paddlepaddle--paddle/paddle/phi/kernels/funcs/gather.h

/* Copyright (c) 2016 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. */

#pragma once
#include <glog/logging.h>
#include <memory.h>

#include <cstring>
#include <vector>

#include "paddle/common/ddim.h"
#include "paddle/common/macros.h"
#include "paddle/phi/common/place.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace phi {
namespace funcs {

/**
 * A thin wrapper for gathering on cpu tensor
 * Return a new tensor from source tensor, gathered according to index
 * input[src]: type-T source Tensor
 * input[index]: type-IndexT index Tensor (1-D)
 * return: output tensor
 */
template <typename T, typename IndexT = int>
void CPUGather(const CPUContext& dev_ctx UNUSED,
               const DenseTensor& src,
               const DenseTensor& index,
               DenseTensor* output) {
  if (src.numel() == 0 || index.numel() == 0) {
    VLOG(6) << "Do nothing for CPUGather since inputs has 0-size tensor.";
    return;
  }

  if (index.dims().size() == 2) {
    PADDLE_ENFORCE_EQ(
        index.dims()[1],
        1,
        common::errors::InvalidArgument(
            "index.dims()[1] should be 1 when index.dims().size() = 2"
            "in gather_op, but received value is [%d].",
            index.dims()[1]));
  } else {
    PADDLE_ENFORCE_EQ(
        index.dims().size() == 1 || index.dims().size() == 0,
        true,
        common::errors::InvalidArgument(
            "The index should be 0D or 1D, when it is not 2D, but we get %d",
            index.dims().size()));
  }

  int64_t index_size = index.dims().size() == 0 ? 1 : index.dims()[0];

  auto src_dims = src.dims();

  const T* p_src = src.data<T>();
  const IndexT* p_index = index.data<IndexT>();
  T* p_output = output->data<T>();

  // slice size
  int64_t slice_size = 1;
  for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i];
  // input size
  // int64_t input_size = src_dims[0] * slice_size;
  int64_t index_dim_size = src_dims[0];

  const size_t slice_bytes = slice_size * sizeof(T);

  for (int64_t i = 0; i < index_size; ++i) {
    PADDLE_ENFORCE_LT(p_index[i],
                      index_dim_size,
                      common::errors::OutOfRange(
                          "The element of Index must be less than the size of "
                          "input dim size of axis which is %d, but received "
                          "index element which is %d in the %d index.",
                          index_dim_size,
                          p_index[i],
                          i));
    PADDLE_ENFORCE_GE(
        p_index[i],
        -index_dim_size,
        common::errors::OutOfRange(
            "The element of Index must be greater than or equal "
            "to %d, but received index element which is %d in the "
            "%d index.",
            -index_dim_size,
            p_index[i],
            i));
    int64_t index_ =
        (p_index[i] < 0 ? p_index[i] + index_dim_size : p_index[i]);
    memcpy(p_output + i * slice_size, p_src + index_ * slice_size, slice_bytes);
  }
}

template <typename T, typename IndexT = int>
void CPUGatherNd(const CPUContext& dev_ctx UNUSED,
                 const DenseTensor& input,
                 const DenseTensor& index,
                 DenseTensor* output) {
  auto index_dims = index.dims();
  auto index_dims_size = index_dims.size();
  auto input_dims = input.dims();
  auto input_dims_size = input_dims.size();

  const T* p_input = input.data<T>();
  const IndexT* p_index = index.data<IndexT>();
  T* p_output = output->data<T>();

  // final dim
  int64_t end_size = index_dims[index_dims_size - 1];
  // remain dim
  auto remain_ddim = slice_ddim(index_dims, 0, index_dims_size - 1);
  int64_t remain_numel = common::product(remain_ddim);
  // slice size
  int64_t slice_size = 1;
  for (int64_t i = end_size; i < input_dims_size; ++i) {
    slice_size *= input_dims[i];
  }
  const size_t slice_bytes = slice_size * sizeof(T);

  for (int64_t i = 0; i < remain_numel; ++i) {
    int64_t index_ = 0;
    int64_t temp = 1;
    for (int64_t j = end_size - 1; j >= 0; --j) {
      int64_t index_value = static_cast<int64_t>(p_index[i * end_size + j]);
      PADDLE_ENFORCE_LT(
          index_value,
          input_dims[j],
          common::errors::InvalidArgument(
              "Input(index[-1)] has wrong value, it is [%d]", index_value));
      PADDLE_ENFORCE_GE(
          index_value,
          -input_dims[j],
          common::errors::InvalidArgument(
              "The value of Input(index) must be no less than [%d]",
              -input_dims[j]));
      if (index_value < 0) {
        index_value += input_dims[j];
      }

      index_ += (index_value * temp);
      temp *= input_dims[j];
    }
    memcpy(
        p_output + i * slice_size, p_input + index_ * slice_size, slice_bytes);
  }
}

template <typename T, typename U>
void GatherV2Function(const CPUContext& dev_ctx,
                      const DenseTensor* input,
                      const DenseTensor* index,
                      int axis,
                      DenseTensor* out) {
  auto* index_data = index->data<U>();
  int64_t index_size = index->numel();
  int64_t input_size = input->numel();
  auto input_dim = input->dims();
  auto* input_data = input->data<T>();

  if (input->numel() == 0) return;
  int axis_index = axis;

  int64_t input_index_dim_size = input_dim[axis_index];
  for (int64_t i = 0; i < index_size; i++) {
    PADDLE_ENFORCE_LT(index_data[i],
                      input_index_dim_size,
                      common::errors::OutOfRange(
                          "The element of Index must be less than the size of "
                          "input dim size of axis which is %d, but received "
                          "index element which is %d in the %d index.",
                          input_index_dim_size,
                          index_data[i],
                          i));
    PADDLE_ENFORCE_GE(
        index_data[i],
        -input_index_dim_size,
        common::errors::OutOfRange(
            "The element of Index must be greater than or equal "
            "to %d, but received index element which is %d in the "
            "%d index.",
            -input_index_dim_size,
            index_data[i],
            i));
  }

  int64_t inner_dim_size = 1;
  int64_t outer_dim_size = 1;
  std::vector<int64_t> out_dim_vec;

  for (int i = 0; i < axis_index; i++) {
    inner_dim_size *= input_dim[i];
    out_dim_vec.push_back(input_dim[i]);
  }
  if (index->dims().size() != 0) {
    out_dim_vec.push_back(index_size);
  }
  for (int i = axis_index + 1; i < input_dim.size(); i++) {
    outer_dim_size *= input_dim[i];
    out_dim_vec.push_back(input_dim[i]);
  }
  auto out_dim = make_ddim(out_dim_vec);

  out->Resize(out_dim);
  auto* out_data = dev_ctx.Alloc<T>(out);

  int out_index = 0;
  for (int64_t i = 0; i < inner_dim_size; i++) {
    for (int64_t j = 0; j < index_size; j++) {
      const int64_t index_data_j =
          (index_data[j] < 0 ? index_data[j] + input_index_dim_size
                             : index_data[j]);
      for (int64_t k = 0; k < outer_dim_size; k++) {
        int64_t index = k + index_data_j * outer_dim_size +
                        (i * input_size / inner_dim_size);
        out_data[out_index] = input_data[index];
        out_index++;
      }
    }
  }
}

template <typename T, typename U>
void GatherV2GradFunction(const CPUContext& dev_ctx,
                          const DenseTensor* input,
                          const DenseTensor* index,
                          const int axis,
                          DenseTensor* out) {
  auto* index_data = index->data<U>();

  auto input_dim = input->dims();
  auto* input_data = input->data<T>();

  if (input->numel() == 0) return;
  int axis_index = axis;
  int64_t input_index_dim_size;
  if (input_dim.size() == out->dims().size()) {
    input_index_dim_size = input_dim[axis_index];
  } else {
    // 0d index
    input_index_dim_size = 1;
  }

  int64_t inner_dim_size = 1;
  int64_t outer_dim_size = 1;

  for (int i = 0; i < axis_index; i++) {
    inner_dim_size *= input_dim[i];
  }
  for (int i = axis_index + 1; i < input_dim.size(); i++) {
    outer_dim_size *= input_dim[i];
  }

  auto* out_data = dev_ctx.Alloc<T>(out);
  auto out_dim = out->dims();
  int64_t out_index_dim_size = out_dim[axis_index];
  // set_constant only supports input of type float value
  funcs::set_constant(dev_ctx, out, static_cast<float>(0.0));

  for (int64_t i = 0; i < inner_dim_size; i++) {
    for (int64_t j = 0; j < input_index_dim_size; j++) {
      const int64_t index_data_j =
          (index_data[j] < 0 ? index_data[j] + out_index_dim_size
                             : index_data[j]);
      for (int64_t k = 0; k < outer_dim_size; k++) {
        int64_t index = k + index_data_j * outer_dim_size +
                        i * outer_dim_size * out_index_dim_size;
        out_data[index] +=
            input_data[i * input_index_dim_size * outer_dim_size +
                       j * outer_dim_size + k];
      }
    }
  }
}

}  // namespace funcs
}  // namespace phi