mirror of
https://github.com/invoke-ai/InvokeAI.git
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d4104be0b8
* test: add if-node execution coverage * feat: short-circuit if-node branch execution * test: cover iterated if-node pruning * style: apply ruff fixes for if-node work * refactor: track prepared exec node metadata * fix: defer iterated if branches until resolution * refactor: extract prepared exec registry * refactor: extract if branch scheduler * refactor: extract execution materializer * refactor: extract execution scheduler * refactor: extract execution runtime * refactor: clarify if branch resolution * refactor: clarify execution materialization * docs: describe graph execution helpers * refactor: clarify execution runtime * refactor: clarify execution scheduling * refactor: clarify iteration node selection * docs: describe execution materializer flow * refactor: clarify collector validation * refactor: clarify iterator validation * refactor: clarify graph validation flow * docs: update shared graph design overview * chore: typegen * fix: harden if-node scheduler edge cases
767 lines
31 KiB
Python
767 lines
31 KiB
Python
from typing import Optional
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from unittest.mock import Mock
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import pytest
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from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
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from invokeai.app.invocations.collections import RangeInvocation
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from invokeai.app.invocations.logic import IfInvocation, IfInvocationOutput
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from invokeai.app.invocations.math import AddInvocation, MultiplyInvocation
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from invokeai.app.invocations.primitives import BooleanCollectionInvocation, BooleanInvocation
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from invokeai.app.services.shared.graph import (
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CollectInvocation,
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Graph,
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GraphExecutionState,
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IterateInvocation,
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)
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# This import must happen before other invoke imports or test in other files(!!) break
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from tests.test_nodes import (
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AnyTypeTestInvocation,
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PromptCollectionTestInvocation,
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PromptTestInvocation,
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TextToImageTestInvocation,
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create_edge,
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)
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@pytest.fixture
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def simple_graph() -> Graph:
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g = Graph()
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g.add_node(PromptTestInvocation(id="1", prompt="Banana sushi"))
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g.add_node(TextToImageTestInvocation(id="2"))
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g.add_edge(create_edge("1", "prompt", "2", "prompt"))
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return g
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def invoke_next(g: GraphExecutionState) -> tuple[Optional[BaseInvocation], Optional[BaseInvocationOutput]]:
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n = g.next()
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if n is None:
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return (None, None)
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print(f"invoking {n.id}: {type(n)}")
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o = n.invoke(Mock(InvocationContext))
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g.complete(n.id, o)
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return (n, o)
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def execute_all_nodes(g: GraphExecutionState) -> list[str]:
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"""Execute the graph to completion and return source node ids in execution order."""
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executed_source_ids: list[str] = []
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while True:
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invocation, _output = invoke_next(g)
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if invocation is None:
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break
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executed_source_ids.append(g.prepared_source_mapping[invocation.id])
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return executed_source_ids
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def test_graph_state_executes_in_order(simple_graph: Graph):
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g = GraphExecutionState(graph=simple_graph)
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n1 = invoke_next(g)
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n2 = invoke_next(g)
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n3 = g.next()
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assert g.prepared_source_mapping[n1[0].id] == "1"
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assert g.prepared_source_mapping[n2[0].id] == "2"
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assert n3 is None
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assert g.results[n1[0].id].prompt == n1[0].prompt
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assert n2[0].prompt == n1[0].prompt
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def test_graph_is_complete(simple_graph: Graph):
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g = GraphExecutionState(graph=simple_graph)
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_ = invoke_next(g)
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_ = invoke_next(g)
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_ = g.next()
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assert g.is_complete()
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def test_graph_is_not_complete(simple_graph: Graph):
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g = GraphExecutionState(graph=simple_graph)
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_ = invoke_next(g)
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_ = g.next()
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assert not g.is_complete()
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# TODO: test completion with iterators/subgraphs
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def test_graph_state_expands_iterator():
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graph = Graph()
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graph.add_node(RangeInvocation(id="0", start=0, stop=3, step=1))
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graph.add_node(IterateInvocation(id="1"))
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graph.add_node(MultiplyInvocation(id="2", b=10))
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graph.add_node(AddInvocation(id="3", b=1))
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graph.add_edge(create_edge("0", "collection", "1", "collection"))
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graph.add_edge(create_edge("1", "item", "2", "a"))
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graph.add_edge(create_edge("2", "value", "3", "a"))
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g = GraphExecutionState(graph=graph)
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while not g.is_complete():
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invoke_next(g)
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prepared_add_nodes = g.source_prepared_mapping["3"]
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results = {g.results[n].value for n in prepared_add_nodes}
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expected = {1, 11, 21}
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assert results == expected
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def test_graph_state_collects():
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graph = Graph()
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test_prompts = ["Banana sushi", "Cat sushi"]
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graph.add_node(PromptCollectionTestInvocation(id="1", collection=list(test_prompts)))
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graph.add_node(IterateInvocation(id="2"))
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graph.add_node(PromptTestInvocation(id="3"))
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graph.add_node(CollectInvocation(id="4"))
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graph.add_edge(create_edge("1", "collection", "2", "collection"))
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graph.add_edge(create_edge("2", "item", "3", "prompt"))
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graph.add_edge(create_edge("3", "prompt", "4", "item"))
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g = GraphExecutionState(graph=graph)
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_ = invoke_next(g)
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_ = invoke_next(g)
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_ = invoke_next(g)
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_ = invoke_next(g)
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_ = invoke_next(g)
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n6 = invoke_next(g)
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assert isinstance(n6[0], CollectInvocation)
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assert sorted(g.results[n6[0].id].collection) == sorted(test_prompts)
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def test_graph_state_prepares_eagerly():
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"""Tests that all prepareable nodes are prepared"""
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graph = Graph()
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test_prompts = ["Banana sushi", "Cat sushi"]
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graph.add_node(PromptCollectionTestInvocation(id="prompt_collection", collection=list(test_prompts)))
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graph.add_node(IterateInvocation(id="iterate"))
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graph.add_node(PromptTestInvocation(id="prompt_iterated"))
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graph.add_edge(create_edge("prompt_collection", "collection", "iterate", "collection"))
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graph.add_edge(create_edge("iterate", "item", "prompt_iterated", "prompt"))
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# separated, fully-preparable chain of nodes
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graph.add_node(PromptTestInvocation(id="prompt_chain_1", prompt="Dinosaur sushi"))
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graph.add_node(PromptTestInvocation(id="prompt_chain_2"))
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graph.add_node(PromptTestInvocation(id="prompt_chain_3"))
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graph.add_edge(create_edge("prompt_chain_1", "prompt", "prompt_chain_2", "prompt"))
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graph.add_edge(create_edge("prompt_chain_2", "prompt", "prompt_chain_3", "prompt"))
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g = GraphExecutionState(graph=graph)
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g.next()
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assert "prompt_collection" in g.source_prepared_mapping
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assert "prompt_chain_1" in g.source_prepared_mapping
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assert "prompt_chain_2" in g.source_prepared_mapping
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assert "prompt_chain_3" in g.source_prepared_mapping
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assert "iterate" not in g.source_prepared_mapping
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assert "prompt_iterated" not in g.source_prepared_mapping
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def test_graph_executes_depth_first():
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"""Tests that the graph executes depth-first, executing a branch as far as possible before moving to the next branch"""
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def assert_topo_order_and_all_executed(state: GraphExecutionState, order: list[str]):
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"""
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Validates:
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1) Every materialized exec node executed exactly once.
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2) Execution order respects all exec-graph dependencies (u→v ⇒ u before v).
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"""
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# order must be EXEC node ids in run order
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exec_nodes = set(state.execution_graph.nodes.keys())
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# 1) coverage: all exec nodes ran, and no duplicates
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pos = {nid: i for i, nid in enumerate(order)}
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assert set(pos.keys()) == exec_nodes, (
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f"Executed {len(pos)} of {len(exec_nodes)} nodes. Missing: {sorted(exec_nodes - set(pos))[:10]}"
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)
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assert len(pos) == len(order), "Duplicate execution detected"
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# 2) topo order: parents before children
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for e in state.execution_graph.edges:
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u = e.source.node_id
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v = e.destination.node_id
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assert pos[u] < pos[v], f"child {v} ran before parent {u}"
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graph = Graph()
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test_prompts = ["Banana sushi", "Cat sushi"]
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graph.add_node(PromptCollectionTestInvocation(id="prompt_collection", collection=list(test_prompts)))
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graph.add_node(IterateInvocation(id="iterate"))
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graph.add_node(PromptTestInvocation(id="prompt_iterated"))
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graph.add_node(PromptTestInvocation(id="prompt_successor"))
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graph.add_edge(create_edge("prompt_collection", "collection", "iterate", "collection"))
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graph.add_edge(create_edge("iterate", "item", "prompt_iterated", "prompt"))
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graph.add_edge(create_edge("prompt_iterated", "prompt", "prompt_successor", "prompt"))
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g = GraphExecutionState(graph=graph)
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order: list[str] = []
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while True:
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n = g.next()
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if n is None:
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break
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o = n.invoke(Mock(InvocationContext))
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g.complete(n.id, o)
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order.append(n.id)
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assert_topo_order_and_all_executed(g, order)
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def test_graph_scheduler_drains_active_class_before_switching():
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graph = Graph()
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graph.add_node(PromptTestInvocation(id="prompt_a", prompt="a"))
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graph.add_node(PromptTestInvocation(id="prompt_b", prompt="b"))
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graph.add_node(TextToImageTestInvocation(id="image"))
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g = GraphExecutionState(graph=graph)
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g.set_ready_order([PromptTestInvocation, TextToImageTestInvocation])
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first = invoke_next(g)[0]
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second = invoke_next(g)[0]
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third = invoke_next(g)[0]
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assert first is not None
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assert g.prepared_source_mapping[first.id] == "prompt_a"
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assert g.prepared_source_mapping[second.id] == "prompt_b"
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assert g.prepared_source_mapping[third.id] == "image"
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def test_graph_scheduler_skips_stale_ready_entries():
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graph = Graph()
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graph.add_node(PromptTestInvocation(id="prompt_a", prompt="a"))
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graph.add_node(PromptTestInvocation(id="prompt_b", prompt="b"))
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g = GraphExecutionState(graph=graph)
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g.set_ready_order([PromptTestInvocation])
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first = invoke_next(g)[0]
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assert first is not None
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prompt_queue = g._queue_for(PromptTestInvocation.__name__)
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prompt_queue.appendleft(first.id)
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second = g.next()
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assert second is not None
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assert second.id != first.id
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assert g.prepared_source_mapping[second.id] == "prompt_b"
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def test_graph_scheduler_falls_back_to_non_priority_ready_classes():
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graph = Graph()
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graph.add_node(TextToImageTestInvocation(id="image"))
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g = GraphExecutionState(graph=graph)
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g.set_ready_order([PromptTestInvocation])
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next_node = g.next()
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assert next_node is not None
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assert g.prepared_source_mapping[next_node.id] == "image"
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# Because this tests deterministic ordering, we run it multiple times
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@pytest.mark.parametrize("execution_number", range(5))
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def test_graph_iterate_execution_order(execution_number: int):
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"""Tests that iterate nodes execution is ordered by the order of the collection"""
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graph = Graph()
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test_prompts = ["Banana sushi", "Cat sushi", "Strawberry Sushi", "Dinosaur Sushi"]
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graph.add_node(PromptCollectionTestInvocation(id="prompt_collection", collection=list(test_prompts)))
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graph.add_node(IterateInvocation(id="iterate"))
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graph.add_node(PromptTestInvocation(id="prompt_iterated"))
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graph.add_edge(create_edge("prompt_collection", "collection", "iterate", "collection"))
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graph.add_edge(create_edge("iterate", "item", "prompt_iterated", "prompt"))
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g = GraphExecutionState(graph=graph)
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_ = invoke_next(g)
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_ = invoke_next(g)
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assert _[1].item == "Banana sushi"
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_ = invoke_next(g)
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assert _[1].item == "Cat sushi"
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_ = invoke_next(g)
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assert _[1].item == "Strawberry Sushi"
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_ = invoke_next(g)
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assert _[1].item == "Dinosaur Sushi"
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_ = invoke_next(g)
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# Because this tests deterministic ordering, we run it multiple times
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@pytest.mark.parametrize("execution_number", range(5))
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def test_graph_nested_iterate_execution_order(execution_number: int):
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"""
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Validates best-effort in-order execution for nodes expanded under nested iterators.
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Expected lexicographic order by (outer_index, inner_index), subject to readiness.
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"""
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graph = Graph()
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# Outer iterator: [0, 1]
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graph.add_node(RangeInvocation(id="outer_range", start=0, stop=2, step=1))
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graph.add_node(IterateInvocation(id="outer_iter"))
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# Inner iterator is derived from the outer item:
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# start = outer_item * 10
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# stop = start + 2 => yields 2 items per outer item
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graph.add_node(MultiplyInvocation(id="mul10", b=10))
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graph.add_node(AddInvocation(id="stop_plus2", b=2))
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graph.add_node(RangeInvocation(id="inner_range", start=0, stop=1, step=1))
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graph.add_node(IterateInvocation(id="inner_iter"))
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# Observe inner items (they encode outer via start=outer*10)
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graph.add_node(AddInvocation(id="sum", b=0))
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graph.add_edge(create_edge("outer_range", "collection", "outer_iter", "collection"))
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graph.add_edge(create_edge("outer_iter", "item", "mul10", "a"))
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graph.add_edge(create_edge("mul10", "value", "stop_plus2", "a"))
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graph.add_edge(create_edge("mul10", "value", "inner_range", "start"))
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graph.add_edge(create_edge("stop_plus2", "value", "inner_range", "stop"))
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graph.add_edge(create_edge("inner_range", "collection", "inner_iter", "collection"))
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graph.add_edge(create_edge("inner_iter", "item", "sum", "a"))
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g = GraphExecutionState(graph=graph)
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sum_values: list[int] = []
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while True:
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n, o = invoke_next(g)
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if n is None:
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break
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if g.prepared_source_mapping[n.id] == "sum":
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sum_values.append(o.value)
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assert sum_values == [0, 1, 10, 11]
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def test_graph_validate_self_iterator_without_collection_input_raises_invalid_edge_error():
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"""Iterator nodes with no collection input should fail validation cleanly.
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This test exposes the bug where validation crashes with IndexError instead of raising InvalidEdgeError.
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"""
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from invokeai.app.services.shared.graph import InvalidEdgeError
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graph = Graph()
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graph.add_node(IterateInvocation(id="iterate"))
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with pytest.raises(InvalidEdgeError):
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graph.validate_self()
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def test_graph_validate_self_collector_without_item_inputs_raises_invalid_edge_error():
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"""Collector nodes with no item inputs should fail validation cleanly.
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This test exposes the bug where validation can crash (e.g. StopIteration) instead of raising InvalidEdgeError.
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"""
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from invokeai.app.services.shared.graph import InvalidEdgeError
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graph = Graph()
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graph.add_node(CollectInvocation(id="collect"))
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with pytest.raises(InvalidEdgeError):
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graph.validate_self()
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def test_if_invocation_selects_true_input_value():
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invocation = IfInvocation(id="if", condition=True, true_input="true", false_input="false")
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output = invocation.invoke(Mock(InvocationContext))
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assert output.value == "true"
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def test_if_invocation_outputs_none_when_selected_input_is_missing():
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invocation = IfInvocation(id="if", condition=False, true_input="true")
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output = invocation.invoke(Mock(InvocationContext))
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assert output.value is None
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def test_if_invocation_output_allows_missing_value_on_deserialization():
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output = IfInvocationOutput.model_validate({"type": "if_output"})
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assert output.value is None
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def test_if_invocation_output_connects_to_downstream_input():
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graph = Graph()
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graph.add_node(IfInvocation(id="if", condition=True, true_input="connected value", false_input="unused"))
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graph.add_node(PromptTestInvocation(id="prompt"))
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graph.add_edge(create_edge("if", "value", "prompt", "prompt"))
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g = GraphExecutionState(graph=graph)
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while not g.is_complete():
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invoke_next(g)
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prepared_prompt_nodes = g.source_prepared_mapping["prompt"]
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assert len(prepared_prompt_nodes) == 1
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prepared_prompt_node_id = next(iter(prepared_prompt_nodes))
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assert g.results[prepared_prompt_node_id].prompt == "connected value"
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@pytest.mark.xfail(strict=True, reason="Legacy eager If-node execution should no longer occur")
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def test_if_graph_current_behavior_executes_both_branches_and_shared_ancestors():
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graph = Graph()
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graph.add_node(BooleanInvocation(id="condition", value=True))
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graph.add_node(PromptTestInvocation(id="shared", prompt="shared value"))
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graph.add_node(PromptTestInvocation(id="true_mid"))
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graph.add_node(PromptTestInvocation(id="true_leaf"))
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graph.add_node(PromptTestInvocation(id="false_mid"))
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graph.add_node(PromptTestInvocation(id="false_leaf"))
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graph.add_node(PromptTestInvocation(id="side_consumer"))
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graph.add_node(IfInvocation(id="if"))
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graph.add_node(PromptTestInvocation(id="selected_output"))
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graph.add_edge(create_edge("condition", "value", "if", "condition"))
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graph.add_edge(create_edge("shared", "prompt", "true_mid", "prompt"))
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graph.add_edge(create_edge("true_mid", "prompt", "true_leaf", "prompt"))
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graph.add_edge(create_edge("true_leaf", "prompt", "if", "true_input"))
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graph.add_edge(create_edge("shared", "prompt", "false_mid", "prompt"))
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graph.add_edge(create_edge("false_mid", "prompt", "false_leaf", "prompt"))
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graph.add_edge(create_edge("false_leaf", "prompt", "if", "false_input"))
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graph.add_edge(create_edge("shared", "prompt", "side_consumer", "prompt"))
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graph.add_edge(create_edge("if", "value", "selected_output", "prompt"))
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g = GraphExecutionState(graph=graph)
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executed_source_ids = execute_all_nodes(g)
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assert set(executed_source_ids) == {
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"condition",
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"shared",
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"true_mid",
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"true_leaf",
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"false_mid",
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"false_leaf",
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"side_consumer",
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"if",
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"selected_output",
|
|
}
|
|
assert executed_source_ids.count("false_mid") == 1
|
|
assert executed_source_ids.count("false_leaf") == 1
|
|
|
|
prepared_selected_output_id = next(iter(g.source_prepared_mapping["selected_output"]))
|
|
assert g.results[prepared_selected_output_id].prompt == "shared value"
|
|
|
|
|
|
@pytest.mark.xfail(strict=True, reason="Legacy eager If-node execution should no longer occur")
|
|
def test_if_graph_current_behavior_executes_both_simple_branches():
|
|
graph = Graph()
|
|
graph.add_node(BooleanInvocation(id="condition", value=True))
|
|
graph.add_node(PromptTestInvocation(id="true_value", prompt="true branch"))
|
|
graph.add_node(PromptTestInvocation(id="false_value", prompt="false branch"))
|
|
graph.add_node(IfInvocation(id="if"))
|
|
graph.add_node(PromptTestInvocation(id="selected_output"))
|
|
|
|
graph.add_edge(create_edge("condition", "value", "if", "condition"))
|
|
graph.add_edge(create_edge("true_value", "prompt", "if", "true_input"))
|
|
graph.add_edge(create_edge("false_value", "prompt", "if", "false_input"))
|
|
graph.add_edge(create_edge("if", "value", "selected_output", "prompt"))
|
|
|
|
g = GraphExecutionState(graph=graph)
|
|
executed_source_ids = execute_all_nodes(g)
|
|
|
|
assert set(executed_source_ids) == {"condition", "true_value", "false_value", "if", "selected_output"}
|
|
prepared_selected_output_id = next(iter(g.source_prepared_mapping["selected_output"]))
|
|
assert g.results[prepared_selected_output_id].prompt == "true branch"
|
|
|
|
|
|
def test_if_graph_optimized_behavior_executes_only_selected_simple_branch():
|
|
graph = Graph()
|
|
graph.add_node(BooleanInvocation(id="condition", value=True))
|
|
graph.add_node(PromptTestInvocation(id="true_value", prompt="true branch"))
|
|
graph.add_node(PromptTestInvocation(id="false_value", prompt="false branch"))
|
|
graph.add_node(IfInvocation(id="if"))
|
|
graph.add_node(PromptTestInvocation(id="selected_output"))
|
|
|
|
graph.add_edge(create_edge("condition", "value", "if", "condition"))
|
|
graph.add_edge(create_edge("true_value", "prompt", "if", "true_input"))
|
|
graph.add_edge(create_edge("false_value", "prompt", "if", "false_input"))
|
|
graph.add_edge(create_edge("if", "value", "selected_output", "prompt"))
|
|
|
|
g = GraphExecutionState(graph=graph)
|
|
executed_source_ids = execute_all_nodes(g)
|
|
|
|
assert set(executed_source_ids) == {"condition", "true_value", "if", "selected_output"}
|
|
assert "false_value" not in executed_source_ids
|
|
|
|
|
|
def test_if_graph_optimized_behavior_records_skipped_branch_in_execution_history():
|
|
graph = Graph()
|
|
graph.add_node(BooleanInvocation(id="condition", value=True))
|
|
graph.add_node(PromptTestInvocation(id="true_value", prompt="true branch"))
|
|
graph.add_node(PromptTestInvocation(id="false_value", prompt="false branch"))
|
|
graph.add_node(IfInvocation(id="if"))
|
|
graph.add_node(PromptTestInvocation(id="selected_output"))
|
|
|
|
graph.add_edge(create_edge("condition", "value", "if", "condition"))
|
|
graph.add_edge(create_edge("true_value", "prompt", "if", "true_input"))
|
|
graph.add_edge(create_edge("false_value", "prompt", "if", "false_input"))
|
|
graph.add_edge(create_edge("if", "value", "selected_output", "prompt"))
|
|
|
|
g = GraphExecutionState(graph=graph)
|
|
execute_all_nodes(g)
|
|
|
|
assert set(g.executed_history) == {"condition", "true_value", "false_value", "if", "selected_output"}
|
|
assert g.executed_history.count("false_value") == 1
|
|
|
|
|
|
def test_if_graph_optimized_behavior_skips_unselected_branch_but_keeps_shared_ancestors():
|
|
graph = Graph()
|
|
graph.add_node(BooleanInvocation(id="condition", value=True))
|
|
graph.add_node(PromptTestInvocation(id="shared", prompt="shared value"))
|
|
graph.add_node(PromptTestInvocation(id="true_mid"))
|
|
graph.add_node(PromptTestInvocation(id="true_leaf"))
|
|
graph.add_node(PromptTestInvocation(id="false_mid"))
|
|
graph.add_node(PromptTestInvocation(id="false_leaf"))
|
|
graph.add_node(PromptTestInvocation(id="side_consumer"))
|
|
graph.add_node(IfInvocation(id="if"))
|
|
graph.add_node(PromptTestInvocation(id="selected_output"))
|
|
|
|
graph.add_edge(create_edge("condition", "value", "if", "condition"))
|
|
graph.add_edge(create_edge("shared", "prompt", "true_mid", "prompt"))
|
|
graph.add_edge(create_edge("true_mid", "prompt", "true_leaf", "prompt"))
|
|
graph.add_edge(create_edge("true_leaf", "prompt", "if", "true_input"))
|
|
graph.add_edge(create_edge("shared", "prompt", "false_mid", "prompt"))
|
|
graph.add_edge(create_edge("false_mid", "prompt", "false_leaf", "prompt"))
|
|
graph.add_edge(create_edge("false_leaf", "prompt", "if", "false_input"))
|
|
graph.add_edge(create_edge("shared", "prompt", "side_consumer", "prompt"))
|
|
graph.add_edge(create_edge("if", "value", "selected_output", "prompt"))
|
|
|
|
g = GraphExecutionState(graph=graph)
|
|
executed_source_ids = execute_all_nodes(g)
|
|
|
|
assert set(executed_source_ids) == {
|
|
"condition",
|
|
"shared",
|
|
"true_mid",
|
|
"true_leaf",
|
|
"side_consumer",
|
|
"if",
|
|
"selected_output",
|
|
}
|
|
assert "false_mid" not in executed_source_ids
|
|
assert "false_leaf" not in executed_source_ids
|
|
|
|
|
|
def test_if_graph_optimized_behavior_skips_distant_unselected_ancestors_only_when_exclusive():
|
|
graph = Graph()
|
|
graph.add_node(BooleanInvocation(id="condition", value=False))
|
|
graph.add_node(PromptTestInvocation(id="shared_root", prompt="shared value"))
|
|
graph.add_node(PromptTestInvocation(id="true_shared_mid"))
|
|
graph.add_node(PromptTestInvocation(id="true_exclusive_leaf"))
|
|
graph.add_node(PromptTestInvocation(id="false_mid"))
|
|
graph.add_node(PromptTestInvocation(id="false_leaf"))
|
|
graph.add_node(PromptTestInvocation(id="shared_observer"))
|
|
graph.add_node(IfInvocation(id="if"))
|
|
graph.add_node(PromptTestInvocation(id="selected_output"))
|
|
|
|
graph.add_edge(create_edge("condition", "value", "if", "condition"))
|
|
graph.add_edge(create_edge("shared_root", "prompt", "true_shared_mid", "prompt"))
|
|
graph.add_edge(create_edge("true_shared_mid", "prompt", "true_exclusive_leaf", "prompt"))
|
|
graph.add_edge(create_edge("true_exclusive_leaf", "prompt", "if", "true_input"))
|
|
graph.add_edge(create_edge("shared_root", "prompt", "false_mid", "prompt"))
|
|
graph.add_edge(create_edge("false_mid", "prompt", "false_leaf", "prompt"))
|
|
graph.add_edge(create_edge("false_leaf", "prompt", "if", "false_input"))
|
|
graph.add_edge(create_edge("true_shared_mid", "prompt", "shared_observer", "prompt"))
|
|
graph.add_edge(create_edge("if", "value", "selected_output", "prompt"))
|
|
|
|
g = GraphExecutionState(graph=graph)
|
|
executed_source_ids = execute_all_nodes(g)
|
|
|
|
assert set(executed_source_ids) == {
|
|
"condition",
|
|
"shared_root",
|
|
"true_shared_mid",
|
|
"false_mid",
|
|
"false_leaf",
|
|
"shared_observer",
|
|
"if",
|
|
"selected_output",
|
|
}
|
|
assert "true_exclusive_leaf" not in executed_source_ids
|
|
|
|
|
|
def test_if_graph_optimized_behavior_allows_selected_missing_branch_input():
|
|
graph = Graph()
|
|
graph.add_node(BooleanInvocation(id="condition", value=False))
|
|
graph.add_node(PromptTestInvocation(id="true_value", prompt="true branch"))
|
|
graph.add_node(IfInvocation(id="if"))
|
|
graph.add_node(AnyTypeTestInvocation(id="selected_output"))
|
|
|
|
graph.add_edge(create_edge("condition", "value", "if", "condition"))
|
|
graph.add_edge(create_edge("true_value", "prompt", "if", "true_input"))
|
|
graph.add_edge(create_edge("if", "value", "selected_output", "value"))
|
|
|
|
g = GraphExecutionState(graph=graph)
|
|
executed_source_ids = execute_all_nodes(g)
|
|
|
|
prepared_selected_output_id = next(iter(g.source_prepared_mapping["selected_output"]))
|
|
assert g.results[prepared_selected_output_id].value is None
|
|
assert set(executed_source_ids) == {"condition", "if", "selected_output"}
|
|
assert "true_value" not in executed_source_ids
|
|
|
|
|
|
def test_if_graph_optimized_behavior_does_not_cross_defer_independent_ifs():
|
|
graph = Graph()
|
|
graph.add_node(BooleanInvocation(id="condition_a", value=True))
|
|
graph.add_node(BooleanInvocation(id="condition_b", value=False))
|
|
graph.add_node(PromptTestInvocation(id="true_a", prompt="true a"))
|
|
graph.add_node(PromptTestInvocation(id="false_a", prompt="false a"))
|
|
graph.add_node(PromptTestInvocation(id="true_b", prompt="true b"))
|
|
graph.add_node(PromptTestInvocation(id="false_b", prompt="false b"))
|
|
graph.add_node(IfInvocation(id="if_a"))
|
|
graph.add_node(IfInvocation(id="if_b"))
|
|
graph.add_node(CollectInvocation(id="collect"))
|
|
|
|
graph.add_edge(create_edge("condition_a", "value", "if_a", "condition"))
|
|
graph.add_edge(create_edge("true_a", "prompt", "if_a", "true_input"))
|
|
graph.add_edge(create_edge("false_a", "prompt", "if_a", "false_input"))
|
|
graph.add_edge(create_edge("condition_b", "value", "if_b", "condition"))
|
|
graph.add_edge(create_edge("true_b", "prompt", "if_b", "true_input"))
|
|
graph.add_edge(create_edge("false_b", "prompt", "if_b", "false_input"))
|
|
graph.add_edge(create_edge("if_a", "value", "collect", "item"))
|
|
graph.add_edge(create_edge("if_b", "value", "collect", "item"))
|
|
|
|
g = GraphExecutionState(graph=graph)
|
|
executed_source_ids = execute_all_nodes(g)
|
|
|
|
prepared_collect_id = next(iter(g.source_prepared_mapping["collect"]))
|
|
assert sorted(g.results[prepared_collect_id].collection) == ["false b", "true a"]
|
|
assert set(executed_source_ids) == {
|
|
"condition_a",
|
|
"condition_b",
|
|
"true_a",
|
|
"false_b",
|
|
"if_a",
|
|
"if_b",
|
|
"collect",
|
|
}
|
|
assert "false_a" not in executed_source_ids
|
|
assert "true_b" not in executed_source_ids
|
|
|
|
|
|
def test_if_graph_optimized_behavior_supports_nested_ifs():
|
|
graph = Graph()
|
|
graph.add_node(BooleanInvocation(id="outer_condition", value=True))
|
|
graph.add_node(BooleanInvocation(id="inner_condition", value=False))
|
|
graph.add_node(PromptTestInvocation(id="outer_false", prompt="outer false"))
|
|
graph.add_node(PromptTestInvocation(id="inner_true", prompt="inner true"))
|
|
graph.add_node(PromptTestInvocation(id="inner_false", prompt="inner false"))
|
|
graph.add_node(IfInvocation(id="inner_if"))
|
|
graph.add_node(IfInvocation(id="outer_if"))
|
|
graph.add_node(PromptTestInvocation(id="selected_output"))
|
|
|
|
graph.add_edge(create_edge("inner_condition", "value", "inner_if", "condition"))
|
|
graph.add_edge(create_edge("inner_true", "prompt", "inner_if", "true_input"))
|
|
graph.add_edge(create_edge("inner_false", "prompt", "inner_if", "false_input"))
|
|
graph.add_edge(create_edge("outer_condition", "value", "outer_if", "condition"))
|
|
graph.add_edge(create_edge("inner_if", "value", "outer_if", "true_input"))
|
|
graph.add_edge(create_edge("outer_false", "prompt", "outer_if", "false_input"))
|
|
graph.add_edge(create_edge("outer_if", "value", "selected_output", "prompt"))
|
|
|
|
g = GraphExecutionState(graph=graph)
|
|
executed_source_ids = execute_all_nodes(g)
|
|
|
|
prepared_selected_output_id = next(iter(g.source_prepared_mapping["selected_output"]))
|
|
assert g.results[prepared_selected_output_id].prompt == "inner false"
|
|
assert set(executed_source_ids) == {
|
|
"outer_condition",
|
|
"inner_condition",
|
|
"inner_false",
|
|
"inner_if",
|
|
"outer_if",
|
|
"selected_output",
|
|
}
|
|
assert "inner_true" not in executed_source_ids
|
|
assert "outer_false" not in executed_source_ids
|
|
|
|
|
|
def test_if_graph_optimized_behavior_prunes_branches_per_iteration():
|
|
graph = Graph()
|
|
graph.add_node(BooleanCollectionInvocation(id="conditions", collection=[True, False, True]))
|
|
graph.add_node(IterateInvocation(id="condition_iter"))
|
|
graph.add_node(AnyTypeTestInvocation(id="true_branch"))
|
|
graph.add_node(AnyTypeTestInvocation(id="false_branch"))
|
|
graph.add_node(IfInvocation(id="if"))
|
|
graph.add_node(CollectInvocation(id="collect"))
|
|
|
|
graph.add_edge(create_edge("conditions", "collection", "condition_iter", "collection"))
|
|
graph.add_edge(create_edge("condition_iter", "item", "if", "condition"))
|
|
graph.add_edge(create_edge("condition_iter", "item", "true_branch", "value"))
|
|
graph.add_edge(create_edge("true_branch", "value", "if", "true_input"))
|
|
graph.add_edge(create_edge("condition_iter", "item", "false_branch", "value"))
|
|
graph.add_edge(create_edge("false_branch", "value", "if", "false_input"))
|
|
graph.add_edge(create_edge("if", "value", "collect", "item"))
|
|
|
|
g = GraphExecutionState(graph=graph)
|
|
executed_source_ids = execute_all_nodes(g)
|
|
|
|
prepared_collect_id = next(iter(g.source_prepared_mapping["collect"]))
|
|
assert g.results[prepared_collect_id].collection == [True, False, True]
|
|
assert executed_source_ids.count("condition_iter") == 3
|
|
assert executed_source_ids.count("true_branch") == 2
|
|
assert executed_source_ids.count("false_branch") == 1
|
|
assert executed_source_ids.count("if") == 3
|
|
|
|
|
|
def test_if_graph_optimized_behavior_keeps_shared_live_consumers_per_iteration():
|
|
graph = Graph()
|
|
graph.add_node(BooleanCollectionInvocation(id="conditions", collection=[True, False, False]))
|
|
graph.add_node(IterateInvocation(id="condition_iter"))
|
|
graph.add_node(AnyTypeTestInvocation(id="shared_branch"))
|
|
graph.add_node(AnyTypeTestInvocation(id="true_leaf"))
|
|
graph.add_node(AnyTypeTestInvocation(id="false_branch"))
|
|
graph.add_node(AnyTypeTestInvocation(id="observer"))
|
|
graph.add_node(IfInvocation(id="if"))
|
|
graph.add_node(CollectInvocation(id="selected_collect"))
|
|
graph.add_node(CollectInvocation(id="observer_collect"))
|
|
|
|
graph.add_edge(create_edge("conditions", "collection", "condition_iter", "collection"))
|
|
graph.add_edge(create_edge("condition_iter", "item", "if", "condition"))
|
|
graph.add_edge(create_edge("condition_iter", "item", "shared_branch", "value"))
|
|
graph.add_edge(create_edge("shared_branch", "value", "true_leaf", "value"))
|
|
graph.add_edge(create_edge("true_leaf", "value", "if", "true_input"))
|
|
graph.add_edge(create_edge("condition_iter", "item", "false_branch", "value"))
|
|
graph.add_edge(create_edge("false_branch", "value", "if", "false_input"))
|
|
graph.add_edge(create_edge("shared_branch", "value", "observer", "value"))
|
|
graph.add_edge(create_edge("if", "value", "selected_collect", "item"))
|
|
graph.add_edge(create_edge("observer", "value", "observer_collect", "item"))
|
|
|
|
g = GraphExecutionState(graph=graph)
|
|
executed_source_ids = execute_all_nodes(g)
|
|
|
|
prepared_selected_collect_id = next(iter(g.source_prepared_mapping["selected_collect"]))
|
|
assert g.results[prepared_selected_collect_id].collection == [True, False, False]
|
|
prepared_observer_collect_id = next(iter(g.source_prepared_mapping["observer_collect"]))
|
|
assert g.results[prepared_observer_collect_id].collection == [True, False, False]
|
|
|
|
assert executed_source_ids.count("condition_iter") == 3
|
|
assert executed_source_ids.count("shared_branch") == 3
|
|
assert executed_source_ids.count("observer") == 3
|
|
assert executed_source_ids.count("true_leaf") == 1
|
|
assert executed_source_ids.count("false_branch") == 2
|
|
|
|
|
|
def test_are_connection_types_compatible_accepts_subclass_to_base():
|
|
"""A subclass output should be connectable to a base-class input.
|
|
|
|
This test exposes the bug where non-Union targets reject valid subclass connections.
|
|
"""
|
|
from invokeai.app.services.shared.graph import are_connection_types_compatible
|
|
|
|
class Base:
|
|
pass
|
|
|
|
class Child(Base):
|
|
pass
|
|
|
|
assert are_connection_types_compatible(Child, Base) is True
|