/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. Licensed 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. */ #include "mkldnn.hpp" #include "paddle/fluid/framework/tensor.h" #include "paddle/fluid/platform/mkldnn_helper.h" #include "paddle/fluid/operators/quantize_op.h" namespace paddle { namespace operators { using mkldnn::memory; using mkldnn::primitive; using mkldnn::reorder; using platform::to_void_cast; using Tensor = framework::Tensor; using framework::DataLayout; using mkldnn::stream; using platform::GetMKLDNNFormat; template class QuantOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto* input = ctx.Input("Input"); auto* scale = ctx.Input("Scale"); auto* output = ctx.Output("Output"); std::cout<<"this is quantize op!!!!!!!!!!!!!!"<(); const auto& engine = dev_ctx.GetEngine(); std::vector pipeline; std::vector src_tz = paddle::framework::vectorize2int(input->dims()); std::vector dst_tz = paddle::framework::vectorize2int(output->dims()); const T* input_data = input->data(); T* output_data = output->mutable_data(ctx.GetPlace()); std::vector scale_data = {*(scale->data())}; mkldnn::primitive_attr attri; int mask = 0; attri.set_output_scales(mask, scale_data); auto src_md = platform::MKLDNNMemDesc( {src_tz}, memory::data_type::f32, input->format()); auto src_pd = mkldnn::memory::primitive_desc(src_md, engine); auto src_memory = std::make_shared(src_pd, to_void_cast(input_data)); std::shared_ptr src_memory_p = std::shared_ptr(new primitive::at(*src_memory)); auto dst_md = platform::MKLDNNMemDesc( {dst_tz}, memory::data_type::u8, memory::format::nhwc); auto dst_pd = mkldnn::memory::primitive_desc(dst_md, engine); auto dst_memory = mkldnn::memory(dst_pd, to_void_cast(output_data)); auto reorder_pd = std::shared_ptr( new reorder::primitive_desc(dst_pd, src_pd, attri)); auto reorder_p= std::shared_ptr(new reorder(*reorder_pd, *src_memory_p, dst_memory)); pipeline.push_back(*reorder_p); stream(stream::kind::eager).submit(pipeline).wait(); output->set_layout(DataLayout::kMKLDNN); output->set_format(GetMKLDNNFormat(dst_memory)); } }; framework::OpKernelType QuantOp::GetExpectedKernelType(const framework::ExecutionContext& ctx) const { framework::LibraryType library_{framework::LibraryType::kPlain}; std::string data_format = ctx.Attr("data_format"); framework::DataLayout layout_ = framework::StringToDataLayout(data_format); #ifdef PADDLE_WITH_MKLDNN if (library_ == framework::LibraryType::kPlain && platform::CanMKLDNNBeUsed(ctx)) { library_ = framework::LibraryType::kMKLDNN; layout_ = framework::DataLayout::kMKLDNN; } #endif return framework::OpKernelType( framework::ToDataType(ctx.Input("Input")->type()),ctx.GetPlace(),layout_, library_); } void QuantOpMaker::Make() { AddInput("Input","input"); AddInput("Scale","scale..."); AddOutput("Output","output"); AddComment(R"DOC( This op will quantize data from FP32 to INT8 )DOC"); } } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OPERATOR(quantize, ops::QuantOp, ops::QuantOpMaker, paddle::framework::DefaultGradOpDescMaker); REGISTER_OP_KERNEL(quantize, MKLDNN, ::paddle::platform::CPUPlace, ops::QuantOpKernel);