mirror of https://github.com/llvm/torch-mlir
Support default padding case for tosa::AvgPool in the presence of count_include_pad (#3868)
Essentially, as part of my earlier
[change](7f9f99c6f8)
, I didn't consider the `padding` value while erroring out for
unsupported `count_include_pad` during `torch-to-tosa` lowering for
AvgPool2d. The fix captured in this change addresses this. Please see
[issue](https://github.com/llvm/torch-mlir/issues/3862) for more details
on this.
Co-authored-by: Hanumanth Hanumantharayappa <hhanuman@ah-hhanuman-l.dhcp.mathworks.com>
main
Hanumanth
GitHub
parent
cd38ecf6c2
commit
30c519369e
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@ -5549,6 +5549,26 @@ static LogicalResult getOutputTypeAndPoolingParameters(
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std::is_same<AtenOpT, AtenAvgPool1dOp>())
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paddingInts.push_back(0);
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if constexpr (std::is_same<AtenOpT, AtenAvgPool1dOp>() ||
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std::is_same<AtenOpT, AtenAvgPool2dOp>()) {
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// Currently, we can not represent `count_include_pad` with the existing
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// TOSA AvgPool2d specification. Without the below check, we produce silent
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// wrong answer (SWA) when the `count_include_pad` value is `true.`
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//
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// Note: We need to check for `count_include_pad` only when the `padding`
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// value is non-zero.
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bool countIncludePad;
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if ((paddingInts[0] != 0 || paddingInts[1] != 0) &&
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(!matchPattern(op.getCountIncludePad(),
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m_TorchConstantBool(&countIncludePad)) ||
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countIncludePad)) {
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return rewriter.notifyMatchFailure(
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op, "Unsupported `count_include_pad` value, for tosa AvgPool "
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"`count_include_pad` value should be `False`.");
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}
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}
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SmallVector<int64_t, 4> padArr = {paddingInts[0], paddingInts[0],
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paddingInts[1], paddingInts[1]};
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kernel = rewriter.getDenseI64ArrayAttr(kernelSizeInts);
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@ -5677,18 +5697,6 @@ public:
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DenseI64ArrayAttr &stride, DenseI64ArrayAttr &pad,
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Type &outputTy) const override {
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// Currently, we can not represent `count_include_pad` with the existing
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// TOSA AvgPool2d specification. Without the below check, we produce silent
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// wrong answers (SWA) when the `count_include_pad` value is `true.`
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bool countIncludePad;
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if (!matchPattern(op.getCountIncludePad(),
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m_TorchConstantBool(&countIncludePad)) ||
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countIncludePad) {
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return rewriter.notifyMatchFailure(
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op, "Unsupported `count_include_pad` value, for tosa AvgPool2dOp "
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"`count_include_pad` value should be `False`.");
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}
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// Currently, we can not represent `divisor_override` with the existing TOSA
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// AvgPool2d specification. Without the below check, we produce silent wrong
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// answers (SWA) when the `divisor_override` value is other than `None.`
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@ -5737,7 +5745,7 @@ public:
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// Expected a rank 3 input tensor
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if (selfTy.getRank() != 3)
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return rewriter.notifyMatchFailure(
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op, "Input tensor for MaxPool1d should have rank 3");
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op, "Input tensor for AvgPool1d should have rank 3");
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// Unsqueeze input tensor to rank 4 to be compatible with tosa::AvgPool2dOp
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SmallVector<int64_t> rank4Shape(selfShape);
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@ -5748,18 +5756,6 @@ public:
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selfTy.getElementType()),
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self, rewriter.getDenseI64ArrayAttr(rank4Shape));
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// Currently, we can not represent `count_include_pad` with the existing
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// TOSA AvgPool2d specification. Without the below check, we produce silent
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// wrong answers (SWA) when the `count_include_pad` value is `true.`
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bool countIncludePad;
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if (!matchPattern(op.getCountIncludePad(),
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m_TorchConstantBool(&countIncludePad)) ||
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countIncludePad) {
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return rewriter.notifyMatchFailure(
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op, "Unsupported `count_include_pad` value, for tosa AvgPool2dOp "
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"`count_include_pad` value should be `False`.");
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}
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SmallVector<int64_t, 2> dilationArray{1, 1};
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if (failed(getOutputTypeAndPoolingParameters<AtenAvgPool1dOp,
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tosa::AvgPool2dOp>(
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@ -1736,6 +1736,12 @@ FX_IMPORTER_TOSA_CRASHING_SET = {
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# Write the TOSA set as a "passing" set as it is very early in development
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# and very few tests work yet.
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TOSA_PASS_SET = {
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"AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
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"AdaptiveAvgPool1dUnitOutputSizeStaticModule_basic",
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"AdaptiveAvgPool2dFixedKernelStrideSizeStaticModule_basic",
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"AdaptiveAvgPool2dNonUnitOutputSizeStaticModule_basic",
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"AdaptiveAvgPool2dOutputSizeDivisibleByInputStaticModule_basic",
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"AdaptiveAvgPool2dUnitOutputSizeStaticModule_basic",
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"ElementwiseAtenLogicalNotOpPromoteModule_basic",
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"ElementwiseCosIntModule_basic",
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"ElementwiseReciprocalIntModule_basic",
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@ -2316,6 +2322,7 @@ TOSA_PASS_SET = {
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"ReshapeExpandModule_basic",
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"ReturnThreeTensorFloat32_basic",
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"ReturnTwoTensorF32I64_basic",
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"ResNet18StaticModule_basic",
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"RsubFloatModule_basic",
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"RsubFloatModule_noalpha_basic",
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"RsubInt0d_NumToTensor_Module_basic",
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@ -3869,26 +3876,11 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
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"ViewSizeFromOtherTensor_basic",
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"VisionTransformerModule_basic",
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"ZerosLikeModule_falsePinMemory",
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# count_include_pad and divisor_override check in TOSA AvgPool2d
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"AdaptiveAvgPool2dNonUnitOutputSizeDynamicModule_basic",
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"AdaptiveAvgPool2dNonUnitOutputSizeStaticModule_basic",
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"AdaptiveAvgPool2dOutputSizeDivisibleByInputDynamicModule_basic",
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"AdaptiveAvgPool2dOutputSizeDivisibleByInputStaticModule_basic",
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"AdaptiveAvgPool2dFixedKernelStrideSizeStaticModule_basic",
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"AdaptiveAvgPool2dUnitOutputSizeDynamicModule_basic",
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"AdaptiveAvgPool2dUnitOutputSizeStaticModule_basic",
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"ResNet18Module_basic",
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"ResNet18StaticModule_basic",
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"MobilenetV3Module_basic",
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# Unexpected failures due to new PyTorch version update
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"AdaptiveAvgPool1dGeneralDynamicNoBatches_basic",
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"AdaptiveAvgPool1dGeneralDynamic_basic",
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"AdaptiveAvgPool1dNonUnitOutputSizeDynamicModule_basic",
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"AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
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"AdaptiveAvgPool1dStaticEvenMultiple_basic",
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"AdaptiveAvgPool1dStaticLargerOutput_basic",
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"AdaptiveAvgPool1dUnitOutputSizeDynamicModule_basic",
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"AdaptiveAvgPool1dUnitOutputSizeStaticModule_basic",
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"AdaptiveAvgPool2dDynamicNoBatch_basic",
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"AdaptiveAvgPool2dDynamic_basic",
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"CrossEntropyLossModule_basic",
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@ -2424,3 +2424,18 @@ func.func @torch.prims.collapse$basic(%arg0: !torch.vtensor<[2,3,4],f32>) -> !to
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%0 = torch.prims.collapse %arg0, %int1, %int2 : !torch.vtensor<[2,3,4],f32>, !torch.int, !torch.int -> !torch.vtensor<[2,12],f32>
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return %0 : !torch.vtensor<[2,12],f32>
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}
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// -----
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func.func @torch.aten.avg_pool1d.count_include_pad_unsupported_value(%arg0: !torch.vtensor<[1,512,10],f32>) -> !torch.vtensor<[1,512,10],f32> {
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%int1 = torch.constant.int 1
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%int3 = torch.constant.int 3
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%false = torch.constant.bool false
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%count_include_pad = torch.constant.bool true
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%0 = torch.prim.ListConstruct %int3 : (!torch.int) -> !torch.list<int>
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%1 = torch.prim.ListConstruct %int1 : (!torch.int) -> !torch.list<int>
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%2 = torch.prim.ListConstruct %int1 : (!torch.int) -> !torch.list<int>
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// expected-error @+1 {{failed to legalize operation 'torch.aten.avg_pool1d' that was explicitly marked illegal}}
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%3 = torch.aten.avg_pool1d %arg0, %0, %1, %2, %false, %count_include_pad : !torch.vtensor<[1,512,10],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.bool -> !torch.vtensor<[1,512,10],f32>
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return %3 : !torch.vtensor<[1,512,10],f32>
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}
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