DepthwiseConvolutionLayer.cc

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/*
 * Copyright (c) 2019 Samsung Electronics Co., Ltd. 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 "DepthwiseConvolutionLayer.h"
 
#include "cker/PortableTensorUtils.h"
#include <cker/operation/DepthwiseConv.h>
 
namespace onert::backend::cpu::ops
{
 
void DepthwiseConvolutionLayer::prepareF32()
{
  if (_dilationWidth != 1 || _dilationHeight != 1 || _strideWidth != _strideHeight)
    return;
 
  // DepthwiseConvOp cpu kernel needs additional memory to perform with multi-
  // threads. So, we allocate it here and pass it to the kernel.
  const int64_t k_packet_size = nnfw::cker::eigen_support::kPacketSize<float>();
 
  const auto out_shape = getShape(_output);
  const auto filter_shape = getShape(_kernel);
  const int batch = out_shape.Dims(0);
  const int out_depth = out_shape.Dims(3);
  const int filter_rows = filter_shape.Dims(1);
  const int filter_cols = filter_shape.Dims(2);
 
  const int filter_spatial_size = filter_rows * filter_cols;
  const int padded_filter_inner_dim_size =
    ((out_depth + k_packet_size - 1) / k_packet_size) * k_packet_size;
 
  _use_padded_filter = (out_depth % k_packet_size) == 0 ? false : true;
 
  // prepare padded_filter buffer for cker
  auto padded_filter_info = ir::OperandInfo(_kernel->get_info());
  padded_filter_info.shape({batch, filter_spatial_size, padded_filter_inner_dim_size});
  _padded_filter = std::make_unique<Tensor>(padded_filter_info, nullptr);
  _padded_filter->setBuffer(std::make_shared<basic::Allocator>(_padded_filter->total_size()));
 
  // prepare out_bprop and in_bprop buffer for cker
  const int thread_count = nnfw::cker::eigen_support::getThreadCount() + 1;
 
  auto filter_buffers_info = ir::OperandInfo(_kernel->get_info());
  filter_buffers_info.shape({thread_count, filter_spatial_size, padded_filter_inner_dim_size});
  _filter_buffers = std::make_unique<Tensor>(filter_buffers_info, nullptr);
  _filter_buffers->setBuffer(std::make_shared<basic::Allocator>(_filter_buffers->total_size()));
}
 
void DepthwiseConvolutionLayer::convFloat32()
{
  float output_activation_min = 0, output_activation_max = 0;
  CalculateActivationRange(_activation, &output_activation_min, &output_activation_max);
 
  nnfw::cker::DepthwiseConvParams op_params;
  op_params.stride_width = _strideWidth;
  op_params.stride_height = _strideHeight;
  op_params.dilation_width_factor = _dilationWidth;
  op_params.dilation_height_factor = _dilationHeight;
  op_params.padding_values.width = _paddingLeft;
  op_params.padding_values.height = _paddingTop;
  op_params.depth_multiplier = _multiplier;
  op_params.float_activation_min = output_activation_min;
  op_params.float_activation_max = output_activation_max;
 
  // Since DepthwiseConvOp does not support dilation and different W/H stride yet,
  // it uses the existing kernel in this case.
  if (_dilationWidth == 1 && _dilationHeight == 1 && _strideWidth == _strideHeight)
  {
    nnfw::cker::DepthwiseConvOp(op_params, getShape(_input), getBuffer<float>(_input),
                                getShape(_kernel), getBuffer<float>(_kernel), getShape(_bias),
                                getBuffer<float>(_bias), getBuffer<float>(_padded_filter.get()),
                                _use_padded_filter, getBuffer<float>(_filter_buffers.get()),
                                getShape(_output), getBuffer<float>(_output));
  }
  else
  {
    nnfw::cker::DepthwiseConv<float, float>(
      op_params, getShape(_input), getBuffer<float>(_input), getShape(_kernel),
      getBuffer<float>(_kernel), getShape(_bias), getBuffer<float>(_bias), getShape(_output),
      getBuffer<float>(_output), _external_context->ruy_context());
  }
}
void DepthwiseConvolutionLayer::convFloat32() {…}
 
void DepthwiseConvolutionLayer::convQ8uPerTensor()
{
  int32_t output_activation_min = 0;
  int32_t output_activation_max = 0;
  CalculateActivationRangeQuantized(_activation, _output, &output_activation_min,
                                    &output_activation_max);
 
  double real_multiplier = 0.0;
  int32_t output_multiplier = 0;
  int32_t output_shift = 0;
  GetQuantizedConvolutionMultiplier(_input, _kernel, _bias, _output, &real_multiplier);
  QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift);
 
  nnfw::cker::DepthwiseConvParams op_params;
  op_params.stride_width = _strideWidth;
  op_params.stride_height = _strideHeight;
  op_params.dilation_width_factor = _dilationWidth;
  op_params.dilation_height_factor = _dilationHeight;
  op_params.padding_values.width = _paddingLeft;
  op_params.padding_values.height = _paddingTop;
  op_params.depth_multiplier = _multiplier;
  op_params.input_offset = -_input->data_zero_point();
  op_params.weights_offset = -_kernel->data_zero_point();
  op_params.output_offset = _output->data_zero_point();
  op_params.output_multiplier = output_multiplier;
  op_params.output_shift = output_shift;
  op_params.quantized_activation_min = output_activation_min;
  op_params.quantized_activation_max = output_activation_max;
 
  nnfw::cker::DepthwiseConv<uint8_t, int32_t>(
    op_params, getShape(_input), getBuffer<uint8_t>(_input), getShape(_kernel),
    getBuffer<uint8_t>(_kernel), getShape(_bias), getBuffer<int32_t>(_bias), getShape(_output),
    getBuffer<uint8_t>(_output), _external_context->ruy_context());
}
void DepthwiseConvolutionLayer::convQ8uPerTensor() {…}
 
void DepthwiseConvolutionLayer::convQ8uPerChannel()
{
  nnfw::cker::DepthwiseConvParams op_params;
  op_params.padding_values.width = _paddingLeft;
  op_params.padding_values.height = _paddingTop;
  op_params.stride_width = _strideWidth;
  op_params.stride_height = _strideHeight;
  op_params.dilation_width_factor = _dilationWidth;
  op_params.dilation_height_factor = _dilationHeight;
  op_params.depth_multiplier = _multiplier;
  op_params.input_offset = -_input->data_zero_point();
  op_params.output_offset = _output->data_zero_point();
  int32_t output_activation_min = 0;
  int32_t output_activation_max = 0;
  CalculateActivationRangeQuantized(_activation, _output, &output_activation_min,
                                    &output_activation_max);
  op_params.quantized_activation_min = output_activation_min;
  op_params.quantized_activation_max = output_activation_max;
  // NOTE: The following fields of ConvParams are not used:
  // padding_type, weights_offset, output_{multiplier,shift}, float_activation_{min,max}
 
  nnfw::cker::reference_integer_ops::DepthwiseConvPerChannel(
    op_params, _per_channel_output_multiplier.data(), _per_channel_output_shift.data(),
    getShape(_input), getBuffer<uint8_t>(_input), getShape(_kernel), getBuffer<uint8_t>(_kernel),
    _kernel->data_zero_points().data(), getShape(_bias), getBuffer<int32_t>(_bias),
    getShape(_output), getBuffer<uint8_t>(_output));
}
void DepthwiseConvolutionLayer::convQ8uPerChannel() {…}
 
void DepthwiseConvolutionLayer::convQ8i()
{
  if (!_prepared)
  {
    prepareQ8i();
    _prepared = true;
  }
 
  int32_t output_activation_min = 0;
  int32_t output_activation_max = 0;
  CalculateActivationRangeQuantized(_activation, _output, &output_activation_min,
                                    &output_activation_max);
 
  nnfw::cker::DepthwiseConvParams op_params;
  op_params.padding_type = nnfw::cker::PaddingType::kSame;
  op_params.padding_values.width = _paddingLeft;
  op_params.padding_values.height = _paddingTop;
  op_params.depth_multiplier = _multiplier;
  op_params.stride_width = _strideWidth;
  op_params.stride_height = _strideHeight;
  op_params.dilation_width_factor = _dilationWidth;
  op_params.dilation_height_factor = _dilationHeight;
  op_params.input_offset = -_input->data_zero_point();
  op_params.weights_offset = 0;
  op_params.output_offset = _output->data_zero_point();
  op_params.quantized_activation_min = output_activation_min;
  op_params.quantized_activation_max = output_activation_max;
 
  nnfw::cker::optimized_integer_ops::DepthwiseConvPerChannel(
    op_params, _per_channel_output_multiplier.data(), _per_channel_output_shift.data(),
    getShape(_input), getBuffer<int8_t>(_input), getShape(_kernel), getBuffer<int8_t>(_kernel),
    getShape(_bias), getBuffer<int32_t>(_bias), getShape(_output), getBuffer<int8_t>(_output),
    _external_context->ruy_context());
}
void DepthwiseConvolutionLayer::convQ8i() {…}
 
void DepthwiseConvolutionLayer::convQ8iHybridPerChannel()
{
  if (!_prepared)
  {
    prepareQ8iHybridPerChannel();
    _prepared = true;
  }
 
  float output_activation_min = 0, output_activation_max = 0;
  CalculateActivationRange(_activation, &output_activation_min, &output_activation_max);
 
  auto input_shape = getShape(_input);
  const int batch_size = input_shape.Dims(0);
  const int input_size = input_shape.FlatSize() / batch_size;
 
  auto scaling_factors_ptr = _input_scaling_factors.data();
  auto input_offsets_ptr = _input_offsets.data();
 
  for (int b = 0; b < batch_size; ++b)
  {
    const int offset = b * input_size;
    nnfw::cker::PortableAsymmetricQuantizeFloats(getBuffer<float>(_input) + offset, input_size,
                                                 _input_quantized.data() + offset,
                                                 &scaling_factors_ptr[b], &input_offsets_ptr[b]);
  }
 
  nnfw::cker::DepthwiseConvParams op_params;
  op_params.padding_values.width = _paddingLeft;
  op_params.padding_values.height = _paddingTop;
  op_params.depth_multiplier = _multiplier;
  op_params.stride_width = _strideWidth;
  op_params.stride_height = _strideHeight;
  op_params.dilation_width_factor = _dilationWidth;
  op_params.dilation_height_factor = _dilationHeight;
  op_params.float_activation_min = output_activation_min;
  op_params.float_activation_max = output_activation_max;
 
  nnfw::cker::reference_integer_ops::DepthwiseConvHybridPerChannel(
    op_params, _input_scaling_factors.data(), getShape(_input), _input_quantized.data(),
    getShape(_kernel), getBuffer<int8_t>(_kernel), getShape(_bias), getBuffer<float>(_bias),
    getShape(_output), getBuffer<float>(_output), _kernel->data_scales().data(),
    _input_offsets.data());
}
void DepthwiseConvolutionLayer::convQ8iHybridPerChannel() {…}
 
void DepthwiseConvolutionLayer::prepareQ8i()
{
  GetQuantizedConvolutionMultipliersAndShifts(
    _input->data_scale(), _output->data_scale(), _kernel->data_scales().data(),
    _kernel->data_scales().size(), getShape(_kernel).Dims(3), _per_channel_output_multiplier,
    _per_channel_output_shift);
}
 
void DepthwiseConvolutionLayer::prepareQ8uPerChannel()
{
  GetQuantizedConvolutionMultipliersAndShifts(
    _input->data_scale(), _output->data_scale(), _kernel->data_scales().data(),
    _kernel->data_scales().size(), getShape(_kernel).Dims(3), _per_channel_output_multiplier,
    _per_channel_output_shift);
}
 
void DepthwiseConvolutionLayer::prepareQ8iHybridPerChannel()
{
  // allocate memory for activation quantization.
  // - quantized values (int8_t type and same shape of original input)
  // - quantization params (= scale/zeropoint for each input)
  auto input_shape = getShape(_input);
  const int batch_size = input_shape.Dims(0);
  const int input_size = input_shape.FlatSize() / batch_size;
  _input_quantized.resize(input_size);
  // TODO: Optimize the case of batch_size = 1
  _input_scaling_factors.resize(batch_size);
  _input_offsets.resize(batch_size);
}
 
void DepthwiseConvolutionLayer::ensureQ8iHybridPerChannel()
{
  // ensure weight is per-channel quantized.
  int32_t kernel_input_channel = getShape(_kernel).Dims(3);
  // zero_points comes from flatbuffer vector. Its size is within uint32_t range.
  size_t kernel_zerop_cnt = _kernel->data_scales().size();
  // promote to int64_t to compare int32_t and uint32_t
  if ((int64_t)kernel_input_channel != (int64_t)kernel_zerop_cnt)
    throw std::runtime_error{"DConv2D hybrid supports only per-channel quantized weight."};
}
 
void DepthwiseConvolutionLayer::configure(
  const IPortableTensor *input, const IPortableTensor *kernel, const IPortableTensor *bias,
  const uint32_t paddingLeft, const uint32_t paddingRight, const uint32_t paddingTop,
  const uint32_t paddingBottom, const uint32_t strideWidth, const uint32_t strideHeight,
  const uint32_t multiplier, const uint32_t dilationWidth, const uint32_t dilationHeight,
  const ir::Activation activation, IPortableTensor *output,
  const std::shared_ptr<ExternalContext> &external_context)
{
  _input = input;
  _kernel = kernel;
  _bias = bias;
  _paddingLeft = paddingLeft;
  _paddingRight = paddingRight;
  _paddingTop = paddingTop;
  _paddingBottom = paddingBottom;
  _strideWidth = strideWidth;
  _strideHeight = strideHeight;
  _multiplier = multiplier;
  _dilationWidth = dilationWidth;
  _dilationHeight = dilationHeight;
  _activation = activation;
  _output = output;
  _external_context = external_context;
  _is_hybrid = _input->data_type() == OperandType::FLOAT32 &&
               _kernel->data_type() == OperandType::QUANT_INT8_SYMM;
 
  if (_is_hybrid)
  {
    ensureQ8iHybridPerChannel();
    prepareQ8iHybridPerChannel();
    _prepared = true;
  }
  else if (_input->data_type() == OperandType::FLOAT32)
  {
    prepareF32();
  }
  else if (_input->data_type() == OperandType::QUANT_INT8_ASYMM)
  {
    if (_kernel->is_constant() && !_input->is_dynamic() && !_output->is_dynamic())
    {
      prepareQ8i();
      _prepared = true;
    }
  }
  else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM && _kernel->is_constant() &&
           !_input->is_dynamic() && !_output->is_dynamic())
  {
    const bool per_channel_quantized = _kernel->data_scales().size() > 1;
    if (per_channel_quantized)
    {
      prepareQ8uPerChannel();
      _prepared = true;
    }
  }
}
void DepthwiseConvolutionLayer::configure( {…}
 
void DepthwiseConvolutionLayer::run()
{
  if (_is_hybrid)
  {
    convQ8iHybridPerChannel();
  }
  else if (_input->data_type() == OperandType::FLOAT32)
  {
    convFloat32();
  }
  else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM)
  {
    const bool per_channel_quantized = _kernel->data_scales().size() > 1;
    if (per_channel_quantized)
      convQ8uPerChannel();
    else
      convQ8uPerTensor();
  }
  else if (_input->data_type() == OperandType::QUANT_INT8_ASYMM)
  {
    convQ8i();
  }
  else
  {
    throw std::runtime_error{"DepthwiseConv: unsupported data type"};
  }
}
void DepthwiseConvolutionLayer::run() {…}
 
} // namespace onert::backend::cpu::ops