wehub-resource-sync
119 lines
3.8 KiB
C++
119 lines
3.8 KiB
C++
// Copyright (c) 2023 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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#pragma once
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/funcs/tensor_formatter.h"
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namespace phi {
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const char kForward[] = "FORWARD";
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const char kBackward[] = "BACKWARD";
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template <typename Context>
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void ShadowFeedKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int dst_place_type,
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DenseTensor* out) {
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Place target_place;
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switch (dst_place_type) {
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case 0: // CPUPlace
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target_place = CPUPlace();
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break;
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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case 1: // CUDAPlace
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target_place = GPUPlace(backends::gpu::GetCurrentDeviceId());
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break;
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#elif defined(PADDLE_WITH_XPU)
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case 1: // XPUPlace
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target_place = XPUPlace(backends::xpu::GetXPUCurrentDeviceId());
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break;
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#elif defined(PADDLE_WITH_CUSTOM_DEVICE)
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case 1: // CustomPlace
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target_place = dev_ctx.GetPlace();
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break;
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#endif
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default:
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PADDLE_THROW(errors::Unimplemented("dst_place_type: %d is not supported.",
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dst_place_type));
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break;
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}
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if (!(x.has_allocation())) {
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if (target_place == CPUPlace()) {
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dev_ctx.HostAlloc(out, out->dtype());
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} else {
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dev_ctx.Alloc(out, out->dtype());
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}
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return;
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}
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if (x.place() == target_place) {
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out->ShareDataWith(x);
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out->set_lod(x.lod());
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} else {
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Copy<Context>(dev_ctx, x, target_place, true, out);
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}
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}
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template <typename Context>
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void ShadowFeedTensorsKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& xs,
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int dst_place_type,
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std::vector<DenseTensor*> outs) {
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for (size_t i = 0; i < xs.size(); ++i) {
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ShadowFeedKernel<Context>(dev_ctx, *(xs[i]), dst_place_type, outs[i]);
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}
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}
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template <typename Context>
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void PrintKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int first_n,
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const std::string& message,
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int summarize,
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bool print_tensor_name,
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bool print_tensor_type,
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bool print_tensor_shape,
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bool print_tensor_layout,
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bool print_tensor_lod,
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const std::string& print_phase,
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bool is_forward,
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DenseTensor* out) {
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Copy<Context>(dev_ctx, x, dev_ctx.GetPlace(), true, out);
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out->set_lod(x.lod());
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if ((is_forward && print_phase == kBackward) ||
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(!is_forward && print_phase == kForward)) {
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return;
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}
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// TODO(phlrain): support first_n using a input tensor
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// if (first_n > 0 && ++times_ > first_n) return;
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// TODO(phlrain): support printed_var_name
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funcs::TensorFormatter formatter;
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const std::string& name = print_tensor_name ? "var" : "";
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formatter.SetPrintTensorType(print_tensor_type);
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formatter.SetPrintTensorShape(print_tensor_shape);
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formatter.SetPrintTensorLod(print_tensor_lod);
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formatter.SetPrintTensorLayout(print_tensor_layout);
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formatter.SetSummarize(summarize);
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formatter.Print(x, name, message);
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}
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} // namespace phi
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