[Mlir-commits] [mlir] 63a2b0b - [mlir][tosa] Support unranked input/weight tensors for convolution ops (#134856)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Fri Apr 25 06:31:20 PDT 2025
Author: Luke Hutton
Date: 2025-04-25T14:31:17+01:00
New Revision: 63a2b0bd3dcb1eb77333d7b21e6086f94a5f540e
URL: https://github.com/llvm/llvm-project/commit/63a2b0bd3dcb1eb77333d7b21e6086f94a5f540e
DIFF: https://github.com/llvm/llvm-project/commit/63a2b0bd3dcb1eb77333d7b21e6086f94a5f540e.diff
LOG: [mlir][tosa] Support unranked input/weight tensors for convolution ops (#134856)
This commit ensures that convolution operators including: conv2d,
depthwise_conv2d, transpose_conv2d and conv3d, can have unranked
input/weight operands.
In order to support operands with unranked tensors, the tablegen
definition was relaxed. The relaxation of tensor type will later be
checked by the validation pass, should the user wish to use it.
Signed-off-by: Luke Hutton <luke.hutton at arm.com>
Added:
Modified:
mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
mlir/test/Dialect/Tosa/invalid.mlir
mlir/test/Dialect/Tosa/ops.mlir
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index c94edad62cac7..cc78aaed911e6 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -125,7 +125,7 @@ def Tosa_Conv2DOp : Tosa_ConvOp<"conv2d"> {
let arguments = (ins
Tosa_Tensor4D:$input,
- TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
+ Tosa_Tensor4D:$weight,
Tosa_Tensor1D:$bias,
Tosa_ScalarIntOrFloatTensor:$input_zp,
Tosa_ScalarIntOrFloatTensor:$weight_zp,
@@ -172,7 +172,7 @@ def Tosa_Conv3DOp : Tosa_ConvOp<"conv3d"> {
let arguments = (ins
Tosa_Tensor5D:$input,
- TosaTensorRankOf<[Tosa_Weight], [5]>:$weight,
+ Tosa_Tensor5D:$weight,
Tosa_Tensor1D:$bias,
Tosa_ScalarIntOrFloatTensor:$input_zp,
Tosa_ScalarIntOrFloatTensor:$weight_zp,
@@ -218,7 +218,7 @@ def Tosa_DepthwiseConv2DOp : Tosa_ConvOp<"depthwise_conv2d"> {
let arguments = (ins
Tosa_Tensor4D:$input,
- TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
+ Tosa_Tensor4D:$weight,
Tosa_Tensor1D:$bias,
Tosa_ScalarIntOrFloatTensor:$input_zp,
Tosa_ScalarIntOrFloatTensor:$weight_zp,
@@ -434,7 +434,7 @@ def Tosa_TransposeConv2DOp : Tosa_ConvOp<"transpose_conv2d"> {
let arguments = (ins
Tosa_Tensor4D:$input,
- TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
+ Tosa_Tensor4D:$weight,
Tosa_Tensor1D:$bias,
Tosa_ScalarIntOrFloatTensor:$input_zp,
Tosa_ScalarIntOrFloatTensor:$weight_zp,
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
index 67011f22fbe2a..b9ac1ff705514 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
@@ -84,11 +84,6 @@ def Tosa_QuantizedInt : AnyTypeOf<[Tosa_QuantizedType<"uint8", [8], 0>,
def Tosa_AnyNumber : AnyTypeOf<[Tosa_Int, Tosa_QuantizedInt, AnyFloat],
"number">;
-// For weight tensors from tosa::Conv2DOp, tosa::Conv3DOp,
-// tosa::DepthwiseConv2DOp, tosa::TransposeConv2DOp
-def Tosa_Weight : AnyTypeOf<[Tosa_Int4, Tosa_Int8,
- Tosa_QuantizedInt, AnyFloat]>;
-
//===----------------------------------------------------------------------===//
// TOSA Tensor Conformance
//===----------------------------------------------------------------------===//
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 107e09a5e7ab4..69b1627583234 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -278,19 +278,8 @@ Value mlir::tosa::createPadConstTensor(OpBuilder &builder, Location loc,
template <typename T>
static LogicalResult verifyConvOp(T op) {
- // All TOSA conv ops have an input and weight arguments which must be ranked
- // tensors.
- auto inputType = llvm::dyn_cast<RankedTensorType>(op.getInput().getType());
- if (!inputType) {
- op.emitOpError("expect a ranked tensor for input, got ") << op.getInput();
- return failure();
- }
-
- auto weightType = llvm::dyn_cast<RankedTensorType>(op.getWeight().getType());
- if (!weightType) {
- op.emitOpError("expect a ranked tensor for weight, got ") << op.getWeight();
- return failure();
- }
+ const auto inputType = llvm::dyn_cast<TensorType>(op.getInput().getType());
+ const auto weightType = llvm::dyn_cast<TensorType>(op.getWeight().getType());
auto inputEType = inputType.getElementType();
auto weightEType = weightType.getElementType();
@@ -3063,14 +3052,6 @@ LogicalResult TransposeConv2DOp::verify() {
return emitOpError("expect all stride values to be >= 1, got [")
<< strides << "]";
- const auto inputType = llvm::dyn_cast<RankedTensorType>(getInput().getType());
-
- const auto outputType =
- llvm::dyn_cast<RankedTensorType>(getOutput().getType());
-
- const auto weightType =
- llvm::dyn_cast<RankedTensorType>(getWeight().getType());
-
const auto checkPadAgainstKernelDim =
[this](int64_t pad_value, int64_t kernel_dim_size,
llvm::StringRef pad_name,
@@ -3084,69 +3065,77 @@ LogicalResult TransposeConv2DOp::verify() {
};
const llvm::ArrayRef<int64_t> padding = getOutPad();
-
const int64_t outPadTop = padding[0];
const int64_t outPadBottom = padding[1];
+ const int64_t outPadLeft = padding[2];
+ const int64_t outPadRight = padding[3];
- const int64_t kernelHeight = weightType.getDimSize(1);
-
- if (!ShapedType::isDynamic(kernelHeight)) {
- if (failed(checkPadAgainstKernelDim(outPadTop, kernelHeight, "out_pad_top",
- "KH")))
- return failure();
-
- if (failed(checkPadAgainstKernelDim(outPadBottom, kernelHeight,
- "out_pad_bottom", "KH")))
- return failure();
- }
+ const auto weightType =
+ llvm::dyn_cast<RankedTensorType>(getWeight().getType());
- const int64_t kernelWidth = weightType.getDimSize(2);
+ if (weightType) {
+ const int64_t kernelHeight = weightType.getDimSize(1);
+ if (!ShapedType::isDynamic(kernelHeight)) {
+ if (failed(checkPadAgainstKernelDim(outPadTop, kernelHeight,
+ "out_pad_top", "KH")))
+ return failure();
- const int64_t outPadLeft = padding[2];
- const int64_t outPadRight = padding[3];
+ if (failed(checkPadAgainstKernelDim(outPadBottom, kernelHeight,
+ "out_pad_bottom", "KH")))
+ return failure();
+ }
- if (!ShapedType::isDynamic(kernelWidth)) {
- if (failed(checkPadAgainstKernelDim(outPadLeft, kernelWidth, "out_pad_left",
- "KW")))
- return failure();
+ const int64_t kernelWidth = weightType.getDimSize(2);
+ if (!ShapedType::isDynamic(kernelWidth)) {
+ if (failed(checkPadAgainstKernelDim(outPadLeft, kernelWidth,
+ "out_pad_left", "KW")))
+ return failure();
- if (failed(checkPadAgainstKernelDim(outPadRight, kernelWidth,
- "out_pad_right", "KW")))
- return failure();
+ if (failed(checkPadAgainstKernelDim(outPadRight, kernelWidth,
+ "out_pad_right", "KW")))
+ return failure();
+ }
}
// Rest of the checks depend on the output type being a RankedTensorType
+ const auto outputType =
+ llvm::dyn_cast<RankedTensorType>(getOutput().getType());
if (!outputType)
return success();
- const int64_t inputHeight = inputType.getDimSize(1);
- const int64_t outputHeight = outputType.getDimSize(1);
-
- if (!ShapedType::isDynamic(inputHeight) &&
- !ShapedType::isDynamic(outputHeight)) {
- if (outputHeight !=
- (inputHeight - 1) * strideY + outPadTop + outPadBottom + kernelHeight)
- return emitOpError(
- "dimension mismatch: expected OH == (IH - 1) * stride_y "
- "+ out_pad_top + out_pad_bottom + KH, but got ")
- << outputHeight << " != (" << inputHeight << " - 1) * " << strideY
- << " + " << outPadTop << " + " << outPadBottom << " + "
- << kernelHeight;
- }
+ const auto inputType = llvm::dyn_cast<RankedTensorType>(getInput().getType());
+ if (inputType && weightType) {
+ const int64_t inputHeight = inputType.getDimSize(1);
+ const int64_t kernelHeight = weightType.getDimSize(1);
+ const int64_t outputHeight = outputType.getDimSize(1);
+
+ if (!ShapedType::isDynamic(inputHeight) &&
+ !ShapedType::isDynamic(outputHeight)) {
+ if (outputHeight !=
+ (inputHeight - 1) * strideY + outPadTop + outPadBottom + kernelHeight)
+ return emitOpError(
+ "dimension mismatch: expected OH == (IH - 1) * stride_y "
+ "+ out_pad_top + out_pad_bottom + KH, but got ")
+ << outputHeight << " != (" << inputHeight << " - 1) * "
+ << strideY << " + " << outPadTop << " + " << outPadBottom
+ << " + " << kernelHeight;
+ }
- const int64_t inputWidth = inputType.getDimSize(2);
- const int64_t outputWidth = outputType.getDimSize(2);
+ const int64_t inputWidth = inputType.getDimSize(2);
+ const int64_t kernelWidth = weightType.getDimSize(2);
+ const int64_t outputWidth = outputType.getDimSize(2);
- if (!ShapedType::isDynamic(inputWidth) &&
- !ShapedType::isDynamic(outputWidth)) {
- if (outputWidth !=
- (inputWidth - 1) * strideX + outPadLeft + outPadRight + kernelWidth)
- return emitOpError(
- "dimension mismatch: expected OW == (IW - 1) * stride_x "
- "+ out_pad_left + out_pad_right + KW, but got ")
- << outputWidth << " != (" << inputWidth << " - 1) * " << strideX
- << " + " << outPadLeft << " + " << outPadRight << " + "
- << kernelWidth;
+ if (!ShapedType::isDynamic(inputWidth) &&
+ !ShapedType::isDynamic(outputWidth)) {
+ if (outputWidth !=
+ (inputWidth - 1) * strideX + outPadLeft + outPadRight + kernelWidth)
+ return emitOpError(
+ "dimension mismatch: expected OW == (IW - 1) * stride_x "
+ "+ out_pad_left + out_pad_right + KW, but got ")
+ << outputWidth << " != (" << inputWidth << " - 1) * " << strideX
+ << " + " << outPadLeft << " + " << outPadRight << " + "
+ << kernelWidth;
+ }
}
const auto biasType = llvm::dyn_cast<RankedTensorType>(getBias().getType());
diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
index 4a341d583426a..4957c1684994d 100644
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -22,22 +22,12 @@ func.func @test_const_non_tensor_attr() {
// -----
-func.func @test_conv2d(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
+func.func @test_conv2d(%arg0: tensor<*xf32>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op expect both input and weight to be float or not together, got 'f32' and 'i8'}}
%0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
- : (tensor<1x29x29x4xf32>, tensor<16x3x3x4xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
- return %0 : tensor<1x27x27x16xi8>
-}
-
-// -----
-
-func.func @test_conv2d(%arg0: tensor<*xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
- %zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- // expected-error at +1 {{'tosa.conv2d' op expect a ranked tensor for input, got <block argument> of type 'tensor<*xi8>' at index: 0}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
- : (tensor<*xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
+ : (tensor<*xf32>, tensor<16x3x3x4xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
}
@@ -45,7 +35,7 @@ func.func @test_conv2d(%arg0: tensor<*xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: t
func.func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<*xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
%zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- // expected-error at +1 {{'tosa.conv2d' op operand #1 must be 4D tensor of 4-bit signless integer or 8-bit signless integer or Quint8 type or Qint4 type or Qint8 type or Qint16 type or Qint32 type or floating-point values, but got 'tensor<*xi8>'}}
+ // expected-error at +1 {{'tosa.conv2d' op illegal: operand/result data types not supported}}
%0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
: (tensor<1x29x29x4xi8>, tensor<*xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
diff --git a/mlir/test/Dialect/Tosa/ops.mlir b/mlir/test/Dialect/Tosa/ops.mlir
index b64074e412ed1..f8273190bde40 100644
--- a/mlir/test/Dialect/Tosa/ops.mlir
+++ b/mlir/test/Dialect/Tosa/ops.mlir
@@ -70,6 +70,13 @@ func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %
return %0 : tensor<1x4x4x8xf32>
}
+// -----
+// CHECK-LABEL: conv2d_unranked_input
+func.func @test_conv2d_unranked_input(%arg0: tensor<*xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<*xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ return %0 : tensor<1x4x4x8xf32>
+}
+
// -----
// CHECK-LABEL: conv2d_quant_uniform
func.func @test_conv2d_quant_uniform(%arg0: tensor<1x4x4x4x!quant.uniform<i8:f32, 0.01>>, %arg1: tensor<8x1x1x4x!quant.uniform<i8:f32, 0.01>>, %arg2: tensor<8x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x4x4x8x!quant.uniform<i32:f32, 0.01>> {
@@ -202,6 +209,20 @@ func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x
return %0 : tensor<1x32x32x16xf32>
}
+// -----
+// CHECK-LABEL: transpose_conv2d_unranked_input
+func.func @test_transpose_conv2d_unranked_input(%arg0: tensor<*xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<*xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+// CHECK-LABEL: transpose_conv2d_unranked_weight
+func.func @test_transpose_conv2d_unranked_weight(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<*xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<*xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ return %0 : tensor<1x32x32x16xf32>
+}
+
// -----
// CHECK-LABEL: transpose_conv2d_with_local_bound
func.func @test_transpose_conv2d_with_local_bound(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
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