wehub-resource-sync
228 lines
8.1 KiB
C++
228 lines
8.1 KiB
C++
// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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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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#pragma once
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#include "paddle/phi/backends/onednn/onednn_reuse.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename T, typename Context>
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class LRNOneDNNHandler
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: public funcs::
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OneDNNHandlerNoCachingT<T, dnnl::lrn_forward, dnnl::lrn_backward> {
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public:
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LRNOneDNNHandler(int n,
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T k_in,
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T alpha_in,
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T beta_in,
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bool is_test,
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const dnnl::engine onednn_engine,
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phi::Place cpu_place,
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const DenseTensor* input)
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: funcs::
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OneDNNHandlerNoCachingT<T, dnnl::lrn_forward, dnnl::lrn_backward>(
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onednn_engine, cpu_place) {
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// MKL-DNN implements LRN in a caffe way:
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// http://caffe.berkeleyvision.org/tutorial/layers/lrn.html
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// Where sum of squares is divided by size of normalization window
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// this is not the case for PaddlePaddle LRN.
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// Hence we need to compensate for this difference by
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// multipliing alpha by size of window(n)
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const float alpha = static_cast<float>(alpha_in) * static_cast<float>(n);
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const float beta = static_cast<float>(beta_in);
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const float k = static_cast<float>(k_in);
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this->AcquireForwardPrimitiveDescriptor(
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is_test ? dnnl::prop_kind::forward_inference
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: dnnl::prop_kind::forward_training,
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dnnl::algorithm::lrn_across_channels,
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input->mem_desc(),
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input->mem_desc(),
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n,
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alpha,
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beta,
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k);
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}
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LRNOneDNNHandler(int n,
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T k_in,
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T alpha_in,
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T beta_in,
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bool is_test,
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const dnnl::engine onednn_engine,
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phi::Place cpu_place,
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const DenseTensor* in_x,
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const DenseTensor* out_grad,
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DenseTensor* in_x_grad)
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: funcs::
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OneDNNHandlerNoCachingT<T, dnnl::lrn_forward, dnnl::lrn_backward>(
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onednn_engine, cpu_place) {
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PADDLE_ENFORCE_EQ(
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is_test,
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false,
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common::errors::PreconditionNotMet(
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"is_test attribute should be set to False in training phase."));
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const float alpha = static_cast<float>(alpha_in) * static_cast<float>(n);
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const float beta = static_cast<float>(beta_in);
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const float k = static_cast<float>(k_in);
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this->AcquireForwardPrimitiveDescriptor(
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dnnl::prop_kind::forward_training,
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dnnl::algorithm::lrn_across_channels,
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in_x->mem_desc(),
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in_x->mem_desc(),
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n,
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alpha,
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beta,
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k);
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this->AcquireBackwardPrimitiveDescriptor(
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dnnl::algorithm::lrn_across_channels,
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out_grad->mem_desc(),
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out_grad->mem_desc(),
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in_x->mem_desc(),
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n,
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alpha,
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beta,
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k);
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}
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std::shared_ptr<dnnl::memory> AcquireWorkspaceMemory(DenseTensor* workspace,
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const Context& dev_ctx) {
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T* ptr = dev_ctx.template HostAlloc<T>(
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workspace, this->fwd_pd_->workspace_desc().get_size());
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return this->AcquireMemoryFromPrimitive(this->fwd_pd_->workspace_desc(),
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ptr);
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}
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std::shared_ptr<dnnl::memory> AcquireBackwardWorkspaceMemory(
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const DenseTensor* workspace) {
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const T* workspace_data = workspace->data<T>();
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return this->AcquireMemoryFromPrimitive(
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this->fwd_pd_->workspace_desc(),
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funcs::to_void_cast<T>(workspace_data));
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}
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};
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template <typename T, typename Context>
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void LRNMKLDNNOpKernel(const Context& dev_ctx,
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const DenseTensor& x_in,
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int n,
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T k,
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T alpha,
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T beta,
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const std::string& data_format,
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DenseTensor* out,
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DenseTensor* mid_out) {
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bool is_test = dev_ctx.HasDnnAttr("is_test")
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? PADDLE_GET_CONST(bool, dev_ctx.GetDnnAttr("is_test"))
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: false;
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const bool is_float_type = std::is_same<T, float>::value;
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PADDLE_ENFORCE_EQ(
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is_float_type,
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true,
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common::errors::PreconditionNotMet("DNNL LRN must use float data."));
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const auto& onednn_engine = dev_ctx.GetEngine();
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auto x = &x_in;
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auto mid = mid_out;
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LRNOneDNNHandler<T, Context> handler(
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n, k, alpha, beta, is_test, onednn_engine, dev_ctx.GetPlace(), x);
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auto src_memory = handler.AcquireSrcMemory(x);
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auto dst_memory = handler.AcquireDstMemory(out);
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auto lrn_p = handler.AcquireForwardPrimitive();
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auto workspace_memory = handler.AcquireWorkspaceMemory(mid, dev_ctx);
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mid->set_layout(DataLayout::ONEDNN);
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auto& astream = OneDNNContext::tls().get_stream();
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if (!workspace_memory->get_desc().is_zero()) {
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mid->set_mem_desc(workspace_memory->get_desc());
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lrn_p->execute(astream,
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{{DNNL_ARG_SRC, *src_memory},
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{DNNL_ARG_DST, *dst_memory},
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{DNNL_ARG_WORKSPACE, *workspace_memory}});
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} else {
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lrn_p->execute(astream,
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{{DNNL_ARG_SRC, *src_memory}, {DNNL_ARG_DST, *dst_memory}});
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}
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astream.wait();
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out->set_mem_desc(dst_memory->get_desc());
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}
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template <typename T, typename Context>
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void LRNMKLDNNGradOpKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out,
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const DenseTensor& mid_out,
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const DenseTensor& out_grad_in,
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int n,
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T k,
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T alpha,
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T beta,
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const std::string& data_format,
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DenseTensor* x_grad) {
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bool is_test = dev_ctx.HasDnnAttr("is_test")
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? PADDLE_GET_CONST(bool, dev_ctx.GetDnnAttr("is_test"))
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: false;
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const bool is_float_type = std::is_same<T, float>::value;
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PADDLE_ENFORCE_EQ(is_float_type,
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true,
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common::errors::PreconditionNotMet(
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"DNNL LRN GradOpKernel must use float data."));
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auto in_x = &x;
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auto mid = &mid_out;
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auto out_grad = &out_grad_in;
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auto in_x_grad = x_grad;
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const auto& onednn_engine = dev_ctx.GetEngine();
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LRNOneDNNHandler<T, Context> handler(n,
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k,
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alpha,
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beta,
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is_test,
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onednn_engine,
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dev_ctx.GetPlace(),
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in_x,
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out_grad,
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in_x_grad);
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auto src_memory = handler.AcquireSrcMemory(in_x);
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auto workspace = handler.AcquireBackwardWorkspaceMemory(mid);
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auto diff_dst_memory = handler.AcquireDiffDstMemory(out_grad);
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auto diff_src_memory = handler.AcquireDiffSrcMemory(in_x_grad);
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auto lrn_bwd = handler.AcquireBackwardPrimitive();
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auto& astream = OneDNNContext::tls().get_stream();
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lrn_bwd->execute(astream,
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{{DNNL_ARG_SRC, *src_memory},
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{DNNL_ARG_DIFF_DST, *diff_dst_memory},
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{DNNL_ARG_DIFF_SRC, *diff_src_memory},
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{DNNL_ARG_WORKSPACE, *workspace}});
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astream.wait();
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in_x_grad->set_mem_desc(diff_src_memory->get_desc());
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}
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} // namespace phi
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