wehub-resource-sync
124 lines
4.8 KiB
C++
124 lines
4.8 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_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 Pool2dCheckIfOneDNNSupport(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 Pool2dKernel(const Context& dev_ctx,
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const DenseTensor& x,
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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* out) {
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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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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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auto src_memory = handler.AcquireSrcMemory(&x);
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auto dst_memory = handler.AcquireDstMemory(out);
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auto pool_p = handler.AcquireForwardPrimitive();
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auto& astream = OneDNNContext::tls().get_stream();
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if (is_test == false && pooling_type == "max") {
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// Training
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auto workspace_memory = handler.AcquireWorkspaceMemory(dev_ctx, "Out");
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pool_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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// Inference
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pool_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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phi::KernelKey PoolOpGetKernelTypeForVar(
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const GetKernelTypeForVarContext* dev_ctx) {
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const DenseTensor& tensor = dev_ctx->GetTensor();
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const phi::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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auto dl = StringToDataLayout(data_format);
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// Some models may have intentionally set "AnyLayout" for pool
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// op. Treat this as NCHW (default data_format value)
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if (dl != DataLayout::ANY) {
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return phi::KernelKey(tensor.place(), dl, expected_kernel_type.dtype());
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}
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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(pool2d,
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OneDNN,
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ONEDNN,
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phi::Pool2dKernel,
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float,
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int8_t,
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uint8_t,
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phi::bfloat16) {
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kernel->get_kerneltype_forvar_fn_ = phi::PoolOpGetKernelTypeForVar;
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kernel->check_if_onednn_kernel_support_ = phi::Pool2dCheckIfOneDNNSupport;
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}
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