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111 lines
4.0 KiB
111 lines
4.0 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "mkldnn.hpp"
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#include "paddle/fluid/framework/data_layout_transform.h"
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#include "paddle/fluid/framework/tensor.h"
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#include "paddle/fluid/operators/requantize_op.h"
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#include "paddle/fluid/platform/mkldnn_helper.h"
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namespace paddle {
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namespace operators {
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using dnnl::memory;
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using dnnl::reorder;
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using platform::to_void_cast;
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using Tensor = framework::Tensor;
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template <typename T>
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class ReQuantOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* input = ctx.Input<Tensor>("Input");
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auto scale_in = ctx.Attr<float>("Scale_in");
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auto scale_out = ctx.Attr<float>("Scale_out");
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auto* output = ctx.Output<Tensor>("Output");
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auto& dev_ctx =
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ctx.template device_context<platform::MKLDNNDeviceContext>();
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const auto& engine = dev_ctx.GetEngine();
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auto src_tz = paddle::framework::vectorize(input->dims());
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std::string key = platform::CreateKey(src_tz, scale_in, scale_out,
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ctx.OutputName("Output"));
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const std::string key_prim = key + "@reorder_p";
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const std::string key_src_mem = key + "@src_mem";
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const std::string key_dst_mem = key + "@dst_mem";
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std::shared_ptr<dnnl::memory> src_memory;
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std::shared_ptr<dnnl::memory> dst_memory;
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std::shared_ptr<reorder> reorder_p;
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reorder_p = std::static_pointer_cast<reorder>(dev_ctx.GetBlob(key_prim));
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const T* input_data = input->data<T>();
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T* output_data = output->mutable_data<T>(ctx.GetPlace());
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if (reorder_p == nullptr) {
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dnnl::primitive_attr attri;
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int mask = 0;
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float scale_shift = scale_out / scale_in;
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attri.set_output_scales(mask, {scale_shift});
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auto dst_tz = paddle::framework::vectorize(output->dims());
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dnnl::memory::data_type src_dt =
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paddle::framework::ToMKLDNNDataType(input->type());
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dnnl::memory::data_type dst_dt = src_dt;
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auto src_md =
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platform::MKLDNNMemDesc({src_tz}, src_dt, MKLDNNMemoryFormat::nhwc);
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src_memory = std::make_shared<dnnl::memory>(src_md, engine,
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to_void_cast<T>(input_data));
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auto dst_md =
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platform::MKLDNNMemDesc({dst_tz}, dst_dt, MKLDNNMemoryFormat::nhwc);
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dst_memory = std::make_shared<dnnl::memory>(dst_md, engine,
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to_void_cast<T>(output_data));
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auto reorder_pd =
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reorder::primitive_desc(*src_memory, *dst_memory, attri);
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reorder_p = std::make_shared<reorder>(reorder_pd);
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dev_ctx.SetBlob(key_prim, reorder_p);
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dev_ctx.SetBlob(key_src_mem, src_memory);
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dev_ctx.SetBlob(key_dst_mem, dst_memory);
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} else {
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src_memory =
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std::static_pointer_cast<dnnl::memory>(dev_ctx.GetBlob(key_src_mem));
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src_memory->set_data_handle(to_void_cast<T>(input_data));
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dst_memory =
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std::static_pointer_cast<dnnl::memory>(dev_ctx.GetBlob(key_dst_mem));
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dst_memory->set_data_handle(output_data);
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}
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dnnl::stream astream(engine);
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reorder_p->execute(astream, *src_memory, *dst_memory);
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astream.wait();
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output->set_layout(framework::DataLayout::kMKLDNN);
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output->set_format(platform::GetMKLDNNFormat(*dst_memory));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_KERNEL(requantize, MKLDNN, ::paddle::platform::CPUPlace,
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ops::ReQuantOpKernel<int8_t>, ops::ReQuantOpKernel<uint8_t>);
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