wehub-resource-sync
116 lines
4.7 KiB
C++
116 lines
4.7 KiB
C++
// Copyright (c) 2022 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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#include "paddle/phi/kernels/pool_grad_kernel.h"
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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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bool Pool2dGradCheckIfOneDNNSupport(const KernelContext* dev_ctx) {
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if (dev_ctx->AttrAt<bool>(8) == false) {
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// adaptive
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return true;
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}
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// oneDNN is supporting only unchangeable in size pool window
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auto src_tz = vectorize(dev_ctx->InputAt<DenseTensor>(0).dims());
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const TensorRef& kernel_size_tmp = dev_ctx->AttrAt<TensorRef>(0);
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IntArray kernel_size_array = IntArray(*kernel_size_tmp.Get());
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std::vector<int64_t> kernel_size = kernel_size_array.GetData();
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// Fast but not exhaustive check
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return ((src_tz[src_tz.size() - 1] % kernel_size[1] == 0) &&
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(src_tz[src_tz.size() - 2] % kernel_size[0] == 0));
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}
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template <typename T, typename Context>
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void Pool2dGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out UNUSED,
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const DenseTensor& dout,
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const IntArray& kernel_size,
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const std::vector<int64_t>& strides,
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const std::vector<int64_t>& paddings,
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bool ceil_mode,
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bool exclusive,
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const std::string& data_format UNUSED,
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const std::string& pooling_type,
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bool global_pooling,
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bool adaptive,
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const std::string& padding_algorithm,
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DenseTensor* dx) {
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funcs::PoolingOneDNNHandler<T> handler(dev_ctx,
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pooling_type,
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kernel_size,
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strides,
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paddings,
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global_pooling,
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padding_algorithm,
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ceil_mode,
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exclusive,
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adaptive,
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&x,
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&dout,
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dx);
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auto diff_dst_memory = handler.AcquireDiffDstMemory(&dout);
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auto diff_src_memory = handler.AcquireDiffSrcMemory(dx);
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auto pool_bwd_p = handler.AcquireBackwardPrimitive();
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auto& astream = OneDNNContext::tls().get_stream();
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if (pooling_type == "max") {
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// Max - pooling needs Workspace
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auto workspace_memory = handler.AcquireWorkspaceMemory(dev_ctx, "Out");
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pool_bwd_p->execute(astream,
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{{DNNL_ARG_DIFF_SRC, *diff_src_memory},
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{DNNL_ARG_DIFF_DST, *diff_dst_memory},
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{DNNL_ARG_WORKSPACE, *workspace_memory}});
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} else {
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// Average Pooling
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pool_bwd_p->execute(astream,
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{{DNNL_ARG_DIFF_SRC, *diff_src_memory},
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{DNNL_ARG_DIFF_DST, *diff_dst_memory}});
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}
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astream.wait();
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dx->set_mem_desc(diff_src_memory->get_desc());
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}
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phi::KernelKey PoolOpGradGetKernelTypeForVar(
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const GetKernelTypeForVarContext* dev_ctx) {
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const DenseTensor& tensor = dev_ctx->GetTensor();
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const KernelKey& expected_kernel_type = dev_ctx->GetKernelKey();
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#ifdef PADDLE_WITH_DNNL
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if ((expected_kernel_type.layout() == DataLayout::ONEDNN) &&
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(tensor.layout() != DataLayout::ONEDNN)) {
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const AttributeMap& attrs = dev_ctx->GetAttrs();
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auto it = attrs.find("data_format");
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const std::string data_format = PADDLE_GET_CONST(std::string, it->second);
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return phi::KernelKey(tensor.place(),
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StringToDataLayout(data_format),
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expected_kernel_type.dtype());
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}
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#endif
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return phi::KernelKey(
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tensor.place(), tensor.layout(), expected_kernel_type.dtype());
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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pool2d_grad, OneDNN, ONEDNN, phi::Pool2dGradKernel, float, phi::bfloat16) {
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kernel->get_kerneltype_forvar_fn_ = phi::PoolOpGradGetKernelTypeForVar;
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kernel->check_if_onednn_kernel_support_ = phi::Pool2dGradCheckIfOneDNNSupport;
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}
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