cl_image.h

/* Copyright (c) 2018 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 <vector>

#include "CL/cl.h"
#include "framework/cl/cl_half.h"
#include "framework/ddim.h"
#include "framework/tensor.h"

namespace paddle_mobile {
namespace framework {

class CLImage {
 public:
  CLImage() = default;

  void Init(cl_context context, float *tensorInput, DDim ddim) {
    tensor_dims_ = ddim;
    cl_image_format cf = {.image_channel_order = CL_RGBA,
                          .image_channel_data_type = CL_HALF_FLOAT};
    // NCHW -> [W * (C+3)/4, H * N]
    DLOG << tensor_dims_;
    size_t N, C, H, W;
    if (tensor_dims_.size() == 4) {
      N = tensor_dims_[0];
      if (N < 0) {
        N = 1;
      }
      C = tensor_dims_[1];
      H = tensor_dims_[2];
      W = tensor_dims_[3];
    } else if (tensor_dims_.size() == 1) {
      N = 1;
      C = tensor_dims_[0];
      H = 1;
      W = 1;
    }

    DLOG << "-------InitMemory-------";

    size_t width = W * ((C + 3) / 4);
    size_t height = H * N;
    std::unique_ptr<half_t[]> imageData{};
    int count = 0;
    if (tensorInput != nullptr) {
      imageData.reset(new half_t[width * height * 4]);
      float *p = tensorInput;
      size_t i0 = 0;
      for (int n = 0; n < N; n++) {
        for (int c = 0; c < C; c++) {
          size_t i1 = i0;
          for (int h = 0; h < H; h++) {
            size_t i2 = (i1 << 2) + c % 4;
            for (int w = 0; w < W; w++) {
              if (i2 >= width * height * 4) {
                printf("%d > %d ----> %d, %d, %d, %d --- %d, %d, %d\n", i2,
                       width * height * 4, n, c, h, w, i0, i1, i2);
              }
              assert(i2 < width * height * 4);

              imageData[i2] = float2half(*p);
              i2 += 4;
              p++;
              //              count++;
              //              DLOG<<count;
            }
            i1 += width;
          }
        }
        i0 += width * H;
      }
    }
    DLOG << "-------InitMemory-------";
    cl_int err;
    cl_image_ = clCreateImage2D(
        context,                                   // cl_context context
        CL_MEM_READ_WRITE | CL_MEM_COPY_HOST_PTR,  // cl_mem_flags flags
        &cf,     // const cl_image_format *image_format
        width,   // size_t image_width
        height,  // size_t image_height
        0,       // size_t image_row_pitch
        reinterpret_cast<void *>(imageData.get()),  // void *host_ptr
        &err);
    if (err != CL_SUCCESS) {
      // TODO(HaiPeng): error handling
    }
  }

  void Init(cl_context context, DDim ddim) { Init(context, nullptr, ddim); }

  inline CLImage &Resize(const DDim &dims) {
    tensor_dims_ = dims;
    return *this;
  }

  const DDim &dims() const { return tensor_dims_; }

  std::vector<size_t> DefaultWorkSize() { return {}; }

  cl_mem GetCLImage() const { return cl_image_; }

  template <typename T>
  T *data() const {
    return reinterpret_cast<T *>(tensor_input_);
  }

  inline int64_t numel() const { return product(tensor_dims_); }

  int ImageWidth() const { return image_width_; }

  int ImageHeight() const { return image_height_; }

  int CBlock() const { return c_block_; }

  int WidthOfOneBlock() const { return width_of_one_block_; }

  int HeightOfOneBlock() const { return height_of_one_block_; }

 private:
  bool initialized_ = false;
  cl_mem cl_image_;
  int image_width_;
  int width_of_one_block_;
  int height_of_one_block_;
  int image_height_;
  int c_block_;
  DDim tensor_dims_;
  float *tensor_input_;
  cl_context context_;
};

// void TensorToCLImage(Tensor *tensor, CLImage *image) {
//
//}
//
// void CLImageToTensor(CLImage *image, Tensor *tensor) {
//
//}

}  // namespace framework
}  // namespace paddle_mobile