/* Copyright (c) 2023 PaddlePaddle 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 "paddle/phi/infermeta/spmd_rules/utils.h"

#include "glog/logging.h"

#include "paddle/phi/core/distributed/auto_parallel/dist_attr.h"
#include "paddle/phi/core/distributed/auto_parallel/utils.h"
#include "paddle/phi/core/enforce.h"

namespace phi {
namespace distributed {

using phi::distributed::auto_parallel::str_join;

std::string GetBroadcastAxes(const int64_t& tenosr_ndim,
                             const int64_t& broadcast_ndim,
                             const std::string& alphabet) {
  PADDLE_ENFORCE_GE(
      alphabet.size(),
      broadcast_ndim,
      phi::errors::InvalidArgument(
          "The size of alphabet [%d] is less than broadcast ndim [%d]",
          alphabet.size(),
          broadcast_ndim));
  PADDLE_ENFORCE_GE(broadcast_ndim,
                    tenosr_ndim,
                    phi::errors::InvalidArgument(
                        "The broadcast ndim [%d] is less than tenosr ndim [%d]",
                        broadcast_ndim,
                        tenosr_ndim));
  if (tenosr_ndim <= 0) {
    return std::string();
  }
  return alphabet.substr(broadcast_ndim - tenosr_ndim, tenosr_ndim);
}

// Rule1: A repicated dimension could be merged by any sharded dimension.
// Rule2: A tensor axis could at most be sharded by one mesh dimension.
// (TODO trigger heuristics cost model and reshard to handle axis sharded by
// multiple dimension case.)
int64_t ShardingMergeForAxis(const std::string& axis,
                             const int64_t& mesh_dim1,
                             const int64_t& mesh_dim2) {
  if (mesh_dim1 != mesh_dim2) {
    if (mesh_dim1 == -1) {
      return mesh_dim2;
    } else if (mesh_dim2 == -1) {
      return mesh_dim1;
    } else {
      // (TODO) local cost model here.
      PADDLE_THROW(
          phi::errors::Unimplemented("Tensor Axis[%s] is Sharded by two "
                                     "different mesh dimension [%d] and [%d].",
                                     axis,
                                     mesh_dim1,
                                     mesh_dim2));
    }

  } else {
    return mesh_dim1;
  }
}

std::unordered_map<std::string, int64_t> ShardingMergeForTensors(
    const std::vector<std::pair<std::string, std::vector<int64_t>>>&
        tensor_axes_to_dim_pairs,
    const bool merge_conflicts) {
  std::unordered_map<std::string, int64_t> axis_to_dim_map;
  std::unordered_map<int64_t, std::string> dim_to_axis_map;
  int64_t merge_dim;

  for (auto& pair : tensor_axes_to_dim_pairs) {
    for (size_t i = 0; i < pair.second.size(); ++i) {
      auto tensor_axis = pair.first.substr(i, 1);
      auto mesh_dim = pair.second[i];

      if (axis_to_dim_map.count(tensor_axis) == 0) {
        merge_dim = mesh_dim;
      } else {
        merge_dim = ShardingMergeForAxis(
            tensor_axis, mesh_dim, axis_to_dim_map[tensor_axis]);
      }
      axis_to_dim_map[tensor_axis] = merge_dim;
      if (merge_dim != -1) {
        if (dim_to_axis_map.count(merge_dim) == 0) {
          dim_to_axis_map.insert({merge_dim, tensor_axis});
        } else if (dim_to_axis_map[merge_dim].find(tensor_axis) ==
                   std::string::npos) {
          dim_to_axis_map[merge_dim] += tensor_axis;
        }
      }
    }
  }

  // Resolute "mesh_dim shard by more than one axis" confict.
  // Now we just naive pick the first axis naively.
  // (TODO) use local cost model to pick the axis with lowest cost(in concern of
  // memory or communication or computation).
  for (auto& it : dim_to_axis_map) {
    if (it.second.size() > 1) {
      if (merge_conflicts) {
        VLOG(4) << "Sharding Conflict: Mesh_Dim [" << it.first
                << "] are Sharding Multiple Tensor Axis: [" << it.second
                << "]. The Axis: [" << it.second[0] << "] is Picked.";
        for (size_t i = 1; i < it.second.size(); ++i) {
          axis_to_dim_map[it.second.substr(i, 1)] = -1;
        }
      } else {
        PADDLE_THROW(phi::errors::PreconditionNotMet(
            "Multiple Tensor Axes [%s] is sharded by same mesh dimension [%d].",
            str_join(it.second),
            it.first));
      }
    }
  }

  return axis_to_dim_map;
}

TensorDistAttr CopyTensorDistAttrForOutput(
    const TensorDistAttr& src_dist_attr) {
  TensorDistAttr new_dist_attr = TensorDistAttr();
  new_dist_attr.set_process_mesh(src_dist_attr.process_mesh());
  new_dist_attr.set_batch_dim(src_dist_attr.batch_dim());
  new_dist_attr.set_dynamic_dims(src_dist_attr.dynamic_dims());
  // new_dist_attr.set_annotated(false); TODO unset field is false by default.
  return new_dist_attr;
}

std::vector<int64_t> ResoluteOutputPartialDimension(
    const std::unordered_map<std::string, int64_t>& axis_to_dim_map,
    const std::string& tensor_axes) {
  std::vector<int64_t> partial_on_dims;

  for (auto& it : axis_to_dim_map) {
    if (tensor_axes.find(it.first) == std::string::npos) {
      if (it.second > -1) {
        partial_on_dims.push_back(it.second);
      }
    }
  }
  return partial_on_dims;
}

}  // namespace distributed
}  // namespace phi