718 lines
25 KiB
Python
718 lines
25 KiB
Python
"""
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Workflow Analyzer Module
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Extracts model references from workflow JSON and identifies missing models.
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"""
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import hashlib
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import json
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import threading
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import time
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from collections import OrderedDict
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from typing import Any, Callable, Dict, List, Optional
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from .log_system import create_module_logger
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log = create_module_logger(__name__)
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_WORKFLOW_MODEL_INVENTORY_CACHE_TTL_SECONDS = 300.0
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_WORKFLOW_MODEL_INVENTORY_CACHE_MAX_ENTRIES = 8
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_WORKFLOW_MODEL_INVENTORY_CACHE_LOCK = threading.Lock()
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_WORKFLOW_MODEL_INVENTORY_CACHE: OrderedDict[str, Dict[str, Any]] = OrderedDict()
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_WORKFLOW_MODEL_INVENTORY_IGNORED_NODE_KEYS = {
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"bgcolor",
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"color",
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"flags",
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"order",
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"pos",
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"shape",
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"size",
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}
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from .workflow import references
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from .workflow import subgraphs as subgraph_utils
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def _normalize_workflow_analysis_value(value: Any) -> Any:
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if isinstance(value, dict):
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is_node = "id" in value and "type" in value
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return {
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key: _normalize_workflow_analysis_value(item)
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for key, item in value.items()
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if not (
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is_node
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and key in _WORKFLOW_MODEL_INVENTORY_IGNORED_NODE_KEYS
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)
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}
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if isinstance(value, list):
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return [_normalize_workflow_analysis_value(item) for item in value]
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return value
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def _get_workflow_node_cache_key(
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node: Dict[str, Any],
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*,
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subgraph_id: Any = "",
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is_top_level: bool,
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) -> tuple[str, str, str]:
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scope = "top" if is_top_level else "subgraph"
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return (scope, "" if is_top_level else str(subgraph_id or ""), str(node.get("id")))
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def _get_workflow_node_fingerprint(node: Dict[str, Any]) -> str:
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serialized_node = json.dumps(
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_normalize_workflow_analysis_value(node),
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ensure_ascii=False,
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sort_keys=True,
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separators=(",", ":"),
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)
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return hashlib.sha256(serialized_node.encode("utf-8")).hexdigest()
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def _get_workflow_node_fingerprints(
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workflow_json: Dict[str, Any],
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) -> Dict[tuple[str, str, str], str]:
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fingerprints = {}
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nodes = workflow_json.get("nodes", [])
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if isinstance(nodes, list):
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for node in nodes:
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if not isinstance(node, dict):
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continue
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key = _get_workflow_node_cache_key(node, is_top_level=True)
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fingerprints[key] = _get_workflow_node_fingerprint(node)
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definitions = workflow_json.get("definitions", {})
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subgraphs = definitions.get("subgraphs", []) if isinstance(definitions, dict) else []
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if not isinstance(subgraphs, list):
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return fingerprints
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for subgraph in subgraphs:
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if not isinstance(subgraph, dict):
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continue
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subgraph_id = subgraph.get("id")
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subgraph_nodes = subgraph.get("nodes", [])
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if not isinstance(subgraph_nodes, list):
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continue
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for node in subgraph_nodes:
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if not isinstance(node, dict):
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continue
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key = _get_workflow_node_cache_key(
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node,
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subgraph_id=subgraph_id,
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is_top_level=False,
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)
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fingerprints[key] = _get_workflow_node_fingerprint(node)
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return fingerprints
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def _order_nodes_for_incremental_analysis(
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nodes: List[Dict[str, Any]],
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previous_node_cache: Optional[
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Dict[tuple[str, str, str], Dict[str, Any]]
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],
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*,
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subgraph_id: Any = "",
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is_top_level: bool,
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) -> List[Dict[str, Any]]:
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"""Keep existing nodes in their previous result positions during refreshes."""
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if previous_node_cache is None or len(nodes) < 2:
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return nodes
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previous_positions = {
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key: index for index, key in enumerate(previous_node_cache)
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}
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existing_nodes = []
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new_nodes = []
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for current_index, node in enumerate(nodes):
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if not isinstance(node, dict):
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new_nodes.append((current_index, node))
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continue
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node_key = _get_workflow_node_cache_key(
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node,
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subgraph_id=subgraph_id,
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is_top_level=is_top_level,
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)
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previous_position = previous_positions.get(node_key)
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if previous_position is None:
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new_nodes.append((current_index, node))
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else:
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existing_nodes.append((previous_position, current_index, node))
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existing_nodes.sort(key=lambda item: (item[0], item[1]))
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new_nodes.sort(key=lambda item: item[0])
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return [
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node for _, _, node in existing_nodes
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] + [
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node for _, node in new_nodes
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]
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def _get_promoted_widget_context_cache_key(
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workflow_json: Dict[str, Any],
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) -> str:
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definitions = workflow_json.get("definitions", {})
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subgraphs = definitions.get("subgraphs", []) if isinstance(definitions, dict) else []
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if not isinstance(subgraphs, list):
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subgraphs = []
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subgraph_ids = {
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str(subgraph.get("id"))
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for subgraph in subgraphs
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if isinstance(subgraph, dict) and subgraph.get("id")
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}
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context_subgraphs = []
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for subgraph in subgraphs:
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if not isinstance(subgraph, dict):
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continue
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context_nodes = []
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nodes = subgraph.get("nodes", [])
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if isinstance(nodes, list):
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for node in nodes:
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if not isinstance(node, dict):
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continue
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widgets_values = node.get("widgets_values", [])
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context_node = {
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**node,
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"widgets_values": (
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[None] * len(widgets_values)
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if isinstance(widgets_values, list)
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else []
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),
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}
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context_nodes.append(
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_normalize_workflow_analysis_value(context_node)
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)
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context_subgraph = {
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key: value
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for key, value in subgraph.items()
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if key != "nodes"
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}
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context_subgraphs.append(
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_normalize_workflow_analysis_value(
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{
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**context_subgraph,
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"nodes": context_nodes,
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}
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)
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)
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context_instances = []
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nodes = workflow_json.get("nodes", [])
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if isinstance(nodes, list):
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for node in nodes:
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if not isinstance(node, dict) or str(node.get("type")) not in subgraph_ids:
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continue
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context_instances.append(
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_normalize_workflow_analysis_value(node)
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)
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serialized_context = json.dumps(
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{
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"subgraphs": context_subgraphs,
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"instances": context_instances,
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},
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ensure_ascii=False,
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sort_keys=True,
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separators=(",", ":"),
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)
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return hashlib.sha256(serialized_context.encode("utf-8")).hexdigest()
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def analyze_workflow_models(
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workflow_json: Dict[str, Any],
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available_models: Optional[List[Dict[str, Any]]] = None,
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progress_callback: Optional[Callable[[Dict[str, Any]], None]] = None,
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*,
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previous_node_cache: Optional[
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Dict[tuple[str, str, str], Dict[str, Any]]
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] = None,
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node_cache_out: Optional[
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Dict[tuple[str, str, str], Dict[str, Any]]
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] = None,
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analysis_stats: Optional[Dict[str, int]] = None,
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) -> List[Dict[str, Any]]:
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"""
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Extract all model references from a workflow, including nested subgraphs.
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Args:
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workflow_json: Complete workflow JSON dictionary
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available_models: Optional list of local model records for existence checks
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progress_callback: Optional callback called while workflow nodes are scanned
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Returns:
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List of model reference dictionaries (same format as get_node_model_info)
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Each dict includes 'subgraph_id' if the model is in a subgraph
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"""
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all_model_refs = []
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reused_nodes = 0
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analyzed_nodes = 0
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# Get subgraph definitions first to check if node types are subgraph UUIDs
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definitions = workflow_json.get("definitions", {})
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subgraphs = definitions.get("subgraphs", [])
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if not isinstance(subgraphs, list):
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subgraphs = []
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subgraph_lookup = {
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sg.get("id"): sg.get("name", sg.get("id"))
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for sg in subgraphs
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if isinstance(sg, dict)
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}
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promoted_widget_contexts = subgraph_utils._build_promoted_widget_contexts(
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workflow_json, subgraphs
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)
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# Analyze top-level nodes
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nodes = workflow_json.get("nodes", [])
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if not isinstance(nodes, list):
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nodes = []
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nodes = _order_nodes_for_incremental_analysis(
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nodes,
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previous_node_cache,
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is_top_level=True,
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)
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total_nodes = len(nodes) + sum(
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len(subgraph_nodes)
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for subgraph in subgraphs
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if isinstance(subgraph, dict)
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for subgraph_nodes in [subgraph.get("nodes", [])]
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if isinstance(subgraph_nodes, list)
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)
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processed_nodes = 0
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def report_node_progress(
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node: Dict[str, Any],
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subgraph_name: Optional[str] = None,
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*,
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reused: bool = False,
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) -> None:
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nonlocal processed_nodes
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processed_nodes += 1
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if not progress_callback:
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return
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node_type = node.get("type", "")
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payload = {
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"stage": "analyzing",
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"message": f"Analyzing workflow node {processed_nodes} of {total_nodes}",
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"current": processed_nodes,
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"total": total_nodes,
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"node_id": node.get("id"),
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"node_type": node_type,
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}
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if reused:
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payload["message"] = (
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f"Reusing workflow node {processed_nodes} of {total_nodes}"
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)
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payload["cached"] = True
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if subgraph_name:
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payload["subgraph_name"] = subgraph_name
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payload["message"] = (
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f"{'Reusing' if reused else 'Analyzing'} subgraph node "
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f"{processed_nodes} of {total_nodes}"
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)
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try:
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progress_callback(payload)
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except Exception as e:
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log.debug(f"Workflow analysis progress callback failed: {e}")
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for node in nodes:
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try:
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node_cache_key = _get_workflow_node_cache_key(
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node,
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is_top_level=True,
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)
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node_fingerprint = _get_workflow_node_fingerprint(node)
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cached_node = (
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previous_node_cache.get(node_cache_key)
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if previous_node_cache is not None
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else None
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)
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reused = bool(
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cached_node
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and cached_node.get("fingerprint") == node_fingerprint
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)
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if reused:
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model_refs = cached_node.get("refs", [])
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reused_nodes += 1
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else:
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model_refs = references.get_node_model_info(
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node,
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available_models=available_models,
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)
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analyzed_nodes += 1
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node_type = node.get("type", "")
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# Check if node type is a subgraph UUID
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subgraph_name = None
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subgraph_id = None
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if node_type in subgraph_lookup:
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subgraph_name = subgraph_lookup[node_type]
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subgraph_id = node_type
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# Mark with subgraph info if it's a subgraph node
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# For top-level subgraph instance nodes, subgraph_path is None
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# This distinguishes them from nodes within subgraph definitions
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if not reused:
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for ref in model_refs:
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ref["subgraph_id"] = subgraph_id
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ref["subgraph_name"] = subgraph_name
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ref["subgraph_path"] = None
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ref["is_top_level"] = True
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if subgraph_id:
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context = promoted_widget_contexts.get(
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"instance_widgets", {}
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).get((str(node.get("id")), ref.get("widget_index")))
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subgraph_utils._apply_instance_promoted_widget_context(
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ref, context, available_models
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)
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if node_cache_out is not None:
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node_cache_out[node_cache_key] = {
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"fingerprint": node_fingerprint,
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"refs": model_refs,
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}
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report_node_progress(node, reused=reused)
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all_model_refs.extend(model_refs)
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except Exception as e:
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log.warning(f"Error analyzing node {node.get('id', 'unknown')}: {e}")
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continue
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# Recursively analyze subgraphs
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for subgraph in subgraphs:
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if not isinstance(subgraph, dict):
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continue
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subgraph_id = subgraph.get("id")
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subgraph_name = subgraph.get("name", subgraph_id)
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subgraph_nodes = subgraph.get("nodes", [])
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if not isinstance(subgraph_nodes, list):
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subgraph_nodes = []
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subgraph_nodes = _order_nodes_for_incremental_analysis(
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subgraph_nodes,
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previous_node_cache,
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subgraph_id=subgraph_id,
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is_top_level=False,
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)
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analyzed_subgraph_nodes = 0
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for node in subgraph_nodes:
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try:
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node_cache_key = _get_workflow_node_cache_key(
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node,
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subgraph_id=subgraph_id,
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is_top_level=False,
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)
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node_fingerprint = _get_workflow_node_fingerprint(node)
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cached_node = (
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previous_node_cache.get(node_cache_key)
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if previous_node_cache is not None
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else None
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)
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reused = bool(
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cached_node
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and cached_node.get("fingerprint") == node_fingerprint
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)
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if reused:
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model_refs = cached_node.get("refs", [])
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reused_nodes += 1
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else:
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model_refs = references.get_node_model_info(
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node,
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available_models=available_models,
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)
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analyzed_nodes += 1
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analyzed_subgraph_nodes += 1
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for ref in model_refs:
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ref["subgraph_id"] = subgraph_id
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ref["subgraph_name"] = subgraph_name
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ref["subgraph_path"] = [
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"definitions",
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"subgraphs",
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subgraph_id,
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"nodes",
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]
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ref["is_top_level"] = False
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subgraph_utils._apply_promoted_widget_locator(
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ref,
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promoted_widget_contexts,
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)
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if node_cache_out is not None:
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node_cache_out[node_cache_key] = {
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"fingerprint": node_fingerprint,
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"refs": model_refs,
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}
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report_node_progress(
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node,
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subgraph_name,
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reused=reused,
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)
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all_model_refs.extend(model_refs)
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except Exception as e:
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log.warning(
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f"Error analyzing subgraph node {node.get('id', 'unknown')}: {e}"
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)
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continue
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if analyzed_subgraph_nodes:
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log.debug(
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f"Analyzing subgraph: {subgraph_name} (ID: {subgraph_id}) "
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f"with {analyzed_subgraph_nodes} changed nodes"
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)
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if analysis_stats is not None:
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analysis_stats.update(
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{
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"total_nodes": reused_nodes + analyzed_nodes,
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"reused_nodes": reused_nodes,
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"analyzed_nodes": analyzed_nodes,
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}
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)
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if previous_node_cache is not None:
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log.debug(
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f"Incremental workflow analysis: analyzed {analyzed_nodes} nodes, "
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f"reused {reused_nodes} nodes"
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)
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return all_model_refs
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|
|
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def _build_workflow_node_cache(
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workflow_json: Dict[str, Any],
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model_refs: List[Dict[str, Any]],
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) -> Dict[tuple[str, str, str], Dict[str, Any]]:
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fingerprints = _get_workflow_node_fingerprints(workflow_json)
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refs_by_node: Dict[tuple[str, str, str], List[Dict[str, Any]]] = {
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key: [] for key in fingerprints
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}
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for ref in model_refs:
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is_top_level = ref.get("is_top_level") is not False
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key = (
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"top" if is_top_level else "subgraph",
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"" if is_top_level else str(ref.get("subgraph_id") or ""),
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str(ref.get("node_id")),
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)
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if key in refs_by_node:
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refs_by_node[key].append(ref)
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return {
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key: {
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"fingerprint": fingerprint,
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"refs": refs_by_node.get(key, []),
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}
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for key, fingerprint in fingerprints.items()
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}
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def _get_workflow_model_inventory_cache_key(
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workflow_json: Dict[str, Any],
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) -> str:
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serialized_workflow = json.dumps(
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_normalize_workflow_analysis_value(workflow_json),
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ensure_ascii=False,
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sort_keys=True,
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separators=(",", ":"),
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)
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return hashlib.sha256(serialized_workflow.encode("utf-8")).hexdigest()
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|
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def invalidate_workflow_model_inventory_cache() -> None:
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"""Clear shared workflow model analysis snapshots."""
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with _WORKFLOW_MODEL_INVENTORY_CACHE_LOCK:
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_WORKFLOW_MODEL_INVENTORY_CACHE.clear()
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|
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def get_workflow_model_inventory(
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workflow_json: Dict[str, Any],
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*,
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force_rescan: bool = False,
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progress_callback: Optional[Callable[[Dict[str, Any]], None]] = None,
|
|
) -> Dict[str, List[Dict[str, Any]]]:
|
|
"""
|
|
Return the shared base model inventory for a workflow.
|
|
|
|
Missing Models and Loaded Models use this snapshot so the same unchanged
|
|
workflow is not parsed separately by both endpoints. Callers must treat the
|
|
returned lists as read-only.
|
|
"""
|
|
from .scanner import get_model_files
|
|
|
|
cache_key = _get_workflow_model_inventory_cache_key(workflow_json)
|
|
promoted_context_key = _get_promoted_widget_context_cache_key(workflow_json)
|
|
current_node_fingerprints = _get_workflow_node_fingerprints(workflow_json)
|
|
now = time.monotonic()
|
|
cached = None
|
|
previous_inventory = None
|
|
|
|
if force_rescan:
|
|
invalidate_workflow_model_inventory_cache()
|
|
else:
|
|
with _WORKFLOW_MODEL_INVENTORY_CACHE_LOCK:
|
|
expired_keys = [
|
|
key
|
|
for key, entry in _WORKFLOW_MODEL_INVENTORY_CACHE.items()
|
|
if now - entry["created_at"]
|
|
>= _WORKFLOW_MODEL_INVENTORY_CACHE_TTL_SECONDS
|
|
]
|
|
for key in expired_keys:
|
|
_WORKFLOW_MODEL_INVENTORY_CACHE.pop(key, None)
|
|
|
|
cached = _WORKFLOW_MODEL_INVENTORY_CACHE.get(cache_key)
|
|
if cached is not None:
|
|
_WORKFLOW_MODEL_INVENTORY_CACHE.move_to_end(cache_key)
|
|
else:
|
|
best_score = 0
|
|
for _, entry in reversed(
|
|
list(_WORKFLOW_MODEL_INVENTORY_CACHE.items())
|
|
):
|
|
if entry.get("promoted_context_key") != promoted_context_key:
|
|
continue
|
|
node_cache = entry.get("node_cache", {})
|
|
score = sum(
|
|
1
|
|
for key, fingerprint in current_node_fingerprints.items()
|
|
if node_cache.get(key, {}).get("fingerprint")
|
|
== fingerprint
|
|
)
|
|
if score > best_score:
|
|
best_score = score
|
|
previous_inventory = entry
|
|
|
|
if cached is not None:
|
|
model_refs = cached["model_refs"]
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "analyzing",
|
|
"message": "Reusing shared workflow analysis...",
|
|
"current": len(cached.get("node_cache", {})),
|
|
"total": len(cached.get("node_cache", {})),
|
|
"cached": True,
|
|
}
|
|
)
|
|
log.debug("Reusing shared workflow model analysis")
|
|
return {
|
|
"available_models": cached["available_models"],
|
|
"model_refs": model_refs,
|
|
}
|
|
|
|
node_cache = {}
|
|
analysis_stats = {}
|
|
if previous_inventory is not None:
|
|
available_models = previous_inventory["available_models"]
|
|
model_refs = analyze_workflow_models(
|
|
workflow_json,
|
|
available_models=available_models,
|
|
progress_callback=progress_callback,
|
|
previous_node_cache=previous_inventory.get("node_cache", {}),
|
|
node_cache_out=node_cache,
|
|
analysis_stats=analysis_stats,
|
|
)
|
|
else:
|
|
available_models = get_model_files(force_rescan=force_rescan)
|
|
model_refs = analyze_workflow_models(
|
|
workflow_json,
|
|
available_models=available_models,
|
|
progress_callback=progress_callback,
|
|
)
|
|
node_cache = _build_workflow_node_cache(workflow_json, model_refs)
|
|
analysis_stats = {
|
|
"total_nodes": len(node_cache),
|
|
"reused_nodes": 0,
|
|
"analyzed_nodes": len(node_cache),
|
|
}
|
|
|
|
with _WORKFLOW_MODEL_INVENTORY_CACHE_LOCK:
|
|
expired_keys = [
|
|
key
|
|
for key, entry in _WORKFLOW_MODEL_INVENTORY_CACHE.items()
|
|
if now - entry["created_at"]
|
|
>= _WORKFLOW_MODEL_INVENTORY_CACHE_TTL_SECONDS
|
|
]
|
|
for key in expired_keys:
|
|
_WORKFLOW_MODEL_INVENTORY_CACHE.pop(key, None)
|
|
|
|
_WORKFLOW_MODEL_INVENTORY_CACHE[cache_key] = {
|
|
"created_at": time.monotonic(),
|
|
"available_models": available_models,
|
|
"model_refs": model_refs,
|
|
"node_cache": node_cache,
|
|
"promoted_context_key": promoted_context_key,
|
|
"analysis_stats": analysis_stats,
|
|
}
|
|
_WORKFLOW_MODEL_INVENTORY_CACHE.move_to_end(cache_key)
|
|
while (
|
|
len(_WORKFLOW_MODEL_INVENTORY_CACHE)
|
|
> _WORKFLOW_MODEL_INVENTORY_CACHE_MAX_ENTRIES
|
|
):
|
|
_WORKFLOW_MODEL_INVENTORY_CACHE.popitem(last=False)
|
|
|
|
return {
|
|
"available_models": available_models,
|
|
"model_refs": model_refs,
|
|
}
|
|
|
|
|
|
def identify_missing_models(
|
|
workflow_models: List[Dict[str, Any]], available_models: List[Dict[str, str]] = None
|
|
) -> List[Dict[str, Any]]:
|
|
"""
|
|
Identify which models from the workflow are missing.
|
|
Deduplicates by category and filename - same model file only appears once even if
|
|
referenced by multiple nodes.
|
|
|
|
Args:
|
|
workflow_models: List of model references from analyze_workflow_models
|
|
available_models: Optional list of available models (if None, checks via folder_paths)
|
|
|
|
Returns:
|
|
List of missing model references (deduplicated by category and filename).
|
|
Each entry has 'all_node_refs' containing all node references for that model.
|
|
"""
|
|
# Group missing models by category and filename to deduplicate workflow
|
|
# references without merging unrelated model folders that happen to share a name.
|
|
missing_by_model: Dict[tuple[str, str], Dict[str, Any]] = {}
|
|
|
|
for model_ref in workflow_models:
|
|
# If exists is False, it's missing
|
|
if not model_ref.get("exists", False):
|
|
filename = model_ref.get("original_path", "")
|
|
category = model_ref.get("category", "")
|
|
group_key = (str(category or ""), str(filename or ""))
|
|
|
|
if group_key not in missing_by_model:
|
|
# First occurrence - use this as the primary entry
|
|
missing_by_model[group_key] = {
|
|
**model_ref,
|
|
"reference_count": 1,
|
|
"all_node_refs": [
|
|
model_ref.copy()
|
|
], # Track all nodes needing this model
|
|
}
|
|
else:
|
|
# Duplicate - just add to the node refs list
|
|
existing = missing_by_model[group_key]
|
|
existing["all_node_refs"].append(model_ref.copy())
|
|
existing["reference_count"] = len(existing["all_node_refs"])
|
|
if model_ref.get("auto_download_capable"):
|
|
existing["auto_download_capable"] = True
|
|
if model_ref.get("auto_download_candidate"):
|
|
existing["auto_download_candidate"] = True
|
|
if model_ref.get("input_choice_matches_value"):
|
|
existing["input_choice_matches_value"] = True
|
|
existing_source = str(existing.get("input_choice_source") or "").lower()
|
|
model_source = str(model_ref.get("input_choice_source") or "").lower()
|
|
if existing_source != "hybrid" and model_source in {
|
|
"static",
|
|
"hybrid",
|
|
"workflow_schema",
|
|
}:
|
|
existing["input_choice_source"] = model_source
|
|
|
|
# Return deduplicated list
|
|
return list(missing_by_model.values())
|