sneaxiy
7 years ago
parent
2c12af76c5
commit
697ba4b13d
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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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#include "paddle/fluid/operators/reader/py_array_feed_queue.h"
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namespace paddle {
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namespace operators {
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namespace reader {
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class PyArrayReader : public framework::ReaderBase {
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public:
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explicit PyArrayReader(const std::shared_ptr<PyArrayFeedQueue>& queue) {
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PADDLE_ENFORCE(queue != nullptr, "PyArrayFeedQueue must not be null");
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queue_ = queue;
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}
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void ReadNext(std::vector<framework::LoDTensor>* out) override {
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*out = queue_->Dequeue();
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}
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void ReInit() override {
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// PADDLE_THROW("PyArrayReader does not support ReInit()");
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}
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private:
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std::shared_ptr<PyArrayFeedQueue> queue_;
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};
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class CreatePyArrayReaderOp : public framework::OperatorBase {
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public:
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using framework::OperatorBase::OperatorBase;
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private:
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void RunImpl(const framework::Scope& scope,
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const platform::Place& dev_place) const override {
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const std::string& feeder_name = Attr<std::string>("feeder_name");
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auto* feeder_holder_var = scope.FindVar(feeder_name);
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PADDLE_ENFORCE(feeder_holder_var != nullptr,
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"No PyArrayFeedQueue variable with name %s found",
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feeder_name);
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auto* feeder_holder =
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feeder_holder_var->template GetMutable<PyArrayFeedQueueHolder>();
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auto* out = scope.FindVar(Output("Out"))
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->template GetMutable<framework::ReaderHolder>();
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out->Reset(new PyArrayReader(feeder_holder->GetFeeder()));
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}
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};
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class CreatePyArrayReaderOpMaker : public FileReaderMakerBase {
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protected:
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void Apply() override {
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AddAttr<std::string>("feeder_name",
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"Name of the `PyArrayFeedQueueHolder` variable");
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AddComment(R"DOC(
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Create PyArrayReader to accept Python data feeding.
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)DOC");
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}
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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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namespace reader = ::paddle::operators::reader;
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REGISTER_FILE_READER_OPERATOR(create_py_array_reader,
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reader::CreatePyArrayReaderOp,
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reader::CreatePyArrayReaderOpMaker);
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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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@ -0,0 +1,125 @@
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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 <condition_variable> // NOLINT
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#include <deque>
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#include "Python.h"
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#include "paddle/fluid/platform/enforce.h"
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#include "pybind11/pybind11.h"
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namespace paddle {
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namespace operators {
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namespace reader {
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// PyBlockingQueue is designed for PyArrayFeedQueue
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// PyBlockingQueue would release GIL of Python when
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// the queue is full to avoid deadlock.
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template <typename T>
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class PyBlockingQueue {
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public:
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explicit PyBlockingQueue(size_t capacity)
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: capacity_(capacity), closed_(false) {
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PADDLE_ENFORCE_GT(
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capacity_, 0,
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"The capacity of a reader::PyBlockingQueue must be greater than 0.");
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}
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~PyBlockingQueue() { Close(); }
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bool Send(const T& elem) {
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std::unique_lock<std::mutex> lock(mutex_);
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receive_cv_.notify_one();
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if (queue_.size() >= capacity_ && (!closed_)) {
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pybind11::gil_scoped_release release;
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send_cv_.wait(lock, [&] { return queue_.size() < capacity_ || closed_; });
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}
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if (closed_) {
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VLOG(5)
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<< "WARNING: Sending an element to a closed reader::BlockingQueue.";
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return false;
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}
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PADDLE_ENFORCE_LT(queue_.size(), capacity_);
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queue_.push_back(elem);
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return true;
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}
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bool Send(T&& elem) {
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std::unique_lock<std::mutex> lock(mutex_);
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receive_cv_.notify_one();
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if (queue_.size() >= capacity_ && (!closed_)) {
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pybind11::gil_scoped_release release;
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send_cv_.wait(lock, [&] { return queue_.size() < capacity_ || closed_; });
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}
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if (closed_) {
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VLOG(5)
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<< "WARNING: Sending an element to a closed reader::BlokcingQueue.";
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return false;
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}
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PADDLE_ENFORCE_LT(queue_.size(), capacity_);
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queue_.emplace_back(std::move(elem));
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return true;
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}
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bool Receive(T* elem) {
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std::unique_lock<std::mutex> lock(mutex_);
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send_cv_.notify_one();
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receive_cv_.wait(lock, [&] { return !queue_.empty() || closed_; });
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if (!queue_.empty()) {
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PADDLE_ENFORCE_NOT_NULL(elem);
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*elem = queue_.front();
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queue_.pop_front();
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return true;
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} else {
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PADDLE_ENFORCE(closed_);
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return false;
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}
|
||||||
|
}
|
||||||
|
|
||||||
|
void Close() {
|
||||||
|
std::lock_guard<std::mutex> lock(mutex_);
|
||||||
|
closed_ = true;
|
||||||
|
send_cv_.notify_all();
|
||||||
|
receive_cv_.notify_all();
|
||||||
|
}
|
||||||
|
|
||||||
|
bool IsClosed() const {
|
||||||
|
std::lock_guard<std::mutex> lock(mutex_);
|
||||||
|
return closed_;
|
||||||
|
}
|
||||||
|
|
||||||
|
size_t Cap() const {
|
||||||
|
std::lock_guard<std::mutex> lock(mutex_);
|
||||||
|
return capacity_;
|
||||||
|
}
|
||||||
|
|
||||||
|
size_t Size() const {
|
||||||
|
std::lock_guard<std::mutex> lock(mutex_);
|
||||||
|
return queue_.size();
|
||||||
|
}
|
||||||
|
|
||||||
|
private:
|
||||||
|
size_t capacity_;
|
||||||
|
bool closed_;
|
||||||
|
std::deque<T> queue_;
|
||||||
|
|
||||||
|
mutable std::mutex mutex_;
|
||||||
|
mutable std::condition_variable receive_cv_;
|
||||||
|
mutable std::condition_variable send_cv_;
|
||||||
|
};
|
||||||
|
} // namespace reader
|
||||||
|
} // namespace operators
|
||||||
|
} // namespace paddle
|
||||||
Loading…
Reference in new issue