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222 lines
8.2 KiB
222 lines
8.2 KiB
/* 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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#pragma once
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#include <vector>
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#include "paddle/fluid/framework/data_type.h"
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#include "paddle/fluid/framework/dlpack_tensor.h"
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/framework.pb.h"
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#include "paddle/fluid/framework/tensor.h"
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#include "paddle/fluid/platform/device_context.h"
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namespace paddle {
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namespace framework {
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class PrintOptions {
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public:
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static PrintOptions& Instance() {
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static PrintOptions instance;
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return instance;
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}
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~PrintOptions() {}
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PrintOptions(const PrintOptions& o) = delete;
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const PrintOptions& operator=(const PrintOptions& o) = delete;
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int precision = 8;
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int threshold = 1000;
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int edgeitems = 3;
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int linewidth = 75;
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bool sci_mode = false;
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private:
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PrintOptions() {}
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};
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// NOTE(zcd): Because TensorCopy is an async operation, when the src_place
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// and dst_place are two different GPU, to ensure that the operation can
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// be carried out correctly, there is a src_ctx wait operation in TensorCopy.
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// If ctx_place and src_place are the same, src_ctx.Wait() is added
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// after memory::Copy; if ctx_place and dst_place are the same,
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// src_ctx.Wait() is added before memory::Copy.
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class Tensor;
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void TensorCopy(const Tensor& src, const platform::Place& dst_place,
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const platform::DeviceContext& ctx, Tensor* dst);
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// NOTE(zcd): If the src.place() and dst_place are two different GPU,
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// the copy operation is carried out on the dst_place's stream. This is
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// very important, because TensorCopy is an async operator, and in most
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// case, once this copy operator returns, dst is to be used in dst_place's
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// stream, if this copy operation is carried out on the src_place's stream,
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// when dst is used in dst_place's stream the copy operation may be
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// not completed.
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void TensorCopy(const Tensor& src, const platform::Place& dst_place,
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Tensor* dst);
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void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
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Tensor* dst);
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template <typename T>
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void TensorFromVector(const std::vector<T>& src,
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const platform::DeviceContext& ctx, Tensor* dst);
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template <typename T>
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void TensorFromVector(const std::vector<T>& src, Tensor* dst);
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template <typename T>
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void TensorToVector(const Tensor& src, const platform::DeviceContext& ctx,
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std::vector<T>* dst);
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template <typename T>
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void TesnorToVector(const Tensor& src, std::vector<T>* dst);
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// copy the result bool to cpu
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bool TensorContainsNAN(const framework::Tensor& tensor);
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bool TensorContainsInf(const framework::Tensor& tensor);
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bool TensorIsfinite(const framework::Tensor& tensor);
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// store the result bool in gpu tensor, async operation. Faster than above ones.
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void TensorContainsNAN(const framework::Tensor& tensor, framework::Tensor* out);
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void TensorContainsInf(const framework::Tensor& tensor, framework::Tensor* out);
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void TensorIsfinite(const framework::Tensor& tensor, framework::Tensor* out);
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void TensorToStream(std::ostream& os, const Tensor& tensor,
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const platform::DeviceContext& dev_ctx);
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void TensorFromStream(std::istream& is, Tensor* tensor,
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const platform::DeviceContext& dev_ctx);
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void TensorFromStream(std::istream& is, Tensor* tensor,
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const platform::DeviceContext& dev_ctx,
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const size_t& seek, const std::vector<int64_t>& shape);
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// store the bool result tensor in out tensor
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void TensorContainsNANV2(const framework::Tensor& tensor,
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framework::Tensor* out);
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void TensorContainsInfV2(const framework::Tensor& tensor,
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framework::Tensor* out);
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void TensorIsfiniteV2(const framework::Tensor& tensor, framework::Tensor* out);
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// convert dlpack's DLTensor to tensor
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void TensorFromDLPack(const ::DLTensor& dl_tensor, framework::Tensor* dst);
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//
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// The implementation of template functions.
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//
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template <typename T>
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void TensorFromArray(const T* src, const size_t& array_size,
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const platform::DeviceContext& ctx, Tensor* dst) {
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auto dst_place = ctx.GetPlace();
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auto src_ptr = static_cast<const void*>(src);
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platform::CPUPlace src_place;
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dst->Resize({static_cast<int64_t>(array_size)});
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auto dst_ptr = static_cast<void*>(dst->mutable_data<T>(dst_place));
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auto size = array_size * sizeof(T);
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if (platform::is_cpu_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
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src_place, src_ptr, size);
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}
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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else if (platform::is_gpu_place(dst_place)) { // NOLINT
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memory::Copy(
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BOOST_GET_CONST(platform::CUDAPlace, dst_place), dst_ptr, src_place,
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src_ptr, size,
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream());
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}
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#endif
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}
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template <typename T>
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void TensorFromVector(const std::vector<T>& src,
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const platform::DeviceContext& ctx, Tensor* dst) {
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auto dst_place = ctx.GetPlace();
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auto src_ptr = static_cast<const void*>(src.data());
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platform::CPUPlace src_place;
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dst->Resize({static_cast<int64_t>(src.size())});
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auto dst_ptr = static_cast<void*>(dst->mutable_data<T>(dst_place));
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auto size = src.size() * sizeof(T);
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if (platform::is_cpu_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
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src_place, src_ptr, size);
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}
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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else if (platform::is_gpu_place(dst_place)) { // NOLINT
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memory::Copy(
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BOOST_GET_CONST(platform::CUDAPlace, dst_place), dst_ptr, src_place,
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src_ptr, size,
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream());
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}
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#endif
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}
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template <typename T>
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void TensorFromVector(const std::vector<T>& src, Tensor* dst) {
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platform::CPUPlace dst_place = platform::CPUPlace();
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auto src_ptr = static_cast<const void*>(src.data());
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platform::CPUPlace src_place;
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dst->Resize({static_cast<int64_t>(src.size())});
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auto dst_ptr = static_cast<void*>(dst->mutable_data<T>(dst_place));
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auto size = src.size() * sizeof(T);
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memory::Copy(dst_place, dst_ptr, src_place, src_ptr, size);
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}
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template <typename T>
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void TensorToVector(const Tensor& src, const platform::DeviceContext& ctx,
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std::vector<T>* dst) {
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auto src_ptr = static_cast<const void*>(src.data<T>());
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auto size = src.numel() * sizeof(T);
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platform::CPUPlace dst_place;
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dst->resize(src.numel());
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auto dst_ptr = static_cast<void*>(dst->data());
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if (platform::is_cpu_place(src.place())) {
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memory::Copy(dst_place, dst_ptr,
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BOOST_GET_CONST(platform::CPUPlace, src.place()), src_ptr,
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size);
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}
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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else if (platform::is_gpu_place(src.place())) { // NOLINT
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memory::Copy(
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dst_place, dst_ptr, BOOST_GET_CONST(platform::CUDAPlace, src.place()),
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src_ptr, size,
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream());
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}
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#endif
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}
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template <typename T>
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void TensorToVector(const Tensor& src, std::vector<T>* dst) {
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auto src_ptr = static_cast<const void*>(src.data<T>());
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auto size = src.numel() * sizeof(T);
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platform::CPUPlace dst_place;
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dst->resize(src.numel());
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auto dst_ptr = static_cast<void*>(dst->data());
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PADDLE_ENFORCE_EQ(
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platform::is_cpu_place(src.place()), true,
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platform::errors::InvalidArgument(
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"The input tensor should be CPU device, but actually it is in %s.",
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src.place()));
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memory::Copy(dst_place, dst_ptr,
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BOOST_GET_CONST(platform::CPUPlace, src.place()), src_ptr, size);
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}
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std::ostream& operator<<(std::ostream& os, const Tensor& t);
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} // namespace framework
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} // namespace paddle
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