api.cpp

/* 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. */

#include "api.h"
#include <fcntl.h>
#include <sys/ioctl.h>
#include <sys/mman.h>
#include <algorithm>
#include <map>
#include "bias_scale.h"
#include "filter.h"
#include "image.h"
#define FPGA_TEST_MODE
#define PADDLE_MOBILE_OS_LINUX

namespace paddle_mobile {
namespace fpga {

static int fd = -1;
static const char *device_path = "/dev/fpgadrv0";
static std::map<void *, size_t> memory_map;

static inline int do_ioctl(int req, const void *arg) {
#ifdef PADDLE_MOBILE_OS_LINUX
  int result = ioctl(fd, req, (uint64_t)arg);
  PADDLE_MOBILE_ENFORCE(result == 0, "ioctl didn't return correctly");
  return result;
#else
  return -1;
#endif
}

int open_device() {
  if (fd == -1) {
    fd = open(device_path, O_RDWR);
  }
  return fd;
}

// memory management;
void *fpga_malloc(size_t size) {
  static uint64_t counter = 0;

#ifdef PADDLE_MOBILE_OS_LINUX
  auto ptr = mmap64(nullptr, size, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);
#else
  auto ptr = malloc(size);
#endif
  counter += size;
  memory_map.insert(std::make_pair(ptr, size));
  DLOG << "Address: " << ptr << ", " << size << " bytes allocated. Total "
       << counter << " bytes";
  return ptr;
}

void fpga_free(void *ptr) {
  static uint64_t counter = 0;
  size_t size = 0;

  auto iter = memory_map.find(ptr);  // std::map<void *, size_t>::iterator
  if (iter != memory_map.end()) {
    size = iter->second;
    memory_map.erase(iter);
#ifdef PADDLE_MOBILE_OS_LINUX
    munmap(ptr, size);
#else
    free(ptr);
#endif
    counter += size;
    DLOG << "Address: " << ptr << ", " << size << " bytes freed. Total "
         << counter << " bytes";
  } else {
    DLOG << "Invalid pointer";
  }
}

void fpga_copy(void *dest, const void *src, size_t num) {
  memcpy(dest, src, num);
}

int fpga_flush(void *address, size_t size) {
  struct MemoryCacheArgs args = {nullptr};
  args.address = address;
  args.size = size;
  return do_ioctl(IOCTL_MEMCACHE_FLUSH, &args);
}

int fpga_invalidate(void *address, size_t size) {
  struct MemoryCacheArgs args = {nullptr};
  args.address = address;
  args.size = size;
  return do_ioctl(IOCTL_MEMCACHE_INVAL, &args);
}

int ComputeBasicConv(const struct ConvArgs &args) {
  DLOG << "======Compute Basic Conv======";
  DLOG << "   relu_enabled:" << args.relu_enabled
       << "   sb_address:" << args.sb_address
       << "   filter_address:" << args.filter_address
       << "   filter_num:" << args.filter_num
       << "   group_num:" << args.group_num;
  DLOG << "   image_address:" << args.image.address
       << "   image_scale_address:" << args.image.scale_address
       << "   image_channels:" << args.image.channels
       << "   image_height:" << args.image.height
       << "   image_width:" << args.image.width
       << "   pad_height:" << args.image.pad_height
       << "   pad_width:" << args.image.pad_width;
  DLOG << "   kernel_height:" << args.kernel.height
       << "   kernel_width:" << args.kernel.width
       << "   stride_h:" << args.kernel.stride_h
       << "   stride_w:" << args.kernel.stride_w;
  DLOG << "   out_address:" << args.output.address
       << "   out_scale_address:" << args.output.scale_address;

  return do_ioctl(IOCTL_CONFIG_CONV, &args);
}

int ComputeFpgaConv(const struct WrapperConvArgs &args) {
#ifdef FPGA_TEST_MODE
  DLOG << "=============ComputeFPGAConv===========";
  DLOG << "   filter_num:" << args.filter_num
       << "   group_num:" << args.group_num
       << "   split_num:" << args.split_num;
#endif

  int split_num = args.split_num;
  for (int i = 0; i < split_num; i++) {
    ComputeBasicConv(args.conv_args[i]);
  }

  if (split_num > 1) {
    ComputeFPGAConcat(args.concat_arg);
  }
}

int ComputeFpgaPool(const struct PoolingArgs &args) {
#ifdef FPGA_TEST_MODE
  DLOG << "=============ComputeFpgaPool===========";
  DLOG << "   image_address:" << args.image.address
       << "   image_scale_address:" << args.image.scale_address
       << "   image_channels:" << args.image.channels
       << "   image_height:" << args.image.height
       << "   image_width:" << args.image.width
       << "   pad_height:" << args.image.pad_height
       << "   pad_width:" << args.image.pad_width;
  DLOG << "   kernel_height:" << args.kernel.height
       << "   kernel_width:" << args.kernel.width
       << "   stride_h:" << args.kernel.stride_h
       << "   stride_w:" << args.kernel.stride_w;
  DLOG << "   out_address:" << args.output.address
       << "   out_scale_address:" << args.output.scale_address;
#endif

  return do_ioctl(IOCTL_CONFIG_POOLING, &args);
}

int ComputeFpgaEWAdd(const struct EWAddArgs &args) {
#ifdef FPGA_TEST_MODE
  DLOG << "=============ComputeFpgaEWAdd===========";
  DLOG << "   relu_enabled:" << args.relu_enabled << "   const0:" << args.const0
       << "   const1:" << args.const1;
  DLOG << "   image0_address:" << args.image0.address
       << "   image0_scale_address:" << args.image0.scale_address
       << "   image0_channels:" << args.image0.channels
       << "   image0_height:" << args.image0.height
       << "   image0_width:" << args.image0.width
       << "   pad0_height:" << args.image0.pad_height
       << "   pad0_width:" << args.image0.pad_width;
  DLOG << "   image1_address:" << args.image1.address
       << "   image1_scale_address:" << args.image1.scale_address
       << "   image1_channels:" << args.image1.channels
       << "   image1_height:" << args.image1.height
       << "   image1_width:" << args.image1.width
       << "   pad1_height:" << args.image1.pad_height
       << "   pad_width:" << args.image1.pad_width;
  DLOG << "   out_address:" << args.output.address
       << "   out_scale_address:" << args.output.scale_address;
#endif

  return do_ioctl(IOCTL_CONFIG_EW, &args);
}
int PerformBypass(const struct BypassArgs &args) {
#ifdef FPGA_TEST_MODE
  DLOG << "=============ComputeFpgaBypass===========";
  DLOG << "   input_type:" << args.input_data_type
       << "   output_type:" << args.output_data_type
       << "   input_layout_type:" << args.input_layout_type
       << "   output_layout_type:" << args.output_layout_type;
  DLOG << "   image_address:" << args.image.address
       << "   image_scale_address:" << args.image.scale_address
       << "   image_channels:" << args.image.channels
       << "   image_height:" << args.image.height
       << "   image_width:" << args.image.width
       << "   pad_height:" << args.image.pad_height
       << "   pad_width:" << args.image.pad_width;
  DLOG << "   out_address:" << args.output.address
       << "   out_scale_address:" << args.output.scale_address;
#endif

  return do_ioctl(IOCTL_CONFIG_BYPASS, &args);
}

int ComputeFPGAConcat(const struct ConcatArgs &args) {
#ifdef FPGA_TEST_MODE
  DLOG << "=============ComputeFpgaConcat===========";
  DLOG << "   Image_num: " << args.image_num
       << "   out_address:" << args.image_out
       << "   out_scale_address:" << args.scale_out;
  DLOG << "   image_height:" << args.height << "   image_width:" << args.width;
  for (int i = 0; i < args.image_num; i++) {
    DLOG << "   " << i << "th:        ";
    DLOG << "   channel_num:" << args.channel_num[i]
         << "   image_address:" << args.images_in[i]
         << "   image_scale_address:" << args.scales_in[i];
  }
#endif

  image::concat_images(args.images_in, args.scales_in, args.image_out,
                       args.scale_out, args.image_num, args.channel_num,
                       args.height, args.width);
  return 0;
}

int get_align_image_cw(int cw) { return align_to_x(cw, IMAGE_ALIGNMENT); }

void format_image(framework::Tensor *image_tensor) {
  auto dims = image_tensor->dims();
  auto channel = dims[1], height = dims[2], width = dims[3];
  auto data_ptr = image_tensor->data<float>();
  size_t memory_size = channel * height * width * sizeof(float);
  auto new_data = (float *)fpga_malloc(memory_size);
  fpga_copy(new_data, data_ptr, memory_size);
  image::format_image(&new_data, channel, height, width);
  image_tensor->reset_data_ptr(new_data);
}

void format_fp16_ofm(framework::Tensor *ofm_tensor) {
  auto dims = ofm_tensor->dims();
  size_t memory_size = 0;
  if (dims.size() == 4) {
    auto channel = dims[1], height = dims[2], width = dims[3];
    memory_size =
        height * align_to_x(channel * width, IMAGE_ALIGNMENT) * sizeof(half);
  } else if (dims.size() == 2) {
    memory_size = align_to_x(dims[1], IMAGE_ALIGNMENT) * sizeof(half);
  } else {
    DLOG << "Wrong ofm dimension";
  }
  auto p = fpga_malloc(memory_size);
  memset(p, 0, memory_size);
  ofm_tensor->reset_data_ptr(p);
}

void format_fp32_ofm(framework::Tensor *ofm_tensor) {
  auto dims = ofm_tensor->dims();
  size_t memory_size = 0;
  if (dims.size() == 4) {
    auto channel = dims[1], height = dims[2], width = dims[3];
    memory_size =
        height * align_to_x(channel * width, IMAGE_ALIGNMENT) * sizeof(float);
  } else if (dims.size() == 2) {
    memory_size = align_to_x(dims[1], IMAGE_ALIGNMENT) * sizeof(float);
  } else {
    DLOG << "Wrong ofm dimension";
  }
  auto p = fpga_malloc(memory_size);
  memset(p, 0, memory_size);
  ofm_tensor->reset_data_ptr(p);
}

float filter_find_max(framework::Tensor *filter_tensor) {
  auto filter_ptr = filter_tensor->data<float>();
  return filter::find_max(filter_ptr, filter_tensor->numel());
}

int get_plit_num(framework::Tensor *filter_tensor) {
  auto dims = filter_tensor->dims();
  auto chw = dims[1] * dims[2] * dims[3];
  auto num = dims[0];
  int div_capacity = filter::calc_division_capacity(chw);
  return filter::calc_split_num(num, div_capacity);
}

int get_filter_num_per_div(framework::Tensor *filter_tensor, int group_num) {
  auto dims = filter_tensor->dims();
  auto chw = dims[1] * dims[2] * dims[3];
  auto num = dims[0];
  int div_capacity = filter::calc_division_capacity(chw);
  return filter::calc_num_per_div(num, group_num, div_capacity);
}

int get_aligned_filter_element_num(int chw) {
  return align_to_x(chw, FILTER_ELEMENT_ALIGNMENT);
}

int get_aligned_filter_num(int num) {
  return align_to_x(num, FILTER_NUM_ALIGNMENT);
}

void format_filter(framework::Tensor *filter_tensor, float max_value,
                   int group_num) {
  filter_tensor->scale[0] = float(max_value / 127.0);
  filter_tensor->scale[1] = float(127.0 / max_value);
  auto dims = filter_tensor->dims();
  auto num = dims[0], channel = dims[1], height = dims[2], width = dims[3];
  auto data_ptr = filter_tensor->data<float>();
  size_t memory_size = num * channel * height * width * sizeof(float);
  auto new_data = (float *)fpga_malloc(memory_size);
  fpga_copy(new_data, data_ptr, memory_size);
  filter::format_filter(&new_data, num, channel, height, width, group_num,
                        max_value);
  filter_tensor->reset_data_ptr(new_data);
}

void format_bias_scale_array(float **bias_scale_array,
                             int element_num_per_division, int num) {
  bias_scale::format_bias_scale_array(bias_scale_array,
                                      element_num_per_division, num);
}

void format_concat_output(framework::Tensor *out, int height, int width,
                          int image_num, uint32_t *channel_num) {
  int sum_channel = 0, sum_cw = 0;
  for (int i = 0; i < image_num; i++) {
    sum_channel += channel_num[i];
  }

  sum_cw = align_to_x(width * sum_channel, IMAGE_ALIGNMENT);
  auto data_ptr = fpga_malloc(height * sum_cw * sizeof(half));
  auto ddim = framework::make_ddim({1, sum_channel, height, width});
  out->Resize(ddim);
  out->reset_data_ptr(data_ptr);
}

void fill_conv_arg(struct WrapperConvArgs *arg, framework::Tensor *input,
                   framework::Tensor *out, framework::Tensor *filter,
                   bool relu_enabled, int group_num, int stride_h, int stride_w,
                   int padding_h, int padding_w, float *bs_ptr) {
  auto input_ptr = input->data<float>();
  auto filter_ptr = filter->data<float>();
  auto out_ptr = out->data<float>();

  arg->group_num = (uint32_t)group_num;
  // Either group_num or split_num = 1;
  arg->split_num = group_num == 1 ? (uint32_t)get_plit_num(filter) : 1;
  arg->filter_num = (uint32_t)filter->dims()[0];
  arg->output.address = out_ptr;
  arg->output.scale_address = out->scale;
  arg->conv_args = (ConvArgs *)fpga_malloc(arg->split_num * sizeof(ConvArgs));

  arg->concat_arg.image_num = arg->split_num;
  arg->concat_arg.image_out = out_ptr;
  arg->concat_arg.scale_out = out->scale;
  arg->concat_arg.height = (uint32_t)filter->dims()[2];
  arg->concat_arg.width = (uint32_t)filter->dims()[3];

  int n = arg->split_num;
  arg->concat_arg.images_in = (half **)fpga_malloc(n * sizeof(int *));
  arg->concat_arg.scales_in = (float **)fpga_malloc(n * sizeof(float *));
  arg->concat_arg.channel_num = (uint32_t *)fpga_malloc(n * sizeof(uint32_t));
  arg->concat_arg.image_out = out_ptr;

  auto channel = (int)out->dims()[1];
  int filter_num_per_div = get_filter_num_per_div(filter, group_num);
  int element_num = get_aligned_filter_element_num(
      filter->dims()[1] * filter->dims()[2] * filter->dims()[3]);

  for (int i = 0; i < n; i++) {
    arg->conv_args[i].relu_enabled = relu_enabled;
    arg->conv_args[i].group_num = (uint32_t)group_num;
    arg->conv_args[i].kernel.stride_h = (uint32_t)stride_h;
    arg->conv_args[i].kernel.stride_w = (uint32_t)stride_w;
    arg->conv_args[i].kernel.height = (uint32_t)filter->dims()[2];
    arg->conv_args[i].kernel.width = (uint32_t)filter->dims()[3];
    arg->conv_args[i].image.address = input_ptr;
    arg->conv_args[i].image.channels = (uint32_t)input->dims()[1];
    arg->conv_args[i].image.height = (uint32_t)input->dims()[2];
    arg->conv_args[i].image.width = (uint32_t)input->dims()[3];
    arg->conv_args[i].image.scale_address = input->scale;
    arg->conv_args[i].image.pad_height = (uint32_t)padding_h;
    arg->conv_args[i].image.pad_width = (uint32_t)padding_w;
    arg->conv_args[i].filter_scale_address = filter->scale;
    arg->conv_args[i].filter_address =
        &((int8_t *)filter_ptr)[i * element_num * filter_num_per_div];
    arg->conv_args[i].sb_address = &bs_ptr[i * filter_num_per_div * 2];
    arg->conv_args[i].filter_num =
        (uint32_t)(i == n - 1 ? channel - (n - 1) * filter_num_per_div
                              : filter_num_per_div);

    if (n > 1) {
      arg->conv_args[i].output.scale_address =
          (float *)fpga_malloc(2 * sizeof(float));
      arg->conv_args[i].output.address = fpga_malloc(
          input->dims()[2] *
          align_to_x(input->dims()[3] * arg->conv_args[i].filter_num,
                     IMAGE_ALIGNMENT) *
          sizeof(half));
    }

    else {
      arg->conv_args[i].output.scale_address = out->scale;
      arg->conv_args[i].output.address = out_ptr;
    }

    arg->concat_arg.images_in[i] = (half *)arg->conv_args[i].output.address;
    arg->concat_arg.scales_in[i] = arg->conv_args[i].output.scale_address;
    arg->concat_arg.channel_num[i] = arg->conv_args[i].filter_num;
  }
}

}  // namespace fpga
}  // namespace paddle_mobile