!4233 Add fp32 conv slide window && Enable int8conv multi thread
Merge pull request !4233 from fuzhiye/tmppull/4233/MERGE
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
fcfc75a30f
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "src/runtime/kernel/arm/fp32/convolution_slidewindow.h"
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#include "src/runtime/kernel/arm/nnacl/common_func.h"
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#include "schema/model_generated.h"
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#include "src/kernel_factory.h"
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#include "include/errorcode.h"
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#include "src/runtime/runtime_api.h"
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namespace mindspore::kernel {
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using mindspore::kernel::KERNEL_ARCH::kCPU;
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using mindspore::lite::KernelRegistrar;
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using mindspore::lite::RET_ERROR;
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using mindspore::lite::RET_INFER_INVALID;
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using mindspore::lite::RET_OK;
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using mindspore::schema::PrimitiveType_Conv2D;
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int ConvolutionSWCPUKernel::InitWeightBias() {
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int kernel_h = conv_param_->kernel_h_;
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int kernel_w = conv_param_->kernel_w_;
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int in_channel = conv_param_->input_channel_;
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int out_channel = conv_param_->output_channel_;
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int ic4 = UP_DIV(in_channel, C4NUM);
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int kernel_plane = kernel_h * kernel_w;
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int oc_block = C4NUM;
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int oc_block_num = UP_DIV(out_channel, C4NUM);
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int pack_weight_size = oc_block_num * oc_block * ic4 * C4NUM * kernel_plane;
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// ==================================init weight======================================//
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auto origin_weight = reinterpret_cast<float *>(inputs_.at(kWeightIndex)->Data());
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packed_weight_ = reinterpret_cast<float *>(malloc(pack_weight_size * sizeof(float)));
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if (packed_weight_ == nullptr) {
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MS_LOG(ERROR) << "malloc packed weight failed.";
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return RET_ERROR;
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}
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memset(packed_weight_, 0, pack_weight_size * sizeof(float));
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for (int oc = 0; oc < out_channel; ++oc) {
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int src_oc_offset = oc * kernel_h * kernel_w * in_channel;
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int dst_oc_offset = oc * kernel_h * kernel_w * ic4 * C4NUM;
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for (int i = 0; i < kernel_h * kernel_w; ++i) {
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const float *src = origin_weight + src_oc_offset + i * in_channel;
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float *dst = packed_weight_ + dst_oc_offset + i * ic4 * C4NUM;
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memcpy(dst, src, in_channel * sizeof(float));
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}
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}
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// ====================================init bias====================================== //
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bias_data_ = reinterpret_cast<float *>(malloc(oc_block_num * oc_block * sizeof(float)));
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if (bias_data_ == nullptr) {
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MS_LOG(ERROR) << "malloc bias failed.";
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return RET_ERROR;
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}
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memset(bias_data_, 0, oc_block_num * oc_block * sizeof(float));
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if (inputs_.size() == kInputSize2) {
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auto ori_bias = reinterpret_cast<float *>(inputs_.at(kBiasIndex)->Data());
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memcpy(bias_data_, ori_bias, out_channel * sizeof(float));
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} else {
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MS_ASSERT(inputs_.size() == kInputSize1);
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}
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return RET_OK;
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}
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int ConvolutionSWCPUKernel::InitTmpBuffer() {
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int in_channel = conv_param_->input_channel_;
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int ic4 = UP_DIV(in_channel, C4NUM);
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int out_channel = conv_param_->output_channel_;
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int oc4 = UP_DIV(out_channel, C4NUM);
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/*=============================nhwc4_input_============================*/
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size_t nhwc4_input_size =
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ic4 * C4NUM * conv_param_->input_batch_ * conv_param_->input_h_ * conv_param_->input_w_ * sizeof(float);
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nhwc4_input_ = malloc(nhwc4_input_size);
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if (nhwc4_input_ == nullptr) {
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MS_LOG(ERROR) << "malloc nhwc4 input failed.";
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return RET_ERROR;
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}
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memset(nhwc4_input_, 0, nhwc4_input_size);
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/*=============================tmp_output_block_============================*/
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tmp_output_block_ = reinterpret_cast<float *>(
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malloc(conv_param_->output_batch_ * conv_param_->output_h_ * conv_param_->output_w_ * oc4 * C4NUM * sizeof(float)));
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if (tmp_output_block_ == nullptr) {
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MS_LOG(ERROR) << "malloc tmp output block failed.";
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return RET_ERROR;
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}
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return RET_OK;
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}
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void ConvolutionSWCPUKernel::ConfigInputOutput() {
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// set output format
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auto output_tensor = outputs_.at(kOutputIndex);
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output_tensor->SetFormat(schema::Format_NHWC);
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// select trans func for input
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auto input_tensor = inputs_.at(kInputIndex);
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auto ret = CheckLayout(input_tensor);
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "Check layout failed.";
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return;
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}
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}
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int ConvolutionSWCPUKernel::Init() {
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if (context_->infer_shape_interrupt_ && !context_->running_) {
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SetNeedReInit();
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return RET_OK;
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}
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auto ret = ConvolutionBaseCPUKernel::Init();
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "ConvolutionBase init failed.";
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return RET_ERROR;
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}
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ret = InitWeightBias();
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "Init weight bias failed.";
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return RET_ERROR;
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}
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// init tmp input, output
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ret = InitTmpBuffer();
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "Init tmp buffer failed.";
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return RET_ERROR;
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}
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// init sliding window param
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slidingWindow_param_ = new SlidingWindowParam;
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InitSlidingParamConv(slidingWindow_param_, conv_param_, C4NUM);
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// config input output
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ConfigInputOutput();
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return RET_OK;
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}
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int ConvolutionSWCPUKernel::ReSize() {
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if (tmp_output_block_ != nullptr) {
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free(tmp_output_block_);
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}
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if (nhwc4_input_ != nullptr) {
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free(nhwc4_input_);
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}
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delete slidingWindow_param_;
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auto ret = ConvolutionBaseCPUKernel::Init();
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "ConvolutionBase init failed.";
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return RET_ERROR;
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}
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// init tmp input, output
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ret = InitTmpBuffer();
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "Init tmp buffer failed.";
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return RET_ERROR;
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}
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// init sliding window param
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slidingWindow_param_ = new SlidingWindowParam;
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InitSlidingParamConv(slidingWindow_param_, conv_param_, C4NUM);
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return RET_OK;
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}
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int ConvolutionSWCPUKernel::RunImpl(int task_id) {
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auto output_addr = reinterpret_cast<float *>(outputs_.at(kOutputIndex)->Data());
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ConvSWFp32(reinterpret_cast<float *>(nhwc4_input_), packed_weight_, reinterpret_cast<float *>(bias_data_),
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tmp_output_block_, output_addr, task_id, conv_param_, slidingWindow_param_);
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return RET_OK;
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}
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int ConvolutionSWImpl(int task_id, LiteParallelGroupEnv *penv, void *cdata) {
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auto conv = reinterpret_cast<ConvolutionSWCPUKernel *>(cdata);
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auto error_code = conv->RunImpl(task_id);
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if (error_code != RET_OK) {
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MS_LOG(ERROR) << "Convolution Sliding Window Run error task_id[" << task_id << "] error_code[" << error_code << "]";
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return RET_ERROR;
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}
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return RET_OK;
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}
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int ConvolutionSWCPUKernel::Run() {
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auto prepare_ret = Prepare();
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if (prepare_ret != RET_OK) {
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MS_LOG(ERROR) << "Prepare fail!ret: " << prepare_ret;
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return prepare_ret;
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}
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auto input_tensor = inputs_.at(kInputIndex);
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auto ori_input_data = input_tensor->Data();
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int in_batch = conv_param_->input_batch_;
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int in_h = conv_param_->input_h_;
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int in_w = conv_param_->input_w_;
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int in_channel = conv_param_->input_channel_;
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convert_func_(ori_input_data, nhwc4_input_, in_batch, in_h * in_w, in_channel);
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int error_code = LiteBackendParallelLaunch(ConvolutionSWImpl, this, thread_count_);
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if (error_code != RET_OK) {
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MS_LOG(ERROR) << "conv error error_code[" << error_code << "]";
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return RET_ERROR;
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}
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return RET_OK;
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}
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} // namespace mindspore::kernel
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@ -0,0 +1,59 @@
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_CONVOLUTION_SLIDEWINDOW_H_
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#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_CONVOLUTION_SLIDEWINDOW_H_
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#include <vector>
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#include "src/lite_kernel.h"
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#include "src/runtime/kernel/arm/nnacl/op_base.h"
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#include "src/runtime/kernel/arm/base/convolution_base.h"
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#include "src/runtime/kernel/arm/nnacl/fp32/conv.h"
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#include "src/runtime/kernel/arm/nnacl/fp32/conv_depthwise.h"
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namespace mindspore::kernel {
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class ConvolutionSWCPUKernel : public ConvolutionBaseCPUKernel {
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public:
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ConvolutionSWCPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
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const std::vector<lite::tensor::Tensor *> &outputs, const lite::Context *ctx,
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const lite::Primitive *primitive)
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: ConvolutionBaseCPUKernel(parameter, inputs, outputs, ctx, primitive) {}
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~ConvolutionSWCPUKernel() override {
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if (packed_weight_ != nullptr) {
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free(packed_weight_);
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}
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if (tmp_output_block_ != nullptr) {
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free(tmp_output_block_);
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}
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delete slidingWindow_param_;
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};
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int Init() override;
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int ReSize() override;
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int Run() override;
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int RunImpl(int task_id);
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int InitWeightBias();
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int InitTmpBuffer();
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void ConfigInputOutput();
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private:
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float *packed_weight_;
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float *tmp_output_block_;
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SlidingWindowParam *slidingWindow_param_;
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};
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} // namespace mindspore::kernel
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#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_CONVOLUTION_SLIDEWINDOW_H_
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