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https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
feat(nodes): simplify processor loop with an early continue
Prefer an early return/continue to reduce the indentation of the processor loop. Easier to read. There are other ways to improve its structure but at first glance, they seem to involve changing the logic in scarier ways.
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psychedelicious
parent
bd9b00a6bf
commit
32d3e4dc5c
@ -127,139 +127,140 @@ class DefaultSessionProcessor(SessionProcessorBase):
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# Get the next session to process
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self._queue_item = self._invoker.services.session_queue.dequeue()
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if self._queue_item is not None:
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self._invoker.services.logger.debug(f"Executing queue item {self._queue_item.item_id}")
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cancel_event.clear()
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if self._queue_item is None:
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# The queue was empty, wait for next polling interval or event to try again
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self._invoker.services.logger.debug("Waiting for next polling interval or event")
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poll_now_event.wait(self._polling_interval)
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continue
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# If profiling is enabled, start the profiler
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if self._profiler is not None:
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self._profiler.start(profile_id=self._queue_item.session_id)
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self._invoker.services.logger.debug(f"Executing queue item {self._queue_item.item_id}")
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cancel_event.clear()
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# Prepare invocations and take the first
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self._invocation = self._queue_item.session.next()
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# If profiling is enabled, start the profiler
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if self._profiler is not None:
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self._profiler.start(profile_id=self._queue_item.session_id)
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# Loop over invocations until the session is complete or canceled
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while self._invocation is not None and not cancel_event.is_set():
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# get the source node id to provide to clients (the prepared node id is not as useful)
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source_invocation_id = self._queue_item.session.prepared_source_mapping[self._invocation.id]
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# Prepare invocations and take the first
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self._invocation = self._queue_item.session.next()
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# Send starting event
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self._invoker.services.events.emit_invocation_started(
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queue_batch_id=self._queue_item.batch_id,
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queue_item_id=self._queue_item.item_id,
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queue_id=self._queue_item.queue_id,
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graph_execution_state_id=self._queue_item.session_id,
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node=self._invocation.model_dump(),
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source_node_id=source_invocation_id,
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)
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# Loop over invocations until the session is complete or canceled
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while self._invocation is not None and not cancel_event.is_set():
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# get the source node id to provide to clients (the prepared node id is not as useful)
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source_invocation_id = self._queue_item.session.prepared_source_mapping[self._invocation.id]
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# Innermost processor try block; any unhandled exception is an invocation error & will fail the graph
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try:
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with self._invoker.services.performance_statistics.collect_stats(
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self._invocation, self._queue_item.session.id
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):
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# Build invocation context (the node-facing API)
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data = InvocationContextData(
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invocation=self._invocation,
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source_invocation_id=source_invocation_id,
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queue_item=self._queue_item,
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)
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context = build_invocation_context(
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data=data,
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services=self._invoker.services,
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cancel_event=self._cancel_event,
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)
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# Send starting event
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self._invoker.services.events.emit_invocation_started(
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queue_batch_id=self._queue_item.batch_id,
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queue_item_id=self._queue_item.item_id,
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queue_id=self._queue_item.queue_id,
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graph_execution_state_id=self._queue_item.session_id,
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node=self._invocation.model_dump(),
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source_node_id=source_invocation_id,
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)
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# Invoke the node
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outputs = self._invocation.invoke_internal(
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context=context, services=self._invoker.services
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)
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# Save outputs and history
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self._queue_item.session.complete(self._invocation.id, outputs)
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# Send complete event
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self._invoker.services.events.emit_invocation_complete(
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queue_batch_id=self._queue_item.batch_id,
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queue_item_id=self._queue_item.item_id,
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queue_id=self._queue_item.queue_id,
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graph_execution_state_id=self._queue_item.session.id,
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node=self._invocation.model_dump(),
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source_node_id=source_invocation_id,
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result=outputs.model_dump(),
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)
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except KeyboardInterrupt:
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# TODO(MM2): Create an event for this
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pass
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except CanceledException:
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# When the user cancels the graph, we first set the cancel event. The event is checked
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# between invocations, in this loop. Some invocations are long-running, and we need to
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# be able to cancel them mid-execution.
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#
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# For example, denoising is a long-running invocation with many steps. A step callback
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# is executed after each step. This step callback checks if the canceled event is set,
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# then raises a CanceledException to stop execution immediately.
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#
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# When we get a CanceledException, we don't need to do anything - just pass and let the
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# loop go to its next iteration, and the cancel event will be handled correctly.
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pass
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except Exception as e:
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error = traceback.format_exc()
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# Save error
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self._queue_item.session.set_node_error(self._invocation.id, error)
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self._invoker.services.logger.error(
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f"Error while invoking session {self._queue_item.session_id}, invocation {self._invocation.id} ({self._invocation.get_type()}):\n{e}"
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# Innermost processor try block; any unhandled exception is an invocation error & will fail the graph
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try:
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with self._invoker.services.performance_statistics.collect_stats(
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self._invocation, self._queue_item.session.id
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):
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# Build invocation context (the node-facing API)
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data = InvocationContextData(
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invocation=self._invocation,
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source_invocation_id=source_invocation_id,
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queue_item=self._queue_item,
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)
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context = build_invocation_context(
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data=data,
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services=self._invoker.services,
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cancel_event=self._cancel_event,
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)
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self._invoker.services.logger.error(error)
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# Send error event
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self._invoker.services.events.emit_invocation_error(
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queue_batch_id=self._queue_item.session_id,
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# Invoke the node
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outputs = self._invocation.invoke_internal(
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context=context, services=self._invoker.services
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)
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# Save outputs and history
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self._queue_item.session.complete(self._invocation.id, outputs)
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# Send complete event
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self._invoker.services.events.emit_invocation_complete(
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queue_batch_id=self._queue_item.batch_id,
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queue_item_id=self._queue_item.item_id,
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queue_id=self._queue_item.queue_id,
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graph_execution_state_id=self._queue_item.session.id,
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node=self._invocation.model_dump(),
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source_node_id=source_invocation_id,
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error_type=e.__class__.__name__,
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error=error,
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result=outputs.model_dump(),
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)
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pass
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# The session is complete if the all invocations are complete or there was an error
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if self._queue_item.session.is_complete() or cancel_event.is_set():
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# Send complete event
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self._invoker.services.events.emit_graph_execution_complete(
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queue_batch_id=self._queue_item.batch_id,
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queue_item_id=self._queue_item.item_id,
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queue_id=self._queue_item.queue_id,
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graph_execution_state_id=self._queue_item.session.id,
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except KeyboardInterrupt:
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# TODO(MM2): Create an event for this
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pass
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except CanceledException:
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# When the user cancels the graph, we first set the cancel event. The event is checked
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# between invocations, in this loop. Some invocations are long-running, and we need to
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# be able to cancel them mid-execution.
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#
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# For example, denoising is a long-running invocation with many steps. A step callback
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# is executed after each step. This step callback checks if the canceled event is set,
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# then raises a CanceledException to stop execution immediately.
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#
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# When we get a CanceledException, we don't need to do anything - just pass and let the
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# loop go to its next iteration, and the cancel event will be handled correctly.
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pass
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except Exception as e:
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error = traceback.format_exc()
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# Save error
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self._queue_item.session.set_node_error(self._invocation.id, error)
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self._invoker.services.logger.error(
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f"Error while invoking session {self._queue_item.session_id}, invocation {self._invocation.id} ({self._invocation.get_type()}):\n{e}"
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)
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self._invoker.services.logger.error(error)
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# Send error event
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self._invoker.services.events.emit_invocation_error(
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queue_batch_id=self._queue_item.session_id,
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queue_item_id=self._queue_item.item_id,
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queue_id=self._queue_item.queue_id,
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graph_execution_state_id=self._queue_item.session.id,
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node=self._invocation.model_dump(),
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source_node_id=source_invocation_id,
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error_type=e.__class__.__name__,
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error=error,
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)
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pass
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# The session is complete if the all invocations are complete or there was an error
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if self._queue_item.session.is_complete() or cancel_event.is_set():
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# Send complete event
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self._invoker.services.events.emit_graph_execution_complete(
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queue_batch_id=self._queue_item.batch_id,
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queue_item_id=self._queue_item.item_id,
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queue_id=self._queue_item.queue_id,
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graph_execution_state_id=self._queue_item.session.id,
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)
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# If we are profiling, stop the profiler and dump the profile & stats
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if self._profiler:
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profile_path = self._profiler.stop()
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stats_path = profile_path.with_suffix(".json")
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self._invoker.services.performance_statistics.dump_stats(
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graph_execution_state_id=self._queue_item.session.id, output_path=stats_path
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)
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# If we are profiling, stop the profiler and dump the profile & stats
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if self._profiler:
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profile_path = self._profiler.stop()
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stats_path = profile_path.with_suffix(".json")
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self._invoker.services.performance_statistics.dump_stats(
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graph_execution_state_id=self._queue_item.session.id, output_path=stats_path
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)
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# We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
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# we don't care about that - suppress the error.
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with suppress(GESStatsNotFoundError):
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self._invoker.services.performance_statistics.log_stats(self._queue_item.session.id)
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self._invoker.services.performance_statistics.reset_stats()
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# We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
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# we don't care about that - suppress the error.
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with suppress(GESStatsNotFoundError):
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self._invoker.services.performance_statistics.log_stats(self._queue_item.session.id)
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self._invoker.services.performance_statistics.reset_stats()
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# Set the invocation to None to prepare for the next session
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self._invocation = None
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else:
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# Prepare the next invocation
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self._invocation = self._queue_item.session.next()
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# The session is complete, immediately poll for next session
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self._queue_item = None
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poll_now_event.set()
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# Set the invocation to None to prepare for the next session
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self._invocation = None
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else:
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# Prepare the next invocation
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self._invocation = self._queue_item.session.next()
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else:
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# The queue was empty, wait for next polling interval or event to try again
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self._invoker.services.logger.debug("Waiting for next polling interval or event")
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