mirror of https://github.com/llvm/torch-mlir
103 lines
3.7 KiB
C++
103 lines
3.7 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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//
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//===----------------------------------------------------------------------===//
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#include "npcomp/Conversion/TCFToLinalg/TCFToLinalg.h"
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#include "../PassDetail.h"
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#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
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#include "mlir/Dialect/Shape/IR/Shape.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/Dialect/Traits.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#include "npcomp/Dialect/TCF/IR/TCFOps.h"
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#include "npcomp/Dialect/TCP/IR/TCPDialect.h"
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#include "npcomp/Dialect/TCP/IR/TCPOps.h"
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using namespace mlir;
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using namespace mlir::NPCOMP;
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static SmallVector<Value, 6> bypassResultShapes(Operation *op,
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OpBuilder &builder) {
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if (auto matmul = dyn_cast<tcf::MatmulOp>(op)) {
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auto lhsRows = builder.create<DimOp>(op->getLoc(), matmul.lhs(), 0);
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auto rhsCols = builder.create<DimOp>(op->getLoc(), matmul.rhs(), 1);
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auto shape = builder.create<TensorFromElementsOp>(
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op->getLoc(), ValueRange({lhsRows, rhsCols}));
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return {shape};
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}
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// No shape transfer function.
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return {};
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}
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namespace {
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class ConvertMatmul : public OpRewritePattern<tcf::MatmulOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(tcf::MatmulOp op,
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PatternRewriter &rewriter) const override {
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// Create the constraints, and the assuming region.
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Value lhsK = rewriter.create<DimOp>(op.getLoc(), op.lhs(), 1);
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Value rhsK = rewriter.create<DimOp>(op.getLoc(), op.rhs(), 0);
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Value matchingK =
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rewriter.create<CmpIOp>(op.getLoc(), CmpIPredicate::eq, lhsK, rhsK);
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Value witness = rewriter.create<shape::CstrRequireOp>(
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op.getLoc(), matchingK, "mismatching contracting dimension for matmul");
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auto assuming = rewriter.create<shape::AssumingOp>(
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op.getLoc(), ArrayRef<Type>{op.getType()}, witness);
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// Build the region body.
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rewriter.createBlock(&assuming.doRegion());
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// Create the init tensor for the matmul.
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// TODO: Expand supported data types.
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Value c0 =
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rewriter.create<ConstantOp>(op.getLoc(), rewriter.getF32FloatAttr(0.0));
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Value shape = bypassResultShapes(op, rewriter)[0];
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Value initTensor =
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rewriter.create<tcp::SplattedOp>(op.getLoc(), op.getType(), c0, shape);
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// Create the matmul.
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auto matmul = rewriter.create<linalg::MatmulOp>(
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op.getLoc(), TypeRange(op.getType()), op.getOperands(), ValueRange(),
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ValueRange(initTensor));
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rewriter.create<shape::AssumingYieldOp>(op.getLoc(), matmul.getResult(0));
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// Finally, replace with the results of the shape.assuming
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rewriter.replaceOp(op, assuming.getResults());
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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 ConvertTCFToLinalg : public ConvertTCFToLinalgBase<ConvertTCFToLinalg> {
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public:
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry.insert<shape::ShapeDialect, tcp::TCPDialect>();
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}
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void runOnOperation() override {
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(void)applyPatternsAndFoldGreedily(getOperation(), getPatterns());
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}
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FrozenRewritePatternList getPatterns() {
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MLIRContext *context = &getContext();
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OwningRewritePatternList patterns;
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patterns.insert<ConvertMatmul>(context);
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return std::move(patterns);
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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::NPCOMP::createConvertTCFToLinalgPass() {
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return std::make_unique<ConvertTCFToLinalg>();
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}
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