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tensorflow/tensorflow/core/kernels/stage_op.cc
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| /* Copyright 2017 The TensorFlow Authors. All Rights Reserved. | |
| Licensed under the Apache License, Version 2.0 (the "License"); | |
| you may not use this file except in compliance with the License. | |
| You may obtain a copy of the License at | |
| http://www.apache.org/licenses/LICENSE-2.0 | |
| Unless required by applicable law or agreed to in writing, software | |
| distributed under the License is distributed on an "AS IS" BASIS, | |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| See the License for the specific language governing permissions and | |
| limitations under the License. | |
| ==============================================================================*/ | |
| #include <cstddef> | |
| #include <deque> | |
| #include <mutex> | |
| #include <numeric> | |
| #include <vector> | |
| #include "tensorflow/core/framework/op_kernel.h" | |
| #include "tensorflow/core/framework/resource_mgr.h" | |
| #include "tensorflow/core/framework/tensor.h" | |
| #include "tensorflow/core/framework/tensor_shape.h" | |
| #include "tensorflow/core/lib/strings/strcat.h" | |
| #include "tensorflow/core/platform/env.h" | |
| #include "tensorflow/core/platform/mutex.h" | |
| namespace tensorflow { | |
| namespace { | |
| class Buffer : public ResourceBase { | |
| public: | |
| using Tuple = std::vector<Tensor>; | |
| explicit Buffer(std::size_t capacity, std::size_t memory_limit) | |
| : capacity_(capacity), memory_limit_(memory_limit), current_bytes_(0) {} | |
| // the Buffer takes ownership of the Tuple | |
| Status Put(Tuple* tuple) { | |
| std::unique_lock<std::mutex> lock(mu_); | |
| std::size_t tuple_bytes = GetTupleBytes(*tuple); | |
| // Sanity check so that we don't block for ever below | |
| if (memory_limit_ > 0 && tuple_bytes > memory_limit_) { | |
| return Status( | |
| errors::ResourceExhausted("Attempted to insert " | |
| "tensors with combined size of '", | |
| tuple_bytes, | |
| "' bytes into " | |
| "Staging Area with a memory limit of '", | |
| memory_limit_, "'.")); | |
| } | |
| // If buffer capacity is bounded wait until elements have been removed | |
| if (IsBounded()) { | |
| full_cond_var_.wait(lock, [tuple_bytes, this]() { | |
| // If there's a memory limit, check if there's space for insertion | |
| bool memory_limit_valid = | |
| memory_limit_ > 0 ? !WouldExceedMemoryLimit(tuple_bytes) : true; | |
| // If we're configured for capacity check if there's space for insertion | |
| bool capacity_valid = capacity_ > 0 ? !IsCapacityFull() : true; | |
| // Stop waiting upon success for both conditions | |
| return capacity_valid && memory_limit_valid; | |
| }); | |
| } | |
| // Update bytes in the Staging Area | |
| current_bytes_ += tuple_bytes; | |
| // Store tuple | |
| buf_.push_back(std::move(*tuple)); | |
| lock.unlock(); | |
| // Notify all removers. Removers | |
| // may be peeking at a specific element or waiting | |
| // for the element at the front of the deque. | |
| // As we don't know the appropriate one to wake up | |
| // we should wake them all. | |
| non_empty_cond_var_.notify_all(); | |
| return Status::OK(); | |
| } | |
| // Get tuple at front of the buffer | |
| void Get(Tuple* tuple) { // TODO(zhifengc): Support cancellation. | |
| std::unique_lock<std::mutex> lock(mu_); | |
| // Wait for data if the buffer is empty | |
| non_empty_cond_var_.wait(lock, [this]() { return !buf_.empty(); }); | |
| // Move data into the output tuple | |
| *tuple = std::move(buf_.front()); | |
| buf_.pop_front(); | |
| // Update bytes in the Staging Area | |
| current_bytes_ -= GetTupleBytes(*tuple); | |
| notify_inserters_if_bounded(&lock); | |
| } | |
| // Return tuple at index | |
| Status Peek(std::size_t index, Tuple* tuple) { | |
| std::unique_lock<std::mutex> lock(mu_); | |
| // Wait if the requested index is not available | |
| non_empty_cond_var_.wait( | |
| lock, [index, this]() { return index < this->buf_.size(); }); | |
| // Place tensors in the output tuple | |
| for (const auto& tensor : buf_[index]) { | |
| tuple->push_back(tensor); | |
| } | |
| return Status::OK(); | |
| } | |
| // Buffer size | |
| size_t Size() { | |
| std::unique_lock<std::mutex> lock(mu_); | |
| return buf_.size(); | |
| } | |
| void Clear() { | |
| std::unique_lock<std::mutex> lock(mu_); | |
| buf_.clear(); | |
| current_bytes_ = 0; | |
| notify_inserters_if_bounded(&lock); | |
| } | |
| string DebugString() const override { | |
| std::unique_lock<std::mutex> lock(mu_); | |
| return strings::StrCat("Staging size: ", buf_.size()); | |
| } | |
| private: | |
| // If the buffer is configured for bounded capacity, notify | |
| // waiting inserters that space is now available | |
| void notify_inserters_if_bounded(std::unique_lock<std::mutex>* lock) { | |
| if (IsBounded()) { | |
| lock->unlock(); | |
| // Notify all inserters. The removal of an element | |
| // may make memory available for many inserters | |
| // to insert new elements | |
| full_cond_var_.notify_all(); | |
| } | |
| } | |
| // Are there a limit number of elements or a memory limit | |
| // configured on this buffer? | |
| bool IsBounded() const { return capacity_ > 0 || memory_limit_ > 0; } | |
| bool IsCapacityFull() const { return buf_.size() >= capacity_; } | |
| bool WouldExceedMemoryLimit(std::size_t bytes) const { | |
| return bytes + current_bytes_ > memory_limit_; | |
| } | |
| std::size_t GetTupleBytes(const Tuple& tuple) { | |
| return std::accumulate(tuple.begin(), tuple.end(), 0, | |
| [](const std::size_t& lhs, const Tensor& rhs) { | |
| return lhs + rhs.TotalBytes(); | |
| }); | |
| } | |
| std::size_t capacity_; | |
| std::size_t memory_limit_; | |
| std::size_t current_bytes_; | |
| mutable std::mutex mu_; | |
| std::condition_variable non_empty_cond_var_; | |
| std::condition_variable full_cond_var_; | |
| std::deque<Tuple> buf_; | |
| }; | |
| Status GetBuffer(OpKernelContext* ctx, const NodeDef& ndef, Buffer** buf) { | |
| auto rm = ctx->resource_manager(); | |
| ContainerInfo cinfo; | |
| // Lambda for creating the Staging Area | |
| auto create_fn = [&ndef](Buffer** ret) -> Status { | |
| int64_t capacity; | |
| int64_t memory_limit; | |
| TF_RETURN_IF_ERROR(GetNodeAttr(ndef, "capacity", &capacity)); | |
| TF_RETURN_IF_ERROR(GetNodeAttr(ndef, "memory_limit", &memory_limit)); | |
| *ret = new Buffer(capacity, memory_limit); | |
| return Status::OK(); | |
| }; | |
| TF_RETURN_IF_ERROR(cinfo.Init(rm, ndef, true /* use name() */)); | |
| TF_RETURN_IF_ERROR(rm->LookupOrCreate<Buffer>(cinfo.container(), cinfo.name(), | |
| buf, create_fn)); | |
| return Status::OK(); | |
| } | |
| } // namespace | |
| class StageOp : public OpKernel { | |
| public: | |
| explicit StageOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} | |
| void Compute(OpKernelContext* ctx) override { | |
| Buffer* buf = nullptr; | |
| OP_REQUIRES_OK(ctx, GetBuffer(ctx, def(), &buf)); | |
| core::ScopedUnref scope(buf); | |
| Buffer::Tuple tuple; | |
| tuple.reserve(ctx->num_inputs()); | |
| for (int i = 0; i < ctx->num_inputs(); ++i) { | |
| tuple.push_back(ctx->input(i)); | |
| } | |
| OP_REQUIRES_OK(ctx, buf->Put(&tuple)); | |
| } | |
| }; | |
| REGISTER_KERNEL_BUILDER(Name("Stage").Device(DEVICE_CPU), StageOp); | |
| REGISTER_KERNEL_BUILDER(Name("Stage").Device(DEVICE_DEFAULT), StageOp); | |
| class UnstageOp : public OpKernel { | |
| public: | |
| explicit UnstageOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} | |
| // Using this op in such a way that it blocks forever | |
| // is an error. As such cancellation is not handled. | |
| void Compute(OpKernelContext* ctx) override { | |
| Buffer* buf = nullptr; | |
| OP_REQUIRES_OK(ctx, GetBuffer(ctx, def(), &buf)); | |
| core::ScopedUnref scope(buf); | |
| Buffer::Tuple tuple; | |
| buf->Get(&tuple); | |
| OP_REQUIRES( | |
| ctx, tuple.size() == (size_t)ctx->num_outputs(), | |
| errors::InvalidArgument("Mismatch stage/unstage: ", tuple.size(), | |
| " vs. ", ctx->num_outputs())); | |
| for (size_t i = 0; i < tuple.size(); ++i) { | |
| ctx->set_output(i, tuple[i]); | |
| } | |
| } | |
| }; | |
| REGISTER_KERNEL_BUILDER(Name("Unstage").Device(DEVICE_CPU), UnstageOp); | |
| REGISTER_KERNEL_BUILDER(Name("Unstage").Device(DEVICE_DEFAULT), UnstageOp); | |
| class StagePeekOp : public OpKernel { | |
| public: | |
| explicit StagePeekOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} | |
| // Using this op in such a way that it blocks forever | |
| // is an error. As such cancellation is not handled. | |
| void Compute(OpKernelContext* ctx) override { | |
| Buffer* buf = nullptr; | |
| OP_REQUIRES_OK(ctx, GetBuffer(ctx, def(), &buf)); | |
| core::ScopedUnref scope(buf); | |
| Buffer::Tuple tuple; | |
| std::size_t index = ctx->input(0).scalar<int>()(); | |
| OP_REQUIRES_OK(ctx, buf->Peek(index, &tuple)); | |
| OP_REQUIRES( | |
| ctx, tuple.size() == (size_t)ctx->num_outputs(), | |
| errors::InvalidArgument("Mismatch stage/unstage: ", tuple.size(), | |
| " vs. ", ctx->num_outputs())); | |
| for (size_t i = 0; i < tuple.size(); ++i) { | |
| ctx->set_output(i, tuple[i]); | |
| } | |
| } | |
| }; | |
| REGISTER_KERNEL_BUILDER(Name("StagePeek").Device(DEVICE_CPU), StagePeekOp); | |
| REGISTER_KERNEL_BUILDER( | |
| Name("StagePeek").HostMemory("index").Device(DEVICE_DEFAULT), StagePeekOp); | |
| class StageSizeOp : public OpKernel { | |
| public: | |
| explicit StageSizeOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} | |
| // Using this op in such a way that it blocks forever | |
| // is an error. As such cancellation is not handled. | |
| void Compute(OpKernelContext* ctx) override { | |
| Buffer* buf = nullptr; | |
| OP_REQUIRES_OK(ctx, GetBuffer(ctx, def(), &buf)); | |
| core::ScopedUnref scope(buf); | |
| // Allocate size output tensor | |
| Tensor* size = nullptr; | |
| OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &size)); | |
| // Set it to the actual size | |
| size->scalar<int32>().setConstant(buf->Size()); | |
| } | |
| }; | |
| REGISTER_KERNEL_BUILDER(Name("StageSize").Device(DEVICE_CPU), StageSizeOp); | |
| REGISTER_KERNEL_BUILDER( | |
| Name("StageSize").HostMemory("size").Device(DEVICE_DEFAULT), StageSizeOp); | |
| class StageClearOp : public OpKernel { | |
| public: | |
| explicit StageClearOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} | |
| // Using this op in such a way that it blocks forever | |
| // is an error. As such cancellation is not handled. | |
| void Compute(OpKernelContext* ctx) override { | |
| Buffer* buf = nullptr; | |
| OP_REQUIRES_OK(ctx, GetBuffer(ctx, def(), &buf)); | |
| core::ScopedUnref scope(buf); | |
| buf->Clear(); | |
| } | |
| }; | |
| REGISTER_KERNEL_BUILDER(Name("StageClear").Device(DEVICE_CPU), StageClearOp); | |
| REGISTER_KERNEL_BUILDER(Name("StageClear").Device(DEVICE_DEFAULT), | |
| StageClearOp); | |
| } // namespace tensorflow |