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126 lines
5.0 KiB
126 lines
5.0 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/dequantize_op.h"
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#include "paddle/fluid/platform/mkldnn_helper.h"
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#include "paddle/fluid/platform/mkldnn_reuse.h"
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namespace paddle {
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namespace operators {
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using mkldnn::memory;
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using mkldnn::primitive;
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using mkldnn::reorder;
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using platform::to_void_cast;
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using Tensor = framework::Tensor;
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using framework::DataLayout;
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using mkldnn::stream;
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using platform::GetMKLDNNFormat;
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std::string CreateKey(const paddle::framework::ExecutionContext& ctx,
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const mkldnn::memory::data_type& src_dt,
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const std::vector<int>& src_tz, const float scale_data) {
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std::string key;
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key.reserve(platform::MKLDNNHandler::MaxKeyLength);
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platform::MKLDNNHandler::AppendKey(&key, std::to_string(src_dt));
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platform::MKLDNNHandler::AppendKeyDims(&key, src_tz);
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platform::MKLDNNHandler::AppendKey(&key, std::to_string(scale_data));
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platform::MKLDNNHandler::AppendKey(&key, ctx.op().Output("Output"));
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return key;
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}
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template <typename T>
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class DeQuantOpKernel : 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_data = ctx.Attr<float>("Scale");
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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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const T* input_data = input->data<T>();
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float* output_data = output->mutable_data<float>(ctx.GetPlace());
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std::vector<float> reorder_scale = {1.0f / scale_data};
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std::vector<primitive> pipeline;
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std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
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std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
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mkldnn::memory::data_type src_dt =
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paddle::framework::ToMKLDNNDataType(input->type());
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mkldnn::memory::format src_fmt = input->format();
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std::string key = CreateKey(ctx, src_dt, src_tz, reorder_scale[0]);
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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<mkldnn::memory> src_memory;
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std::shared_ptr<mkldnn::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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if (reorder_p == nullptr) {
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mkldnn::primitive_attr attri;
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int mask = 0;
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attri.set_output_scales(mask, reorder_scale);
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auto src_md = platform::MKLDNNMemDesc({src_tz}, src_dt, src_fmt);
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auto src_pd = mkldnn::memory::primitive_desc(src_md, engine);
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src_memory =
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std::make_shared<mkldnn::memory>(src_pd, to_void_cast<T>(input_data));
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std::shared_ptr<primitive::at> src_memory_p =
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std::shared_ptr<primitive::at>(new primitive::at(*src_memory));
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auto dst_md = platform::MKLDNNMemDesc({dst_tz}, memory::data_type::f32,
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memory::format::nchw);
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auto dst_pd = mkldnn::memory::primitive_desc(dst_md, engine);
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dst_memory = std::make_shared<mkldnn::memory>(
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dst_pd, to_void_cast<float>(output_data));
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auto reorder_pd = std::shared_ptr<reorder::primitive_desc>(
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new reorder::primitive_desc(src_pd, dst_pd, attri));
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reorder_p = std::shared_ptr<reorder>(
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new reorder(*reorder_pd, *src_memory_p, *dst_memory));
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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 = std::static_pointer_cast<mkldnn::memory>(
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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 = std::static_pointer_cast<mkldnn::memory>(
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dev_ctx.GetBlob(key_dst_mem));
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dst_memory->set_data_handle(output->mutable_data<float>(ctx.GetPlace()));
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}
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pipeline.push_back(*reorder_p);
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stream(stream::kind::eager).submit(pipeline).wait();
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output->set_format(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(dequantize, MKLDNN, ::paddle::platform::CPUPlace,
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ops::DeQuantOpKernel<uint8_t>, ops::DeQuantOpKernel<int8_t>);
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