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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.
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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