conv_depthwise.cc

// 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.

#include "lite/kernels/arm/conv_depthwise.h"
#include "lite/backends/arm/math/conv_block_utils.h"
#include "lite/backends/arm/math/conv_impl.h"

namespace paddle {
namespace lite {
namespace kernels {
namespace arm {

template <>
void DepthwiseConv<PRECISION(kFloat), PRECISION(kFloat)>::PrepareForRun() {
  auto& param = this->Param<param_t>();
  CHECK(this->ctx_);
  auto& ctx = this->ctx_->template As<ARMContext>();
  auto w_dims = param.filter->dims();
  auto kw = w_dims[3];
  // select dw conv kernel
  if (kw == 3) {
    VLOG(5) << "invoke 3x3 dw conv fp32";
    /// trans weights
    constexpr int cblock = 4;
    auto oc = w_dims[0];
    auto kh = w_dims[2];
    auto cround = ROUNDUP(oc, cblock);
    weights_.Resize({cround, 1, kh, kw});
    auto w_data = weights_.mutable_data<float>();
    auto w_data_in = param.filter->data<float>();
    lite::arm::math::conv_trans_weights_numc(
        w_data_in, w_data, oc, 1, cblock, kh * kw);
    impl_ = lite::arm::math::conv_depthwise_3x3_fp32;
    flag_trans_weights_ = true;
  } else if (kw == 5) {
    VLOG(5) << "invoke 5x5 dw conv fp32";
    impl_ = lite::arm::math::conv_depthwise_5x5_fp32;
  } else {
    LOG(FATAL) << "this type dw conv not impl";
  }
}

template <>
void DepthwiseConv<PRECISION(kInt8), PRECISION(kFloat)>::PrepareForRun() {
  auto& param = this->Param<param_t>();
  CHECK(this->ctx_);
  auto& ctx = this->ctx_->template As<ARMContext>();
  auto w_dims = param.filter->dims();
  int kh = w_dims[2];
  int kw = w_dims[3];
  int oc = w_dims[0];
  /// update scale
  float in_scale = param.input_scale;
  auto& scale = param.weight_scale;
  CHECK(scale.size() == 1 || scale.size() == oc)
      << "weights scale size must = filter size or = 1";
  w_scale_.resize(oc);
  for (int i = 0; i < oc; ++i) {
    if (scale.size() == 1) {
      w_scale_[i] = scale[0] * in_scale;
    } else {
      w_scale_[i] = scale[i] * in_scale;
    }
  }
  /// select dw conv kernel
  if (kw == 3) {
    VLOG(5) << "invoke 3x3 dw conv int8 kernel fp32 out";
    impl_ = lite::arm::math::conv_depthwise_3x3_int8_fp32;
    int cround = ROUNDUP(w_dims[0], 8);
    weights_.Resize({cround / 8, 1, kh * kw, 8});
    auto wptr = param.filter->data<int8_t>();
    auto wptr_new = weights_.mutable_data<int8_t>();
    lite::arm::math::conv_trans_weights_numc(wptr, wptr_new, oc, 1, 8, 9);
    flag_trans_weights_ = true;
  } else {
    LOG(FATAL) << "this type dw conv not impl";
  }
}

template <>
void DepthwiseConv<PRECISION(kInt8), PRECISION(kInt8)>::PrepareForRun() {
  auto& param = this->Param<param_t>();
  CHECK(this->ctx_);
  auto& ctx = this->ctx_->template As<ARMContext>();
  auto w_dims = param.filter->dims();
  int kw = w_dims[3];
  int kh = w_dims[2];
  int oc = w_dims[0];
  /// update scale
  float in_scale = param.input_scale;
  float out_scale = param.output_scale;
  auto& scale = param.weight_scale;
  CHECK(scale.size() == 1 || scale.size() == oc)
      << "weights scale size must = filter size or = 1";
  w_scale_.resize(oc);
  for (int i = 0; i < oc; ++i) {
    if (scale.size() == 1) {
      w_scale_[i] = scale[0] * in_scale / out_scale;
    } else {
      w_scale_[i] = scale[i] * in_scale / out_scale;
    }
  }
  /// update bias
  if (param.bias) {
    bias_.Resize(param.bias->dims());
    auto ptr = bias_.mutable_data<float>();
    auto ptr_in = param.bias->data<float>();
    for (int i = 0; i < bias_.numel(); ++i) {
      ptr[i] = ptr_in[i] / out_scale;
    }
    flag_trans_bias_ = true;
  }
  /// select dw conv kernel
  if (kw == 3) {
    VLOG(5) << "invoke 3x3 dw conv int8 kernel int8 out";
    impl_ = lite::arm::math::conv_depthwise_3x3_int8_int8;
    int cround = ROUNDUP(w_dims[0], 8);
    weights_.Resize({cround / 8, 1, kh * kw, 8});
    auto wptr = param.filter->data<int8_t>();
    auto wptr_new = weights_.mutable_data<int8_t>();
    lite::arm::math::conv_trans_weights_numc(wptr, wptr_new, oc, 1, 8, 9);
    flag_trans_weights_ = true;
  } else {
    LOG(FATAL) << "this type dw conv not impl";
  }
}

template <>
void DepthwiseConv<PRECISION(kFloat), PRECISION(kFloat)>::Run() {
  auto& param = this->Param<param_t>();
  CHECK(this->ctx_);
  auto& ctx = this->ctx_->template As<ARMContext>();
  const auto* i_data = param.x->data<float>();
  const auto* w_data = flag_trans_weights_ ? weights_.data<float>()
                                           : param.filter->data<float>();
  const auto* b_data = param.bias ? param.bias->data<float>() : nullptr;
  if (flag_trans_bias_) {
    b_data = bias_.data<float>();
  }
  auto* o_data = param.output->mutable_data<float>();

  auto x_dims = param.x->dims();
  auto w_dims = param.filter->dims();
  auto o_dims = param.output->dims();

  int iw = x_dims[3];  // nchw
  int ih = x_dims[2];
  int ic = x_dims[1];
  int bs = x_dims[0];
  int oh = o_dims[2];
  int ow = o_dims[3];
  int oc = o_dims[1];

  impl_(i_data,
        o_data,
        bs,
        oc,
        oh,
        ow,
        ic,
        ih,
        iw,
        w_data,
        b_data,
        param,
        &ctx,
        w_scale_.data());
}

template <>
void DepthwiseConv<PRECISION(kInt8), PRECISION(kFloat)>::Run() {
  auto& param = this->Param<param_t>();
  CHECK(this->ctx_);
  auto& ctx = this->ctx_->template As<ARMContext>();
  const auto* i_data = param.x->data<int8_t>();
  const auto* w_data = flag_trans_weights_ ? weights_.data<int8_t>()
                                           : param.filter->data<int8_t>();
  const auto* b_data = param.bias ? param.bias->data<float>() : nullptr;
  if (flag_trans_bias_) {
    b_data = bias_.data<float>();
  }
  auto* o_data = param.output->mutable_data<float>();

  auto x_dims = param.x->dims();
  auto w_dims = param.filter->dims();
  auto o_dims = param.output->dims();

  int iw = x_dims[3];  // nchw
  int ih = x_dims[2];
  int ic = x_dims[1];
  int bs = x_dims[0];
  int oh = o_dims[2];
  int ow = o_dims[3];
  int oc = o_dims[1];

  impl_(i_data,
        o_data,
        bs,
        oc,
        oh,
        ow,
        ic,
        ih,
        iw,
        w_data,
        b_data,
        param,
        &ctx,
        w_scale_.data());
}

template <>
void DepthwiseConv<PRECISION(kInt8), PRECISION(kInt8)>::Run() {
  auto& param = this->Param<param_t>();
  CHECK(this->ctx_);
  auto& ctx = this->ctx_->template As<ARMContext>();
  const auto* i_data = param.x->data<int8_t>();
  const auto* w_data = flag_trans_weights_ ? weights_.data<int8_t>()
                                           : param.filter->data<int8_t>();
  const auto* b_data = param.bias ? param.bias->data<float>() : nullptr;
  if (flag_trans_bias_) {
    b_data = bias_.data<float>();
  }
  auto* o_data = param.output->mutable_data<int8_t>();

  auto x_dims = param.x->dims();
  auto w_dims = param.filter->dims();
  auto o_dims = param.output->dims();

  int iw = x_dims[3];  // nchw
  int ih = x_dims[2];
  int ic = x_dims[1];
  int bs = x_dims[0];
  int oh = o_dims[2];
  int ow = o_dims[3];
  int oc = o_dims[1];

  impl_(i_data,
        o_data,
        bs,
        oc,
        oh,
        ow,
        ic,
        ih,
        iw,
        w_data,
        b_data,
        param,
        &ctx,
        w_scale_.data());
}

}  // namespace arm
}  // namespace kernels
}  // namespace lite
}  // namespace paddle