09dfc5713d
The greedy rewriter is used in many different flows and it has a lot of
convenience (work list management, debugging actions, tracing, etc). But
it combines two kinds of greedy behavior 1) how ops are matched, 2)
folding wherever it can.
These are independent forms of greedy and leads to inefficiency. E.g.,
cases where one need to create different phases in lowering and is
required to applying patterns in specific order split across different
passes. Using the driver one ends up needlessly retrying folding/having
multiple rounds of folding attempts, where one final run would have
sufficed.
Of course folks can locally avoid this behavior by just building their
own, but this is also a common requested feature that folks keep on
working around locally in suboptimal ways.
For downstream users, there should be no behavioral change. Updating
from the deprecated should just be a find and replace (e.g., `find ./
-type f -exec sed -i
's|applyPatternsAndFoldGreedily|applyPatternsGreedily|g' {} \;` variety)
as the API arguments hasn't changed between the two.
257 lines
9.6 KiB
C++
257 lines
9.6 KiB
C++
//===- TosaMakeBroadcastable.cpp ------------------------------------------===//
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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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//
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// Insert reshape to binary op's input if needed to match rank
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/Func/IR/FuncOps.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Dialect/Tosa/IR/TosaOps.h"
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#include "mlir/Dialect/Tosa/Transforms/Passes.h"
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#include "mlir/Dialect/Tosa/Utils/ConversionUtils.h"
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#include "mlir/Dialect/Tosa/Utils/QuantUtils.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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namespace mlir {
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namespace tosa {
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#define GEN_PASS_DEF_TOSAMAKEBROADCASTABLE
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#include "mlir/Dialect/Tosa/Transforms/Passes.h.inc"
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} // namespace tosa
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} // namespace mlir
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using namespace mlir;
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using namespace mlir::tosa;
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namespace {
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/// Common code to create the reshape op where necessary to make the rank of the
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/// operations equal. input1 and input2 will be updated when the rank has
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/// changed. The caller is expected to use these to rewrite the original
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/// operator with the RESHAPE now in the graph.
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/// return failure when (1) no reshape needed, or (2) output_type is specified
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/// and it has different rank
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LogicalResult reshapeLowerToHigher(PatternRewriter &rewriter, Location loc,
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RankedTensorType outputType, Value &input1,
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Value &input2) {
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auto input1Ty = dyn_cast<RankedTensorType>(input1.getType());
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auto input2Ty = dyn_cast<RankedTensorType>(input2.getType());
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if (!input1Ty || !input2Ty) {
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return rewriter.notifyMatchFailure(loc, "input not a ranked tensor");
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}
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int64_t input1Rank = input1Ty.getRank();
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int64_t input2Rank = input2Ty.getRank();
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if (input1Rank == input2Rank)
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return rewriter.notifyMatchFailure(loc,
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"cannot rewrite as its already correct");
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Value input1Copy = input1;
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Value input2Copy = input2;
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if (EqualizeRanks(rewriter, loc, input1Copy, input2Copy).failed()) {
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return rewriter.notifyMatchFailure(loc, "failed to reshape inputs");
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}
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// Verify the rank agrees with the output type if the output type is ranked.
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if (outputType) {
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if (outputType.getRank() !=
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llvm::cast<RankedTensorType>(input1Copy.getType()).getRank() ||
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outputType.getRank() !=
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llvm::cast<RankedTensorType>(input2Copy.getType()).getRank())
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return rewriter.notifyMatchFailure(
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loc, "the reshaped type doesn't agrees with the ranked output type");
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}
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input1 = input1Copy;
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input2 = input2Copy;
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return success();
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}
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template <typename OpTy>
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struct ConvertTosaOp : public OpRewritePattern<OpTy> {
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using OpRewritePattern<OpTy>::OpRewritePattern;
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LogicalResult matchAndRewrite(OpTy tosaBinaryOp,
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PatternRewriter &rewriter) const override {
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Value input1 = tosaBinaryOp.getInput1();
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Value input2 = tosaBinaryOp.getInput2();
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Value output = tosaBinaryOp.getResult();
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auto outputType = dyn_cast<RankedTensorType>(output.getType());
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if (!outputType)
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return failure();
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if (reshapeLowerToHigher(rewriter, tosaBinaryOp.getLoc(), outputType,
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input1, input2)
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.failed())
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return failure();
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rewriter.replaceOpWithNewOp<OpTy>(tosaBinaryOp, outputType, input1, input2);
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return success();
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}
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};
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// The MulOp has an extra parameter 'shift' not present in other elementwise
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// binary ops, that necessitates special handling of its builder.
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template <>
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struct ConvertTosaOp<tosa::MulOp> : public OpRewritePattern<tosa::MulOp> {
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using OpRewritePattern<tosa::MulOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(tosa::MulOp tosaBinaryOp,
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PatternRewriter &rewriter) const override {
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Value input1 = tosaBinaryOp.getInput1();
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Value input2 = tosaBinaryOp.getInput2();
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int32_t shift = tosaBinaryOp.getShift();
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Value output = tosaBinaryOp.getResult();
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auto outputType = dyn_cast<RankedTensorType>(output.getType());
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if (!outputType)
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return failure();
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if (reshapeLowerToHigher(rewriter, tosaBinaryOp.getLoc(), outputType,
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input1, input2)
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.failed())
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return failure();
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rewriter.replaceOpWithNewOp<tosa::MulOp>(tosaBinaryOp, outputType, input1,
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input2, shift);
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return success();
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}
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};
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// The ArithmeticRightShiftOp has an extra parameter 'round' not present in
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// other elementwise binary ops, that necessitates special handling of its
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// builder.
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template <>
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struct ConvertTosaOp<tosa::ArithmeticRightShiftOp>
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: public OpRewritePattern<tosa::ArithmeticRightShiftOp> {
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using OpRewritePattern<tosa::ArithmeticRightShiftOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(tosa::ArithmeticRightShiftOp tosaBinaryOp,
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PatternRewriter &rewriter) const override {
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Value input1 = tosaBinaryOp.getInput1();
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Value input2 = tosaBinaryOp.getInput2();
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int32_t round = tosaBinaryOp.getRound();
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Value output = tosaBinaryOp.getResult();
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auto outputType = dyn_cast<RankedTensorType>(output.getType());
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if (!outputType)
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return failure();
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if (reshapeLowerToHigher(rewriter, tosaBinaryOp.getLoc(), outputType,
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input1, input2)
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.failed())
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return failure();
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rewriter.replaceOpWithNewOp<tosa::ArithmeticRightShiftOp>(
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tosaBinaryOp, outputType, input1, input2, round);
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return success();
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}
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};
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template <>
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struct ConvertTosaOp<tosa::SelectOp> : public OpRewritePattern<tosa::SelectOp> {
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using OpRewritePattern<tosa::SelectOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(tosa::SelectOp tosaOp,
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PatternRewriter &rewriter) const override {
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Value input1 = tosaOp.getPred();
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Value input2 = tosaOp.getOnTrue();
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Value input3 = tosaOp.getOnFalse();
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Value output = tosaOp.getResult();
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auto outputType = dyn_cast<RankedTensorType>(output.getType());
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if (!outputType)
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return rewriter.notifyMatchFailure(tosaOp, "output not a ranked tensor");
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// Apply broadcasting to each pair of inputs separately, and chain them as
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// compound as below so that the broadcasting happens all at once.
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bool reshaped1 = reshapeLowerToHigher(rewriter, tosaOp.getLoc(), outputType,
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input1, input2)
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.succeeded();
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bool reshaped2 = reshapeLowerToHigher(rewriter, tosaOp.getLoc(), outputType,
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input1, input3)
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.succeeded();
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bool reshaped3 = reshapeLowerToHigher(rewriter, tosaOp.getLoc(), outputType,
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input2, input3)
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.succeeded();
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if (!reshaped1 && !reshaped2 && !reshaped3)
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return rewriter.notifyMatchFailure(
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tosaOp,
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"cannot rewrite as the rank of all operands is already aligned");
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int32_t result1Rank = cast<RankedTensorType>(input1.getType()).getRank();
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int32_t result2Rank = cast<RankedTensorType>(input2.getType()).getRank();
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int32_t result3Rank = cast<RankedTensorType>(input3.getType()).getRank();
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int32_t outputRank = outputType.getRank();
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if ((result1Rank != result2Rank) || (result2Rank != result3Rank) ||
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(result1Rank != outputRank))
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return rewriter.notifyMatchFailure(
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tosaOp, "not all ranks are aligned with each other");
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rewriter.replaceOpWithNewOp<tosa::SelectOp>(tosaOp, outputType, input1,
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input2, input3);
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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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/// Pass that enables broadcast by making all input arrays have the same
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/// number of dimensions. Insert RESHAPE operations to lower rank operand
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struct TosaMakeBroadcastable
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: public tosa::impl::TosaMakeBroadcastableBase<TosaMakeBroadcastable> {
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public:
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void runOnOperation() override {
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auto func = getOperation();
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RewritePatternSet patterns(func.getContext());
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MLIRContext *ctx = func.getContext();
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// Add the generated patterns to the list.
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patterns.add<ConvertTosaOp<tosa::BitwiseAndOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::BitwiseOrOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::BitwiseXorOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::AddOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::SubOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::MulOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::IntDivOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::MaximumOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::MinimumOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::EqualOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::GreaterOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::GreaterEqualOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::LogicalLeftShiftOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::ArithmeticRightShiftOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::LogicalRightShiftOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::LogicalAndOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::LogicalOrOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::LogicalXorOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::SelectOp>>(ctx);
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patterns.add<ConvertTosaOp<tosa::PowOp>>(ctx);
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(void)applyPatternsGreedily(func, std::move(patterns));
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}
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};
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} // namespace
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std::unique_ptr<Pass> mlir::tosa::createTosaMakeBroadcastablePass() {
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return std::make_unique<TosaMakeBroadcastable>();
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}
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