// Copyright (c) 2021 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/fluid/eager/grad_tensor_holder.h"
#include "paddle/fluid/imperative/gradient_accumulator.h"

#include "paddle/fluid/framework/var_type.h"
#include "paddle/pten/kernels/funcs/math_function.h"

namespace egr {

void GradTensorHolder::add(size_t slot_id, size_t rank,
                           const paddle::experimental::Tensor& t,
                           bool fill_one) {
  // TODO(jiabin): We need to deal with empty input_buffer with slot size not
  // empty;
  PADDLE_ENFORCE(slot_id < buffer_.size(),
                 paddle::platform::errors::Fatal(
                     "Invalid slot_id for GradTensorHolder::add() "
                     "which exceeds size of buffer"));
  VLOG(6) << "Add Tensor for buffer_ slot: " << slot_id
          << ", size: " << buffer_[slot_id].size();
  if (buffer_[slot_id].empty()) {
    VLOG(6) << "Pass add Tensor for buffer_ slot: " << slot_id
            << " since its buffer_ is empty ";
    return;
  }
  PADDLE_ENFORCE(
      rank < buffer_[slot_id].size(),
      paddle::platform::errors::Fatal(
          "Invalid rank for GradTensorHolder::add() which exceeds size "
          "of buffer slot %d, got slot size is: %d rank is: %d",
          slot_id, buffer_[slot_id].size(), rank));
  if (!fill_one) {
    paddle::experimental::Tensor& buffer_tensor = buffer_[slot_id][rank];
    // TODO(jiabin): Code bellow is ugly to divide which inner var we used,
    // remove framework::Variable
    // related code later.
    // This if statement is trying to test neither pten::Tensor nor
    // framework::Variable is initialized.
    if ((!buffer_tensor.defined() || !buffer_tensor.initialized())) {
      // Simply copy tensor->impl
      buffer_tensor = t;
    } else {
      // Accumulation
      PADDLE_ENFORCE_EQ(t.initialized(), true,
                        paddle::platform::errors::Fatal(
                            "We can only accumulate initialized tensor, but we "
                            "got tensor: %s is empty please check you network "
                            "and make sure it creates grads.",
                            t.name()));
      if (t.is_dense_tensor()) {
        if (buffer_tensor.is_dense_tensor()) {
          paddle::imperative::TensorAdd<paddle::experimental::Tensor>(
              t, &buffer_tensor);
        } else {
          // TODO(jiabin): Support Other TensorBase later
          paddle::experimental::Tensor new_buffer(
              std::make_shared<pten::DenseTensor>(), "tmp_accumulator");
          paddle::imperative::SelectedRowsAddTensor(buffer_tensor, t,
                                                    &new_buffer);
          buffer_tensor.set_impl(new_buffer.impl());
        }
      } else {
        // TODO(jiabin): Support Other TensorBase later
        if (buffer_tensor.is_dense_tensor()) {
          paddle::imperative::SelectedRowsAddToTensor(t, &buffer_tensor);
        } else {
          PADDLE_THROW(paddle::platform::errors::Fatal(
              "We don't support Selected Rows merge for now, support it later "
              "and make all kinds of grads can be merged."));
        }
      }
    }
  } else {
    // Create new tensor->impl and fill it with 1.0
    if (t.defined()) {
      // Fill 1.0
      buffer_[slot_id][rank] = paddle::experimental::ones_like(t);
    }
  }
}

}  // namespace egr
