mirror of https://github.com/llvm/torch-mlir
[MLIR][ONNX] Add OnnxToTorch support for Maxpool Op (#2695)
Add Maxpool ONNX op support. Add Utils.h/cpp files to create a constant int list for ONNX.pull/2754/head
Chi_Liu
GitHub
parent
670a99ae19
commit
c7452af4fa
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@ -0,0 +1,23 @@
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//===------------------------------------------------------------*- C++ -*-===//
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//
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// This file is licensed under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// Also available under a BSD-style license. See LICENSE.
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//
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//===----------------------------------------------------------------------===//
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#ifndef TORCHMLIR_CONVERSION_TORCHONNXTOTORCH_UTILS_H
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#define TORCHMLIR_CONVERSION_TORCHONNXTOTORCH_UTILS_H
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#include "torch-mlir/Conversion/TorchOnnxToTorch/Patterns.h"
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namespace mlir::torch::onnx_c {
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Value createConstantIntList(OpBinder binder,
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ConversionPatternRewriter &rewriter,
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SmallVector<int64_t> cstInput);
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} // namespace mlir::torch::onnx_c
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#endif // TORCHMLIR_CONVERSION_TORCHONNXTOTORCH_UTILS_H
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@ -5,6 +5,7 @@ add_mlir_conversion_library(TorchMLIRTorchOnnxToTorch
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Passes.cpp
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Patterns.cpp
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TorchOnnxToTorch.cpp
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Utils.cpp
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ADDITIONAL_HEADER_DIRS
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${PROJECT_SOURCE_DIR}/include/torch-mlir/Conversion/TorchOnnxToTorch
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@ -8,6 +8,7 @@
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//===----------------------------------------------------------------------===//
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#include "torch-mlir/Conversion/TorchOnnxToTorch/Patterns.h"
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#include "torch-mlir/Conversion/TorchOnnxToTorch/Utils.h"
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#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
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using namespace mlir;
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@ -148,6 +149,84 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
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binder.op, resultType, lhs, rhs);
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return success();
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});
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patterns.onOp(
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"MaxPool", 12, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
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std::string autoPad;
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if (binder.customOpNameStringAttr(autoPad, "auto_pad", "NOTSET"))
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return rewriter.notifyMatchFailure(binder.op,
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"auto_pad bind failure");
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if (autoPad != "NOTSET")
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return rewriter.notifyMatchFailure(
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binder.op, "unsupported conversion: auto_pad != NOTSET");
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Torch::ValueTensorType resultType;
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Value operand;
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bool ceilMode;
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int64_t storageOrder;
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// TODO: Add support for indices output and storage_order
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if (binder.tensorOperand(operand) ||
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binder.s64BoolAttr(ceilMode, "ceil_mode", false) ||
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binder.s64IntegerAttr(storageOrder, "storage_order", 0) ||
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binder.tensorResultType(resultType))
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return rewriter.notifyMatchFailure(
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binder.op,
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"operand/ceil_mode/storage_order/resultType bind failure");
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if (storageOrder != 0)
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return rewriter.notifyMatchFailure(
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binder.op, "storage_order setting is not supported.");
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// Determine the rank of input tensor.
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std::optional<unsigned> maybeRank = Torch::getTensorRank(operand);
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if (!maybeRank)
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return rewriter.notifyMatchFailure(binder.op,
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"Unimplemented: unranked tensor");
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unsigned rank = *maybeRank;
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SmallVector<int64_t> kernel, padding, strides, dilations;
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if (binder.s64IntegerArrayAttr(kernel, "kernel_shape", {}))
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return rewriter.notifyMatchFailure(binder.op,
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"kernel_shape bind failure");
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if (kernel.size() != rank - 2)
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return rewriter.notifyMatchFailure(
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binder.op, "kernel list size does not match the number of axes");
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if (binder.s64IntegerArrayAttr(padding, "pads", {0}))
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return rewriter.notifyMatchFailure(binder.op, "pads bind failure");
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if (padding.size() != 1 && padding.size() != rank - 2)
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return rewriter.notifyMatchFailure(
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binder.op, "padding list size does not match the number of axes");
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if (binder.s64IntegerArrayAttr(strides, "strides", {1}))
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return rewriter.notifyMatchFailure(binder.op, "strides bind failure");
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if (strides.size() != 1 && strides.size() != rank - 2)
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return rewriter.notifyMatchFailure(
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binder.op, "strides list size does not match the number of axes");
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if (binder.s64IntegerArrayAttr(dilations, "dilations", {}))
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return rewriter.notifyMatchFailure(binder.op,
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"dilations bind failure");
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Value kernelSizeList = createConstantIntList(binder, rewriter, kernel);
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Value paddingList = createConstantIntList(binder, rewriter, padding);
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Value stridesList = createConstantIntList(binder, rewriter, strides);
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Value dilationsList =
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createConstantIntList(binder, rewriter, dilations);
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Value cstCeilMode =
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rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), ceilMode);
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if (rank == 3)
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return rewriter.notifyMatchFailure(binder.op,
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"Unimplemented: AtenMaxPool1dOp");
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if (rank == 4) {
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rewriter.replaceOpWithNewOp<Torch::AtenMaxPool2dOp>(
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binder.op, resultType, operand, kernelSizeList, stridesList,
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paddingList, dilationsList, cstCeilMode);
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return success();
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}
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if (rank == 5) {
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rewriter.replaceOpWithNewOp<Torch::AtenMaxPool3dOp>(
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binder.op, resultType, operand, kernelSizeList, stridesList,
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paddingList, dilationsList, cstCeilMode);
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return success();
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}
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return rewriter.notifyMatchFailure(binder.op, "No rank is matched.");
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});
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patterns.onOp("Greater", 16,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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@ -0,0 +1,28 @@
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//===------------------------------------------------------------*- C++ -*-===//
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//
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// This file is licensed under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// Also available under a BSD-style license. See LICENSE.
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//
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//===----------------------------------------------------------------------===//
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#include "torch-mlir/Conversion/TorchOnnxToTorch/Utils.h"
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using namespace mlir;
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using namespace mlir::torch;
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using namespace mlir::torch::onnx_c;
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Value mlir::torch::onnx_c::createConstantIntList(
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OpBinder binder, ConversionPatternRewriter &rewriter,
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SmallVector<int64_t> cstInput) {
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SmallVector<Value> cstValue;
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for (int64_t i : cstInput) {
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cstValue.push_back(rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(i)));
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}
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return rewriter.create<Torch::PrimListConstructOp>(
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binder.getLoc(),
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Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
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cstValue);
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}
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@ -13,6 +13,8 @@ func.func @test_greater(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtenso
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return %0 : !torch.vtensor<[3,4,5],i1>
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}
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// -----
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// CHECK-LABEL: func.func @test_greater_or_equal
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func.func @test_greater_or_equal(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],i1> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 16 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[3,4,5],f32>
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@ -22,6 +24,8 @@ func.func @test_greater_or_equal(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !tor
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return %0 : !torch.vtensor<[3,4,5],i1>
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}
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// -----
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// CHECK-LABEL: func.func @test_less
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func.func @test_less(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],i1> 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 = ""} {
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// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[3,4,5],f32>
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@ -31,6 +35,8 @@ func.func @test_less(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[
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return %0 : !torch.vtensor<[3,4,5],i1>
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}
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// -----
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// CHECK-LABEL: func.func @test_gather_elements
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func.func @test_gather_elements(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[3,4,5], si64>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.opset_version = 13 : si64} {
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// CHECK-DAG: %[[INT0:.+]] = torch.constant.int 0
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@ -99,7 +105,7 @@ func.func @test_gemm_beta(%arg0: !torch.vtensor<[3,5],f32>, %arg1: !torch.vtenso
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return %0 : !torch.vtensor<[3,4],f32>
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}
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// -----
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// -----
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// CHECK-LABEL: func.func @test_gemm_alpha_beta
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func.func @test_gemm_alpha_beta(%arg0: !torch.vtensor<[3,5],f32>, %arg1: !torch.vtensor<[5,4],f32>, %arg2: !torch.vtensor<[1,4],f32>) -> !torch.vtensor<[3,4],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64} {
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@ -137,6 +143,8 @@ func.func @test_leaky_relu(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor
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return %0 : !torch.vtensor<[3,4,5],f32>
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}
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// -----
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// CHECK-LABEL: @test_matmul_2d
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func.func @test_matmul_2d(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[4,3],f32>) -> !torch.vtensor<[3,3],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 = ""} {
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// CHECK: torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[3,4],f32>, !torch.vtensor<[4,3],f32> -> !torch.vtensor<[3,3],f32>
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@ -173,6 +181,62 @@ func.func @test_matmul_4d(%arg0: !torch.vtensor<[1,2,3,4],f32>, %arg1: !torch.vt
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// -----
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// CHECK-LABEL: func.func @test_maxpool_2d_default
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func.func @test_maxpool_2d_default(%arg0: !torch.vtensor<[1,3,32,32],f32>) -> !torch.vtensor<[1,3,31,31],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 12 : si64} {
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// CHECK: %[[I2:.*]] = torch.constant.int 2
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// CHECK: %[[I2_1:.*]] = torch.constant.int 2
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// CHECK: %[[LIST22:.*]] = torch.prim.ListConstruct %[[I2]], %[[I2_1]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[I0:.*]] = torch.constant.int 0
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// CHECK: %[[LIST0:.*]] = torch.prim.ListConstruct %[[I0]] : (!torch.int) -> !torch.list<int>
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// CHECK: %[[I1:.*]] = torch.constant.int 1
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// CHECK: %[[LIST1:.*]] = torch.prim.ListConstruct %[[I1]] : (!torch.int) -> !torch.list<int>
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// CHECK: %[[LIST:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
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// CHECK: %[[FALSE:.*]] = torch.constant.bool false
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// CHECK: torch.aten.max_pool2d %arg0, %[[LIST22]], %[[LIST1]], %[[LIST0]], %[[LIST]], %[[FALSE]] : !torch.vtensor<[1,3,32,32],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool -> !torch.vtensor<[1,3,31,31],f32>
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%0 = torch.operator "onnx.MaxPool"(%arg0) {torch.onnx.kernel_shape = [2 : si64, 2 : si64]} : (!torch.vtensor<[1,3,32,32],f32>) -> !torch.vtensor<[1,3,31,31],f32>
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return %0 : !torch.vtensor<[1,3,31,31],f32>
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}
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// -----
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// CHECK-LABEL: func.func @test_maxpool_2d_ceil
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func.func @test_maxpool_2d_ceil(%arg0: !torch.vtensor<[1,1,4,4],f32>) -> !torch.vtensor<[1,1,2,2],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 12 : si64} {
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// CHECK: %[[I3:.*]] = torch.constant.int 3
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// CHECK: %[[I3_1:.*]] = torch.constant.int 3
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// CHECK: %[[LIST33:.*]] = torch.prim.ListConstruct %[[I3]], %[[I3_1]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[I0:.*]] = torch.constant.int 0
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// CHECK: %[[LIST0:.*]] = torch.prim.ListConstruct %[[I0]] : (!torch.int) -> !torch.list<int>
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// CHECK: %[[I2:.*]] = torch.constant.int 2
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// CHECK: %[[I2_1:.*]] = torch.constant.int 2
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// CHECK: %[[LIST22:.*]] = torch.prim.ListConstruct %[[I2]], %[[I2_1]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[LIST:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
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// CHECK: %[[TRUE:.*]] = torch.constant.bool true
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// CHECK: torch.aten.max_pool2d %arg0, %[[LIST33]], %[[LIST22]], %[[LIST0]], %[[LIST]], %[[TRUE]] : !torch.vtensor<[1,1,4,4],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool -> !torch.vtensor<[1,1,2,2],f32>
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%0 = torch.operator "onnx.MaxPool"(%arg0) {torch.onnx.ceil_mode = 1 : si64, torch.onnx.kernel_shape = [3 : si64, 3 : si64], torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[1,1,4,4],f32>) -> !torch.vtensor<[1,1,2,2],f32>
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return %0 : !torch.vtensor<[1,1,2,2],f32>
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}
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// -----
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// CHECK-LABEL: func.func @test_maxpool_3d_default
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func.func @test_maxpool_3d_default(%arg0: !torch.vtensor<[1,3,32,32,32],f32>) -> !torch.vtensor<[1,3,31,31,31],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 12 : si64} {
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// CHECK: %[[I2:.*]] = torch.constant.int 2
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// CHECK: %[[I2_1:.*]] = torch.constant.int 2
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// CHECK: %[[I2_2:.*]] = torch.constant.int 2
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// CHECK: %[[LIST222:.*]] = torch.prim.ListConstruct %[[I2]], %[[I2_1]], %[[I2_2]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[I0:.*]] = torch.constant.int 0
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// CHECK: %[[LIST0:.*]] = torch.prim.ListConstruct %[[I0]] : (!torch.int) -> !torch.list<int>
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// CHECK: %[[I1:.*]] = torch.constant.int 1
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// CHECK: %[[LIST1:.*]] = torch.prim.ListConstruct %[[I1]] : (!torch.int) -> !torch.list<int>
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// CHECK: %[[LIST:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
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// CHECK: %[[FALSE:.*]] = torch.constant.bool false
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// CHECK: torch.aten.max_pool3d %arg0, %[[LIST222]], %[[LIST1]], %[[LIST0]], %[[LIST]], %[[FALSE]] : !torch.vtensor<[1,3,32,32,32],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool -> !torch.vtensor<[1,3,31,31,31],f32>
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%0 = torch.operator "onnx.MaxPool"(%arg0) {torch.onnx.kernel_shape = [2 : si64, 2 : si64, 2 : si64]} : (!torch.vtensor<[1,3,32,32,32],f32>) -> !torch.vtensor<[1,3,31,31,31],f32>
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return %0 : !torch.vtensor<[1,3,31,31,31],f32>
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}
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// -----
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// CHECK-LABEL: @test_gelu_default_1
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func.func @test_gelu_default_1(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[STR1:.*]] = torch.constant.str "none"
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@ -222,6 +286,8 @@ func.func @test_less_or_equal(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.
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return %0 : !torch.vtensor<[3,4,5],i1>
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}
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// -----
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// CHECK-LABEL: func.func @test_pow
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func.func @test_pow(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 15 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: torch.aten.pow.Tensor_Tensor %arg0, %arg1 : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
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@ -229,6 +295,8 @@ func.func @test_less_or_equal(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.
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return %0 : !torch.vtensor<[3,4,5],f32>
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}
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// -----
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// CHECK-LABEL: @test_hardsigmoid_example
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func.func @test_hardsigmoid_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 6 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[ALPHA_FLOAT:.*]] = torch.constant.float 5.000000e-01
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@ -252,6 +320,8 @@ func.func @test_hardsigmoid_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vt
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return %0 : !torch.vtensor<[3],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: @test_hardsigmoid
|
||||
func.func @test_hardsigmoid(%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 = 6 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK: %[[ALPHA_FLOAT:.*]] = torch.constant.float 5.000000e-01
|
||||
|
|
@ -274,6 +344,8 @@ func.func @test_hardsigmoid(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtenso
|
|||
return %0 : !torch.vtensor<[3,4,5],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: @test_hardsigmoid_default
|
||||
func.func @test_hardsigmoid_default(%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 = 6 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK: %[[ALPHA_FLOAT:.*]] = torch.constant.float 0.20000000298023224
|
||||
|
|
@ -331,6 +403,8 @@ func.func @test_globalaveragepool_precomputed(%arg0: !torch.vtensor<[1,1,3,3],f3
|
|||
return %0 : !torch.vtensor<[1,1,1,1],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @test_max_example
|
||||
func.func @test_max_example(%arg0: !torch.vtensor<[3],f32>, %arg1: !torch.vtensor<[3],f32>, %arg2: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],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.maximum %arg0, %arg1 : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
|
||||
|
|
@ -338,6 +412,8 @@ func.func @test_globalaveragepool_precomputed(%arg0: !torch.vtensor<[1,1,3,3],f3
|
|||
return %0 : !torch.vtensor<[3],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @test_min_example
|
||||
func.func @test_min_example(%arg0: !torch.vtensor<[3],f32>, %arg1: !torch.vtensor<[3],f32>, %arg2: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],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.minimum %arg0, %arg1 : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
|
||||
|
|
@ -345,6 +421,7 @@ func.func @test_globalaveragepool_precomputed(%arg0: !torch.vtensor<[1,1,3,3],f3
|
|||
return %0 : !torch.vtensor<[3],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// 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 = ""} {
|
||||
|
|
@ -353,6 +430,8 @@ func.func @test_globalaveragepool_precomputed(%arg0: !torch.vtensor<[1,1,3,3],f3
|
|||
return %0 : !torch.vtensor<[3,4,5],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @test_neg
|
||||
func.func @test_neg(%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.neg %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
|
||||
|
|
@ -360,6 +439,8 @@ func.func @test_globalaveragepool_precomputed(%arg0: !torch.vtensor<[1,1,3,3],f3
|
|||
return %0 : !torch.vtensor<[3,4,5],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @test_not_2d
|
||||
func.func @test_not_2d(%arg0: !torch.vtensor<[3,4],i1>) -> !torch.vtensor<[3,4],i1> 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: torch.aten.bitwise_not %arg0 : !torch.vtensor<[3,4],i1> -> !torch.vtensor<[3,4],i1>
|
||||
|
|
@ -367,6 +448,8 @@ func.func @test_not_2d(%arg0: !torch.vtensor<[3,4],i1>) -> !torch.vtensor<[3,4],
|
|||
return %0 : !torch.vtensor<[3,4],i1>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @test_or2d
|
||||
func.func @test_or2d(%arg0: !torch.vtensor<[3,4],i1>, %arg1: !torch.vtensor<[3,4],i1>) -> !torch.vtensor<[3,4],i1> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 7 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK: torch.aten.bitwise_or.Tensor %arg0, %arg1 : !torch.vtensor<[3,4],i1>, !torch.vtensor<[3,4],i1> -> !torch.vtensor<[3,4],i1>
|
||||
|
|
|
|||
Loading…
Reference in New Issue