/* 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 "absl/algorithm/container.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/compiler/xla/stream_executor/lib/statusor.h"
#include "tensorflow/core/lib/core/errors.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::CustomCall(&existing_gemm,
                                   {kGemmCallTarget, kCublasLtMatmulCallTarget})
                         .WithOperand(0, m::Broadcast(&bcast, m::Op()))) ||
        (Match(instr, m::CustomCall(&existing_gemm, {kGemmCallTarget,
                                                     kCublasLtMatmulCallTarget})
                          .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();

      const tsl::protobuf::RepeatedField<int64_t> &batch_dimensions =
          (bcast_operand_index == 1) ? dim_nums->rhs_batch_dimensions()
                                     : dim_nums->lhs_batch_dimensions();
      // 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 i.e. all new
      // dimensions must be in [0, num_bcast_dims) or equivalently all original
      // dimensions are >= num_bcast_dims.
      for (int64_t bcast_dim : bcast->dimensions()) {
        if (bcast_dim < num_bcast_dims) {
          return OkStatus();
        }
        // bcast_dim should not be in batch_dimensions.
        if (absl::c_linear_search(batch_dimensions, bcast_dim)) {
          return 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 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));
      MarkAsChanged();
    }
    return 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,
    const absl::flat_hash_set<absl::string_view> &execution_threads) {
  bool changed = false;
  for (HloComputation *computation :
       module->MakeNonfusionComputations(execution_threads)) {
    TF_ASSIGN_OR_RETURN(bool result, RunOnComputation(computation));
    changed |= result;
  }
  return changed;
}

}  // namespace gpu
}  // namespace xla
