chore: import upstream snapshot with attribution

paddle/fluid/framework/data_device_transform.cc

@@ -0,0 +1,54 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/framework/data_device_transform.h"
+
+namespace paddle::framework {
+
+void TransDataDevice(const DenseTensor &in,
+                     const Place &dst_place,
+                     DenseTensor *out) {
+  VLOG(3) << "DeviceTransform in, src_place " << in.place()
+          << " dst_place: " << dst_place;
+
+  PADDLE_ENFORCE_NE(
+      in.place().GetType(),
+      dst_place.GetType(),
+      common::errors::Unavailable("Currently, model parallelism is only "
+                                  "supported between CPU and CUDA."));
+
+  // NOTE(zhiqiu): Special case for CPU->NPU, avoid stream sync.
+  if (phi::is_cpu_place(in.place())) {
+    paddle::framework::TensorCopy(
+        in, dst_place, *phi::DeviceContextPool::Instance().Get(dst_place), out);
+    return;
+  }
+
+  // NOTE(yy): TransDataDevice should wait for computation of input.
+  if (!phi::is_cuda_pinned_place(in.place())) {
+    phi::DeviceContextPool::Instance().Get(in.place())->Wait();
+    phi::DeviceContextPool::Instance().Get(dst_place)->Wait();
+  }
+
+  // FIXME(zcd): TransDataDevice is used to transform data from GPU to CPU and
+  // the enforced checks have been done in GetDeviceContext, so the
+  // `dev_ctx->Wait()` is necessary. But `dev_ctx->Wait()` will make the program
+  // slow, especially when the number of elements is little, for example,
+  // the elements of learning rate are one and it's CPU side.
+  // One solution is to use a CUDA kernel to complete the copy operation when
+  // the transforming is from CPU to GPU and the number of elements is little.
+  // But the embarrassment is that this solution makes training slower.
+  TensorCopySync(in, dst_place, out);
+}
+
+}  // namespace paddle::framework