525 lines
21 KiB
C++
525 lines
21 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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// This file implements logic for lowering HLO dialect to LHLO dialect.
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#include "third_party/absl/memory/memory.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/Dialect/StandardOps/IR/Ops.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/AffineMap.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/Attributes.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/BlockAndValueMapping.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/Builders.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/Function.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/Location.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/MLIRContext.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/Operation.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/PatternMatch.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/StandardTypes.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/Pass/Pass.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/Transforms/BufferPlacement.h"
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#include "third_party/llvm/llvm-project/mlir/include/mlir/Transforms/DialectConversion.h"
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#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
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#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/IR/lhlo_ops.h"
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#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/transforms/map_hlo_to_lhlo_op.h"
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#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/transforms/passes.h"
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#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/transforms/rewriters.h"
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namespace mlir {
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namespace mhlo {
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namespace {
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template <typename T>
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using BaseOpConversion = BufferAssignmentOpConversionPattern<T>;
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using StdReturnOpConverter =
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detail::BufferAssignmentReturnOpConverter<mlir::ReturnOp, mlir::ReturnOp,
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xla_lhlo::CopyOp, true>;
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Value InsertDynamicAllocAndDealloc(Location loc, Value result,
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Value shape_operand,
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ConversionPatternRewriter* rewriter) {
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auto result_type = result.getType().dyn_cast<ShapedType>();
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if (!result_type) {
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result.getDefiningOp()->emitOpError()
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<< "tensor to buffer conversion expects ranked results";
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}
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auto memref_type =
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MemRefType::get(result_type.getShape(), result_type.getElementType());
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Operation* op = result.getDefiningOp();
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// Extract the required element out of the vector.
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SmallVector<Value, 4> dynamic_operands;
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for (auto shape_element : llvm::enumerate(result_type.getShape())) {
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if (shape_element.value() != ShapedType::kDynamicSize) continue;
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Value index = rewriter->create<ConstantOp>(
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loc, rewriter->getIntegerAttr(rewriter->getIndexType(),
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shape_element.index()));
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Value alloc_operand = rewriter->create<ExtractElementOp>(loc, shape_operand,
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ValueRange{index});
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if (!alloc_operand.getType().isIndex()) {
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alloc_operand = rewriter->create<IndexCastOp>(loc, alloc_operand,
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rewriter->getIndexType());
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}
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dynamic_operands.push_back(alloc_operand);
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}
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// Insert in front of op to ensure sizes are available.
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OpBuilder allocBuilder(op);
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auto alloc = allocBuilder.create<AllocOp>(loc, memref_type, dynamic_operands);
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return alloc;
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}
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Value InsertAlloc(Location loc, OpResult result,
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BufferAssignmentPlacer* bufferAssignment,
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ConversionPatternRewriter* rewriter) {
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auto result_type = result.getType().dyn_cast<ShapedType>();
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if (!result_type || !result_type.hasStaticShape()) {
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result.getDefiningOp()->emitOpError()
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<< "tensor to buffer conversion expects statically shaped results";
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}
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auto memref_type =
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MemRefType::get(result_type.getShape(), result_type.getElementType());
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OpBuilder::InsertionGuard guard(*rewriter);
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rewriter->restoreInsertionPoint(
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bufferAssignment->computeAllocPosition(result));
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auto alloc = rewriter->create<AllocOp>(loc, memref_type);
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return alloc;
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}
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template <typename HloOpTy>
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class HloToLhloOpConverter : public BaseOpConversion<HloOpTy> {
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public:
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using BaseOpConversion<HloOpTy>::BaseOpConversion;
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LogicalResult matchAndRewrite(
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HloOpTy hloOp, ArrayRef<Value> operands,
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ConversionPatternRewriter& rewriter) const final {
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Operation* op = hloOp.getOperation();
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const auto& original_results = op->getResults();
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SmallVector<Value, 4> buffer_args(operands.begin(), operands.end());
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for (auto result : llvm::enumerate(original_results)) {
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RankedTensorType resultType =
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result.value().getType().dyn_cast<RankedTensorType>();
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if (!resultType) {
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return failure();
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}
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if (resultType.hasStaticShape()) {
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buffer_args.push_back(InsertAlloc(op->getLoc(), result.value(),
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this->bufferAssignment, &rewriter));
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} else {
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SmallVector<Value, 1> results_shape;
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auto shape_type_op = dyn_cast<InferShapedTypeOpInterface>(op);
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if (!shape_type_op) return failure();
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if (failed(
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shape_type_op.reifyReturnTypeShapes(rewriter, results_shape)))
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return failure();
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buffer_args.push_back(InsertDynamicAllocAndDealloc(
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op->getLoc(), result.value(), results_shape.front(), &rewriter));
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}
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}
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rewriter.create<mhlo::HloToLhloOp<HloOpTy>>(op->getLoc(), llvm::None,
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buffer_args, op->getAttrs());
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rewriter.replaceOp(op, ArrayRef<Value>(buffer_args).slice(operands.size()));
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return success();
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}
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};
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struct HloToLhloDynamicBroadcastInDimOpConverter
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: public BaseOpConversion<mhlo::DynamicBroadcastInDimOp> {
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public:
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using BaseOpConversion<mhlo::DynamicBroadcastInDimOp>::BaseOpConversion;
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LogicalResult matchAndRewrite(
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mhlo::DynamicBroadcastInDimOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter& rewriter) const final {
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auto loc = op.getLoc();
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Value resultBuffer = InsertDynamicAllocAndDealloc(
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loc, op.getResult(), op.output_dimensions(), &rewriter);
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Value transformed_operand =
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InsertDynamicMemrefCastOp(op, operands.front(), &rewriter);
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rewriter.create<xla_lhlo::BroadcastInDimOp>(
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loc, transformed_operand, resultBuffer, op.broadcast_dimensions());
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rewriter.replaceOp(op, {resultBuffer});
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return success();
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}
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private:
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// Inserts dynamic memref to change the layout of the memref to put 0-stride
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// and size of the target dimension if size-1 dimension expansion is
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// necessary.
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xla_lhlo::DynamicMemRefCastOp InsertDynamicMemrefCastOp(
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mhlo::DynamicBroadcastInDimOp op, Value operand, OpBuilder* b) const {
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auto loc = op.getLoc();
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auto operand_type = operand.getType().cast<MemRefType>();
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auto operand_shape = operand_type.getShape();
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SmallVector<Value, 2> sizes, strides;
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sizes.reserve(operand_shape.size());
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strides.reserve(operand_shape.size());
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Value zero = b->create<ConstantIndexOp>(loc, 0);
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Value one = b->create<ConstantIndexOp>(loc, 1);
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for (auto dim : llvm::enumerate(op.broadcast_dimensions())) {
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Value broadcast_dim_value =
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b->create<ConstantIndexOp>(loc, dim.value().getSExtValue());
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Value result_dim_size = b->create<ExtractElementOp>(
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loc, op.output_dimensions(), broadcast_dim_value);
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Value operand_dim_size =
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ShapedType::isDynamic(operand_shape[dim.index()])
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? b->create<DimOp>(loc, operand, dim.index()).getResult()
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: b->create<ConstantIndexOp>(loc, operand_shape[dim.index()])
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.getResult();
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// TODO(pifon): Revisit if this cast is needed. Maybe we can use
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// tensor<index> for `output_dimensions` as well.
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if (!result_dim_size.getType().isIndex()) {
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result_dim_size =
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b->create<IndexCastOp>(loc, result_dim_size, b->getIndexType());
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}
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// There can be two cases:
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// 1) Operand dim == result dim => expansion is not needed => stride := 1.
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// 2) Operand dim < result dim => expansion is needed => stride := 0.
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Value is_expansion = b->create<CmpIOp>(loc, CmpIPredicate::slt,
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operand_dim_size, result_dim_size);
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strides.push_back(
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b->create<mlir::SelectOp>(loc, is_expansion, zero, one));
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// Size of input dim can be set to the size of the corresponding output
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// dimension for both cases.
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sizes.push_back(result_dim_size);
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}
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// Type-erased memref type with static rank, dynamic sizes and strides.
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SmallVector<int64_t, 2> dynamic_layout(operand_shape.size(),
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MemRefType::kDynamicStrideOrOffset);
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SmallVector<int64_t, 2> dynamic_shape(operand_shape.size(),
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MemRefType::kDynamicSize);
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auto type_erased_memref_type = MemRefType::get(
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dynamic_shape, operand_type.getElementType(),
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makeStridedLinearLayoutMap(dynamic_layout,
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/*offset=*/0, b->getContext()));
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auto transformed_operand = b->create<xla_lhlo::DynamicMemRefCastOp>(
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loc, type_erased_memref_type, operand, sizes, strides);
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return transformed_operand;
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}
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};
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struct HloToLhloDynamicReshapeConverter
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: public BaseOpConversion<mhlo::DynamicReshapeOp> {
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public:
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using BaseOpConversion<mhlo::DynamicReshapeOp>::BaseOpConversion;
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LogicalResult matchAndRewrite(
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mhlo::DynamicReshapeOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter& rewriter) const final {
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Type result_type;
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if (auto ranked_type = op.getType().dyn_cast<RankedTensorType>()) {
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result_type =
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MemRefType::get(ranked_type.getShape(), ranked_type.getElementType());
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} else if (auto unranked_type =
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op.getType().dyn_cast<UnrankedTensorType>()) {
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result_type = UnrankedMemRefType::get(unranked_type.getElementType(), 0);
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} else {
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return failure();
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}
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mhlo::DynamicReshapeOp::Adaptor adaptor(operands);
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rewriter.replaceOpWithNewOp<xla_lhlo::ReshapeMemRefCastOp>(
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op, result_type, adaptor.operand(), adaptor.output_shape());
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return success();
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}
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};
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struct HloToLhloReduceOpConverter : public BaseOpConversion<mhlo::ReduceOp> {
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public:
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using BaseOpConversion<mhlo::ReduceOp>::BaseOpConversion;
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LogicalResult matchAndRewrite(
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mhlo::ReduceOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter& rewriter) const final {
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auto loc = op.getLoc();
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// TODO(b/137624192) Implement variadic reduce.
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if (op.getNumResults() != 1) return failure();
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if (!llvm::hasSingleElement(op.body())) {
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return op.emitOpError()
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<< "tensor to buffer conversion expects a single block "
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"in the region containing the operation";
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}
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const auto& original_results = op.getResults();
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SmallVector<Value, 4> buffer_args(operands.begin(), operands.end());
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for (auto result : original_results) {
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buffer_args.push_back(
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InsertAlloc(loc, result, this->bufferAssignment, &rewriter));
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}
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auto new_op = rewriter.create<xla_lhlo::ReduceOp>(
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loc, llvm::None, buffer_args, op.getAttrs());
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// Copy over the operations inside the region.
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rewriter.inlineRegionBefore(op.body(), new_op.body(), new_op.body().end());
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// Create new block arguments with correct type.
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auto& entry_block = new_op.body().front();
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int original_arg_count = entry_block.getNumArguments();
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for (int i = 0; i < original_arg_count; ++i) {
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auto old_arg = entry_block.getArgument(i);
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auto old_type = old_arg.getType().cast<TensorType>();
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auto new_type =
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MemRefType::get(old_type.getShape(), old_type.getElementType());
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auto new_arg = entry_block.addArgument(new_type);
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rewriter.replaceUsesOfBlockArgument(old_arg, new_arg);
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}
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// Add an argument for the result.
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entry_block.addArgument(
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entry_block.getArgument(original_arg_count).getType());
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// Remove the old arguments.
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for (int i = original_arg_count - 1; i >= 0; --i) {
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entry_block.eraseArgument(i);
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}
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// Insert terminator at the end.
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rewriter.setInsertionPointToEnd(&entry_block);
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rewriter.create<xla_lhlo::TerminatorOp>(loc);
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rewriter.replaceOp(op, ArrayRef<Value>(buffer_args).slice(operands.size()));
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return success();
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}
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};
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class HloToLhloTensorLoadOpConverter
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: public BaseOpConversion<mlir::TensorLoadOp> {
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public:
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using BaseOpConversion<mlir::TensorLoadOp>::BaseOpConversion;
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LogicalResult matchAndRewrite(
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mlir::TensorLoadOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter& rewriter) const final {
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rewriter.replaceOp(op, operands);
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return success();
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}
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};
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// TODO(b/137624192): Rewrite into a copy and elide copy if possible.
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class HloToLhloTensorStoreOpConverter
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: public BaseOpConversion<mlir::TensorStoreOp> {
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public:
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using BaseOpConversion<mlir::TensorStoreOp>::BaseOpConversion;
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LogicalResult matchAndRewrite(
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mlir::TensorStoreOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter& rewriter) const final {
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rewriter.replaceOpWithNewOp<xla_lhlo::CopyOp>(
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op, llvm::None, operands.front(), operands.back());
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return success();
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}
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};
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// Lowers from HLO dialect to LHLO dialect allocating/deallocating temporary
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// buffers if necessary.
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//
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// Example fusion with HLO ops.
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//
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// func @fusion(%arg0: memref<2x2xf32>,
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// %arg1: memref<2x2xf32>,
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// %arg2: memref<2x2xf32>,
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// %arg3: memref<2x2xf32>) {
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// "xla_lhlo.fusion"() ({
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// %0 = tensor_load %arg1 : memref<2x2xf32>
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// %1 = tensor_load %arg2 : memref<2x2xf32>
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// %2 = "mhlo.add"(%0, %1) :
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// (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
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// %3 = tensor_load %arg0 : memref<2x2xf32>
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// %4 = "mhlo.multiply"(%2, %3) :
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// (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
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// tensor_store %4, %arg3 : memref<2x2xf32>
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// "xla_lhlo.terminator"() : () -> ()
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// }) : () -> ()
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// return
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// }
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//
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// Transformed fusion with LHLO ops.
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// func @fusion(%arg0: memref<2x2xf32>,
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// %arg1: memref<2x2xf32>,
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// %arg2: memref<2x2xf32>,
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// %arg3: memref<2x2xf32>) {
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// "xla_lhlo.fusion"() ( {
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// %0 = alloc() : memref<2x2xf32>
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// "xla_lhlo.add"(%arg1, %arg2, %0) :
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// (memref<2x2xf32>, memref<2x2xf32>, memref<2x2xf32>) -> ()
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// "xla_lhlo.multiply"(%0, %arg0, %arg3) :
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// (memref<2x2xf32>, memref<2x2xf32>, memref<2x2xf32>) -> ()
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// "xla_lhlo.terminator"() : () -> ()
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// }) : () -> ()
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// return
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// }
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//
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// FuncOp signature conversion example:
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//
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// func @func_op(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {
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// %0 = "mhlo.maximum"(%arg0, %arg1) : (tensor<4xf32>, tensor<4xf32>) ->
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// tensor<4xf32> %1 = "mhlo.add"(%arg0, %0) : (tensor<4xf32>,
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// tensor<4xf32>) -> tensor<4xf32> return %1 : tensor<4xf32>
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// }
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//
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// Transformed function with an extra argument for the result. The types have
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// been converted from tensor to memref.
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//
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// func @func_op(%arg0: memref<4xf32>,
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// %arg1: memref<4xf32>,
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// %arg2: memref<4xf32>) {
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// %0 = alloc() : memref<4xf32>
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// "xla_lhlo.maximum"(%arg0, %arg1, %0) :
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// (memref<4xf32>, memref<4xf32>, memref<4xf32>) -> ()
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// %1 = alloc() : memref<4xf32>
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// "xla_lhlo.add"(%arg0, %0, %1) :
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// (memref<4xf32>, memref<4xf32>, memref<4xf32>) -> ()
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// "xla_lhlo.copy"(%1, %arg2) : (memref<4xf32>, memref<4xf32>) -> ()
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// "xla_lhlo.terminator"() : () -> ()
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// }
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struct HloLegalizeToLhlo
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: public PassWrapper<HloLegalizeToLhlo, OperationPass<ModuleOp>> {
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public:
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HloLegalizeToLhlo() = default;
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HloLegalizeToLhlo(const HloLegalizeToLhlo& o) {
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this->results_escape_function = o.results_escape_function.getValue();
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}
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explicit HloLegalizeToLhlo(bool results_escape_function) {
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this->results_escape_function.setValue(results_escape_function);
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}
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void runOnOperation() override {
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OwningRewritePatternList patterns;
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auto& context = getContext();
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ConversionTarget target(context);
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target.addLegalDialect<xla_lhlo::XlaLhloDialect>();
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target.addLegalDialect<StandardOpsDialect>();
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target.addLegalOp<ModuleOp>();
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target.addIllegalOp<mlir::TensorLoadOp>();
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target.addIllegalOp<mlir::TensorStoreOp>();
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target.addLegalOp<ModuleTerminatorOp>();
|
|
target.addLegalOp<TensorFromElementsOp>();
|
|
target.addIllegalDialect<mhlo::XlaHloDialect>();
|
|
|
|
BufferAssignmentTypeConverter converter;
|
|
auto isMemRefType = [](Type type) { return type.isa<BaseMemRefType>(); };
|
|
target.addDynamicallyLegalOp<FuncOp>([&](FuncOp op) {
|
|
auto inputs = op.getType().getInputs();
|
|
return llvm::all_of(inputs, isMemRefType) &&
|
|
converter.isLegal(&op.getBody());
|
|
});
|
|
target.addDynamicallyLegalOp<CallOp>([&](CallOp op) {
|
|
return std::all_of(op.operand_type_begin(), op.operand_type_end(),
|
|
isMemRefType) &&
|
|
std::all_of(op.result_type_begin(), op.result_type_end(),
|
|
isMemRefType);
|
|
});
|
|
target.addDynamicallyLegalOp<mlir::ReturnOp>([&](mlir::ReturnOp op) {
|
|
return std::all_of(op.operand_type_begin(), op.operand_type_end(),
|
|
isMemRefType);
|
|
});
|
|
|
|
auto module = getOperation();
|
|
WalkResult result = module.walk([&](FuncOp func) -> WalkResult {
|
|
BufferAssignmentPlacer bufferAssignment(func);
|
|
OwningRewritePatternList patterns;
|
|
populateHLOToLHLOConversionPattern(func.getContext(), &bufferAssignment,
|
|
&converter, &patterns);
|
|
if (results_escape_function) {
|
|
populateWithBufferAssignmentOpConversionPatterns<
|
|
mlir::ReturnOp, mlir::ReturnOp, xla_lhlo::CopyOp,
|
|
/*allowMemrefFunctionResults=*/true>(&context, &bufferAssignment,
|
|
&converter, &patterns);
|
|
} else {
|
|
populateWithBufferAssignmentOpConversionPatterns<
|
|
mlir::ReturnOp, mlir::ReturnOp, xla_lhlo::CopyOp,
|
|
/*allowMemrefFunctionResults=*/false>(&context, &bufferAssignment,
|
|
&converter, &patterns);
|
|
}
|
|
return applyPartialConversion(func, target, patterns);
|
|
});
|
|
if (result.wasInterrupted()) {
|
|
signalPassFailure();
|
|
}
|
|
}
|
|
|
|
private:
|
|
Option<bool> results_escape_function{
|
|
*this, "results-escape-function",
|
|
llvm::cl::desc(
|
|
"Allocate the results of functions within the functions body"),
|
|
llvm::cl::init(false)};
|
|
};
|
|
} // namespace
|
|
|
|
void populateHLOToLHLOConversionPattern(
|
|
MLIRContext* context, BufferAssignmentPlacer* bufferAssignment,
|
|
TypeConverter* converter, OwningRewritePatternList* patterns) {
|
|
// clang-format off
|
|
patterns->insert<
|
|
HloToLhloDynamicBroadcastInDimOpConverter,
|
|
HloToLhloDynamicReshapeConverter,
|
|
HloToLhloOpConverter<mhlo::AbsOp>,
|
|
HloToLhloOpConverter<mhlo::AddOp>,
|
|
HloToLhloOpConverter<mhlo::AndOp>,
|
|
HloToLhloOpConverter<mhlo::BroadcastInDimOp>,
|
|
HloToLhloOpConverter<mhlo::CeilOp>,
|
|
HloToLhloOpConverter<mhlo::CompareOp>,
|
|
HloToLhloOpConverter<mhlo::ComplexOp>,
|
|
HloToLhloOpConverter<mhlo::ConstOp>,
|
|
HloToLhloOpConverter<mhlo::ConvOp>,
|
|
HloToLhloOpConverter<mhlo::ConvertOp>,
|
|
HloToLhloOpConverter<mhlo::CopyOp>,
|
|
HloToLhloOpConverter<mhlo::CosOp>,
|
|
HloToLhloOpConverter<mhlo::DivOp>,
|
|
HloToLhloOpConverter<mhlo::DotOp>,
|
|
HloToLhloOpConverter<mhlo::ExpOp>,
|
|
HloToLhloOpConverter<mhlo::GatherOp>,
|
|
HloToLhloOpConverter<mhlo::ImagOp>,
|
|
HloToLhloOpConverter<mhlo::IotaOp>,
|
|
HloToLhloOpConverter<mhlo::LogOp>,
|
|
HloToLhloOpConverter<mhlo::MaxOp>,
|
|
HloToLhloOpConverter<mhlo::MinOp>,
|
|
HloToLhloOpConverter<mhlo::MulOp>,
|
|
HloToLhloOpConverter<mhlo::NegOp>,
|
|
HloToLhloOpConverter<mhlo::RealOp>,
|
|
HloToLhloOpConverter<mhlo::RemOp>,
|
|
HloToLhloOpConverter<mhlo::RsqrtOp>,
|
|
HloToLhloOpConverter<mhlo::ReshapeOp>,
|
|
HloToLhloOpConverter<mhlo::SelectOp>,
|
|
HloToLhloOpConverter<mhlo::SignOp>,
|
|
HloToLhloOpConverter<mhlo::SqrtOp>,
|
|
HloToLhloOpConverter<mhlo::SubOp>,
|
|
HloToLhloOpConverter<mhlo::TanhOp>,
|
|
HloToLhloReduceOpConverter,
|
|
HloToLhloTensorLoadOpConverter,
|
|
HloToLhloTensorStoreOpConverter
|
|
>(context, bufferAssignment, converter);
|
|
// clang-format on
|
|
}
|
|
|
|
std::unique_ptr<OperationPass<ModuleOp>> createLegalizeToLhloPass(
|
|
bool results_escape_function) {
|
|
return absl::make_unique<HloLegalizeToLhlo>(results_escape_function);
|
|
}
|
|
|
|
static PassRegistration<HloLegalizeToLhlo> legalize_pass(
|
|
"hlo-legalize-to-lhlo", "Legalize from HLO dialect to LHLO dialect");
|
|
|
|
} // namespace mhlo
|
|
} // namespace mlir
|