ciyongch commented on a change in pull request #17147: [WIP] Quantized Elemwise Mul Operator URL: https://github.com/apache/incubator-mxnet/pull/17147#discussion_r360804532
########## File path: src/operator/quantization/quantized_elemwise_mul.cc ########## @@ -0,0 +1,265 @@ +/* + * 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. + */ + +/*! + * Copyright (c) 2016 by Contributors + * \file quantized_elemwise_mul.cc + * \brief CPU Implementation of basic elementwise binary broadcast operators + */ +#include <mxnet/op_attr_types.h> +#include "../tensor/elemwise_binary_op-inl.h" +#include "./quantized_elemwise_mul-inl.h" +#include "./quantization_utils.h" + +namespace mxnet { +namespace op { + +DMLC_REGISTER_PARAMETER(QuantizeElemwiseMulParam); + +static std::vector<std::string> QuantizedElemwiseMulOutputNames(const NodeAttrs &attrs) { + const QuantizeElemwiseMulParam& params = nnvm::get<QuantizeElemwiseMulParam>(attrs.parsed); + if (params.enable_float_output) + return std::vector<std::string>{"output"}; + else + return std::vector<std::string>{"output", "min_output", "max_output"}; +} + +inline bool QuantizedElemwiseMulOpShape(const nnvm::NodeAttrs& attrs, + mxnet::ShapeVector *in_attrs, + mxnet::ShapeVector *out_attrs) { + using namespace mshadow; + const QuantizeElemwiseMulParam& params = nnvm::get<QuantizeElemwiseMulParam>(attrs.parsed); + const mxnet::TShape &lshape = (*in_attrs)[quantized_elemwise_mul::kLhs]; + const mxnet::TShape &rshape = (*in_attrs)[quantized_elemwise_mul::kRhs]; + if (!ndim_is_known(lshape) || !ndim_is_known(rshape)) return false; + CHECK_EQ(lshape.ndim(), rshape.ndim()) << "Currently, quantized elemwise multiply doesn't support broadcast."; + for (int i = 0; i < lshape.ndim(); ++i) { + CHECK_EQ(lshape[i], rshape[i]); + } + SHAPE_ASSIGN_CHECK(*in_attrs, quantized_elemwise_mul::kLhsMin, mxnet::TShape(1, 1)); + SHAPE_ASSIGN_CHECK(*in_attrs, quantized_elemwise_mul::kLhsMax, mxnet::TShape(1, 1)); + SHAPE_ASSIGN_CHECK(*in_attrs, quantized_elemwise_mul::kRhsMin, mxnet::TShape(1, 1)); + SHAPE_ASSIGN_CHECK(*in_attrs, quantized_elemwise_mul::kRhsMax, mxnet::TShape(1, 1)); + out_attrs->clear(); + + mxnet::TShape oshape(lshape); + out_attrs->push_back(oshape); + if (!params.enable_float_output) { + out_attrs->push_back(mxnet::TShape(1, 1)); + out_attrs->push_back(mxnet::TShape(1, 1)); + } + return shape_is_known(oshape); +} + +inline bool QuantizedElemwiseMulOpType(const nnvm::NodeAttrs& attrs, + std::vector<int> *in_type, + std::vector<int> *out_type) { + const QuantizeElemwiseMulParam& params = nnvm::get<QuantizeElemwiseMulParam>(attrs.parsed); + for (int i = 0; i < 2; ++i) { + if (in_type->at(i) == mshadow::kInt8) { + TYPE_ASSIGN_CHECK(*in_type, i, mshadow::kInt8); + } else { + LOG(ERROR) << "currently, quantized elemwise mul only support int8 inputs."; + } + } + TYPE_ASSIGN_CHECK(*in_type, 2, mshadow::kFloat32); + TYPE_ASSIGN_CHECK(*in_type, 3, mshadow::kFloat32); + TYPE_ASSIGN_CHECK(*in_type, 4, mshadow::kFloat32); + TYPE_ASSIGN_CHECK(*in_type, 5, mshadow::kFloat32); + + int dtype = mshadow::kInt32; + if (params.max_calib_range.has_value() && params.min_calib_range.has_value()) { + dtype = mshadow::kInt8; + } + if (!params.enable_float_output) { + TYPE_ASSIGN_CHECK(*out_type, 0, dtype); + TYPE_ASSIGN_CHECK(*out_type, 1, mshadow::kFloat32); + TYPE_ASSIGN_CHECK(*out_type, 2, mshadow::kFloat32); + } else { + TYPE_ASSIGN_CHECK(*out_type, 0, mshadow::kFloat32); + } + return true; +} + +inline bool QuantizedElemwiseMulOpStorageType(const nnvm::NodeAttrs& attrs, + int dev_mask, + DispatchMode* dispatch_mode, + std::vector<int> *in_attrs, + std::vector<int> *out_attrs) { + using namespace common; + *dispatch_mode = DispatchMode::kFCompute; + + for (auto &v : *out_attrs) { + v = kDefaultStorage; + if (common::stype_string(v).compare("unknown") == 0) { + return false; + } + } + + for (auto &v : *in_attrs) { + v = kDefaultStorage; + if (common::stype_string(v).compare("unknown") == 0) { + return false; + } + } + return true; +} + +void QuantizedElemwiseMulOpForward(const nnvm::NodeAttrs &attrs, + const OpContext &ctx, + const std::vector<TBlob> &inputs, + const std::vector<OpReqType> &req, + const std::vector<TBlob> &outputs) { + const QuantizeElemwiseMulParam& params = nnvm::get<QuantizeElemwiseMulParam>(attrs.parsed); + using namespace mxnet_op; + + float lhs_min = inputs[quantized_elemwise_mul::kLhsMin].dptr<float>()[0]; + float lhs_max = inputs[quantized_elemwise_mul::kLhsMax].dptr<float>()[0]; + float rhs_min = inputs[quantized_elemwise_mul::kRhsMin].dptr<float>()[0]; + float rhs_max = inputs[quantized_elemwise_mul::kRhsMax].dptr<float>()[0]; + + float cached_output_min_ = 0.f; + float cached_output_max_ = 0.f; + float out_data_scale = 1.f; + float out_scale = 1.f; + // output default set as int32 + float output_data_range = kInt32Range; + // dataA && dataB are uint8 + if (outputs[quantized_elemwise_mul::kOut].type_flag_ == mshadow::kInt8) { + output_data_range = kInt8Range; + } else { + output_data_range = kInt32Range; + } + if (!params.enable_float_output) { + if (params.max_calib_range.has_value() && params.min_calib_range.has_value()) { + cached_output_min_ = params.min_calib_range.value(); + cached_output_max_ = params.max_calib_range.value(); + out_data_scale = output_data_range / MaxAbs(cached_output_min_, cached_output_max_); + auto lhs_scale = kInt8Range / MaxAbs(lhs_min, lhs_max); + auto rhs_scale = kInt8Range / MaxAbs(rhs_min, rhs_max); + out_scale = out_data_scale / lhs_scale / rhs_scale; + } else { + Stream<cpu> *s = ctx.get_stream<cpu>(); + if (inputs[quantized_elemwise_mul::kLhs].type_flag_ == mshadow::kInt8 && + inputs[quantized_elemwise_mul::kRhs].type_flag_ == mshadow::kInt8) { + mxnet_op::Kernel<QuantizationRangeForS8S8MultiplicationStruct, cpu>::Launch( + s, 1, &cached_output_min_, &cached_output_max_, &lhs_min, &lhs_max, &rhs_min, &rhs_max); + } else { + LOG(ERROR) << "lhs and rhs only support iny8 dtype."; + } + } + } else { + auto lhs_scale = kInt8Range / MaxAbs(lhs_min, lhs_max); + auto rhs_scale = kInt8Range / MaxAbs(rhs_min, rhs_max); + out_scale = 1.0 / lhs_scale / rhs_scale; + } + + size_t out_size = outputs[quantized_elemwise_mul::kOut].Size(); + auto *input_l = inputs[quantized_elemwise_mul::kLhs].dptr<int8_t>(); + auto *input_r = inputs[quantized_elemwise_mul::kRhs].dptr<int8_t>(); + // TODO(Xinyu): a temp solution to enable Elemwise INT8 computation, + // will be refactored after the DNNL primitive is done. + if (!params.enable_float_output) { + if (params.max_calib_range.has_value() && params.min_calib_range.has_value()) { + typedef int8_t out_type; + auto *out_data = outputs[quantized_elemwise_mul::kOut].dptr<out_type>(); + #pragma omp simd + for (size_t i = 0; i < out_size; ++i) { + const int8_t a = input_l[i]; + const int8_t b = input_r[i]; + out_data[i] = static_cast<out_type>(a * b * out_scale); + } + } else { + typedef int32_t out_type; + auto *out_data = outputs[quantized_elemwise_mul::kOut].dptr<out_type>(); + #pragma omp simd + for (size_t i = 0; i < out_size; ++i) { + const int8_t a = input_l[i]; + const int8_t b = input_r[i]; + out_data[i] = static_cast<out_type>(a * b * out_scale); + } + } + } else { + typedef float_t out_type; + auto *out_data = outputs[quantized_elemwise_mul::kOut].dptr<out_type>(); +#pragma omp simd + for (size_t i = 0; i < out_size; ++i) { + const int8_t a = input_l[i]; + const int8_t b = input_r[i]; + out_data[i] = static_cast<out_type>(a * b * out_scale); + } + } + + if (!params.enable_float_output) { + outputs[quantized_elemwise_mul::kOutMin].dptr<float>()[0] = cached_output_min_; + outputs[quantized_elemwise_mul::kOutMax].dptr<float>()[0] = cached_output_max_; + } +} + +NNVM_REGISTER_OP(_contrib_quantized_elemwise_mul) +.describe(R"code(Multiplies arguments int8 element-wise. +)code" ADD_FILELINE) +.set_num_inputs(6) +.set_num_outputs([](const NodeAttrs& attrs) { + const QuantizeElemwiseMulParam& params = nnvm::get<QuantizeElemwiseMulParam>(attrs.parsed); + return (!params.enable_float_output) ? 3 : 1; +}) +.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", QuantizedElemwiseMulOutputNames) +.set_attr<mxnet::FInferShape>("FInferShape", QuantizedElemwiseMulOpShape) +.set_attr<nnvm::FInferType>("FInferType", QuantizedElemwiseMulOpType) +.set_attr<FInferStorageType>("FInferStorageType", QuantizedElemwiseMulOpStorageType) +.set_attr<FResourceRequest>("FResourceRequest", + [](const NodeAttrs& attrs) { + return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; + }) +.set_attr<FCompute>("FCompute<cpu>", QuantizedElemwiseMulOpForward) +// 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) { 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", "Minimum value of first input.") +.add_argument("lhs_max", "NDArray-or-Symbol", "Maximum value of first input.") +.add_argument("rhs_min", "NDArray-or-Symbol", "Minimum value of second input.") +.add_argument("rhs_max", "NDArray-or-Symbol", "Maximum value of second input.") +.set_attr_parser(ParamParser<QuantizeElemwiseMulParam>) +.add_arguments(QuantizeElemwiseMulParam::__FIELDS__()); + +NNVM_REGISTER_OP(elemwise_mul) +.set_attr<FQuantizable>("FQuantizable", [](const NodeAttrs& attrs) { + return QuantizeType::kMust; Review comment: Suppose this ops is `kSupport` instead of `kMust`, right? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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