pengzhao-intel commented on a change in pull request #9552: [REQUEST FOR REVIEW | DO NOT MERGE] Model Quantization with Calibration URL: https://github.com/apache/incubator-mxnet/pull/9552#discussion_r165646442
########## File path: src/operator/quantization/quantized_conv.cc ########## @@ -0,0 +1,171 @@ +/* + * 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) 2017 by Contributors + * \file quantized_conv.cc + * \brief + * \author Ziheng Jiang, Jun Wu +*/ +#include "../nn/convolution-inl.h" + +namespace mxnet { +namespace op { + +// TODO(junwu): Reuse the InferShape function of convolution op after +// this pr is merged: https://github.com/apache/incubator-mxnet/pull/8302 +bool QuantizedConvShape(const nnvm::NodeAttrs& attrs, + std::vector<TShape>* in_shape, + std::vector<TShape>* 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 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"; + + 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, 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; }; + 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, TShape({1})); + SHAPE_ASSIGN_CHECK(*out_shape, 2, TShape({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); + TYPE_ASSIGN_CHECK(*in_type, 0, mshadow::kInt8); + 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; +} + +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(ParamParser<ConvolutionParam>) +.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<nnvm::FInferShape>("FInferShape", QuantizedConvShape) +.set_attr<nnvm::FInferType>("FInferType", QuantizedConvType) +.set_attr<FResourceRequest>("FResourceRequest", + [](const NodeAttrs& attrs) { + return std::vector<ResourceRequest>(1, ResourceRequest::kTempSpace); + }) +.set_attr<FNeedRequantize>("FNeedRequantize", [](const NodeAttrs& attrs) { return true; }) Review comment: I agree we can focus on the accuracy first and then optimized flow :) @wentingj will answer your other questions later. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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