wehub-resource-sync
137 lines
4.9 KiB
C++
137 lines
4.9 KiB
C++
// Copyright (c) 2021 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/fluid/eager/accumulation/accumulation_node.h"
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#include "paddle/fluid/eager/api/utils/global_utils.h"
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#include "paddle/fluid/eager/autograd_meta.h"
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#include "paddle/fluid/eager/eager_tensor.h"
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#include "paddle/fluid/eager/utils.h"
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#include "paddle/fluid/platform/init.h"
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#include "paddle/phi/api/all.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/memory/memcpy.h"
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#include "paddle/phi/core/platform/device_context.h"
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#include "paddle/phi/core/tensor_meta.h"
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namespace eager_test {
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inline paddle::Tensor CreateTensorWithValue(const phi::DDim& ddim,
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const phi::Place& place,
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const phi::DataType& dtype,
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const phi::DataLayout& layout,
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float value,
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bool is_leaf = true) {
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paddle::Tensor out =
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paddle::experimental::full(common::vectorize(ddim),
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paddle::experimental::Scalar(value),
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dtype,
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place);
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auto meta = egr::EagerUtils::autograd_meta(&out);
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if (is_leaf) {
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auto accumulation_node = std::make_shared<egr::GradNodeAccumulation>(out);
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meta->SetGradNode(accumulation_node);
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meta->SetStopGradient(false);
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}
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return out;
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}
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template <typename T>
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bool CompareGradTensorWithValue(const paddle::Tensor& target, T value) {
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egr::AutogradMeta* meta = egr::EagerUtils::unsafe_autograd_meta(target);
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auto grad_dense =
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std::dynamic_pointer_cast<phi::DenseTensor>(meta->Grad().impl());
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T* ptr = grad_dense->data<T>();
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std::vector<T> host_data(grad_dense->numel());
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if (phi::is_gpu_place(grad_dense->place())) {
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#ifdef PADDLE_WITH_CUDA
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phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
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auto* dev_ctx = dynamic_cast<phi::GPUContext*>(pool.Get(phi::GPUPlace()));
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auto stream = dev_ctx->stream();
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paddle::memory::Copy(phi::CPUPlace(),
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host_data.data(),
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phi::GPUPlace(),
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ptr,
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sizeof(T) * grad_dense->numel(),
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stream);
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ptr = host_data.data();
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#endif
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}
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VLOG(6) << "CompareGradTensorWithValue";
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for (int i = 0; i < grad_dense->numel(); i++) {
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PADDLE_ENFORCE(value == ptr[i],
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common::errors::PreconditionNotMet(
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"Numerical Error in Compare Grad Variable With Value of "
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"%d, we expected got value: %f, but got: %f instead. "
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"Please check it later.",
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i,
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value,
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ptr[i]));
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}
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return true;
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}
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template <typename T>
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bool CompareTensorWithValue(const paddle::Tensor& target, T value) {
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// TODO(jiabin): Support Selected Rows later
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auto dense_t = std::dynamic_pointer_cast<phi::DenseTensor>(target.impl());
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T* ptr = dense_t->data<T>();
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std::vector<T> host_data(dense_t->numel());
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if (phi::is_gpu_place(dense_t->place())) {
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#ifdef PADDLE_WITH_CUDA
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phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
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auto* dev_ctx = dynamic_cast<phi::GPUContext*>(pool.Get(phi::GPUPlace()));
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auto stream = dev_ctx->stream();
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paddle::memory::Copy(phi::CPUPlace(),
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host_data.data(),
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phi::GPUPlace(),
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ptr,
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sizeof(T) * dense_t->numel(),
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stream);
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ptr = host_data.data();
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#endif
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}
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VLOG(6) << "CompareTensorWithValue";
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for (int i = 0; i < dense_t->numel(); i++) {
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PADDLE_ENFORCE(value == ptr[i],
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common::errors::PreconditionNotMet(
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"Numerical Error in Compare Grad Variable With Value of "
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"%d, we expected got value: %f, but got: %f instead. "
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"Please check it later.",
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i,
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value,
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ptr[i]));
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}
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return true;
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}
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inline void InitEnv(phi::Place place) {
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// Prepare Device Contexts
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// Init DeviceContextPool
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paddle::framework::InitDevices();
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// Init Tracer Place
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egr::Controller::Instance().SetExpectedPlace(place);
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}
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} // namespace eager_test
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