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

src/operator/quantization/quantized_elemwise_add.cc
192 строки8 KB
Andrzej Kotłowski
[FEATURE] Add quantization for npi_add with oneDNN (#21041)
22 июн 2022, 09:27
22 июн 2022, 09:2763aea9e
/*
 * 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_elemwise_add.cc
 * \brief
 */
#include "../tensor/elemwise_unary_op.h"
#include "./quantized_elemwise_add-inl.h"

namespace mxnet {
namespace op {

static bool ElemwiseAddShape(const nnvm::NodeAttrs& attrs,
                             mxnet::ShapeVector* in_shape,
                             mxnet::ShapeVector* out_shape) {
  // A, B, A_min, A_max, B_min, B_max
  CHECK_EQ(in_shape->size(), 6U);
  // C, C_min, C_max
  CHECK_EQ(out_shape->size(), 3U);
  CHECK_EQ((*in_shape)[0], (*in_shape)[1]);

  SHAPE_ASSIGN_CHECK(*in_shape, 2, TShape{1});
  SHAPE_ASSIGN_CHECK(*in_shape, 3, TShape{1});
  SHAPE_ASSIGN_CHECK(*in_shape, 4, TShape{1});
  SHAPE_ASSIGN_CHECK(*in_shape, 5, TShape{1});

  SHAPE_ASSIGN_CHECK(*out_shape, 0, (*in_shape)[0]);
  SHAPE_ASSIGN_CHECK(*out_shape, 1, TShape{1});
  SHAPE_ASSIGN_CHECK(*out_shape, 2, TShape{1});
  return true;
}

static bool ElemwiseAddType(const nnvm::NodeAttrs& attrs,
                            std::vector<int>* in_type,
                            std::vector<int>* out_type) {
  // A, B, A_min, A_max, B_min, B_max
  CHECK_EQ(in_type->size(), 6U);
  // C, C_min, C_max
  CHECK_EQ(out_type->size(), 3U);

  // A, B
  const int elem_add_num = 2;
  for (int i = 0; i < elem_add_num; ++i) {
    if (in_type->at(i) == mshadow::kInt8) {
      TYPE_ASSIGN_CHECK(*in_type, i, mshadow::kInt8);
    } else {
      TYPE_ASSIGN_CHECK(*in_type, i, mshadow::kUint8);
    }
  }
  // C
  int dtype                              = mshadow::kInt32;
  const QuantizeElemwiseAddParam& params = nnvm::get<QuantizeElemwiseAddParam>(attrs.parsed);
  if (params.max_calib_range.has_value() && params.min_calib_range.has_value()) {
    dtype = (in_type->at(0) == in_type->at(1)) ? in_type->at(0) : mshadow::kInt8;
  }
  TYPE_ASSIGN_CHECK(*out_type, 0, dtype);
  // C_min
  TYPE_ASSIGN_CHECK(*out_type, 1, mshadow::kFloat32);
  // C_max
  TYPE_ASSIGN_CHECK(*out_type, 2, mshadow::kFloat32);

  return true;
}

void QuantizedElemwiseAddForward(const nnvm::NodeAttrs& attrs,
                                 const OpContext& ctx,
                                 const std::vector<TBlob>& in_data,
                                 const std::vector<OpReqType>& req,
                                 const std::vector<TBlob>& out_data) {
  LOG(FATAL) << "Not supported for MXNet built without oneDNN. "
                "Please install oneDNN enabled MXNet.";
}

NNVM_REGISTER_OP(_contrib_quantized_elemwise_add)
    .add_alias("_npx_quantized_elemwise_add")
    .describe(R"code(elemwise_add operator for input dataA and input dataB 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 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")
    .set_num_inputs([](const NodeAttrs& attrs) {
      // A, B, A_min, A_max, B_min, B_max
      return 6;
    })
    // C, C_min, C_max
    .set_num_outputs(3)
    .set_attr<nnvm::FListInputNames>(
        "FListInputNames",
        [](const NodeAttrs& attrs) {
          return std::vector<std::string>{"lhs", "rhs", "lhs_min", "lhs_max", "rhs_min", "rhs_max"};
        })
    .set_attr<nnvm::FListOutputNames>(
        "FListOutputNames",
        [](const NodeAttrs& attrs) {
          return std::vector<std::string>{"output", "min_output", "max_output"};
        })
    .set_attr<nnvm::FInferType>("FInferType", ElemwiseAddType)
    .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseAddShape)
    .set_attr<FCompute>("FCompute<cpu>", QuantizedElemwiseAddForward)
    .set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return true; })
    .add_argument("lhs", "NDArray-or-Symbol", "first input")
    .add_argument("rhs", "NDArray-or-Symbol", "second input")
    .add_argument("lhs_min", "NDArray-or-Symbol", "3rd input")
    .add_argument("lhs_max", "NDArray-or-Symbol", "4th input")
    .add_argument("rhs_min", "NDArray-or-Symbol", "5th input")
    .add_argument("rhs_max", "NDArray-or-Symbol", "6th input");

NNVM_REGISTER_OP(elemwise_add).set_attr<FQuantizedOp>("FQuantizedOp", [](const NodeAttrs& attrs) {
  nnvm::ObjectPtr node = nnvm::Node::Create();
  node->attrs.op       = Op::Get("_contrib_quantized_elemwise_add");
  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;
});

NNVM_REGISTER_OP(_contrib_quantized_npi_add)
    .add_alias("_npx_quantized_npi_add")
    .describe(R"code(elemwise_add operator for input dataA and input dataB 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 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")
    .set_num_inputs([](const NodeAttrs& attrs) {
      // A, B, A_min, A_max, B_min, B_max
      return 6;
    })
    // C, C_min, C_max
    .set_num_outputs(3)
    .set_attr<nnvm::FListInputNames>(
        "FListInputNames",
        [](const NodeAttrs& attrs) {
          return std::vector<std::string>{"lhs", "rhs", "lhs_min", "lhs_max", "rhs_min", "rhs_max"};
        })
    .set_attr<nnvm::FListOutputNames>(
        "FListOutputNames",
        [](const NodeAttrs& attrs) {
          return std::vector<std::string>{"output", "min_output", "max_output"};
        })
    .set_attr<nnvm::FInferType>("FInferType", ElemwiseAddType)
    .set_attr<mxnet::FInferShape>("FInferShape", QuantizedBinaryBroadcastShape)
    .set_attr<FCompute>("FCompute<cpu>", QuantizedElemwiseAddForward)
    .set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return true; })
    .add_argument("lhs", "NDArray-or-Symbol", "first input")
    .add_argument("rhs", "NDArray-or-Symbol", "second input")
    .add_argument("lhs_min", "NDArray-or-Symbol", "3rd input")
    .add_argument("lhs_max", "NDArray-or-Symbol", "4th input")
    .add_argument("rhs_min", "NDArray-or-Symbol", "5th input")
    .add_argument("rhs_max", "NDArray-or-Symbol", "6th input");

NNVM_REGISTER_OP(_npi_add).set_attr<FQuantizedOp>("FQuantizedOp", [](const NodeAttrs& attrs) {
  nnvm::ObjectPtr node = nnvm::Node::Create();
  node->attrs.op       = Op::Get("_contrib_quantized_npi_add");
  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