Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[MLIR][ONNX] Add OnnxToTorch support for GlobalAveragePool op. #2692

Merged
merged 1 commit into from
Dec 26, 2023
Merged

[MLIR][ONNX] Add OnnxToTorch support for GlobalAveragePool op. #2692

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
71 changes: 71 additions & 0 deletions lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -242,6 +242,77 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
binder.op, resultType, mm, c, constBeta);
return success();
});
patterns.onOp(
"GlobalAveragePool", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();

auto inputTensorType = operand.getType().cast<Torch::ValueTensorType>();
if (!inputTensorType || !inputTensorType.hasSizes()) {
return rewriter.notifyMatchFailure(
binder.op, "Expected input type having sizes");
}
ArrayRef<int64_t> inputShape = inputTensorType.getSizes();
unsigned inputRank = inputShape.size();
if (!resultType || !resultType.hasSizes()) {
return rewriter.notifyMatchFailure(
binder.op, "Expected result type having sizes");
}
ArrayRef<int64_t> resultShape = resultType.getSizes();

SmallVector<Value> cstKernel, cstPadding, cstStrides;
Value cstZero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(0));
Value cstOne = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(1));
for (unsigned i = 2; i < inputRank; i++) {
int64_t kernelSize = inputShape[i] - resultShape[i] + 1;
cstKernel.push_back(rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(kernelSize)));
cstPadding.push_back(cstZero);
cstStrides.push_back(cstOne);
}
Value kernelSizeList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
cstKernel);
Value paddingList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
cstPadding);
Value stridesList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
cstStrides);
Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
Value cstCeilMode = cstFalse;
Value cstCountIncludePad = cstFalse;
Value cstNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());

if (inputRank == 3) {
rewriter.replaceOpWithNewOp<Torch::AtenAvgPool1dOp>(
binder.op, resultType, operand, kernelSizeList, stridesList,
paddingList, cstCeilMode, cstCountIncludePad);
return success();
} else if (inputRank == 4) {
rewriter.replaceOpWithNewOp<Torch::AtenAvgPool2dOp>(
binder.op, resultType, operand, kernelSizeList, stridesList,
paddingList, cstCeilMode, cstCountIncludePad,
/*divisor_override=*/cstNone);
return success();
} else if (inputRank == 5) {
rewriter.replaceOpWithNewOp<Torch::AtenAvgPool3dOp>(
binder.op, resultType, operand, kernelSizeList, stridesList,
paddingList, cstCeilMode, cstCountIncludePad,
/*divisor_override=*/cstNone);
return success();
}
return failure();
});
patterns.onOp("LeakyRelu", 16,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Expand Down
36 changes: 36 additions & 0 deletions test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -265,3 +265,39 @@ func.func @test_hardsigmoid_default(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torc
%0 = torch.operator "onnx.HardSigmoid"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
return %0 : !torch.vtensor<[3,4,5],f32>
}

// -----

// CHECK-LABEL: @test_globalaveragepool
func.func @test_globalaveragepool(%arg0: !torch.vtensor<[1,3,5,5],f32>) -> !torch.vtensor<[1,3,1,1],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 1 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[C0:.*]] = torch.constant.int 0
// CHECK: %[[C1:.*]] = torch.constant.int 1
// CHECK: %[[C5:.*]] = torch.constant.int 5
// CHECK: %[[C5_0:.*]] = torch.constant.int 5
// CHECK: %[[KERNELSIZE:.*]] = torch.prim.ListConstruct %[[C5]], %[[C5_0]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C1]], %[[C1]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: torch.aten.avg_pool2d %arg0, %[[KERNELSIZE]], %[[STRIDE]], %[[PADDING]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[1,3,5,5],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[1,3,1,1],f32>
%0 = torch.operator "onnx.GlobalAveragePool"(%arg0) : (!torch.vtensor<[1,3,5,5],f32>) -> !torch.vtensor<[1,3,1,1],f32>
return %0 : !torch.vtensor<[1,3,1,1],f32>
}

// -----

// CHECK-LABEL: @test_globalaveragepool_precomputed
func.func @test_globalaveragepool_precomputed(%arg0: !torch.vtensor<[1,1,3,3],f32>) -> !torch.vtensor<[1,1,1,1],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 1 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[C0:.*]] = torch.constant.int 0
// CHECK: %[[C1:.*]] = torch.constant.int 1
// CHECK: %[[C3:.*]] = torch.constant.int 3
// CHECK: %[[C3_0:.*]] = torch.constant.int 3
// CHECK: %[[KERNELSIZE:.*]] = torch.prim.ListConstruct %[[C3]], %[[C3_0]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C1]], %[[C1]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: torch.aten.avg_pool2d %arg0, %[[KERNELSIZE]], %[[STRIDE]], %[[PADDING]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[1,1,3,3],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[1,1,1,1],f32>
%0 = torch.operator "onnx.GlobalAveragePool"(%arg0) : (!torch.vtensor<[1,1,3,3],f32>) -> !torch.vtensor<[1,1,1,1],f32>
return %0 : !torch.vtensor<[1,1,1,1],f32>
}