sneaxiy
7 years ago
parent
748e204eff
commit
2dcf0e4e66
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// Copyright (c) 2018 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 <algorithm>
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#include <condition_variable> //NOLINT
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#include <memory>
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#include <mutex> // NOLINT
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#include <vector>
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#include "glog/logging.h"
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/operators/reader/py_blocking_queue.h"
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#include "paddle/fluid/operators/reader/reader_op_registry.h"
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#include "paddle/fluid/pybind/tensor_py.h"
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namespace paddle {
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namespace operators {
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namespace reader {
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using PyTuple = ::pybind11::tuple;
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using PyArray = ::pybind11::array;
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template <typename T>
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using PyArrayT = ::pybind11::array_t<T, ::pybind11::array::c_style |
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::pybind11::array::forcecast>;
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class PyArrayToTensorVisitor : public boost::static_visitor<void> {
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public:
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#define PY_ARRAY_TO_TENSOR_WITH_TYPE(dtype, func_name) \
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pybind::func_name(tensor_, static_cast<const PyArrayT<dtype>&>(py_array_), \
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place)
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#define PY_ARRAY_TO_TENSOR(func_name) \
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if (IsType<size_t>()) { \
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PY_ARRAY_TO_TENSOR_WITH_TYPE(size_t, func_name); \
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} else if (IsType<int64_t>()) { \
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PY_ARRAY_TO_TENSOR_WITH_TYPE(int64_t, func_name); \
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} else if (IsType<int32_t>()) { \
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PY_ARRAY_TO_TENSOR_WITH_TYPE(int32_t, func_name); \
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} else if (IsType<int16_t>()) { \
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PY_ARRAY_TO_TENSOR_WITH_TYPE(int16_t, func_name); \
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} else if (IsType<uint8_t>()) { \
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PY_ARRAY_TO_TENSOR_WITH_TYPE(uint8_t, func_name); \
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} else if (IsType<float>()) { \
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PY_ARRAY_TO_TENSOR_WITH_TYPE(float, func_name); \
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} else if (IsType<double>()) { \
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PY_ARRAY_TO_TENSOR_WITH_TYPE(double, func_name); \
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} else { \
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PADDLE_THROW("unsupported dtype of python array"); \
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}
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PyArrayToTensorVisitor(const PyArray& py_array, framework::Tensor* tensor)
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: py_array_(py_array), tensor_(tensor) {}
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void operator()(const platform::CPUPlace& place) {
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PY_ARRAY_TO_TENSOR(PyCPUTensorSetFromArray);
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}
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void operator()(const platform::CUDAPlace& place) {
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#ifdef PADDLE_WITH_CUDA
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PY_ARRAY_TO_TENSOR(PyCUDATensorSetFromArray);
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#else
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PADDLE_THROW("CUDAPlace is not supported in CPU only version");
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#endif
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}
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void operator()(const platform::CUDAPinnedPlace& place) {
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#ifdef PADDLE_WITH_CUDA
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PY_ARRAY_TO_TENSOR(PyCUDAPinnedTensorSetFromArray);
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#else
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PADDLE_THROW("CUDAPinnedPlace is not supported in CPU only version");
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#endif
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}
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#undef PY_ARRAY_TO_TENSOR
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#undef PY_ARRAY_TO_TENSOR_WITH_TYPE
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private:
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template <typename T>
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inline bool IsType() const {
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return ::pybind11::isinstance<PyArrayT<T>>(py_array_);
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}
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private:
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const PyArray& py_array_;
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framework::Tensor* tensor_;
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};
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class PyArrayFeedQueueHolder;
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// PyArrayFeedQueue must be thread-safe
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class PyArrayFeedQueue {
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friend class PyArrayFeedQueueHolder;
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private:
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PyArrayFeedQueue(size_t capacity, const std::vector<framework::DDim>& dims,
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const platform::Place& place)
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: dims_(dims), place_(place) {
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queue_.reset(
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new PyBlockingQueue<std::vector<framework::LoDTensor>>(capacity));
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}
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public:
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~PyArrayFeedQueue() { Close(); }
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bool Enqueue(const std::vector<PyArray>& py_array_vec) {
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auto lod_tensor_vec = PyArrayVecToLoDTensorVec(py_array_vec);
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VLOG(5) << "Enqueue at address " << reinterpret_cast<void*>(this);
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return queue_->Send(std::move(lod_tensor_vec));
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}
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bool Enqueue(const std::vector<framework::LoDTensor>& tensor_vec) {
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VLOG(5) << "Enqueue at address " << reinterpret_cast<void*>(this);
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return queue_->Send(tensor_vec);
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}
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std::vector<framework::LoDTensor> Dequeue() {
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VLOG(5) << "Dequeue at address " << reinterpret_cast<void*>(this);
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std::vector<framework::LoDTensor> ret;
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return queue_->Receive(&ret) ? ret : std::vector<framework::LoDTensor>();
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}
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inline size_t Size() const { return queue_->Size(); }
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inline size_t Cap() const { return queue_->Cap(); }
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inline bool IsClosed() const { return queue_->IsClosed(); }
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inline void Close() { queue_->Close(); }
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private:
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std::vector<framework::LoDTensor> PyArrayVecToLoDTensorVec(
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const std::vector<PyArray>& py_array_vec) {
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PADDLE_ENFORCE(dims_.size() == py_array_vec.size(),
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"expected input tensor number %d but found %d", dims_.size(),
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py_array_vec.size());
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size_t i = 0;
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if (py_array_vec.size() > 1) {
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size_t dim0 = py_array_vec[0].shape()[0];
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for (size_t j = 1; j < py_array_vec.size(); ++j) {
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PADDLE_ENFORCE(dim0 == py_array_vec[j].shape()[0],
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"0-dim of the %d-th input tensor is %d, but 0-dim of "
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"the 0-th input tensor is %d",
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j, py_array_vec[j].shape()[0], dim0);
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}
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}
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std::vector<framework::LoDTensor> lod_tensor_vec;
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lod_tensor_vec.reserve(py_array_vec.size());
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std::for_each(
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py_array_vec.begin(), py_array_vec.end(), [&](const PyArray& py_array) {
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for (int64_t j = 1; j < dims_[i].size(); ++j) {
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PADDLE_ENFORCE(
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dims_[i][j] == static_cast<int64_t>(py_array.shape()[j]),
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"expected %d-dim of %d-th input tensor is %d but found %d", j,
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i, dims_[i][j], py_array.shape()[j]);
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}
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lod_tensor_vec.emplace_back(framework::LoDTensor());
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PyArrayToTensorVisitor visitor(py_array, &(lod_tensor_vec.back()));
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boost::apply_visitor(visitor, place_);
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++i;
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});
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return lod_tensor_vec;
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}
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std::unique_ptr<PyBlockingQueue<std::vector<framework::LoDTensor>>> queue_;
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std::vector<framework::DDim> dims_;
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platform::Place place_;
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};
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class PyArrayFeedQueueHolder {
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public:
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PyArrayFeedQueueHolder() {}
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void InitOnce(size_t capacity, const std::vector<framework::DDim>& dims,
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const platform::Place& place) {
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PADDLE_ENFORCE(
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feeder_ == nullptr,
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"PyArrayFeedQueueHolder::InitOnce() can only be called once");
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feeder_.reset(new PyArrayFeedQueue(capacity, dims, place));
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}
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std::shared_ptr<PyArrayFeedQueue> GetFeeder() { return feeder_; }
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const std::shared_ptr<PyArrayFeedQueue>& GetFeeder() const { return feeder_; }
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private:
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std::shared_ptr<PyArrayFeedQueue> feeder_;
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};
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} // namespace reader
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} // namespace operators
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} // namespace paddle
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