tensorflow/compiler/mlir/tensorflow/transforms/batchmatmul_to_einsum.cc

/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include <climits>
#include <cstdint>
#include <numeric>

#include "absl/memory/memory.h"
#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/StringSwitch.h"
#include "llvm/Support/Casting.h"
#include "llvm/Support/Debug.h"
#include "mlir/Dialect/Affine/Analysis/LoopAnalysis.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/Attributes.h"  // from @llvm-project
#include "mlir/IR/BuiltinTypes.h"  // from @llvm-project
#include "mlir/IR/OpImplementation.h"  // from @llvm-project
#include "mlir/IR/PatternMatch.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes_detail.h"
#include "tensorflow/core/util/matmul_bcast.h"

namespace mlir {
namespace TF {

namespace {

// Replace TF BatchMatMul by TF Einsum op
template <typename BatchMatMulOpType>
class ConvertTFBatchMatMulToEinsumOp
    : public OpRewritePattern<BatchMatMulOpType> {
  using OpRewritePattern<BatchMatMulOpType>::OpRewritePattern;

  LogicalResult matchAndRewrite(BatchMatMulOpType op,
                                PatternRewriter& rewriter) const override {
    Value input_lhs = op.x();
    Value input_rhs = op.y();

    // LHS and RHS must be a ranked tensor type
    auto lhs_type = input_lhs.getType().dyn_cast<RankedTensorType>();
    auto rhs_type = input_rhs.getType().dyn_cast<RankedTensorType>();

    if (!lhs_type || !rhs_type) return failure();

    auto lhs_shape = lhs_type.getShape();
    auto rhs_shape = rhs_type.getShape();

    // Ensure that input ranks are at least 2.
    const int dims_a = lhs_shape.size();
    const int dims_b = rhs_shape.size();
    if (dims_a < 2 || dims_b < 2) {
      return failure();
    }

    // einsum equation for batchmatmul
    std::string equation("...mk,...kn->...mn");
    if (op.adj_x()) std::swap(equation[3], equation[4]);
    if (op.adj_y()) std::swap(equation[6 + 3], equation[6 + 4]);

    rewriter.replaceOpWithNewOp<TF::EinsumOp>(
        op, op.getType(),
        /*inputs=*/ValueRange({input_lhs, input_rhs}),
        /*equation=*/equation);

    return success();
  }
};

struct BatchMatMulToEinsumPass
    : public BatchMatMulToEinsumPassBase<BatchMatMulToEinsumPass> {
  void runOnOperation() override;
};

void BatchMatMulToEinsumPass::runOnOperation() {
  RewritePatternSet patterns(&getContext());
  auto func = getOperation();

  patterns.add<ConvertTFBatchMatMulToEinsumOp<TF::BatchMatMulOp>,
               ConvertTFBatchMatMulToEinsumOp<TF::BatchMatMulV2Op>>(
      &getContext());
  (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
}

}  // namespace

std::unique_ptr<OperationPass<func::FuncOp>> CreateBatchMatMulToEinsumPass() {
  return std::make_unique<BatchMatMulToEinsumPass>();
}

}  // namespace TF
}  // namespace mlir