Superjom
8 years ago
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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 "paddle/memory/memcpy.h"
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namespace paddle {
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namespace framework {
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template <typename T>
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inline void Tensor::check_memory_size() const {
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PADDLE_ENFORCE(holder_ != nullptr,
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"Tenosr holds no memory. Call Tensor::mutable_data first.");
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PADDLE_ENFORCE(holder_->size() >= product(dims_) * sizeof(T) + offset_,
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"Tensor's dims_ is out of bound. Call Tensor::mutable_data "
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"first to re-allocate memory.");
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}
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template <typename T>
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inline const T* Tensor::data() const {
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check_memory_size<T>();
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return reinterpret_cast<const T*>(
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reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
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}
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template <typename T>
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inline T* Tensor::data() {
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check_memory_size<T>();
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return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
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offset_);
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}
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template <typename T>
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inline T* Tensor::mutable_data(DDim dims, platform::Place place) {
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static_assert(std::is_pod<T>::value, "T must be POD");
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Resize(dims);
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return mutable_data<T>(place);
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}
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template <typename T>
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inline T* Tensor::mutable_data(platform::Place place) {
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static_assert(std::is_pod<T>::value, "T must be POD");
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PADDLE_ENFORCE(product(dims_) > 0,
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"Tensor's numel must be larger than zero to call "
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"Tensor::mutable_data. Call Tensor::set_dim first.");
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/* some versions of boost::variant don't have operator!= */
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size_t size = product(dims_) * sizeof(T);
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if (holder_ == nullptr || !(holder_->place() == place) ||
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holder_->size() < size + offset_) {
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if (platform::is_cpu_place(place)) {
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holder_.reset(new PlaceholderImpl<T, platform::CPUPlace>(
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boost::get<platform::CPUPlace>(place), size));
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}
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#ifndef PADDLE_ONLY_CPU
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else if (platform::is_gpu_place(place)) {
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holder_.reset(new PlaceholderImpl<T, platform::GPUPlace>(
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boost::get<platform::GPUPlace>(place), size));
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}
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#endif
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offset_ = 0;
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}
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return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
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offset_);
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}
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template <typename T>
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inline void Tensor::ShareDataWith(const Tensor& src) {
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src.check_memory_size<T>();
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*this = src;
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}
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template <typename T>
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inline void Tensor::CopyFrom(const Tensor& src,
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const platform::CPUDeviceContext& ctx) {
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src.check_memory_size<T>();
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Resize(src.dims());
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auto src_place = src.holder_->place();
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auto src_ptr = static_cast<const void*>(src.data<T>());
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auto dst_place = ctx.GetPlace();
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auto dst_ptr = static_cast<void*>(mutable_data<T>(dst_place));
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auto size = product(src.dims_) * sizeof(T);
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if (platform::is_cpu_place(src_place)) {
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memory::Copy(boost::get<platform::CPUPlace>(dst_place), dst_ptr,
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boost::get<platform::CPUPlace>(src_place), src_ptr, size);
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}
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#ifndef PADDLE_ONLY_CPU
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else if (platform::is_gpu_place(src_place)) {
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memory::Copy(boost::get<platform::CPUPlace>(dst_place), dst_ptr,
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boost::get<platform::GPUPlace>(src_place), src_ptr, size, 0);
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}
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#endif
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}
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#ifndef PADDLE_ONLY_CPU
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template <typename T>
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inline void Tensor::CopyFrom(const Tensor& src,
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const platform::CUDADeviceContext& ctx) {
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src.check_memory_size<T>();
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Resize(src.dims());
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auto src_place = src.holder_->place();
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auto src_ptr = static_cast<const void*>(src.data<T>());
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auto dst_place = ctx.GetPlace();
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auto dst_ptr = static_cast<void*>(mutable_data<T>(dst_place));
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auto size = product(src.dims_) * sizeof(T);
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if (platform::is_cpu_place(src_place)) {
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memory::Copy(boost::get<platform::GPUPlace>(dst_place), dst_ptr,
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boost::get<platform::CPUPlace>(src_place), src_ptr, size,
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ctx.stream());
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} else if (platform::is_gpu_place(src_place)) {
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memory::Copy(boost::get<platform::GPUPlace>(dst_place), dst_ptr,
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boost::get<platform::GPUPlace>(src_place), src_ptr, size,
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ctx.stream());
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}
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}
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#endif
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template <typename T>
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inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
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check_memory_size<T>();
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PADDLE_ENFORCE(begin_idx >= 0, "Slice begin index is less than zero.");
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PADDLE_ENFORCE(end_idx <= dims_[0], "Slice end index is out of bound.");
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PADDLE_ENFORCE(begin_idx < end_idx,
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"Begin index must be less than end index.");
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PADDLE_ENFORCE(dims_[0] != 1, "Can not slice a tensor with dims_[0] = 1.");
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int base = product(dims_) / dims_[0];
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Tensor dst;
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dst.holder_ = holder_;
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DDim dst_dims = dims_;
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dst_dims[0] = end_idx - begin_idx;
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dst.Resize(dst_dims);
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dst.offset_ = offset_ + begin_idx * base * sizeof(T);
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return dst;
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}
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inline void Tensor::Resize(const DDim& dims) { dims_ = dims; }
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inline const DDim& Tensor::dims() const { return dims_; }
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} // namespace framework
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} // namespace paddle
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@ -1,15 +0,0 @@
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syntax="proto2";
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package paddle.framework;
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import "op_proto.proto";
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message NetDesc {
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// network identification
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optional string name = 1;
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// operator contains in network
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repeated OpProto operators = 2;
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// network type to run with. e.g "plainNet", "DAG"
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optional string net_type = 3;
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// num worker always
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optional int32 num_workers = 4;
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}
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