tensor<1x32x32x8x!quant.uniform<i8:f32, 0.015684768557548523>>, tensor<16x1x1x8x!quant.uniform<i8<-127:127>:f32, 0.015680249780416489>>, tensor<16xi32>) -> tensor<1x32x32x16x!quant.uniform<i32:f32, 0.078431375324726104>>
+ return %0 : tensor<1x32x32x16x!quant.uniform<i32:f32, 0.078431375324726104>>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-decompose-conv2d.mlir b/mlir/test/Dialect/Tosa/tosa-decompose-conv2d.mlir
index d876ccfb3b9110..9e53b013cb8339 100644
--- a/mlir/test/Dialect/Tosa/tosa-decompose-conv2d.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-decompose-conv2d.mlir
@@ -14,7 +14,7 @@ func.func @conv2d_as_fully_connected(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor
// CHECK: %[[VAR3:.*]] = tosa.reshape %[[VAR2]] {new_shape = array<i64: 4, 10, 10, 3>}
// CHECK-SAME: -> tensor<4x10x10x3xf32>
// CHECK: return %[[VAR3]]
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<4x10x10x3xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<4x10x10x3xf32>
return %0 : tensor<4x10x10x3xf32>
}
@@ -33,7 +33,7 @@ func.func @conv2d_as_fully_connected_quant(%arg0: tensor<4x10x10x2xi8>, %arg1: t
// CHECK: %[[VAR3:.*]] = tosa.reshape %[[VAR2]] {new_shape = array<i64: 4, 10, 10, 3>}
// CHECK-SAME: -> tensor<4x10x10x3xi32>
// CHECK: return %[[VAR3]]
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>, quantization_info = #tosa.conv_quant<input_zp = 42, weight_zp = 24>} : (tensor<4x10x10x2xi8>, tensor<3x1x1x2xi8>, tensor<3xi32>) -> tensor<4x10x10x3xi32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = i32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>, quantization_info = #tosa.conv_quant<input_zp = 42, weight_zp = 24>} : (tensor<4x10x10x2xi8>, tensor<3x1x1x2xi8>, tensor<3xi32>) -> tensor<4x10x10x3xi32>
return %0 : tensor<4x10x10x3xi32>
}
@@ -50,7 +50,7 @@ func.func @conv_with_dynamic_dim(%arg0: tensor<?x14x14x64xi8>, %arg1: tensor<384
// CHECK: %[[VAL_6:.*]] = tosa.reshape %[[VAL_5]] {new_shape = array<i64: -1, 14, 14, 384>} : (tensor<?x384xi32>) -> tensor<?x14x14x384xi32>
// CHECK: return %[[VAL_6]] : tensor<?x14x14x384xi32>
// CHECK: }
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -6, weight_zp = 11>, stride = array<i64: 1, 1>} : (tensor<?x14x14x64xi8>, tensor<384x1x1x64xi8>, tensor<384xi32>) -> tensor<?x14x14x384xi32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -6, weight_zp = 11>, stride = array<i64: 1, 1>} : (tensor<?x14x14x64xi8>, tensor<384x1x1x64xi8>, tensor<384xi32>) -> tensor<?x14x14x384xi32>
return %0 : tensor<?x14x14x384xi32>
}
@@ -65,6 +65,6 @@ func.func @conv2d_as_fully_connected_padded(%arg0: tensor<4x10x10x2xi8>, %arg1:
// CHECK-DAG: %[[RESHAPE_FILTER:.+]] = tosa.reshape %arg1 {new_shape = array<i64: 3, 2>}
// CHECK-DAG: %[[FULLY:.+]] = tosa.fully_connected %[[RESHAPE_INPUT]], %[[RESHAPE_FILTER]], %arg2 {quantization_info = #tosa.conv_quant<input_zp = 42, weight_zp = 24>}
// CHECK: %[[RESHAPE:.+]] = tosa.reshape %[[FULLY]] {new_shape = array<i64: 4, 12, 12, 3>}
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>, quantization_info = #tosa.conv_quant<input_zp = 42, weight_zp = 24>} : (tensor<4x10x10x2xi8>, tensor<3x1x1x2xi8>, tensor<3xi32>) -> tensor<4x12x12x3xi32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = i32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>, quantization_info = #tosa.conv_quant<input_zp = 42, weight_zp = 24>} : (tensor<4x10x10x2xi8>, tensor<3x1x1x2xi8>, tensor<3xi32>) -> tensor<4x12x12x3xi32>
return %0 : tensor<4x12x12x3xi32>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir b/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
index 2224bf3f57b255..f35a3f8761bb5b 100644
--- a/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
@@ -18,7 +18,7 @@ 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]]
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {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<4x10x10x6xf32>
return %0 : tensor<4x10x10x6xf32>
}
@@ -38,7 +38,7 @@ func.func @depthwise_conv2d_as_mul_q(%arg0: tensor<4x10x10x2xi8>, %arg1: tensor<
// CHECK: %[[reO:.+]] = tosa.reshape %[[mul]] {new_shape = array<i64: 4, 10, 10, 6>}
// CHECK: %[[reArg2:.+]] = tosa.reshape %arg2 {new_shape = array<i64: 1, 1, 1, 6>}
// CHECK: %[[add:.+]] = tosa.add %[[reO]], %[[reArg2]]
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>, quantization_info = #tosa.conv_quant<input_zp = 7, weight_zp = 11>} : (tensor<4x10x10x2xi8>, tensor<1x1x2x3xi8>, tensor<6xi32>) -> tensor<4x10x10x6xi32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = i32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>, quantization_info = #tosa.conv_quant<input_zp = 7, weight_zp = 11>} : (tensor<4x10x10x2xi8>, tensor<1x1x2x3xi8>, tensor<6xi32>) -> tensor<4x10x10x6xi32>
return %0 : tensor<4x10x10x6xi32>
}
@@ -55,6 +55,6 @@ func.func @depthwise_conv2d_as_mul_padded(%arg0: tensor<4x10x10x2xf32>, %arg1: t
// CHECK: %[[reOut:.+]] = tosa.reshape %[[mul]] {new_shape = array<i64: 4, 12, 12, 6>}
// CHECK: %[[reArg2:.+]] = tosa.reshape %arg2 {new_shape = array<i64: 1, 1, 1, 6>}
// CHECK: %[[add:.+]] = tosa.add %[[reOut]], %[[reArg2]]
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>) -> tensor<4x12x12x6xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {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<4x12x12x6xf32>
return %0 : tensor<4x12x12x6xf32>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir b/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
index 1f2bb3fb9a3657..41c146d707d65e 100644
--- a/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
@@ -6,7 +6,7 @@ func.func @transpose_conv2d(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3x
// CHECK: %[[REV2:.+]] = tosa.reverse %[[REV1]] {axis = 2 : i32}
// CHECK: tosa.conv2d %arg0, %[[REV2]], %arg2
// CHECK-SAME: dilation = array<i64: 1, 1>, pad = array<i64: 2, 2, 5, 5>, stride = array<i64: 1, 1>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<2x18x19x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<2x18x19x5xf32>
return %0 : tensor<2x18x19x5xf32>
}
@@ -17,8 +17,8 @@ func.func @transpose_conv2d(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3x
func.func @transpose_conv2d_quantized(%arg0: tensor<2x16x14x3xi8>, %arg1: tensor<5x3x6x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x18x19x5xi32>) {
// CHECK: %[[REV1:.+]] = tosa.reverse %arg1 {axis = 1 : i32}
// CHECK: %[[REV2:.+]] = tosa.reverse %[[REV1]] {axis = 2 : i32}
- // CHECK: tosa.conv2d %arg0, %[[REV2]], %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 2, 2, 5, 5>, quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, stride = array<i64: 1, 1>}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {out_pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xi8>, tensor<5x3x6x3xi8>, tensor<5xi32>) -> tensor<2x18x19x5xi32>
+ // CHECK: tosa.conv2d %arg0, %[[REV2]], %arg2 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 2, 2, 5, 5>, quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, stride = array<i64: 1, 1>}
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xi8>, tensor<5x3x6x3xi8>, tensor<5xi32>) -> tensor<2x18x19x5xi32>
return %0 : tensor<2x18x19x5xi32>
}
@@ -32,6 +32,7 @@ func.func @transpose_conv2d_quantized_padded(%arg0: tensor<2x16x14x3xi8>, %arg1:
// CHECK-SAME: dilation = array<i64: 1, 1>, pad = array<i64: 3, 4, 8, 9>,
// CHECK-SAME: quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, stride = array<i64: 1, 1>}
%0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {
+ acc_type = i32,
out_pad = array<i64: 1, 2, 3, 4>,
quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>,
out_shape = array<i64: -1, -1, -1, -1>,
@@ -60,14 +61,14 @@ func.func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<
// Manipulate the final shape.
// CHECK-DAG: %[[BIAS:.+]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<30xf32>}
- // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]] {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
// CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = tosa.reshape %[[CONV]] {new_shape = array<i64: 2, 18, 16, 2, 3, 5>}
// CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]], %[[TRANS2]]
// CHECK-DAG: %[[RESHAPE_OUT_2:.+]] = tosa.reshape %[[TRANS_OUT]] {new_shape = array<i64: 2, 36, 48, 5>}
// CHECK-DAG: %[[SLICE:.+]] = tosa.slice %[[RESHAPE_OUT_2]] {size = array<i64: 2, 35, 47, 5>, start = array<i64: 0, 0, 0, 0>}
// CHECK-DAG: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2 {new_shape = array<i64: 1, 1, 1, 5>}
// CHECK: %[[ADD:.+]] = tosa.add %[[SLICE]], %[[RESHAPE_ARG2]]
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<2x35x47x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<2x35x47x5xf32>
%1 = tensor.cast %0 : tensor<2x35x47x5xf32> to tensor<2x?x?x5xf32>
return %1 : tensor<2x?x?x5xf32>
}
@@ -93,14 +94,14 @@ func.func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1
// Manipulate the final shape.
// CHECK-DAG: %[[BIAS:.+]] = "tosa.const"() <{value = dense<0> : tensor<30xi32>}
- // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]] {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, stride = array<i64: 1, 1>}
+ // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]] {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, stride = array<i64: 1, 1>}
// CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = tosa.reshape %[[CONV]] {new_shape = array<i64: 2, 18, 16, 2, 3, 5>}
// CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]], %[[TRANS2]]
// CHECK-DAG: %[[RESHAPE_OUT_2:.+]] = tosa.reshape %[[TRANS_OUT]] {new_shape = array<i64: 2, 36, 48, 5>}
// CHECK-DAG: %[[SLICE:.+]] = tosa.slice %[[RESHAPE_OUT_2]] {size = array<i64: 2, 35, 47, 5>, start = array<i64: 0, 0, 0, 0>}
// CHECK-DAG: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2 {new_shape = array<i64: 1, 1, 1, 5>}
// CHECK: %[[ADD:.+]] = tosa.add %[[SLICE]], %[[RESHAPE_ARG2]]
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {out_pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 2, 3>} : (tensor<2x17x15x3xi8>, tensor<5x3x5x3xi8>, tensor<5xi32>) -> tensor<2x35x47x5xi32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 2, 3>} : (tensor<2x17x15x3xi8>, tensor<5x3x5x3xi8>, tensor<5xi32>) -> tensor<2x35x47x5xi32>
return %0 : tensor<2x35x47x5xi32>
}
@@ -129,6 +130,7 @@ func.func @transpose_conv2d_strided_overpad(%arg0 : tensor<1x16x1x1xi8>, %arg1 :
// CHECK: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2 {new_shape = array<i64: 1, 1, 1, 1>}
// CHECK: %[[ADD:.+]] = tosa.add %[[PAD_RESULT]], %[[RESHAPE_ARG2]]
%2 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {
+ acc_type = i32,
out_pad = array<i64: 2, 0, 0, 1>,
out_shape = array<i64: 1, -1, -1, 1>,
stride = array<i64: 1, 2>,
diff --git a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
index d46de740800e93..88e9d5a4654de0 100644
--- a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
@@ -674,7 +674,7 @@ func.func @test_pool_static(%arg0: tensor<3x5x6x7xf32>) {
// CHECK-LABEL: @conv2d_static
func.func @conv2d_static(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>) -> () {
// CHECK: -> tensor<2x6x4x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -683,7 +683,7 @@ func.func @conv2d_static(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf
// CHECK-LABEL: @conv2d_dynamic_input
func.func @conv2d_dynamic_input(%input: tensor<?x?x?x?xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>) -> () {
// CHECK: -> tensor<?x?x?x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -716,7 +716,7 @@ func.func @test_pool_padded(%arg0: tensor<3x5x6x7xf32>) {
// CHECK-LABEL: @conv2d_dynamic_weight
func.func @conv2d_dynamic_weight(%input: tensor<2x8x9x3xf32>, %weights: tensor<?x?x?x?xf32>, %bias: tensor<5xf32>) -> () {
// CHECK: -> tensor<2x?x?x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -725,7 +725,7 @@ func.func @conv2d_dynamic_weight(%input: tensor<2x8x9x3xf32>, %weights: tensor<?
// CHECK-LABEL: @conv2d_dynamic_bias
func.func @conv2d_dynamic_bias(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<?xf32>) -> () {
// CHECK: -> tensor<2x6x4x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -746,7 +746,7 @@ func.func @test_pool_stride(%arg0: tensor<3x11x12x7xf32>) {
// CHECK-LABEL: @conv2d_padded
func.func @conv2d_padded(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>) -> () {
// CHECK: -> tensor<2x9x11x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias {pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -755,7 +755,7 @@ func.func @conv2d_padded(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf
// CHECK-LABEL: @conv2d_dilated
func.func @conv2d_dilated(%input: tensor<2x12x14x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>) -> () {
// CHECK: -> tensor<2x6x4x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 3, 2>} : (tensor<2x12x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 3, 2>} : (tensor<2x12x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -764,7 +764,7 @@ func.func @conv2d_dilated(%input: tensor<2x12x14x3xf32>, %weights: tensor<5x3x6x
// CHECK-LABEL: @conv2d_strided
func.func @conv2d_strided(%input: tensor<1x13x14x1xf32>, %weights: tensor<1x1x1x1xf32>, %bias: tensor<1xf32>) -> () {
// CHECK: -> tensor<1x5x7x1xf32>
- %0 = tosa.conv2d %input, %weights, %bias {pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 3, 2>, dilation = array<i64: 1, 1>} : (tensor<1x13x14x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 3, 2>, dilation = array<i64: 1, 1>} : (tensor<1x13x14x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -773,7 +773,7 @@ func.func @conv2d_strided(%input: tensor<1x13x14x1xf32>, %weights: tensor<1x1x1x
// CHECK-LABEL: @conv3d_static
func.func @conv3d_static(%input: tensor<2x8x9x10x3xf32>, %weights: tensor<5x3x6x4x3xf32>, %bias: tensor<5xf32>) -> () {
// CHECK: -> tensor<2x6x4x7x5xf32>
- %0 = tosa.conv3d %input, %weights, %bias {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %input, %weights, %bias {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -782,7 +782,7 @@ func.func @conv3d_static(%input: tensor<2x8x9x10x3xf32>, %weights: tensor<5x3x6x
// CHECK-LABEL: @conv3d_dynamic_input
func.func @conv3d_dynamic_input(%arg0: tensor<?x?x?x?x?xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<5xf32>) {
// CHECK: -> tensor<?x?x?x?x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<?x?x?x?x?xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<?x?x?x?x?xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -791,7 +791,7 @@ func.func @conv3d_dynamic_input(%arg0: tensor<?x?x?x?x?xf32>, %arg1: tensor<5x3x
// CHECK-LABEL: @conv3d_dynamic_weight
func.func @conv3d_dynamic_weight(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<?x?x?x?x?xf32>, %arg2: tensor<5xf32>) {
// CHECK: -> tensor<2x?x?x?x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<?x?x?x?x?xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<?x?x?x?x?xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -800,7 +800,7 @@ func.func @conv3d_dynamic_weight(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<?x
// CHECK-LABEL: @conv3d_dynamic_bias
func.func @conv3d_dynamic_bias(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<?xf32>) {
// CHECK: -> tensor<2x6x4x7x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<?xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<?xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -809,7 +809,7 @@ func.func @conv3d_dynamic_bias(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x
// CHECK-LABEL: @conv3d_padded
func.func @conv3d_padded(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<5xf32>) {
// CHECK: -> tensor<2x9x11x18x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1, 1>, pad = array<i64: 1, 2, 3, 4, 5, 6>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 1, 2, 3, 4, 5, 6>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -818,7 +818,7 @@ func.func @conv3d_padded(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x6x4x3x
// CHECK-LABEL: @conv3d_dilated
func.func @conv3d_dilated(%arg0: tensor<2x12x14x16x3xf32>, %arg1: tensor<5x3x6x2x3xf32>, %arg2: tensor<5xf32>) {
// CHECK: -> tensor<2x6x4x12x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {dilation = array<i64: 3, 2, 4>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x12x14x16x3xf32>, tensor<5x3x6x2x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 3, 2, 4>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x12x14x16x3xf32>, tensor<5x3x6x2x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -827,7 +827,7 @@ func.func @conv3d_dilated(%arg0: tensor<2x12x14x16x3xf32>, %arg1: tensor<5x3x6x2
// CHECK-LABEL: @conv3d_strided
func.func @conv3d_strided(%arg0: tensor<1x13x14x15x1xf32>, %arg1: tensor<1x1x1x1x1xf32>, %arg2: tensor<1xf32>) {
// CHECK: -> tensor<1x5x7x4x1xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 3, 2, 4>} : (tensor<1x13x14x15x1xf32>, tensor<1x1x1x1x1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 3, 2, 4>} : (tensor<1x13x14x15x1xf32>, tensor<1x1x1x1x1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
@@ -836,7 +836,7 @@ func.func @conv3d_strided(%arg0: tensor<1x13x14x15x1xf32>, %arg1: tensor<1x1x1x1
// CHECK-LABEL: @depthwise_conv2d_static
func.func @depthwise_conv2d_static(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>) {
// CHECK: -> tensor<2x6x4x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x6x4x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x6x4x15xf32>
return
}
@@ -845,7 +845,7 @@ func.func @depthwise_conv2d_static(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6
// CHECK-LABEL: @depthwise_conv2d_dynamic_input
func.func @depthwise_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>) {
// CHECK: -> tensor<?x?x?x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<?x?x?x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<?x?x?x15xf32>
return
}
@@ -854,7 +854,7 @@ func.func @depthwise_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: ten
// CHECK-LABEL: @depthwise_conv2d_dynamic_weight
func.func @depthwise_conv2d_dynamic_weight(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<15xf32>) {
// CHECK: -> tensor<2x?x?x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<15xf32>) -> tensor<2x?x?x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<15xf32>) -> tensor<2x?x?x15xf32>
return
}
@@ -863,7 +863,7 @@ func.func @depthwise_conv2d_dynamic_weight(%arg0: tensor<2x8x9x3xf32>, %arg1: te
// CHECK-LABEL: @depthwise_conv2d_dynamic_bias
func.func @depthwise_conv2d_dynamic_bias(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<?xf32>) {
// CHECK: -> tensor<2x6x4x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<?xf32>) -> tensor<2x6x4x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<?xf32>) -> tensor<2x6x4x15xf32>
return
}
@@ -872,7 +872,7 @@ func.func @depthwise_conv2d_dynamic_bias(%arg0: tensor<2x8x9x3xf32>, %arg1: tens
// CHECK-LABEL: @depthwise_conv2d_padded
func.func @depthwise_conv2d_padded(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>) {
// CHECK: -> tensor<2x9x11x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x9x11x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x9x11x15xf32>
return
}
@@ -881,7 +881,7 @@ func.func @depthwise_conv2d_padded(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6
// CHECK-LABEL: @depthwise_conv2d_dilated
func.func @depthwise_conv2d_dilated(%arg0: tensor<2x12x14x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>) {
// CHECK: -> tensor<2x6x4x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 3, 2>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x12x14x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x6x4x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 3, 2>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x12x14x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x6x4x15xf32>
return
}
@@ -890,7 +890,7 @@ func.func @depthwise_conv2d_dilated(%arg0: tensor<2x12x14x3xf32>, %arg1: tensor<
// CHECK-LABEL: @depthwise_conv2d_strided
func.func @depthwise_conv2d_strided(%arg0: tensor<1x13x14x1xf32>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1xf32>) {
// CHECK: -> tensor<1x5x7x1xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 3, 2>} : (tensor<1x13x14x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x5x7x1xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 3, 2>} : (tensor<1x13x14x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x5x7x1xf32>
return
}
@@ -899,7 +899,7 @@ func.func @depthwise_conv2d_strided(%arg0: tensor<1x13x14x1xf32>, %arg1: tensor<
// CHECK-LABEL: @transpose_conv2d_out_shape
func.func @transpose_conv2d_out_shape(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
// CHECK: -> tensor<2x8x9x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, 8, 9, -1>, stride = array<i64: 1, 1>} : (tensor<2x?x?x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x8x9x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, 8, 9, -1>, stride = array<i64: 1, 1>} : (tensor<2x?x?x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x8x9x5xf32>
return
}
@@ -908,7 +908,7 @@ func.func @transpose_conv2d_out_shape(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<
// CHECK-LABEL: @transpose_conv2d_static
func.func @transpose_conv2d_static(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
// CHECK: -> tensor<2x18x19x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<2x?x?x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<2x?x?x5xf32>
return
}
@@ -917,7 +917,7 @@ func.func @transpose_conv2d_static(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5
// CHECK-LABEL: @transpose_conv2d_static_strided
func.func @transpose_conv2d_static_strided(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
// CHECK: -> tensor<2x33x45x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 2, 3>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
return
}
@@ -926,7 +926,7 @@ func.func @transpose_conv2d_static_strided(%arg0: tensor<2x16x14x3xf32>, %arg1:
// CHECK-LABEL: @transpose_conv2d_dynamic_input
func.func @transpose_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
// CHECK: -> tensor<?x?x?x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x5xf32>
return
}
@@ -935,7 +935,7 @@ func.func @transpose_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: ten
// CHECK-LABEL: @transpose_conv2d_dynamic_weights
func.func @transpose_conv2d_dynamic_weights(%arg0: tensor<2x6x4x3xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<5xf32>) {
// CHECK: -> tensor<2x?x?x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x6x4x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<2x6x4x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
return
}
@@ -944,7 +944,7 @@ func.func @transpose_conv2d_dynamic_weights(%arg0: tensor<2x6x4x3xf32>, %arg1: t
// CHECK-LABEL: @transpose_conv2d_dynamic_bias
func.func @transpose_conv2d_dynamic_bias(%arg0: tensor<2x6x4x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<?xf32>) {
// CHECK: -> tensor<2x8x9x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x6x4x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>) -> tensor<2x8x9x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<2x6x4x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>) -> tensor<2x8x9x5xf32>
return
}
@@ -953,14 +953,14 @@ func.func @transpose_conv2d_dynamic_bias(%arg0: tensor<2x6x4x3xf32>, %arg1: tens
// CHECK-LABEL: @transpose_conv2d_padded
func.func @transpose_conv2d_padded(%arg0: tensor<2x9x11x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
// CHECK: -> tensor<2x10x13x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {out_pad = array<i64: 1, 0, 3, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x9x11x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x10x13x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = f32, out_pad = array<i64: 1, 0, 3, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x9x11x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x10x13x5xf32>
return
}
// CHECK-LABEL: @transpose_conv2d_strided
func.func @transpose_conv2d_strided(%arg0: tensor<1x5x7x1xf32>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1xf32>) {
// CHECK: -> tensor<1x13x13x1xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 3, 2>} : (tensor<1x5x7x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x13x13x1xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 3, 2>} : (tensor<1x5x7x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x13x13x1xf32>
return
}
@@ -1368,7 +1368,7 @@ func.func @test_non_tosa_consumer_still_propagates(%arg0: tensor<1x1x8xf32>, %ar
func.func @test_tosa_use_def_chain(%arg0: tensor<1x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>) -> tensor<?x16x16x16xf32> {
// CHECK: [[CONV:%.+]] = tosa.conv2d %arg0, %arg1, %arg2
// CHECK: (tensor<1x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>} : (tensor<1x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<?x32x32x16xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>} : (tensor<1x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<?x32x32x16xf32>
// CHECK: tosa.max_pool2d [[CONV]]
// CHECK: (tensor<1x32x32x16xf32>) -> tensor<1x16x16x16xf32>
%1 = tosa.max_pool2d %0 {kernel = array<i64: 2, 2>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>} : (tensor<?x32x32x16xf32>) -> tensor<?x16x16x16xf32>
diff --git a/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp b/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
index ac904c3e01c938..83db1188861ab0 100644
--- a/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
+++ b/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
@@ -149,7 +149,7 @@ ConvertTosaConv2DOp::matchAndRewrite(Operation *op,
op->getLoc(), newTosaConv2DOpType, tosaConv2DOp.getInput(),
tosaConv2DOp.getWeight(), tosaConv2DOp.getBias(),
tosaConv2DOp.getPadAttr(), tosaConv2DOp.getStrideAttr(),
- tosaConv2DOp.getDilationAttr());
+ tosaConv2DOp.getDilationAttr(), tosaConv2DOp.getAccTypeAttr());
// Create rescale to quantized type
double inputScale = inputQType.getScale();
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