src/operator/quantization/quantized_pooling.cc - master - githubmirror/incubator-mxnet

src/operator/quantization/quantized_pooling.cc
240 строк10 KB
bartekkuncer
Unify all names used to refer to oneDNN library in logs and docs to oneDNN (#20719)
22 ноя 2021, 09:41
22 ноя 2021, 09:41024d01e
/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * 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.
 */

/*!
 * \file quantized_pooling.cc
 */
#include <mxnet/op_attr_types.h>
#include "../nn/pooling-inl.h"
#if MXNET_USE_ONEDNN == 1
#include "../nn/dnnl/dnnl_pooling-inl.h"
#endif

namespace mxnet {
namespace op {

bool QuantizedPoolingShape(const nnvm::NodeAttrs& attrs,
                           mxnet::ShapeVector* in_shape,
                           mxnet::ShapeVector* out_shape) {
  const PoolingParam& param = nnvm::get<PoolingParam>(attrs.parsed);
  CHECK_EQ(in_shape->size(), 3U);
  if (!shape_is_known(in_shape->at(0)))
    return false;
  const mxnet::TShape& dshape = (*in_shape)[0];

  const int data_ndims   = dshape.ndim();
  const int kernel_ndims = param.kernel.ndim();
  const int layout       = param.GetLayout(data_ndims);

#if MXNET_USE_ONEDNN == 1
  CHECK(data_ndims == 4U || data_ndims == 5U)
      << "oneDNN QuantizedPoolingOp only supports 4D/5D layout for now, input should be 4D in "
      << "(batch, channel, y, x) or 5D in (batch, channel, d, y, x)";
  CHECK(layout == mshadow::kNCHW || layout == mshadow::kNCDHW)
      << "oneDNN QuantizedPoolingOp only supports NCHW/NCDHW layout for now, saw " << layout;
  CHECK(kernel_ndims == 2U || kernel_ndims == 3U)
      << "oneDNN QuantizedPoolingOp only supports 2D/3D pooling for now, saw" << kernel_ndims;
#else
  CHECK_EQ(data_ndims, 4U) << "quantized_pooling: Input data should be 4D in "
                           << "(batch, channel, y, x)";
  CHECK_EQ(layout, mshadow::kNCHW)
      << "QuantizedPoolingOp only supports NCHW layout for now, saw " << layout;
  CHECK_EQ(kernel_ndims, 2U) << "QuantizedPoolingOp only supports 2D pooling for now";
#endif

  const int D = (data_ndims == 5) ? 2 : 1;
  const int N = 0, H = D + 1, W = D + 2, C = 1;
  mxnet::TShape oshape(data_ndims, -1);

  int idx = 0;
  if (kernel_ndims == 3) {
    CHECK(param.kernel[idx] <= dshape[D] + 2 * param.pad[idx])
        << "kernel size (" << param.kernel[0] << ") exceeds input (" << dshape[D] << " padded to "
        << (dshape[D] + 2 * param.pad[idx]) << ")";
    ++idx;
  }
  CHECK(param.kernel[idx] <= dshape[H] + 2 * param.pad[idx])
      << "kernel size (" << param.kernel[idx] << ") exceeds input (" << dshape[H] << " padded to "
      << (dshape[H] + 2 * param.pad[idx]) << ")";
  ++idx;
  CHECK(param.kernel[idx] <= dshape[W] + 2 * param.pad[idx])
      << "kernel size (" << param.kernel[idx] << ") exceeds input (" << dshape[W] << " padded to "
      << (dshape[W] + 2 * param.pad[idx]) << ")";

#define OUTPUT_SHAPE_VALID_ASSIGN(spatial_dim, idx)                                             \
  {                                                                                             \
    oshape[spatial_dim] =                                                                       \
        1 + (dshape[spatial_dim] + 2 * param.pad[idx] - param.kernel[idx]) / param.stride[idx]; \
  }
#define OUTPUT_SHAPE_FULL_ASSIGN(spatial_dim, idx)                                                 \
  {                                                                                                \
    oshape[spatial_dim] =                                                                          \
        1 + static_cast<int>(std::ceil(                                                            \
                static_cast<float>(dshape[spatial_dim] + 2 * param.pad[idx] - param.kernel[idx]) / \
                param.stride[idx]));                                                               \
  }

  oshape[N] = dshape[N];
  oshape[C] = dshape[C];
  if (param.global_pool) {
    if (data_ndims == 5)
      oshape[D] = 1;
    oshape[H] = 1;
    oshape[W] = 1;
  } else {
    if (param.pooling_convention == pool_enum::kValid) {
      int idx = 0;
      if (data_ndims == 5) {
        OUTPUT_SHAPE_VALID_ASSIGN(D, idx);
        ++idx;
      }
      OUTPUT_SHAPE_VALID_ASSIGN(H, idx);
      ++idx;
      OUTPUT_SHAPE_VALID_ASSIGN(W, idx);
    } else {
      int idx = 0;
      if (data_ndims == 5) {
        OUTPUT_SHAPE_FULL_ASSIGN(D, idx);
        ++idx;
      }
      OUTPUT_SHAPE_FULL_ASSIGN(H, idx);
      ++idx;
      OUTPUT_SHAPE_FULL_ASSIGN(W, idx);
    }
  }

  SHAPE_ASSIGN_CHECK(*in_shape, 1, mxnet::TShape{1});
  SHAPE_ASSIGN_CHECK(*in_shape, 2, mxnet::TShape{1});

  out_shape->clear();
  out_shape->push_back(oshape);
  out_shape->push_back(mxnet::TShape{1});
  out_shape->push_back(mxnet::TShape{1});
  return true;
}

bool QuantizedPoolingType(const nnvm::NodeAttrs& attrs,
                          std::vector<int>* in_type,
                          std::vector<int>* out_type) {
  const PoolingParam& param = nnvm::get<PoolingParam>(attrs.parsed);
  CHECK_EQ(in_type->size(), 3U);
  CHECK_EQ(out_type->size(), 3U);
  if (param.pool_type == pool_enum::kMaxPooling || param.pool_type == pool_enum::kAvgPooling) {
#if MXNET_USE_ONEDNN == 1
    TYPE_ASSIGN_CHECK(*out_type, 0, (*in_type)[0]);
#else
    TYPE_ASSIGN_CHECK(*in_type, 0, mshadow::kInt8);
    TYPE_ASSIGN_CHECK(*out_type, 0, mshadow::kInt8);
#endif
  } else {
    LOG(FATAL) << "QuantizedPoolingOp only supports pool_type=max/avg for now";
  }
  TYPE_ASSIGN_CHECK(*in_type, 1, mshadow::kFloat32);
  TYPE_ASSIGN_CHECK(*in_type, 2, mshadow::kFloat32);
  TYPE_ASSIGN_CHECK(*out_type, 1, mshadow::kFloat32);
  TYPE_ASSIGN_CHECK(*out_type, 2, mshadow::kFloat32);
  return true;
}

inline static bool QuantizedPoolingStorageType(const nnvm::NodeAttrs& attrs,
                                               const int dev_mask,
                                               DispatchMode* dispatch_mode,
                                               std::vector<int>* in_attrs,
                                               std::vector<int>* out_attrs) {
  CHECK_EQ(in_attrs->size(), 3);

  *dispatch_mode = DispatchMode::kFCompute;
#if MXNET_USE_ONEDNN == 1
  const PoolingParam& param = nnvm::get<PoolingParam>(attrs.parsed);
  if (dev_mask == mshadow::cpu::kDevMask && SupportDNNLPooling(param)) {
    *dispatch_mode = DispatchMode::kFComputeEx;
  }
#else
  CHECK_EQ(out_attrs->size(), 3);
#endif
  for (int& out_attr : *out_attrs)
    out_attr = kDefaultStorage;
  return true;
}

NNVM_REGISTER_OP(_contrib_quantized_pooling)
    .add_alias("_npx_quantized_pooling")
    .describe(R"code(Pooling 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.
    This operator only supports `pool_type` of `avg` or `max`.)code" ADD_FILELINE)
    .set_num_inputs(3)
    .set_num_outputs(3)
    .set_attr_parser(PoolingParamParser)
    .set_attr<nnvm::FListInputNames>(
        "FListInputNames",
        [](const NodeAttrs& attrs) {
          return std::vector<std::string>{"data", "min_data", "max_data"};
        })
    .set_attr<nnvm::FListOutputNames>(
        "FListOutputNames",
        [](const NodeAttrs& attrs) {
          return std::vector<std::string>{"output", "min_output", "max_output"};
        })
    .set_attr<mxnet::FInferShape>("FInferShape", QuantizedPoolingShape)
    .set_attr<nnvm::FInferType>("FInferType", QuantizedPoolingType)
    .set_attr<FInferStorageType>("FInferStorageType", QuantizedPoolingStorageType)
    // 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<FNeedRequantize>(
        "FNeedRequantize",
        [](const NodeAttrs& attrs) {
          const PoolingParam& param = nnvm::get<PoolingParam>(attrs.parsed);
          CHECK(param.pool_type == pool_enum::kMaxPooling ||
                param.pool_type == pool_enum::kAvgPooling)
              << "QuantizedPoolingOp only supports pool_type=max/avg for now";
          return false;
        })
    .add_argument("data", "NDArray-or-Symbol", "Input data.")
    .add_argument("min_data", "NDArray-or-Symbol", "Minimum value of data.")
    .add_argument("max_data", "NDArray-or-Symbol", "Maximum value of data.")
    .add_arguments(PoolingParam::__FIELDS__());

NNVM_REGISTER_OP(Pooling).set_attr<FQuantizedOp>("FQuantizedOp", [](const NodeAttrs& attrs) {
  PoolingParam param;
  param.Init(attrs.dict);
  // TODO(junwu): Uncomment the following line and remove the above lines
  // after pooling op is refactored
  // const PoolingParam& param = nnvm::get<PoolingParam>(attrs.parsed);
  nnvm::ObjectPtr node = nnvm::Node::Create();
  if (param.pool_type == pool_enum::kMaxPooling || param.pool_type == pool_enum::kAvgPooling) {
    node->attrs.op   = Op::Get("_contrib_quantized_pooling");
    node->attrs.name = "quantized_" + attrs.name;
  } else {
    node->attrs.op   = Op::Get("Pooling");
    node->attrs.name = 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