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[onnx] support for lowering mod op from onnx to torch (#2859) 

nod-ai/Shark-Turbine#267

---------

Authored-by: boddu.pavani@research.iiit.ac.in
Co-authored-by: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
pull/3034/head
Pavani Chowdary 2024-03-18 17:54:37 +05:30 committed by GitHub
parent d8a52e82c2
commit c51e2130f2
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GPG Key ID: B5690EEEBB952194
3 changed files with 39 additions and 10 deletions
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21
lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
View File

@ -1153,4 +1153,25 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
binder.op, resultType, tensor, slope); binder.op, resultType, tensor, slope);
return success(); return success();
}); });
patterns.onOp("Mod", 13,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value self, other;
int64_t fmod;
if (binder.tensorOperands(self, other) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(fmod, "fmod", 0)) {
return failure();
}
if (fmod) {
rewriter.replaceOpWithNewOp<Torch::AtenFmodTensorOp>(
binder.op, resultType, self, other);
return success();
}
rewriter.replaceOpWithNewOp<Torch::AtenRemainderTensorOp>(
binder.op, resultType, self, other);
return success();
});
} }

10
projects/pt1/e2e_testing/xfail_sets.py
View File

@ -1611,8 +1611,6 @@ ONNX_XFAIL_SET = {
"ElementwiseOrTensorStaticShapeModule_basic", "ElementwiseOrTensorStaticShapeModule_basic",
"ElementwiseQuantizePerTensorModule_basic", "ElementwiseQuantizePerTensorModule_basic",
"ElementwiseRemainderTensorModule_Int_basic", "ElementwiseRemainderTensorModule_Int_basic",
"ElementwiseFmodTensor_Float_basic",
"ElementwiseFmodTensor_Int_Float_basic",
"ElementwiseFmodTensor_Int_basic", "ElementwiseFmodTensor_Int_basic",
"EmptyStridedModule_basic", "EmptyStridedModule_basic",
"EmptyStridedSizeIntStrideModule_basic", "EmptyStridedSizeIntStrideModule_basic",
@ -1908,14 +1906,6 @@ ONNX_XFAIL_SET = {
"MaxPool2dWithIndicesNonDefaultPaddingModule_basic", "MaxPool2dWithIndicesNonDefaultPaddingModule_basic",
"MaxPool2dWithIndicesStaticModule_basic", "MaxPool2dWithIndicesStaticModule_basic",
# Failure - onnx_lowering: onnx.Mod
"ElementwiseRemainderScalarModule_Bool_basic",
"ElementwiseRemainderScalarModule_Int_basic",
"UnflattenIntNegativeOneDimStaticModule_basic",
"UnflattenIntNegativeOneSizeStaticModule_basic",
"UnflattenIntStaticModule_basic",
"UnflattenStaticModule_basic",
# Failure - onnx_lowering: onnx.OneHot # Failure - onnx_lowering: onnx.OneHot
"OneHotModule_basic", "OneHotModule_basic",

18
test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
View File

@ -629,6 +629,24 @@ func.func @test_globalaveragepool_precomputed(%arg0: !torch.vtensor<[1,1,3,3],f3
// ----- // -----
// CHECK-LABEL: func.func @test_mod_int64_fmod
func.func @test_mod_int64_fmod(%arg0: !torch.vtensor<[6],si64>, %arg1: !torch.vtensor<[6],si64>) -> !torch.vtensor<[6],si64> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: torch.aten.fmod.Tensor %arg0, %arg1 : !torch.vtensor<[6],si64>, !torch.vtensor<[6],si64> -> !torch.vtensor<[6],si64>
%0 = torch.operator "onnx.Mod"(%arg0, %arg1) {torch.onnx.fmod = 1 : si64} : (!torch.vtensor<[6],si64>, !torch.vtensor<[6],si64>) -> !torch.vtensor<[6],si64>
return %0 : !torch.vtensor<[6],si64>
}
// -----
// CHECK-LABEL: func.func @test_mod_int64_no_fmod
func.func @test_mod_int64_no_fmod(%arg0: !torch.vtensor<[6],si64>, %arg1: !torch.vtensor<[6],si64>) -> !torch.vtensor<[6],si64> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: torch.aten.remainder.Tensor %arg0, %arg1 : !torch.vtensor<[6],si64>, !torch.vtensor<[6],si64> -> !torch.vtensor<[6],si64>
%0 = torch.operator "onnx.Mod"(%arg0, %arg1) : (!torch.vtensor<[6],si64>, !torch.vtensor<[6],si64>) -> !torch.vtensor<[6],si64>
return %0 : !torch.vtensor<[6],si64>
}
// -----
// CHECK-LABEL: func.func @test_log // CHECK-LABEL: func.func @test_log
func.func @test_log(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} { func.func @test_log(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: torch.aten.log %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32> // CHECK: torch.aten.log %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>