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214 lines
7.8 KiB
214 lines
7.8 KiB
// Copyright (c) 2018 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/fluid/framework/data_layout_transform.h"
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#include <string>
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#include "paddle/fluid/operators/math/math_function.h"
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#ifdef PADDLE_WITH_MKLDNN
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#include "paddle/fluid/platform/mkldnn_reuse.h"
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#endif
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namespace paddle {
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namespace framework {
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std::vector<int> GetAxis(const DataLayout& from, const DataLayout& to) {
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PADDLE_ENFORCE_NE(
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from, to,
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platform::errors::InvalidArgument(
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"Layout transform should transform between different layout."));
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if (from == DataLayout::kNCHW && to == DataLayout::kNHWC) {
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return {0, 2, 3, 1};
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} else if (from == DataLayout::kNHWC && to == DataLayout::kNCHW) {
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return {0, 3, 1, 2};
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} else {
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PADDLE_THROW(
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platform::errors::InvalidArgument("Unsupported layout transform."));
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}
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}
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struct CastDataLayout {
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CastDataLayout(const platform::DeviceContext* ctx,
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const std::vector<int>& axis, const framework::Tensor& in,
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framework::Tensor* out)
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: in_(in), out_(out), ctx_(ctx), axis_(axis) {}
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const framework::Tensor in_;
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framework::Tensor* out_;
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const platform::DeviceContext* ctx_;
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const std::vector<int> axis_;
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template <typename T>
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void apply() {
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auto place = ctx_->GetPlace();
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if (platform::is_cpu_place(place)) {
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operators::math::Transpose<platform::CPUDeviceContext, T, 4> trans4;
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auto* context = static_cast<const platform::CPUDeviceContext*>(ctx_);
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trans4(*context, in_, out_, axis_);
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} else {
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PADDLE_THROW(platform::errors::PreconditionNotMet(
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"Unsupported data layout cast from CPU to GPU."));
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}
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}
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};
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void TransDataLayout(const OpKernelType& kernel_type_for_var,
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const OpKernelType& expected_kernel_type, const Tensor& in,
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Tensor* out) {
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PADDLE_ENFORCE(
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platform::places_are_same_class(kernel_type_for_var.place_,
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expected_kernel_type.place_),
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platform::errors::PreconditionNotMet(
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"TransDataLayout only support DataLayout transform on same place."));
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PADDLE_ENFORCE_EQ(
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arity(in.dims()), 4,
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platform::errors::InvalidArgument(
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"Input dimension arity only can be 4, the input dimension is %s.",
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in.dims()));
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auto& pool = platform::DeviceContextPool::Instance();
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auto src_dim = in.dims();
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std::vector<int64_t> dst_dim;
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auto axis = GetAxis(kernel_type_for_var.data_layout_,
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expected_kernel_type.data_layout_);
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dst_dim.resize(axis.size());
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for (size_t i = 0; i < axis.size(); i++) {
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dst_dim[i] = src_dim[axis[i]];
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}
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out->Resize(make_ddim(dst_dim));
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out->mutable_data(expected_kernel_type.place_, in.type());
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framework::VisitDataType(
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in.type(),
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CastDataLayout(pool.Get(expected_kernel_type.place_), axis, in, out));
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out->set_layout(expected_kernel_type.data_layout_);
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}
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#ifdef PADDLE_WITH_MKLDNN
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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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void* GetDataFromTensor(const Tensor& tensor, mkldnn::memory::data_type type) {
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switch (type) {
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case mkldnn::memory::data_type::f32:
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return platform::to_void_cast(tensor.data<float>());
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case mkldnn::memory::data_type::s8:
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return platform::to_void_cast(tensor.data<int8_t>());
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case mkldnn::memory::data_type::u8:
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return platform::to_void_cast(tensor.data<unsigned char>());
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case mkldnn::memory::data_type::s32:
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return platform::to_void_cast(tensor.data<int32_t>());
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case mkldnn::memory::data_type::bf16:
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return platform::to_void_cast(tensor.data<paddle::platform::bfloat16>());
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default:
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PADDLE_THROW(
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platform::errors::InvalidArgument("Wrong mkldnn type provided."));
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}
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}
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void TransDataLayoutFromMKLDNN(const OpKernelType& kernel_type_for_var,
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const OpKernelType& expected_kernel_type,
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const Tensor& in, Tensor* out) {
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auto in_layout = kernel_type_for_var.data_layout_;
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auto out_layout = expected_kernel_type.data_layout_;
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auto place = expected_kernel_type.place_;
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PADDLE_ENFORCE(
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in_layout == DataLayout::kMKLDNN && out_layout != DataLayout::kMKLDNN,
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platform::errors::InvalidArgument(
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"TransDataLayoutFromMKLDNN only supports transform from MKLDNN to "
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"non-MKLDNN"));
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innerTransDataLayoutFromMKLDNN(
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in_layout,
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paddle::platform::MKLDNNDeviceContext::tls().get_cur_paddle_data_layout(),
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in, out, place);
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}
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void innerTransDataLayoutFromMKLDNN(DataLayout in_layout, DataLayout out_layout,
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const Tensor& in, Tensor* out,
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platform::Place place) {
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PADDLE_ENFORCE_NE(in.format(), MKLDNNMemoryFormat::undef,
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platform::errors::InvalidArgument(
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"Input tensor format is invalid. Input tensor should "
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"have specified memory format."));
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PADDLE_ENFORCE_NE(in.format(), MKLDNNMemoryFormat::any,
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platform::errors::InvalidArgument(
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"Input tensor format is invalid. Input tensor should "
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"have specified memory format."));
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// Set default as NCHW in case not specified
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out_layout =
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out_layout == DataLayout::kAnyLayout ? DataLayout::kNCHW : out_layout;
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auto& pool = platform::DeviceContextPool::Instance();
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auto* dev_ctx = dynamic_cast<platform::MKLDNNDeviceContext*>(pool.Get(place));
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auto& cpu_engine = dev_ctx->GetEngine();
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auto in_tz = paddle::framework::vectorize<int64_t>(in.dims());
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auto out_tz = in_tz;
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memory::data_type in_type = ToMKLDNNDataType(in.type());
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PADDLE_ENFORCE_NE(in_type, memory::data_type::undef,
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platform::errors::InvalidArgument(
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"Input tensor type (%s) is not supported.",
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DataTypeToString(in.type())));
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auto in_format = platform::MKLDNNFormatForSize(in_tz.size(), in.format());
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auto out_format =
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platform::MKLDNNFormatForSize(in_tz.size(), ToMKLDNNFormat(out_layout));
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// output tensor has the same dims as input. Reorder don't change dims
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out->Resize(in.dims());
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if (in_format != out_format) {
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void* in_data = GetDataFromTensor(in, in_type);
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const std::string key =
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platform::CreateKey(in_tz, in_format, out_format, in_type);
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platform::ReorderMKLDNNHandler handler(in_tz, in.type(), in_type, *dev_ctx,
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cpu_engine, key);
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auto reorder_src_memory_p = handler.AcquireSrcMemory(in_format, in_data);
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auto reorder_dst_memory_p =
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handler.AcquireDstMemory(out, out_format, place);
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auto reorder_p =
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handler.AcquireReorder(reorder_dst_memory_p, reorder_src_memory_p);
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mkldnn::stream astream(cpu_engine);
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reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
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astream.wait();
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} else {
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out->ShareDataWith(in);
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}
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// For exepected NHWC data format we need to reshape the Output tensor
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// As MKL-DNN description was in NCHW and paddle is expecting NHWC
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platform::MatchShapeToLayout(out, in_layout, out_layout);
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out->set_layout(DataLayout::kNCHW);
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// reset format since the out tensor will be feed to non-MKLDNN OPkernel
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out->set_format(MKLDNNMemoryFormat::undef);
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}
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#endif
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} // namespace framework
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} // namespace paddle
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