1379 lines
54 KiB
Python
1379 lines
54 KiB
Python
"""Prompt interception and graph rewriting for Modal-backed execution."""
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from __future__ import annotations
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import copy
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import logging
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import time
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import uuid
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from collections import defaultdict
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from dataclasses import dataclass, field
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from typing import Any, Iterator
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from aiohttp import web
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from .modal_executor_node import ensure_modal_component_proxy_node_registered
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from .settings import ModalSyncSettings, get_settings
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from .sync_engine import ModalAssetSyncEngine, SyncedAsset
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logger = logging.getLogger(__name__)
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_ROUTE_REGISTERED = False
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_TRANSPORTABLE_OUTPUT_TYPES = frozenset(
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{
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"*",
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"BOOLEAN",
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"FLOAT",
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"IMAGE",
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"INT",
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"LATENT",
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"MASK",
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"NOISE",
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"SIGMAS",
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"STRING",
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}
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)
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@dataclass(frozen=True)
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class LinkedOutputRef:
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"""Reference a node output slot within a prompt graph."""
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node_id: str
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output_index: int
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@dataclass(frozen=True)
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class InputTarget:
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"""Describe a target input inside a remote component."""
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node_id: str
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input_name: str
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@dataclass
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class BoundaryInputSpec:
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"""Describe one local-to-remote boundary value for a component."""
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proxy_input_name: str
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source: LinkedOutputRef
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targets: list[InputTarget] = field(default_factory=list)
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@dataclass
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class BoundaryOutputSpec:
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"""Describe one remote-to-local boundary value for a component."""
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proxy_output_name: str
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source: LinkedOutputRef
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io_type: str
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is_list: bool
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@dataclass
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class RemoteComponentPlan:
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"""Execution and rewrite plan for one connected remote component."""
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node_ids: list[str]
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representative_node_id: str
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boundary_inputs: list[BoundaryInputSpec]
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boundary_outputs: list[BoundaryOutputSpec]
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execute_node_ids: list[str]
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contains_output_node: bool
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@dataclass
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class RewriteSummary:
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"""Summary of the prompt rewrite performed for a queue request."""
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remote_node_ids: list[str] = field(default_factory=list)
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remote_component_ids: list[str] = field(default_factory=list)
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component_node_ids_by_representative: dict[str, list[str]] = field(default_factory=dict)
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rewritten_node_id_map: dict[str, str] = field(default_factory=dict)
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synced_assets: list[SyncedAsset] = field(default_factory=list)
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custom_nodes_bundle: SyncedAsset | None = None
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@dataclass(frozen=True)
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class RemoteExpansionReason:
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"""Describe why one upstream node had to join a remote component."""
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node_id: str
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class_type: str
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required_by_node_id: str
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required_by_class_type: str
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output_index: int
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io_type: str
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@dataclass
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class RemoteNodeAnalysis:
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"""Structured dry-run result for context-menu remote expansion."""
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requested_node_ids: list[str] = field(default_factory=list)
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requested_workflow_node_paths: list[str] = field(default_factory=list)
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current_remote_node_ids: list[str] = field(default_factory=list)
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current_remote_workflow_node_paths: list[str] = field(default_factory=list)
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resolved_remote_node_ids: list[str] = field(default_factory=list)
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resolved_workflow_node_paths: list[str] = field(default_factory=list)
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added_node_ids: list[str] = field(default_factory=list)
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added_workflow_node_paths: list[str] = field(default_factory=list)
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reasons: list[RemoteExpansionReason] = field(default_factory=list)
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class ModalPromptValidationError(ValueError):
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"""Raised when a prompt cannot be executed with the current Modal transport."""
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def _emit_modal_status(
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prompt_server: Any,
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phase: str,
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*,
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client_id: str | None,
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prompt_id: str | None,
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node_ids: list[str],
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component_node_ids_by_representative: dict[str, list[str]] | None = None,
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active_node_id: str | None = None,
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active_node_class_type: str | None = None,
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active_node_role: str | None = None,
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error_message: str | None = None,
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) -> None:
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"""Send a Modal execution status event to the active websocket client."""
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if client_id is None:
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logger.debug("Skipping Modal status event %s because no client id is available.", phase)
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return
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payload: dict[str, Any] = {
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"phase": phase,
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"prompt_id": prompt_id,
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"node_ids": list(node_ids),
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}
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if component_node_ids_by_representative:
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payload["components"] = [
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{
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"representative_node_id": representative_node_id,
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"node_ids": list(component_node_ids),
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}
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for representative_node_id, component_node_ids in sorted(
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component_node_ids_by_representative.items()
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)
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]
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if active_node_id is not None:
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payload["active_node_id"] = active_node_id
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if active_node_class_type is not None:
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payload["active_node_class_type"] = active_node_class_type
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if active_node_role is not None:
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payload["active_node_role"] = active_node_role
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if error_message is not None:
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payload["error_message"] = error_message
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prompt_server.send_sync("modal_status", payload, client_id)
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def _get_nodes_module() -> Any:
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"""Import the ComfyUI nodes module lazily."""
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import nodes
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return nodes
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def _get_server_module() -> Any:
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"""Import the ComfyUI server module lazily."""
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import server
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return server
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def _get_execution_module() -> Any:
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"""Import the ComfyUI execution module lazily."""
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import execution
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return execution
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def _is_link(value: Any) -> bool:
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"""Return whether a prompt input value is a ComfyUI link."""
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return (
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isinstance(value, list)
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and len(value) == 2
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and all(not isinstance(item, dict) for item in value)
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)
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def _looks_like_workflow_node(fragment: dict[str, Any]) -> bool:
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"""Return whether a JSON fragment resembles a saved ComfyUI workflow node."""
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return "id" in fragment and "properties" in fragment
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def _iter_workflow_nodes(
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workflow_fragment: Any,
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visited_object_ids: set[int] | None = None,
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) -> Iterator[dict[str, Any]]:
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"""Yield workflow node dictionaries from a nested saved workflow fragment."""
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if visited_object_ids is None:
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visited_object_ids = set()
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if isinstance(workflow_fragment, dict):
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object_id = id(workflow_fragment)
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if object_id in visited_object_ids:
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return
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visited_object_ids.add(object_id)
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if _looks_like_workflow_node(workflow_fragment):
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yield workflow_fragment
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for value in workflow_fragment.values():
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yield from _iter_workflow_nodes(value, visited_object_ids)
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return
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if isinstance(workflow_fragment, list):
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object_id = id(workflow_fragment)
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if object_id in visited_object_ids:
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return
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visited_object_ids.add(object_id)
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for item in workflow_fragment:
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yield from _iter_workflow_nodes(item, visited_object_ids)
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def _iter_workflow_nodes_with_ancestors(
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workflow_fragment: Any,
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ancestor_node_ids: tuple[str, ...] = (),
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visited_object_ids: set[int] | None = None,
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) -> Iterator[tuple[dict[str, Any], tuple[str, ...]]]:
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"""Yield workflow nodes together with their ancestor workflow-node ids."""
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if visited_object_ids is None:
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visited_object_ids = set()
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if isinstance(workflow_fragment, dict):
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object_id = id(workflow_fragment)
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if object_id in visited_object_ids:
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return
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visited_object_ids.add(object_id)
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next_ancestor_node_ids = ancestor_node_ids
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if _looks_like_workflow_node(workflow_fragment):
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node_id = str(workflow_fragment.get("id"))
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yield workflow_fragment, ancestor_node_ids
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next_ancestor_node_ids = ancestor_node_ids + (node_id,)
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for value in workflow_fragment.values():
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yield from _iter_workflow_nodes_with_ancestors(
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value,
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next_ancestor_node_ids,
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visited_object_ids,
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)
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return
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if isinstance(workflow_fragment, list):
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object_id = id(workflow_fragment)
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if object_id in visited_object_ids:
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return
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visited_object_ids.add(object_id)
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for item in workflow_fragment:
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yield from _iter_workflow_nodes_with_ancestors(
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item,
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ancestor_node_ids,
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visited_object_ids,
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)
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def _resolve_prompt_node_ids_for_workflow_node(
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workflow_node_id: str,
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ancestor_node_ids: tuple[str, ...],
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prompt_node_ids: set[str],
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) -> set[str]:
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"""Resolve one saved workflow node id to matching queued prompt node ids."""
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path_segments = ancestor_node_ids + (workflow_node_id,)
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resolved_prompt_node_ids: set[str] = set()
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for index in range(len(path_segments)):
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candidate = ":".join(path_segments[index:])
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if candidate in prompt_node_ids:
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resolved_prompt_node_ids.add(candidate)
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descendant_prefix = f"{candidate}:"
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resolved_prompt_node_ids.update(
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prompt_node_id
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for prompt_node_id in prompt_node_ids
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if prompt_node_id.startswith(descendant_prefix)
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)
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if resolved_prompt_node_ids:
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return resolved_prompt_node_ids
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for ancestor_node_id in reversed(ancestor_node_ids):
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if ancestor_node_id in prompt_node_ids:
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resolved_prompt_node_ids.add(ancestor_node_id)
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descendant_prefix = f"{ancestor_node_id}:"
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resolved_prompt_node_ids.update(
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prompt_node_id
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for prompt_node_id in prompt_node_ids
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if prompt_node_id.startswith(descendant_prefix)
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)
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break
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return resolved_prompt_node_ids
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def _workflow_node_path(workflow_node_id: str, ancestor_node_ids: tuple[str, ...]) -> str:
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"""Return one workflow node's composed path, including subgraph ancestors."""
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if not ancestor_node_ids:
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return workflow_node_id
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return ":".join((*ancestor_node_ids, workflow_node_id))
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def _extract_marked_workflow_node_paths(
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workflow: dict[str, Any] | None,
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settings: ModalSyncSettings | None = None,
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) -> set[str]:
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"""Return composed workflow paths for nodes explicitly marked remote in metadata."""
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if workflow is None:
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return set()
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marker = (settings or get_settings()).marker_property
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marked_workflow_node_paths: set[str] = set()
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for node, ancestor_node_ids in _iter_workflow_nodes_with_ancestors(workflow):
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properties = node.get("properties") or {}
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if not properties.get(marker):
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continue
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marked_workflow_node_paths.add(
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_workflow_node_path(str(node.get("id")), ancestor_node_ids)
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)
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return marked_workflow_node_paths
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def _build_workflow_prompt_resolution_maps(
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workflow: dict[str, Any] | None,
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prompt_node_ids: set[str],
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) -> tuple[dict[str, set[str]], dict[str, set[str]]]:
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"""Return bidirectional mappings between workflow paths and queued prompt ids."""
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workflow_path_to_prompt_node_ids: dict[str, set[str]] = {}
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prompt_node_id_to_workflow_paths: dict[str, set[str]] = defaultdict(set)
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if workflow is None:
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for prompt_node_id in prompt_node_ids:
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workflow_path_to_prompt_node_ids[prompt_node_id] = {prompt_node_id}
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prompt_node_id_to_workflow_paths[prompt_node_id].add(prompt_node_id)
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return workflow_path_to_prompt_node_ids, prompt_node_id_to_workflow_paths
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for node, ancestor_node_ids in _iter_workflow_nodes_with_ancestors(workflow):
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workflow_node_id = str(node.get("id"))
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workflow_node_path = _workflow_node_path(workflow_node_id, ancestor_node_ids)
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resolved_prompt_node_ids = _resolve_prompt_node_ids_for_workflow_node(
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workflow_node_id,
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ancestor_node_ids,
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prompt_node_ids,
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)
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if workflow_node_id in prompt_node_ids:
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resolved_prompt_node_ids.add(workflow_node_id)
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if workflow_node_path in prompt_node_ids:
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resolved_prompt_node_ids.add(workflow_node_path)
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if not resolved_prompt_node_ids:
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continue
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workflow_path_to_prompt_node_ids[workflow_node_path] = resolved_prompt_node_ids
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for prompt_node_id in resolved_prompt_node_ids:
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prompt_node_id_to_workflow_paths[prompt_node_id].add(workflow_node_path)
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for prompt_node_id in prompt_node_ids:
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workflow_path_to_prompt_node_ids.setdefault(prompt_node_id, {prompt_node_id})
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prompt_node_id_to_workflow_paths[prompt_node_id].add(prompt_node_id)
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return workflow_path_to_prompt_node_ids, prompt_node_id_to_workflow_paths
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def _resolve_requested_prompt_node_ids(
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requested_workflow_node_paths: set[str],
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prompt_node_ids: set[str],
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workflow_path_to_prompt_node_ids: dict[str, set[str]],
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) -> set[str]:
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"""Resolve requested workflow-node paths to the prompt ids that queue-time rewrite sees."""
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requested_prompt_node_ids: set[str] = set()
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for requested_workflow_node_path in requested_workflow_node_paths:
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if requested_workflow_node_path in workflow_path_to_prompt_node_ids:
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requested_prompt_node_ids.update(
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workflow_path_to_prompt_node_ids[requested_workflow_node_path]
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)
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continue
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if requested_workflow_node_path in prompt_node_ids:
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requested_prompt_node_ids.add(requested_workflow_node_path)
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return requested_prompt_node_ids
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def _best_workflow_path_for_prompt_node(
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prompt_node_id: str,
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prompt_node_id_to_workflow_paths: dict[str, set[str]],
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) -> str:
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"""Choose the most specific workflow path for one queued prompt node id."""
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workflow_node_paths = prompt_node_id_to_workflow_paths.get(prompt_node_id, {prompt_node_id})
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return max(
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workflow_node_paths,
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key=lambda workflow_node_path: (
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workflow_node_path.count(":"),
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len(workflow_node_path),
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workflow_node_path,
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),
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)
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def _resolve_workflow_node_paths_for_prompt_nodes(
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prompt_node_ids: set[str],
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prompt_node_id_to_workflow_paths: dict[str, set[str]],
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) -> set[str]:
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"""Map queued prompt ids back to the workflow node paths the UI can mark remote."""
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return {
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_best_workflow_path_for_prompt_node(prompt_node_id, prompt_node_id_to_workflow_paths)
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for prompt_node_id in prompt_node_ids
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}
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def extract_remote_node_ids(
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workflow: dict[str, Any] | None,
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settings: ModalSyncSettings | None = None,
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prompt_node_ids: set[str] | None = None,
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) -> set[str]:
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"""Return the node ids marked for remote execution in the workflow metadata."""
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if workflow is None:
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return set()
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marker = (settings or get_settings()).marker_property
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remote_node_ids: set[str] = set()
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for node, ancestor_node_ids in _iter_workflow_nodes_with_ancestors(workflow):
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properties = node.get("properties") or {}
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if properties.get(marker):
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node_id = str(node.get("id"))
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if prompt_node_ids is None or node_id in prompt_node_ids:
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remote_node_ids.add(node_id)
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continue
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resolved_prompt_node_ids = _resolve_prompt_node_ids_for_workflow_node(
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node_id,
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ancestor_node_ids,
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prompt_node_ids,
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)
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if resolved_prompt_node_ids:
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remote_node_ids.update(resolved_prompt_node_ids)
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logger.info(
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"Mapped workflow Modal marker from node %s with ancestors %s to prompt nodes %s.",
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node_id,
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list(ancestor_node_ids),
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sorted(resolved_prompt_node_ids),
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)
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return remote_node_ids
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def _normalize_output_metadata(node_class: type[Any]) -> tuple[tuple[str, ...], tuple[str, ...], tuple[bool, ...]]:
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"""Return normalized output metadata for a node class."""
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if hasattr(node_class, "GET_SCHEMA"):
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node_class.GET_SCHEMA()
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output_types = tuple(getattr(node_class, "RETURN_TYPES", ("*",))) or ("*",)
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default_names = tuple(f"output_{index}" for index, _ in enumerate(output_types))
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output_names = tuple(getattr(node_class, "RETURN_NAMES", default_names))
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output_is_list = tuple(getattr(node_class, "OUTPUT_IS_LIST", (False,) * len(output_types)))
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if len(output_names) < len(output_types):
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output_names = output_names + default_names[len(output_names) :]
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if len(output_is_list) < len(output_types):
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output_is_list = output_is_list + (False,) * (len(output_types) - len(output_is_list))
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return output_types, output_names[: len(output_types)], output_is_list[: len(output_types)]
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def _is_transportable_output_type(io_type: str) -> bool:
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"""Return whether a ComfyUI output type can cross the current transport."""
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normalized_parts = [part.strip() for part in io_type.split(",") if part.strip()]
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return bool(normalized_parts) and all(part in _TRANSPORTABLE_OUTPUT_TYPES for part in normalized_parts)
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|
|
def _build_consumer_map(prompt: dict[str, Any]) -> dict[LinkedOutputRef, list[InputTarget]]:
|
|
"""Build a reverse map from node outputs to downstream prompt inputs."""
|
|
consumers: dict[LinkedOutputRef, list[InputTarget]] = defaultdict(list)
|
|
for node_id, prompt_node in prompt.items():
|
|
for input_name, input_value in (prompt_node.get("inputs") or {}).items():
|
|
if not _is_link(input_value):
|
|
continue
|
|
source = LinkedOutputRef(node_id=str(input_value[0]), output_index=int(input_value[1]))
|
|
consumers[source].append(InputTarget(node_id=str(node_id), input_name=str(input_name)))
|
|
return consumers
|
|
|
|
|
|
def _build_remote_components(prompt: dict[str, Any], remote_node_ids: set[str]) -> list[list[str]]:
|
|
"""Partition remote-marked nodes into connected components."""
|
|
adjacency: dict[str, set[str]] = {node_id: set() for node_id in remote_node_ids}
|
|
for node_id in remote_node_ids:
|
|
prompt_node = prompt.get(node_id)
|
|
if prompt_node is None:
|
|
continue
|
|
for input_value in (prompt_node.get("inputs") or {}).values():
|
|
if not _is_link(input_value):
|
|
continue
|
|
upstream_node_id = str(input_value[0])
|
|
if upstream_node_id not in remote_node_ids:
|
|
continue
|
|
adjacency[node_id].add(upstream_node_id)
|
|
adjacency[upstream_node_id].add(node_id)
|
|
|
|
components: list[list[str]] = []
|
|
visited: set[str] = set()
|
|
for node_id in sorted(remote_node_ids):
|
|
if node_id in visited:
|
|
continue
|
|
pending = [node_id]
|
|
component: list[str] = []
|
|
while pending:
|
|
current = pending.pop()
|
|
if current in visited:
|
|
continue
|
|
visited.add(current)
|
|
component.append(current)
|
|
pending.extend(sorted(adjacency[current] - visited))
|
|
components.append(sorted(component))
|
|
logger.info(
|
|
"Partitioned %d remote nodes into %d remote components: %s",
|
|
len(remote_node_ids),
|
|
len(components),
|
|
components,
|
|
)
|
|
return components
|
|
|
|
|
|
def _expand_remote_node_ids_for_non_transportable_inputs(
|
|
prompt: dict[str, Any],
|
|
remote_node_ids: set[str],
|
|
nodes_module: Any,
|
|
) -> tuple[set[str], list[RemoteExpansionReason]]:
|
|
"""Grow the remote set upstream until non-transportable inputs stay inside the remote island."""
|
|
expanded_remote_node_ids = set(remote_node_ids)
|
|
added_node_ids: set[str] = set()
|
|
reasons: list[RemoteExpansionReason] = []
|
|
|
|
changed = True
|
|
while changed:
|
|
changed = False
|
|
for node_id in sorted(expanded_remote_node_ids):
|
|
prompt_node = prompt.get(node_id)
|
|
if prompt_node is None:
|
|
continue
|
|
for input_value in (prompt_node.get("inputs") or {}).values():
|
|
if not _is_link(input_value):
|
|
continue
|
|
upstream_node_id = str(input_value[0])
|
|
if upstream_node_id in expanded_remote_node_ids:
|
|
continue
|
|
|
|
upstream_prompt_node = prompt.get(upstream_node_id)
|
|
if upstream_prompt_node is None:
|
|
continue
|
|
|
|
upstream_class_type = str(upstream_prompt_node["class_type"])
|
|
upstream_class = nodes_module.NODE_CLASS_MAPPINGS.get(upstream_class_type)
|
|
if upstream_class is None:
|
|
continue
|
|
|
|
output_types, _, _ = _normalize_output_metadata(upstream_class)
|
|
output_index = int(input_value[1])
|
|
if output_index >= len(output_types):
|
|
continue
|
|
|
|
io_type = str(output_types[output_index])
|
|
if _is_transportable_output_type(io_type):
|
|
continue
|
|
|
|
expanded_remote_node_ids.add(upstream_node_id)
|
|
reason = RemoteExpansionReason(
|
|
node_id=upstream_node_id,
|
|
class_type=upstream_class_type,
|
|
required_by_node_id=node_id,
|
|
required_by_class_type=str(prompt_node["class_type"]),
|
|
output_index=output_index,
|
|
io_type=io_type,
|
|
)
|
|
reasons.append(reason)
|
|
added_node_ids.add(upstream_node_id)
|
|
changed = True
|
|
logger.info(
|
|
"Auto-expanded remote execution upstream: added node %s (%s) because node %s depends on non-transportable type '%s'.",
|
|
reason.node_id,
|
|
reason.class_type,
|
|
reason.required_by_node_id,
|
|
reason.io_type,
|
|
)
|
|
break
|
|
if changed:
|
|
break
|
|
|
|
if added_node_ids:
|
|
logger.info(
|
|
"Expanded remote node set from %d to %d nodes by absorbing upstream non-transportable dependencies: %s",
|
|
len(remote_node_ids),
|
|
len(expanded_remote_node_ids),
|
|
sorted(added_node_ids),
|
|
)
|
|
return expanded_remote_node_ids, reasons
|
|
|
|
|
|
def analyze_remote_node_selection(
|
|
prompt: dict[str, Any],
|
|
workflow: dict[str, Any] | None,
|
|
seed_workflow_node_paths: list[str],
|
|
settings: ModalSyncSettings | None = None,
|
|
nodes_module: Any | None = None,
|
|
) -> RemoteNodeAnalysis:
|
|
"""Return the nodes the UI should mark remote for one context-menu expansion request."""
|
|
resolved_settings = settings or get_settings()
|
|
resolved_nodes_module = nodes_module or _get_nodes_module()
|
|
prompt_node_ids = {str(node_id) for node_id in prompt.keys()}
|
|
requested_workflow_node_paths = {
|
|
str(seed_workflow_node_path)
|
|
for seed_workflow_node_path in seed_workflow_node_paths
|
|
if str(seed_workflow_node_path)
|
|
}
|
|
current_remote_node_ids = extract_remote_node_ids(
|
|
workflow,
|
|
resolved_settings,
|
|
prompt_node_ids,
|
|
)
|
|
current_remote_workflow_node_paths = _extract_marked_workflow_node_paths(
|
|
workflow,
|
|
resolved_settings,
|
|
)
|
|
workflow_path_to_prompt_node_ids, prompt_node_id_to_workflow_paths = (
|
|
_build_workflow_prompt_resolution_maps(workflow, prompt_node_ids)
|
|
)
|
|
requested_node_ids = _resolve_requested_prompt_node_ids(
|
|
requested_workflow_node_paths,
|
|
prompt_node_ids,
|
|
workflow_path_to_prompt_node_ids,
|
|
)
|
|
initial_remote_node_ids = current_remote_node_ids | requested_node_ids
|
|
resolved_remote_node_ids, reasons = _expand_remote_node_ids_for_non_transportable_inputs(
|
|
prompt=prompt,
|
|
remote_node_ids=initial_remote_node_ids,
|
|
nodes_module=resolved_nodes_module,
|
|
)
|
|
resolved_workflow_node_paths = (
|
|
_resolve_workflow_node_paths_for_prompt_nodes(
|
|
resolved_remote_node_ids,
|
|
prompt_node_id_to_workflow_paths,
|
|
)
|
|
| current_remote_workflow_node_paths
|
|
)
|
|
added_node_ids = resolved_remote_node_ids - current_remote_node_ids
|
|
added_workflow_node_paths = resolved_workflow_node_paths - current_remote_workflow_node_paths
|
|
|
|
return RemoteNodeAnalysis(
|
|
requested_node_ids=sorted(requested_node_ids),
|
|
requested_workflow_node_paths=sorted(requested_workflow_node_paths),
|
|
current_remote_node_ids=sorted(current_remote_node_ids),
|
|
current_remote_workflow_node_paths=sorted(current_remote_workflow_node_paths),
|
|
resolved_remote_node_ids=sorted(resolved_remote_node_ids),
|
|
resolved_workflow_node_paths=sorted(resolved_workflow_node_paths),
|
|
added_node_ids=sorted(added_node_ids),
|
|
added_workflow_node_paths=sorted(added_workflow_node_paths),
|
|
reasons=reasons,
|
|
)
|
|
|
|
|
|
def _build_component_plan(
|
|
component_node_ids: list[str],
|
|
prompt: dict[str, Any],
|
|
consumers: dict[LinkedOutputRef, list[InputTarget]],
|
|
nodes_module: Any,
|
|
) -> RemoteComponentPlan:
|
|
"""Build rewrite metadata for a connected remote component."""
|
|
component_node_id_set = set(component_node_ids)
|
|
representative_node_id = component_node_ids[0]
|
|
boundary_inputs_by_source: dict[LinkedOutputRef, BoundaryInputSpec] = {}
|
|
boundary_outputs_by_source: dict[LinkedOutputRef, BoundaryOutputSpec] = {}
|
|
output_execution_targets: set[str] = set()
|
|
contains_output_node = False
|
|
|
|
for node_id in component_node_ids:
|
|
prompt_node = prompt[node_id]
|
|
class_type = str(prompt_node["class_type"])
|
|
node_class = nodes_module.NODE_CLASS_MAPPINGS[class_type]
|
|
output_types, output_names, output_is_list = _normalize_output_metadata(node_class)
|
|
|
|
if getattr(node_class, "OUTPUT_NODE", False):
|
|
contains_output_node = True
|
|
output_execution_targets.add(node_id)
|
|
|
|
for input_name, input_value in (prompt_node.get("inputs") or {}).items():
|
|
if not _is_link(input_value):
|
|
continue
|
|
upstream_node_id = str(input_value[0])
|
|
if upstream_node_id in component_node_id_set:
|
|
continue
|
|
source = LinkedOutputRef(node_id=upstream_node_id, output_index=int(input_value[1]))
|
|
spec = boundary_inputs_by_source.get(source)
|
|
if spec is None:
|
|
spec = BoundaryInputSpec(
|
|
proxy_input_name=f"remote_input_{len(boundary_inputs_by_source)}",
|
|
source=source,
|
|
)
|
|
boundary_inputs_by_source[source] = spec
|
|
spec.targets.append(InputTarget(node_id=node_id, input_name=str(input_name)))
|
|
|
|
for output_index, io_type in enumerate(output_types):
|
|
source = LinkedOutputRef(node_id=node_id, output_index=output_index)
|
|
local_consumers = [
|
|
consumer for consumer in consumers.get(source, []) if consumer.node_id not in component_node_id_set
|
|
]
|
|
if not local_consumers:
|
|
continue
|
|
output_execution_targets.add(node_id)
|
|
if source in boundary_outputs_by_source:
|
|
continue
|
|
output_name = output_names[output_index]
|
|
boundary_outputs_by_source[source] = BoundaryOutputSpec(
|
|
proxy_output_name=f"{node_id}_{output_name}",
|
|
source=source,
|
|
io_type=str(io_type),
|
|
is_list=bool(output_is_list[output_index]),
|
|
)
|
|
|
|
component = RemoteComponentPlan(
|
|
node_ids=component_node_ids,
|
|
representative_node_id=representative_node_id,
|
|
boundary_inputs=sorted(
|
|
boundary_inputs_by_source.values(),
|
|
key=lambda spec: (spec.source.node_id, spec.source.output_index),
|
|
),
|
|
boundary_outputs=sorted(
|
|
boundary_outputs_by_source.values(),
|
|
key=lambda spec: (spec.source.node_id, spec.source.output_index),
|
|
),
|
|
execute_node_ids=sorted(output_execution_targets),
|
|
contains_output_node=contains_output_node,
|
|
)
|
|
logger.info(
|
|
"Planned remote component %s: nodes=%s boundary_inputs=%d boundary_outputs=%d execute_nodes=%s output_node=%s",
|
|
component.representative_node_id,
|
|
component.node_ids,
|
|
len(component.boundary_inputs),
|
|
len(component.boundary_outputs),
|
|
component.execute_node_ids,
|
|
component.contains_output_node,
|
|
)
|
|
return component
|
|
|
|
|
|
def _build_component_plans(
|
|
prompt: dict[str, Any],
|
|
remote_node_ids: set[str],
|
|
nodes_module: Any,
|
|
) -> list[RemoteComponentPlan]:
|
|
"""Build plans for every connected remote component."""
|
|
consumers = _build_consumer_map(prompt)
|
|
components = _build_remote_components(prompt, remote_node_ids)
|
|
return [
|
|
_build_component_plan(component, prompt, consumers, nodes_module)
|
|
for component in components
|
|
]
|
|
|
|
|
|
def _describe_output_boundary_error(
|
|
component: RemoteComponentPlan,
|
|
source: LinkedOutputRef,
|
|
source_class_type: str,
|
|
io_type: str,
|
|
local_consumer: InputTarget,
|
|
local_consumer_class_type: str,
|
|
) -> str:
|
|
"""Format a human-readable remote-to-local transport validation error."""
|
|
return (
|
|
"Remote component rooted at node "
|
|
f"{component.representative_node_id} exports node {source.node_id} "
|
|
f"({source_class_type}) output index {source.output_index} of type '{io_type}' "
|
|
f"to local node {local_consumer.node_id} ({local_consumer_class_type}) input "
|
|
f"'{local_consumer.input_name}', which cannot cross the current local/remote boundary. "
|
|
"Current ComfyUI-Modal transport only supports JSON-compatible values, bytes, "
|
|
"and tensor-like outputs such as IMAGE, MASK, LATENT, SIGMAS, NOISE, INT, "
|
|
"FLOAT, BOOLEAN, and STRING."
|
|
)
|
|
|
|
|
|
def _describe_input_boundary_error(
|
|
component: RemoteComponentPlan,
|
|
target: InputTarget,
|
|
target_class_type: str,
|
|
source: LinkedOutputRef,
|
|
source_class_type: str,
|
|
io_type: str,
|
|
) -> str:
|
|
"""Format a human-readable local-to-remote transport validation error."""
|
|
return (
|
|
"Remote node "
|
|
f"{target.node_id} ({target_class_type}) input '{target.input_name}' "
|
|
f"depends on upstream node {source.node_id} ({source_class_type}) output index "
|
|
f"{source.output_index} of type '{io_type}', which cannot cross the current "
|
|
"local/remote boundary. Current ComfyUI-Modal transport only supports "
|
|
"JSON-compatible values, bytes, and tensor-like outputs such as IMAGE, MASK, "
|
|
"LATENT, SIGMAS, NOISE, INT, FLOAT, BOOLEAN, and STRING."
|
|
)
|
|
|
|
|
|
def validate_remote_component_transport_compatibility(
|
|
prompt: dict[str, Any],
|
|
components: list[RemoteComponentPlan],
|
|
nodes_module: Any,
|
|
) -> None:
|
|
"""Reject remote components whose true graph boundaries require unsupported transport."""
|
|
validation_errors: list[str] = []
|
|
consumers = _build_consumer_map(prompt)
|
|
logger.info("Validating %d remote components for transport compatibility.", len(components))
|
|
|
|
for component in components:
|
|
for boundary_input in component.boundary_inputs:
|
|
source_prompt_node = prompt.get(boundary_input.source.node_id)
|
|
if source_prompt_node is None:
|
|
continue
|
|
source_class_type = str(source_prompt_node["class_type"])
|
|
source_class = nodes_module.NODE_CLASS_MAPPINGS.get(source_class_type)
|
|
if source_class is None:
|
|
continue
|
|
|
|
source_output_types, _, _ = _normalize_output_metadata(source_class)
|
|
if boundary_input.source.output_index >= len(source_output_types):
|
|
continue
|
|
io_type = str(source_output_types[boundary_input.source.output_index])
|
|
if _is_transportable_output_type(io_type):
|
|
continue
|
|
|
|
for target in boundary_input.targets:
|
|
target_class_type = str(prompt[target.node_id]["class_type"])
|
|
validation_errors.append(
|
|
_describe_input_boundary_error(
|
|
component=component,
|
|
target=target,
|
|
target_class_type=target_class_type,
|
|
source=boundary_input.source,
|
|
source_class_type=source_class_type,
|
|
io_type=io_type,
|
|
)
|
|
)
|
|
|
|
for boundary_output in component.boundary_outputs:
|
|
if _is_transportable_output_type(boundary_output.io_type):
|
|
continue
|
|
|
|
source_class_type = str(prompt[boundary_output.source.node_id]["class_type"])
|
|
for local_consumer in consumers.get(boundary_output.source, []):
|
|
if local_consumer.node_id in component.node_ids:
|
|
continue
|
|
local_consumer_class_type = str(prompt[local_consumer.node_id]["class_type"])
|
|
validation_errors.append(
|
|
_describe_output_boundary_error(
|
|
component=component,
|
|
source=boundary_output.source,
|
|
source_class_type=source_class_type,
|
|
io_type=boundary_output.io_type,
|
|
local_consumer=local_consumer,
|
|
local_consumer_class_type=local_consumer_class_type,
|
|
)
|
|
)
|
|
|
|
if validation_errors:
|
|
raise ModalPromptValidationError("\n".join(validation_errors))
|
|
logger.info("Remote component transport validation passed.")
|
|
|
|
|
|
def _sync_component_prompt_inputs(
|
|
component: RemoteComponentPlan,
|
|
rewritten_prompt: dict[str, Any],
|
|
sync_engine: ModalAssetSyncEngine,
|
|
) -> tuple[dict[str, Any], list[SyncedAsset]]:
|
|
"""Build a synced prompt payload for one remote component."""
|
|
component_prompt: dict[str, Any] = {}
|
|
synced_assets: list[SyncedAsset] = []
|
|
logger.info(
|
|
"Syncing prompt inputs for remote component %s with %d nodes.",
|
|
component.representative_node_id,
|
|
len(component.node_ids),
|
|
)
|
|
for node_id in component.node_ids:
|
|
prompt_node = rewritten_prompt[node_id]
|
|
synced_inputs, node_assets = sync_engine.sync_prompt_inputs(copy.deepcopy(prompt_node.get("inputs", {})))
|
|
synced_assets.extend(node_assets)
|
|
component_prompt[node_id] = {
|
|
"class_type": str(prompt_node["class_type"]),
|
|
"inputs": synced_inputs,
|
|
"_meta": copy.deepcopy(prompt_node.get("_meta", {})),
|
|
}
|
|
logger.info(
|
|
"Prepared remote node %s (%s) with %d synced assets.",
|
|
node_id,
|
|
component_prompt[node_id]["class_type"],
|
|
len(node_assets),
|
|
)
|
|
logger.info(
|
|
"Finished syncing remote component %s with %d total synced assets.",
|
|
component.representative_node_id,
|
|
len(synced_assets),
|
|
)
|
|
return component_prompt, synced_assets
|
|
|
|
|
|
def _build_component_payload(
|
|
component: RemoteComponentPlan,
|
|
component_prompt: dict[str, Any],
|
|
extra_data: dict[str, Any] | None,
|
|
synced_assets: list[SyncedAsset],
|
|
custom_nodes_bundle: SyncedAsset | None,
|
|
) -> dict[str, Any]:
|
|
"""Build the serialized execution payload for one remote component."""
|
|
requires_volume_reload = any(asset.uploaded for asset in synced_assets) or (
|
|
custom_nodes_bundle is not None and custom_nodes_bundle.uploaded
|
|
)
|
|
payload = {
|
|
"payload_kind": "subgraph",
|
|
"component_id": component.representative_node_id,
|
|
"prompt_id": (extra_data or {}).get("prompt_id"),
|
|
"component_node_ids": list(component.node_ids),
|
|
"subgraph_prompt": component_prompt,
|
|
"boundary_inputs": [
|
|
{
|
|
"proxy_input_name": boundary_input.proxy_input_name,
|
|
"targets": [
|
|
{"node_id": target.node_id, "input_name": target.input_name}
|
|
for target in boundary_input.targets
|
|
],
|
|
}
|
|
for boundary_input in component.boundary_inputs
|
|
],
|
|
"boundary_outputs": [
|
|
{
|
|
"proxy_output_name": boundary_output.proxy_output_name,
|
|
"node_id": boundary_output.source.node_id,
|
|
"output_index": boundary_output.source.output_index,
|
|
"io_type": boundary_output.io_type,
|
|
"is_list": boundary_output.is_list,
|
|
}
|
|
for boundary_output in component.boundary_outputs
|
|
],
|
|
"execute_node_ids": list(component.execute_node_ids),
|
|
"extra_data": copy.deepcopy(extra_data or {}),
|
|
"requires_volume_reload": requires_volume_reload,
|
|
"custom_nodes_bundle": (
|
|
custom_nodes_bundle.remote_path if custom_nodes_bundle is not None else None
|
|
),
|
|
}
|
|
logger.info(
|
|
"Built remote payload for component %s: boundary_inputs=%d boundary_outputs=%d execute_nodes=%s",
|
|
component.representative_node_id,
|
|
len(payload["boundary_inputs"]),
|
|
len(payload["boundary_outputs"]),
|
|
payload["execute_node_ids"],
|
|
)
|
|
logger.info(
|
|
"Remote payload for component %s requires_volume_reload=%s",
|
|
component.representative_node_id,
|
|
requires_volume_reload,
|
|
)
|
|
return payload
|
|
|
|
|
|
def _rewrite_component_into_proxy(
|
|
component: RemoteComponentPlan,
|
|
rewritten_prompt: dict[str, Any],
|
|
payload: dict[str, Any],
|
|
nodes_module: Any,
|
|
) -> str:
|
|
"""Replace a remote component with a single proxy node in the prompt."""
|
|
output_types = tuple(spec.io_type for spec in component.boundary_outputs)
|
|
output_names = tuple(spec.proxy_output_name for spec in component.boundary_outputs)
|
|
output_is_list = tuple(spec.is_list for spec in component.boundary_outputs)
|
|
proxy_node_id = ensure_modal_component_proxy_node_registered(
|
|
output_types=output_types,
|
|
output_names=output_names,
|
|
output_is_list=output_is_list,
|
|
nodes_module=nodes_module,
|
|
is_output_node=component.contains_output_node,
|
|
)
|
|
|
|
proxy_inputs: dict[str, Any] = {
|
|
boundary_input.proxy_input_name: [boundary_input.source.node_id, boundary_input.source.output_index]
|
|
for boundary_input in component.boundary_inputs
|
|
}
|
|
proxy_inputs["original_node_data"] = payload
|
|
|
|
representative_node_id = component.representative_node_id
|
|
representative_meta = copy.deepcopy(rewritten_prompt[representative_node_id].get("_meta", {}))
|
|
rewritten_prompt[representative_node_id] = {
|
|
"class_type": proxy_node_id,
|
|
"inputs": proxy_inputs,
|
|
"_meta": representative_meta,
|
|
}
|
|
|
|
boundary_output_indices = {
|
|
spec.source: index for index, spec in enumerate(component.boundary_outputs)
|
|
}
|
|
component_node_id_set = set(component.node_ids)
|
|
|
|
for node_id, prompt_node in list(rewritten_prompt.items()):
|
|
if node_id in component_node_id_set and node_id != representative_node_id:
|
|
del rewritten_prompt[node_id]
|
|
continue
|
|
if node_id == representative_node_id:
|
|
continue
|
|
|
|
for input_name, input_value in list((prompt_node.get("inputs") or {}).items()):
|
|
if not _is_link(input_value):
|
|
continue
|
|
source = LinkedOutputRef(node_id=str(input_value[0]), output_index=int(input_value[1]))
|
|
if source not in boundary_output_indices:
|
|
continue
|
|
prompt_node["inputs"][input_name] = [representative_node_id, boundary_output_indices[source]]
|
|
|
|
logger.info(
|
|
"Rewrote remote component %s with %d nodes to Modal proxy %s.",
|
|
representative_node_id,
|
|
len(component.node_ids),
|
|
proxy_node_id,
|
|
)
|
|
return representative_node_id
|
|
|
|
|
|
def rewrite_prompt_for_modal(
|
|
prompt: dict[str, Any],
|
|
workflow: dict[str, Any] | None,
|
|
sync_engine: ModalAssetSyncEngine | None = None,
|
|
settings: ModalSyncSettings | None = None,
|
|
nodes_module: Any | None = None,
|
|
extra_data: dict[str, Any] | None = None,
|
|
) -> tuple[dict[str, Any], RewriteSummary]:
|
|
"""Rewrite connected remote components into Modal proxy nodes."""
|
|
resolved_settings = settings or get_settings()
|
|
remote_node_ids = extract_remote_node_ids(workflow, resolved_settings, set(prompt.keys()))
|
|
summary = RewriteSummary(remote_node_ids=sorted(remote_node_ids))
|
|
logger.info("Found %d workflow nodes marked for Modal execution.", len(remote_node_ids))
|
|
|
|
if not remote_node_ids:
|
|
return copy.deepcopy(prompt), summary
|
|
|
|
resolved_nodes_module = nodes_module or _get_nodes_module()
|
|
resolved_sync_engine = sync_engine or ModalAssetSyncEngine.from_environment(resolved_settings)
|
|
rewritten_prompt = copy.deepcopy(prompt)
|
|
expanded_remote_node_ids, _ = _expand_remote_node_ids_for_non_transportable_inputs(
|
|
prompt=rewritten_prompt,
|
|
remote_node_ids=remote_node_ids,
|
|
nodes_module=resolved_nodes_module,
|
|
)
|
|
summary.remote_node_ids = sorted(expanded_remote_node_ids)
|
|
components = _build_component_plans(
|
|
rewritten_prompt,
|
|
expanded_remote_node_ids,
|
|
resolved_nodes_module,
|
|
)
|
|
validate_remote_component_transport_compatibility(
|
|
prompt=rewritten_prompt,
|
|
components=components,
|
|
nodes_module=resolved_nodes_module,
|
|
)
|
|
|
|
if resolved_settings.sync_custom_nodes:
|
|
summary.custom_nodes_bundle = resolved_sync_engine.sync_custom_nodes_directory()
|
|
else:
|
|
logger.info(
|
|
"Skipping custom_nodes bundle sync because sync is disabled for execution_mode=%s.",
|
|
resolved_settings.execution_mode,
|
|
)
|
|
|
|
for component in components:
|
|
summary.remote_component_ids.append(component.representative_node_id)
|
|
summary.component_node_ids_by_representative[component.representative_node_id] = list(
|
|
component.node_ids
|
|
)
|
|
for node_id in component.node_ids:
|
|
summary.rewritten_node_id_map[node_id] = component.representative_node_id
|
|
|
|
logger.info(
|
|
"Rewriting remote component %s covering nodes %s.",
|
|
component.representative_node_id,
|
|
component.node_ids,
|
|
)
|
|
component_prompt, synced_assets = _sync_component_prompt_inputs(
|
|
component=component,
|
|
rewritten_prompt=rewritten_prompt,
|
|
sync_engine=resolved_sync_engine,
|
|
)
|
|
summary.synced_assets.extend(synced_assets)
|
|
payload = _build_component_payload(
|
|
component=component,
|
|
component_prompt=component_prompt,
|
|
extra_data=extra_data,
|
|
synced_assets=synced_assets,
|
|
custom_nodes_bundle=summary.custom_nodes_bundle,
|
|
)
|
|
_rewrite_component_into_proxy(
|
|
component=component,
|
|
rewritten_prompt=rewritten_prompt,
|
|
payload=payload,
|
|
nodes_module=resolved_nodes_module,
|
|
)
|
|
|
|
return rewritten_prompt, summary
|
|
|
|
|
|
async def _queue_prompt_json(prompt_server: Any, json_data: dict[str, Any]) -> web.Response:
|
|
"""Queue a possibly rewritten prompt using ComfyUI's native semantics."""
|
|
execution = _get_execution_module()
|
|
json_data = prompt_server.trigger_on_prompt(json_data)
|
|
|
|
if "number" in json_data:
|
|
number = float(json_data["number"])
|
|
else:
|
|
number = prompt_server.number
|
|
if json_data.get("front"):
|
|
number = -number
|
|
prompt_server.number += 1
|
|
|
|
if "prompt" not in json_data:
|
|
return web.json_response(
|
|
{
|
|
"error": {
|
|
"type": "no_prompt",
|
|
"message": "No prompt provided",
|
|
"details": "No prompt provided",
|
|
"extra_info": {},
|
|
}
|
|
},
|
|
status=400,
|
|
)
|
|
|
|
prompt = json_data["prompt"]
|
|
prompt_id = str(json_data.get("prompt_id", uuid.uuid4()))
|
|
partial_execution_targets = json_data.get("partial_execution_targets")
|
|
valid = await execution.validate_prompt(prompt_id, prompt, partial_execution_targets)
|
|
|
|
extra_data = dict(json_data.get("extra_data", {}))
|
|
if "client_id" in json_data:
|
|
extra_data["client_id"] = json_data["client_id"]
|
|
|
|
if not valid[0]:
|
|
logger.warning("invalid prompt: %s", valid[1])
|
|
return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400)
|
|
|
|
outputs_to_execute = valid[2]
|
|
sensitive: dict[str, Any] = {}
|
|
for sensitive_key in execution.SENSITIVE_EXTRA_DATA_KEYS:
|
|
if sensitive_key in extra_data:
|
|
sensitive[sensitive_key] = extra_data.pop(sensitive_key)
|
|
|
|
extra_data["create_time"] = int(time.time() * 1000)
|
|
prompt_server.prompt_queue.put(
|
|
(number, prompt_id, prompt, extra_data, outputs_to_execute, sensitive)
|
|
)
|
|
return web.json_response(
|
|
{"prompt_id": prompt_id, "number": number, "node_errors": valid[3]}
|
|
)
|
|
|
|
|
|
def _analysis_route_path(route_path: str) -> str:
|
|
"""Return the sibling HTTP route used for dry-run remote-node expansion."""
|
|
if route_path.endswith("/queue_prompt"):
|
|
return f"{route_path.removesuffix('/queue_prompt')}/analyze_remote_nodes"
|
|
return f"{route_path.rstrip('/')}/analyze_remote_nodes"
|
|
|
|
|
|
def setup_modal_queue_route(
|
|
prompt_server: Any | None = None,
|
|
sync_engine: ModalAssetSyncEngine | None = None,
|
|
settings: ModalSyncSettings | None = None,
|
|
) -> None:
|
|
"""Register the `/modal/queue_prompt` route once for the active PromptServer."""
|
|
global _ROUTE_REGISTERED
|
|
if _ROUTE_REGISTERED:
|
|
return
|
|
|
|
try:
|
|
resolved_server_module = _get_server_module()
|
|
except ModuleNotFoundError:
|
|
logger.debug("ComfyUI server module is not available; skipping route registration.")
|
|
return
|
|
|
|
resolved_settings = settings or get_settings()
|
|
prompt_server = prompt_server or getattr(resolved_server_module.PromptServer, "instance", None)
|
|
if prompt_server is None:
|
|
logger.debug("PromptServer.instance is not available; skipping route registration.")
|
|
return
|
|
|
|
resolved_sync_engine = sync_engine or ModalAssetSyncEngine.from_environment(resolved_settings)
|
|
analysis_route_path = _analysis_route_path(resolved_settings.route_path)
|
|
|
|
@prompt_server.routes.post(analysis_route_path)
|
|
async def modal_analyze_remote_nodes(request: web.Request) -> web.Response:
|
|
"""Analyze which workflow nodes should be marked remote for the current graph."""
|
|
logger.info("Received Modal remote-node analysis request.")
|
|
try:
|
|
request_started_at = time.perf_counter()
|
|
json_data = await request.json()
|
|
prompt = json_data.get("prompt")
|
|
if not isinstance(prompt, dict):
|
|
raise ValueError("Modal remote-node analysis requires a 'prompt' object.")
|
|
|
|
workflow = json_data.get("workflow")
|
|
seed_node_ids = json_data.get("seed_node_ids") or []
|
|
if not isinstance(seed_node_ids, list):
|
|
raise ValueError("Modal remote-node analysis requires 'seed_node_ids' to be a list.")
|
|
|
|
analysis = analyze_remote_node_selection(
|
|
prompt=prompt,
|
|
workflow=workflow if isinstance(workflow, dict) else None,
|
|
seed_workflow_node_paths=[str(seed_node_id) for seed_node_id in seed_node_ids],
|
|
settings=resolved_settings,
|
|
)
|
|
logger.info(
|
|
"Modal remote-node analysis finished in %.3fs with %d requested nodes and %d additions.",
|
|
time.perf_counter() - request_started_at,
|
|
len(analysis.requested_workflow_node_paths),
|
|
len(analysis.added_workflow_node_paths),
|
|
)
|
|
return web.json_response(
|
|
{
|
|
"requested_node_ids": analysis.requested_node_ids,
|
|
"requested_workflow_node_paths": analysis.requested_workflow_node_paths,
|
|
"current_remote_node_ids": analysis.current_remote_node_ids,
|
|
"current_remote_workflow_node_paths": (
|
|
analysis.current_remote_workflow_node_paths
|
|
),
|
|
"resolved_remote_node_ids": analysis.resolved_remote_node_ids,
|
|
"resolved_workflow_node_paths": analysis.resolved_workflow_node_paths,
|
|
"added_node_ids": analysis.added_node_ids,
|
|
"added_workflow_node_paths": analysis.added_workflow_node_paths,
|
|
"reasons": [
|
|
{
|
|
"node_id": reason.node_id,
|
|
"class_type": reason.class_type,
|
|
"required_by_node_id": reason.required_by_node_id,
|
|
"required_by_class_type": reason.required_by_class_type,
|
|
"output_index": reason.output_index,
|
|
"io_type": reason.io_type,
|
|
}
|
|
for reason in analysis.reasons
|
|
],
|
|
}
|
|
)
|
|
except (TypeError, ValueError) as exc:
|
|
logger.warning("Modal remote-node analysis request was invalid: %s", exc)
|
|
return web.json_response({"error": str(exc), "node_errors": []}, status=400)
|
|
|
|
@prompt_server.routes.post(resolved_settings.route_path)
|
|
async def modal_queue_prompt(request: web.Request) -> web.Response:
|
|
"""Handle prompt queue requests that include Modal remote markers."""
|
|
logger.info("Received Modal queue request.")
|
|
json_data: dict[str, Any] | None = None
|
|
workflow: dict[str, Any] | None = None
|
|
remote_node_ids: list[str] = []
|
|
summary = RewriteSummary()
|
|
try:
|
|
request_started_at = time.perf_counter()
|
|
json_data = await request.json()
|
|
json_data.setdefault("prompt_id", str(uuid.uuid4()))
|
|
json_data.setdefault("extra_data", {})
|
|
json_data["extra_data"]["prompt_id"] = json_data["prompt_id"]
|
|
if json_data.get("client_id") is not None:
|
|
json_data["extra_data"]["client_id"] = json_data["client_id"]
|
|
extra_pnginfo = ((json_data.get("extra_data") or {}).get("extra_pnginfo") or {})
|
|
workflow = extra_pnginfo.get("workflow")
|
|
prompt_node_ids = (
|
|
{str(node_id) for node_id in json_data.get("prompt", {}).keys()}
|
|
if "prompt" in json_data
|
|
else None
|
|
)
|
|
remote_node_ids = sorted(
|
|
extract_remote_node_ids(workflow, resolved_settings, prompt_node_ids)
|
|
)
|
|
if "prompt" in json_data:
|
|
rewrite_started_at = time.perf_counter()
|
|
rewritten_prompt, summary = rewrite_prompt_for_modal(
|
|
prompt=json_data["prompt"],
|
|
workflow=workflow,
|
|
sync_engine=resolved_sync_engine,
|
|
settings=resolved_settings,
|
|
extra_data=json_data.get("extra_data"),
|
|
)
|
|
logger.info(
|
|
"Modal prompt rewrite finished in %.3fs for %d remote nodes across %d components.",
|
|
time.perf_counter() - rewrite_started_at,
|
|
len(summary.remote_node_ids),
|
|
len(summary.remote_component_ids),
|
|
)
|
|
remote_node_ids = list(summary.remote_node_ids)
|
|
json_data["prompt"] = rewritten_prompt
|
|
if json_data.get("partial_execution_targets"):
|
|
rewritten_targets = {
|
|
summary.rewritten_node_id_map.get(str(target), str(target))
|
|
for target in json_data["partial_execution_targets"]
|
|
}
|
|
json_data["partial_execution_targets"] = sorted(rewritten_targets)
|
|
json_data.setdefault("extra_data", {}).setdefault("modal", {})
|
|
json_data["extra_data"]["modal"]["remote_node_ids"] = summary.remote_node_ids
|
|
json_data["extra_data"]["modal"]["remote_component_ids"] = summary.remote_component_ids
|
|
json_data["extra_data"]["modal"]["synced_assets"] = [
|
|
asset.remote_path for asset in summary.synced_assets
|
|
]
|
|
if summary.custom_nodes_bundle is not None:
|
|
json_data["extra_data"]["modal"]["custom_nodes_bundle"] = (
|
|
summary.custom_nodes_bundle.remote_path
|
|
)
|
|
_emit_modal_status(
|
|
prompt_server=prompt_server,
|
|
phase="setup",
|
|
client_id=str(json_data.get("client_id")) if json_data.get("client_id") else None,
|
|
prompt_id=str(json_data.get("prompt_id")) if json_data.get("prompt_id") else None,
|
|
node_ids=remote_node_ids,
|
|
component_node_ids_by_representative=summary.component_node_ids_by_representative,
|
|
)
|
|
response = await _queue_prompt_json(prompt_server, json_data)
|
|
logger.info(
|
|
"Modal queue request completed in %.3fs.",
|
|
time.perf_counter() - request_started_at,
|
|
)
|
|
return response
|
|
except FileNotFoundError as exc:
|
|
logger.exception("Modal asset sync failed.")
|
|
if json_data is not None:
|
|
_emit_modal_status(
|
|
prompt_server=prompt_server,
|
|
phase="error",
|
|
client_id=str(json_data.get("client_id")) if json_data.get("client_id") else None,
|
|
prompt_id=str(json_data.get("prompt_id")) if json_data.get("prompt_id") else None,
|
|
node_ids=remote_node_ids,
|
|
error_message=str(exc),
|
|
)
|
|
return web.json_response({"error": str(exc), "node_errors": []}, status=400)
|
|
except ModalPromptValidationError as exc:
|
|
logger.exception("Modal prompt validation failed.")
|
|
if json_data is not None:
|
|
_emit_modal_status(
|
|
prompt_server=prompt_server,
|
|
phase="error",
|
|
client_id=str(json_data.get("client_id")) if json_data.get("client_id") else None,
|
|
prompt_id=str(json_data.get("prompt_id")) if json_data.get("prompt_id") else None,
|
|
node_ids=remote_node_ids,
|
|
error_message=str(exc),
|
|
)
|
|
return web.json_response({"error": str(exc), "node_errors": []}, status=400)
|
|
except Exception as exc:
|
|
logger.exception("Modal queue handler failed.")
|
|
if json_data is not None:
|
|
_emit_modal_status(
|
|
prompt_server=prompt_server,
|
|
phase="error",
|
|
client_id=str(json_data.get("client_id")) if json_data.get("client_id") else None,
|
|
prompt_id=str(json_data.get("prompt_id")) if json_data.get("prompt_id") else None,
|
|
node_ids=remote_node_ids,
|
|
error_message=str(exc),
|
|
)
|
|
return web.json_response({"error": str(exc), "node_errors": []}, status=500)
|
|
|
|
_ROUTE_REGISTERED = True
|
|
logger.info(
|
|
"Registered Modal queue route at %s and analysis route at %s",
|
|
resolved_settings.route_path,
|
|
analysis_route_path,
|
|
)
|