mirror of https://github.com/llvm/torch-mlir
130 lines
4.2 KiB
C++
130 lines
4.2 KiB
C++
//===------------------------------------------------------------*- 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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#include "torch-mlir/Dialect/Torch/IR/TorchTypes.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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Type mlir::torch::onnx_c::getQTorchTypeFromTorchIntType(Type ty) {
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Torch::ValueTensorType tty = dyn_cast<Torch::ValueTensorType>(ty);
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if (!tty)
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return nullptr;
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auto ctx = ty.getContext();
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Type dty = tty.getDtype();
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if (dty.isUnsignedInteger(8))
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dty = Torch::QUInt8Type::get(ctx);
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if (dty.isSignedInteger(8))
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dty = Torch::QInt8Type::get(ctx);
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if (dty.isSignedInteger(32))
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dty = Torch::QInt32Type::get(ctx);
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if (!dty)
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return nullptr;
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return Torch::ValueTensorType::get(ctx, tty.getOptionalSizes(), dty);
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}
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bool mlir::torch::onnx_c::areAllElementsDistinct(SmallVector<int64_t> array) {
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int n = array.size();
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llvm::SetVector<int64_t> set;
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for (int i = 0; i < n; i++) {
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set.insert(array[i]);
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}
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// If all elements are distinct, then the size of set should be same
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// as array's size.
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return (set.size() == array.size());
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}
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std::optional<int64_t>
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mlir::torch::onnx_c::onnxDtypeIntToTorchDtypeInt(int64_t dtypeIntOnnx) {
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// TODO: Add complete mapping.
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// Where are the ONNX and PyTorch dtype enums defined?
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// ONNX:
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// https://github.com/shouxieai/tensorRT_Pro/blob/main/onnx/onnx-ml.proto
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// PyTorch:
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// https://github.com/llvm/torch-mlir/blob/main/include/torch-mlir/Dialect/Torch/Utils/TorchUpstream.h#L88
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std::optional<int64_t> dtypeIntTorch =
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[dtypeIntOnnx]() -> std::optional<int64_t> {
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switch (dtypeIntOnnx) {
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case 1:
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return 6; // float
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case 2:
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return 0; // uint8
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case 3:
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return 1; // int8
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case 6:
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return 3; // int32
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case 7:
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return 4; // int64
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case 9:
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return 11; // bool
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case 10:
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return 5; // half
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case 11:
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return 7; // double
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case 16:
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return 15; // bfloat16
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default:
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return std::nullopt; // No dtype
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}
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}();
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return dtypeIntTorch;
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}
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LogicalResult mlir::torch::onnx_c::createTorchTransposeOp(
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ConversionPatternRewriter &rewriter, Location loc, Value input,
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int64_t dimA, int64_t dimB, Value &transposed) {
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Type transposedType;
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if (failed(getTransposedType(cast<Torch::BaseTensorType>(input.getType()),
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dimA, dimB, transposedType)))
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return failure();
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Value cstDimA = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(dimA));
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Value cstDimB = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(dimB));
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transposed = rewriter.create<Torch::AtenTransposeIntOp>(
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loc, transposedType, input, cstDimA, cstDimB);
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return success();
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}
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LogicalResult mlir::torch::onnx_c::createTorchPermuteOp(
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OpBinder binder, ConversionPatternRewriter &rewriter, Location loc,
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Value input, SmallVector<int64_t> permuteDims, Value &permuted) {
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Type permutedType;
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if (failed(
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Torch::getPermutedType(cast<Torch::BaseTensorType>(input.getType()),
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permuteDims, permutedType)))
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return failure();
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Value permuteDimsList = createConstantIntList(binder, rewriter, permuteDims);
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permuted = rewriter.create<Torch::AtenPermuteOp>(loc, permutedType, input,
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permuteDimsList);
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return success();
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}
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