mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
25b9c19eed
We were not handling node preparation errors as node errors before. Here's the explanation, copied from a comment that is no longer required: --- TODO(psyche): Sessions only support errors on nodes, not on the session itself. When an error occurs outside node execution, it bubbles up to the processor where it is treated as a queue item error. Nodes are pydantic models. When we prepare a node in `session.next()`, we set its inputs. This can cause a pydantic validation error. For example, consider a resize image node which has a constraint on its `width` input field - it must be greater than zero. During preparation, if the width is set to zero, pydantic will raise a validation error. When this happens, it breaks the flow before `invocation` is set. We can't set an error on the invocation because we didn't get far enough to get it - we don't know its id. Hence, we just set it as a queue item error. --- This change wraps the node preparation step with exception handling. A new `NodeInputError` exception is raised when there is a validation error. This error has the node (in the state it was in just prior to the error) and an identifier of the input that failed. This allows us to mark the node that failed preparation as errored, correctly making such errors _node_ errors and not _processor_ errors. It's much easier to diagnose these situations. The error messages look like this: > Node b5ac87c6-0678-4b8c-96b9-d215aee12175 has invalid incoming input for height Some of the exception handling logic is cleaned up.
1177 lines
48 KiB
Python
1177 lines
48 KiB
Python
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
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import copy
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import itertools
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from typing import Annotated, Any, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
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import networkx as nx
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from pydantic import (
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BaseModel,
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GetJsonSchemaHandler,
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ValidationError,
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field_validator,
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)
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from pydantic.fields import Field
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from pydantic.json_schema import JsonSchemaValue
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from pydantic_core import CoreSchema
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# Importing * is bad karma but needed here for node detection
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from invokeai.app.invocations import * # noqa: F401 F403
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from invokeai.app.invocations.baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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invocation,
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invocation_output,
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)
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from invokeai.app.invocations.fields import Input, InputField, OutputField, UIType
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.app.util.misc import uuid_string
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# in 3.10 this would be "from types import NoneType"
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NoneType = type(None)
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class EdgeConnection(BaseModel):
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node_id: str = Field(description="The id of the node for this edge connection")
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field: str = Field(description="The field for this connection")
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def __eq__(self, other):
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return (
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isinstance(other, self.__class__)
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and getattr(other, "node_id", None) == self.node_id
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and getattr(other, "field", None) == self.field
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)
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def __hash__(self):
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return hash(f"{self.node_id}.{self.field}")
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class Edge(BaseModel):
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source: EdgeConnection = Field(description="The connection for the edge's from node and field")
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destination: EdgeConnection = Field(description="The connection for the edge's to node and field")
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def get_output_field(node: BaseInvocation, field: str) -> Any:
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node_type = type(node)
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node_outputs = get_type_hints(node_type.get_output_annotation())
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node_output_field = node_outputs.get(field) or None
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return node_output_field
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def get_input_field(node: BaseInvocation, field: str) -> Any:
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node_type = type(node)
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node_inputs = get_type_hints(node_type)
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node_input_field = node_inputs.get(field) or None
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return node_input_field
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def is_union_subtype(t1, t2):
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t1_args = get_args(t1)
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t2_args = get_args(t2)
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if not t1_args:
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# t1 is a single type
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return t1 in t2_args
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else:
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# t1 is a Union, check that all of its types are in t2_args
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return all(arg in t2_args for arg in t1_args)
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def is_list_or_contains_list(t):
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t_args = get_args(t)
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# If the type is a List
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if get_origin(t) is list:
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return True
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# If the type is a Union
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elif t_args:
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# Check if any of the types in the Union is a List
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for arg in t_args:
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if get_origin(arg) is list:
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return True
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return False
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def are_connection_types_compatible(from_type: Any, to_type: Any) -> bool:
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if not from_type:
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return False
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if not to_type:
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return False
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# TODO: this is pretty forgiving on generic types. Clean that up (need to handle optionals and such)
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if from_type and to_type:
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# Ports are compatible
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if (
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from_type == to_type
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or from_type == Any
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or to_type == Any
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or Any in get_args(from_type)
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or Any in get_args(to_type)
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):
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return True
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if from_type in get_args(to_type):
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return True
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if to_type in get_args(from_type):
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return True
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# allow int -> float, pydantic will cast for us
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if from_type is int and to_type is float:
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return True
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# allow int|float -> str, pydantic will cast for us
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if (from_type is int or from_type is float) and to_type is str:
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return True
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# if not issubclass(from_type, to_type):
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if not is_union_subtype(from_type, to_type):
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return False
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else:
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return False
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return True
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def are_connections_compatible(
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from_node: BaseInvocation, from_field: str, to_node: BaseInvocation, to_field: str
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) -> bool:
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"""Determines if a connection between fields of two nodes is compatible."""
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# TODO: handle iterators and collectors
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from_node_field = get_output_field(from_node, from_field)
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to_node_field = get_input_field(to_node, to_field)
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return are_connection_types_compatible(from_node_field, to_node_field)
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T = TypeVar("T")
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def copydeep(obj: T) -> T:
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"""Deep-copies an object. If it is a pydantic model, use the model's copy method."""
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if isinstance(obj, BaseModel):
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return obj.model_copy(deep=True)
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return copy.deepcopy(obj)
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class NodeAlreadyInGraphError(ValueError):
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pass
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class InvalidEdgeError(ValueError):
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pass
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class NodeNotFoundError(ValueError):
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pass
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class NodeAlreadyExecutedError(ValueError):
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pass
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class DuplicateNodeIdError(ValueError):
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pass
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class NodeFieldNotFoundError(ValueError):
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pass
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class NodeIdMismatchError(ValueError):
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pass
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class CyclicalGraphError(ValueError):
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pass
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class UnknownGraphValidationError(ValueError):
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pass
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class NodeInputError(ValueError):
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"""Raised when a node fails preparation. This occurs when a node's inputs are being set from its incomers, but an
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input fails validation.
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Attributes:
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node: The node that failed preparation. Note: only successfully set fields will be accurate. Review the error to
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determine which field caused the failure.
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"""
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def __init__(self, node: BaseInvocation, e: ValidationError):
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self.original_error = e
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self.node = node
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# When preparing a node, we set each input one-at-a-time. We may thus safely assume that the first error
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# represents the first input that failed.
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self.failed_input = loc_to_dot_sep(e.errors()[0]["loc"])
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super().__init__(f"Node {node.id} has invalid incoming input for {self.failed_input}")
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def loc_to_dot_sep(loc: tuple[Union[str, int], ...]) -> str:
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"""Helper to pretty-print pydantic error locations as dot-separated strings.
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Taken from https://docs.pydantic.dev/latest/errors/errors/#customize-error-messages
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"""
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path = ""
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for i, x in enumerate(loc):
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if isinstance(x, str):
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if i > 0:
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path += "."
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path += x
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else:
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path += f"[{x}]"
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return path
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@invocation_output("iterate_output")
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class IterateInvocationOutput(BaseInvocationOutput):
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"""Used to connect iteration outputs. Will be expanded to a specific output."""
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item: Any = OutputField(
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description="The item being iterated over", title="Collection Item", ui_type=UIType._CollectionItem
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)
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index: int = OutputField(description="The index of the item", title="Index")
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total: int = OutputField(description="The total number of items", title="Total")
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# TODO: Fill this out and move to invocations
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@invocation("iterate", version="1.1.0")
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class IterateInvocation(BaseInvocation):
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"""Iterates over a list of items"""
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collection: list[Any] = InputField(
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description="The list of items to iterate over", default=[], ui_type=UIType._Collection
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)
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index: int = InputField(description="The index, will be provided on executed iterators", default=0, ui_hidden=True)
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def invoke(self, context: InvocationContext) -> IterateInvocationOutput:
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"""Produces the outputs as values"""
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return IterateInvocationOutput(item=self.collection[self.index], index=self.index, total=len(self.collection))
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@invocation_output("collect_output")
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class CollectInvocationOutput(BaseInvocationOutput):
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collection: list[Any] = OutputField(
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description="The collection of input items", title="Collection", ui_type=UIType._Collection
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)
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@invocation("collect", version="1.0.0")
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class CollectInvocation(BaseInvocation):
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"""Collects values into a collection"""
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item: Optional[Any] = InputField(
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default=None,
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description="The item to collect (all inputs must be of the same type)",
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ui_type=UIType._CollectionItem,
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title="Collection Item",
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input=Input.Connection,
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)
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collection: list[Any] = InputField(
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description="The collection, will be provided on execution", default=[], ui_hidden=True
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)
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def invoke(self, context: InvocationContext) -> CollectInvocationOutput:
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"""Invoke with provided services and return outputs."""
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return CollectInvocationOutput(collection=copy.copy(self.collection))
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class Graph(BaseModel):
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id: str = Field(description="The id of this graph", default_factory=uuid_string)
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# TODO: use a list (and never use dict in a BaseModel) because pydantic/fastapi hates me
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nodes: dict[str, BaseInvocation] = Field(description="The nodes in this graph", default_factory=dict)
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edges: list[Edge] = Field(
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description="The connections between nodes and their fields in this graph",
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default_factory=list,
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)
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@field_validator("nodes", mode="plain")
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@classmethod
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def validate_nodes(cls, v: dict[str, Any]):
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"""Validates the nodes in the graph by retrieving a union of all node types and validating each node."""
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# Invocations register themselves as their python modules are executed. The union of all invocations is
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# constructed at runtime. We use pydantic to validate `Graph.nodes` using that union.
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#
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# It's possible that when `graph.py` is executed, not all invocation-containing modules will have executed. If
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# we construct the invocation union as `graph.py` is executed, we may miss some invocations. Those missing
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# invocations will cause a graph to fail if they are used.
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#
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# We can get around this by validating the nodes in the graph using a "plain" validator, which overrides the
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# pydantic validation entirely. This allows us to validate the nodes using the union of invocations at runtime.
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#
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# This same pattern is used in `GraphExecutionState`.
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nodes: dict[str, BaseInvocation] = {}
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typeadapter = BaseInvocation.get_typeadapter()
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for node_id, node in v.items():
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nodes[node_id] = typeadapter.validate_python(node)
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return nodes
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@classmethod
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def __get_pydantic_json_schema__(cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue:
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# We use a "plain" validator to validate the nodes in the graph. Pydantic is unable to create a JSON Schema for
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# fields that use "plain" validators, so we have to hack around this. Also, we need to add all invocations to
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# the generated schema as options for the `nodes` field.
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#
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# The workaround is to create a new BaseModel that has the same fields as `Graph` but without the validator and
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# with the invocation union as the type for the `nodes` field. Pydantic then generates the JSON Schema as
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# expected.
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#
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# You might be tempted to do something like this:
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#
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# ```py
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# cloned_model = create_model(cls.__name__, __base__=cls, nodes=...)
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# delattr(cloned_model, "validate_nodes")
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# cloned_model.model_rebuild(force=True)
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# json_schema = handler(cloned_model.__pydantic_core_schema__)
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# ```
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#
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# Unfortunately, this does not work. Calling `handler` here results in infinite recursion as pydantic attempts
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# to build the JSON Schema for the cloned model. Instead, we have to manually clone the model.
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#
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# This same pattern is used in `GraphExecutionState`.
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class Graph(BaseModel):
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id: Optional[str] = Field(default=None, description="The id of this graph")
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nodes: dict[
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str, Annotated[Union[tuple(BaseInvocation._invocation_classes)], Field(discriminator="type")]
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] = Field(description="The nodes in this graph")
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edges: list[Edge] = Field(description="The connections between nodes and their fields in this graph")
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json_schema = handler(Graph.__pydantic_core_schema__)
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json_schema = handler.resolve_ref_schema(json_schema)
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return json_schema
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def add_node(self, node: BaseInvocation) -> None:
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"""Adds a node to a graph
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:raises NodeAlreadyInGraphError: the node is already present in the graph.
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"""
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if node.id in self.nodes:
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raise NodeAlreadyInGraphError()
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self.nodes[node.id] = node
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def delete_node(self, node_id: str) -> None:
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"""Deletes a node from a graph"""
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try:
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# Delete edges for this node
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input_edges = self._get_input_edges(node_id)
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output_edges = self._get_output_edges(node_id)
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for edge in input_edges:
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self.delete_edge(edge)
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for edge in output_edges:
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self.delete_edge(edge)
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del self.nodes[node_id]
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except NodeNotFoundError:
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pass # Ignore, not doesn't exist (should this throw?)
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def add_edge(self, edge: Edge) -> None:
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"""Adds an edge to a graph
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:raises InvalidEdgeError: the provided edge is invalid.
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"""
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self._validate_edge(edge)
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if edge not in self.edges:
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self.edges.append(edge)
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else:
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raise InvalidEdgeError()
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def delete_edge(self, edge: Edge) -> None:
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"""Deletes an edge from a graph"""
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try:
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self.edges.remove(edge)
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except KeyError:
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pass
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def validate_self(self) -> None:
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"""
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Validates the graph.
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Raises an exception if the graph is invalid:
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- `DuplicateNodeIdError`
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- `NodeIdMismatchError`
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- `InvalidSubGraphError`
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- `NodeNotFoundError`
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- `NodeFieldNotFoundError`
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- `CyclicalGraphError`
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- `InvalidEdgeError`
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"""
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# Validate that all node ids are unique
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node_ids = [n.id for n in self.nodes.values()]
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duplicate_node_ids = {node_id for node_id in node_ids if node_ids.count(node_id) >= 2}
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if duplicate_node_ids:
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raise DuplicateNodeIdError(f"Node ids must be unique, found duplicates {duplicate_node_ids}")
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# Validate that all node ids match the keys in the nodes dict
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for k, v in self.nodes.items():
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if k != v.id:
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raise NodeIdMismatchError(f"Node ids must match, got {k} and {v.id}")
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# Validate that all edges match nodes and fields in the graph
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for edge in self.edges:
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source_node = self.nodes.get(edge.source.node_id, None)
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if source_node is None:
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raise NodeNotFoundError(f"Edge source node {edge.source.node_id} does not exist in the graph")
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destination_node = self.nodes.get(edge.destination.node_id, None)
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if destination_node is None:
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raise NodeNotFoundError(f"Edge destination node {edge.destination.node_id} does not exist in the graph")
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# output fields are not on the node object directly, they are on the output type
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if edge.source.field not in source_node.get_output_annotation().model_fields:
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raise NodeFieldNotFoundError(
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f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}"
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)
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# input fields are on the node
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if edge.destination.field not in destination_node.model_fields:
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raise NodeFieldNotFoundError(
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f"Edge destination field {edge.destination.field} does not exist in node {edge.destination.node_id}"
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)
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# Validate there are no cycles
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g = self.nx_graph_flat()
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if not nx.is_directed_acyclic_graph(g):
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raise CyclicalGraphError("Graph contains cycles")
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# Validate all edge connections are valid
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for edge in self.edges:
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if not are_connections_compatible(
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self.get_node(edge.source.node_id),
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edge.source.field,
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self.get_node(edge.destination.node_id),
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edge.destination.field,
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):
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raise InvalidEdgeError(
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f"Invalid edge from {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
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)
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# Validate all iterators & collectors
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# TODO: may need to validate all iterators & collectors in subgraphs so edge connections in parent graphs will be available
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for node in self.nodes.values():
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if isinstance(node, IterateInvocation) and not self._is_iterator_connection_valid(node.id):
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raise InvalidEdgeError(f"Invalid iterator node {node.id}")
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if isinstance(node, CollectInvocation) and not self._is_collector_connection_valid(node.id):
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raise InvalidEdgeError(f"Invalid collector node {node.id}")
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return None
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def is_valid(self) -> bool:
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"""
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Checks if the graph is valid.
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Raises `UnknownGraphValidationError` if there is a problem validating the graph (not a validation error).
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"""
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try:
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self.validate_self()
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return True
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except (
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DuplicateNodeIdError,
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NodeIdMismatchError,
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NodeNotFoundError,
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NodeFieldNotFoundError,
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CyclicalGraphError,
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InvalidEdgeError,
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):
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return False
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except Exception as e:
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raise UnknownGraphValidationError(f"Problem validating graph {e}") from e
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|
def _is_destination_field_Any(self, edge: Edge) -> bool:
|
|
"""Checks if the destination field for an edge is of type typing.Any"""
|
|
return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == Any
|
|
|
|
def _is_destination_field_list_of_Any(self, edge: Edge) -> bool:
|
|
"""Checks if the destination field for an edge is of type typing.Any"""
|
|
return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == list[Any]
|
|
|
|
def _validate_edge(self, edge: Edge):
|
|
"""Validates that a new edge doesn't create a cycle in the graph"""
|
|
|
|
# Validate that the nodes exist
|
|
try:
|
|
from_node = self.get_node(edge.source.node_id)
|
|
to_node = self.get_node(edge.destination.node_id)
|
|
except NodeNotFoundError:
|
|
raise InvalidEdgeError("One or both nodes don't exist: {edge.source.node_id} -> {edge.destination.node_id}")
|
|
|
|
# Validate that an edge to this node+field doesn't already exist
|
|
input_edges = self._get_input_edges(edge.destination.node_id, edge.destination.field)
|
|
if len(input_edges) > 0 and not isinstance(to_node, CollectInvocation):
|
|
raise InvalidEdgeError(
|
|
f"Edge to node {edge.destination.node_id} field {edge.destination.field} already exists"
|
|
)
|
|
|
|
# Validate that no cycles would be created
|
|
g = self.nx_graph_flat()
|
|
g.add_edge(edge.source.node_id, edge.destination.node_id)
|
|
if not nx.is_directed_acyclic_graph(g):
|
|
raise InvalidEdgeError(
|
|
f"Edge creates a cycle in the graph: {edge.source.node_id} -> {edge.destination.node_id}"
|
|
)
|
|
|
|
# Validate that the field types are compatible
|
|
if not are_connections_compatible(from_node, edge.source.field, to_node, edge.destination.field):
|
|
raise InvalidEdgeError(
|
|
f"Fields are incompatible: cannot connect {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
|
|
)
|
|
|
|
# Validate if iterator output type matches iterator input type (if this edge results in both being set)
|
|
if isinstance(to_node, IterateInvocation) and edge.destination.field == "collection":
|
|
if not self._is_iterator_connection_valid(edge.destination.node_id, new_input=edge.source):
|
|
raise InvalidEdgeError(
|
|
f"Iterator input type does not match iterator output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
|
|
)
|
|
|
|
# Validate if iterator input type matches output type (if this edge results in both being set)
|
|
if isinstance(from_node, IterateInvocation) and edge.source.field == "item":
|
|
if not self._is_iterator_connection_valid(edge.source.node_id, new_output=edge.destination):
|
|
raise InvalidEdgeError(
|
|
f"Iterator output type does not match iterator input type:, {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
|
|
)
|
|
|
|
# Validate if collector input type matches output type (if this edge results in both being set)
|
|
if isinstance(to_node, CollectInvocation) and edge.destination.field == "item":
|
|
if not self._is_collector_connection_valid(edge.destination.node_id, new_input=edge.source):
|
|
raise InvalidEdgeError(
|
|
f"Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
|
|
)
|
|
|
|
# Validate that we are not connecting collector to iterator (currently unsupported)
|
|
if isinstance(from_node, CollectInvocation) and isinstance(to_node, IterateInvocation):
|
|
raise InvalidEdgeError(
|
|
f"Cannot connect collector to iterator: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
|
|
)
|
|
|
|
# Validate if collector output type matches input type (if this edge results in both being set) - skip if the destination field is not Any or list[Any]
|
|
if (
|
|
isinstance(from_node, CollectInvocation)
|
|
and edge.source.field == "collection"
|
|
and not self._is_destination_field_list_of_Any(edge)
|
|
and not self._is_destination_field_Any(edge)
|
|
):
|
|
if not self._is_collector_connection_valid(edge.source.node_id, new_output=edge.destination):
|
|
raise InvalidEdgeError(
|
|
f"Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
|
|
)
|
|
|
|
def has_node(self, node_id: str) -> bool:
|
|
"""Determines whether or not a node exists in the graph."""
|
|
try:
|
|
_ = self.get_node(node_id)
|
|
return True
|
|
except NodeNotFoundError:
|
|
return False
|
|
|
|
def get_node(self, node_id: str) -> BaseInvocation:
|
|
"""Gets a node from the graph."""
|
|
try:
|
|
return self.nodes[node_id]
|
|
except KeyError as e:
|
|
raise NodeNotFoundError(f"Node {node_id} not found in graph") from e
|
|
|
|
def update_node(self, node_id: str, new_node: BaseInvocation) -> None:
|
|
"""Updates a node in the graph."""
|
|
node = self.nodes[node_id]
|
|
|
|
# Ensure the node type matches the new node
|
|
if type(node) is not type(new_node):
|
|
raise TypeError(f"Node {node_id} is type {type(node)} but new node is type {type(new_node)}")
|
|
|
|
# Ensure the new id is either the same or is not in the graph
|
|
if new_node.id != node.id and self.has_node(new_node.id):
|
|
raise NodeAlreadyInGraphError(f"Node with id {new_node.id} already exists in graph")
|
|
|
|
# Set the new node in the graph
|
|
self.nodes[new_node.id] = new_node
|
|
if new_node.id != node.id:
|
|
input_edges = self._get_input_edges(node_id)
|
|
output_edges = self._get_output_edges(node_id)
|
|
|
|
# Delete node and all edges
|
|
self.delete_node(node_id)
|
|
|
|
# Create new edges for each input and output
|
|
for edge in input_edges:
|
|
self.add_edge(
|
|
Edge(
|
|
source=edge.source,
|
|
destination=EdgeConnection(node_id=new_node.id, field=edge.destination.field),
|
|
)
|
|
)
|
|
|
|
for edge in output_edges:
|
|
self.add_edge(
|
|
Edge(
|
|
source=EdgeConnection(node_id=new_node.id, field=edge.source.field),
|
|
destination=edge.destination,
|
|
)
|
|
)
|
|
|
|
def _get_input_edges(self, node_id: str, field: Optional[str] = None) -> list[Edge]:
|
|
"""Gets all input edges for a node. If field is provided, only edges to that field are returned."""
|
|
|
|
edges = [e for e in self.edges if e.destination.node_id == node_id]
|
|
|
|
if field is None:
|
|
return edges
|
|
|
|
filtered_edges = [e for e in edges if e.destination.field == field]
|
|
|
|
return filtered_edges
|
|
|
|
def _get_output_edges(self, node_id: str, field: Optional[str] = None) -> list[Edge]:
|
|
"""Gets all output edges for a node. If field is provided, only edges from that field are returned."""
|
|
edges = [e for e in self.edges if e.source.node_id == node_id]
|
|
|
|
if field is None:
|
|
return edges
|
|
|
|
filtered_edges = [e for e in edges if e.source.field == field]
|
|
|
|
return filtered_edges
|
|
|
|
def _is_iterator_connection_valid(
|
|
self,
|
|
node_id: str,
|
|
new_input: Optional[EdgeConnection] = None,
|
|
new_output: Optional[EdgeConnection] = None,
|
|
) -> bool:
|
|
inputs = [e.source for e in self._get_input_edges(node_id, "collection")]
|
|
outputs = [e.destination for e in self._get_output_edges(node_id, "item")]
|
|
|
|
if new_input is not None:
|
|
inputs.append(new_input)
|
|
if new_output is not None:
|
|
outputs.append(new_output)
|
|
|
|
# Only one input is allowed for iterators
|
|
if len(inputs) > 1:
|
|
return False
|
|
|
|
# Get input and output fields (the fields linked to the iterator's input/output)
|
|
input_field = get_output_field(self.get_node(inputs[0].node_id), inputs[0].field)
|
|
output_fields = [get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
|
|
|
|
# Input type must be a list
|
|
if get_origin(input_field) != list:
|
|
return False
|
|
|
|
# Validate that all outputs match the input type
|
|
input_field_item_type = get_args(input_field)[0]
|
|
if not all((are_connection_types_compatible(input_field_item_type, f) for f in output_fields)):
|
|
return False
|
|
|
|
return True
|
|
|
|
def _is_collector_connection_valid(
|
|
self,
|
|
node_id: str,
|
|
new_input: Optional[EdgeConnection] = None,
|
|
new_output: Optional[EdgeConnection] = None,
|
|
) -> bool:
|
|
inputs = [e.source for e in self._get_input_edges(node_id, "item")]
|
|
outputs = [e.destination for e in self._get_output_edges(node_id, "collection")]
|
|
|
|
if new_input is not None:
|
|
inputs.append(new_input)
|
|
if new_output is not None:
|
|
outputs.append(new_output)
|
|
|
|
# Get input and output fields (the fields linked to the iterator's input/output)
|
|
input_fields = [get_output_field(self.get_node(e.node_id), e.field) for e in inputs]
|
|
output_fields = [get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
|
|
|
|
# Validate that all inputs are derived from or match a single type
|
|
input_field_types = {
|
|
t
|
|
for input_field in input_fields
|
|
for t in ([input_field] if get_origin(input_field) is None else get_args(input_field))
|
|
if t != NoneType
|
|
} # Get unique types
|
|
type_tree = nx.DiGraph()
|
|
type_tree.add_nodes_from(input_field_types)
|
|
type_tree.add_edges_from([e for e in itertools.permutations(input_field_types, 2) if issubclass(e[1], e[0])])
|
|
type_degrees = type_tree.in_degree(type_tree.nodes)
|
|
if sum((t[1] == 0 for t in type_degrees)) != 1: # type: ignore
|
|
return False # There is more than one root type
|
|
|
|
# Get the input root type
|
|
input_root_type = next(t[0] for t in type_degrees if t[1] == 0) # type: ignore
|
|
|
|
# Verify that all outputs are lists
|
|
if not all(is_list_or_contains_list(f) for f in output_fields):
|
|
return False
|
|
|
|
# Verify that all outputs match the input type (are a base class or the same class)
|
|
if not all(
|
|
is_union_subtype(input_root_type, get_args(f)[0]) or issubclass(input_root_type, get_args(f)[0])
|
|
for f in output_fields
|
|
):
|
|
return False
|
|
|
|
return True
|
|
|
|
def nx_graph(self) -> nx.DiGraph:
|
|
"""Returns a NetworkX DiGraph representing the layout of this graph"""
|
|
# TODO: Cache this?
|
|
g = nx.DiGraph()
|
|
g.add_nodes_from(list(self.nodes.keys()))
|
|
g.add_edges_from({(e.source.node_id, e.destination.node_id) for e in self.edges})
|
|
return g
|
|
|
|
def nx_graph_with_data(self) -> nx.DiGraph:
|
|
"""Returns a NetworkX DiGraph representing the data and layout of this graph"""
|
|
g = nx.DiGraph()
|
|
g.add_nodes_from(list(self.nodes.items()))
|
|
g.add_edges_from({(e.source.node_id, e.destination.node_id) for e in self.edges})
|
|
return g
|
|
|
|
def nx_graph_flat(self, nx_graph: Optional[nx.DiGraph] = None) -> nx.DiGraph:
|
|
"""Returns a flattened NetworkX DiGraph, including all subgraphs (but not with iterations expanded)"""
|
|
g = nx_graph or nx.DiGraph()
|
|
|
|
# Add all nodes from this graph except graph/iteration nodes
|
|
g.add_nodes_from([n.id for n in self.nodes.values() if not isinstance(n, IterateInvocation)])
|
|
|
|
# TODO: figure out if iteration nodes need to be expanded
|
|
|
|
unique_edges = {(e.source.node_id, e.destination.node_id) for e in self.edges}
|
|
g.add_edges_from([(e[0], e[1]) for e in unique_edges])
|
|
return g
|
|
|
|
|
|
class GraphExecutionState(BaseModel):
|
|
"""Tracks the state of a graph execution"""
|
|
|
|
id: str = Field(description="The id of the execution state", default_factory=uuid_string)
|
|
# TODO: Store a reference to the graph instead of the actual graph?
|
|
graph: Graph = Field(description="The graph being executed")
|
|
|
|
# The graph of materialized nodes
|
|
execution_graph: Graph = Field(
|
|
description="The expanded graph of activated and executed nodes",
|
|
default_factory=Graph,
|
|
)
|
|
|
|
# Nodes that have been executed
|
|
executed: set[str] = Field(description="The set of node ids that have been executed", default_factory=set)
|
|
executed_history: list[str] = Field(
|
|
description="The list of node ids that have been executed, in order of execution",
|
|
default_factory=list,
|
|
)
|
|
|
|
# The results of executed nodes
|
|
results: dict[str, BaseInvocationOutput] = Field(description="The results of node executions", default_factory=dict)
|
|
|
|
# Errors raised when executing nodes
|
|
errors: dict[str, str] = Field(description="Errors raised when executing nodes", default_factory=dict)
|
|
|
|
# Map of prepared/executed nodes to their original nodes
|
|
prepared_source_mapping: dict[str, str] = Field(
|
|
description="The map of prepared nodes to original graph nodes",
|
|
default_factory=dict,
|
|
)
|
|
|
|
# Map of original nodes to prepared nodes
|
|
source_prepared_mapping: dict[str, set[str]] = Field(
|
|
description="The map of original graph nodes to prepared nodes",
|
|
default_factory=dict,
|
|
)
|
|
|
|
@field_validator("results", mode="plain")
|
|
@classmethod
|
|
def validate_results(cls, v: dict[str, BaseInvocationOutput]):
|
|
"""Validates the results in the GES by retrieving a union of all output types and validating each result."""
|
|
|
|
# See the comment in `Graph.validate_nodes` for an explanation of this logic.
|
|
results: dict[str, BaseInvocationOutput] = {}
|
|
typeadapter = BaseInvocationOutput.get_typeadapter()
|
|
for result_id, result in v.items():
|
|
results[result_id] = typeadapter.validate_python(result)
|
|
return results
|
|
|
|
@field_validator("graph")
|
|
def graph_is_valid(cls, v: Graph):
|
|
"""Validates that the graph is valid"""
|
|
v.validate_self()
|
|
return v
|
|
|
|
@classmethod
|
|
def __get_pydantic_json_schema__(cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue:
|
|
# See the comment in `Graph.__get_pydantic_json_schema__` for an explanation of this logic.
|
|
class GraphExecutionState(BaseModel):
|
|
"""Tracks the state of a graph execution"""
|
|
|
|
id: str = Field(description="The id of the execution state")
|
|
graph: Graph = Field(description="The graph being executed")
|
|
execution_graph: Graph = Field(description="The expanded graph of activated and executed nodes")
|
|
executed: set[str] = Field(description="The set of node ids that have been executed")
|
|
executed_history: list[str] = Field(
|
|
description="The list of node ids that have been executed, in order of execution"
|
|
)
|
|
results: dict[
|
|
str, Annotated[Union[tuple(BaseInvocationOutput._output_classes)], Field(discriminator="type")]
|
|
] = Field(description="The results of node executions")
|
|
errors: dict[str, str] = Field(description="Errors raised when executing nodes")
|
|
prepared_source_mapping: dict[str, str] = Field(
|
|
description="The map of prepared nodes to original graph nodes"
|
|
)
|
|
source_prepared_mapping: dict[str, set[str]] = Field(
|
|
description="The map of original graph nodes to prepared nodes"
|
|
)
|
|
|
|
json_schema = handler(GraphExecutionState.__pydantic_core_schema__)
|
|
json_schema = handler.resolve_ref_schema(json_schema)
|
|
return json_schema
|
|
|
|
def next(self) -> Optional[BaseInvocation]:
|
|
"""Gets the next node ready to execute."""
|
|
|
|
# TODO: enable multiple nodes to execute simultaneously by tracking currently executing nodes
|
|
# possibly with a timeout?
|
|
|
|
# If there are no prepared nodes, prepare some nodes
|
|
next_node = self._get_next_node()
|
|
if next_node is None:
|
|
prepared_id = self._prepare()
|
|
|
|
# Prepare as many nodes as we can
|
|
while prepared_id is not None:
|
|
prepared_id = self._prepare()
|
|
next_node = self._get_next_node()
|
|
|
|
# Get values from edges
|
|
if next_node is not None:
|
|
try:
|
|
self._prepare_inputs(next_node)
|
|
except ValidationError as e:
|
|
raise NodeInputError(next_node, e)
|
|
|
|
# If next is still none, there's no next node, return None
|
|
return next_node
|
|
|
|
def complete(self, node_id: str, output: BaseInvocationOutput) -> None:
|
|
"""Marks a node as complete"""
|
|
|
|
if node_id not in self.execution_graph.nodes:
|
|
return # TODO: log error?
|
|
|
|
# Mark node as executed
|
|
self.executed.add(node_id)
|
|
self.results[node_id] = output
|
|
|
|
# Check if source node is complete (all prepared nodes are complete)
|
|
source_node = self.prepared_source_mapping[node_id]
|
|
prepared_nodes = self.source_prepared_mapping[source_node]
|
|
|
|
if all(n in self.executed for n in prepared_nodes):
|
|
self.executed.add(source_node)
|
|
self.executed_history.append(source_node)
|
|
|
|
def set_node_error(self, node_id: str, error: str):
|
|
"""Marks a node as errored"""
|
|
self.errors[node_id] = error
|
|
|
|
def is_complete(self) -> bool:
|
|
"""Returns true if the graph is complete"""
|
|
node_ids = set(self.graph.nx_graph_flat().nodes)
|
|
return self.has_error() or all((k in self.executed for k in node_ids))
|
|
|
|
def has_error(self) -> bool:
|
|
"""Returns true if the graph has any errors"""
|
|
return len(self.errors) > 0
|
|
|
|
def _create_execution_node(self, node_id: str, iteration_node_map: list[tuple[str, str]]) -> list[str]:
|
|
"""Prepares an iteration node and connects all edges, returning the new node id"""
|
|
|
|
node = self.graph.get_node(node_id)
|
|
|
|
self_iteration_count = -1
|
|
|
|
# If this is an iterator node, we must create a copy for each iteration
|
|
if isinstance(node, IterateInvocation):
|
|
# Get input collection edge (should error if there are no inputs)
|
|
input_collection_edge = next(iter(self.graph._get_input_edges(node_id, "collection")))
|
|
input_collection_prepared_node_id = next(
|
|
n[1] for n in iteration_node_map if n[0] == input_collection_edge.source.node_id
|
|
)
|
|
input_collection_prepared_node_output = self.results[input_collection_prepared_node_id]
|
|
input_collection = getattr(input_collection_prepared_node_output, input_collection_edge.source.field)
|
|
self_iteration_count = len(input_collection)
|
|
|
|
new_nodes: list[str] = []
|
|
if self_iteration_count == 0:
|
|
# TODO: should this raise a warning? It might just happen if an empty collection is input, and should be valid.
|
|
return new_nodes
|
|
|
|
# Get all input edges
|
|
input_edges = self.graph._get_input_edges(node_id)
|
|
|
|
# Create new edges for this iteration
|
|
# For collect nodes, this may contain multiple inputs to the same field
|
|
new_edges: list[Edge] = []
|
|
for edge in input_edges:
|
|
for input_node_id in (n[1] for n in iteration_node_map if n[0] == edge.source.node_id):
|
|
new_edge = Edge(
|
|
source=EdgeConnection(node_id=input_node_id, field=edge.source.field),
|
|
destination=EdgeConnection(node_id="", field=edge.destination.field),
|
|
)
|
|
new_edges.append(new_edge)
|
|
|
|
# Create a new node (or one for each iteration of this iterator)
|
|
for i in range(self_iteration_count) if self_iteration_count > 0 else [-1]:
|
|
# Create a new node
|
|
new_node = copy.deepcopy(node)
|
|
|
|
# Create the node id (use a random uuid)
|
|
new_node.id = uuid_string()
|
|
|
|
# Set the iteration index for iteration invocations
|
|
if isinstance(new_node, IterateInvocation):
|
|
new_node.index = i
|
|
|
|
# Add to execution graph
|
|
self.execution_graph.add_node(new_node)
|
|
self.prepared_source_mapping[new_node.id] = node_id
|
|
if node_id not in self.source_prepared_mapping:
|
|
self.source_prepared_mapping[node_id] = set()
|
|
self.source_prepared_mapping[node_id].add(new_node.id)
|
|
|
|
# Add new edges to execution graph
|
|
for edge in new_edges:
|
|
new_edge = Edge(
|
|
source=edge.source,
|
|
destination=EdgeConnection(node_id=new_node.id, field=edge.destination.field),
|
|
)
|
|
self.execution_graph.add_edge(new_edge)
|
|
|
|
new_nodes.append(new_node.id)
|
|
|
|
return new_nodes
|
|
|
|
def _iterator_graph(self) -> nx.DiGraph:
|
|
"""Gets a DiGraph with edges to collectors removed so an ancestor search produces all active iterators for any node"""
|
|
g = self.graph.nx_graph_flat()
|
|
collectors = (n for n in self.graph.nodes if isinstance(self.graph.get_node(n), CollectInvocation))
|
|
for c in collectors:
|
|
g.remove_edges_from(list(g.in_edges(c)))
|
|
return g
|
|
|
|
def _get_node_iterators(self, node_id: str) -> list[str]:
|
|
"""Gets iterators for a node"""
|
|
g = self._iterator_graph()
|
|
iterators = [n for n in nx.ancestors(g, node_id) if isinstance(self.graph.get_node(n), IterateInvocation)]
|
|
return iterators
|
|
|
|
def _prepare(self) -> Optional[str]:
|
|
# Get flattened source graph
|
|
g = self.graph.nx_graph_flat()
|
|
|
|
# Find next node that:
|
|
# - was not already prepared
|
|
# - is not an iterate node whose inputs have not been executed
|
|
# - does not have an unexecuted iterate ancestor
|
|
sorted_nodes = nx.topological_sort(g)
|
|
next_node_id = next(
|
|
(
|
|
n
|
|
for n in sorted_nodes
|
|
# exclude nodes that have already been prepared
|
|
if n not in self.source_prepared_mapping
|
|
# exclude iterate nodes whose inputs have not been executed
|
|
and not (
|
|
isinstance(self.graph.get_node(n), IterateInvocation) # `n` is an iterate node...
|
|
and not all((e[0] in self.executed for e in g.in_edges(n))) # ...that has unexecuted inputs
|
|
)
|
|
# exclude nodes who have unexecuted iterate ancestors
|
|
and not any(
|
|
(
|
|
isinstance(self.graph.get_node(a), IterateInvocation) # `a` is an iterate ancestor of `n`...
|
|
and a not in self.executed # ...that is not executed
|
|
for a in nx.ancestors(g, n) # for all ancestors `a` of node `n`
|
|
)
|
|
)
|
|
),
|
|
None,
|
|
)
|
|
|
|
if next_node_id is None:
|
|
return None
|
|
|
|
# Get all parents of the next node
|
|
next_node_parents = [e[0] for e in g.in_edges(next_node_id)]
|
|
|
|
# Create execution nodes
|
|
next_node = self.graph.get_node(next_node_id)
|
|
new_node_ids = []
|
|
if isinstance(next_node, CollectInvocation):
|
|
# Collapse all iterator input mappings and create a single execution node for the collect invocation
|
|
all_iteration_mappings = list(
|
|
itertools.chain(*(((s, p) for p in self.source_prepared_mapping[s]) for s in next_node_parents))
|
|
)
|
|
# all_iteration_mappings = list(set(itertools.chain(*prepared_parent_mappings)))
|
|
create_results = self._create_execution_node(next_node_id, all_iteration_mappings)
|
|
if create_results is not None:
|
|
new_node_ids.extend(create_results)
|
|
else: # Iterators or normal nodes
|
|
# Get all iterator combinations for this node
|
|
# Will produce a list of lists of prepared iterator nodes, from which results can be iterated
|
|
iterator_nodes = self._get_node_iterators(next_node_id)
|
|
iterator_nodes_prepared = [list(self.source_prepared_mapping[n]) for n in iterator_nodes]
|
|
iterator_node_prepared_combinations = list(itertools.product(*iterator_nodes_prepared))
|
|
|
|
# Select the correct prepared parents for each iteration
|
|
# For every iterator, the parent must either not be a child of that iterator, or must match the prepared iteration for that iterator
|
|
# TODO: Handle a node mapping to none
|
|
eg = self.execution_graph.nx_graph_flat()
|
|
prepared_parent_mappings = [
|
|
[(n, self._get_iteration_node(n, g, eg, it)) for n in next_node_parents]
|
|
for it in iterator_node_prepared_combinations
|
|
] # type: ignore
|
|
|
|
# Create execution node for each iteration
|
|
for iteration_mappings in prepared_parent_mappings:
|
|
create_results = self._create_execution_node(next_node_id, iteration_mappings) # type: ignore
|
|
if create_results is not None:
|
|
new_node_ids.extend(create_results)
|
|
|
|
return next(iter(new_node_ids), None)
|
|
|
|
def _get_iteration_node(
|
|
self,
|
|
source_node_id: str,
|
|
graph: nx.DiGraph,
|
|
execution_graph: nx.DiGraph,
|
|
prepared_iterator_nodes: list[str],
|
|
) -> Optional[str]:
|
|
"""Gets the prepared version of the specified source node that matches every iteration specified"""
|
|
prepared_nodes = self.source_prepared_mapping[source_node_id]
|
|
if len(prepared_nodes) == 1:
|
|
return next(iter(prepared_nodes))
|
|
|
|
# Check if the requested node is an iterator
|
|
prepared_iterator = next((n for n in prepared_nodes if n in prepared_iterator_nodes), None)
|
|
if prepared_iterator is not None:
|
|
return prepared_iterator
|
|
|
|
# Filter to only iterator nodes that are a parent of the specified node, in tuple format (prepared, source)
|
|
iterator_source_node_mapping = [(n, self.prepared_source_mapping[n]) for n in prepared_iterator_nodes]
|
|
parent_iterators = [itn for itn in iterator_source_node_mapping if nx.has_path(graph, itn[1], source_node_id)]
|
|
|
|
return next(
|
|
(n for n in prepared_nodes if all(nx.has_path(execution_graph, pit[0], n) for pit in parent_iterators)),
|
|
None,
|
|
)
|
|
|
|
def _get_next_node(self) -> Optional[BaseInvocation]:
|
|
"""Gets the deepest node that is ready to be executed"""
|
|
g = self.execution_graph.nx_graph()
|
|
|
|
# Perform a topological sort using depth-first search
|
|
topo_order = list(nx.dfs_postorder_nodes(g))
|
|
|
|
# Get all IterateInvocation nodes
|
|
iterate_nodes = [n for n in topo_order if isinstance(self.execution_graph.nodes[n], IterateInvocation)]
|
|
|
|
# Sort the IterateInvocation nodes based on their index attribute
|
|
iterate_nodes.sort(key=lambda x: self.execution_graph.nodes[x].index)
|
|
|
|
# Prioritize IterateInvocation nodes and their children
|
|
for iterate_node in iterate_nodes:
|
|
if iterate_node not in self.executed and all((e[0] in self.executed for e in g.in_edges(iterate_node))):
|
|
return self.execution_graph.nodes[iterate_node]
|
|
|
|
# Check the children of the IterateInvocation node
|
|
for child_node in nx.dfs_postorder_nodes(g, iterate_node):
|
|
if child_node not in self.executed and all((e[0] in self.executed for e in g.in_edges(child_node))):
|
|
return self.execution_graph.nodes[child_node]
|
|
|
|
# If no IterateInvocation node or its children are ready, return the first ready node in the topological order
|
|
for node in topo_order:
|
|
if node not in self.executed and all((e[0] in self.executed for e in g.in_edges(node))):
|
|
return self.execution_graph.nodes[node]
|
|
|
|
# If no node is found, return None
|
|
return None
|
|
|
|
def _prepare_inputs(self, node: BaseInvocation):
|
|
input_edges = [e for e in self.execution_graph.edges if e.destination.node_id == node.id]
|
|
# Inputs must be deep-copied, else if a node mutates the object, other nodes that get the same input
|
|
# will see the mutation.
|
|
if isinstance(node, CollectInvocation):
|
|
output_collection = [
|
|
copydeep(getattr(self.results[edge.source.node_id], edge.source.field))
|
|
for edge in input_edges
|
|
if edge.destination.field == "item"
|
|
]
|
|
node.collection = output_collection
|
|
else:
|
|
for edge in input_edges:
|
|
setattr(
|
|
node,
|
|
edge.destination.field,
|
|
copydeep(getattr(self.results[edge.source.node_id], edge.source.field)),
|
|
)
|
|
|
|
# TODO: Add API for modifying underlying graph that checks if the change will be valid given the current execution state
|
|
def _is_edge_valid(self, edge: Edge) -> bool:
|
|
try:
|
|
self.graph._validate_edge(edge)
|
|
except InvalidEdgeError:
|
|
return False
|
|
|
|
# Invalid if destination has already been prepared or executed
|
|
if edge.destination.node_id in self.source_prepared_mapping:
|
|
return False
|
|
|
|
# Otherwise, the edge is valid
|
|
return True
|
|
|
|
def _is_node_updatable(self, node_id: str) -> bool:
|
|
# The node is updatable as long as it hasn't been prepared or executed
|
|
return node_id not in self.source_prepared_mapping
|
|
|
|
def add_node(self, node: BaseInvocation) -> None:
|
|
self.graph.add_node(node)
|
|
|
|
def update_node(self, node_id: str, new_node: BaseInvocation) -> None:
|
|
if not self._is_node_updatable(node_id):
|
|
raise NodeAlreadyExecutedError(
|
|
f"Node {node_id} has already been prepared or executed and cannot be updated"
|
|
)
|
|
self.graph.update_node(node_id, new_node)
|
|
|
|
def delete_node(self, node_id: str) -> None:
|
|
if not self._is_node_updatable(node_id):
|
|
raise NodeAlreadyExecutedError(
|
|
f"Node {node_id} has already been prepared or executed and cannot be deleted"
|
|
)
|
|
self.graph.delete_node(node_id)
|
|
|
|
def add_edge(self, edge: Edge) -> None:
|
|
if not self._is_node_updatable(edge.destination.node_id):
|
|
raise NodeAlreadyExecutedError(
|
|
f"Destination node {edge.destination.node_id} has already been prepared or executed and cannot be linked to"
|
|
)
|
|
self.graph.add_edge(edge)
|
|
|
|
def delete_edge(self, edge: Edge) -> None:
|
|
if not self._is_node_updatable(edge.destination.node_id):
|
|
raise NodeAlreadyExecutedError(
|
|
f"Destination node {edge.destination.node_id} has already been prepared or executed and cannot have a source edge deleted"
|
|
)
|
|
self.graph.delete_edge(edge)
|