src/operator/quantization/quantized_batch_norm.cc

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 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * 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,
 * 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.
 */

/*!
 * Copyright (c) 2017 by Contributors
 * \file quantized_batch_norm.cc
 * \brief
 * \author Yixin Bao
*/
#include <mxnet/op_attr_types.h>
#include "../nn/batch_norm-inl.h"
#if MXNET_USE_MKLDNN == 1
#include "../nn/mkldnn/mkldnn_batch_norm-inl.h"
#endif

namespace mxnet {
namespace op {

bool QuantizedBatchNormShape(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector* in_shape,
                             mxnet::ShapeVector* out_shape) {
  const BatchNormParam& param = nnvm::get<BatchNormParam>(attrs.parsed);
  using namespace mshadow;
  CHECK_EQ(in_shape->size(), 7U)
      << "Input:[data, gamma, beta, moving_mean, moving_var, min_data, max_data]";
  CHECK_EQ(out_shape->size(), 3U);

  const mxnet::TShape& dshape = in_shape->at(batchnorm::kData);
  if (!mxnet::ndim_is_known(dshape)) {
    return false;
  }
  const int channelAxis = param.axis < 0 ? dshape.ndim() + param.axis : param.axis;
  CHECK_LT(channelAxis, dshape.ndim()) << "Channel axis out of range: " << param.axis;
  const int channelCount = dshape[channelAxis];

  SHAPE_ASSIGN_CHECK(*in_shape, 1, mxnet::TShape(Shape1(channelCount)))  // gamma,beta
  SHAPE_ASSIGN_CHECK(*in_shape, 2, mxnet::TShape(Shape1(channelCount)))
  SHAPE_ASSIGN_CHECK(*in_shape, 3, mxnet::TShape(Shape1(channelCount)));  // moving_mean, moving_var
  SHAPE_ASSIGN_CHECK(*in_shape, 4, mxnet::TShape(Shape1(channelCount)))
  SHAPE_ASSIGN_CHECK(*in_shape, 5, mxnet::TShape(1, 1));  // min_data, max_data
  SHAPE_ASSIGN_CHECK(*in_shape, 6, mxnet::TShape(1, 1));

  SHAPE_ASSIGN_CHECK(*out_shape, 0, dshape);
  SHAPE_ASSIGN_CHECK(*out_shape, 1, mxnet::TShape(1, 1));  // min_output, max_output
  SHAPE_ASSIGN_CHECK(*out_shape, 2, mxnet::TShape(1, 1));
  return true;
}

bool QuantizedBatchNormType(const nnvm::NodeAttrs& attrs, std::vector<int>* in_type,
                            std::vector<int>* out_type) {
  using namespace mshadow;
  CHECK_EQ(in_type->size(), 7U);
  CHECK_EQ(out_type->size(), 3U);

#if MXNET_USE_MKLDNN == 1
  CHECK(in_type->at(0) == mshadow::kInt8 || in_type->at(0) == mshadow::kUint8)
      << "QuantizedBatchNorm with MKLDNN backend only supports int8/uint8 input, while "
      << in_type->at(0) << " is given.";
#else
  TYPE_ASSIGN_CHECK(*in_type, 0, mshadow::kInt8);
#endif
  for (size_t i = 1; i < 7; ++i) {
    TYPE_ASSIGN_CHECK(*in_type, i, mshadow::kFloat32);
  }

  TYPE_ASSIGN_CHECK(*out_type, 0, mshadow::kInt8);
  TYPE_ASSIGN_CHECK(*out_type, 1, mshadow::kFloat32);
  TYPE_ASSIGN_CHECK(*out_type, 2, mshadow::kFloat32);

  return true;
}

NNVM_REGISTER_OP(_contrib_quantized_batch_norm)
.describe(R"code(BatchNorm operator for input and output data type of int8.
The input and output data comes with min and max thresholds for quantizing
the float32 data into int8.

.. Note::
    This operator only supports forward propogation. DO NOT use it in training.
)code" ADD_FILELINE)
.set_num_inputs(7)
.set_num_outputs(3)
.set_attr_parser(ParamParser<BatchNormParam>)
.set_attr<nnvm::FListInputNames>("FListInputNames",
  [](const NodeAttrs& attrs) {
    return std::vector<std::string>{"data", "gamma", "beta",
    "moving_mean", "moving_var", "min_data", "max_data"};
  })
.set_attr<nnvm::FListOutputNames>("FListOutputNames",
  [](const NodeAttrs& attrs) {
    return std::vector<std::string>{"output", "min_output", "max_output"};
  })
.set_attr<nnvm::FMutateInputs>("FMutateInputs", [](const nnvm::NodeAttrs& attrs) {
  return std::vector<uint32_t>{3, 4};
})
.set_attr<mxnet::FInferShape>("FInferShape", QuantizedBatchNormShape)
.set_attr<nnvm::FInferType>("FInferType", QuantizedBatchNormType)
.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
.set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return false; })
.set_attr<FNeedCalibrateInput>("FNeedCalibrateOutput", [](const NodeAttrs& attrs){
  return std::vector<int>{0};
})
.add_argument("data", "NDArray-or-Symbol", "Input data.")
.add_argument("gamma", "NDArray-or-Symbol", "gamma.")
.add_argument("beta", "NDArray-or-Symbol", "beta.")
.add_argument("moving_mean", "NDArray-or-Symbol", "moving_mean.")
.add_argument("moving_var", "NDArray-or-Symbol", "moving_var.")
.add_argument("min_data", "NDArray-or-Symbol", "Minimum value of data.")
.add_argument("max_data", "NDArray-or-Symbol", "Maximum value of data.")
.add_arguments(BatchNormParam::__FIELDS__());

NNVM_REGISTER_OP(BatchNorm)
.set_attr<FQuantizedOp>("FQuantizedOp", [](const NodeAttrs& attrs) {
    nnvm::ObjectPtr node = nnvm::Node::Create();
    node->attrs.op = Op::Get("_contrib_quantized_batch_norm");
    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;
  })
.set_attr<FAvoidQuantizeInput>("FAvoidQuantizeInput", [](
  const NodeAttrs &attrs, const size_t index, const std::string quantize_granularity) {
  return (index != 0);
});

}  // namespace op
}  // namespace mxnet