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255 lines
9.6 KiB
Python
255 lines
9.6 KiB
Python
import json
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import pytest
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from pydantic import TypeAdapter, ValidationError
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from invokeai.app.services.session_queue.session_queue_common import (
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Batch,
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BatchDataCollection,
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BatchDatum,
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NodeFieldValue,
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calc_session_count,
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create_session_nfv_tuples,
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prepare_values_to_insert,
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)
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from invokeai.app.services.shared.graph import Graph, GraphExecutionState
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from tests.test_nodes import PromptTestInvocation
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@pytest.fixture
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def batch_data_collection() -> BatchDataCollection:
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return [
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[
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# zipped
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BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi", "Grape sushi"]),
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BatchDatum(node_path="2", field_name="prompt", items=["Strawberry sushi", "Blueberry sushi"]),
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],
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[
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BatchDatum(node_path="3", field_name="prompt", items=["Orange sushi", "Apple sushi"]),
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],
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]
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@pytest.fixture
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def batch_graph() -> Graph:
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g = Graph()
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g.add_node(PromptTestInvocation(id="1", prompt="Chevy"))
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g.add_node(PromptTestInvocation(id="2", prompt="Toyota"))
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g.add_node(PromptTestInvocation(id="3", prompt="Subaru"))
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g.add_node(PromptTestInvocation(id="4", prompt="Nissan"))
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return g
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def test_create_sessions_from_batch_with_runs(batch_data_collection, batch_graph):
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b = Batch(graph=batch_graph, data=batch_data_collection, runs=2)
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t = list(create_session_nfv_tuples(batch=b, maximum=1000))
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# 2 list[BatchDatum] * length 2 * 2 runs = 8
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assert len(t) == 8
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assert json.loads(t[0][1])["graph"]["nodes"]["1"]["prompt"] == "Banana sushi"
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assert json.loads(t[0][1])["graph"]["nodes"]["2"]["prompt"] == "Strawberry sushi"
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assert json.loads(t[0][1])["graph"]["nodes"]["3"]["prompt"] == "Orange sushi"
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assert json.loads(t[0][1])["graph"]["nodes"]["4"]["prompt"] == "Nissan"
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assert json.loads(t[1][1])["graph"]["nodes"]["1"]["prompt"] == "Banana sushi"
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assert json.loads(t[1][1])["graph"]["nodes"]["2"]["prompt"] == "Strawberry sushi"
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assert json.loads(t[1][1])["graph"]["nodes"]["3"]["prompt"] == "Apple sushi"
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assert json.loads(t[1][1])["graph"]["nodes"]["4"]["prompt"] == "Nissan"
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assert json.loads(t[2][1])["graph"]["nodes"]["1"]["prompt"] == "Grape sushi"
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assert json.loads(t[2][1])["graph"]["nodes"]["2"]["prompt"] == "Blueberry sushi"
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assert json.loads(t[2][1])["graph"]["nodes"]["3"]["prompt"] == "Orange sushi"
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assert json.loads(t[2][1])["graph"]["nodes"]["4"]["prompt"] == "Nissan"
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assert json.loads(t[3][1])["graph"]["nodes"]["1"]["prompt"] == "Grape sushi"
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assert json.loads(t[3][1])["graph"]["nodes"]["2"]["prompt"] == "Blueberry sushi"
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assert json.loads(t[3][1])["graph"]["nodes"]["3"]["prompt"] == "Apple sushi"
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assert json.loads(t[3][1])["graph"]["nodes"]["4"]["prompt"] == "Nissan"
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# repeat for second run
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assert json.loads(t[4][1])["graph"]["nodes"]["1"]["prompt"] == "Banana sushi"
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assert json.loads(t[4][1])["graph"]["nodes"]["2"]["prompt"] == "Strawberry sushi"
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assert json.loads(t[4][1])["graph"]["nodes"]["3"]["prompt"] == "Orange sushi"
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assert json.loads(t[4][1])["graph"]["nodes"]["4"]["prompt"] == "Nissan"
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assert json.loads(t[5][1])["graph"]["nodes"]["1"]["prompt"] == "Banana sushi"
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assert json.loads(t[5][1])["graph"]["nodes"]["2"]["prompt"] == "Strawberry sushi"
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assert json.loads(t[5][1])["graph"]["nodes"]["3"]["prompt"] == "Apple sushi"
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assert json.loads(t[5][1])["graph"]["nodes"]["4"]["prompt"] == "Nissan"
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assert json.loads(t[6][1])["graph"]["nodes"]["1"]["prompt"] == "Grape sushi"
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assert json.loads(t[6][1])["graph"]["nodes"]["2"]["prompt"] == "Blueberry sushi"
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assert json.loads(t[6][1])["graph"]["nodes"]["3"]["prompt"] == "Orange sushi"
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assert json.loads(t[6][1])["graph"]["nodes"]["4"]["prompt"] == "Nissan"
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assert json.loads(t[7][1])["graph"]["nodes"]["1"]["prompt"] == "Grape sushi"
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assert json.loads(t[7][1])["graph"]["nodes"]["2"]["prompt"] == "Blueberry sushi"
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assert json.loads(t[7][1])["graph"]["nodes"]["3"]["prompt"] == "Apple sushi"
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assert json.loads(t[7][1])["graph"]["nodes"]["4"]["prompt"] == "Nissan"
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def test_create_sessions_from_batch_without_runs(batch_data_collection, batch_graph):
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b = Batch(graph=batch_graph, data=batch_data_collection)
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t = list(create_session_nfv_tuples(batch=b, maximum=1000))
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# 2 list[BatchDatum] * length 2 * 1 runs = 8
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assert len(t) == 4
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def test_create_sessions_from_batch_without_batch(batch_graph):
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b = Batch(graph=batch_graph, runs=2)
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t = list(create_session_nfv_tuples(batch=b, maximum=1000))
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# 2 runs
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assert len(t) == 2
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def test_create_sessions_from_batch_without_batch_or_runs(batch_graph):
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b = Batch(graph=batch_graph)
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t = list(create_session_nfv_tuples(batch=b, maximum=1000))
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# 1 run
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assert len(t) == 1
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def test_create_sessions_from_batch_with_runs_and_max(batch_data_collection, batch_graph):
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b = Batch(graph=batch_graph, data=batch_data_collection, runs=2)
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t = list(create_session_nfv_tuples(batch=b, maximum=5))
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# 2 list[BatchDatum] * length 2 * 2 runs = 8, but max is 5
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assert len(t) == 5
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def test_calc_session_count(batch_data_collection, batch_graph):
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b = Batch(graph=batch_graph, data=batch_data_collection, runs=2)
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# 2 list[BatchDatum] * length 2 * 2 runs = 8
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assert calc_session_count(batch=b) == 8
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def test_prepare_values_to_insert(batch_data_collection, batch_graph):
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b = Batch(graph=batch_graph, data=batch_data_collection, runs=2)
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values = prepare_values_to_insert(queue_id="default", batch=b, priority=0, max_new_queue_items=1000)
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assert len(values) == 8
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GraphExecutionStateValidator = TypeAdapter(GraphExecutionState)
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# graph should be serialized
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ges = GraphExecutionStateValidator.validate_json(values[0][1])
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# graph values should be populated
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assert ges.graph.get_node("1").prompt == "Banana sushi"
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assert ges.graph.get_node("2").prompt == "Strawberry sushi"
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assert ges.graph.get_node("3").prompt == "Orange sushi"
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assert ges.graph.get_node("4").prompt == "Nissan"
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# session ids should match deserialized graph
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assert [v[2] for v in values] == [GraphExecutionStateValidator.validate_json(v[1]).id for v in values]
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# should unique session ids
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sids = [v[2] for v in values]
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assert len(sids) == len(set(sids))
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NodeFieldValueValidator = TypeAdapter(list[NodeFieldValue])
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# should have 3 node field values
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assert isinstance(values[0][4], str)
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assert len(NodeFieldValueValidator.validate_json(values[0][4])) == 3
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# should have batch id and priority
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assert all(v[3] == b.batch_id for v in values)
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assert all(v[5] == 0 for v in values)
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def test_prepare_values_to_insert_with_priority(batch_data_collection, batch_graph):
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b = Batch(graph=batch_graph, data=batch_data_collection, runs=2)
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values = prepare_values_to_insert(queue_id="default", batch=b, priority=1, max_new_queue_items=1000)
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assert all(v[5] == 1 for v in values)
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def test_prepare_values_to_insert_with_max(batch_data_collection, batch_graph):
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b = Batch(graph=batch_graph, data=batch_data_collection, runs=2)
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values = prepare_values_to_insert(queue_id="default", batch=b, priority=1, max_new_queue_items=5)
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assert len(values) == 5
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def test_cannot_create_bad_batch_items_length(batch_graph):
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with pytest.raises(ValidationError, match="Zipped batch items must all have the same length"):
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Batch(
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graph=batch_graph,
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data=[
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[
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BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi"]), # 1 item
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BatchDatum(node_path="2", field_name="prompt", items=["Toyota", "Nissan"]), # 2 items
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],
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],
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)
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def test_cannot_create_bad_batch_items_type(batch_graph):
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with pytest.raises(ValidationError, match="All items in a batch must have the same type"):
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Batch(
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graph=batch_graph,
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data=[
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[
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BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi", 123]),
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]
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],
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)
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def test_number_type_interop(batch_graph):
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# integers and floats can be mixed, should not throw an error
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Batch(
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graph=batch_graph,
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data=[
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[
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BatchDatum(node_path="1", field_name="prompt", items=[1, 1.5]),
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]
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],
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)
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Batch(
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graph=batch_graph,
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data=[
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[
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BatchDatum(node_path="1", field_name="prompt", items=[1.5, 1]),
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]
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],
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)
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def test_cannot_create_bad_batch_unique_ids(batch_graph):
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with pytest.raises(ValidationError, match="Each batch data must have unique node_id and field_name"):
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Batch(
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graph=batch_graph,
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data=[
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[
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BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi"]),
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],
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[
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BatchDatum(node_path="1", field_name="prompt", items=["Banana sushi"]),
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],
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],
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)
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def test_cannot_create_bad_batch_nodes_exist(
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batch_graph,
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):
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with pytest.raises(ValidationError, match=r"Node .* not found in graph"):
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Batch(
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graph=batch_graph,
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data=[
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[
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BatchDatum(node_path="batman", field_name="prompt", items=["Banana sushi"]),
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],
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],
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)
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def test_cannot_create_bad_batch_fields_exist(
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batch_graph,
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):
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with pytest.raises(ValidationError, match=r"Field .* not found in node"):
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Batch(
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graph=batch_graph,
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data=[
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[
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BatchDatum(node_path="1", field_name="batman", items=["Banana sushi"]),
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],
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],
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)
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