ZhennanQin commented on a change in pull request #12530: Implement mkldnn convolution fusion and quantization. URL: https://github.com/apache/incubator-mxnet/pull/12530#discussion_r220034161
########## File path: src/operator/subgraph/mkldnn/mkldnn_conv.cc ########## @@ -0,0 +1,678 @@ +/* +* 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. +*/ + +#if MXNET_USE_MKLDNN == 1 + +#include <utility> +#include <vector> +#include <string> +#include "../common.h" +#include "../../nn/mkldnn/mkldnn_base-inl.h" +#include "../../nn/mkldnn/mkldnn_ops-inl.h" +#include "../../quantization/quantization_utils.h" +#include "mkldnn_conv-inl.h" + +namespace mxnet { +namespace op { + +template <typename DType> +static void UpdateConvWeightBias(NDArray *weight, NDArray *bias, bool no_bias, + const NDArray &gamma, const NDArray &beta, + const NDArray &mean, const NDArray &variance, + const BatchNormParam *param) { + // TODO(Zhennan): Handle the case weight is not in dims 4. + NDArray update_weight = NDArray(weight->storage_type(), weight->shape(), + weight->ctx(), true, weight->dtype()); + NDArray update_bias = NDArray(beta.storage_type(), beta.shape(), beta.ctx(), + true, beta.dtype()); + DType *weight_ptr = weight->data().dptr<DType>(); + DType *bias_ptr = no_bias ? nullptr : bias->data().dptr<DType>(); + DType *gamma_ptr = gamma.Reorder2Default().data().dptr<DType>(); + DType *beta_ptr = beta.Reorder2Default().data().dptr<DType>(); + DType *mean_ptr = mean.Reorder2Default().data().dptr<DType>(); + DType *var_ptr = variance.Reorder2Default().data().dptr<DType>(); + DType *update_weight_ptr = update_weight.data().dptr<DType>(); + DType *update_bias_ptr = update_bias.data().dptr<DType>(); + size_t channel = gamma.shape()[0]; + size_t offset = weight->shape()[1] * weight->shape()[2] * weight->shape()[3]; +#pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount()) + for (int c = 0; c < static_cast<int>(channel); ++c) { + DType *p1 = reinterpret_cast<DType *>(weight_ptr + c * offset); + DType *p2 = reinterpret_cast<DType *>(update_weight_ptr + c * offset); + DType alpha = (param->fix_gamma ? static_cast<DType>(1.0f) : gamma_ptr[c]) / + sqrt(var_ptr[c] + param->eps); + + if (bias_ptr) + update_bias_ptr[c] = beta_ptr[c] + alpha * (bias_ptr[c] - mean_ptr[c]); + else + update_bias_ptr[c] = beta_ptr[c] - alpha * mean_ptr[c]; + + for (size_t k = 0; k < offset; ++k) { + p2[k] = p1[k] * alpha; + } + } + *weight = update_weight; + *bias = update_bias; +} + +static inline size_t GetInSumIndex(const MKLDNNConvFusionParam ¶m) { + return 2 + (param.full_conv_param.conv_param.no_bias ? 0 : 1) + + (param.full_conv_param.mkldnn_param.with_bn ? 4 : 0); +} + +template <typename DType> +static void QuantizeConvWeightBias(NDArray *weight, NDArray *bias, + bool has_bias, float data_min, + float data_max, + bool weight_channelwise_scale, + std::vector<float> *weight_scales) { + using red::limits::MaxValue; + using red::limits::MinValue; + DType *weight_ptr = weight->data().dptr<DType>(); + NDArray quantized_weight = NDArray(weight->storage_type(), weight->shape(), + weight->ctx(), true, mshadow::kInt8); + int8_t *quan_weight_ptr = quantized_weight.data().dptr<int8_t>(); + size_t channel = weight->shape()[0]; + + // TODO(Zhennan): Handle the case weight is not in dims 4. + size_t offset = weight->shape()[1] * weight->shape()[2] * weight->shape()[3]; + std::vector<DType> weight_c_min(channel, MaxValue<DType>()); + std::vector<DType> weight_c_max(channel, MinValue<DType>()); +#pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount()) + for (int c = 0; c < static_cast<int>(channel); ++c) { + DType *p1 = weight_ptr + c * offset; + for (size_t k = 0; k < offset; ++k) { + if (weight_c_min[c] > p1[k]) + weight_c_min[c] = p1[k]; + if (weight_c_max[c] < p1[k]) + weight_c_max[c] = p1[k]; + } + } + + if (weight_channelwise_scale) { + weight_scales->resize(channel); +#pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount()) + for (int c = 0; c < static_cast<int>(channel); ++c) { + DType weight_range = MaxAbs(weight_c_min[c], weight_c_max[c]); + weight_scales->at(c) = int8_range / weight_range; + DType *fp_ptr = weight_ptr + c * offset; + int8_t *quan_ptr = quan_weight_ptr + c * offset; + for (size_t k = 0; k < offset; ++k) { + quan_ptr[k] = std::round(weight_scales->at(c) * fp_ptr[k]); + } + } + } else { + DType total_min = weight_c_min[0]; + DType total_max = weight_c_max[0]; + for (size_t c = 0; c < channel; ++c) { + if (total_min > weight_c_min[c]) total_min = weight_c_min[c]; + if (total_max < weight_c_max[c]) total_max = weight_c_max[c]; + } + weight_scales->resize(1); + DType weight_range = MaxAbs(total_min, total_max); + weight_scales->at(0) = int8_range / weight_range; +#pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount()) + for (int c = 0; c < static_cast<int>(channel); ++c) { + DType *fp_ptr = weight_ptr + c * offset; + int8_t *quan_ptr = quan_weight_ptr + c * offset; + for (size_t k = 0; k < offset; ++k) { + quan_ptr[k] = std::round(weight_scales->at(0) * fp_ptr[k]); + } + } + } + + *weight = quantized_weight; + if (has_bias) { + DType *bias_ptr = bias->data().dptr<DType>(); + NDArray quantized_bias = NDArray(bias->storage_type(), bias->shape(), + bias->ctx(), true, mshadow::kInt32); + int32_t *quan_bias_ptr = quantized_bias.data().dptr<int32_t>(); + DType data_scale = uint8_range / MaxAbs(data_min, data_max); + for (size_t c = 0; c < channel; ++c) { + auto weight_scale = + weight_channelwise_scale ? weight_scales->at(c) : weight_scales->at(0); + float bias_scale = weight_scale * data_scale; + quan_bias_ptr[c] = std::round(bias_scale * bias_ptr[c]); + } + *bias = quantized_bias; + } +} + +static void ConvFusionFallBackCompute() { + LOG(FATAL) << "Don't know how to do ConvFusionFallBackCompute!"; +} + +static void ConvolutionFusionComputeExCPU(const MKLDNNConvFullParam &full_param, + const OpContext &ctx, + MKLDNNConvForward *fwd, + const std::vector<NDArray> &inputs, + const std::vector<OpReqType> &req, + const std::vector<NDArray> &outputs) { + if (SupportMKLDNNConv(full_param.conv_param, inputs[0])) { + // MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs); + MKLDNNConvolutionForwardFullFeature(full_param, ctx, fwd, inputs, req, outputs); + // MKLDNN_OPCHECK_RUN(ConvolutionCompute<cpu>, attrs, ctx, inputs, req, + // outputs); Review comment: Will remove it. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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