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2b653e8c18
* Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
import contextvars
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from typing import Optional, NamedTuple
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class ExecutionContext(NamedTuple):
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"""
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Context information about the currently executing node.
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Attributes:
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node_id: The ID of the currently executing node
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list_index: The index in a list being processed (for operations on batches/lists)
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"""
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prompt_id: str
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node_id: str
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list_index: Optional[int]
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current_executing_context: contextvars.ContextVar[Optional[ExecutionContext]] = contextvars.ContextVar("current_executing_context", default=None)
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def get_executing_context() -> Optional[ExecutionContext]:
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return current_executing_context.get(None)
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class CurrentNodeContext:
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"""
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Context manager for setting the current executing node context.
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Sets the current_executing_context on enter and resets it on exit.
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Example:
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with CurrentNodeContext(node_id="123", list_index=0):
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# Code that should run with the current node context set
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process_image()
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"""
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def __init__(self, prompt_id: str, node_id: str, list_index: Optional[int] = None):
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self.context = ExecutionContext(
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prompt_id= prompt_id,
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node_id= node_id,
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list_index= list_index
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)
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self.token = None
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def __enter__(self):
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self.token = current_executing_context.set(self.context)
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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if self.token is not None:
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current_executing_context.reset(self.token)
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