src/operator/quantization/quantized_conv.cc

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 * to you under the Apache License, Version 2.0 (the
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 *
 *   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.
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/*!
 * Copyright (c) 2017 by Contributors
 * \file quantized_conv.cc
 * \brief
 * \author Ziheng Jiang, Jun Wu
*/
#include "../nn/convolution-inl.h"
#if MXNET_USE_MKLDNN == 1
#include "../nn/mkldnn/mkldnn_ops-inl.h"
#endif

namespace mxnet {
namespace op {

bool QuantizedConvShape(const nnvm::NodeAttrs& attrs,
                        mxnet::ShapeVector* in_shape,
                        mxnet::ShapeVector* out_shape) {
  using namespace mshadow;
  const ConvolutionParam& param = nnvm::get<ConvolutionParam>(attrs.parsed);
  CHECK_EQ(param.num_group, 1U) << "quantized_conv only supports num_group=1 for now";
  CHECK_EQ(in_shape->size(), param.no_bias? 6U : 9U);
  CHECK_EQ(out_shape->size(), 3U);
  if (param.layout.has_value()) {
    CHECK_EQ(param.layout.value(), mshadow::kNCHW) << "quantized_conv only supports NCHW for now";
  }
  CHECK_EQ(param.kernel.ndim(), 2U) << "quantized_conv only supports 2D convolution for now";
  CHECK(param.dilate.ndim() == 0U || param.dilate.Size() == 1U)
    << "quantized_conv only supports dilation=1 for all dimensions";
  const mxnet::TShape& dshape =  in_shape->at(0);
  CHECK_EQ(dshape.ndim(), 4U);
  if (dshape.ndim() == 0U) return false;

  const int N = 0, H = 2, W = 3, C = 1;
  CHECK_EQ(dshape[C] % 4,  0U)
    << "for 8bit cudnn conv, the number of channel must be multiple of 4";
  CHECK_EQ(param.num_filter % 4, 0U)
    << "for 8bit cudnn conv, the number of channel must be multiple of 4";

  mxnet::TShape wshape{0, 0, 0, 0};
  wshape[N] = param.num_filter;
  wshape[H] = param.kernel[0];
  wshape[W] = param.kernel[1];
  wshape[C] = dshape[C];
  SHAPE_ASSIGN_CHECK(*in_shape, 1, wshape);
  const int start = param.no_bias? 2 : 3;
  const int end = param.no_bias? 6 : 9;
  for (int i = start; i < end; ++i) {
    SHAPE_ASSIGN_CHECK(*in_shape, i, mxnet::TShape{1});
  }
  if (!param.no_bias) {
    SHAPE_ASSIGN_CHECK(*in_shape, 2, Shape1(param.num_filter));
  }

  auto AddPad = [](index_t dsize, index_t pad) { return dsize + 2 * pad; };
  mxnet::TShape oshape{1, 1, 1, 1};
  oshape[N] = dshape[N];
  oshape[C] = wshape[N];
  oshape[H] = (AddPad(dshape[H], param.pad[0]) - wshape[H]) / param.stride[0] + 1;
  oshape[W] = (AddPad(dshape[W], param.pad[1]) - wshape[W]) / param.stride[1] + 1;

  SHAPE_ASSIGN_CHECK(*out_shape, 0, oshape);
  SHAPE_ASSIGN_CHECK(*out_shape, 1, mxnet::TShape(1, 1));
  SHAPE_ASSIGN_CHECK(*out_shape, 2, mxnet::TShape(1, 1));
  return true;
}

bool QuantizedConvType(const nnvm::NodeAttrs& attrs,
                       std::vector<int> *in_type,
                       std::vector<int> *out_type) {
  const ConvolutionParam& param = nnvm::get<ConvolutionParam>(attrs.parsed);
  CHECK_EQ(in_type->size(), param.no_bias? 6U : 9U);
  CHECK_EQ(out_type->size(), 3U);
#ifndef MXNET_USE_MKLDNN
  TYPE_ASSIGN_CHECK(*in_type, 0, mshadow::kInt8);
#endif
  TYPE_ASSIGN_CHECK(*in_type, 1, mshadow::kInt8);
  if (!param.no_bias) {
    TYPE_ASSIGN_CHECK(*in_type, 2, mshadow::kInt8);
  }
  const size_t start = param.no_bias? 2 : 3;
  const size_t end = param.no_bias? 6 : 9;
  for (size_t i = start; i < end; ++i) {
    TYPE_ASSIGN_CHECK(*in_type, i, mshadow::kFloat32);
  }

  TYPE_ASSIGN_CHECK(*out_type, 0, mshadow::kInt32);
  TYPE_ASSIGN_CHECK(*out_type, 1, mshadow::kFloat32);
  TYPE_ASSIGN_CHECK(*out_type, 2, mshadow::kFloat32);
  return true;
}

bool QuantizedConvStorageType(const nnvm::NodeAttrs& attrs,
                              const int dev_mask,
                              DispatchMode* dispatch_mode,
                              std::vector<int> *in_attrs,
                              std::vector<int> *out_attrs) {
  *dispatch_mode = DispatchMode::kFCompute;
#if MXNET_USE_MKLDNN == 1
  if (dev_mask == mshadow::cpu::kDevMask) {
    *dispatch_mode = DispatchMode::kFComputeEx;
  }
#endif

  (*out_attrs)[0] = kDefaultStorage;
  (*out_attrs)[1] = kDefaultStorage;
  (*out_attrs)[2] = kDefaultStorage;
  return true;
}

NNVM_REGISTER_OP(_contrib_quantized_conv)
.describe(R"code(Convolution operator for input, weight and bias data type of int8,
and accumulates in type int32 for the output. For each argument, two more arguments of type
float32 must be provided representing the thresholds of quantizing argument from data
type float32 to int8. The final outputs contain the convolution result in int32, and min
and max thresholds representing the threholds for quantizing the float32 output into int32.

.. Note::
    This operator only supports forward propogation. DO NOT use it in training.)code" ADD_FILELINE)
.set_num_inputs(
  [](const NodeAttrs& attrs) {
    const ConvolutionParam& param = nnvm::get<ConvolutionParam>(attrs.parsed);
    return param.no_bias? 6 : 9;
  })
.set_num_outputs(3)
.set_attr_parser(ConvolutionParamParser)
.set_attr<nnvm::FListInputNames>("FListInputNames",
  [](const NodeAttrs& attrs) {
    const ConvolutionParam& param = nnvm::get<ConvolutionParam>(attrs.parsed);
    if (param.no_bias) {
      return std::vector<std::string>{"data", "weight", "min_data", "max_data",
                                      "min_weight", "max_weight"};
    } else {
      return std::vector<std::string>{"data", "weight", "bias", "min_data", "max_data",
                                      "min_weight", "max_weight", "min_bias", "max_bias"};
    }
  })
.set_attr<nnvm::FListOutputNames>("FListOutputNames",
  [](const NodeAttrs& attrs) {
    return std::vector<std::string>{"output", "min_output", "max_output"};
  })
.set_attr<mxnet::FInferShape>("FInferShape", QuantizedConvShape)
.set_attr<nnvm::FInferType>("FInferType", QuantizedConvType)
.set_attr<FInferStorageType>("FInferStorageType", QuantizedConvStorageType)
// TODO(Xinyu): a temp solution to enable GluonCV INT8 flow,
// will be reverted after the improvement of CachedOP is done.
.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
.set_attr<FResourceRequest>("FResourceRequest",
  [](const NodeAttrs& attrs) {
    return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace);
  })
.set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return true; })
.add_argument("data", "NDArray-or-Symbol", "Input data.")
.add_argument("weight", "NDArray-or-Symbol", "weight.")
.add_argument("bias", "NDArray-or-Symbol", "bias.")
.add_argument("min_data", "NDArray-or-Symbol", "Minimum value of data.")
.add_argument("max_data", "NDArray-or-Symbol", "Maximum value of data.")
.add_argument("min_weight", "NDArray-or-Symbol", "Minimum value of weight.")
.add_argument("max_weight", "NDArray-or-Symbol", "Maximum value of weight.")
.add_argument("min_bias", "NDArray-or-Symbol", "Minimum value of bias.")
.add_argument("max_bias", "NDArray-or-Symbol", "Maximum value of bias.")
.add_arguments(ConvolutionParam::__FIELDS__());

NNVM_REGISTER_OP(Convolution)
.set_attr<FQuantizable>("FQuantizable", [](const NodeAttrs& attrs) {
    return QuantizeType::kMust;
})
.set_attr<FQuantizedOp>("FQuantizedOp", [](const NodeAttrs& attrs) {
    nnvm::NodePtr node = nnvm::Node::Create();
    node->attrs.op = Op::Get("_contrib_quantized_conv");
    node->attrs.name = "quantized_" + attrs.name;
    node->attrs.dict = attrs.dict;
    if (node->op()->attr_parser != nullptr) {
      node->op()->attr_parser(&(node->attrs));
    }
    return node;
  });

}  // namespace op
}  // namespace mxnet