text: /* Copyright 2019 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 "tensorflow/compiler/xla/service/gpu/gemm_broadcast_folding_rewriter.h"
  
  #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
  #include "tensorflow/compiler/xla/service/gpu/backend_configs.pb.h"
  #include "tensorflow/compiler/xla/service/gpu/cublas_cudnn.h"
  #include "tensorflow/compiler/xla/service/hlo_casting_utils.h"
  #include "tensorflow/compiler/xla/service/hlo_computation.h"
  #include "tensorflow/compiler/xla/service/hlo_instruction.h"
  #include "tensorflow/compiler/xla/service/hlo_instructions.h"
  #include "tensorflow/compiler/xla/service/hlo_opcode.h"
  #include "tensorflow/compiler/xla/service/pattern_matcher.h"
  #include "tensorflow/compiler/xla/status_macros.h"
  #include "tensorflow/compiler/xla/statusor.h"
  #include "tensorflow/core/lib/core/errors.h"
  #include "tensorflow/stream_executor/lib/statusor.h"
  
  namespace xla {
  namespace gpu {
  
  namespace m = match;
  
  class GemmBroadcastFoldingVisitor : public DfsHloRewriteVisitor {
   public:
    Status HandleCustomCall(HloInstruction *instr) override {
      HloInstruction *existing_gemm;
      HloInstruction *bcast;
      if (Match(instr, m::Op(&existing_gemm)
                           .WithCustomCallTarget(kGemmCallTarget)
                           .WithOperand(0, m::Broadcast(&bcast, m::Op()))) ||
          (Match(instr, m::Op(&existing_gemm)
                            .WithCustomCallTarget(kGemmCallTarget)
                            .WithOperand(1, m::Broadcast(&bcast, m::Op()))))) {
        TF_ASSIGN_OR_RETURN(auto config,
                            existing_gemm->backend_config<GemmBackendConfig>());
        DotDimensionNumbers *dim_nums = config.mutable_dot_dimension_numbers();
        int bcast_operand_index = instr->operand_index(bcast);
        int num_bcast_dims = (bcast->shape().dimensions_size() -
                              bcast->operand(0)->shape().dimensions_size());
        int num_batch_dims = dim_nums->lhs_batch_dimensions_size();
  
        // This optimization is only valid if the set of broadcasted dimensions
        // is exactly the set of batch dimensions. First, check that all newly
        // broadcast dimensions have been inserted on the left.
        for (int64_t bcast_dim : bcast->dimensions()) {
          if (bcast_dim < num_bcast_dims) {
            return ::tensorflow::OkStatus();
          }
        }
  
        // Then check that all batch dimensions are being broadcast, and that
        // there is at least one batch dimension.
        CHECK_GT(num_bcast_dims, 0);
        if (num_bcast_dims != num_batch_dims) {
          return ::tensorflow::OkStatus();
        }
  
        if (bcast_operand_index == 1) {
          CHECK_EQ(dim_nums->rhs_contracting_dimensions_size(), 1);
          dim_nums->set_rhs_contracting_dimensions(
              0, dim_nums->rhs_contracting_dimensions(0) - num_batch_dims);
          dim_nums->clear_rhs_batch_dimensions();
        } else {
          CHECK_EQ(dim_nums->lhs_contracting_dimensions_size(), 1);
          dim_nums->set_lhs_contracting_dimensions(
              0, dim_nums->lhs_contracting_dimensions(0) - num_batch_dims);
          dim_nums->clear_lhs_batch_dimensions();
        }
        TF_RETURN_IF_ERROR(existing_gemm->ReplaceOperandWithDifferentShape(
            bcast_operand_index, bcast->mutable_operand(0)));
        TF_RETURN_IF_ERROR(existing_gemm->set_backend_config(config));
      }
      return ::tensorflow::OkStatus();
    }
  };
  
  static StatusOr<bool> RunOnComputation(HloComputation *computation) {
    GemmBroadcastFoldingVisitor visitor;
    TF_RETURN_IF_ERROR(computation->Accept(&visitor));
    return visitor.changed();
  }
  
  StatusOr<bool> GemmBroadcastFoldingRewriter::Run(HloModule *module) {
    bool changed = false;
    for (HloComputation *computation : module->MakeNonfusionComputations()) {
      TF_ASSIGN_OR_RETURN(bool result, RunOnComputation(computation));
      changed |= result;
    }
    return changed;
  }
  
  }  // namespace gpu
  }  // namespace xla
  