src/operator/quantization/mkldnn/mkldnn_quantized_conv.cc

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 * distributed with this work for additional information
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 * to you 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,
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/*!
 * \file mkldnn_quantized_conv.cc
 * \brief
 * \author Wenting Jiang, Xinyu Chen
*/

#if MXNET_USE_MKLDNN == 1
#include "../../nn/mkldnn/mkldnn_base-inl.h"
#include "../../nn/mkldnn/mkldnn_convolution-inl.h"
#include "../../nn/convolution-inl.h"
#include "../quantization_utils.h"
#include "../../tensor/matrix_op-inl.h"
#include "../../elemwise_op_common.h"
namespace mxnet {
namespace op {

static void MKLDNNQuantizedConvForward(const nnvm::NodeAttrs& attrs,
                                       const OpContext &ctx,
                                       const std::vector<NDArray> &in_data,
                                       const std::vector<OpReqType> &req,
                                       const std::vector<NDArray> &out_data) {
  CHECK_EQ(in_data[0].dtype(), mshadow::kUint8)
    << "mkldnn_quantized_conv op only supports uint8 as input type";
  TmpMemMgr::Get()->Init(ctx.requested[conv::kTempSpace]);
  NDArray weight = in_data[conv::kWeight];
  ConvolutionParam param = nnvm::get<ConvolutionParam>(attrs.parsed);
  MKLDNNConvFullParam full_param;
  full_param.conv_param = param;
  full_param.mkldnn_param.Init(std::unordered_map<std::string, std::string>());
  auto &fwd = GetConvFwd(full_param, ctx.is_train, in_data[conv::kData], in_data[conv::kWeight],
                         param.no_bias ? nullptr : &in_data[conv::kBias], out_data[conv::kOut]);
  auto data_mem = in_data[conv::kData].GetMKLDNNDataReorder(fwd.GetPd().src_desc());
  const mkldnn::memory *weight_mem;
  // For inference, we want to reorder the weight array so we don't need to
  // reorder data every time.
  if (weight.IsDefaultData()) {
    // We also need to modify the layout on the original weight array.
    // Don't switch below sequence because naive engine will executes
    // pushAsync synchronously.
    weight.MKLDNNDataReorderAsync(fwd.GetPd().weights_desc());
    weight_mem = GetWeights(weight, fwd.GetPd().weights_desc(), param.num_group);
  } else {
    weight_mem = weight.GetMKLDNNData();
  }
  auto out_mem = CreateMKLDNNMem(out_data[conv::kOut], fwd.GetPd().dst_desc(),
                                 req[conv::kOut]);
  mkldnn_args_map_t net_args;
  if (!param.no_bias) {
    const mkldnn::memory *bias_mem =
        in_data[conv::kBias].GetMKLDNNDataReorder(fwd.GetPd().bias_desc());
    net_args.insert({MKLDNN_ARG_BIAS, *bias_mem});
  }
  net_args.insert({MKLDNN_ARG_SRC, *data_mem});
  net_args.insert({MKLDNN_ARG_WEIGHTS, *weight_mem});
  net_args.insert({MKLDNN_ARG_DST, *out_mem.second});
  MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
  CommitOutput(out_data[conv::kOut], out_mem);
  MKLDNNStream::Get()->Submit();
  Stream<cpu> *s = ctx.get_stream<cpu>();
  const size_t num_inputs = param.no_bias ? 2 : 3;
  mxnet_op::Kernel<QuantizationRangeForS8S8MultiplicationStruct, cpu>::Launch(s, 1,
           out_data[1].data().dptr<float>(), out_data[2].data().dptr<float>(),
           in_data[num_inputs].data().dptr<float>(),
           in_data[num_inputs+1].data().dptr<float>(),
           in_data[num_inputs+2].data().dptr<float>(),
           in_data[num_inputs+3].data().dptr<float>());
}

NNVM_REGISTER_OP(_contrib_quantized_conv)
.set_attr<FComputeEx>("FComputeEx<cpu>", MKLDNNQuantizedConvForward);

}  // namespace op
}  // namespace mxnet

#endif  // MXNET_USE_MKLDNN == 1