mirror of https://github.com/llvm/torch-mlir
101 lines
3.8 KiB
C++
101 lines
3.8 KiB
C++
//===----------------------------------------------------------------------===//
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//
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// Part of the LLVM Project, 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 "PassDetail.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
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#include "torch-mlir/Dialect/Torch/Transforms/Passes.h"
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#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
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#include "llvm/ADT/StringExtras.h"
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using namespace mlir;
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using namespace mlir::torch;
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using namespace mlir::torch::Torch;
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// Decompose softmax into: exp(x) / sum(exp(x))
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namespace {
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class DecomposeAtenSoftmaxIntOp : public OpRewritePattern<AtenSoftmaxIntOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(AtenSoftmaxIntOp op,
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PatternRewriter &rewriter) const override {
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Location loc = op.getLoc();
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Value self = op.self();
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Value dim = op.dim();
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if (!op.dtype().getType().isa<Torch::NoneType>())
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return rewriter.notifyMatchFailure(
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op, "Unimplemented non-None dtype for softmax");
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BaseTensorType tensorType = self.getType().cast<BaseTensorType>();
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if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
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return rewriter.notifyMatchFailure(op, "Only support floating type");
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// exp(x)
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Value exp = rewriter.create<AtenExpOp>(loc, tensorType, self);
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// sum(exp(x))
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Value dimList = rewriter.create<PrimListConstructOp>(
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loc, Torch::ListType::get(dim.getType()), dim);
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Value keepDim = rewriter.create<ConstantBoolOp>(loc, true);
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Value dtype = rewriter.create<ConstantNoneOp>(loc);
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SmallVector<int64_t> sizes;
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int64_t dimInt;
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if (tensorType.hasSizes()) {
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ArrayRef<int64_t> inputShape = tensorType.getSizes();
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int64_t inputRank = inputShape.size();
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if (matchPattern(dim, m_TorchConstantInt(&dimInt))) {
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dimInt = toPositiveDim(dimInt, inputRank);
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if (!isValidDim(dimInt, inputRank))
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return rewriter.notifyMatchFailure(op, "dim is not a valid dim");
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sizes.append(inputShape.begin(), inputShape.end());
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sizes[dimInt] = 1;
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} else {
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sizes.resize(inputRank, kUnknownSize);
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}
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}
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Type resultType = tensorType.getWithSizesAndDtype(
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sizes.size() == 0 ? Optional<ArrayRef<int64_t>>()
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: llvm::makeArrayRef(sizes),
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tensorType.getDtype());
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Value sum = rewriter.create<AtenSumDimIntListOp>(loc, resultType, exp,
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dimList, keepDim, dtype);
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// exp(x) / sum(exp(x))
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Value result = rewriter.create<AtenDivTensorOp>(loc, tensorType, exp, sum);
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rewriter.replaceOpWithNewOp<TensorStaticInfoCastOp>(op, op.getType(),
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result);
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return success();
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}
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};
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} // namespace
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namespace {
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class DecomposeComplexOpsPass
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: public DecomposeComplexOpsBase<DecomposeComplexOpsPass> {
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void runOnOperation() override {
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MLIRContext *context = &getContext();
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RewritePatternSet patterns(context);
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ConversionTarget target(*context);
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target.addLegalDialect<Torch::TorchDialect>();
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patterns.add<DecomposeAtenSoftmaxIntOp>(context);
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target.addIllegalOp<AtenSoftmaxIntOp>();
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if (failed(applyPartialConversion(getOperation(), target,
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std::move(patterns)))) {
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return signalPassFailure();
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}
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}
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};
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} // namespace
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std::unique_ptr<OperationPass<FuncOp>>
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mlir::torch::Torch::createDecomposeComplexOpsPass() {
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return std::make_unique<DecomposeComplexOpsPass>();
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}
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