| /* Copyright 2016 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. |
| ==============================================================================*/ |
| // See docs in ../ops/data_flow_ops.cc. |
| |
| #include <deque> |
| #include <queue> |
| #include <vector> |
| |
| #include "tensorflow/core/framework/node_def.pb.h" |
| #include "tensorflow/core/framework/tensor.h" |
| #include "tensorflow/core/framework/tensor_shape.h" |
| #include "tensorflow/core/framework/types.h" |
| #include "tensorflow/core/kernels/priority_queue.h" |
| #include "tensorflow/core/kernels/queue_base.h" |
| #include "tensorflow/core/lib/core/errors.h" |
| #include "tensorflow/core/lib/gtl/priority_queue_util.h" |
| #include "tensorflow/core/platform/logging.h" |
| #include "tensorflow/core/platform/mutex.h" |
| #include "tensorflow/core/platform/types.h" |
| #include "tensorflow/core/util/batch_util.h" |
| |
| namespace tensorflow { |
| |
| PriorityQueue::PriorityQueue(int32 capacity, |
| const DataTypeVector& component_dtypes, |
| const std::vector<TensorShape>& component_shapes, |
| const string& name) |
| : TypedQueue(capacity, component_dtypes, component_shapes, name) {} |
| |
| Status PriorityQueue::Initialize() { |
| Status s = TypedQueue::Initialize(); |
| if (!s.ok()) return s; |
| |
| mutex_lock lock(mu_); |
| if (component_dtypes_[0] != DT_INT64) { |
| return errors::InvalidArgument( |
| "PriorityQueue priority index component must be type int64, but " |
| "dtype is: ", |
| DataTypeString(component_dtypes_[0])); |
| } |
| if (specified_shapes() && !TensorShapeUtils::IsScalar(component_shapes_[0])) { |
| return errors::InvalidArgument( |
| "PriorityQueue priority index component must be a scalar, but shape " |
| "is: ", |
| component_shapes_[0].DebugString()); |
| } |
| return Status::OK(); |
| } |
| |
| void PriorityQueue::DequeueLocked(OpKernelContext* ctx, Tuple* tuple) { |
| DCHECK_GT(queues_[0].size(), 0); |
| (*tuple).reserve(num_components()); |
| for (int i = 0; i < num_components(); ++i) { |
| PersistentTensor persistent_tensor = gtl::ConsumeTop(&queues_[i]).second; |
| (*tuple).push_back(*persistent_tensor.AccessTensor(ctx)); |
| } |
| } |
| |
| void PriorityQueue::TryEnqueue(const Tuple& tuple, OpKernelContext* ctx, |
| DoneCallback callback) { |
| CancellationManager* cm = ctx->cancellation_manager(); |
| CancellationToken token = cm->get_cancellation_token(); |
| bool already_cancelled; |
| { |
| mutex_lock l(mu_); |
| already_cancelled = !cm->RegisterCallback( |
| token, [this, cm, token]() { Cancel(kEnqueue, cm, token); }); |
| if (!already_cancelled) { |
| enqueue_attempts_.emplace_back( |
| 1, callback, ctx, cm, token, |
| [tuple, this](Attempt* attempt) EXCLUSIVE_LOCKS_REQUIRED(mu_) { |
| if (closed_) { |
| attempt->context->SetStatus( |
| errors::Cancelled("PriorityQueue '", name_, "' is closed.")); |
| return kComplete; |
| } |
| if (queues_[0].size() < static_cast<size_t>(capacity_)) { |
| if (!TensorShapeUtils::IsScalar(tuple[0].shape())) { |
| attempt->context->SetStatus(errors::InvalidArgument( |
| "Expected the priority element to be a scalar, but " |
| "received shape: ", |
| tuple[0].shape().DebugString())); |
| return kComplete; |
| } |
| const int64 priority = tuple[0].scalar<int64>()(); |
| for (int i = 0; i < num_components(); ++i) { |
| queues_[i].emplace(priority, PersistentTensor(tuple[i])); |
| } |
| return kComplete; |
| } else { |
| return kNoProgress; |
| } |
| }); |
| } |
| } |
| if (!already_cancelled) { |
| FlushUnlocked(); |
| } else { |
| ctx->SetStatus(errors::Cancelled("Enqueue operation was cancelled")); |
| callback(); |
| } |
| } |
| |
| /* static */ |
| Status PriorityQueue::GetElementComponentFromBatch( |
| const PriorityQueue::Tuple& tuple, int index, int component, |
| OpKernelContext* ctx, PersistentTensor* out_tensor) { |
| TensorShape element_shape(tuple[component].shape()); |
| element_shape.RemoveDim(0); |
| Tensor* element_access = nullptr; |
| TF_RETURN_IF_ERROR(ctx->allocate_persistent( |
| tuple[component].dtype(), element_shape, out_tensor, &element_access)); |
| TF_RETURN_IF_ERROR( |
| batch_util::CopySliceToElement(tuple[component], element_access, index)); |
| return Status::OK(); |
| } |
| |
| void PriorityQueue::TryEnqueueMany(const Tuple& tuple, OpKernelContext* ctx, |
| DoneCallback callback) { |
| const int64 batch_size = tuple[0].dim_size(0); |
| if (batch_size == 0) { |
| callback(); |
| return; |
| } |
| |
| CancellationManager* cm = ctx->cancellation_manager(); |
| CancellationToken token = cm->get_cancellation_token(); |
| bool already_cancelled; |
| { |
| mutex_lock l(mu_); |
| already_cancelled = !cm->RegisterCallback( |
| token, [this, cm, token]() { Cancel(kEnqueue, cm, token); }); |
| if (!already_cancelled) { |
| enqueue_attempts_.emplace_back( |
| batch_size, callback, ctx, cm, token, |
| [tuple, this, ctx](Attempt* attempt) EXCLUSIVE_LOCKS_REQUIRED(mu_) { |
| if (closed_) { |
| attempt->context->SetStatus( |
| errors::Cancelled("PriorityQueue '", name_, "' is closed.")); |
| return kComplete; |
| } |
| RunResult result = kNoProgress; |
| while (queues_[0].size() < static_cast<size_t>(capacity_)) { |
| result = kProgress; |
| const int index = |
| tuple[0].dim_size(0) - attempt->elements_requested; |
| |
| PersistentTensor priority_element; |
| attempt->context->SetStatus(GetElementComponentFromBatch( |
| tuple, index, 0, attempt->context, &priority_element)); |
| if (!attempt->context->status().ok()) return kComplete; |
| Tensor* priority_tensor = priority_element.AccessTensor(ctx); |
| if (!TensorShapeUtils::IsScalar(priority_tensor->shape())) { |
| attempt->context->SetStatus(errors::InvalidArgument( |
| "Expected the priority element to be a scalar, but " |
| "received shape: ", |
| priority_tensor->shape().DebugString())); |
| return kComplete; |
| } |
| const int64 priority = priority_tensor->scalar<int64>()(); |
| for (int i = 0; i < num_components(); ++i) { |
| PersistentTensor element; |
| attempt->context->SetStatus(GetElementComponentFromBatch( |
| tuple, index, i, attempt->context, &element)); |
| if (!attempt->context->status().ok()) return kComplete; |
| queues_[i].emplace(priority, element); |
| } |
| --attempt->elements_requested; |
| if (attempt->elements_requested == 0) { |
| return kComplete; |
| } |
| } |
| return result; |
| }); |
| } |
| } |
| if (!already_cancelled) { |
| FlushUnlocked(); |
| } else { |
| ctx->SetStatus(errors::Cancelled("Enqueue operation was cancelled")); |
| callback(); |
| } |
| } |
| |
| void PriorityQueue::TryDequeue(OpKernelContext* ctx, |
| CallbackWithTuple callback) { |
| CancellationManager* cm = ctx->cancellation_manager(); |
| CancellationToken token = cm->get_cancellation_token(); |
| bool already_cancelled; |
| { |
| mutex_lock l(mu_); |
| already_cancelled = !cm->RegisterCallback( |
| token, [this, cm, token]() { Cancel(kDequeue, cm, token); }); |
| if (!already_cancelled) { |
| // TODO(josh11b): This makes two copies of callback, avoid this if possible. |
| dequeue_attempts_.emplace_back( |
| 1, [callback]() { callback(Tuple()); }, ctx, cm, token, |
| [callback, this](Attempt* attempt) EXCLUSIVE_LOCKS_REQUIRED(mu_) { |
| const int32 s = queues_[0].size(); |
| if (closed_ && s == 0) { |
| attempt->context->SetStatus(errors::OutOfRange( |
| "PriorityQueue '", name_, "' is closed and has ", |
| "insufficient elements (requested ", 1, ", current size ", s, |
| ")")); |
| return kComplete; |
| } |
| if (s > 0) { |
| Tuple tuple; |
| DequeueLocked(attempt->context, &tuple); |
| attempt->done_callback = [callback, tuple]() { callback(tuple); }; |
| return kComplete; |
| } else { |
| return kNoProgress; |
| } |
| }); |
| } |
| } |
| if (!already_cancelled) { |
| FlushUnlocked(); |
| } else { |
| ctx->SetStatus(errors::Cancelled("Dequeue operation was cancelled")); |
| callback(Tuple()); |
| } |
| } |
| |
| void PriorityQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx, |
| bool allow_small_batch, |
| CallbackWithTuple callback) { |
| if (!specified_shapes()) { |
| ctx->SetStatus( |
| errors::InvalidArgument("PriorityQueue's DequeueMany requires the " |
| "components to have specified shapes.")); |
| callback(Tuple()); |
| return; |
| } |
| if (num_elements == 0) { |
| Tuple tuple; |
| tuple.reserve(num_components()); |
| for (int i = 0; i < num_components(); ++i) { |
| // TODO(josh11b,misard): Switch to allocate_output(). Problem is |
| // this breaks the abstraction boundary since we don't *really* |
| // know if and how the Tensors in the tuple we pass to callback |
| // correspond to the outputs of *ctx. For example, the |
| // ReaderRead Op uses TryDequeue() to get a filename out of a |
| // queue that is used internally by the reader and is not |
| // associated with any output of the ReaderRead. |
| // mrry@ adds: |
| // Maybe we need to pass a std::function<Tensor*(...)> (or |
| // better signature) that calls the appropriate allocator |
| // function in addition to ctx? (Or support a shim Allocator |
| // that has an internal OpKernelContext*, and dispatches to the |
| // appropriate method?) |
| // misard@ adds: |
| // I don't see that a std::function would help. The problem is |
| // that at this point (allocation time) the system doesn't know |
| // what is going to happen to the element read out of the |
| // queue. As long as we keep the generality that TensorFlow Ops |
| // do their own dynamic allocation in arbitrary C++ code, we |
| // need to preserve robustness to allocating output Tensors with |
| // the 'wrong' attributes, and fixing up with a copy. The only |
| // improvement I can see here in the future would be to support |
| // an optimized case where the queue 'knows' what attributes to |
| // use, and plumbs them through here. |
| Tensor element; |
| Status status = ctx->allocate_temp(component_dtypes_[i], |
| ManyOutShape(i, 0), &element); |
| if (!status.ok()) { |
| ctx->SetStatus(status); |
| callback(Tuple()); |
| return; |
| } |
| tuple.emplace_back(element); |
| } |
| callback(tuple); |
| return; |
| } |
| |
| CancellationManager* cm = ctx->cancellation_manager(); |
| CancellationToken token = cm->get_cancellation_token(); |
| bool already_cancelled; |
| { |
| mutex_lock l(mu_); |
| already_cancelled = !cm->RegisterCallback( |
| token, [this, cm, token]() { Cancel(kDequeue, cm, token); }); |
| if (!already_cancelled) { |
| // TODO(josh11b): This makes two copies of callback, avoid this if possible. |
| dequeue_attempts_.emplace_back( |
| num_elements, [callback]() { callback(Tuple()); }, ctx, cm, token, |
| [callback, this, |
| allow_small_batch](Attempt* attempt) EXCLUSIVE_LOCKS_REQUIRED(mu_) { |
| int32 s = queues_[0].size(); |
| // Return OutOfRange if closed and there are fewer elements |
| // available than requested. *Unless* allow_small_batch |
| // is true, in which case we return as many elements as |
| // possible. |
| if (closed_) { |
| if (s == 0 || |
| (!allow_small_batch && s < attempt->elements_requested)) { |
| attempt->context->SetStatus(errors::OutOfRange( |
| "PriorityQueue '", name_, "' is closed and has ", |
| "insufficient elements (requested ", |
| attempt->elements_requested, ", current size ", s, ")")); |
| return kComplete; |
| } |
| } |
| |
| // The PriorityQueue is expected to always return a |
| // sorted set of entries. In order to do this, the underlying |
| // queue must have at least this many entries already. |
| // Doing the dynamic thing and pulling out a portion at a |
| // time leads to unordered output in calls to DequeueMany. |
| // |
| // An alternative solution is to store the attempt tuple |
| // entries in an identical priority_queue and push onto |
| // this queue dynamically, then when it is full, do all |
| // the Tensor concatenation at the very end. |
| // TODO(ebrevdo): Change approach if this leads to locking issues. |
| if (s < attempt->elements_requested) { |
| // If we have no elements at all, then wait. |
| // Otherwise proceed if closed and allow small batch is true. |
| // Otherwise wait until we have more enqueued elements. |
| if (s == 0 || !(closed_ && allow_small_batch)) { |
| return kNoProgress; |
| } |
| } |
| |
| RunResult result = kNoProgress; |
| for (; s > 0; --s) { |
| if (attempt->tuple.empty()) { |
| // Only allocate tuple when we have something to dequeue |
| // so we don't use excessive memory when there are many |
| // blocked dequeue attempts waiting. |
| attempt->tuple.reserve(num_components()); |
| for (int i = 0; i < num_components(); ++i) { |
| const TensorShape shape = |
| ManyOutShape(i, attempt->elements_requested); |
| Tensor element; |
| attempt->context->SetStatus(attempt->context->allocate_temp( |
| component_dtypes_[i], shape, &element)); |
| if (!attempt->context->status().ok()) return kComplete; |
| attempt->tuple.emplace_back(element); |
| } |
| } |
| result = kProgress; |
| Tuple tuple; |
| DequeueLocked(attempt->context, &tuple); |
| const int index = |
| attempt->tuple[0].dim_size(0) - attempt->elements_requested; |
| for (int i = 0; i < num_components(); ++i) { |
| attempt->context->SetStatus(batch_util::CopyElementToSlice( |
| std::move(tuple[i]), &attempt->tuple[i], index)); |
| if (!attempt->context->status().ok()) return kComplete; |
| } |
| tuple.clear(); |
| --attempt->elements_requested; |
| if (attempt->elements_requested == 0) { |
| tuple = attempt->tuple; |
| attempt->done_callback = [callback, tuple]() { |
| callback(tuple); |
| }; |
| return kComplete; |
| } |
| } |
| return result; |
| }); |
| } |
| } |
| if (!already_cancelled) { |
| FlushUnlocked(); |
| } else { |
| ctx->SetStatus(errors::Cancelled("Dequeue operation was cancelled")); |
| callback(Tuple()); |
| } |
| } |
| |
| Status PriorityQueue::MatchesNodeDef(const NodeDef& node_def) { |
| if (!MatchesNodeDefOp(node_def, "PriorityQueue").ok() && |
| !MatchesNodeDefOp(node_def, "PriorityQueueV2").ok()) { |
| return errors::InvalidArgument("Expected PriorityQueue, found ", |
| node_def.op()); |
| } |
| TF_RETURN_IF_ERROR(MatchesNodeDefCapacity(node_def, capacity_)); |
| TF_RETURN_IF_ERROR(MatchesPriorityNodeDefTypes(node_def)); |
| TF_RETURN_IF_ERROR(MatchesPriorityNodeDefShapes(node_def)); |
| return Status::OK(); |
| } |
| |
| Status PriorityQueue::MatchesPriorityNodeDefTypes( |
| const NodeDef& node_def) const { |
| DataTypeVector requested_dtypes; |
| TF_RETURN_IF_ERROR( |
| GetNodeAttr(node_def, "component_types", &requested_dtypes)); |
| requested_dtypes.insert(requested_dtypes.begin(), DT_INT64); |
| if (requested_dtypes != component_dtypes_) { |
| return errors::InvalidArgument("Shared queue '", name_, |
| "' has component types ", |
| DataTypeSliceString(component_dtypes_), |
| " but requested component types were ", |
| DataTypeSliceString(requested_dtypes)); |
| } |
| return Status::OK(); |
| } |
| |
| Status PriorityQueue::MatchesPriorityNodeDefShapes( |
| const NodeDef& node_def) const { |
| std::vector<TensorShape> requested_shapes; |
| TF_RETURN_IF_ERROR(GetNodeAttr(node_def, "shapes", &requested_shapes)); |
| requested_shapes.insert(requested_shapes.begin(), TensorShape({})); |
| if (requested_shapes != component_shapes_) { |
| return errors::InvalidArgument("Shared queue '", name_, |
| "' has component shapes ", |
| ShapeListString(component_shapes_), |
| " but requested component shapes were ", |
| ShapeListString(requested_shapes)); |
| } |
| return Status::OK(); |
| } |
| |
| } // namespace tensorflow |
