mirror of https://github.com/llvm/torch-mlir
Fix onnx acosh lowering (#3262)
iree tests `test_acosh` and `test_acosh_example` passedpull/3269/head
parent
aa471f1d96
commit
fb499192df
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@ -242,15 +242,27 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
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binder.op, resultType, operand);
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binder.op, resultType, operand);
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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("Acosh", 9,
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patterns.onOp(
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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"Acosh", 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::AtenAcoshOp>(
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binder.op, resultType, operand);
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// log(x + sqrt(x**2 - 1))
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Value square = rewriter.create<Torch::AtenSquareOp>(
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binder.getLoc(), resultType, operand);
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Value cstOne = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(1));
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Value sub = rewriter.create<Torch::AtenSubScalarOp>(
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binder.getLoc(), resultType, square, cstOne, cstOne);
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Value sqrt = rewriter.create<Torch::AtenSqrtOp>(binder.getLoc(),
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resultType, sub);
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Value add = rewriter.create<Torch::AtenAddTensorOp>(
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binder.getLoc(), resultType, operand, sqrt, cstOne);
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rewriter.replaceOpWithNewOp<Torch::AtenLogOp>(binder.op, resultType,
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add);
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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("BatchNormalization", 15,
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patterns.onOp("BatchNormalization", 15,
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@ -695,7 +695,12 @@ func.func @test_cosh(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,
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// CHECK-LABEL: @test_acosh_example
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// CHECK-LABEL: @test_acosh_example
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func.func @test_acosh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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func.func @test_acosh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: torch.aten.acosh %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
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// CHECK: %[[SQUARE:.+]] = torch.aten.square %arg0 : !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.Scalar %[[SQUARE]], %[[C1]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.int, !torch.int -> !torch.vtensor<[3],f32>
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// CHECK: %[[SQRT:.+]] = torch.aten.sqrt %[[SUB]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
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// CHECK: %[[ADD:.+]] = torch.aten.add.Tensor %arg0, %[[SQRT]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
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// CHECK: torch.aten.log %[[ADD]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
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%0 = torch.operator "onnx.Acosh"(%arg0) : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
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%0 = torch.operator "onnx.Acosh"(%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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@ -704,7 +709,12 @@ func.func @test_acosh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<
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// CHECK-LABEL: @test_acosh
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// CHECK-LABEL: @test_acosh
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func.func @test_acosh(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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func.func @test_acosh(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: torch.aten.acosh %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
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// CHECK: %[[SQUARE:.+]] = torch.aten.square %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
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// CHECK: %[[C1:.+]] = torch.constant.int 1
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// CHECK: %[[SUB:.+]] = torch.aten.sub.Scalar %[[SQUARE]], %[[C1]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,4,5],f32>
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// CHECK: %[[SQRT:.+]] = torch.aten.sqrt %[[SUB]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
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// CHECK: %[[ADD:.+]] = torch.aten.add.Tensor %arg0, %[[SQRT]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32>, !torch.int -> !torch.vtensor<[3,4,5],f32>
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// CHECK: torch.aten.log %[[ADD]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
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%0 = torch.operator "onnx.Acosh"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
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%0 = torch.operator "onnx.Acosh"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
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return %0 : !torch.vtensor<[3,4,5],f32>
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return %0 : !torch.vtensor<[3,4,5],f32>
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}
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}
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