DepthwiseConv.h

Go to the documentation of this file.

/*
 * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
 * Copyright 2017 The TensorFlow 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.
 */
 
#ifndef __NNFW_CKER_DEPTHWISE_CONV_H__
#define __NNFW_CKER_DEPTHWISE_CONV_H__
 
#include "cker/Shape.h"
#include "cker/Types.h"
#include "cker/Utils.h"
#include "cker/neon/neon_check.h"
#include "cker/operation/optimized/DepthwiseConvFloat.h"
#include "cker/operation/optimized/DepthwiseConvUint8.h"
#include "cker/operation/optimized/integer_ops/DepthwiseConvInt8.h"
#include "cker/operation/reference/integer_ops/DepthwiseConvUInt8.h"
#include "cker/operation/reference/integer_ops/DepthwiseConvHybrid.h"
#include "cker/CpuBackendThreadpool.h"
#include "cker/eigen/depthwise_conv_op.h"
#include "cker/eigen/bias_op.h"
 
namespace nnfw
{
namespace cker
{
 
// TODO(luwa): add multithread to per-channel depthwise_conv
// DepthwiseConv can run with multi threads on the dim specified by thread_dim.
// Each thread processes output elements on dim, thread_dim, in the range of
// [thread_start, thread_end).
// For example, assume thread_start = 2, thread_end = 6, and thread_dim = 1, it
// means that it will calculate DepthwiseConv for output_data[:, 2:5, :, :].
template <typename T, typename TS> struct DepthwiseConvWorkerTask : cpu_backend_threadpool::Task
{
  DepthwiseConvWorkerTask(const DepthwiseConvParams &params, const Shape &input_shape,
                          const T *input_data, const Shape &filter_shape, const T *filter_data,
                          const Shape &bias_shape, const TS *bias_data, const Shape &output_shape,
                          T *output_data, int thread_start, int thread_end, int thread_dim)
    : params_(params), input_shape_(input_shape), input_data_(input_data),
      filter_shape_(filter_shape), filter_data_(filter_data), bias_shape_(bias_shape),
      bias_data_(bias_data), output_shape_(output_shape), output_data_(output_data),
      thread_start_(thread_start), thread_end_(thread_end), thread_dim_(thread_dim)
  {
  }
 
  void Run() override
  {
    optimized::DepthwiseConvImpl(params_, input_shape_, input_data_, filter_shape_, filter_data_,
                                 bias_shape_, bias_data_, output_shape_, output_data_,
                                 thread_start_, thread_end_, thread_dim_);
  }
 
private:
  const DepthwiseConvParams &params_;
  const Shape &input_shape_;
  const T *input_data_;
  const Shape &filter_shape_;
  const T *filter_data_;
  const Shape &bias_shape_;
  const TS *bias_data_;
  const Shape &output_shape_;
  T *output_data_;
  // const CpuFlags& cpu_flags_;
  int thread_start_;
  int thread_end_;
  int thread_dim_;
};
 
inline int HowManyConvThreads(const Shape &output_shape, const Shape &filter_shape)
{
  // How many scalar multiplications are needed to make it worth using one
  // more thread
  static constexpr int kMinMulPerThread = 1 << 13; // 8k
  const int filter_height = filter_shape.Dims(1);
  const int filter_width = filter_shape.Dims(2);
  const int num_muls = output_shape.FlatSize() * filter_height * filter_width;
  // Try to avoid real runtime divisions if possible by dividing by a
  // compile-time constant.
  int thread_count = std::max(1, num_muls / kMinMulPerThread);
  return thread_count;
}
 
inline bool MultithreadAlongBatches(int thread_count, int batches)
{
  assert(thread_count >= 2);
  // If there are fewer batch entries than the number of threads we want to use,
  // then better do intra-batch-entry multithreading.
  if (batches < thread_count)
  {
    return false;
  }
  // If there are at least 2 batch entries to be handed to each thread, then
  // it's safe to proceed with batch-wise multithreading: each thread will have
  // approximately equal number of batch entries to handle, so the load
  // balancing will be reasonable, and the amount to which the load is not
  // perfectly balanced will be offset by the inherent advantages of
  // batch-wise multithreading (each thread is more efficient thanks to working
  // on larger buffers with less boundary-handling overhead).
  if (batches >= 2 * thread_count)
  {
    return true;
  }
  // In the limit case were there are at least 1 but not much more than 1
  // batch entries per thread, it may be a good idea to do per-batch
  // multithreading if the number of batch entries is a multiple of the number
  // of threads, so that each thread will have the same number of batch entries
  // to process.
  return ((batches % thread_count) == 0);
}
 
template <typename T, typename TS>
inline void DepthwiseConv(const DepthwiseConvParams &params, const Shape &input_shape,
                          const T *input_data, const Shape &filter_shape, const T *filter_data,
                          const Shape &bias_shape, const TS *bias_data, const Shape &output_shape,
                          T *output_data, ruy::Context *ruy_context)
{
  assert(input_shape.DimensionsCount() == 4);
  assert(filter_shape.DimensionsCount() == 4);
  assert(output_shape.DimensionsCount() == 4);
 
  int thread_count = HowManyConvThreads(output_shape, filter_shape);
 
  // NOTE Borrow RuyContext to get max_num_threads setting
  // TODO Define and use max_num_threads for CPU backend
  const auto max_threads = (ruy_context == nullptr) ? 1 : ruy_context->max_num_threads();
 
  thread_count = std::max(1, std::min(thread_count, max_threads));
  // Cap the number of threads to 2 for float path to avoid regression in
  // performance (b/132294857).
  if (std::is_floating_point<T>::value)
  {
    thread_count = std::min(thread_count, 2);
  }
 
  const int output_batches = output_shape.Dims(0);
  const int output_height = output_shape.Dims(1);
 
  if (thread_count == 1)
  {
    optimized::DepthwiseConvImpl(params, input_shape, input_data, filter_shape, filter_data,
                                 bias_shape, bias_data, output_shape, output_data, 0, output_height,
                                 1);
    return;
  }
 
  int thread_dim, thread_dim_size;
  if (MultithreadAlongBatches(thread_count, output_batches))
  {
    thread_dim = 0;
    thread_dim_size = output_batches;
  }
  else
  {
    thread_dim = 1;
    thread_dim_size = output_height;
  }
 
  std::vector<DepthwiseConvWorkerTask<T, TS>> tasks;
  // TODO(b/131746020) don't create new heap allocations every time.
  // At least we make it a single heap allocation by using reserve().
  tasks.reserve(thread_count);
  int thread_start = 0;
  for (int i = 0; i < thread_count; ++i)
  {
    int thread_end = thread_start + (thread_dim_size - thread_start) / (thread_count - i);
    tasks.emplace_back(params, input_shape, input_data, filter_shape, filter_data, bias_shape,
                       bias_data, output_shape, output_data, thread_start, thread_end, thread_dim);
    thread_start = thread_end;
  }
  cpu_backend_threadpool::Execute(tasks.size(), tasks.data(), ruy_context);
}
 
void DepthwiseConvOp(const DepthwiseConvParams &params, const Shape &input_shape,
                     const float *input_data, const Shape &filter_shape, const float *filter_data,
                     const Shape &bias_shape, const float *bias_data, float *padded_filter_data,
                     bool pad_filter, float *filter_buffers_data, const Shape &output_shape,
                     float *output_data)
{
  if (params.stride_height != params.stride_width)
    throw std::runtime_error("Not support different length strides");
 
  if (params.dilation_height_factor != 1 || params.dilation_width_factor != 1)
    throw std::runtime_error{"Not support dilation other than 1."};
 
  const int batch = MatchingDim(input_shape, 0, output_shape, 0);
  const int input_depth = input_shape.Dims(3);
  const int output_depth = output_shape.Dims(3);
  const int input_height = input_shape.Dims(1);
  const int input_width = input_shape.Dims(2);
  const int filter_height = filter_shape.Dims(1);
  const int filter_width = filter_shape.Dims(2);
  const int output_height = output_shape.Dims(1);
  const int output_width = output_shape.Dims(2);
  const int stride = params.stride_height;
  const int depth_multiplier = params.depth_multiplier;
  const int pad_height = params.padding_values.height;
  const int pad_width = params.padding_values.width;
  const float activation_min = params.float_activation_min;
  const float activation_max = params.float_activation_max;
 
  depthwise_conv_op::LaunchDepthwiseConvOp<Eigen::ThreadPoolDevice, float>()(
    batch, input_height, input_width, input_depth, filter_height, filter_width, depth_multiplier,
    stride, pad_height, pad_width, output_height, output_width, output_depth, input_data,
    filter_data, padded_filter_data, pad_filter, filter_buffers_data, output_data);
 
  if (bias_data != nullptr)
  {
    bias_op::biasHelper<float>(bias_shape, bias_data, output_shape, output_data, activation_min,
                               activation_max);
  }
}
 
} // namespace cker
} // namespace nnfw
 
#endif // __NNFW_CKER_DEPTHWISE_CONV_H__