uniform_random_op.h

// Copyright (c) 2019 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 <algorithm>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"

namespace paddle {
namespace operators {
using Tensor = framework::Tensor;

inline std::vector<int64_t> GetNewDataFromShapeTensor(
    const Tensor *new_data_tensor) {
  if (new_data_tensor->type() == framework::proto::VarType::INT64) {
    auto *new_data = new_data_tensor->data<int64_t>();
    framework::Tensor cpu_starts_tensor;
    if (platform::is_gpu_place(new_data_tensor->place())) {
      TensorCopySync(*new_data_tensor, platform::CPUPlace(),
                     &cpu_starts_tensor);
      new_data = cpu_starts_tensor.data<int64_t>();
    }
    std::vector<int64_t> vec_new_data(new_data,
                                      new_data + new_data_tensor->numel());
    return vec_new_data;
  } else if (new_data_tensor->type() == framework::proto::VarType::INT32) {
    auto *new_data = new_data_tensor->data<int32_t>();
    std::vector<int64_t> vec_new_data;
    framework::Tensor cpu_starts_tensor;
    if (platform::is_gpu_place(new_data_tensor->place())) {
      TensorCopySync(*new_data_tensor, platform::CPUPlace(),
                     &cpu_starts_tensor);
      new_data = cpu_starts_tensor.data<int32_t>();
    }
    for (int i = 0; i < new_data_tensor->numel(); ++i) {
      vec_new_data.push_back(static_cast<int64_t>(*(new_data + i)));
    }
    return vec_new_data;
  } else {
    PADDLE_THROW("The dtype of shape tensor must be int32 or int64.");
  }
}

inline std::vector<int64_t> GetNewDataFromShapeTensorList(
    const std::vector<const Tensor *> &list_new_shape_tensor) {
  std::vector<int64_t> vec_new_shape;
  vec_new_shape.reserve(list_new_shape_tensor.size());
  for (size_t i = 0; i < list_new_shape_tensor.size(); ++i) {
    auto tensor = list_new_shape_tensor[i];
    PADDLE_ENFORCE_EQ(
        tensor->dims(), framework::make_ddim({1}),
        platform::errors::InvalidArgument(
            "Shape of dim tensor in uniform_random_op should be [1]"
            "But received tensor's dim=%s.",
            tensor->dims()));

    if (tensor->type() == framework::proto::VarType::INT32) {
      if (platform::is_gpu_place(tensor->place())) {
        framework::Tensor temp;
        TensorCopySync(*tensor, platform::CPUPlace(), &temp);
        vec_new_shape.push_back(static_cast<int64_t>(*temp.data<int32_t>()));
      } else {
        vec_new_shape.push_back(static_cast<int64_t>(*tensor->data<int32_t>()));
      }
    } else if (tensor->type() == framework::proto::VarType::INT64) {
      if (platform::is_gpu_place(tensor->place())) {
        framework::Tensor temp;
        TensorCopySync(*tensor, platform::CPUPlace(), &temp);
        vec_new_shape.push_back(*temp.data<int64_t>());
      } else {
        vec_new_shape.push_back(*tensor->data<int64_t>());
      }
    } else {
      PADDLE_THROW("The dtype of shape tensor must be int32 or int64.");
    }
  }

  return vec_new_shape;
}

}  // namespace operators
}  // namespace paddle