mirror of https://github.com/llvm/torch-mlir
parent
122cf22cc2
commit
b64c22cfc1
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@ -1449,16 +1449,29 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
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return success();
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return success();
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});
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});
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patterns.onOp("Sinh", 9,
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patterns.onOp(
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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"Sinh", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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Torch::ValueTensorType resultType;
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Value operand;
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Value operand;
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if (binder.tensorOperand(operand) ||
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if (binder.tensorOperand(operand) ||
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binder.tensorResultType(resultType))
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binder.tensorResultType(resultType))
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return failure();
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return failure();
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rewriter.replaceOpWithNewOp<Torch::AtenSinhOp>(
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// 1/2 * (exp(x) – exp(-x))
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binder.op, resultType, operand);
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Value x = rewriter.create<Torch::AtenExpOp>(binder.getLoc(), resultType,
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operand);
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Value neg = rewriter.create<Torch::AtenNegOp>(binder.getLoc(),
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resultType, operand);
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Value y =
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rewriter.create<Torch::AtenExpOp>(binder.getLoc(), resultType, neg);
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Value cstOne = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(1));
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Value z = rewriter.create<Torch::AtenSubTensorOp>(
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binder.getLoc(), resultType, x, y, cstOne);
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Value cstTwo = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(2));
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rewriter.replaceOpWithNewOp<Torch::AtenDivScalarOp>(
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binder.op, resultType, z, cstTwo);
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return success();
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return success();
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});
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});
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@ -1265,9 +1265,15 @@ func.func @test_reduce_prod_keepdims_random(%arg0: !torch.vtensor<[3,2,2],f32>,
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// -----
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// -----
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// CHECK-LABEL: func.func @test_sinh
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// CHECK-LABEL: func.func @test_sinh_example
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func.func @test_sinh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 4 : si64, torch.onnx_meta.opset_version = 9 : si64} {
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func.func @test_sinh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 4 : si64, torch.onnx_meta.opset_version = 9 : si64} {
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// CHECK: torch.aten.sinh %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
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// CHECK: %[[X:.+]] = torch.aten.exp %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
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// CHECK: %[[NEG:.+]] = torch.aten.neg %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
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// CHECK: %[[Y:.+]] = torch.aten.exp %[[NEG]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
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// CHECK: %[[C1:.+]] = torch.constant.int 1
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// CHECK: %[[SUB:.+]] = torch.aten.sub.Tensor %[[X]], %[[Y]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
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// CHECK: %[[C2:.+]] = torch.constant.int 2
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// CHECK: torch.aten.div.Scalar %[[SUB]], %[[C2]] : !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
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%0 = torch.operator "onnx.Sinh"(%arg0) : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
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%0 = torch.operator "onnx.Sinh"(%arg0) : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
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return %0 : !torch.vtensor<[3],f32>
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return %0 : !torch.vtensor<[3],f32>
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}
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}
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