177 lines
9.0 KiB
Markdown
177 lines
9.0 KiB
Markdown
---
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layout: forward
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target: https://developers.google.com/mediapipe/framework/framework_concepts/synchronization
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title: Synchronization
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parent: Framework Concepts
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nav_order: 4
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---
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# Synchronization
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{: .no_toc }
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1. TOC
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{:toc}
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---
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## Scheduling mechanics
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Data processing in a MediaPipe graph occurs inside processing nodes defined as
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[`CalculatorBase`] subclasses. The scheduling system decides when each
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calculator should run.
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Each graph has at least one **scheduler queue**. Each scheduler queue has
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exactly one **executor**. Nodes are statically assigned to a queue (and
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therefore to an executor). By default there is one queue, whose executor is a
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thread pool with a number of threads based on the system’s capabilities.
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Each node has a scheduling state, which can be *not ready*, *ready*, or
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*running*. A readiness function determines whether a node is ready to run. This
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function is invoked at graph initialization, whenever a node finishes running,
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and whenever the state of a node’s inputs changes.
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The readiness function used depends on the type of node. A node with no stream
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inputs is known as a **source node**; source nodes are always ready to run,
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until they tell the framework they have no more data to output, at which point
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they are closed.
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Non-source nodes are ready if they have inputs to process, and if those inputs
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form a valid input set according to the conditions set by the node’s **input
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policy** (discussed below). Most nodes use the default input policy, but some
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nodes specify a different one.
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Note: Because changing the input policy changes the guarantees the calculator’s
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code can expect from its inputs, it is not generally possible to mix and match
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calculators with arbitrary input policies. Thus a calculator that uses a special
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input policy should be written for it, and declare it in its contract.
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When a node becomes ready, a task is added to the corresponding scheduler queue,
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which is a priority queue. The priority function is currently fixed, and takes
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into account static properties of the nodes and their topological sorting within
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the graph. For example, nodes closer to the output side of the graph have higher
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priority, while source nodes have the lowest priority.
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Each queue is served by an executor, which is responsible for actually running
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the task by invoking the calculator’s code. Different executors can be provided
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and configured; this can be used to customize the use of execution resources,
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e.g. by running certain nodes on lower-priority threads.
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## Timestamp Synchronization
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MediaPipe graph execution is decentralized: there is no global clock, and
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different nodes can process data from different timestamps at the same time.
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This allows higher throughput via pipelining.
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However, time information is very important for many perception workflows. Nodes
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that receive multiple input streams generally need to coordinate them in some
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way. For example, an object detector may output a list of boundary rectangles
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from a frame, and this information may be fed into a rendering node, which
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should process it together with the original frame.
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Therefore, one of the key responsibilities of the MediaPipe framework is to
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provide input synchronization for nodes. In terms of framework mechanics, the
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primary role of a timestamp is to serve as a **synchronization key**.
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Furthermore, MediaPipe is designed to support deterministic operations, which is
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important in many scenarios (testing, simulation, batch processing, etc.), while
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allowing graph authors to relax determinism where needed to meet real-time
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constraints.
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The two objectives of synchronization and determinism underlie several design
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choices. Notably, the packets pushed into a given stream must have monotonically
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increasing timestamps: this is not just a useful assumption for many nodes, but
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it is also relied upon by the synchronization logic. Each stream has a
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**timestamp bound**, which is the lowest possible timestamp allowed for a new
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packet on the stream. When a packet with timestamp `T` arrives, the bound
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automatically advances to `T+1`, reflecting the monotonic requirement. This
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allows the framework to know for certain that no more packets with timestamp
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lower than `T` will arrive.
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## Input policies
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Synchronization is handled locally on each node, using the input policy
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specified by the node.
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The default input policy, defined by [`DefaultInputStreamHandler`], provides
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deterministic synchronization of inputs, with the following guarantees:
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* If packets with the same timestamp are provided on multiple input streams,
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they will always be processed together regardless of their arrival order in
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real time.
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* Input sets are processed in strictly ascending timestamp order.
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* No packets are dropped, and the processing is fully deterministic.
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* The node becomes ready to process data as soon as possible given the
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guarantees above.
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Note: An important consequence of this is that if the calculator always uses the
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current input timestamp when outputting packets, the output will inherently obey
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the monotonically increasing timestamp requirement.
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Warning: On the other hand, it is not guaranteed that an input packet will
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always be available for all streams.
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To explain how it works, we need to introduce the definition of a settled
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timestamp. We say that a timestamp in a stream is *settled* if it is lower than
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the timestamp bound. In other words, a timestamp is settled for a stream once
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the state of the input at that timestamp is irrevocably known: either there is a
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packet, or there is the certainty that a packet with that timestamp will not
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arrive.
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Note: For this reason, MediaPipe also allows a stream producer to explicitly
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advance the timestamp bound farther than what the last packet implies, i.e. to
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provide a tighter bound. This can allow the downstream nodes to settle their
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inputs sooner.
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A timestamp is settled across multiple streams if it is settled on each of those
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streams. Furthermore, if a timestamp is settled it implies that all previous
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timestamps are also settled. Thus settled timestamps can be processed
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deterministically in ascending order.
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Given this definition, a calculator with the default input policy is ready if
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there is a timestamp which is settled across all input streams and contains a
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packet on at least one input stream. The input policy provides all available
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packets for a settled timestamp as a single *input set* to the calculator.
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One consequence of this deterministic behavior is that, for nodes with multiple
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input streams, there can be a theoretically unbounded wait for a timestamp to be
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settled, and an unbounded number of packets can be buffered in the meantime.
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(Consider a node with two input streams, one of which keeps sending packets
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while the other sends nothing and does not advance the bound.)
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Therefore, we also provide for custom input policies: for example, splitting the
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inputs in different synchronization sets defined by
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[`SyncSetInputStreamHandler`], or avoiding synchronization altogether and
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processing inputs immediately as they arrive defined by
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[`ImmediateInputStreamHandler`].
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## Flow control
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There are two main flow control mechanisms. A backpressure mechanism throttles
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the execution of upstream nodes when the packets buffered on a stream reach a
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(configurable) limit defined by [`CalculatorGraphConfig::max_queue_size`]. This
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mechanism maintains deterministic behavior, and includes a deadlock avoidance
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system that relaxes configured limits when needed.
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The second system consists of inserting special nodes which can drop packets
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according to real-time constraints (typically using custom input policies)
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defined by [`FlowLimiterCalculator`]. For example, a common pattern places a
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flow-control node at the input of a subgraph, with a loopback connection from
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the final output to the flow-control node. The flow-control node is thus able to
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keep track of how many timestamps are being processed in the downstream graph,
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and drop packets if this count hits a (configurable) limit; and since packets
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are dropped upstream, we avoid the wasted work that would result from partially
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processing a timestamp and then dropping packets between intermediate stages.
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This calculator-based approach gives the graph author control of where packets
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can be dropped, and allows flexibility in adapting and customizing the graph’s
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behavior depending on resource constraints.
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[`CalculatorBase`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/calculator_base.h
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[`DefaultInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/default_input_stream_handler.h
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[`SyncSetInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/sync_set_input_stream_handler.cc
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[`ImmediateInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/immediate_input_stream_handler.cc
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[`CalculatorGraphConfig::max_queue_size`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/calculator.proto
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[`FlowLimiterCalculator`]: https://github.com/google/mediapipe/tree/master/mediapipe/calculators/core/flow_limiter_calculator.cc
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