wehub-resource-sync
55 lines
2.2 KiB
C++
55 lines
2.2 KiB
C++
/* 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 "paddle/fluid/framework/data_device_transform.h"
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namespace paddle::framework {
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void TransDataDevice(const DenseTensor &in,
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const Place &dst_place,
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DenseTensor *out) {
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VLOG(3) << "DeviceTransform in, src_place " << in.place()
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<< " dst_place: " << dst_place;
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PADDLE_ENFORCE_NE(
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in.place().GetType(),
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dst_place.GetType(),
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common::errors::Unavailable("Currently, model parallelism is only "
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"supported between CPU and CUDA."));
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// NOTE(zhiqiu): Special case for CPU->NPU, avoid stream sync.
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if (phi::is_cpu_place(in.place())) {
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paddle::framework::TensorCopy(
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in, dst_place, *phi::DeviceContextPool::Instance().Get(dst_place), out);
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return;
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}
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// NOTE(yy): TransDataDevice should wait for computation of input.
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if (!phi::is_cuda_pinned_place(in.place())) {
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phi::DeviceContextPool::Instance().Get(in.place())->Wait();
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phi::DeviceContextPool::Instance().Get(dst_place)->Wait();
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}
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// FIXME(zcd): TransDataDevice is used to transform data from GPU to CPU and
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// the enforced checks have been done in GetDeviceContext, so the
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// `dev_ctx->Wait()` is necessary. But `dev_ctx->Wait()` will make the program
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// slow, especially when the number of elements is little, for example,
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// the elements of learning rate are one and it's CPU side.
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// One solution is to use a CUDA kernel to complete the copy operation when
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// the transforming is from CPU to GPU and the number of elements is little.
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// But the embarrassment is that this solution makes training slower.
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TensorCopySync(in, dst_place, out);
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}
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} // namespace paddle::framework
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