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.
79 lines
2.6 KiB
C++
79 lines
2.6 KiB
C++
//===- TosaLayerwiseConstantFoldPass.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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// This file implements constant folding transformations on TOSA operations
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/Tosa/Transforms/Passes.h"
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#include "mlir/Dialect/Func/IR/FuncOps.h"
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#include "mlir/Dialect/Tosa/IR/TosaOps.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_TOSALAYERWISECONSTANTFOLDPASS
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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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template <typename... Args>
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void addOpsCanonicalizations(MLIRContext *ctx, RewritePatternSet &patterns) {
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(Args::getCanonicalizationPatterns(patterns, ctx), ...);
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}
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void populateTosaOpsCanonicalizationPatterns(MLIRContext *ctx,
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RewritePatternSet &patterns) {
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addOpsCanonicalizations<
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#define GET_OP_LIST
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#include "mlir/Dialect/Tosa/IR/TosaOps.cpp.inc"
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>(ctx, patterns);
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}
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struct TosaLayerwiseConstantFoldPass
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: public tosa::impl::TosaLayerwiseConstantFoldPassBase<
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TosaLayerwiseConstantFoldPass> {
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TosaLayerwiseConstantFoldPass(
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const TosaLayerwiseConstantFoldPassOptions &options)
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: TosaLayerwiseConstantFoldPassBase(options) {}
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void runOnOperation() override {
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auto *ctx = &getContext();
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RewritePatternSet patterns(ctx);
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auto func = getOperation();
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mlir::tosa::populateTosaFoldConstantReciprocalPatterns(ctx, patterns);
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mlir::tosa::populateTosaFoldConstantTransposePatterns(ctx, patterns);
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mlir::tosa::populateTosaConstantReduction(ctx, patterns,
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aggressiveReduceConstant);
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populateTosaOpsCanonicalizationPatterns(ctx, patterns);
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if (applyPatternsGreedily(func, std::move(patterns)).failed())
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signalPassFailure();
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}
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};
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} // namespace
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std::unique_ptr<Pass> mlir::tosa::createTosaLayerwiseConstantFoldPass() {
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return std::make_unique<TosaLayerwiseConstantFoldPass>(
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TosaLayerwiseConstantFoldPassOptions{false});
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}
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std::unique_ptr<Pass> mlir::tosa::createTosaLayerwiseConstantFoldPass(
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const TosaLayerwiseConstantFoldPassOptions &options) {
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return std::make_unique<TosaLayerwiseConstantFoldPass>(options);
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}
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