695c341b84
Support ALLOW filters and DENY filters. This is needed for compatibility with existing code that specifies more complex op filters. Differential Revision: https://reviews.llvm.org/D119820
52 lines
2.0 KiB
C++
52 lines
2.0 KiB
C++
//===- Bufferize.cpp - Bufferization for `tensor` dialect ops -------------===//
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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 bufferization of `tensor` dialect ops
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/Bufferization/Transforms/Bufferize.h"
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#include "PassDetail.h"
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#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
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#include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
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#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/Dialect/SCF/SCF.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.h"
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#include "mlir/Dialect/Tensor/Transforms/Passes.h"
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#include "mlir/IR/ImplicitLocOpBuilder.h"
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#include "mlir/Transforms/DialectConversion.h"
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using namespace mlir;
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using namespace bufferization;
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namespace {
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struct TensorBufferizePass : public TensorBufferizeBase<TensorBufferizePass> {
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void runOnOperation() override {
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BufferizationOptions options = getPartialBufferizationOptions();
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options.allowDialectInFilter<tensor::TensorDialect>();
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if (failed(bufferizeOp(getOperation(), options)))
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signalPassFailure();
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}
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry.insert<bufferization::BufferizationDialect, memref::MemRefDialect,
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tensor::TensorDialect, scf::SCFDialect,
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arith::ArithmeticDialect>();
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tensor::registerBufferizableOpInterfaceExternalModels(registry);
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}
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};
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} // namespace
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std::unique_ptr<Pass> mlir::createTensorBufferizePass() {
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return std::make_unique<TensorBufferizePass>();
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}
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