"""Prompt interception and graph rewriting for Modal-backed execution.""" from __future__ import annotations import asyncio import copy import hashlib import json import logging import math import os import threading import time import uuid from collections import defaultdict, deque from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass, field, replace from pathlib import Path from typing import Any, Callable, Iterable, Iterator, Mapping from aiohttp import web from .modal_executor_node import ( MODAL_ARTIFACT_FINALIZER_MAX_COMPONENTS, MODAL_ARTIFACT_FINALIZER_NODE_ID, MODAL_COMPONENT_COMPLETION_OUTPUT_NAME, MODAL_LOCAL_BRIDGE_MATERIALIZER_NODE_ID, MODAL_MAP_INPUT_NODE_ID, MODAL_PARALLEL_LOCAL_PASSTHROUGH_NODE_ID, MODAL_PROMPT_ID_EXTRA_PNGINFO_KEY, ensure_modal_artifact_finalizer_registered, ensure_modal_component_proxy_node_registered, ensure_modal_local_bridge_materializer_registered, ensure_modal_parallel_local_passthrough_registered, register_cache_friendly_proxy_payload, register_modal_map_input_warmup_context, registered_proxy_execution_payload, update_registered_proxy_payload_fields, ) from .session_state import RemoteSessionHandle from .settings import ( ModalSyncSettings, get_settings, discover_comfyui_user_directory, modal_gpu_from_workflow, settings_for_modal_gpu, ) from .execution_environments import ( ComponentResourceRequirements, CostAwareEnvironmentScheduler, EnvironmentCapabilities, EnvironmentHealth, EnvironmentSchedulingState, ExecutionAssignment, ExecutionPolicy, ExecutionProvider, GpuCapability, NoCompatibleExecutionEnvironmentError, WorkflowExecutionPreferences, ) from .execution_history import ExecutionHistory from .sync_engine import ( AssetSyncRequestCache, MODEL_FILE_EXTENSIONS, ModalAssetSyncEngine, ModalVolumeBackend, SyncCancelledError, SyncedAsset, modal, resolve_model_path, ) from .remote_hosts import RemoteExecutionConfig, RemoteHostRegistry, SshHostConfig from .remote_configuration_nodes import ( REMOTE_CONFIGURATION_NODE_IDS, REMOTE_EXECUTION_CONFIGURATOR_NODE_ID, compile_remote_configuration_set, ) from .remote_configurations import ( ModalRemoteConfiguration, RemoteConfiguration, RemoteConfigurationSet, SshRemoteConfiguration, VastRemoteConfiguration, ) from .ssh_docker import SshDockerController, SshDockerVolumeBackend from .vast_config_node import extract_vast_profiles from .vast_service import VastProfileQuote, VastSearchRequirements, VastService from .vast_api import VastApiClient from .vast_leases import VastLeaseRegistry logger = logging.getLogger(__name__) SetupStatusCallback = Callable[[str, int | None, int | None], None] ExecutionPlanStatusCallback = Callable[ [dict[str, dict[str, Any]], list[dict[str, Any]]], None ] _ROUTE_REGISTERED = False _MODAL_UI_EVENT_RETENTION_SECONDS = 2 * 60 * 60 _MODAL_UI_EVENT_LIMIT_PER_CLIENT = 512 _MODAL_UI_EVENTS_LOCK = threading.Lock() _MODAL_UI_EVENTS_BY_CLIENT: dict[str, deque[dict[str, Any]]] = {} _MODAL_INTERRUPT_QUEUE_BRIDGE_ATTR = "__comfy_modal_interrupt_queue_bridge_installed" _REMOTE_PREPARATION_PROMPTS_ATTR = "__comfy_modal_remote_preparation_prompts" _REMOTE_PREPARATION_CANCELLATIONS_ATTR = ( "__comfy_modal_remote_preparation_cancellations" ) _REMOTE_PREPARATION_LOCK_ATTR = "__comfy_modal_remote_preparation_lock" _REMOTE_TOGGLE_WIDGET_NAME = "Run Remotely" _LEGACY_REMOTE_TOGGLE_WIDGET_NAME = "Run on Modal" _LOCAL_ONLY_REMOTE_CLASS_TYPES = frozenset( { "ModalEndpointChat", "VastAILeaseConfiguration", *REMOTE_CONFIGURATION_NODE_IDS, MODAL_ARTIFACT_FINALIZER_NODE_ID, MODAL_LOCAL_BRIDGE_MATERIALIZER_NODE_ID, MODAL_MAP_INPUT_NODE_ID, MODAL_PARALLEL_LOCAL_PASSTHROUGH_NODE_ID, } ) _TRANSPORTABLE_OUTPUT_TYPES = frozenset( { "*", "AUDIO", "BOOLEAN", "FLOAT", "IMAGE", "INT", "LATENT", "MASK", "SIGMAS", "STRING", "VIDEO", } ) _INEXPENSIVE_REMOTE_BOUNDARY_TYPES = frozenset( { "BOOLEAN", "FLOAT", "INT", "STRING", } ) _ROOT_LOADER_PREWARM_CLASS_TYPES = frozenset( { "CheckpointLoaderSimple", "UNETLoader", "CLIPLoader", "DualCLIPLoader", } ) _SPECULATIVE_PREWARM_TARGET_KEY = "speculative_remote_prewarm_target" _SPECULATIVE_PREWARM_PAYLOAD_FIELDS = frozenset( { "component_id", "prompt_id", "modal_gpu", "execution_provider", "execution_environment_id", "remote_worker_affinity_group", "remote_local_gap_pool", "subgraph_prompt", "requires_volume_reload", "volume_reload_marker", "uploaded_volume_paths", "custom_nodes_bundle", "snapshot_profile_key", } ) _MODEL_VRAM_WEIGHT_MULTIPLIER = 1.20 _MODEL_VRAM_HEADROOM_BYTES = 4 * 1024**3 _MODEL_RAM_HEADROOM_BYTES = 4 * 1024**3 _REMOTE_REPLICA_NODE_PREFIX = "__ComfyModalReplica__" _REPLICABLE_REMOTE_OBJECT_TYPES = frozenset( {"CLIP", "MODEL", "NOISE", "SAMPLER", "VAE"} ) _ADDITIVE_MODEL_CLASS_TOKENS = ( "adapter", "controlnet", "ipadapter", "lora", ) @dataclass(frozen=True) class ComponentMemoryEstimate: """Describe conservative scheduler memory floors inferred from model assets.""" minimum_vram_bytes: int minimum_ram_bytes: int model_asset_count: int = 0 largest_model_bytes: int = 0 @dataclass(frozen=True) class LinkedOutputRef: """Reference a node output slot within a prompt graph.""" node_id: str output_index: int @dataclass(frozen=True) class InputTarget: """Describe a target input inside a remote component.""" node_id: str input_name: str @dataclass class BoundaryInputSpec: """Describe one local-to-remote boundary value for a component.""" proxy_input_name: str source: LinkedOutputRef io_type: str targets: list[InputTarget] = field(default_factory=list) @dataclass class BoundaryOutputSpec: """Describe one value exported across a remote component boundary.""" proxy_output_name: str source: LinkedOutputRef io_type: str is_list: bool preview_target_node_ids: list[str] = field(default_factory=list) session_output: bool = False session_consumer_node_ids: list[str] = field(default_factory=list) local_materializer_node_id: str | None = None local_materializer_consumer_node_ids: list[str] = field(default_factory=list) @dataclass class StaticToMappedBoundarySpec: """Describe one static-phase output injected into each mapped item run.""" proxy_name: str source: LinkedOutputRef io_type: str is_list: bool targets: list[InputTarget] = field(default_factory=list) @dataclass(frozen=True) class ProducedPhaseOutputSpec: """Describe one output published by an earlier split-proxy phase.""" proxy_output_name: str source: LinkedOutputRef io_type: str is_list: bool session_output: bool = False @dataclass class RemoteComponentPlan: """Execution and rewrite plan for one connected remote component.""" node_ids: list[str] representative_node_id: str boundary_inputs: list[BoundaryInputSpec] boundary_outputs: list[BoundaryOutputSpec] execute_node_ids: list[str] contains_output_node: bool mapped_boundary_input_name: str | None = None mapped_boundary_input_io_type: str | None = None mapped_boundary_source_node_id: str | None = None static_node_ids: list[str] = field(default_factory=list) mapped_node_ids: list[str] = field(default_factory=list) mapped_execute_node_ids: list[str] = field(default_factory=list) static_execute_node_ids: list[str] = field(default_factory=list) static_to_mapped_boundaries: list[StaticToMappedBoundarySpec] = field( default_factory=list ) local_tap_node_ids: list[str] = field(default_factory=list) local_tap_terminal_node_ids: list[str] = field(default_factory=list) @dataclass class RewriteSummary: """Summary of the prompt rewrite performed for a queue request.""" remote_node_ids: list[str] = field(default_factory=list) remote_component_ids: list[str] = field(default_factory=list) component_node_ids_by_representative: dict[str, list[str]] = field( default_factory=dict ) component_dependency_ids_by_representative: dict[str, list[str]] = field( default_factory=dict ) component_execution_stages: list[list[str]] = field(default_factory=list) mapped_component_ids: list[str] = field(default_factory=list) estimated_max_parallel_requests: int = 0 max_parallel_requests_upper_bound: int | None = None requires_volume_reload: bool = False volume_reload_marker: str | None = None uploaded_volume_paths: list[str] = field(default_factory=list) rewritten_node_id_map: dict[str, str] = field(default_factory=dict) sandwiched_local_node_ids: list[str] = field(default_factory=list) parallel_local_branch_node_ids: list[str] = field(default_factory=list) synced_assets: list[SyncedAsset] = field(default_factory=list) custom_nodes_bundle: SyncedAsset | None = None artifact_finalizer_node_id: str | None = None execution_assignments_by_representative: dict[str, ExecutionAssignment] = field( default_factory=dict ) execution_worker_indices_by_representative: dict[str, int] = field( default_factory=dict ) custom_nodes_bundles_by_environment: dict[str, SyncedAsset | None] = field( default_factory=dict ) remote_configurations: list[dict[str, Any]] = field(default_factory=list) execution_locations_by_environment: dict[str, str] = field( default_factory=dict ) @dataclass class ComponentExecutionPlan: """Hold assignments and prepared provider state for one prompt rewrite.""" assignments: dict[str, ExecutionAssignment] configuration_set: RemoteConfigurationSet | None = None configurations_by_id: dict[str, RemoteConfiguration] = field( default_factory=dict ) ssh_hosts_by_id: dict[str, SshHostConfig] = field(default_factory=dict) vast_service: VastService | None = None vast_leases_by_environment: dict[str, Any] = field(default_factory=dict) @dataclass(frozen=True) class RemoteExpansionReason: """Describe why one upstream node had to join a remote component.""" node_id: str class_type: str required_by_node_id: str required_by_class_type: str output_index: int io_type: str @dataclass class RemoteNodeAnalysis: """Structured dry-run result for context-menu remote expansion.""" requested_node_ids: list[str] = field(default_factory=list) requested_workflow_node_paths: list[str] = field(default_factory=list) current_remote_node_ids: list[str] = field(default_factory=list) current_remote_workflow_node_paths: list[str] = field(default_factory=list) resolved_remote_node_ids: list[str] = field(default_factory=list) resolved_workflow_node_paths: list[str] = field(default_factory=list) added_node_ids: list[str] = field(default_factory=list) added_workflow_node_paths: list[str] = field(default_factory=list) sandwiched_local_node_ids: list[str] = field(default_factory=list) reasons: list[RemoteExpansionReason] = field(default_factory=list) @dataclass(frozen=True) class PromptGraphLink: """Describe one direct prompt dependency edge.""" source_node_id: str source_output_index: int target_node_id: str target_input_name: str source_class_type: str target_class_type: str class ModalPromptValidationError(ValueError): """Raised when a prompt cannot be executed with the current Modal transport.""" _MODAL_GPU_VRAM_GB: dict[str, float] = { "T4": 16, "L4": 24, "A10": 24, "L40S": 48, "A100": 40, "A100-40GB": 40, "A100-80GB": 80, "RTX-PRO-6000": 96, "H100": 80, "H100!": 80, "H200": 141, "B200": 180, "B200+": 180, "B300": 288, } # Modal public pricing verified 2026-08-22; CPU and memory charges are excluded. _MODAL_GPU_COST_USD_PER_SECOND: dict[str, float] = { "T4": 0.000164, "L4": 0.000222, "A10": 0.000306, "L40S": 0.000542, "A100": 0.000583, "A100-40GB": 0.000583, "A100-80GB": 0.000694, "RTX-PRO-6000": 0.000842, "H100": 0.001097, "H100!": 0.001097, "H200": 0.001261, "B200": 0.001736, "B200+": 0.001736, "B300": 0.001972, } def _modal_environment_state(settings: ModalSyncSettings) -> EnvironmentSchedulingState: """Return the scheduler-facing state of the selected Modal GPU target.""" modal_gpu = settings.modal_gpu vram_bytes = int(_MODAL_GPU_VRAM_GB.get(modal_gpu, 0.0) * 1024**3) capabilities = EnvironmentCapabilities( architecture="x86_64", operating_system="linux", cpu_count=1, total_ram_bytes=max(vram_bytes, 1), available_ram_bytes=None, available_disk_bytes=None, docker_version="modal-managed", docker_rootless=False, nvidia_container_runtime=True, gpus=( GpuCapability( uuid=f"modal-{modal_gpu.lower()}", name=modal_gpu, total_vram_bytes=vram_bytes, ), ), ) return EnvironmentSchedulingState( environment_id=f"modal:{modal_gpu}", provider=ExecutionProvider.MODAL, enabled=True, health=EnvironmentHealth.READY, cost_usd_per_second=_MODAL_GPU_COST_USD_PER_SECOND.get(modal_gpu), capabilities=capabilities, maximum_workers=settings.max_containers or 1, ) def _modal_configuration_environment_state( configuration: ModalRemoteConfiguration, settings: ModalSyncSettings, ) -> EnvironmentSchedulingState: """Return one scheduler state for a workflow-declared Modal capacity pool.""" selected_settings = settings_for_modal_gpu(settings, configuration.gpu_type) base_state = _modal_environment_state(selected_settings) return replace( base_state, environment_id=( f"modal:{configuration.configuration_id}:{configuration.gpu_type}" ), configuration_id=configuration.configuration_id, display_name=configuration.display_name, maximum_workers=configuration.instance_count, ) def _ssh_host_registry(settings: ModalSyncSettings) -> RemoteHostRegistry | None: """Return the persistent SSH host registry when a user directory exists.""" user_directory = discover_comfyui_user_directory(settings) if user_directory is None: return None return RemoteHostRegistry.for_user_directory(user_directory) def _configured_ssh_hosts(settings: ModalSyncSettings) -> tuple[SshHostConfig, ...]: """Return configured SSH hosts without probing during queue submission.""" registry = _ssh_host_registry(settings) if registry is None: return () return registry.load().hosts def _schedulable_ssh_hosts(settings: ModalSyncSettings) -> tuple[SshHostConfig, ...]: """Probe every enabled host immediately before cost-aware scheduling.""" hosts = _configured_ssh_hosts(settings) registry = _ssh_host_registry(settings) if registry is None: return hosts probe_hosts = [host for host in hosts if host.enabled and not host.draining] if not probe_hosts: return hosts with ThreadPoolExecutor(max_workers=min(8, len(probe_hosts))) as executor: refreshed = { host.environment_id: future.result() for host, future in ( (host, executor.submit(_refresh_ssh_host, host, registry)) for host in probe_hosts ) } return tuple(refreshed.get(host.environment_id, host) for host in hosts) def _refresh_ssh_host( host: SshHostConfig, registry: RemoteHostRegistry, ) -> SshHostConfig: """Probe and persist one host without aborting other candidates.""" try: capabilities = SshDockerController(host).probe_capabilities() return registry.update_probe_result( host.environment_id, capabilities=capabilities, health=EnvironmentHealth.READY, last_error=None, ) except (OSError, RuntimeError, ValueError) as exc: logger.warning( "Remote environment probe failed environment=%s: %s", host.environment_id, exc, ) try: return registry.update_probe_result( host.environment_id, capabilities=host.capabilities, health=EnvironmentHealth.UNAVAILABLE, last_error=str(exc), ) except (KeyError, OSError, ValueError): return replace( host, health=EnvironmentHealth.UNAVAILABLE, last_error=str(exc), ) def _prompt_llm_model_references(prompt: Mapping[str, Any]) -> tuple[str, ...]: """Return every fixed Modal LLM model reference in a prompt.""" references: set[str] = set() for prompt_node in prompt.values(): if not isinstance(prompt_node, Mapping): continue if str(prompt_node.get("class_type") or "") != "ModalLLM": continue inputs = prompt_node.get("inputs") if not isinstance(inputs, Mapping): continue model_reference = inputs.get("model_profile") if isinstance(model_reference, str) and model_reference.strip(): references.add(model_reference.strip()) return tuple(sorted(references)) def _resolve_prompt_llm_profiles( prompt: Mapping[str, Any], settings: ModalSyncSettings, ) -> dict[str, Any]: """Resolve LLM metadata before environment admission and cost ranking.""" from .llm_profiles import get_llm_profile from .llm_resolver import resolve_model_profile storage_root = Path( getattr(settings, "local_storage_root", "/tmp/comfyui-modal-sync-storage") ) profiles: dict[str, Any] = {} for model_reference in _prompt_llm_model_references(prompt): try: profile = get_llm_profile(model_reference, storage_root=storage_root) except ValueError as profile_error: if model_reference.startswith("hf-"): raise ModalPromptValidationError(str(profile_error)) from profile_error try: profile = resolve_model_profile(model_reference, storage_root).profile except ValueError as resolution_error: raise ModalPromptValidationError( str(resolution_error) ) from resolution_error profiles[model_reference] = profile logger.info( "Resolved planner LLM profile model=%s profile=%s weights_gib=%.2f " "estimated_vram_gib=%.2f.", model_reference, profile.profile_id, profile.artifact_bytes / 1024**3, profile.estimated_vram_gb, ) return profiles def _component_profile_memory_estimate( component: RemoteComponentPlan, prompt: Mapping[str, Any], preferences: WorkflowExecutionPreferences, resolved_profiles: Mapping[str, Any], ) -> ComponentMemoryEstimate: """Estimate a component's RAM and VRAM floors from resolved LLM profiles.""" minimum_vram_bytes = preferences.minimum_vram_bytes profiles: dict[str, Any] = {} for node_id in component.node_ids: prompt_node = prompt.get(node_id) if not isinstance(prompt_node, Mapping): continue if str(prompt_node.get("class_type") or "") != "ModalLLM": continue inputs = prompt_node.get("inputs") if not isinstance(inputs, Mapping): continue model_profile = inputs.get("model_profile") if not isinstance(model_profile, str) or not model_profile.strip(): continue profile = resolved_profiles.get(model_profile.strip()) if profile is None: continue profiles[profile.profile_id] = profile minimum_vram_bytes = max( minimum_vram_bytes, int(max(0.0, profile.estimated_vram_gb) * 1024**3), ) artifact_sizes = [int(profile.artifact_bytes) for profile in profiles.values()] return ComponentMemoryEstimate( minimum_vram_bytes=minimum_vram_bytes, minimum_ram_bytes=(sum(artifact_sizes) + _MODEL_RAM_HEADROOM_BYTES) if artifact_sizes else 0, model_asset_count=len(artifact_sizes), largest_model_bytes=max(artifact_sizes, default=0), ) def _component_required_provider( component: RemoteComponentPlan, prompt: Mapping[str, Any], resolved_profiles: Mapping[str, Any], ) -> ExecutionProvider | None: """Require SSH when a component uses an SSH-only resident backend.""" for node_id in component.node_ids: prompt_node = prompt.get(node_id) if not isinstance(prompt_node, Mapping): continue if str(prompt_node.get("class_type") or "") != "ModalLLM": continue inputs = prompt_node.get("inputs") if not isinstance(inputs, Mapping): continue model_reference = inputs.get("model_profile") if not isinstance(model_reference, str): continue profile = resolved_profiles.get(model_reference.strip()) if ( profile is not None and getattr(profile, "backend", "") == "llama_cpp_server" ): return ExecutionProvider.SSH_DOCKER return None def _prompt_node_required_provider( prompt_node: Mapping[str, Any], ) -> ExecutionProvider | None: """Return the provider required by one curated node backend, when known.""" if str(prompt_node.get("class_type") or "") != "ModalLLM": return None inputs = prompt_node.get("inputs") if not isinstance(inputs, Mapping): return None model_reference = inputs.get("model_profile") if not isinstance(model_reference, str) or not model_reference.strip(): return None from .llm_profiles import get_llm_profile try: profile = get_llm_profile(model_reference.strip()) except ValueError: return None if profile.backend == "llama_cpp_server": return ExecutionProvider.SSH_DOCKER return None def _iter_prompt_string_values(value: object) -> Iterator[str]: """Yield nested prompt string values while ignoring graph links and scalars.""" if isinstance(value, str): yield value return if isinstance(value, Mapping): for child in value.values(): yield from _iter_prompt_string_values(child) return if isinstance(value, (list, tuple)) and not _is_link(value): for child in value: yield from _iter_prompt_string_values(child) def _is_additive_model_node(class_type: str) -> bool: """Return whether a loader contributes weights alongside a primary model.""" normalized = class_type.casefold() return any(token in normalized for token in _ADDITIVE_MODEL_CLASS_TOKENS) def _component_model_asset_sizes( component: RemoteComponentPlan, prompt: Mapping[str, Any], settings: ModalSyncSettings, ) -> tuple[tuple[int, ...], tuple[int, ...]]: """Return unique primary and additive model sizes referenced by a component.""" asset_roles: dict[Path, bool] = {} for node_id in component.node_ids: prompt_node = prompt.get(node_id) if not isinstance(prompt_node, Mapping): continue inputs = prompt_node.get("inputs") if not isinstance(inputs, Mapping): continue additive = _is_additive_model_node(str(prompt_node.get("class_type") or "")) for value in _iter_prompt_string_values(inputs): resolved_path = resolve_model_path( value, comfyui_root=getattr(settings, "comfyui_root", None), extensions=MODEL_FILE_EXTENSIONS, ) if resolved_path is None: continue asset_roles[resolved_path] = ( asset_roles.get(resolved_path, True) and additive ) primary_sizes: list[int] = [] additive_sizes: list[int] = [] for asset_path, additive in asset_roles.items(): try: size_bytes = asset_path.stat().st_size except OSError as exc: logger.warning( "Unable to inspect model size for placement path=%s: %s", asset_path, exc, ) continue if size_bytes <= 0: continue target = additive_sizes if additive else primary_sizes target.append(size_bytes) return tuple(primary_sizes), tuple(additive_sizes) def _component_memory_estimate( component: RemoteComponentPlan, prompt: Mapping[str, Any], preferences: WorkflowExecutionPreferences, settings: ModalSyncSettings, resolved_llm_profiles: Mapping[str, Any] | None = None, ) -> ComponentMemoryEstimate: """Infer conservative RAM and VRAM floors from resident model weight sizes.""" profile_estimate = _component_profile_memory_estimate( component, prompt, preferences, resolved_llm_profiles if resolved_llm_profiles is not None else _resolve_prompt_llm_profiles(prompt, settings), ) primary_sizes, additive_sizes = _component_model_asset_sizes( component, prompt, settings, ) all_sizes = primary_sizes + additive_sizes if not all_sizes: return profile_estimate primary_peak_bytes = max(primary_sizes or all_sizes) additive_bytes = sum(additive_sizes) if primary_sizes else 0 resident_weight_bytes = primary_peak_bytes + additive_bytes model_vram_bytes = ( math.ceil(resident_weight_bytes * _MODEL_VRAM_WEIGHT_MULTIPLIER) + _MODEL_VRAM_HEADROOM_BYTES ) return ComponentMemoryEstimate( minimum_vram_bytes=max( profile_estimate.minimum_vram_bytes, model_vram_bytes, ), minimum_ram_bytes=max( profile_estimate.minimum_ram_bytes, resident_weight_bytes + _MODEL_RAM_HEADROOM_BYTES, ), model_asset_count=len(all_sizes) + profile_estimate.model_asset_count, largest_model_bytes=max( max(all_sizes), profile_estimate.largest_model_bytes, ), ) def _component_execution_signature( component: RemoteComponentPlan, prompt: Mapping[str, Any], ) -> str: """Return a stable workload signature for runtime-history estimates.""" cost_shaping_input_names = frozenset( { "batch_size", "duration", "frames", "height", "max_new_tokens", "model_profile", "num_frames", "steps", "width", } ) nodes: list[dict[str, Any]] = [] for node_id in sorted(component.node_ids): prompt_node = prompt.get(node_id) if not isinstance(prompt_node, Mapping): continue inputs = prompt_node.get("inputs") input_mapping = inputs if isinstance(inputs, Mapping) else {} nodes.append( { "class_type": str(prompt_node.get("class_type") or ""), "inputs": { name: input_mapping[name] for name in sorted(cost_shaping_input_names & set(input_mapping)) if not _is_link(input_mapping[name]) }, } ) serialized = json.dumps(nodes, separators=(",", ":"), sort_keys=True).encode( "utf-8" ) return hashlib.sha256(serialized).hexdigest() def _execution_history(settings: ModalSyncSettings) -> ExecutionHistory | None: """Return local runtime history when the ComfyUI user directory is known.""" user_directory = discover_comfyui_user_directory(settings) if user_directory is None: return None return ExecutionHistory.for_user_directory(user_directory) def _maximum_capacity_state( environment: EnvironmentSchedulingState, ) -> EnvironmentSchedulingState: """Return an optimistic state used only to identify reclaimable SSH capacity.""" capabilities = environment.capabilities if environment.provider is not ExecutionProvider.SSH_DOCKER or capabilities is None: return environment return replace( environment, capabilities=replace( capabilities, available_ram_bytes=capabilities.total_ram_bytes, gpus=tuple( replace(gpu, free_vram_bytes=gpu.total_vram_bytes) for gpu in capabilities.gpus ), ), ) def _reprobe_reclaimed_ssh_host( host: SshHostConfig, settings: ModalSyncSettings, ) -> SshHostConfig: """Probe a reclaimed host and persist its new free-memory measurements.""" registry = _ssh_host_registry(settings) if registry is not None: return _refresh_ssh_host(host, registry) try: capabilities = SshDockerController(host).probe_capabilities() except (OSError, RuntimeError, ValueError) as exc: logger.warning( "Remote environment re-probe failed after worker reclaim " "environment=%s: %s", host.environment_id, exc, ) return replace( host, health=EnvironmentHealth.UNAVAILABLE, last_error=str(exc), ) return replace( host, capabilities=capabilities, health=EnvironmentHealth.READY, last_error=None, ) def _probe_workflow_ssh_configuration( configuration: SshRemoteConfiguration, ) -> SshHostConfig: """Probe one workflow-declared SSH host without mutating the global registry.""" host = configuration.host try: capabilities = SshDockerController(host).probe_capabilities() except (OSError, RuntimeError, ValueError) as exc: logger.warning( "Workflow SSH environment probe failed configuration=%s environment=%s: %s", configuration.display_name, host.environment_id, exc, ) return replace( host, capabilities=None, health=EnvironmentHealth.UNAVAILABLE, last_error=str(exc), ) return replace( host, capabilities=capabilities, health=EnvironmentHealth.READY, last_error=None, ) def _optional_scheduler_choice( scheduler: CostAwareEnvironmentScheduler, environments: list[EnvironmentSchedulingState], requirements: ComponentResourceRequirements, ) -> tuple[ ExecutionAssignment | None, NoCompatibleExecutionEnvironmentError | None, ]: """Return a scheduler choice and preserve a compatibility failure for fallback.""" try: return scheduler.choose(environments, requirements), None except NoCompatibleExecutionEnvironmentError as exc: return None, exc def _require_scheduler_choice( assignment: ExecutionAssignment | None, error: NoCompatibleExecutionEnvironmentError | None, ) -> ExecutionAssignment: """Return an existing scheduler choice or raise its compatibility detail.""" if assignment is not None: return assignment if error is not None: raise error raise RuntimeError("Scheduler failed without an incompatibility reason.") def _remove_idle_ssh_workers_for_reclaim(host: SshHostConfig) -> tuple[str, ...]: """Recycle idle managed workers without disrupting fallback placement.""" try: return SshDockerController(host).remove_idle_managed_workers() except (OSError, RuntimeError, ValueError) as exc: logger.warning( "Could not reclaim managed SSH worker capacity environment=%s: %s", host.environment_id, exc, ) return () def _reclaim_improves_assignment( optimistic: ExecutionAssignment, actual: ExecutionAssignment | None, requirements: ComponentResourceRequirements, ) -> bool: """Return whether reclaim unlocks required or materially better placement.""" if actual is None: return True if optimistic.environment_id == actual.environment_id: return False preferred = requirements.preferred_environment_ids if optimistic.environment_id in preferred: if actual.environment_id not in preferred: return True return preferred.index(optimistic.environment_id) < preferred.index( actual.environment_id ) if optimistic.predicted_cost_usd is None: return False if actual.predicted_cost_usd is None: return True return optimistic.predicted_cost_usd < actual.predicted_cost_usd def _choose_with_idle_ssh_worker_reclaim( *, scheduler: CostAwareEnvironmentScheduler, environments: list[EnvironmentSchedulingState], requirements: ComponentResourceRequirements, ssh_hosts_by_id: dict[str, SshHostConfig], settings: ModalSyncSettings, ) -> ExecutionAssignment: """Choose an environment, recycling a cheaper idle SSH worker when necessary.""" actual_assignment, actual_error = _optional_scheduler_choice( scheduler, environments, requirements, ) optimistic_assignment, _ = _optional_scheduler_choice( scheduler, [_maximum_capacity_state(environment) for environment in environments], requirements, ) if optimistic_assignment is None: return _require_scheduler_choice(actual_assignment, actual_error) if ( optimistic_assignment.provider is not ExecutionProvider.SSH_DOCKER or not _reclaim_improves_assignment( optimistic_assignment, actual_assignment, requirements, ) ): return _require_scheduler_choice(actual_assignment, actual_error) host = ssh_hosts_by_id.get(optimistic_assignment.environment_id) if host is None: return _require_scheduler_choice(actual_assignment, actual_error) removed_workers = _remove_idle_ssh_workers_for_reclaim(host) if not removed_workers: return _require_scheduler_choice(actual_assignment, actual_error) logger.info( "Recycled idle managed SSH worker(s) environment=%s containers=%s so the " "planner can re-evaluate full host capacity.", host.environment_id, removed_workers, ) refreshed_host = _reprobe_reclaimed_ssh_host(host, settings) ssh_hosts_by_id[host.environment_id] = refreshed_host refreshed_environments = [ ( refreshed_host.scheduling_state() if environment.environment_id == host.environment_id else environment ) for environment in environments ] return scheduler.choose(refreshed_environments, requirements) def _plan_component_execution_assignments( *, components: list[RemoteComponentPlan], prompt: Mapping[str, Any], workflow: Mapping[str, Any] | None, settings: ModalSyncSettings, status_callback: SetupStatusCallback | None = None, ) -> dict[str, ExecutionAssignment]: """Assign components across Modal, SSH Docker, and workflow-declared Vast pools.""" preferences = WorkflowExecutionPreferences.from_workflow(workflow) resolved_llm_profiles = _resolve_prompt_llm_profiles(prompt, settings) if preferences.policy is ExecutionPolicy.MODAL: incompatible_component_ids = [ component.representative_node_id for component in components if _component_required_provider( component, prompt, resolved_llm_profiles, ) is ExecutionProvider.SSH_DOCKER ] if incompatible_component_ids: raise ModalPromptValidationError( "Modal-only execution cannot run SSH-only component(s) " f"{incompatible_component_ids}. Select Automatic or Self-hosted " "execution for the workflow." ) modal_state = _modal_environment_state(settings) return { component.representative_node_id: ExecutionAssignment( environment_id=modal_state.environment_id, provider=ExecutionProvider.MODAL, predicted_cost_usd=None, predicted_completion_seconds=0.0, reasons=("workflow policy requires Modal",), ) for component in components } try: vast_profiles = extract_vast_profiles(prompt) except ValueError as exc: raise ModalPromptValidationError(str(exc)) from exc vast_service: VastService | None = None if preferences.policy in {ExecutionPolicy.VAST, ExecutionPolicy.AUTOMATIC}: if preferences.policy is ExecutionPolicy.VAST and not vast_profiles: raise ModalPromptValidationError( "Vast.ai-only execution requires at least one disconnected " "Vast.ai Lease Configuration node in the workflow." ) if vast_profiles: try: vast_service = VastService.from_environment( settings, repo_root=Path(__file__).resolve().parent, ) except (OSError, RuntimeError, ValueError) as exc: if preferences.policy is ExecutionPolicy.VAST: raise ModalPromptValidationError(str(exc)) from exc logger.warning("Skipping Vast.ai automatic placement: %s", exc) ssh_hosts = ( _schedulable_ssh_hosts(settings) if preferences.policy in {ExecutionPolicy.SELF_HOSTED, ExecutionPolicy.AUTOMATIC} else () ) ssh_hosts_by_id = {host.environment_id: host for host in ssh_hosts} modal_state = ( _modal_environment_state(settings) if preferences.policy is ExecutionPolicy.AUTOMATIC else None ) if not ssh_hosts and modal_state is None and vast_service is None: raise ModalPromptValidationError( "This workflow requests remote execution, but no compatible provider is configured." ) scheduler = CostAwareEnvironmentScheduler() history = _execution_history(settings) component_signatures: dict[str, str] = {} component_memory_estimates: dict[str, ComponentMemoryEstimate] = {} component_required_providers: dict[str, ExecutionProvider | None] = {} for component in components: component_id = component.representative_node_id component_signatures[component_id] = _component_execution_signature( component, prompt, ) memory_estimate = _component_memory_estimate( component, prompt, preferences, settings, resolved_llm_profiles, ) component_memory_estimates[component_id] = memory_estimate component_required_providers[component_id] = _component_required_provider( component, prompt, resolved_llm_profiles, ) if memory_estimate.model_asset_count: logger.info( "Estimated remote component=%s memory floor vram_gib=%.2f ram_gib=%.2f " "from model_assets=%d largest_model_gib=%.2f.", component_id, memory_estimate.minimum_vram_bytes / 1024**3, memory_estimate.minimum_ram_bytes / 1024**3, memory_estimate.model_asset_count, memory_estimate.largest_model_bytes / 1024**3, ) if vast_service is not None: try: vast_service.prefetch_offers_sync( vast_profiles, tuple( VastSearchRequirements( minimum_vram_bytes=memory_estimate.minimum_vram_bytes, minimum_ram_bytes=memory_estimate.minimum_ram_bytes, ) for memory_estimate in component_memory_estimates.values() ), ) except (OSError, RuntimeError, ValueError) as exc: if preferences.policy is ExecutionPolicy.VAST: raise ModalPromptValidationError(str(exc)) from exc logger.warning( "Unable to prefetch Vast.ai marketplace offers: %s", exc, ) assignments: dict[str, ExecutionAssignment] = {} for component in components: component_id = component.representative_node_id environments = [ host.scheduling_state() for host in ssh_hosts_by_id.values() ] if modal_state is not None: environments.append(modal_state) component_signature = component_signatures[component_id] memory_estimate = component_memory_estimates[component_id] vast_quote: VastProfileQuote | None = None if vast_service is not None: try: vast_quote = vast_service.quote_best_profile_sync( vast_profiles, minimum_vram_bytes=memory_estimate.minimum_vram_bytes, minimum_ram_bytes=memory_estimate.minimum_ram_bytes, predicted_execution_seconds=60.0, ) except (OSError, RuntimeError, ValueError) as exc: if preferences.policy is ExecutionPolicy.VAST: raise ModalPromptValidationError(str(exc)) from exc logger.warning( "No Vast.ai automatic candidate for component=%s: %s", component.representative_node_id, exc, ) else: environments.append(vast_service.scheduling_state(vast_quote)) environment_ids = [ environment.environment_id for environment in environments ] historical_estimates = ( history.estimates(component_signature, environment_ids) if history is not None else {} ) component_required_provider = component_required_providers[component_id] if preferences.policy is ExecutionPolicy.VAST: if component_required_provider is ExecutionProvider.SSH_DOCKER: raise ModalPromptValidationError( "Vast.ai-only execution cannot run an SSH-only component. " "Select Automatic or Self-hosted execution for this workflow." ) component_required_provider = ExecutionProvider.VAST requirements = ComponentResourceRequirements( minimum_vram_bytes=memory_estimate.minimum_vram_bytes, minimum_ram_bytes=memory_estimate.minimum_ram_bytes, gpu_required=True, architecture="x86_64", estimated_execution_seconds=60.0, estimated_execution_seconds_by_environment={ environment_id: estimate.execution_seconds for environment_id, estimate in historical_estimates.items() }, preferred_environment_ids=preferences.preferred_environment_ids, required_provider=component_required_provider, ) try: assignment = _choose_with_idle_ssh_worker_reclaim( scheduler=scheduler, environments=environments, requirements=requirements, ssh_hosts_by_id=ssh_hosts_by_id, settings=settings, ) except NoCompatibleExecutionEnvironmentError as exc: raise ModalPromptValidationError(str(exc)) from exc if assignment.provider is ExecutionProvider.VAST: if vast_service is None or vast_quote is None: raise ModalPromptValidationError( "Vast.ai was selected without a current marketplace quote." ) try: if status_callback is None: lease = vast_service.acquire_sync(vast_quote) else: lease = vast_service.acquire_sync( vast_quote, status_callback=lambda message: status_callback( message, None, None, ), ) except (OSError, RuntimeError, ValueError) as exc: raise ModalPromptValidationError( f"Unable to acquire Vast.ai capacity: {exc}" ) from exc assignment = replace( assignment, environment_id=lease.environment_id, predicted_cost_usd=vast_quote.predicted_incremental_cost_usd, reasons=assignment.reasons + ( f"Vast profile {vast_quote.profile.profile_name}", f"idle retention {lease.idle_retention_seconds / 3600:.1f}h", ), ) assignments[component.representative_node_id] = assignment logger.info( "Assigned remote component=%s provider=%s environment=%s predicted_cost_usd=%s reasons=%s.", component.representative_node_id, assignment.provider.value, assignment.environment_id, assignment.predicted_cost_usd, assignment.reasons, ) return assignments def _plan_component_execution( *, components: list[RemoteComponentPlan], prompt: Mapping[str, Any], workflow: Mapping[str, Any] | None, settings: ModalSyncSettings, status_callback: SetupStatusCallback | None = None, plan_callback: ExecutionPlanStatusCallback | None = None, ) -> ComponentExecutionPlan: """Plan through connected configurations or synthesize the legacy behavior.""" try: configuration_set = compile_remote_configuration_set(prompt) except (TypeError, ValueError) as exc: raise ModalPromptValidationError(str(exc)) from exc if configuration_set is None: return ComponentExecutionPlan( assignments=_plan_component_execution_assignments( components=components, prompt=prompt, workflow=workflow, settings=settings, status_callback=status_callback, ) ) return _plan_configured_component_execution( components=components, prompt=prompt, workflow=workflow, settings=settings, configuration_set=configuration_set, status_callback=status_callback, plan_callback=plan_callback, ) def _plan_configured_component_execution( *, components: list[RemoteComponentPlan], prompt: Mapping[str, Any], workflow: Mapping[str, Any] | None, settings: ModalSyncSettings, configuration_set: RemoteConfigurationSet, status_callback: SetupStatusCallback | None = None, plan_callback: ExecutionPlanStatusCallback | None = None, ) -> ComponentExecutionPlan: """Resolve and prepare a capacity-aware plan from connected configurations.""" configurations_by_id = { configuration.configuration_id: configuration for configuration in configuration_set.configurations } ssh_hosts_by_id = _probe_configured_ssh_hosts(configuration_set) vast_service, vast_unavailable_reason = _configured_vast_service( configuration_set, settings, ) requirements_by_component = _configured_component_requirements( components=components, prompt=prompt, workflow=workflow, settings=settings, ) _prefetch_configured_vast_offers( configuration_set=configuration_set, requirements_by_component=requirements_by_component, vast_service=vast_service, ) environments_by_component, vast_quotes = _configured_candidate_environments( configuration_set=configuration_set, requirements_by_component=requirements_by_component, settings=settings, ssh_hosts_by_id=ssh_hosts_by_id, vast_service=vast_service, vast_unavailable_reason=vast_unavailable_reason, ) _apply_historical_execution_estimates( components=components, environments_by_component=environments_by_component, requirements_by_component=requirements_by_component, prompt=prompt, settings=settings, ) execution_stages = _component_execution_stages( dict(prompt), { component.representative_node_id: set(component.node_ids) for component in components }, ) try: assignments = CostAwareEnvironmentScheduler().plan( execution_stages=execution_stages, environments_by_component=environments_by_component, requirements_by_component=requirements_by_component, ) except NoCompatibleExecutionEnvironmentError as exc: raise ModalPromptValidationError(str(exc)) from exc if plan_callback is not None: plan_callback( _planned_execution_assignments_payload(assignments, components), configuration_set.to_safe_list(), ) vast_leases = _prepare_selected_vast_capacity( assignments=assignments, configuration_set=configuration_set, requirements_by_component=requirements_by_component, vast_quotes=vast_quotes, vast_service=vast_service, status_callback=status_callback, ) return ComponentExecutionPlan( assignments=assignments, configuration_set=configuration_set, configurations_by_id=configurations_by_id, ssh_hosts_by_id=ssh_hosts_by_id, vast_service=vast_service, vast_leases_by_environment=vast_leases, ) def _planned_execution_assignments_payload( assignments: Mapping[str, ExecutionAssignment], components: Sequence[RemoteComponentPlan], ) -> dict[str, dict[str, Any]]: """Serialize scheduler choices before provider capacity is acquired.""" component_nodes = { component.representative_node_id: list(component.node_ids) for component in components } return { component_id: { "provider": assignment.provider.value, "environment_id": assignment.environment_id, "configuration_id": assignment.configuration_id, "node_ids": component_nodes.get(component_id, [component_id]), "predicted_cost_usd": assignment.predicted_cost_usd, "predicted_completion_seconds": assignment.predicted_completion_seconds, "worker_index": assignment.capacity_slot_index, "reasons": list(assignment.reasons), } for component_id, assignment in sorted(assignments.items()) } def _probe_configured_ssh_hosts( configuration_set: RemoteConfigurationSet, ) -> dict[str, SshHostConfig]: """Probe all workflow-declared SSH hosts concurrently.""" ssh_configurations = [ configuration for configuration in configuration_set.configurations if isinstance(configuration, SshRemoteConfiguration) ] if not ssh_configurations: return {} with ThreadPoolExecutor(max_workers=min(8, len(ssh_configurations))) as executor: probed_hosts = { configuration.configuration_id: future.result() for configuration, future in ( ( configuration, executor.submit( _probe_workflow_ssh_configuration, configuration, ), ) for configuration in ssh_configurations ) } return probed_hosts def _configured_vast_service( configuration_set: RemoteConfigurationSet, settings: ModalSyncSettings, ) -> tuple[VastService | None, str | None]: """Construct one Vast service only when the connected graph requests it.""" vast_configured = any( isinstance(configuration, VastRemoteConfiguration) for configuration in configuration_set.configurations ) if not vast_configured: return None, None try: return ( VastService.from_environment( settings, repo_root=Path(__file__).resolve().parent, ), None, ) except (OSError, RuntimeError, ValueError) as exc: has_fallback_provider = any( not isinstance(configuration, VastRemoteConfiguration) for configuration in configuration_set.configurations ) if not has_fallback_provider: raise ModalPromptValidationError(str(exc)) from exc logger.warning( "Skipping unavailable workflow Vast.ai capacity because another " "configured provider can be planned: %s", exc, ) return None, str(exc) def _configured_component_requirements( *, components: list[RemoteComponentPlan], prompt: Mapping[str, Any], workflow: Mapping[str, Any] | None, settings: ModalSyncSettings, ) -> dict[str, ComponentResourceRequirements]: """Build provider-neutral requirements for every remote component.""" preferences = WorkflowExecutionPreferences.from_workflow(workflow) resolved_llm_profiles = _resolve_prompt_llm_profiles(prompt, settings) requirements: dict[str, ComponentResourceRequirements] = {} for component in components: memory_estimate = _component_memory_estimate( component, prompt, preferences, settings, resolved_llm_profiles, ) component_id = component.representative_node_id requirements[component_id] = ComponentResourceRequirements( minimum_vram_bytes=memory_estimate.minimum_vram_bytes, minimum_ram_bytes=memory_estimate.minimum_ram_bytes, gpu_required=True, architecture="x86_64", estimated_execution_seconds=60.0, preferred_environment_ids=preferences.preferred_environment_ids, required_provider=_component_required_provider( component, prompt, resolved_llm_profiles, ), ) return requirements def _configured_candidate_environments( *, configuration_set: RemoteConfigurationSet, requirements_by_component: Mapping[str, ComponentResourceRequirements], settings: ModalSyncSettings, ssh_hosts_by_id: Mapping[str, SshHostConfig], vast_service: VastService | None, vast_unavailable_reason: str | None, ) -> tuple[ dict[str, list[EnvironmentSchedulingState]], dict[tuple[str, str], VastProfileQuote], ]: """Resolve candidate pool states for every component without acquiring capacity.""" candidates: dict[str, list[EnvironmentSchedulingState]] = { component_id: [] for component_id in requirements_by_component } vast_quotes: dict[tuple[str, str], VastProfileQuote] = {} for component_id, requirements in requirements_by_component.items(): for configuration in configuration_set.configurations: state, quote = _configured_candidate_environment( configuration=configuration, requirements=requirements, settings=settings, ssh_hosts_by_id=ssh_hosts_by_id, vast_service=vast_service, vast_unavailable_reason=vast_unavailable_reason, ) if state is not None: candidates[component_id].append(state) if quote is not None: vast_quotes[(component_id, configuration.configuration_id)] = quote return candidates, vast_quotes def _prefetch_configured_vast_offers( *, configuration_set: RemoteConfigurationSet, requirements_by_component: Mapping[str, ComponentResourceRequirements], vast_service: VastService | None, ) -> None: """Warm distinct Vast searches in parallel before deterministic planning.""" if vast_service is None: return profiles = [ configuration.profile for configuration in configuration_set.configurations if isinstance(configuration, VastRemoteConfiguration) ] prefetch = getattr(vast_service, "prefetch_offers_sync", None) if not callable(prefetch): return try: prefetch( profiles, tuple( VastSearchRequirements( minimum_vram_bytes=requirements.minimum_vram_bytes, minimum_ram_bytes=requirements.minimum_ram_bytes, ) for requirements in requirements_by_component.values() ), ) except (OSError, RuntimeError, ValueError) as exc: logger.warning( "Unable to prefetch configured Vast.ai marketplace offers: %s", exc, ) def _configured_candidate_environment( *, configuration: RemoteConfiguration, requirements: ComponentResourceRequirements, settings: ModalSyncSettings, ssh_hosts_by_id: Mapping[str, SshHostConfig], vast_service: VastService | None, vast_unavailable_reason: str | None, ) -> tuple[EnvironmentSchedulingState | None, VastProfileQuote | None]: """Resolve one configuration as a candidate for one component.""" if isinstance(configuration, ModalRemoteConfiguration): return _modal_configuration_environment_state(configuration, settings), None if isinstance(configuration, SshRemoteConfiguration): host = ssh_hosts_by_id[configuration.configuration_id] return replace( host.scheduling_state(), configuration_id=configuration.configuration_id, display_name=configuration.display_name, ), None if not isinstance(configuration, VastRemoteConfiguration): raise TypeError( f"Unsupported remote configuration type {type(configuration).__name__}." ) if vast_service is None: return EnvironmentSchedulingState( environment_id=f"vast:{configuration.configuration_id}", provider=ExecutionProvider.VAST, enabled=True, health=EnvironmentHealth.UNAVAILABLE, cost_usd_per_second=None, capabilities=None, configuration_id=configuration.configuration_id, display_name=configuration.display_name, unavailable_reason=( vast_unavailable_reason or "Vast.ai controller is unavailable." ), maximum_workers=configuration.capacity_limit, ), None try: quote = vast_service.quote_best_profile_sync( [configuration.profile], minimum_vram_bytes=requirements.minimum_vram_bytes, minimum_ram_bytes=requirements.minimum_ram_bytes, predicted_execution_seconds=requirements.estimated_execution_seconds, ) except (OSError, RuntimeError, ValueError) as exc: logger.warning( "Vast configuration has no candidate configuration=%s: %s", configuration.display_name, exc, ) return EnvironmentSchedulingState( environment_id=f"vast:{configuration.configuration_id}", provider=ExecutionProvider.VAST, enabled=True, health=EnvironmentHealth.UNAVAILABLE, cost_usd_per_second=None, capabilities=None, configuration_id=configuration.configuration_id, display_name=configuration.display_name, unavailable_reason=str(exc), maximum_workers=configuration.capacity_limit, ), None state = vast_service.scheduling_state(quote) return replace( state, environment_id=f"vast:{configuration.configuration_id}", configuration_id=configuration.configuration_id, display_name=configuration.display_name, maximum_workers=configuration.capacity_limit, ), quote def _apply_historical_execution_estimates( *, components: list[RemoteComponentPlan], environments_by_component: Mapping[str, list[EnvironmentSchedulingState]], requirements_by_component: dict[str, ComponentResourceRequirements], prompt: Mapping[str, Any], settings: ModalSyncSettings, ) -> None: """Add environment-specific historical timing estimates in place.""" history = _execution_history(settings) if history is None: return for component in components: component_id = component.representative_node_id environment_ids = [ environment.environment_id for environment in environments_by_component.get(component_id, []) ] estimates = history.estimates( _component_execution_signature(component, prompt), environment_ids, ) requirements_by_component[component_id] = replace( requirements_by_component[component_id], estimated_execution_seconds_by_environment={ environment_id: estimate.execution_seconds for environment_id, estimate in estimates.items() }, ) def _prepare_selected_vast_capacity( *, assignments: dict[str, ExecutionAssignment], configuration_set: RemoteConfigurationSet, requirements_by_component: Mapping[str, ComponentResourceRequirements], vast_quotes: Mapping[tuple[str, str], VastProfileQuote], vast_service: VastService | None, status_callback: SetupStatusCallback | None = None, ) -> dict[str, Any]: """Acquire only Vast slots selected by the completed provider-neutral plan.""" selected_slots: dict[tuple[str, int], list[str]] = defaultdict(list) for component_id, assignment in assignments.items(): if assignment.provider is not ExecutionProvider.VAST: continue if assignment.configuration_id is None: raise ModalPromptValidationError( "Vast assignment is missing its configuration identity." ) selected_slots[ (assignment.configuration_id, assignment.capacity_slot_index) ].append(component_id) if not selected_slots: return {} if vast_service is None: raise ModalPromptValidationError( "Vast capacity was selected without an initialized Vast service." ) configurations_by_id = { configuration.configuration_id: configuration for configuration in configuration_set.configurations if isinstance(configuration, VastRemoteConfiguration) } leases_by_environment: dict[str, Any] = {} ordered_slots = sorted(selected_slots.items()) total_slots = len(ordered_slots) for slot_number, ((configuration_id, slot_index), component_ids) in enumerate( ordered_slots, start=1, ): configuration = configurations_by_id[configuration_id] quote = _quote_selected_vast_slot( configuration=configuration, component_ids=component_ids, requirements_by_component=requirements_by_component, vast_quotes=vast_quotes, vast_service=vast_service, ) try: if status_callback is not None: status_callback( f"Acquiring Vast.ai capacity {slot_number} of {total_slots}", slot_number - 1, total_slots, ) if status_callback is None: lease = vast_service.acquire_sync(quote, slot=slot_index) else: lease = vast_service.acquire_sync( quote, slot=slot_index, status_callback=( lambda message, current=slot_number - 1: status_callback( message, current, total_slots, ) ), ) except (OSError, RuntimeError, ValueError) as exc: raise ModalPromptValidationError( f"Unable to acquire Vast.ai capacity for configuration " f"{configuration.display_name!r} slot {slot_index}: {exc}" ) from exc if status_callback is not None: status_callback( f"Vast.ai capacity {slot_number} of {total_slots} is ready", slot_number, total_slots, ) leases_by_environment[lease.environment_id] = lease cost_share = quote.predicted_incremental_cost_usd / len(component_ids) for component_id in component_ids: assignment = assignments[component_id] assignments[component_id] = replace( assignment, environment_id=lease.environment_id, predicted_cost_usd=cost_share, reasons=assignment.reasons + ( f"Vast profile {configuration.display_name}", f"Vast capacity slot {slot_index}", ), ) return leases_by_environment def _quote_selected_vast_slot( *, configuration: VastRemoteConfiguration, component_ids: list[str], requirements_by_component: Mapping[str, ComponentResourceRequirements], vast_quotes: Mapping[tuple[str, str], VastProfileQuote], vast_service: VastService, ) -> VastProfileQuote: """Quote aggregate slot requirements immediately before acquisition.""" minimum_vram_bytes = max( requirements_by_component[component_id].minimum_vram_bytes for component_id in component_ids ) minimum_ram_bytes = max( requirements_by_component[component_id].minimum_ram_bytes for component_id in component_ids ) predicted_execution_seconds = sum( requirements_by_component[component_id].estimated_execution_seconds for component_id in component_ids ) if len(component_ids) == 1: existing_quote = vast_quotes.get( (component_ids[0], configuration.configuration_id) ) if existing_quote is not None: return existing_quote return vast_service.quote_best_profile_sync( [configuration.profile], minimum_vram_bytes=minimum_vram_bytes, minimum_ram_bytes=minimum_ram_bytes, predicted_execution_seconds=predicted_execution_seconds, ) def _ssh_sync_engine( *, host: SshHostConfig, settings: ModalSyncSettings, ) -> ModalAssetSyncEngine: """Build a content-addressed sync engine for one SSH Docker host.""" ssh_settings = replace( settings, execution_mode="ssh_docker", volume_name=host.resolved_storage_volume_name, local_storage_root=( settings.local_storage_root / "ssh" / host.environment_id ).resolve(), remote_storage_root="/storage", ) volume = SshDockerVolumeBackend( SshDockerController(host), host.resolved_storage_volume_name, ) return ModalAssetSyncEngine(volume=volume, settings=ssh_settings) def _stamp_execution_assignment( payload: dict[str, Any], assignment: ExecutionAssignment, worker_index: int = 0, execution_history_signature: str | None = None, execution_location: str | None = None, provider_metadata: Mapping[str, Any] | None = None, ) -> None: """Attach provider placement to a payload and every nested proxy phase.""" payload["execution_provider"] = assignment.provider.value payload["execution_environment_id"] = assignment.environment_id payload["execution_worker_index"] = worker_index if execution_location: payload["execution_location"] = execution_location if execution_history_signature: payload["execution_history_signature"] = execution_history_signature if provider_metadata: payload.update(provider_metadata) split_payloads = payload.get("split_proxy_payloads") nested_payloads: list[dict[str, Any]] = [] if isinstance(split_payloads, dict): nested_payloads.extend( nested_payload for nested_payload in split_payloads.values() if isinstance(nested_payload, dict) ) elif isinstance(split_payloads, list): nested_payloads.extend( nested_payload for nested_payload in split_payloads if isinstance(nested_payload, dict) ) for nested_payload in nested_payloads: _stamp_execution_assignment( nested_payload, assignment, worker_index, execution_history_signature, execution_location, provider_metadata, ) def _ssh_hostname(ssh_target: str) -> str: """Return the host portion of one validated OpenSSH destination.""" host = ssh_target.rsplit("@", 1)[-1].strip() if host.startswith("[") and "]" in host: return host[1 : host.index("]")] return host def _execution_location_for_assignment( assignment: ExecutionAssignment, ssh_hosts_by_id: Mapping[str, SshHostConfig], vast_leases_by_environment: Mapping[str, Any] | None = None, ) -> str | None: """Return the runtime location label known before remote dispatch.""" if assignment.provider is ExecutionProvider.MODAL: return None if assignment.provider is ExecutionProvider.VAST: lease = (vast_leases_by_environment or {}).get(assignment.environment_id) if lease is not None: return str(lease.ssh_host or lease.gpu_name) return assignment.environment_id host = ssh_hosts_by_id.get(assignment.environment_id) return _ssh_hostname(host.ssh_target) if host is not None else assignment.environment_id def _vast_provider_metadata(lease: Any) -> dict[str, Any]: """Return credential-free lease details for execution and local status output.""" return { "vast_instance_id": lease.instance_id, "vast_profile_id": lease.profile_id, "vast_profile_name": lease.profile_name, "vast_gpu_name": lease.gpu_name, "vast_gpu_count": lease.gpu_count, "vast_gpu_ram_mb": lease.gpu_ram_mb, "vast_hourly_cost_usd": lease.hourly_cost_usd, "vast_idle_retention_seconds": lease.idle_retention_seconds, } def _configured_provider_metadata( *, execution_plan: ComponentExecutionPlan, assignment: ExecutionAssignment, vast_leases_by_environment: Mapping[str, Any], ) -> dict[str, Any] | None: """Return safe provider metadata needed to execute one prepared assignment.""" if assignment.provider is ExecutionProvider.VAST: return _vast_provider_metadata( vast_leases_by_environment[assignment.environment_id] ) configuration_id = assignment.configuration_id if configuration_id is None: return None configuration = execution_plan.configurations_by_id.get(configuration_id) if isinstance(configuration, ModalRemoteConfiguration): return { "remote_configuration_id": configuration.configuration_id, "remote_configuration_name": configuration.display_name, "modal_gpu": configuration.gpu_type, "modal_max_containers": configuration.instance_count, } if isinstance(configuration, SshRemoteConfiguration): host = execution_plan.ssh_hosts_by_id[configuration.configuration_id] portable_host = replace( host, capabilities=None, health=EnvironmentHealth.UNKNOWN, last_error=None, ) return { "remote_configuration_id": configuration.configuration_id, "remote_configuration_name": configuration.display_name, "ssh_host_config": portable_host.to_dict(), } return None def _ensure_remote_sync_backend( settings: ModalSyncSettings, sync_engine: ModalAssetSyncEngine, ) -> None: """Fail before queueing when remote execution cannot write to Modal-visible storage.""" if settings.execution_mode == "local": return if isinstance(sync_engine.volume, ModalVolumeBackend): return raise ModalPromptValidationError( "Remote Modal execution requires asset sync to use the Modal volume backend, " f"but the active sync backend is {type(sync_engine.volume).__name__}. " "Restart ComfyUI with COMFY_MODAL_EXECUTION_MODE=remote and the Modal SDK available " "so synced assets and custom_nodes bundles are visible inside Modal workers." ) def _emit_modal_status( prompt_server: Any, phase: str, *, client_id: str | None, prompt_id: str | None, node_ids: list[str], configurator_node_id: str | None = None, modal_gpu: str | None = None, component_node_ids_by_representative: dict[str, list[str]] | None = None, active_node_id: str | None = None, active_node_class_type: str | None = None, active_node_role: str | None = None, error_message: str | None = None, status_message: str | None = None, status_current: int | None = None, status_total: int | None = None, remote_execution_assignments: Mapping[str, Mapping[str, Any]] | None = None, remote_execution_configurations: Sequence[Mapping[str, Any]] | None = None, ) -> None: """Send a Modal execution status event to the active websocket client.""" if client_id is None: logger.debug( "Skipping Modal status event %s because no client id is available.", phase ) return payload: dict[str, Any] = { "phase": phase, "prompt_id": prompt_id, "node_ids": list(node_ids), } if modal_gpu is not None: payload["modal_gpu"] = modal_gpu if configurator_node_id is not None: payload["configurator_node_id"] = configurator_node_id if component_node_ids_by_representative: payload["components"] = [ { "representative_node_id": representative_node_id, "node_ids": list(component_node_ids), } for representative_node_id, component_node_ids in sorted( component_node_ids_by_representative.items() ) ] if active_node_id is not None: payload["active_node_id"] = active_node_id if active_node_class_type is not None: payload["active_node_class_type"] = active_node_class_type if active_node_role is not None: payload["active_node_role"] = active_node_role if error_message is not None: payload["error_message"] = error_message if status_message is not None: payload["status_message"] = status_message if status_current is not None: payload["status_current"] = int(status_current) if status_total is not None: payload["status_total"] = int(status_total) if remote_execution_assignments is not None: payload["remote_execution_assignments"] = copy.deepcopy( dict(remote_execution_assignments) ) if remote_execution_configurations is not None: payload["remote_execution_configurations"] = copy.deepcopy( list(remote_execution_configurations) ) record_modal_ui_event("modal_status", payload, client_id) prompt_server.send_sync("modal_status", payload, client_id) def record_modal_ui_event( event: str, payload: Mapping[str, Any], client_id: str | None ) -> None: """Store one client-scoped Modal UI event so the browser can replay it after refocus.""" if client_id is None: return event_record = { "event": event, "payload": copy.deepcopy(dict(payload)), "updated_at": time.time(), } with _MODAL_UI_EVENTS_LOCK: client_events = _MODAL_UI_EVENTS_BY_CLIENT.setdefault( client_id, deque(maxlen=_MODAL_UI_EVENT_LIMIT_PER_CLIENT), ) client_events.append(event_record) _prune_modal_ui_events_locked(client_events) def modal_ui_events_for_client(client_id: str | None) -> list[dict[str, Any]]: """Return recent Modal UI events for one websocket client.""" if not client_id: return [] with _MODAL_UI_EVENTS_LOCK: client_events = _MODAL_UI_EVENTS_BY_CLIENT.get(client_id) if client_events is None: return [] _prune_modal_ui_events_locked(client_events) return [copy.deepcopy(event_record) for event_record in client_events] def _prune_modal_ui_events_locked(client_events: deque[dict[str, Any]]) -> None: """Discard stale Modal UI events while the caller holds the event lock.""" cutoff = time.time() - _MODAL_UI_EVENT_RETENTION_SECONDS while client_events and float(client_events[0].get("updated_at", 0.0)) < cutoff: client_events.popleft() def _get_nodes_module() -> Any: """Import the ComfyUI nodes module lazily.""" import nodes return nodes def _get_server_module() -> Any: """Import the ComfyUI server module lazily.""" import server return server def _get_execution_module() -> Any: """Import the ComfyUI execution module lazily.""" import execution return execution def _is_link(value: Any) -> bool: """Return whether a prompt input value is a ComfyUI link.""" return ( isinstance(value, list) and len(value) == 2 and all(not isinstance(item, dict) for item in value) ) def _prompt_node_class_type(prompt: Mapping[str, Any], node_id: str) -> str: """Return the class type for a prompt node, or a diagnostic placeholder.""" prompt_node = prompt.get(str(node_id)) if not isinstance(prompt_node, Mapping): return "" return str(prompt_node.get("class_type", "")) def _prompt_graph_links(prompt: Mapping[str, Any]) -> list[PromptGraphLink]: """Return direct prompt dependency edges from linked inputs.""" links: list[PromptGraphLink] = [] for target_node_id in sorted(str(node_id) for node_id in prompt): prompt_node = prompt.get(target_node_id) if not isinstance(prompt_node, Mapping): continue inputs = prompt_node.get("inputs") or {} if not isinstance(inputs, Mapping): continue for input_name, input_value in sorted( inputs.items(), key=lambda item: str(item[0]) ): if not _is_link(input_value): continue source_node_id = str(input_value[0]) links.append( PromptGraphLink( source_node_id=source_node_id, source_output_index=int(input_value[1]), target_node_id=target_node_id, target_input_name=str(input_name), source_class_type=_prompt_node_class_type(prompt, source_node_id), target_class_type=_prompt_node_class_type(prompt, target_node_id), ) ) return links def _prompt_link_dict(link: PromptGraphLink) -> dict[str, Any]: """Return a JSON-safe representation of one prompt dependency edge.""" return { "source_node_id": link.source_node_id, "source_output_index": link.source_output_index, "source_class_type": link.source_class_type, "target_node_id": link.target_node_id, "target_input_name": link.target_input_name, "target_class_type": link.target_class_type, } def _find_prompt_dependency_cycles(prompt: Mapping[str, Any]) -> list[list[str]]: """Return representative dependency cycles in the prompt graph.""" links = _prompt_graph_links(prompt) adjacency: dict[str, list[str]] = defaultdict(list) for link in links: if link.source_node_id not in prompt or link.target_node_id not in prompt: continue adjacency[link.source_node_id].append(link.target_node_id) visited_node_ids: set[str] = set() active_node_ids: set[str] = set() path: list[str] = [] cycles: list[list[str]] = [] seen_cycle_keys: set[tuple[str, ...]] = set() def normalize_cycle(cycle: list[str]) -> tuple[str, ...]: """Return a rotation-stable key for a detected cycle.""" if not cycle: return () rotations = [ tuple(cycle[index:] + cycle[:index]) for index in range(len(cycle)) ] return min(rotations) def visit(node_id: str) -> None: """Depth-first search one prompt node for dependency back-edges.""" if node_id in active_node_ids: cycle_start_index = path.index(node_id) cycle = path[cycle_start_index:] + [node_id] cycle_key = normalize_cycle(cycle[:-1]) if cycle_key not in seen_cycle_keys: seen_cycle_keys.add(cycle_key) cycles.append(cycle) return if node_id in visited_node_ids: return visited_node_ids.add(node_id) active_node_ids.add(node_id) path.append(node_id) for downstream_node_id in sorted(adjacency.get(node_id, [])): visit(downstream_node_id) path.pop() active_node_ids.remove(node_id) for node_id in sorted(str(node_id) for node_id in prompt): visit(node_id) return cycles def _modal_proxy_payload_summaries(prompt: Mapping[str, Any]) -> list[dict[str, Any]]: """Return compact summaries for Modal proxy payloads embedded in a rewritten prompt.""" summaries: list[dict[str, Any]] = [] for node_id in sorted(str(node_id) for node_id in prompt): prompt_node = prompt.get(node_id) if not isinstance(prompt_node, Mapping): continue inputs = prompt_node.get("inputs") or {} if not isinstance(inputs, Mapping): continue payload = inputs.get("original_node_data") if not isinstance(payload, Mapping): continue boundary_inputs = [ { "proxy_input_name": str(boundary_input.get("proxy_input_name")), "io_type": str(boundary_input.get("io_type")), "targets": copy.deepcopy(boundary_input.get("targets", [])), } for boundary_input in payload.get("boundary_inputs", []) if isinstance(boundary_input, Mapping) ] boundary_outputs = [ { "proxy_output_name": str(boundary_output.get("proxy_output_name")), "node_id": str(boundary_output.get("node_id")), "output_index": int(boundary_output.get("output_index", 0)), "io_type": str(boundary_output.get("io_type")), "is_list": bool(boundary_output.get("is_list", False)), "session_output": bool(boundary_output.get("session_output", False)), } for boundary_output in payload.get("boundary_outputs", []) if isinstance(boundary_output, Mapping) ] summaries.append( { "proxy_node_id": node_id, "proxy_class_type": _prompt_node_class_type(prompt, node_id), "payload_kind": str(payload.get("payload_kind")), "component_id": str(payload.get("component_id")), "component_node_ids": [ str(value) for value in payload.get("component_node_ids", []) ], "execute_node_ids": [ str(value) for value in payload.get("execute_node_ids", []) ], "boundary_inputs": boundary_inputs, "boundary_outputs": boundary_outputs, } ) return summaries def _modal_rewritten_prompt_diagnostics( prompt: Mapping[str, Any], summary: RewriteSummary | None = None, ) -> dict[str, Any]: """Return compact diagnostics for a rewritten Modal prompt graph.""" node_class_types = { str(node_id): _prompt_node_class_type(prompt, str(node_id)) for node_id in sorted(str(node_id) for node_id in prompt) } diagnostics: dict[str, Any] = { "node_count": len(prompt), "node_class_types": node_class_types, "links": [_prompt_link_dict(link) for link in _prompt_graph_links(prompt)], "cycles": _find_prompt_dependency_cycles(prompt), "modal_proxy_payloads": _modal_proxy_payload_summaries(prompt), } if summary is not None: diagnostics["remote_node_ids"] = list(summary.remote_node_ids) diagnostics["remote_component_ids"] = list(summary.remote_component_ids) diagnostics["component_node_ids_by_representative"] = copy.deepcopy( summary.component_node_ids_by_representative ) diagnostics["component_dependency_ids_by_representative"] = copy.deepcopy( summary.component_dependency_ids_by_representative ) diagnostics["component_execution_stages"] = copy.deepcopy( summary.component_execution_stages ) diagnostics["parallel_local_branch_node_ids"] = list( summary.parallel_local_branch_node_ids ) diagnostics["rewritten_node_id_map"] = copy.deepcopy( summary.rewritten_node_id_map ) return diagnostics def _log_modal_rewritten_prompt_diagnostics( *, prompt_id: str | None, prompt: Mapping[str, Any], summary: RewriteSummary | None = None, reason: str, level: int = logging.INFO, ) -> None: """Log compact diagnostics for a rewritten Modal prompt graph.""" diagnostics = _modal_rewritten_prompt_diagnostics(prompt, summary) cycles = diagnostics.get("cycles") or [] if cycles: logger.warning( "Modal rewritten prompt contains dependency cycle(s) prompt_id=%s reason=%s cycles=%s", prompt_id, reason, cycles, ) logger.log( level, "Modal rewritten prompt diagnostics prompt_id=%s reason=%s diagnostics=%s", prompt_id, reason, json.dumps(diagnostics, sort_keys=True), ) def _prompt_value_signature_fragment( prompt: dict[str, Any], value: Any, *, memo: dict[str, str], ) -> Any: """Return a stable structural signature fragment for one prompt input value.""" if _is_link(value): source_node_id = str(value[0]) return { "kind": "link", "source_node_id": source_node_id, "output_index": int(value[1]), "source_digest": _prompt_node_signature_digest( prompt, source_node_id, memo=memo, ), } if value is None or isinstance(value, bool | int | str): return value if isinstance(value, float): if value != value: return {"kind": "float", "value": "nan"} if value == float("inf"): return {"kind": "float", "value": "inf"} if value == float("-inf"): return {"kind": "float", "value": "-inf"} return value if isinstance(value, list): return { "kind": "list", "items": [ _prompt_value_signature_fragment(prompt, item, memo=memo) for item in value ], } if isinstance(value, tuple): return { "kind": "tuple", "items": [ _prompt_value_signature_fragment(prompt, item, memo=memo) for item in value ], } if isinstance(value, dict): return { "kind": "dict", "items": [ { "key": str(key), "value": _prompt_value_signature_fragment( prompt, value[key], memo=memo ), } for key in sorted(value) ], } return { "kind": "repr", "type": type(value).__name__, "value": repr(value), } def _prompt_node_signature_digest( prompt: dict[str, Any], node_id: str, *, memo: dict[str, str], ) -> str: """Return a stable digest for one prompt node and its upstream prompt inputs.""" if node_id in memo: return memo[node_id] prompt_node = prompt.get(str(node_id)) if prompt_node is None: digest = hashlib.sha256( json.dumps( {"kind": "missing-node", "node_id": str(node_id)}, sort_keys=True, separators=(",", ":"), ).encode("utf-8") ).hexdigest() memo[str(node_id)] = digest return digest payload = { "kind": "prompt-node", "node_id": str(node_id), "class_type": str(prompt_node.get("class_type", "")), "inputs": [ { "name": str(input_name), "value": _prompt_value_signature_fragment( prompt, input_value, memo=memo, ), } for input_name, input_value in sorted( (prompt_node.get("inputs") or {}).items() ) ], } digest = hashlib.sha256( json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("utf-8") ).hexdigest() memo[str(node_id)] = digest return digest def _iter_loader_snapshot_prompt_payloads( payload: Mapping[str, Any] ) -> Iterator[Mapping[str, Any]]: """Yield prompt-bearing payload fragments that may contain root loader nodes.""" split_proxy_payloads = payload.get("split_proxy_payloads") if isinstance(split_proxy_payloads, dict): for phase_payload in split_proxy_payloads.values(): if isinstance(phase_payload, Mapping): yield phase_payload return if isinstance(split_proxy_payloads, list): for phase_payload in split_proxy_payloads: if isinstance(phase_payload, Mapping): yield phase_payload return if isinstance(payload.get("subgraph_prompt"), Mapping): yield payload def _is_root_literal_loader_node(prompt_node: Mapping[str, Any]) -> bool: """Return whether one prompt node is a supported root loader with literal inputs.""" class_type = str(prompt_node.get("class_type") or "") if class_type not in _ROOT_LOADER_PREWARM_CLASS_TYPES: return False inputs = prompt_node.get("inputs") if not isinstance(inputs, Mapping): return False return not any(_is_link(input_value) for input_value in inputs.values()) def _loader_prewarm_plan_signature(class_type: str, inputs: Mapping[str, Any]) -> str: """Return a stable signature for one synthetic loader-prewarm plan.""" return json.dumps( { "class_type": class_type, "inputs": copy.deepcopy(dict(inputs)), }, sort_keys=True, default=str, ) def _uses_llm_worker_affinity(payload: Mapping[str, Any]) -> bool: """Return whether an execution payload belongs to the isolated LLM worker pool.""" affinity_group = ( str(payload.get("remote_worker_affinity_group") or "").strip().lower() ) return affinity_group == "llm" def _payload_loader_snapshot_profile_key(payload: Mapping[str, Any]) -> str: """Return the stable loader snapshot profile key derivable from one payload.""" prompt_id = payload.get("prompt_id") normalized_prompt_id = str(prompt_id) if prompt_id is not None else None plan_signatures: set[str] = set() for prompt_payload in _iter_loader_snapshot_prompt_payloads(payload): subgraph_prompt = prompt_payload.get("subgraph_prompt") if not isinstance(subgraph_prompt, Mapping): continue for node_id, prompt_node in subgraph_prompt.items(): if not isinstance(prompt_node, Mapping) or not _is_root_literal_loader_node( prompt_node ): continue class_type = str(prompt_node.get("class_type") or "") inputs = prompt_node.get("inputs") if not isinstance(inputs, Mapping): continue plan_signatures.add(_loader_prewarm_plan_signature(class_type, inputs)) logger.debug( "Derived rewrite-time loader prewarm plan for component=%s node=%s class_type=%s prompt_id=%s.", payload.get("component_id"), node_id, class_type, normalized_prompt_id, ) if not plan_signatures: return "" profile_digest = hashlib.sha256( json.dumps({"plan_signatures": sorted(plan_signatures)}, sort_keys=True).encode( "utf-8" ) ).hexdigest() return f"loader-profile:{profile_digest}" def _stamp_snapshot_profile_key( payload: dict[str, Any], snapshot_profile_key: str ) -> None: """Attach one loader snapshot profile to eligible Comfy payload descendants.""" if not snapshot_profile_key: return if _uses_llm_worker_affinity(payload): payload.pop("snapshot_profile_key", None) logger.info( "Omitting Comfy loader snapshot profile from LLM worker component=%s.", payload.get("component_id"), ) else: payload["snapshot_profile_key"] = snapshot_profile_key split_proxy_payloads = payload.get("split_proxy_payloads") if isinstance(split_proxy_payloads, dict): for phase_payload in split_proxy_payloads.values(): if isinstance(phase_payload, dict): _stamp_snapshot_profile_key(phase_payload, snapshot_profile_key) return if isinstance(split_proxy_payloads, list): for phase_payload in split_proxy_payloads: if isinstance(phase_payload, dict): _stamp_snapshot_profile_key(phase_payload, snapshot_profile_key) def _attach_snapshot_profile_key( payload: dict[str, Any], settings: ModalSyncSettings ) -> dict[str, Any]: """Stamp a deterministic loader snapshot profile onto one payload when enabled.""" if not settings.enable_gpu_memory_snapshot or not settings.enable_loader_prewarm: return payload snapshot_profile_key = _payload_loader_snapshot_profile_key(payload) if snapshot_profile_key: _stamp_snapshot_profile_key(payload, snapshot_profile_key) logger.info( "Attached rewrite-time loader snapshot profile %s to component=%s payload_kind=%s.", snapshot_profile_key, payload.get("component_id"), payload.get("payload_kind"), ) return payload def _boundary_source_signature( prompt: dict[str, Any], source: LinkedOutputRef, ) -> str: """Return a stable prompt-structural fingerprint for one boundary source output.""" payload = { "kind": "boundary-source", "source_node_id": source.node_id, "output_index": int(source.output_index), "source_digest": _prompt_node_signature_digest( prompt, source.node_id, memo={}, ), } digest = hashlib.sha256( json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("utf-8") ).hexdigest() return f"SRC_{digest}" def _serialize_boundary_input_specs( boundary_inputs: list[BoundaryInputSpec], *, signature_prompt: dict[str, Any], ) -> list[dict[str, Any]]: """Return serialized boundary input payloads with stable provenance for non-transportable inputs.""" serialized_boundary_inputs: list[dict[str, Any]] = [] for boundary_input in boundary_inputs: serialized_boundary_input = { "proxy_input_name": boundary_input.proxy_input_name, "io_type": boundary_input.io_type, "targets": [ {"node_id": target.node_id, "input_name": target.input_name} for target in boundary_input.targets ], } if not _is_transportable_output_type(boundary_input.io_type): serialized_boundary_input["source_signature"] = _boundary_source_signature( signature_prompt, boundary_input.source, ) serialized_boundary_inputs.append(serialized_boundary_input) return serialized_boundary_inputs def _iter_payload_input_strings(value: Any) -> Iterator[str]: """Yield string literals nested inside one serialized prompt input value.""" if isinstance(value, str): yield value return if isinstance(value, list): if len(value) == 2 and isinstance(value[0], str): return for item in value: yield from _iter_payload_input_strings(item) return if isinstance(value, dict): for nested_value in value.values(): yield from _iter_payload_input_strings(nested_value) def _looks_like_workflow_node(fragment: dict[str, Any]) -> bool: """Return whether a JSON fragment resembles a saved ComfyUI workflow node.""" return "id" in fragment and "properties" in fragment def _iter_workflow_nodes( workflow_fragment: Any, visited_object_ids: set[int] | None = None, ) -> Iterator[dict[str, Any]]: """Yield workflow node dictionaries from a nested saved workflow fragment.""" if visited_object_ids is None: visited_object_ids = set() if isinstance(workflow_fragment, dict): object_id = id(workflow_fragment) if object_id in visited_object_ids: return visited_object_ids.add(object_id) if _looks_like_workflow_node(workflow_fragment): yield workflow_fragment for value in workflow_fragment.values(): yield from _iter_workflow_nodes(value, visited_object_ids) return if isinstance(workflow_fragment, list): object_id = id(workflow_fragment) if object_id in visited_object_ids: return visited_object_ids.add(object_id) for item in workflow_fragment: yield from _iter_workflow_nodes(item, visited_object_ids) def _workflow_subgraph_definitions( workflow_fragment: Any, ) -> dict[str, dict[str, Any]]: """Return reusable ComfyUI subgraph definitions keyed by their node type id.""" if not isinstance(workflow_fragment, dict): return {} definitions = workflow_fragment.get("definitions") if not isinstance(definitions, dict): return {} subgraphs = definitions.get("subgraphs") if not isinstance(subgraphs, list): return {} return { str(subgraph["id"]): subgraph for subgraph in subgraphs if isinstance(subgraph, dict) and subgraph.get("id") is not None } def _iter_workflow_nodes_with_ancestors( workflow_fragment: Any, ancestor_node_ids: tuple[str, ...] = (), visited_fragments: set[tuple[int, tuple[str, ...], tuple[str, ...]]] | None = None, subgraph_definitions: Mapping[str, dict[str, Any]] | None = None, active_subgraph_definition_ids: tuple[str, ...] = (), ) -> Iterator[tuple[dict[str, Any], tuple[str, ...]]]: """Yield workflow nodes with instance paths across embedded and defined subgraphs.""" if visited_fragments is None: visited_fragments = set() if subgraph_definitions is None: subgraph_definitions = _workflow_subgraph_definitions(workflow_fragment) if isinstance(workflow_fragment, dict): fragment_identity = ( id(workflow_fragment), ancestor_node_ids, active_subgraph_definition_ids, ) if fragment_identity in visited_fragments: return visited_fragments.add(fragment_identity) next_ancestor_node_ids = ancestor_node_ids if _looks_like_workflow_node(workflow_fragment): node_id = str(workflow_fragment.get("id")) yield workflow_fragment, ancestor_node_ids next_ancestor_node_ids = ancestor_node_ids + (node_id,) subgraph_definition_id = str(workflow_fragment.get("type") or "") subgraph_definition = subgraph_definitions.get(subgraph_definition_id) if ( subgraph_definition is not None and subgraph_definition_id not in active_subgraph_definition_ids ): yield from _iter_workflow_nodes_with_ancestors( subgraph_definition.get("nodes", []), next_ancestor_node_ids, visited_fragments, subgraph_definitions, active_subgraph_definition_ids + (subgraph_definition_id,), ) for key, value in workflow_fragment.items(): if key == "definitions": continue yield from _iter_workflow_nodes_with_ancestors( value, next_ancestor_node_ids, visited_fragments, subgraph_definitions, active_subgraph_definition_ids, ) return if isinstance(workflow_fragment, list): fragment_identity = ( id(workflow_fragment), ancestor_node_ids, active_subgraph_definition_ids, ) if fragment_identity in visited_fragments: return visited_fragments.add(fragment_identity) for item in workflow_fragment: yield from _iter_workflow_nodes_with_ancestors( item, ancestor_node_ids, visited_fragments, subgraph_definitions, active_subgraph_definition_ids, ) def _resolve_prompt_node_ids_for_workflow_node( workflow_node_id: str, ancestor_node_ids: tuple[str, ...], prompt_node_ids: set[str], ) -> set[str]: """Resolve one saved workflow node id to matching queued prompt node ids.""" path_segments = ancestor_node_ids + (workflow_node_id,) for index in range(len(path_segments)): candidate = ":".join(path_segments[index:]) resolved_prompt_node_ids: set[str] = set() if candidate in prompt_node_ids: resolved_prompt_node_ids.add(candidate) descendant_prefix = f"{candidate}:" resolved_prompt_node_ids.update( prompt_node_id for prompt_node_id in prompt_node_ids if prompt_node_id.startswith(descendant_prefix) ) if resolved_prompt_node_ids: return resolved_prompt_node_ids resolved_prompt_node_ids: set[str] = set() for ancestor_node_id in reversed(ancestor_node_ids): if ancestor_node_id in prompt_node_ids: resolved_prompt_node_ids.add(ancestor_node_id) descendant_prefix = f"{ancestor_node_id}:" resolved_prompt_node_ids.update( prompt_node_id for prompt_node_id in prompt_node_ids if prompt_node_id.startswith(descendant_prefix) ) break return resolved_prompt_node_ids def _workflow_node_path( workflow_node_id: str, ancestor_node_ids: tuple[str, ...] ) -> str: """Return one workflow node's composed path, including subgraph ancestors.""" if not ancestor_node_ids: return workflow_node_id return ":".join((*ancestor_node_ids, workflow_node_id)) def _extract_marked_workflow_node_paths( workflow: dict[str, Any] | None, settings: ModalSyncSettings | None = None, ) -> set[str]: """Return composed workflow paths for nodes explicitly marked remote in metadata.""" if workflow is None: return set() marker = (settings or get_settings()).marker_property marked_workflow_node_paths: set[str] = set() for node, ancestor_node_ids in _iter_workflow_nodes_with_ancestors(workflow): properties = node.get("properties") or {} if not properties.get(marker): continue marked_workflow_node_paths.add( _workflow_node_path(str(node.get("id")), ancestor_node_ids) ) return marked_workflow_node_paths def _build_workflow_prompt_resolution_maps( workflow: dict[str, Any] | None, prompt_node_ids: set[str], ) -> tuple[dict[str, set[str]], dict[str, set[str]]]: """Return bidirectional mappings between workflow paths and queued prompt ids.""" workflow_path_to_prompt_node_ids: dict[str, set[str]] = {} prompt_node_id_to_workflow_paths: dict[str, set[str]] = defaultdict(set) if workflow is None: for prompt_node_id in prompt_node_ids: workflow_path_to_prompt_node_ids[prompt_node_id] = {prompt_node_id} prompt_node_id_to_workflow_paths[prompt_node_id].add(prompt_node_id) return workflow_path_to_prompt_node_ids, prompt_node_id_to_workflow_paths for node, ancestor_node_ids in _iter_workflow_nodes_with_ancestors(workflow): workflow_node_id = str(node.get("id")) workflow_node_path = _workflow_node_path(workflow_node_id, ancestor_node_ids) resolved_prompt_node_ids = _resolve_prompt_node_ids_for_workflow_node( workflow_node_id, ancestor_node_ids, prompt_node_ids, ) if not ancestor_node_ids and workflow_node_id in prompt_node_ids: resolved_prompt_node_ids.add(workflow_node_id) if workflow_node_path in prompt_node_ids: resolved_prompt_node_ids.add(workflow_node_path) if not resolved_prompt_node_ids: continue workflow_path_to_prompt_node_ids[workflow_node_path] = resolved_prompt_node_ids for prompt_node_id in resolved_prompt_node_ids: prompt_node_id_to_workflow_paths[prompt_node_id].add(workflow_node_path) for prompt_node_id in prompt_node_ids: workflow_path_to_prompt_node_ids.setdefault(prompt_node_id, {prompt_node_id}) prompt_node_id_to_workflow_paths[prompt_node_id].add(prompt_node_id) return workflow_path_to_prompt_node_ids, prompt_node_id_to_workflow_paths def _resolve_requested_prompt_node_ids( requested_workflow_node_paths: set[str], prompt_node_ids: set[str], workflow_path_to_prompt_node_ids: dict[str, set[str]], ) -> set[str]: """Resolve requested workflow-node paths to the prompt ids that queue-time rewrite sees.""" requested_prompt_node_ids: set[str] = set() for requested_workflow_node_path in requested_workflow_node_paths: if requested_workflow_node_path in workflow_path_to_prompt_node_ids: requested_prompt_node_ids.update( workflow_path_to_prompt_node_ids[requested_workflow_node_path] ) continue if requested_workflow_node_path in prompt_node_ids: requested_prompt_node_ids.add(requested_workflow_node_path) return requested_prompt_node_ids def _best_workflow_path_for_prompt_node( prompt_node_id: str, prompt_node_id_to_workflow_paths: dict[str, set[str]], ) -> str: """Choose the most specific workflow path for one queued prompt node id.""" workflow_node_paths = prompt_node_id_to_workflow_paths.get( prompt_node_id, {prompt_node_id} ) return max( workflow_node_paths, key=lambda workflow_node_path: ( workflow_node_path.count(":"), len(workflow_node_path), workflow_node_path, ), ) def _resolve_workflow_node_paths_for_prompt_nodes( prompt_node_ids: set[str], prompt_node_id_to_workflow_paths: dict[str, set[str]], ) -> set[str]: """Map queued prompt ids back to the workflow node paths the UI can mark remote.""" return { _best_workflow_path_for_prompt_node( prompt_node_id, prompt_node_id_to_workflow_paths ) for prompt_node_id in prompt_node_ids } def extract_remote_node_ids( workflow: dict[str, Any] | None, settings: ModalSyncSettings | None = None, prompt_node_ids: set[str] | None = None, ) -> set[str]: """Return the node ids marked for remote execution in the workflow metadata.""" if workflow is None: return set() marker = (settings or get_settings()).marker_property remote_node_ids: set[str] = set() for node, ancestor_node_ids in _iter_workflow_nodes_with_ancestors(workflow): if _workflow_node_remote_enabled(node, marker): node_id = str(node.get("id")) if prompt_node_ids is None: remote_node_ids.add(node_id) continue resolved_prompt_node_ids = _resolve_prompt_node_ids_for_workflow_node( node_id, ancestor_node_ids, prompt_node_ids, ) if resolved_prompt_node_ids: remote_node_ids.update(resolved_prompt_node_ids) logger.info( "Mapped workflow Modal marker from node %s with ancestors %s to prompt nodes %s.", node_id, list(ancestor_node_ids), sorted(resolved_prompt_node_ids), ) return remote_node_ids def requested_remote_node_ids( *, prompt: Mapping[str, Any], workflow: Mapping[str, Any] | None, settings: ModalSyncSettings, ) -> set[str]: """Return explicit remote markers or every eligible node for auto placement.""" preferences = WorkflowExecutionPreferences.from_workflow(workflow) if not preferences.auto_place: return extract_remote_node_ids( dict(workflow) if workflow is not None else None, settings, set(prompt), ) selected_node_ids = { str(node_id) for node_id, prompt_node in prompt.items() if isinstance(prompt_node, Mapping) and str(prompt_node.get("class_type") or "") not in _LOCAL_ONLY_REMOTE_CLASS_TYPES and not str(prompt_node.get("class_type") or "").startswith( "ModalUniversalExecutor" ) } logger.info( "Automatic remote placement selected %d eligible prompt nodes.", len(selected_node_ids), ) return selected_node_ids def _workflow_node_remote_enabled(node: Mapping[str, Any], marker: str) -> bool: """Return the visible remote toggle, falling back to the saved marker property.""" named_widget_values = node.get("widgets_values_named") if isinstance(named_widget_values, Mapping): visible_toggle_value = named_widget_values.get(_REMOTE_TOGGLE_WIDGET_NAME) if not isinstance(visible_toggle_value, bool): visible_toggle_value = named_widget_values.get( _LEGACY_REMOTE_TOGGLE_WIDGET_NAME ) if isinstance(visible_toggle_value, bool): return visible_toggle_value properties = node.get("properties") return isinstance(properties, Mapping) and bool(properties.get(marker)) def _normalize_output_metadata( node_class: type[Any], ) -> tuple[tuple[str, ...], tuple[str, ...], tuple[bool, ...]]: """Return normalized output metadata for a node class.""" if hasattr(node_class, "GET_SCHEMA"): node_class.GET_SCHEMA() output_types = tuple(getattr(node_class, "RETURN_TYPES", ("*",))) or ("*",) default_names = tuple(f"output_{index}" for index, _ in enumerate(output_types)) output_names = tuple(getattr(node_class, "RETURN_NAMES", default_names)) output_is_list = tuple( getattr(node_class, "OUTPUT_IS_LIST", (False,) * len(output_types)) ) if len(output_names) < len(output_types): output_names = output_names + default_names[len(output_names) :] if len(output_is_list) < len(output_types): output_is_list = output_is_list + (False,) * ( len(output_types) - len(output_is_list) ) return ( output_types, output_names[: len(output_types)], output_is_list[: len(output_types)], ) def _is_transportable_output_type(io_type: str) -> bool: """Return whether a ComfyUI output type can cross the current transport.""" normalized_parts = [part.strip() for part in io_type.split(",") if part.strip()] return bool(normalized_parts) and all( part in _TRANSPORTABLE_OUTPUT_TYPES for part in normalized_parts ) def _is_inexpensive_remote_boundary_type(io_type: str) -> bool: """Return whether a remote edge is cheap enough to remain a component boundary.""" normalized_parts = [part.strip() for part in io_type.split(",") if part.strip()] return bool(normalized_parts) and all( part in _INEXPENSIVE_REMOTE_BOUNDARY_TYPES for part in normalized_parts ) 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 _sandwiched_local_node_ids( prompt: dict[str, Any], remote_node_ids: set[str], ) -> set[str]: """Return local nodes on paths that leave and then re-enter remote execution.""" downstream_node_ids: dict[str, set[str]] = defaultdict(set) upstream_node_ids: dict[str, set[str]] = defaultdict(set) for target_node_id, prompt_node in prompt.items(): for input_value in (prompt_node.get("inputs") or {}).values(): if not _is_link(input_value): continue source_node_id = str(input_value[0]) safe_target_node_id = str(target_node_id) downstream_node_ids[source_node_id].add(safe_target_node_id) upstream_node_ids[safe_target_node_id].add(source_node_id) def local_closure(adjacency: Mapping[str, set[str]]) -> set[str]: """Return local nodes reachable from the remote seed set in one direction.""" reachable_local_node_ids: set[str] = set() pending_node_ids = list(sorted(remote_node_ids)) visited_node_ids = set(remote_node_ids) while pending_node_ids: current_node_id = pending_node_ids.pop() for adjacent_node_id in adjacency.get(current_node_id, set()): if adjacent_node_id in visited_node_ids: continue visited_node_ids.add(adjacent_node_id) if adjacent_node_id in remote_node_ids: continue if adjacent_node_id not in prompt: continue reachable_local_node_ids.add(adjacent_node_id) pending_node_ids.append(adjacent_node_id) return reachable_local_node_ids downstream_local_node_ids = local_closure(downstream_node_ids) upstream_local_node_ids = local_closure(upstream_node_ids) return downstream_local_node_ids & upstream_local_node_ids def _node_output_refs( prompt: dict[str, Any], node_id: str, nodes_module: Any ) -> list[LinkedOutputRef]: """Return declared output refs for one prompt node.""" prompt_node = prompt.get(node_id) if prompt_node is None: return [] node_class = nodes_module.NODE_CLASS_MAPPINGS.get( str(prompt_node.get("class_type")) ) if node_class is None: return [] output_types, _, _ = _normalize_output_metadata(node_class) return [ LinkedOutputRef(node_id=node_id, output_index=output_index) for output_index, _io_type in enumerate(output_types) ] def _downstream_node_ids_from_targets( *, prompt: dict[str, Any], consumers: dict[LinkedOutputRef, list[InputTarget]], seed_targets: list[InputTarget], nodes_module: Any, ) -> set[str]: """Return every node reachable downstream from the supplied input targets.""" visited_node_ids: set[str] = set() pending_node_ids = deque(str(target.node_id) for target in seed_targets) while pending_node_ids: node_id = pending_node_ids.popleft() if node_id in visited_node_ids or node_id not in prompt: continue visited_node_ids.add(node_id) for output_ref in _node_output_refs(prompt, node_id, nodes_module): for downstream_target in consumers.get(output_ref, []): downstream_node_id = str(downstream_target.node_id) if downstream_node_id not in visited_node_ids: pending_node_ids.append(downstream_node_id) return visited_node_ids def _is_non_returning_tap_terminal_node( *, prompt_node: dict[str, Any] | None, nodes_module: Any, ) -> bool: """Return whether a local tap node should stay local instead of being absorbed.""" if prompt_node is None: return False class_type = str(prompt_node.get("class_type")) if class_type == "PreviewImage": return True node_class = nodes_module.NODE_CLASS_MAPPINGS.get(class_type) return bool(getattr(node_class, "OUTPUT_NODE", False)) def _non_returning_local_tap_node_ids( *, prompt: dict[str, Any], component_node_ids: set[str], remote_node_ids: set[str], consumers: dict[LinkedOutputRef, list[InputTarget]], nodes_module: Any, ) -> tuple[set[str], set[str]]: """Return non-returning local tap branches without making local nodes remote.""" local_tap_node_ids: set[str] = set() local_tap_terminal_node_ids: set[str] = set() for component_node_id in sorted(component_node_ids): for source in _node_output_refs(prompt, component_node_id, nodes_module): output_consumers = consumers.get(source, []) local_consumers = [ target for target in output_consumers if target.node_id not in component_node_ids and target.node_id not in remote_node_ids ] remote_consumers = [ target for target in output_consumers if target.node_id in remote_node_ids ] if not local_consumers or not remote_consumers: continue for local_consumer in local_consumers: branch_node_ids = _downstream_node_ids_from_targets( prompt=prompt, consumers=consumers, seed_targets=[local_consumer], nodes_module=nodes_module, ) if branch_node_ids & remote_node_ids: continue local_tap_terminal_node_ids.update(branch_node_ids) return local_tap_node_ids, local_tap_terminal_node_ids def _non_returning_local_output_consumers( *, prompt: dict[str, Any], source: LinkedOutputRef, remote_node_ids: set[str], consumers: dict[LinkedOutputRef, list[InputTarget]], nodes_module: Any, ) -> tuple[list[InputTarget], list[InputTarget]]: """Partition local consumers by whether their branch later returns remote.""" non_returning_consumers: list[InputTarget] = [] returning_consumers: list[InputTarget] = [] for target in consumers.get(source, []): if target.node_id in remote_node_ids: continue branch_node_ids = _downstream_node_ids_from_targets( prompt=prompt, consumers=consumers, seed_targets=[target], nodes_module=nodes_module, ) if branch_node_ids and not (branch_node_ids & remote_node_ids): non_returning_consumers.append(target) else: returning_consumers.append(target) return non_returning_consumers, returning_consumers def _output_supports_parallel_local_materialization( *, prompt: dict[str, Any], source: LinkedOutputRef, remote_node_ids: set[str], consumers: dict[LinkedOutputRef, list[InputTarget]], nodes_module: Any, ) -> bool: """Return whether every local consumer is independent of later remote work.""" ( non_returning_consumers, returning_consumers, ) = _non_returning_local_output_consumers( prompt=prompt, source=source, remote_node_ids=remote_node_ids, consumers=consumers, nodes_module=nodes_module, ) return bool(non_returning_consumers) and not returning_consumers def _expand_component_for_non_transportable_local_outputs( *, prompt: dict[str, Any], component_node_ids: set[str], remote_node_ids: set[str], consumers: dict[LinkedOutputRef, list[InputTarget]], nodes_module: Any, ) -> tuple[set[str], set[str]]: """Absorb local consumers needed to avoid non-transportable component outputs.""" expanded_node_ids = set(component_node_ids) added_node_ids: set[str] = set() changed = True while changed: changed = False ( upstream_expanded_node_ids, _expansion_reasons, ) = _expand_remote_node_ids_for_non_transportable_inputs( prompt=prompt, remote_node_ids=expanded_node_ids, nodes_module=nodes_module, ) upstream_added_node_ids = upstream_expanded_node_ids - expanded_node_ids if upstream_added_node_ids: expanded_node_ids = upstream_expanded_node_ids added_node_ids.update(upstream_added_node_ids) changed = True for node_id in sorted(expanded_node_ids): prompt_node = prompt.get(node_id) if prompt_node is None: continue node_class = nodes_module.NODE_CLASS_MAPPINGS.get( str(prompt_node.get("class_type")) ) if node_class is None: continue output_types, _, _ = _normalize_output_metadata(node_class) for output_index, io_type in enumerate(output_types): if _is_transportable_output_type(str(io_type)): continue source = LinkedOutputRef(node_id=node_id, output_index=output_index) for target in consumers.get(source, []): target_node_id = str(target.node_id) if ( target_node_id in expanded_node_ids or target_node_id in remote_node_ids ): continue if target_node_id not in prompt: continue if _is_non_returning_tap_terminal_node( prompt_node=prompt.get(target_node_id), nodes_module=nodes_module, ): continue expanded_node_ids.add(target_node_id) added_node_ids.add(target_node_id) changed = True logger.info( "Absorbing local node %s because it consumes non-transportable output %s:%d from a preview-tap remote component.", target_node_id, source.node_id, source.output_index, ) return expanded_node_ids, added_node_ids def _remote_output_io_type( *, prompt: dict[str, Any], node_id: str, output_index: int, nodes_module: Any, ) -> str | None: """Return the declared output type for one prompt node output when available.""" prompt_node = prompt.get(node_id) if prompt_node is None: return None class_type = str(prompt_node["class_type"]) node_class = nodes_module.NODE_CLASS_MAPPINGS.get(class_type) if node_class is None: return None output_types, _, _ = _normalize_output_metadata(node_class) if output_index < 0 or output_index >= len(output_types): return None return str(output_types[output_index]) def _remote_output_is_list( *, prompt: dict[str, Any], node_id: str, output_index: int, nodes_module: Any, ) -> bool: """Return whether one prompt node output is declared as a list output.""" prompt_node = prompt.get(node_id) if prompt_node is None: return False class_type = str(prompt_node["class_type"]) node_class = nodes_module.NODE_CLASS_MAPPINGS.get(class_type) if node_class is None: return False output_types, _output_names, output_is_list = _normalize_output_metadata(node_class) if output_index < 0 or output_index >= len(output_types): return False return bool(output_is_list[output_index]) def _remote_group_dependency_edges( prompt: Mapping[str, Any], remote_node_ids: set[str], group_for_node: Callable[[str], str], ) -> dict[str, set[str]]: """Return current coarse dependency edges for a remote-node grouping.""" dependency_edges: dict[str, set[str]] = defaultdict(set) for downstream_node_id in remote_node_ids: downstream_node = prompt.get(downstream_node_id) if not isinstance(downstream_node, Mapping): continue downstream_group = group_for_node(downstream_node_id) for input_value in (downstream_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 upstream_group = group_for_node(upstream_node_id) if upstream_group != downstream_group: dependency_edges[upstream_group].add(downstream_group) return dependency_edges def _has_alternate_group_path_through_protected_group( dependency_edges: Mapping[str, set[str]], start_group: str, target_group: str, protected_groups: set[str], ) -> bool: """Return whether a non-direct path crosses a provider-constrained group.""" pending = [ (downstream_group, downstream_group in protected_groups) for downstream_group in dependency_edges.get(start_group, set()) if downstream_group != target_group ] visited: set[tuple[str, bool]] = set() while pending: current_group, crossed_protected_group = pending.pop() if current_group == target_group and crossed_protected_group: return True state = (current_group, crossed_protected_group) if state in visited: continue visited.add(state) pending.extend( ( downstream_group, crossed_protected_group or downstream_group in protected_groups, ) for downstream_group in dependency_edges.get(current_group, set()) ) return False def _remote_component_partition_groups( prompt: dict[str, Any], remote_node_ids: set[str], consumers: dict[LinkedOutputRef, list[InputTarget]], nodes_module: Any, *, preserve_nontransportable_affinity: bool = True, ) -> dict[str, set[str]]: """Return component groups after merging remote nodes across costly boundaries.""" parent: dict[str, str] = {node_id: node_id for node_id in remote_node_ids} downstream_remote_node_ids_by_node_id: dict[str, set[str]] = defaultdict(set) provider_constrained_node_ids = { node_id for node_id in remote_node_ids if isinstance((prompt_node := prompt.get(node_id)), Mapping) and _prompt_node_required_provider(prompt_node) is not None } def find(node_id: str) -> str: """Return the canonical union-find representative for one remote node.""" root = parent[node_id] while root != parent[root]: root = parent[root] while node_id != root: next_node_id = parent[node_id] parent[node_id] = root node_id = next_node_id return root def union( left_node_id: str, right_node_id: str, *, preserve_coarse_dag: bool | None = None, ) -> bool: """Merge two remote nodes unless doing so creates a coarse dependency cycle.""" left_root = find(left_node_id) right_root = find(right_node_id) if left_root == right_root: return True should_preserve_coarse_dag = ( bool(provider_constrained_node_ids) if preserve_coarse_dag is None else preserve_coarse_dag ) if should_preserve_coarse_dag: dependency_edges = _remote_group_dependency_edges( prompt, remote_node_ids, find, ) protected_groups = { find(node_id) for node_id in provider_constrained_node_ids } creates_cycle = _has_alternate_group_path_through_protected_group( dependency_edges, left_root, right_root, protected_groups, ) or _has_alternate_group_path_through_protected_group( dependency_edges, right_root, left_root, protected_groups, ) if creates_cycle: logger.info( "Splitting remote nodes %s and %s to preserve an acyclic " "component graph.", left_node_id, right_node_id, ) return False canonical_root = min(left_root, right_root) merged_root = max(left_root, right_root) parent[merged_root] = canonical_root return True for node_id in sorted(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 downstream_remote_node_ids_by_node_id[upstream_node_id].add(node_id) upstream_prompt_node = prompt.get(upstream_node_id) source = LinkedOutputRef( node_id=upstream_node_id, output_index=int(input_value[1]) ) if ( upstream_prompt_node is not None and str(upstream_prompt_node.get("class_type")) == MODAL_MAP_INPUT_NODE_ID ): union(node_id, upstream_node_id, preserve_coarse_dag=False) continue io_type = _remote_output_io_type( prompt=prompt, node_id=upstream_node_id, output_index=source.output_index, nodes_module=nodes_module, ) if ( preserve_nontransportable_affinity and io_type is not None and not _is_transportable_output_type(io_type) ): logger.info( "Co-locating remote nodes %s -> %s because %s output %d " "cannot cross a component boundary.", upstream_node_id, node_id, io_type, source.output_index, ) union(node_id, upstream_node_id, preserve_coarse_dag=False) continue downstream_provider = _prompt_node_required_provider(prompt_node) upstream_provider = ( _prompt_node_required_provider(upstream_prompt_node) if isinstance(upstream_prompt_node, Mapping) else None ) if downstream_provider != upstream_provider and ( downstream_provider is not None or upstream_provider is not None ): logger.info( "Splitting remote nodes %s -> %s across provider boundary %s -> %s.", upstream_node_id, node_id, upstream_provider.value if upstream_provider is not None else "any", downstream_provider.value if downstream_provider is not None else "any", ) continue if _output_supports_parallel_local_materialization( prompt=prompt, source=source, remote_node_ids=remote_node_ids, consumers=consumers, nodes_module=nodes_module, ): logger.info( "Splitting remote nodes %s -> %s because output %d also feeds only non-returning local work.", upstream_node_id, node_id, source.output_index, ) continue if io_type is not None and _is_inexpensive_remote_boundary_type(io_type): continue if io_type is not None and _is_transportable_output_type(io_type): logger.info( "Co-locating remote nodes %s -> %s because %s output %d is " "transportable but expensive.", upstream_node_id, node_id, io_type, source.output_index, ) union(node_id, upstream_node_id) for remote_node_id in sorted(remote_node_ids): prompt_node = prompt.get(remote_node_id) if ( prompt_node is None or str(prompt_node.get("class_type")) != MODAL_MAP_INPUT_NODE_ID ): continue pending_node_ids = [remote_node_id] visited_node_ids: set[str] = set() while pending_node_ids: current_node_id = pending_node_ids.pop() if current_node_id in visited_node_ids: continue visited_node_ids.add(current_node_id) union( remote_node_id, current_node_id, preserve_coarse_dag=False, ) for downstream_node_id in sorted( downstream_remote_node_ids_by_node_id.get(current_node_id, set()) ): pending_node_ids.append(downstream_node_id) groups: dict[str, set[str]] = defaultdict(set) for node_id in sorted(remote_node_ids): groups[find(node_id)].add(node_id) return groups def _component_topological_order( prompt: dict[str, Any], component_groups: dict[str, set[str]], ) -> list[list[str]]: """Return component node ids ordered from upstream to downstream.""" _, dependency_edges, indegree_by_component_id = _component_dependency_graph( prompt, component_groups ) merged_component_groups = _merge_cyclic_component_groups( component_groups=component_groups, dependency_edges=dependency_edges, ) if merged_component_groups != component_groups: return _component_topological_order(prompt, merged_component_groups) ready_component_ids = deque( sorted( [ component_id for component_id, indegree in indegree_by_component_id.items() if indegree == 0 ] ) ) ordered_components: list[list[str]] = [] emitted_component_ids: set[str] = set() while ready_component_ids: component_id = ready_component_ids.popleft() if component_id in emitted_component_ids: continue emitted_component_ids.add(component_id) ordered_components.append(sorted(component_groups[component_id])) for downstream_component_id in sorted(dependency_edges[component_id]): indegree_by_component_id[downstream_component_id] -= 1 if indegree_by_component_id[downstream_component_id] == 0: ready_component_ids.append(downstream_component_id) if len(emitted_component_ids) == len(component_groups): return ordered_components logger.warning( "Transport-aware component ordering encountered a cycle or unresolved dependency; falling back to stable component order." ) for component_id in sorted(component_groups): if component_id in emitted_component_ids: continue ordered_components.append(sorted(component_groups[component_id])) return ordered_components def _component_dependency_graph( prompt: dict[str, Any], component_groups: dict[str, set[str]], ) -> tuple[dict[str, str], dict[str, set[str]], dict[str, int]]: """Return component membership, downstream edges, and indegrees for the coarse component DAG.""" component_id_by_node_id: dict[str, str] = {} for representative_node_id, component_node_ids in component_groups.items(): for node_id in component_node_ids: component_id_by_node_id[node_id] = representative_node_id dependency_edges: dict[str, set[str]] = { representative_node_id: set() for representative_node_id in component_groups } indegree_by_component_id: dict[str, int] = { representative_node_id: 0 for representative_node_id in component_groups } for node_id, representative_node_id in component_id_by_node_id.items(): 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]) upstream_component_id = component_id_by_node_id.get(upstream_node_id) if ( upstream_component_id is None or upstream_component_id == representative_node_id ): continue if representative_node_id in dependency_edges[upstream_component_id]: continue dependency_edges[upstream_component_id].add(representative_node_id) indegree_by_component_id[representative_node_id] += 1 return component_id_by_node_id, dependency_edges, indegree_by_component_id def _component_execution_stages( prompt: dict[str, Any], component_groups: dict[str, set[str]], ) -> list[list[str]]: """Return one best-effort stage decomposition for concurrent remote component execution.""" _, dependency_edges, indegree_by_component_id = _component_dependency_graph( prompt, component_groups ) merged_component_groups = _merge_cyclic_component_groups( component_groups=component_groups, dependency_edges=dependency_edges, ) if merged_component_groups != component_groups: return _component_execution_stages(prompt, merged_component_groups) remaining_indegrees = dict(indegree_by_component_id) ready_component_ids = sorted( [ component_id for component_id, indegree in remaining_indegrees.items() if indegree == 0 ] ) execution_stages: list[list[str]] = [] emitted_component_ids: set[str] = set() while ready_component_ids: current_stage = list(ready_component_ids) execution_stages.append(current_stage) next_ready_component_ids: set[str] = set() for component_id in current_stage: emitted_component_ids.add(component_id) for downstream_component_id in sorted( dependency_edges.get(component_id, set()) ): remaining_indegrees[downstream_component_id] -= 1 if remaining_indegrees[downstream_component_id] == 0: next_ready_component_ids.add(downstream_component_id) ready_component_ids = sorted( component_id for component_id in next_ready_component_ids if component_id not in emitted_component_ids ) if len(emitted_component_ids) == len(component_groups): return execution_stages fallback_stage = [ component_id for component_id in sorted(component_groups) if component_id not in emitted_component_ids ] if fallback_stage: logger.warning( "Transport-aware execution-stage planning encountered a cycle or unresolved dependency; appending fallback stage %s.", fallback_stage, ) execution_stages.append(fallback_stage) return execution_stages def _estimated_stage_parallelism( execution_stages: list[list[str]], mapped_component_ids: set[str], *, mapped_component_weight: int, max_parallelism_cap: int | None = None, ) -> int: """Return the weighted best-effort parallelism estimate over staged remote execution.""" stage_parallelism = 0 for stage in execution_stages: current_stage_parallelism = sum( mapped_component_weight if component_id in mapped_component_ids else 1 for component_id in stage ) stage_parallelism = max(stage_parallelism, current_stage_parallelism) if max_parallelism_cap is not None: return min(stage_parallelism, max_parallelism_cap) return stage_parallelism def _merge_cyclic_component_groups( *, component_groups: dict[str, set[str]], dependency_edges: dict[str, set[str]], ) -> dict[str, set[str]]: """Collapse cyclic coarse component groups into SCC-merged groups.""" component_ids = sorted(component_groups) reverse_edges: dict[str, set[str]] = { component_id: set() for component_id in component_ids } for upstream_component_id, downstream_component_ids in dependency_edges.items(): for downstream_component_id in downstream_component_ids: reverse_edges.setdefault(downstream_component_id, set()).add( upstream_component_id ) visited_component_ids: set[str] = set() finish_order: list[str] = [] def visit_forward(component_id: str) -> None: """Record reverse-topological finish order over the coarse graph.""" if component_id in visited_component_ids: return visited_component_ids.add(component_id) for downstream_component_id in sorted( dependency_edges.get(component_id, set()) ): visit_forward(downstream_component_id) finish_order.append(component_id) for component_id in component_ids: visit_forward(component_id) assigned_component_ids: set[str] = set() merged_groups: dict[str, set[str]] = {} merged_sccs = 0 def visit_reverse(component_id: str, scc_component_ids: set[str]) -> None: """Collect one SCC by walking reverse edges from the finish-order seed.""" if component_id in assigned_component_ids: return assigned_component_ids.add(component_id) scc_component_ids.add(component_id) for upstream_component_id in sorted(reverse_edges.get(component_id, set())): visit_reverse(upstream_component_id, scc_component_ids) for component_id in reversed(finish_order): if component_id in assigned_component_ids: continue scc_component_ids: set[str] = set() visit_reverse(component_id, scc_component_ids) merged_node_ids: set[str] = set() for scc_component_id in scc_component_ids: merged_node_ids.update(component_groups[scc_component_id]) representative_node_id = min(merged_node_ids) merged_groups[representative_node_id] = merged_node_ids if len(scc_component_ids) > 1: merged_sccs += 1 if merged_sccs: logger.warning( "Transport-aware coarse component graph contained %d cyclic SCC(s); merging them back into larger remote components.", merged_sccs, ) return merged_groups def _build_remote_components( prompt: dict[str, Any], remote_node_ids: set[str], consumers: dict[LinkedOutputRef, list[InputTarget]], nodes_module: Any, ) -> list[list[str]]: """Partition remote-marked nodes into transport-aware DAG components.""" component_groups = _remote_component_partition_groups( prompt, remote_node_ids, consumers, nodes_module, ) components = _component_topological_order(prompt, component_groups) logger.info( "Partitioned %d remote nodes into %d transport-aware 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 _terminal_remote_video_source( *, node_id: str, prompt: dict[str, Any], remote_node_ids: set[str], consumers: dict[LinkedOutputRef, list[InputTarget]], nodes_module: Any, ) -> LinkedOutputRef | None: """Return the remote VIDEO feeding one safe terminal SaveVideo artifact sink.""" prompt_node = prompt.get(node_id) if prompt_node is None or str(prompt_node.get("class_type")) != "SaveVideo": return None inputs = prompt_node.get("inputs") or {} video_input = inputs.get("video") if not _is_link(video_input): return None video_source = LinkedOutputRef(str(video_input[0]), int(video_input[1])) linked_source_node_ids = { str(input_value[0]) for input_value in inputs.values() if _is_link(input_value) } if not linked_source_node_ids.issubset(remote_node_ids): return None if any( consumers.get(output_ref) for output_ref in _node_output_refs(prompt, node_id, nodes_module) ): return None io_type = _remote_output_io_type( prompt=prompt, node_id=video_source.node_id, output_index=video_source.output_index, nodes_module=nodes_module, ) return video_source if io_type == "VIDEO" else None def _expand_remote_node_ids_for_terminal_video_sinks( prompt: dict[str, Any], remote_node_ids: set[str], nodes_module: Any, ) -> set[str]: """Run terminal SaveVideo consumers remotely to avoid exporting raw VIDEO tensors.""" expanded_remote_node_ids = set(remote_node_ids) consumers = _build_consumer_map(prompt) added_node_ids: set[str] = set() for node_id in sorted(prompt): if node_id in expanded_remote_node_ids: continue video_source = _terminal_remote_video_source( node_id=node_id, prompt=prompt, remote_node_ids=expanded_remote_node_ids, consumers=consumers, nodes_module=nodes_module, ) if video_source is None: continue expanded_remote_node_ids.add(node_id) added_node_ids.add(node_id) logger.info( "Auto-expanded terminal SaveVideo node %s into remote execution because its " "VIDEO input comes from remote node %s; the encoded output artifact will be " "materialized locally without exporting raw frames.", node_id, video_source.node_id, ) if added_node_ids: logger.info( "Expanded remote node set with terminal video artifact sinks: %s.", sorted(added_node_ids), ) return expanded_remote_node_ids 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_remote_node_ids = _expand_remote_node_ids_for_terminal_video_sinks( prompt=prompt, remote_node_ids=resolved_remote_node_ids, nodes_module=resolved_nodes_module, ) sandwiched_local_node_ids = _sandwiched_local_node_ids( prompt, resolved_remote_node_ids, ) 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), sandwiched_local_node_ids=sorted(sandwiched_local_node_ids), reasons=reasons, ) def _build_component_plan( component_node_ids: list[str], prompt: dict[str, Any], consumers: dict[LinkedOutputRef, list[InputTarget]], remote_node_ids: set[str], nodes_module: Any, ) -> RemoteComponentPlan: """Build rewrite metadata for a connected remote component.""" original_component_node_id_set = set(component_node_ids) representative_node_id = component_node_ids[0] local_tap_node_ids, local_tap_terminal_node_ids = _non_returning_local_tap_node_ids( prompt=prompt, component_node_ids=original_component_node_id_set, remote_node_ids=remote_node_ids, consumers=consumers, nodes_module=nodes_module, ) if local_tap_node_ids: logger.info( "Absorbing non-returning local tap nodes into remote component %s: %s", representative_node_id, sorted(local_tap_node_ids), ) if local_tap_terminal_node_ids: logger.info( "Keeping non-returning local tap terminal nodes outside remote component %s: %s", representative_node_id, sorted(local_tap_terminal_node_ids), ) component_node_id_set = original_component_node_id_set | local_tap_node_ids if local_tap_node_ids: ( expanded_component_node_id_set, tap_dependency_node_ids, ) = _expand_component_for_non_transportable_local_outputs( prompt=prompt, component_node_ids=component_node_id_set, remote_node_ids=remote_node_ids, consumers=consumers, nodes_module=nodes_module, ) if tap_dependency_node_ids: logger.info( "Absorbing non-transportable boundary dependencies for remote component %s preview taps: %s", representative_node_id, sorted(tap_dependency_node_ids), ) local_tap_node_ids.update(tap_dependency_node_ids) component_node_id_set = expanded_component_node_id_set component_node_ids = sorted(component_node_id_set) 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: source_io_type = _remote_output_io_type( prompt=prompt, node_id=source.node_id, output_index=source.output_index, nodes_module=nodes_module, ) spec = BoundaryInputSpec( proxy_input_name=f"remote_input_{len(boundary_inputs_by_source)}", source=source, io_type=source_io_type, ) boundary_inputs_by_source[source] = spec spec.targets.append( InputTarget(node_id=node_id, input_name=str(input_name)) ) has_downstream_consumer = False for output_index, io_type in enumerate(output_types): source = LinkedOutputRef(node_id=node_id, output_index=output_index) output_consumers = consumers.get(source, []) if output_consumers: has_downstream_consumer = True local_consumers = [ consumer for consumer in output_consumers 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]), preview_target_node_ids=_preview_target_node_ids( prompt=prompt, local_consumers=local_consumers, ), ) if not has_downstream_consumer: output_execution_targets.add(node_id) mapped_boundary_spec: BoundaryInputSpec | None = None mapped_boundary_input_io_type: str | None = None for boundary_input in boundary_inputs_by_source.values(): source_prompt_node = prompt.get(boundary_input.source.node_id) source_class_type = ( str(source_prompt_node.get("class_type")) if source_prompt_node is not None else None ) mapped_targets = [ target for target in boundary_input.targets if str(prompt[target.node_id]["class_type"]) == MODAL_MAP_INPUT_NODE_ID ] source_is_modal_map_input = source_class_type == MODAL_MAP_INPUT_NODE_ID if not mapped_targets and not source_is_modal_map_input: continue if mapped_targets and len(mapped_targets) != len(boundary_input.targets): raise ModalPromptValidationError( "Mapped remote execution requires the mapped boundary input to feed only ModalMapInput nodes." ) if mapped_boundary_spec is not None: raise ModalPromptValidationError( "Remote components currently support only one mapped ModalMapInput boundary." ) mapped_boundary_spec = boundary_input mapped_boundary_input_io_type = _mapped_boundary_origin_io_type( prompt, boundary_input, nodes_module, ) mapped_node_ids: list[str] = [] mapped_execute_node_ids: list[str] = [] static_execute_node_ids: list[str] = [] static_node_ids: list[str] = [] static_to_mapped_boundaries: list[StaticToMappedBoundarySpec] = [] if mapped_boundary_spec is not None: mapped_reachable_node_ids = _component_downstream_closure( seed_node_ids={target.node_id for target in mapped_boundary_spec.targets}, component_node_id_set=component_node_id_set, consumers=consumers, ) mapped_node_ids = sorted(mapped_reachable_node_ids) mapped_node_id_set = set(mapped_node_ids) static_node_ids = sorted(component_node_id_set - mapped_node_id_set) static_to_mapped_boundaries_by_source: dict[ LinkedOutputRef, StaticToMappedBoundarySpec ] = {} for mapped_node_id in mapped_node_ids: prompt_node = prompt[mapped_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 not in component_node_id_set or upstream_node_id in mapped_node_id_set ): continue source = LinkedOutputRef( node_id=upstream_node_id, output_index=int(input_value[1]), ) boundary_spec = static_to_mapped_boundaries_by_source.get(source) if boundary_spec is None: boundary_spec = StaticToMappedBoundarySpec( proxy_name=f"static_input_{len(static_to_mapped_boundaries_by_source)}", source=source, io_type=str( _remote_output_io_type( prompt=prompt, node_id=source.node_id, output_index=source.output_index, nodes_module=nodes_module, ) or "*" ), is_list=_remote_output_is_list( prompt=prompt, node_id=source.node_id, output_index=source.output_index, nodes_module=nodes_module, ), ) static_to_mapped_boundaries_by_source[source] = boundary_spec boundary_spec.targets.append( InputTarget(node_id=mapped_node_id, input_name=str(input_name)) ) static_to_mapped_boundaries = sorted( static_to_mapped_boundaries_by_source.values(), key=lambda spec: (spec.source.node_id, spec.source.output_index), ) mapped_execute_node_ids = sorted(output_execution_targets & mapped_node_id_set) static_execute_node_ids = sorted( (output_execution_targets - mapped_node_id_set) | { boundary_spec.source.node_id for boundary_spec in static_to_mapped_boundaries } ) 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, mapped_boundary_input_name=( mapped_boundary_spec.proxy_input_name if mapped_boundary_spec is not None else None ), mapped_boundary_input_io_type=mapped_boundary_input_io_type, mapped_boundary_source_node_id=( mapped_boundary_spec.source.node_id if mapped_boundary_spec is not None and prompt.get(mapped_boundary_spec.source.node_id, {}).get("class_type") == MODAL_MAP_INPUT_NODE_ID else None ), static_node_ids=static_node_ids, mapped_node_ids=mapped_node_ids, mapped_execute_node_ids=mapped_execute_node_ids, static_execute_node_ids=static_execute_node_ids, static_to_mapped_boundaries=static_to_mapped_boundaries, local_tap_node_ids=sorted(local_tap_node_ids), local_tap_terminal_node_ids=sorted(local_tap_terminal_node_ids), ) logger.info( "Planned remote component %s: nodes=%s boundary_inputs=%d boundary_outputs=%d execute_nodes=%s output_node=%s mapped_input=%s static_nodes=%s mapped_nodes=%s mapped_execute_nodes=%s static_execute_nodes=%s local_tap_nodes=%s local_tap_terminal_nodes=%s static_to_mapped_boundaries=%s", component.representative_node_id, component.node_ids, len(component.boundary_inputs), len(component.boundary_outputs), component.execute_node_ids, component.contains_output_node, component.mapped_boundary_input_name, component.static_node_ids, component.mapped_node_ids, component.mapped_execute_node_ids, component.static_execute_node_ids, component.local_tap_node_ids, component.local_tap_terminal_node_ids, [ { "proxy_name": boundary_spec.proxy_name, "source": ( boundary_spec.source.node_id, boundary_spec.source.output_index, ), "targets": [ (target.node_id, target.input_name) for target in boundary_spec.targets ], } for boundary_spec in component.static_to_mapped_boundaries ], ) return component def _filter_boundary_inputs_for_node_ids( boundary_inputs: list[BoundaryInputSpec], allowed_node_ids: set[str], ) -> list[BoundaryInputSpec]: """Return boundary inputs whose targets belong to one node-id subset.""" filtered_boundary_inputs: list[BoundaryInputSpec] = [] for boundary_input in boundary_inputs: filtered_targets = [ target for target in boundary_input.targets if target.node_id in allowed_node_ids ] if not filtered_targets: continue filtered_boundary_inputs.append( BoundaryInputSpec( proxy_input_name=boundary_input.proxy_input_name, source=boundary_input.source, io_type=boundary_input.io_type, targets=filtered_targets, ) ) return filtered_boundary_inputs def _filter_boundary_outputs_for_node_ids( boundary_outputs: list[BoundaryOutputSpec], allowed_node_ids: set[str], ) -> list[BoundaryOutputSpec]: """Return boundary outputs exported by one node-id subset.""" return [ boundary_output for boundary_output in boundary_outputs if boundary_output.source.node_id in allowed_node_ids ] def _subset_component_prompt( component_prompt: dict[str, Any], node_ids: list[str], ) -> dict[str, Any]: """Return one deep-copied prompt subset for a phase-local node set.""" return { node_id: copy.deepcopy(component_prompt[node_id]) for node_id in node_ids if node_id in component_prompt } def _preview_target_node_ids( *, prompt: dict[str, Any], local_consumers: list[InputTarget], ) -> list[str]: """Return direct local PreviewImage consumers for one remote boundary output.""" preview_target_node_ids: set[str] = set() for local_consumer in local_consumers: consumer_prompt_node = prompt.get(local_consumer.node_id) if consumer_prompt_node is None: continue if str(consumer_prompt_node.get("class_type")) != "PreviewImage": continue preview_target_node_ids.add(str(local_consumer.node_id)) return sorted(preview_target_node_ids) def _component_downstream_closure( *, seed_node_ids: set[str], component_node_id_set: set[str], consumers: dict[LinkedOutputRef, list[InputTarget]], ) -> set[str]: """Return component-local nodes reachable downstream from one seed set.""" reachable_node_ids: set[str] = set() pending_node_ids = list(sorted(seed_node_ids)) while pending_node_ids: current_node_id = pending_node_ids.pop() if ( current_node_id in reachable_node_ids or current_node_id not in component_node_id_set ): continue reachable_node_ids.add(current_node_id) for consumer_source, consumer_targets in consumers.items(): if consumer_source.node_id != current_node_id: continue for consumer_target in consumer_targets: if consumer_target.node_id in component_node_id_set: pending_node_ids.append(consumer_target.node_id) return reachable_node_ids def _component_upstream_closure( *, prompt: dict[str, Any], seed_node_ids: set[str], candidate_node_ids: set[str], ) -> set[str]: """Return one candidate-local upstream closure for the supplied seed nodes.""" reachable_node_ids: set[str] = set() pending_node_ids = list(sorted(seed_node_ids)) while pending_node_ids: current_node_id = pending_node_ids.pop() if ( current_node_id in reachable_node_ids or current_node_id not in candidate_node_ids ): continue reachable_node_ids.add(current_node_id) prompt_node = prompt.get(current_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 candidate_node_ids: pending_node_ids.append(upstream_node_id) return reachable_node_ids def _component_ancestors_of_local_source( *, prompt: dict[str, Any], source_node_id: str, component_node_id_set: set[str], ) -> set[str]: """Return component nodes that feed one local boundary-input source.""" ancestor_node_ids: set[str] = set() visited_node_ids: set[str] = set() pending_node_ids = [source_node_id] while pending_node_ids: current_node_id = str(pending_node_ids.pop()) if current_node_id in visited_node_ids: continue visited_node_ids.add(current_node_id) if current_node_id in component_node_id_set: ancestor_node_ids.add(current_node_id) continue prompt_node = prompt.get(current_node_id) if prompt_node is None: continue for input_value in (prompt_node.get("inputs") or {}).values(): if _is_link(input_value): pending_node_ids.append(str(input_value[0])) return ancestor_node_ids def _component_has_local_reentry_dependency( *, prompt: dict[str, Any], component: RemoteComponentPlan, ) -> bool: """Return whether a component boundary input depends on that same component's output.""" component_node_id_set = set(component.node_ids) for boundary_input in component.boundary_inputs: if boundary_input.source.node_id in component_node_id_set: continue if _component_ancestors_of_local_source( prompt=prompt, source_node_id=boundary_input.source.node_id, component_node_id_set=component_node_id_set, ): return True return False def _component_has_parallel_local_remote_fanout( component: RemoteComponentPlan, ) -> bool: """Return whether one remote output feeds local work and a remote continuation.""" return any( boundary_output.local_materializer_node_id is not None and bool(boundary_output.session_consumer_node_ids) for boundary_output in component.boundary_outputs ) def _order_execute_node_ids_for_transportable_splits( *, prompt: dict[str, Any], component_prompt: dict[str, Any], component_node_ids: set[str], execute_node_ids: list[str], ) -> list[str]: """Return split execute targets ordered by component and local feedback dependencies.""" base_order = list(execute_node_ids) if len(base_order) <= 1: return base_order closure_by_execute_node_id = { execute_node_id: _component_upstream_closure( prompt=component_prompt, seed_node_ids={execute_node_id}, candidate_node_ids=component_node_ids, ) for execute_node_id in base_order } producer_execute_node_ids_by_component_node_id: dict[str, list[str]] = defaultdict( list ) for execute_node_id in base_order: for component_node_id in closure_by_execute_node_id[execute_node_id]: producer_execute_node_ids_by_component_node_id[component_node_id].append( execute_node_id ) base_index_by_execute_node_id = { execute_node_id: index for index, execute_node_id in enumerate(base_order) } dependency_edges: dict[str, set[str]] = { execute_node_id: set() for execute_node_id in base_order } indegree_by_execute_node_id: dict[str, int] = { execute_node_id: 0 for execute_node_id in base_order } def producer_for_component_node(component_node_id: str) -> str | None: """Return the earliest execute target that produces a component node.""" producer_execute_node_ids = producer_execute_node_ids_by_component_node_id.get( component_node_id, [], ) if not producer_execute_node_ids: return None exact_producers = [ execute_node_id for execute_node_id in producer_execute_node_ids if execute_node_id == component_node_id ] if exact_producers: return exact_producers[0] return min( producer_execute_node_ids, key=lambda execute_node_id: base_index_by_execute_node_id[execute_node_id], ) for target_execute_node_id in base_order: target_closure = closure_by_execute_node_id[target_execute_node_id] for phase_node_id in sorted(target_closure): prompt_node = component_prompt.get(phase_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 source_node_id = str(input_value[0]) if source_node_id in component_node_ids: continue source_ancestor_node_ids = _component_ancestors_of_local_source( prompt=prompt, source_node_id=source_node_id, component_node_id_set=component_node_ids, ) for source_ancestor_node_id in sorted(source_ancestor_node_ids): if source_ancestor_node_id in target_closure: continue producer_execute_node_id = producer_for_component_node( source_ancestor_node_id ) if ( producer_execute_node_id is None or producer_execute_node_id == target_execute_node_id or target_execute_node_id in dependency_edges[producer_execute_node_id] ): continue dependency_edges[producer_execute_node_id].add( target_execute_node_id ) indegree_by_execute_node_id[target_execute_node_id] += 1 ready_execute_node_ids = [ execute_node_id for execute_node_id in base_order if indegree_by_execute_node_id[execute_node_id] == 0 ] ordered_execute_node_ids: list[str] = [] while ready_execute_node_ids: ready_execute_node_ids.sort( key=lambda node_id: base_index_by_execute_node_id[node_id] ) execute_node_id = ready_execute_node_ids.pop(0) ordered_execute_node_ids.append(execute_node_id) for downstream_execute_node_id in sorted( dependency_edges[execute_node_id], key=lambda node_id: base_index_by_execute_node_id[node_id], ): indegree_by_execute_node_id[downstream_execute_node_id] -= 1 if indegree_by_execute_node_id[downstream_execute_node_id] == 0: ready_execute_node_ids.append(downstream_execute_node_id) if len(ordered_execute_node_ids) != len(base_order): logger.warning( "Split phase local-feedback dependency ordering encountered a cycle; keeping base execute order %s.", base_order, ) return base_order if ordered_execute_node_ids != base_order: logger.info( "Reordered split execute targets from %s to %s using local-feedback dependencies %s.", base_order, ordered_execute_node_ids, { execute_node_id: sorted(downstream_execute_node_ids) for execute_node_id, downstream_execute_node_ids in dependency_edges.items() if downstream_execute_node_ids }, ) return ordered_execute_node_ids def _subgraph_topological_node_order( prompt: dict[str, Any], node_ids: set[str], ) -> list[str]: """Return one best-effort topological order for a prompt node subset.""" indegree_by_node_id: dict[str, int] = {node_id: 0 for node_id in node_ids} downstream_node_ids_by_node_id: dict[str, set[str]] = { node_id: set() for node_id in node_ids } for node_id in sorted(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 node_ids: continue if node_id in downstream_node_ids_by_node_id[upstream_node_id]: continue downstream_node_ids_by_node_id[upstream_node_id].add(node_id) indegree_by_node_id[node_id] += 1 ready_node_ids = deque( sorted( node_id for node_id, indegree in indegree_by_node_id.items() if indegree == 0 ) ) ordered_node_ids: list[str] = [] while ready_node_ids: node_id = ready_node_ids.popleft() ordered_node_ids.append(node_id) for downstream_node_id in sorted(downstream_node_ids_by_node_id[node_id]): indegree_by_node_id[downstream_node_id] -= 1 if indegree_by_node_id[downstream_node_id] == 0: ready_node_ids.append(downstream_node_id) if len(ordered_node_ids) == len(node_ids): return ordered_node_ids logger.warning( "Component phase planning encountered a node-level cycle; falling back to stable node order for %s.", sorted(node_ids), ) return sorted(node_ids) def _remote_dependency_closure( prompt: Mapping[str, Any], source_node_id: str, ) -> set[str]: """Return the complete upstream dependency closure for one remote producer.""" closure: set[str] = set() pending = [source_node_id] while pending: node_id = pending.pop() if node_id in closure: continue prompt_node = prompt.get(node_id) if not isinstance(prompt_node, Mapping): continue closure.add(node_id) pending.extend( str(input_value[0]) for input_value in (prompt_node.get("inputs") or {}).values() if _is_link(input_value) ) return closure def _can_replicate_remote_dependency_closure( *, prompt: Mapping[str, Any], source_node_id: str, dependency_node_ids: set[str], remote_node_ids: set[str], ) -> bool: """Return whether a producer closure can be safely rebuilt on another worker.""" if not dependency_node_ids or not dependency_node_ids.issubset(remote_node_ids): return False source_node = prompt.get(source_node_id) if not isinstance(source_node, Mapping): return False source_inputs = source_node.get("inputs") if not isinstance(source_inputs, Mapping): return False if not any(_is_link(value) for value in source_inputs.values()): return True return all( "sampler" not in str((prompt.get(node_id) or {}).get("class_type") or "").casefold() for node_id in dependency_node_ids ) def _replica_node_id_mapping( *, prompt: Mapping[str, Any], dependency_node_ids: set[str], source_node_id: str, target_group_id: str, ) -> dict[str, str]: """Return collision-free deterministic ids for one replicated dependency closure.""" replica_digest = hashlib.sha256( f"{source_node_id}\0{target_group_id}".encode("utf-8") ).hexdigest()[:12] reserved_node_ids = set(prompt) mapping: dict[str, str] = {} for node_id in sorted(dependency_node_ids): base_id = f"{_REMOTE_REPLICA_NODE_PREFIX}{replica_digest}__{node_id}" replica_node_id = base_id suffix = 1 while replica_node_id in reserved_node_ids: replica_node_id = f"{base_id}_{suffix}" suffix += 1 mapping[node_id] = replica_node_id reserved_node_ids.add(replica_node_id) return mapping def _install_remote_dependency_replica( *, prompt: dict[str, Any], remote_node_ids: set[str], dependency_node_ids: set[str], source_node_id: str, target_group_id: str, ) -> str: """Clone one safe producer closure and return its replicated source node id.""" replica_ids = _replica_node_id_mapping( prompt=prompt, dependency_node_ids=dependency_node_ids, source_node_id=source_node_id, target_group_id=target_group_id, ) for node_id in sorted(dependency_node_ids): replica_node = copy.deepcopy(prompt[node_id]) for input_name, input_value in list( (replica_node.get("inputs") or {}).items() ): if _is_link(input_value) and str(input_value[0]) in replica_ids: replica_node["inputs"][input_name] = [ replica_ids[str(input_value[0])], int(input_value[1]), ] prompt[replica_ids[node_id]] = replica_node remote_node_ids.update(replica_ids.values()) return replica_ids[source_node_id] def _cross_group_replicable_boundaries( *, prompt: dict[str, Any], remote_node_ids: set[str], component_groups: Mapping[str, set[str]], nodes_module: Any, ) -> list[tuple[str, int, str, str, str]]: """Return replicable non-transportable edges crossing tentative components.""" group_by_node_id = { node_id: group_id for group_id, node_ids in component_groups.items() for node_id in node_ids } boundaries: list[tuple[str, int, str, str, str]] = [] for target_node_id in sorted(remote_node_ids): target_node = prompt.get(target_node_id) if not isinstance(target_node, Mapping): continue for input_name, input_value in (target_node.get("inputs") or {}).items(): if not _is_link(input_value): continue source_node_id = str(input_value[0]) if ( source_node_id not in remote_node_ids or group_by_node_id.get(source_node_id) == group_by_node_id.get(target_node_id) ): continue output_index = int(input_value[1]) io_type = _remote_output_io_type( prompt=prompt, node_id=source_node_id, output_index=output_index, nodes_module=nodes_module, ) if io_type not in _REPLICABLE_REMOTE_OBJECT_TYPES: continue boundaries.append( ( source_node_id, output_index, target_node_id, str(input_name), str(group_by_node_id[target_node_id]), ) ) return boundaries def _replicate_safe_nontransportable_provider_boundaries( *, prompt: dict[str, Any], remote_node_ids: set[str], nodes_module: Any, ) -> None: """Replicate safe object producers instead of forcing provider cycles together.""" for _iteration in range(len(prompt) + 1): consumers = _build_consumer_map(prompt) tentative_groups = _remote_component_partition_groups( prompt, remote_node_ids, consumers, nodes_module, preserve_nontransportable_affinity=False, ) boundaries = _cross_group_replicable_boundaries( prompt=prompt, remote_node_ids=remote_node_ids, component_groups=tentative_groups, nodes_module=nodes_module, ) replica_source_by_group: dict[tuple[str, str], str] = {} changed = False for ( source_node_id, output_index, target_node_id, input_name, group_id, ) in boundaries: dependency_node_ids = _remote_dependency_closure(prompt, source_node_id) if not _can_replicate_remote_dependency_closure( prompt=prompt, source_node_id=source_node_id, dependency_node_ids=dependency_node_ids, remote_node_ids=remote_node_ids, ): continue replica_key = (source_node_id, group_id) replica_source_node_id = replica_source_by_group.get(replica_key) if replica_source_node_id is None: replica_source_node_id = _install_remote_dependency_replica( prompt=prompt, remote_node_ids=remote_node_ids, dependency_node_ids=dependency_node_ids, source_node_id=source_node_id, target_group_id=group_id, ) replica_source_by_group[replica_key] = replica_source_node_id logger.info( "Replicated non-transportable producer %s and dependencies %s " "for remote component group %s.", source_node_id, sorted(dependency_node_ids), group_id, ) prompt[target_node_id]["inputs"][input_name] = [ replica_source_node_id, output_index, ] changed = True if not changed: return raise ModalPromptValidationError( "Unable to stabilize provider-aware non-transportable dependency replicas." ) def _workflow_visible_remote_node_ids(node_ids: Iterable[str]) -> list[str]: """Exclude internal dependency replicas from workflow-facing rewrite metadata.""" return [ str(node_id) for node_id in node_ids if not str(node_id).startswith(_REMOTE_REPLICA_NODE_PREFIX) ] 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.""" _replicate_safe_nontransportable_provider_boundaries( prompt=prompt, remote_node_ids=remote_node_ids, nodes_module=nodes_module, ) consumers = _build_consumer_map(prompt) components = _build_remote_components( prompt, remote_node_ids, consumers, nodes_module ) return [ _build_component_plan( component, prompt, consumers, remote_node_ids, nodes_module ) for component in components ] def _mark_remote_to_remote_session_boundaries( prompt: dict[str, Any], components: list[RemoteComponentPlan], nodes_module: Any, assignments_by_component_id: Mapping[str, ExecutionAssignment], ) -> set[str]: """Keep only same-environment remote edges in provider-local session storage.""" consumers = _build_consumer_map(prompt) component_id_by_node_id = { node_id: component.representative_node_id for component in components for node_id in component.node_ids } session_sources: set[LinkedOutputRef] = set() for component in components: for boundary_output in component.boundary_outputs: output_consumers = consumers.get(boundary_output.source, []) if not output_consumers: continue remote_consumers = [ consumer for consumer in output_consumers if consumer.node_id in component_id_by_node_id ] if not remote_consumers: continue producer_assignment = assignments_by_component_id[ component.representative_node_id ] consumer_component_ids = { component_id_by_node_id[consumer.node_id] for consumer in remote_consumers } cross_environment_consumers = sorted( consumer_component_id for consumer_component_id in consumer_component_ids if assignments_by_component_id[consumer_component_id].environment_id != producer_assignment.environment_id ) if cross_environment_consumers: logger.info( "Materializing remote boundary output through ComfyUI across " "execution environments source=%s:%d producer=%s environment=%s " "consumer_components=%s.", boundary_output.source.node_id, boundary_output.source.output_index, component.representative_node_id, producer_assignment.environment_id, cross_environment_consumers, ) continue ( non_returning_local_consumers, returning_local_consumers, ) = _non_returning_local_output_consumers( prompt=prompt, source=boundary_output.source, remote_node_ids=set(component_id_by_node_id), consumers=consumers, nodes_module=nodes_module, ) if returning_local_consumers: logger.info( "Materializing remote boundary output normally because local consumers %s later return to remote execution source=%s:%d.", [consumer.node_id for consumer in returning_local_consumers], boundary_output.source.node_id, boundary_output.source.output_index, ) continue if boundary_output.is_list and _is_transportable_output_type( boundary_output.io_type ): logger.info( "Materializing transportable list boundary output through ComfyUI " "to preserve scheduler item semantics source=%s:%d io_type=%s " "producer_component=%s consumer_components=%s.", boundary_output.source.node_id, boundary_output.source.output_index, boundary_output.io_type, component.representative_node_id, sorted(consumer_component_ids), ) continue boundary_output.session_output = True boundary_output.session_consumer_node_ids = sorted( {consumer.node_id for consumer in remote_consumers} ) if non_returning_local_consumers: materializer_node_id = ( f"__ModalLocalBridgeMaterializer__" f"{boundary_output.source.node_id}_" f"{boundary_output.source.output_index}" ) reserved_node_ids = { output.local_materializer_node_id for planned_component in components for output in planned_component.boundary_outputs if output.local_materializer_node_id is not None } while ( materializer_node_id in prompt or materializer_node_id in reserved_node_ids ): materializer_node_id = f"{materializer_node_id}_proxy" boundary_output.local_materializer_node_id = materializer_node_id boundary_output.local_materializer_consumer_node_ids = sorted( {consumer.node_id for consumer in non_returning_local_consumers} ) boundary_output.preview_target_node_ids = [] session_sources.add(boundary_output.source) logger.info( "Keeping remote boundary output in Modal storage source=%s:%d io_type=%s producer_component=%s consumer_components=%s local_materializer=%s local_consumers=%s.", boundary_output.source.node_id, boundary_output.source.output_index, boundary_output.io_type, component.representative_node_id, sorted( { component_id_by_node_id[consumer.node_id] for consumer in output_consumers if consumer.node_id in component_id_by_node_id } ), boundary_output.local_materializer_node_id, boundary_output.local_materializer_consumer_node_ids, ) return _remote_session_component_ids( components=components, session_sources=session_sources, ) def _remote_session_component_ids( *, components: list[RemoteComponentPlan], session_sources: set[LinkedOutputRef], ) -> set[str]: """Return components that produce or consume Modal-backed boundary refs.""" return { component.representative_node_id for component in components if any(output.session_output for output in component.boundary_outputs) or any( boundary_input.source in session_sources for boundary_input in component.boundary_inputs ) } def _boundary_output_payload( boundary_output: BoundaryOutputSpec, *, mapped_output: bool | None = None, ) -> dict[str, Any]: """Serialize one component boundary output for a remote payload.""" payload = { "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, "preview_target_node_ids": list(boundary_output.preview_target_node_ids), } if boundary_output.session_output: payload["session_output"] = True if mapped_output is not None: payload["mapped_output"] = mapped_output if mapped_output: payload["scheduler_is_list"] = True return payload def _proxy_boundary_output_is_list(boundary_output: Mapping[str, Any]) -> bool: """Return whether ComfyUI should expose one proxy boundary as a list output.""" return bool( boundary_output.get("is_list", False) or boundary_output.get("scheduler_is_list", False) ) def _implicitly_mapped_boundary_output_sources( *, component: RemoteComponentPlan, original_prompt: dict[str, Any], rewritten_prompt: dict[str, Any], nodes_module: Any, ) -> set[LinkedOutputRef]: """Return component outputs transitively driven by scheduler-list proxy inputs.""" mapped_target_node_ids: set[str] = set() list_boundary_input_names: set[str] = set() for boundary_input in component.boundary_inputs: input_is_scheduler_list = False for target in boundary_input.targets: target_prompt_node = rewritten_prompt.get(target.node_id) if target_prompt_node is None: continue current_input_value = (target_prompt_node.get("inputs") or {}).get( target.input_name ) if not _is_link(current_input_value): continue if not _remote_output_is_list( prompt=rewritten_prompt, node_id=str(current_input_value[0]), output_index=int(current_input_value[1]), nodes_module=nodes_module, ): continue input_is_scheduler_list = True mapped_target_node_ids.add(target.node_id) if input_is_scheduler_list: list_boundary_input_names.add(boundary_input.proxy_input_name) if not mapped_target_node_ids: return set() consumers = _build_consumer_map(original_prompt) mapped_node_ids = _component_downstream_closure( seed_node_ids=mapped_target_node_ids, component_node_id_set=set(component.node_ids), consumers=consumers, ) mapped_output_sources = { boundary_output.source for boundary_output in component.boundary_outputs if boundary_output.source.node_id in mapped_node_ids } logger.info( "Propagating scheduler-list metadata through remote component %s from " "boundary inputs=%s to outputs=%s.", component.representative_node_id, sorted(list_boundary_input_names), sorted( f"{source.node_id}:{source.output_index}" for source in mapped_output_sources ), ) return mapped_output_sources def _mark_payload_scheduler_list_outputs( payload: dict[str, Any], scheduler_list_output_sources: set[LinkedOutputRef], ) -> None: """Mark matching outputs across a component payload and any split phase payloads.""" if not scheduler_list_output_sources: return boundary_outputs = payload.get("boundary_outputs") if isinstance(boundary_outputs, list): for boundary_output in boundary_outputs: if not isinstance(boundary_output, dict): continue source = LinkedOutputRef( node_id=str(boundary_output.get("node_id")), output_index=int(boundary_output.get("output_index", -1)), ) if source in scheduler_list_output_sources: boundary_output["scheduler_is_list"] = True for phase_name in ("static_phase", "mapped_phase"): phase_payload = payload.get(phase_name) if isinstance(phase_payload, dict): _mark_payload_scheduler_list_outputs( phase_payload, scheduler_list_output_sources, ) split_proxy_payloads = payload.get("split_proxy_payloads") if isinstance(split_proxy_payloads, dict): for split_payload in split_proxy_payloads.values(): if isinstance(split_payload, dict): _mark_payload_scheduler_list_outputs( split_payload, scheduler_list_output_sources, ) elif isinstance(split_proxy_payloads, list): for split_payload in split_proxy_payloads: if isinstance(split_payload, dict): _mark_payload_scheduler_list_outputs( split_payload, scheduler_list_output_sources, ) def _mapped_boundary_origin_io_type( prompt: dict[str, Any], boundary_input: BoundaryInputSpec, nodes_module: Any, ) -> str | None: """Return the effective io_type for one mapped boundary, unwrapping local ModalMapInput markers.""" source_prompt_node = prompt.get(boundary_input.source.node_id) if source_prompt_node is None: return boundary_input.io_type if str(source_prompt_node.get("class_type")) != MODAL_MAP_INPUT_NODE_ID: return _remote_output_io_type( prompt=prompt, node_id=boundary_input.source.node_id, output_index=boundary_input.source.output_index, nodes_module=nodes_module, ) mapped_value = (source_prompt_node.get("inputs") or {}).get("value") if not _is_link(mapped_value): return boundary_input.io_type return _remote_output_io_type( prompt=prompt, node_id=str(mapped_value[0]), output_index=int(mapped_value[1]), nodes_module=nodes_module, ) 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 component-output 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 node {local_consumer.node_id} ({local_consumer_class_type}) outside that " f"component at input '{local_consumer.input_name}', which cannot cross the " "current component boundary. " "Current ComfyUI-Modal transport only supports JSON-compatible values, bytes, " "and media or tensor-like outputs such as VIDEO, AUDIO, IMAGE, MASK, LATENT, " "SIGMAS, 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 component-input transport validation error.""" return ( "Remote node " f"{target.node_id} ({target_class_type}) input '{target.input_name}' " f"depends on node {source.node_id} ({source_class_type}) outside its component, " f"using output index {source.output_index} of type '{io_type}', which cannot " "cross the current component boundary. Current ComfyUI-Modal transport only " "supports JSON-compatible values, bytes, and media or tensor-like outputs such " "as VIDEO, AUDIO, IMAGE, MASK, LATENT, SIGMAS, 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, request_cache: AssetSyncRequestCache, status_callback: Any | None = None, ) -> 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", {})), status_callback=status_callback, request_cache=request_cache, ) 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 _deduplicate_synced_assets(synced_assets: list[SyncedAsset]) -> list[SyncedAsset]: """Return one request summary record per content-addressed remote path.""" unique_assets_by_remote_path: dict[str, SyncedAsset] = {} for synced_asset in synced_assets: unique_assets_by_remote_path.setdefault(synced_asset.remote_path, synced_asset) return list(unique_assets_by_remote_path.values()) def _build_component_payload( component: RemoteComponentPlan, component_prompt: dict[str, Any], signature_prompt: dict[str, Any], extra_data: dict[str, Any] | None, settings: ModalSyncSettings, requires_volume_reload: bool, volume_reload_marker: str | None, custom_nodes_bundle: SyncedAsset | None, uploaded_volume_paths: list[str], terminate_container_on_error: bool, nodes_module: Any, remote_session: dict[str, Any] | None = None, ) -> dict[str, Any]: """Build the serialized execution payload for one remote component.""" prompt_id = (extra_data or {}).get("prompt_id") custom_nodes_bundle_path = ( custom_nodes_bundle.remote_path if custom_nodes_bundle is not None else None ) def remote_worker_affinity_group(component_node_ids: list[str]) -> str: """Return the worker-pool group required by one remote component phase.""" class_types = { str((component_prompt.get(node_id) or {}).get("class_type") or "") for node_id in component_node_ids } return "llm" if "ModalLLM" in class_types else "comfy" def build_subgraph_payload( *, component_id: str, component_node_ids: list[str], boundary_inputs: list[BoundaryInputSpec], boundary_outputs: list[dict[str, Any]], execute_node_ids: list[str], remote_session: dict[str, Any] | None = None, clear_remote_session: bool = False, mapped_progress_display_node_id: str | None = None, ) -> dict[str, Any]: """Build one ordinary subgraph payload for a proxy node.""" payload = { "payload_kind": "subgraph", "component_id": component_id, "prompt_id": prompt_id, "modal_gpu": settings.modal_gpu, "remote_worker_affinity_group": remote_worker_affinity_group( component_node_ids ), "component_node_ids": list(component_node_ids), "subgraph_prompt": _subset_component_prompt( component_prompt, component_node_ids ), "boundary_inputs": _serialize_boundary_input_specs( boundary_inputs, signature_prompt=signature_prompt, ), "boundary_outputs": copy.deepcopy(boundary_outputs), "execute_node_ids": list(execute_node_ids), "extra_data": copy.deepcopy(extra_data or {}), "requires_volume_reload": requires_volume_reload, "volume_reload_marker": volume_reload_marker, "uploaded_volume_paths": list(uploaded_volume_paths), "terminate_container_on_error": terminate_container_on_error, "custom_nodes_bundle": custom_nodes_bundle_path, } if remote_session is not None: payload["remote_session"] = copy.deepcopy(remote_session) if clear_remote_session: payload["clear_remote_session"] = True if mapped_progress_display_node_id is not None: payload["mapped_progress_display_node_id"] = mapped_progress_display_node_id return payload def build_phase_payloads_for_transportable_splits() -> list[dict[str, Any]] | None: """Return ordered phases for local feedback or parallel local fanout.""" if len(component.execute_node_ids) <= 1: return None has_local_reentry_dependency = _component_has_local_reentry_dependency( prompt=signature_prompt, component=component, ) has_parallel_local_remote_fanout = _component_has_parallel_local_remote_fanout( component ) if not has_local_reentry_dependency and not has_parallel_local_remote_fanout: logger.info( "Keeping remote component %s as one proxy because execute targets %s " "have neither a local re-entry dependency nor parallel local/remote fanout.", component.representative_node_id, component.execute_node_ids, ) return None if component.local_tap_node_ids: logger.info( "Allowing remote component %s with local tap nodes %s to split because it has a local re-entry dependency.", component.representative_node_id, component.local_tap_node_ids, ) if has_parallel_local_remote_fanout: logger.info( "Forcing ordered phases for remote component %s because a remote output " "feeds both a non-returning local branch and later remote execution.", component.representative_node_id, ) component_node_id_set = set(component.node_ids) topological_node_ids = _subgraph_topological_node_order( component_prompt, component_node_id_set ) remaining_node_ids = set(component.node_ids) remaining_execute_node_ids = [ node_id for node_id in topological_node_ids if node_id in set(component.execute_node_ids) ] remaining_execute_node_ids = _order_execute_node_ids_for_transportable_splits( prompt=signature_prompt, component_prompt=component_prompt, component_node_ids=component_node_id_set, execute_node_ids=remaining_execute_node_ids, ) if len(remaining_execute_node_ids) <= 1: return None phase_payloads: list[dict[str, Any]] = [] produced_outputs_by_source: dict[LinkedOutputRef, ProducedPhaseOutputSpec] = {} local_boundary_outputs_by_source = { boundary_output.source: boundary_output for boundary_output in component.boundary_outputs } bridge_output_counter = 0 while remaining_execute_node_ids: target_node_id = remaining_execute_node_ids[0] phase_node_ids = sorted( _component_upstream_closure( prompt=component_prompt, seed_node_ids={target_node_id}, candidate_node_ids=remaining_node_ids, ) ) phase_node_id_set = set(phase_node_ids) if not phase_node_ids: raise ModalPromptValidationError( f"Unable to derive split phase nodes for remote component {component.representative_node_id}." ) phase_boundary_inputs = _filter_boundary_inputs_for_node_ids( component.boundary_inputs, phase_node_id_set, ) phase_boundary_inputs_by_name = { boundary_input.proxy_input_name: boundary_input for boundary_input in phase_boundary_inputs } for phase_node_id in phase_node_ids: prompt_node = component_prompt.get(phase_node_id) if prompt_node is None: continue 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]), ) if source.node_id in phase_node_id_set: continue produced_output = produced_outputs_by_source.get(source) if produced_output is None: continue boundary_input = phase_boundary_inputs_by_name.get( produced_output.proxy_output_name ) if boundary_input is None: boundary_input = BoundaryInputSpec( proxy_input_name=produced_output.proxy_output_name, source=source, io_type=produced_output.io_type, ) phase_boundary_inputs.append(boundary_input) phase_boundary_inputs_by_name[ boundary_input.proxy_input_name ] = boundary_input boundary_input.targets.append( InputTarget(node_id=phase_node_id, input_name=str(input_name)) ) phase_boundary_outputs: list[dict[str, Any]] = [] phase_output_names_by_source: dict[LinkedOutputRef, str] = {} for boundary_output in _filter_boundary_outputs_for_node_ids( component.boundary_outputs, phase_node_id_set, ): phase_boundary_outputs.append(_boundary_output_payload(boundary_output)) phase_output_names_by_source[ boundary_output.source ] = boundary_output.proxy_output_name pending_node_ids = remaining_node_ids - phase_node_id_set for pending_node_id in sorted(pending_node_ids): prompt_node = component_prompt.get(pending_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 source = LinkedOutputRef( node_id=str(input_value[0]), output_index=int(input_value[1]), ) if ( source.node_id not in phase_node_id_set or source in produced_outputs_by_source ): continue local_boundary_output = local_boundary_outputs_by_source.get(source) io_type = ( local_boundary_output.io_type if local_boundary_output is not None else str( _remote_output_io_type( prompt=component_prompt, node_id=source.node_id, output_index=source.output_index, nodes_module=nodes_module, ) or "*" ) ) is_list = ( local_boundary_output.is_list if local_boundary_output is not None else _remote_output_is_list( prompt=component_prompt, node_id=source.node_id, output_index=source.output_index, nodes_module=nodes_module, ) ) proxy_output_name = phase_output_names_by_source.get(source) if proxy_output_name is None: proxy_output_name = f"phase_bridge_{bridge_output_counter}" bridge_output_counter += 1 phase_boundary_outputs.append( { "proxy_output_name": proxy_output_name, "node_id": source.node_id, "output_index": source.output_index, "io_type": io_type, "is_list": is_list, "preview_target_node_ids": [], "session_output": True, } ) phase_output_names_by_source[source] = proxy_output_name produced_outputs_by_source[source] = ProducedPhaseOutputSpec( proxy_output_name=proxy_output_name, source=source, io_type=io_type, is_list=is_list, session_output=True, ) phase_execute_node_ids = [ node_id for node_id in component.execute_node_ids if node_id in phase_node_id_set ] phase_payloads.append( build_subgraph_payload( component_id=str(target_node_id), component_node_ids=phase_node_ids, boundary_inputs=phase_boundary_inputs, boundary_outputs=phase_boundary_outputs, execute_node_ids=phase_execute_node_ids, ) ) remaining_node_ids -= phase_node_id_set remaining_execute_node_ids = [ node_id for node_id in remaining_execute_node_ids if node_id not in phase_node_id_set ] has_session_bridges = any( bool(boundary_output.get("session_output")) for phase_payload in phase_payloads for boundary_output in phase_payload.get("boundary_outputs", []) ) if not phase_payloads or len(phase_payloads) <= 1: return None active_remote_session = remote_session if active_remote_session is None and has_session_bridges: active_remote_session = RemoteSessionHandle( session_id=uuid.uuid4().hex, prompt_id=(str(prompt_id) if prompt_id is not None else None), owner_component_id=component.representative_node_id, ).to_payload() if active_remote_session is not None: for phase_index, phase_payload in enumerate(phase_payloads): phase_payload["remote_session"] = copy.deepcopy(active_remote_session) if phase_index == len(phase_payloads) - 1: phase_payload["clear_remote_session"] = True logger.info( "Split ordinary remote component %s into ordered phases: %s", component.representative_node_id, [ { "component_id": phase_payload["component_id"], "component_node_ids": phase_payload["component_node_ids"], "execute_node_ids": phase_payload["execute_node_ids"], } for phase_payload in phase_payloads ], ) return phase_payloads split_phase_payloads = build_phase_payloads_for_transportable_splits() if split_phase_payloads is not None: return _attach_snapshot_profile_key( {"split_proxy_payloads": split_phase_payloads}, settings, ) payload = { "payload_kind": "mapped_subgraph" if component.mapped_boundary_input_name else "subgraph", "component_id": component.representative_node_id, "prompt_id": prompt_id, "modal_gpu": settings.modal_gpu, "remote_worker_affinity_group": remote_worker_affinity_group( list(component.node_ids) ), "component_node_ids": list(component.node_ids), "subgraph_prompt": component_prompt, "boundary_inputs": _serialize_boundary_input_specs( component.boundary_inputs, signature_prompt=signature_prompt, ), "boundary_outputs": [ _boundary_output_payload( boundary_output, mapped_output=( boundary_output.source.node_id in set(component.mapped_node_ids) if component.mapped_boundary_input_name else None ), ) for boundary_output in component.boundary_outputs ], "execute_node_ids": list(component.execute_node_ids), "mapped_execute_node_ids": list(component.mapped_execute_node_ids), "static_execute_node_ids": list(component.static_execute_node_ids), "extra_data": copy.deepcopy(extra_data or {}), "requires_volume_reload": requires_volume_reload, "volume_reload_marker": volume_reload_marker, "uploaded_volume_paths": list(uploaded_volume_paths), "terminate_container_on_error": terminate_container_on_error, "custom_nodes_bundle": custom_nodes_bundle_path, "mapped_input": ( { "proxy_input_name": component.mapped_boundary_input_name, "io_type": str(component.mapped_boundary_input_io_type or "*"), } if component.mapped_boundary_input_name else None ), } if remote_session is not None: payload["remote_session"] = copy.deepcopy(remote_session) payload["clear_remote_session"] = True logger.info( "Built remote payload for component %s: boundary_inputs=%d boundary_outputs=%d execute_nodes=%s custom_nodes_bundle=%s", component.representative_node_id, len(payload["boundary_inputs"]), len(payload["boundary_outputs"]), payload["execute_node_ids"], payload["custom_nodes_bundle"], ) logger.info( "Remote payload for component %s requires_volume_reload=%s volume_reload_marker=%s", component.representative_node_id, requires_volume_reload, volume_reload_marker, ) if component.mapped_boundary_input_name: static_node_id_set = set(component.static_node_ids) mapped_node_id_set = set(component.mapped_node_ids) static_boundary_inputs = _filter_boundary_inputs_for_node_ids( component.boundary_inputs, static_node_id_set, ) mapped_boundary_inputs = _filter_boundary_inputs_for_node_ids( component.boundary_inputs, mapped_node_id_set, ) static_boundary_outputs = _filter_boundary_outputs_for_node_ids( component.boundary_outputs, static_node_id_set, ) mapped_boundary_outputs = _filter_boundary_outputs_for_node_ids( component.boundary_outputs, mapped_node_id_set, ) static_bridge_outputs = [ { "proxy_output_name": boundary_spec.proxy_name, "node_id": boundary_spec.source.node_id, "output_index": boundary_spec.source.output_index, "io_type": boundary_spec.io_type, "is_list": boundary_spec.is_list, "preview_target_node_ids": [], "session_output": True, } for boundary_spec in component.static_to_mapped_boundaries ] payload["static_to_mapped_boundaries"] = [ { "proxy_name": boundary_spec.proxy_name, "node_id": boundary_spec.source.node_id, "output_index": boundary_spec.source.output_index, "io_type": boundary_spec.io_type, "is_list": boundary_spec.is_list, "targets": [ {"node_id": target.node_id, "input_name": target.input_name} for target in boundary_spec.targets ], } for boundary_spec in component.static_to_mapped_boundaries ] payload["static_phase"] = { "component_node_ids": list(component.static_node_ids), "subgraph_prompt": _subset_component_prompt( component_prompt, component.static_node_ids ), "boundary_inputs": _serialize_boundary_input_specs( static_boundary_inputs, signature_prompt=signature_prompt, ), "boundary_outputs": [ _boundary_output_payload(boundary_output) for boundary_output in static_boundary_outputs ] + static_bridge_outputs, "execute_node_ids": list(component.static_execute_node_ids), } payload["mapped_phase"] = { "component_node_ids": list(component.mapped_node_ids), "subgraph_prompt": _subset_component_prompt( component_prompt, component.mapped_node_ids ), "boundary_inputs": _serialize_boundary_input_specs( mapped_boundary_inputs + [ BoundaryInputSpec( proxy_input_name=boundary_spec.proxy_name, source=boundary_spec.source, io_type=boundary_spec.io_type, targets=list(boundary_spec.targets), ) for boundary_spec in component.static_to_mapped_boundaries ], signature_prompt=signature_prompt, ), "boundary_outputs": [ _boundary_output_payload(boundary_output, mapped_output=True) for boundary_output in mapped_boundary_outputs ], "execute_node_ids": list(component.mapped_execute_node_ids), } if not component.static_node_ids: return _attach_snapshot_profile_key(payload, settings) if remote_session is None: remote_session = RemoteSessionHandle( session_id=uuid.uuid4().hex, prompt_id=(str(prompt_id) if prompt_id is not None else None), owner_component_id=component.representative_node_id, ).to_payload() logger.info( "Split hybrid component %s into static nodes=%s and mapped nodes=%s using remote_session session_id=%s with %d static bridge outputs.", component.representative_node_id, component.static_node_ids, component.mapped_node_ids, remote_session["session_id"], len(static_bridge_outputs), ) payload = { "split_proxy_payloads": { "static": build_subgraph_payload( component_id=component.static_node_ids[0], component_node_ids=list(component.static_node_ids), boundary_inputs=static_boundary_inputs, boundary_outputs=[ _boundary_output_payload(boundary_output) for boundary_output in static_boundary_outputs ] + static_bridge_outputs, execute_node_ids=list(component.static_execute_node_ids), remote_session=remote_session, ), "mapped": build_subgraph_payload( component_id=f"{component.representative_node_id}__mapped", component_node_ids=list(component.mapped_node_ids), boundary_inputs=mapped_boundary_inputs + [ BoundaryInputSpec( proxy_input_name=boundary_spec.proxy_name, source=boundary_spec.source, io_type=boundary_spec.io_type, targets=list(boundary_spec.targets), ) for boundary_spec in component.static_to_mapped_boundaries ], boundary_outputs=[ _boundary_output_payload(boundary_output) for boundary_output in mapped_boundary_outputs ], execute_node_ids=list(component.mapped_execute_node_ids), remote_session=remote_session, clear_remote_session=True, mapped_progress_display_node_id=component.static_node_ids[0], ), } } payload["split_proxy_payloads"]["mapped"]["static_to_mapped_boundaries"] = [ { "proxy_name": boundary_spec.proxy_name, "node_id": boundary_spec.source.node_id, "output_index": boundary_spec.source.output_index, "io_type": boundary_spec.io_type, "is_list": boundary_spec.is_list, "targets": [ {"node_id": target.node_id, "input_name": target.input_name} for target in boundary_spec.targets ], } for boundary_spec in component.static_to_mapped_boundaries ] payload["split_proxy_payloads"]["mapped"]["static_phase"] = { "component_node_ids": list(component.static_node_ids), "subgraph_prompt": _subset_component_prompt( component_prompt, component.static_node_ids ), "boundary_inputs": _serialize_boundary_input_specs( static_boundary_inputs, signature_prompt=signature_prompt, ), "boundary_outputs": copy.deepcopy(static_bridge_outputs), "execute_node_ids": list( dict.fromkeys( boundary_spec.source.node_id for boundary_spec in component.static_to_mapped_boundaries ) ), } return _attach_snapshot_profile_key(payload, settings) def _component_uploaded_volume_paths( *, component_prompt: dict[str, Any], synced_assets: list[SyncedAsset], custom_nodes_bundle: SyncedAsset | None, ) -> list[str]: """Return newly uploaded mounted-volume paths that this component can actually reference.""" referenced_paths: set[str] = set() for prompt_node in component_prompt.values(): if not isinstance(prompt_node, dict): continue inputs = prompt_node.get("inputs", {}) if not isinstance(inputs, dict): continue for input_value in inputs.values(): for candidate_path in _iter_payload_input_strings(input_value): if isinstance(candidate_path, str) and candidate_path.startswith("/"): referenced_paths.add(candidate_path) uploaded_paths = { asset.remote_path for asset in synced_assets if asset.uploaded and asset.remote_path in referenced_paths } if custom_nodes_bundle is not None and custom_nodes_bundle.uploaded: uploaded_paths.add(custom_nodes_bundle.remote_path) return sorted(uploaded_paths) def _rewrite_component_into_proxy( component: RemoteComponentPlan, rewritten_prompt: dict[str, Any], payload: dict[str, Any], nodes_module: Any, ) -> list[str]: """Replace a remote component with a single proxy node in the prompt.""" def contains_output_node(node_ids: list[str]) -> bool: """Return whether one node subset contains an output node.""" for node_id in node_ids: prompt_node = rewritten_prompt.get(node_id) if prompt_node is None: continue node_class = nodes_module.NODE_CLASS_MAPPINGS.get( str(prompt_node["class_type"]) ) if node_class is not None and getattr(node_class, "OUTPUT_NODE", False): return True return False def proxy_inputs_for_boundary_inputs( boundary_inputs: list[dict[str, Any]] ) -> dict[str, Any]: """Resolve one proxy input mapping from the current prompt graph.""" proxy_inputs: dict[str, Any] = {} for boundary_input in boundary_inputs: current_input_value: Any = None for target in boundary_input.get("targets", []): target_prompt_node = rewritten_prompt.get(str(target["node_id"])) if target_prompt_node is None: continue target_input_value = (target_prompt_node.get("inputs") or {}).get( str(target["input_name"]) ) if _is_link(target_input_value): current_input_value = list(target_input_value) break if current_input_value is None: raise ModalPromptValidationError( "Unable to resolve proxy boundary input wiring while rewriting split Modal proxies." ) proxy_inputs[str(boundary_input["proxy_input_name"])] = current_input_value return proxy_inputs def register_proxy_node( *, prompt_node_id: str, payload_mapping: dict[str, Any], proxy_inputs: dict[str, Any], meta: dict[str, Any], is_output_node: bool, ) -> None: """Insert one dynamic proxy node into the rewritten prompt.""" boundary_outputs = list(payload_mapping.get("boundary_outputs", [])) proxy_node_id = ensure_modal_component_proxy_node_registered( output_types=tuple(str(output["io_type"]) for output in boundary_outputs), output_names=tuple( str(output["proxy_output_name"]) for output in boundary_outputs ), output_is_list=tuple( _proxy_boundary_output_is_list(output) for output in boundary_outputs ), nodes_module=nodes_module, is_output_node=is_output_node, include_completion_output=True, ) proxy_inputs["original_node_data"] = register_cache_friendly_proxy_payload( prompt_node_id, payload_mapping, ) rewritten_prompt[prompt_node_id] = { "class_type": proxy_node_id, "inputs": proxy_inputs, "_meta": copy.deepcopy(meta), } split_proxy_payloads = payload.get("split_proxy_payloads") component_node_id_set = set(component.node_ids) if isinstance(split_proxy_payloads, list): phase_payloads = [dict(phase_payload) for phase_payload in split_proxy_payloads] phase_proxy_node_ids: list[str] = [] produced_output_indices_by_name: dict[str, list[Any]] = {} replacement_output_indices: dict[LinkedOutputRef, list[Any]] = {} materializer_output_by_consumer_and_source: dict[ tuple[str, LinkedOutputRef], list[Any] ] = {} proxy_output_by_consumer_and_source: dict[ tuple[str, LinkedOutputRef], list[Any] ] = {} materializer_proxy_output_by_node_id: dict[str, list[Any]] = {} boundary_output_specs_by_source = { boundary_output.source: boundary_output for boundary_output in component.boundary_outputs } component_proxy_node_ids: set[str] = set() phase_proxy_inputs_by_node_id: dict[str, dict[str, Any]] = {} phase_proxy_meta_by_node_id: dict[str, dict[str, Any]] = {} if any( spec.local_materializer_node_id is not None for spec in component.boundary_outputs ): ensure_modal_local_bridge_materializer_registered(nodes_module) for phase_payload in phase_payloads: phase_proxy_node_id = str(phase_payload["component_id"]) while ( phase_proxy_node_id in rewritten_prompt and phase_proxy_node_id not in component_node_id_set ): phase_proxy_node_id = f"{phase_proxy_node_id}_proxy" phase_payload["component_id"] = phase_proxy_node_id phase_proxy_node_ids.append(phase_proxy_node_id) component_proxy_node_ids.add(phase_proxy_node_id) for phase_payload in phase_payloads: phase_proxy_node_id = str(phase_payload["component_id"]) phase_proxy_inputs = proxy_inputs_for_boundary_inputs( list(phase_payload["boundary_inputs"]) ) for boundary_input in phase_payload.get("boundary_inputs", []): proxy_input_name = str(boundary_input["proxy_input_name"]) produced_output_index = produced_output_indices_by_name.get( proxy_input_name ) if produced_output_index is None: continue phase_proxy_inputs[proxy_input_name] = list(produced_output_index) first_phase_node_id = str(phase_payload["component_node_ids"][0]) phase_proxy_meta = copy.deepcopy( rewritten_prompt[first_phase_node_id].get("_meta", {}) ) phase_proxy_inputs_by_node_id[phase_proxy_node_id] = phase_proxy_inputs phase_proxy_meta_by_node_id[phase_proxy_node_id] = phase_proxy_meta for output_index, boundary_output in enumerate( phase_payload.get("boundary_outputs", []) ): proxy_output_name = str(boundary_output["proxy_output_name"]) proxy_output = [phase_proxy_node_id, output_index] produced_output_indices_by_name[proxy_output_name] = proxy_output source = LinkedOutputRef( node_id=str(boundary_output["node_id"]), output_index=int(boundary_output["output_index"]), ) boundary_output_spec = boundary_output_specs_by_source.get(source) if boundary_output_spec is not None: for ( consumer_node_id ) in boundary_output_spec.session_consumer_node_ids: proxy_output_by_consumer_and_source[ (consumer_node_id, source) ] = proxy_output materializer_node_id = ( boundary_output_spec.local_materializer_node_id ) if materializer_node_id is not None: materializer_proxy_output_by_node_id[ materializer_node_id ] = proxy_output for ( consumer_node_id ) in boundary_output_spec.local_materializer_consumer_node_ids: materializer_output_by_consumer_and_source[ (consumer_node_id, source) ] = [materializer_node_id, 0] if bool(boundary_output.get("session_output")): continue replacement_output_indices[source] = proxy_output for node_id in component.node_ids: rewritten_prompt.pop(node_id, None) for phase_payload in phase_payloads: phase_proxy_node_id = str(phase_payload["component_id"]) register_proxy_node( prompt_node_id=phase_proxy_node_id, payload_mapping=phase_payload, proxy_inputs=phase_proxy_inputs_by_node_id[phase_proxy_node_id], meta=phase_proxy_meta_by_node_id[phase_proxy_node_id], is_output_node=contains_output_node( list(phase_payload["component_node_ids"]) ), ) for ( materializer_node_id, proxy_output, ) in materializer_proxy_output_by_node_id.items(): rewritten_prompt[materializer_node_id] = { "class_type": MODAL_LOCAL_BRIDGE_MATERIALIZER_NODE_ID, "inputs": {"bridge_ref": list(proxy_output)}, "_meta": {"title": "Modal Local Bridge Materializer"}, } if component.mapped_boundary_source_node_id is not None: mapped_node_id_set = set(component.mapped_node_ids) for phase_payload in phase_payloads: phase_node_ids = { str(node_id) for node_id in phase_payload.get("component_node_ids", []) } if not (phase_node_ids & mapped_node_id_set): continue register_modal_map_input_warmup_context( component.mapped_boundary_source_node_id, phase_payload, str(component.mapped_boundary_input_io_type or "*"), ) break for node_id, prompt_node in list(rewritten_prompt.items()): if node_id in component_proxy_node_ids: 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]) ) replacement_output = materializer_output_by_consumer_and_source.get( (node_id, source) ) if replacement_output is None: replacement_output = proxy_output_by_consumer_and_source.get( (node_id, source) ) if replacement_output is None: replacement_output = replacement_output_indices.get(source) if replacement_output is not None: prompt_node["inputs"][input_name] = list(replacement_output) logger.info( "Rewrote remote component %s into ordered proxies %s.", component.representative_node_id, phase_proxy_node_ids, ) return phase_proxy_node_ids if isinstance(split_proxy_payloads, dict): static_payload = dict(split_proxy_payloads["static"]) mapped_payload = dict(split_proxy_payloads["mapped"]) static_proxy_node_id = str(static_payload["component_id"]) mapped_proxy_node_id = str(mapped_payload["component_id"]) while ( mapped_proxy_node_id in rewritten_prompt and mapped_proxy_node_id not in component_node_id_set ): mapped_proxy_node_id = f"{mapped_proxy_node_id}_proxy" mapped_payload["component_id"] = mapped_proxy_node_id static_proxy_inputs = proxy_inputs_for_boundary_inputs( list(static_payload["boundary_inputs"]) ) static_boundary_outputs = list(static_payload["boundary_outputs"]) static_proxy_meta = copy.deepcopy( rewritten_prompt[static_proxy_node_id].get("_meta", {}) ) mapped_proxy_meta = copy.deepcopy( rewritten_prompt[component.mapped_node_ids[0]].get("_meta", {}) ) bridge_output_indices = { str(boundary_output["proxy_output_name"]): output_index for output_index, boundary_output in enumerate(static_boundary_outputs) if bool(boundary_output.get("session_output")) } mapped_proxy_inputs = proxy_inputs_for_boundary_inputs( list(mapped_payload["boundary_inputs"]) ) mapped_static_phase = mapped_payload.get("static_phase") if isinstance(mapped_static_phase, dict): for input_name, input_value in proxy_inputs_for_boundary_inputs( list(mapped_static_phase.get("boundary_inputs", [])) ).items(): mapped_proxy_inputs.setdefault(input_name, input_value) for boundary_input in mapped_payload.get("boundary_inputs", []): proxy_input_name = str(boundary_input["proxy_input_name"]) if proxy_input_name not in bridge_output_indices: continue mapped_proxy_inputs[proxy_input_name] = [ static_proxy_node_id, bridge_output_indices[proxy_input_name], ] replacement_output_indices = { LinkedOutputRef( node_id=str(boundary_output["node_id"]), output_index=int(boundary_output["output_index"]), ): [static_proxy_node_id, output_index] for output_index, boundary_output in enumerate(static_boundary_outputs) if not bool(boundary_output.get("session_output")) } replacement_output_indices.update( { LinkedOutputRef( node_id=str(boundary_output["node_id"]), output_index=int(boundary_output["output_index"]), ): [mapped_proxy_node_id, output_index] for output_index, boundary_output in enumerate( mapped_payload.get("boundary_outputs", []) ) } ) for node_id in component.node_ids: rewritten_prompt.pop(node_id, None) register_proxy_node( prompt_node_id=static_proxy_node_id, payload_mapping=static_payload, proxy_inputs=static_proxy_inputs, meta=static_proxy_meta, is_output_node=contains_output_node(component.static_node_ids), ) register_proxy_node( prompt_node_id=mapped_proxy_node_id, payload_mapping=mapped_payload, proxy_inputs=mapped_proxy_inputs, meta=mapped_proxy_meta, is_output_node=contains_output_node(component.mapped_node_ids), ) if component.mapped_boundary_source_node_id is not None: register_modal_map_input_warmup_context( component.mapped_boundary_source_node_id, mapped_payload, str(component.mapped_boundary_input_io_type or "*"), ) for node_id, prompt_node in list(rewritten_prompt.items()): if node_id in {static_proxy_node_id, mapped_proxy_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 in replacement_output_indices: prompt_node["inputs"][input_name] = list( replacement_output_indices[source] ) logger.info( "Rewrote hybrid remote component %s into static proxy %s and mapped proxy %s.", component.representative_node_id, static_proxy_node_id, mapped_proxy_node_id, ) return [static_proxy_node_id, mapped_proxy_node_id] boundary_outputs = list(payload.get("boundary_outputs", [])) proxy_node_id = ensure_modal_component_proxy_node_registered( output_types=tuple(str(output["io_type"]) for output in boundary_outputs), output_names=tuple( str(output["proxy_output_name"]) for output in boundary_outputs ), output_is_list=tuple( _proxy_boundary_output_is_list(output) for output in boundary_outputs ), nodes_module=nodes_module, is_output_node=component.contains_output_node, include_completion_output=True, ) representative_node_id = component.representative_node_id proxy_inputs = proxy_inputs_for_boundary_inputs( list(payload.get("boundary_inputs", [])) ) proxy_inputs["original_node_data"] = register_cache_friendly_proxy_payload( representative_node_id, payload, ) 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, } if component.mapped_boundary_source_node_id is not None: register_modal_map_input_warmup_context( component.mapped_boundary_source_node_id, payload, str(component.mapped_boundary_input_io_type or "*"), ) materializer_output_by_consumer_and_source: dict[ tuple[str, LinkedOutputRef], list[Any] ] = {} proxy_output_by_consumer_and_source: dict[ tuple[str, LinkedOutputRef], list[Any] ] = {} default_proxy_output_by_source: dict[LinkedOutputRef, list[Any]] = {} if any( spec.local_materializer_node_id is not None for spec in component.boundary_outputs ): ensure_modal_local_bridge_materializer_registered(nodes_module) for output_index, spec in enumerate(component.boundary_outputs): proxy_output = [representative_node_id, output_index] default_proxy_output_by_source.setdefault(spec.source, proxy_output) for consumer_node_id in spec.session_consumer_node_ids: proxy_output_by_consumer_and_source[ (consumer_node_id, spec.source) ] = proxy_output materializer_node_id = spec.local_materializer_node_id if materializer_node_id is None: continue rewritten_prompt[materializer_node_id] = { "class_type": MODAL_LOCAL_BRIDGE_MATERIALIZER_NODE_ID, "inputs": {"bridge_ref": proxy_output}, "_meta": {"title": "Modal Local Bridge Materializer"}, } for consumer_node_id in spec.local_materializer_consumer_node_ids: materializer_output_by_consumer_and_source[ (consumer_node_id, spec.source) ] = [materializer_node_id, 0] 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]) ) replacement_output = materializer_output_by_consumer_and_source.get( (node_id, source) ) if replacement_output is None: replacement_output = proxy_output_by_consumer_and_source.get( (node_id, source) ) if replacement_output is None: replacement_output = default_proxy_output_by_source.get(source) if replacement_output is not None: prompt_node["inputs"][input_name] = list(replacement_output) 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 _modal_component_completion_output_index( *, proxy_node_id: str, rewritten_prompt: dict[str, Any], nodes_module: Any, ) -> int: """Return the synthetic completion-token output index for one Modal proxy.""" prompt_node = rewritten_prompt.get(proxy_node_id) if prompt_node is None: raise ModalPromptValidationError( f"Modal proxy node {proxy_node_id!r} is missing before artifact finalization." ) class_type = str(prompt_node.get("class_type")) node_class = nodes_module.NODE_CLASS_MAPPINGS.get(class_type) if node_class is None: raise ModalPromptValidationError( f"Modal proxy class {class_type!r} is not registered before artifact finalization." ) _output_types, output_names, _output_is_list = _normalize_output_metadata( node_class ) try: return output_names.index(MODAL_COMPONENT_COMPLETION_OUTPUT_NAME) except ValueError as exc: raise ModalPromptValidationError( f"Modal proxy {proxy_node_id!r} does not expose its completion token." ) from exc def _attach_modal_artifact_finalizer( *, rewritten_prompt: dict[str, Any], remote_component_ids: list[str], nodes_module: Any, ) -> str: """Attach one internal output sink to every rewritten Modal component.""" if not remote_component_ids: raise ModalPromptValidationError( "Modal artifact finalization requires at least one remote component." ) if len(remote_component_ids) > MODAL_ARTIFACT_FINALIZER_MAX_COMPONENTS: raise ModalPromptValidationError( "Modal artifact finalization supports at most " f"{MODAL_ARTIFACT_FINALIZER_MAX_COMPONENTS} remote components per prompt." ) ensure_modal_artifact_finalizer_registered(nodes_module) finalizer_node_id = f"__{MODAL_ARTIFACT_FINALIZER_NODE_ID}__" while finalizer_node_id in rewritten_prompt: finalizer_node_id = f"{finalizer_node_id}_proxy" finalizer_inputs = { f"components.component_{component_index}": [ proxy_node_id, _modal_component_completion_output_index( proxy_node_id=proxy_node_id, rewritten_prompt=rewritten_prompt, nodes_module=nodes_module, ), ] for component_index, proxy_node_id in enumerate(remote_component_ids) } rewritten_prompt[finalizer_node_id] = { "class_type": MODAL_ARTIFACT_FINALIZER_NODE_ID, "inputs": finalizer_inputs, "_meta": {"title": "Modal Artifact Finalizer"}, } logger.info( "Attached Modal artifact finalizer %s to remote components %s.", finalizer_node_id, remote_component_ids, ) return finalizer_node_id def _nearest_downstream_remote_component_ids( *, rewritten_prompt: dict[str, Any], consumers: dict[LinkedOutputRef, list[InputTarget]], seed_targets: list[InputTarget], remote_component_id_set: set[str], nodes_module: Any, ) -> list[str]: """Return the first remote proxies reached along every downstream branch.""" nearest_remote_component_ids: set[str] = set() visited_node_ids: set[str] = set() pending_node_ids = deque(str(target.node_id) for target in seed_targets) while pending_node_ids: node_id = pending_node_ids.popleft() if node_id in visited_node_ids or node_id not in rewritten_prompt: continue visited_node_ids.add(node_id) if node_id in remote_component_id_set: nearest_remote_component_ids.add(node_id) continue for output_ref in _node_output_refs( rewritten_prompt, node_id, nodes_module, ): for downstream_target in consumers.get(output_ref, []): pending_node_ids.append(str(downstream_target.node_id)) return sorted(nearest_remote_component_ids) def _parallelize_non_returning_local_branches( *, rewritten_prompt: dict[str, Any], remote_component_ids: list[str], nodes_module: Any, ) -> list[str]: """Release local-only taps once the nearest remote continuations are in flight.""" remote_component_id_set = set(remote_component_ids) if len(remote_component_id_set) < 2: return [] consumers = _build_consumer_map(rewritten_prompt) ensure_modal_parallel_local_passthrough_registered(nodes_module) passthrough_node_ids: list[str] = [] dispatch_group_id = uuid.uuid4().hex for component_id in remote_component_ids: embedded_payload = ( rewritten_prompt.get(component_id, {}) .get("inputs", {}) .get("original_node_data") ) if not isinstance(embedded_payload, Mapping): continue payload_prompt_id = embedded_payload.get("prompt_id") if payload_prompt_id is not None and str(payload_prompt_id).strip(): dispatch_group_id = str(payload_prompt_id) break for source_component_id in remote_component_ids: for source in _node_output_refs( rewritten_prompt, source_component_id, nodes_module, ): output_consumers = list(consumers.get(source, [])) if not output_consumers: continue downstream_remote_component_ids = _nearest_downstream_remote_component_ids( rewritten_prompt=rewritten_prompt, consumers=consumers, seed_targets=output_consumers, remote_component_id_set=remote_component_id_set, nodes_module=nodes_module, ) downstream_remote_component_ids = [ component_id for component_id in downstream_remote_component_ids if component_id != source_component_id ] if not downstream_remote_component_ids: continue local_only_consumers: list[InputTarget] = [] for target in output_consumers: if target.node_id in remote_component_id_set: continue branch_node_ids = _downstream_node_ids_from_targets( prompt=rewritten_prompt, consumers=consumers, seed_targets=[target], nodes_module=nodes_module, ) if branch_node_ids & remote_component_id_set: continue local_only_consumers.append(target) if not local_only_consumers: continue passthrough_node_id = ( f"__{MODAL_PARALLEL_LOCAL_PASSTHROUGH_NODE_ID}__" f"{len(passthrough_node_ids)}" ) while passthrough_node_id in rewritten_prompt: passthrough_node_id = f"{passthrough_node_id}_proxy" passthrough_inputs: dict[str, Any] = { "value": [source.node_id, source.output_index], "dispatch_context": { "dispatch_group_id": dispatch_group_id, "component_ids": downstream_remote_component_ids, }, } rewritten_prompt[passthrough_node_id] = { "class_type": MODAL_PARALLEL_LOCAL_PASSTHROUGH_NODE_ID, "inputs": passthrough_inputs, "_meta": {"title": "Modal Parallel Local Branch"}, } for target in local_only_consumers: rewritten_prompt[target.node_id]["inputs"][target.input_name] = [ passthrough_node_id, 0, ] for component_id in downstream_remote_component_ids: prompt_inputs = rewritten_prompt[component_id].get("inputs", {}) embedded_payload = prompt_inputs.get("original_node_data") if not isinstance(embedded_payload, Mapping): continue prompt_inputs[ "original_node_data" ] = update_registered_proxy_payload_fields( component_id, embedded_payload, { "parallel_local_dispatch_group_id": dispatch_group_id, "signal_parallel_local_dispatch": True, }, ) passthrough_node_ids.append(passthrough_node_id) logger.info( "Parallelized local-only consumers %s of remote output %s:%d after downstream Modal dispatches %s via passthrough=%s.", [target.node_id for target in local_only_consumers], source.node_id, source.output_index, downstream_remote_component_ids, passthrough_node_id, ) return passthrough_node_ids def _configure_local_gap_keepalive_payloads( *, rewritten_prompt: dict[str, Any], remote_component_ids: list[str], sandwiched_local_node_ids: set[str], ) -> None: """Mark only remote proxies that precede a local-to-remote execution gap.""" if not sandwiched_local_node_ids: return payloads_by_component_id: dict[str, Mapping[str, Any]] = {} for component_id in remote_component_ids: payload = ( rewritten_prompt.get(component_id, {}) .get("inputs", {}) .get("original_node_data") ) if not isinstance(payload, dict): continue payloads_by_component_id[component_id] = payload keepalive_component_ids: set[str] = set() continuation_component_ids: set[str] = set() for local_node_id in sandwiched_local_node_ids: upstream_component_ids: set[str] = set() downstream_component_ids: set[str] = set() for component_id in remote_component_ids: if _component_ancestors_of_local_source( prompt=rewritten_prompt, source_node_id=local_node_id, component_node_id_set={component_id}, ): upstream_component_ids.add(component_id) if _component_ancestors_of_local_source( prompt=rewritten_prompt, source_node_id=component_id, component_node_id_set={local_node_id}, ): downstream_component_ids.add(component_id) for upstream_component_id in upstream_component_ids: upstream_payload = payloads_by_component_id.get(upstream_component_id) if upstream_payload is None: continue upstream_environment_id = str( upstream_payload.get("execution_environment_id") or "" ) if upstream_payload.get("execution_provider") != ExecutionProvider.MODAL.value: continue for downstream_component_id in downstream_component_ids: downstream_payload = payloads_by_component_id.get( downstream_component_id ) if ( downstream_payload is None or downstream_payload.get("execution_provider") != ExecutionProvider.MODAL.value or str(downstream_payload.get("execution_environment_id") or "") != upstream_environment_id ): continue keepalive_component_ids.add(upstream_component_id) continuation_component_ids.add(downstream_component_id) for component_id, embedded_payload in payloads_by_component_id.items(): if component_id not in keepalive_component_ids | continuation_component_ids: continue payload_fields: dict[str, Any] = {"remote_local_gap_pool": True} if component_id in keepalive_component_ids: payload_fields["keepalive_after_remote_component"] = True if component_id in continuation_component_ids: payload_fields["stop_local_gap_keepalive_before_remote_component"] = True rewritten_prompt[component_id]["inputs"][ "original_node_data" ] = update_registered_proxy_payload_fields( component_id, embedded_payload, payload_fields, ) logger.info( "Configured local-gap Modal pool for components=%s keepalive_producers=%s continuations=%s.", sorted(keepalive_component_ids | continuation_component_ids), sorted(keepalive_component_ids), sorted(continuation_component_ids), ) def _remote_proxy_payload( rewritten_prompt: Mapping[str, Any], component_id: str ) -> Mapping[str, Any] | None: """Return the registered execution payload embedded in one remote proxy.""" prompt_node = rewritten_prompt.get(component_id) if not isinstance(prompt_node, Mapping): return None inputs = prompt_node.get("inputs") if not isinstance(inputs, Mapping): return None payload = inputs.get("original_node_data") if not isinstance(payload, Mapping): return None return registered_proxy_execution_payload(component_id, payload) def _remote_proxy_dependency_edges( rewritten_prompt: Mapping[str, Any], component_ids: set[str], ) -> dict[str, set[str]]: """Return nearest remote dependencies while traversing intervening local nodes.""" dependency_edges = {component_id: set() for component_id in component_ids} for downstream_component_id in component_ids: downstream_node = rewritten_prompt.get(downstream_component_id) if not isinstance(downstream_node, Mapping): continue pending_node_ids = [ str(input_value[0]) for input_value in (downstream_node.get("inputs") or {}).values() if _is_link(input_value) ] visited_node_ids: set[str] = set() while pending_node_ids: upstream_node_id = pending_node_ids.pop() if upstream_node_id in visited_node_ids: continue visited_node_ids.add(upstream_node_id) if upstream_node_id in component_ids: dependency_edges[upstream_node_id].add(downstream_component_id) continue upstream_node = rewritten_prompt.get(upstream_node_id) if not isinstance(upstream_node, Mapping): continue pending_node_ids.extend( str(input_value[0]) for input_value in (upstream_node.get("inputs") or {}).values() if _is_link(input_value) ) return dependency_edges def _component_descendant_distances( component_id: str, dependency_edges: Mapping[str, set[str]], ) -> dict[str, int]: """Return shortest downstream distances from one remote component.""" distances: dict[str, int] = {} pending = deque( (descendant_id, 1) for descendant_id in dependency_edges.get(component_id, set()) ) while pending: descendant_id, distance = pending.popleft() previous_distance = distances.get(descendant_id) if previous_distance is not None and previous_distance <= distance: continue distances[descendant_id] = distance pending.extend( (nested_descendant_id, distance + 1) for nested_descendant_id in dependency_edges.get(descendant_id, set()) ) return distances def _speculative_prewarm_target_payload( payload: Mapping[str, Any], ) -> dict[str, Any]: """Return the minimal side-effect-free payload needed to prepare one worker.""" return { field_name: copy.deepcopy(payload[field_name]) for field_name in _SPECULATIVE_PREWARM_PAYLOAD_FIELDS if field_name in payload } def _configure_speculative_affinity_prewarm_payloads( *, rewritten_prompt: dict[str, Any], execution_stages: list[list[str]], ) -> None: """Attach one next-affinity worker preparation target to each eligible proxy.""" stage_index_by_component_id = { component_id: stage_index for stage_index, stage in enumerate(execution_stages) for component_id in stage } payloads_by_component_id = { component_id: payload for component_id in stage_index_by_component_id if (payload := _remote_proxy_payload(rewritten_prompt, component_id)) is not None } dependency_edges = _remote_proxy_dependency_edges( rewritten_prompt, set(payloads_by_component_id), ) configured_targets: dict[str, str] = {} for component_id, payload in payloads_by_component_id.items(): if payload.get("execution_provider") != ExecutionProvider.MODAL.value: continue execution_environment_id = str( payload.get("execution_environment_id") or "" ) affinity_group = ( str(payload.get("remote_worker_affinity_group") or "comfy").strip().lower() ) descendant_distances = _component_descendant_distances( component_id, dependency_edges ) candidate_ids = sorted( ( descendant_id for descendant_id in descendant_distances if descendant_id in payloads_by_component_id and payloads_by_component_id[descendant_id].get( "execution_provider" ) == ExecutionProvider.MODAL.value and str( payloads_by_component_id[descendant_id].get( "execution_environment_id" ) or "" ) == execution_environment_id and str( payloads_by_component_id[descendant_id].get( "remote_worker_affinity_group" ) or "comfy" ) .strip() .lower() != affinity_group ), key=lambda descendant_id: ( descendant_distances[descendant_id], stage_index_by_component_id[descendant_id], descendant_id, ), ) if not candidate_ids: continue target_component_id = candidate_ids[0] target_payload = _speculative_prewarm_target_payload( payloads_by_component_id[target_component_id] ) rewritten_prompt[component_id]["inputs"][ "original_node_data" ] = update_registered_proxy_payload_fields( component_id, payload, {_SPECULATIVE_PREWARM_TARGET_KEY: target_payload}, ) configured_targets[component_id] = target_component_id if configured_targets: logger.info( "Configured one-step speculative Modal worker prewarm targets=%s.", configured_targets, ) 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, status_callback: SetupStatusCallback | None = None, plan_callback: ExecutionPlanStatusCallback | None = None, cancellation_check: Callable[[], bool] | None = None, ) -> tuple[dict[str, Any], RewriteSummary]: """Rewrite connected remote components into Modal proxy nodes.""" if cancellation_check is not None and cancellation_check(): raise SyncCancelledError("Remote workflow preparation was cancelled.") resolved_settings = settings or get_settings() remote_node_ids = requested_remote_node_ids( prompt=prompt, workflow=workflow, settings=resolved_settings, ) summary = RewriteSummary(remote_node_ids=sorted(remote_node_ids)) logger.info( "Found %d workflow nodes marked for remote 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, ) expanded_remote_node_ids = _expand_remote_node_ids_for_terminal_video_sinks( prompt=rewritten_prompt, remote_node_ids=expanded_remote_node_ids, nodes_module=resolved_nodes_module, ) summary.remote_node_ids = sorted(expanded_remote_node_ids) summary.sandwiched_local_node_ids = sorted( _sandwiched_local_node_ids(rewritten_prompt, expanded_remote_node_ids) ) if summary.sandwiched_local_node_ids: logger.warning( "Detected local nodes sandwiched between remote graph regions; " "these nodes may force additional remote phases: %s", summary.sandwiched_local_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, ) execution_plan = _plan_component_execution( components=components, prompt=rewritten_prompt, workflow=workflow, settings=resolved_settings, status_callback=status_callback, plan_callback=plan_callback, ) assignments_by_component_id = execution_plan.assignments if execution_plan.configuration_set is not None: summary.remote_configurations = ( execution_plan.configuration_set.to_safe_list() ) for assignment in assignments_by_component_id.values(): execution_location = _execution_location_for_assignment( assignment, execution_plan.ssh_hosts_by_id, execution_plan.vast_leases_by_environment, ) if execution_location: summary.execution_locations_by_environment[ assignment.environment_id ] = execution_location session_component_ids = _mark_remote_to_remote_session_boundaries( rewritten_prompt, components, resolved_nodes_module, assignments_by_component_id, ) prompt_id = (extra_data or {}).get("prompt_id") remote_sessions_by_component_id = { component.representative_node_id: RemoteSessionHandle( session_id=uuid.uuid4().hex, prompt_id=(str(prompt_id) if prompt_id is not None else None), owner_component_id=component.representative_node_id, ).to_payload() for component in components if component.representative_node_id in session_component_ids } if remote_sessions_by_component_id: logger.info( "Enabled environment-local remote references for components=%s.", sorted(remote_sessions_by_component_id), ) summary.execution_assignments_by_representative = dict(assignments_by_component_id) vast_service = execution_plan.vast_service vast_leases_by_environment = dict( execution_plan.vast_leases_by_environment ) if vast_service is None and any( assignment.provider is ExecutionProvider.VAST for assignment in assignments_by_component_id.values() ): try: vast_service = VastService.from_environment( resolved_settings, repo_root=Path(__file__).resolve().parent, ) vast_leases_by_environment = { assignment.environment_id: vast_service.lease_for_environment_id( assignment.environment_id ) for assignment in assignments_by_component_id.values() if assignment.provider is ExecutionProvider.VAST } except (KeyError, OSError, RuntimeError, ValueError) as exc: raise ModalPromptValidationError( f"Unable to resolve the acquired Vast.ai lease: {exc}" ) from exc sync_engines_by_environment: dict[str, ModalAssetSyncEngine] = {} ssh_hosts_by_id = dict(execution_plan.ssh_hosts_by_id) if not ssh_hosts_by_id: ssh_hosts_by_id = { host.environment_id: host for host in _configured_ssh_hosts(resolved_settings) } session_environment_ids = { assignments_by_component_id[component_id].environment_id for component_id in session_component_ids } worker_counts_by_environment: dict[str, int] = defaultdict(int) for component in components: component_id = component.representative_node_id assignment = assignments_by_component_id[component_id] if assignment.provider is ExecutionProvider.MODAL: summary.execution_worker_indices_by_representative[component_id] = 0 continue if assignment.provider is ExecutionProvider.VAST: summary.execution_worker_indices_by_representative[component_id] = 0 continue if execution_plan.configuration_set is not None: summary.execution_worker_indices_by_representative[component_id] = ( assignment.capacity_slot_index ) logger.info( "Assigned workflow-configured remote component=%s environment=%s " "worker_index=%d.", component_id, assignment.environment_id, assignment.capacity_slot_index, ) continue host = ssh_hosts_by_id.get(assignment.environment_id) maximum_workers = max(1, host.maximum_workers if host is not None else 1) if component_id in session_component_ids: worker_index = 0 else: first_worker = ( 1 if assignment.environment_id in session_environment_ids and maximum_workers > 1 else 0 ) available_workers = max(1, maximum_workers - first_worker) worker_index = first_worker + ( worker_counts_by_environment[assignment.environment_id] % available_workers ) worker_counts_by_environment[assignment.environment_id] += 1 summary.execution_worker_indices_by_representative[component_id] = worker_index logger.info( "Assigned remote component=%s environment=%s worker_index=%d.", component_id, assignment.environment_id, worker_index, ) for assignment in assignments_by_component_id.values(): if assignment.environment_id in sync_engines_by_environment: continue if assignment.provider is ExecutionProvider.MODAL: _ensure_remote_sync_backend(resolved_settings, resolved_sync_engine) sync_engines_by_environment[ assignment.environment_id ] = resolved_sync_engine continue if assignment.provider is ExecutionProvider.VAST: if vast_service is None: raise ModalPromptValidationError( "Vast.ai assignment has no active controller service." ) lease = vast_leases_by_environment[assignment.environment_id] vast_sync_engine = vast_service.sync_engine(lease) vast_sync_engine.cancellation_check = cancellation_check sync_engines_by_environment[assignment.environment_id] = vast_sync_engine continue host = ssh_hosts_by_id.get(assignment.environment_id) if host is None: raise ModalPromptValidationError( f"Assigned SSH execution environment {assignment.environment_id!r} is no longer configured." ) sync_engines_by_environment[assignment.environment_id] = _ssh_sync_engine( host=host, settings=resolved_settings, ) if status_callback is not None: status_callback("Preparing assets for remote execution", None, None) if resolved_settings.sync_custom_nodes: for ( environment_id, environment_sync_engine, ) in sync_engines_by_environment.items(): summary.custom_nodes_bundles_by_environment[ environment_id ] = environment_sync_engine.sync_custom_nodes_directory( status_callback=status_callback, ) modal_bundle = next( ( summary.custom_nodes_bundles_by_environment[assignment.environment_id] for assignment in assignments_by_component_id.values() if assignment.provider is ExecutionProvider.MODAL ), None, ) summary.custom_nodes_bundle = modal_bundle or next( iter(summary.custom_nodes_bundles_by_environment.values()), None, ) else: logger.info( "Skipping custom_nodes bundle sync because sync is disabled for execution_mode=%s.", resolved_settings.execution_mode, ) synced_component_prompts: dict[str, dict[str, Any]] = {} synced_assets_by_component_id: dict[str, list[SyncedAsset]] = {} request_asset_caches_by_environment = { environment_id: environment_sync_engine.create_request_asset_cache( rewritten_prompt[node_id].get("inputs", {}) for component in components if assignments_by_component_id[ component.representative_node_id ].environment_id == environment_id for node_id in component.node_ids ) for environment_id, environment_sync_engine in sync_engines_by_environment.items() } for component in components: assignment = assignments_by_component_id[component.representative_node_id] component_sync_engine = sync_engines_by_environment[assignment.environment_id] component_prompt, synced_assets = _sync_component_prompt_inputs( component=component, rewritten_prompt=rewritten_prompt, sync_engine=component_sync_engine, request_cache=request_asset_caches_by_environment[ assignment.environment_id ], status_callback=status_callback, ) synced_component_prompts[component.representative_node_id] = component_prompt synced_assets_by_component_id[component.representative_node_id] = list( synced_assets ) summary.synced_assets.extend(synced_assets) summary.synced_assets = _deduplicate_synced_assets(summary.synced_assets) modal_environment_ids = { assignment.environment_id for assignment in assignments_by_component_id.values() if assignment.provider is ExecutionProvider.MODAL } modal_component_ids = { component.representative_node_id for component in components if assignments_by_component_id[component.representative_node_id].provider is ExecutionProvider.MODAL } requires_volume_reload = any( asset.uploaded for component_id, assets in synced_assets_by_component_id.items() if component_id in modal_component_ids for asset in assets ) or any( bundle is not None and bundle.uploaded for environment_id, bundle in summary.custom_nodes_bundles_by_environment.items() if environment_id in modal_environment_ids ) volume_reload_marker = uuid.uuid4().hex if requires_volume_reload else None logger.info( "Resolved request-wide Modal volume reload requirement: requires_volume_reload=%s volume_reload_marker=%s synced_assets=%d custom_nodes_uploaded=%s", requires_volume_reload, volume_reload_marker, len(summary.synced_assets), bool( summary.custom_nodes_bundle is not None and summary.custom_nodes_bundle.uploaded ), ) summary.requires_volume_reload = requires_volume_reload summary.volume_reload_marker = volume_reload_marker summary.uploaded_volume_paths = [ asset.remote_path for asset in summary.synced_assets if asset.uploaded ] mapped_proxy_component_ids: set[str] = set() sandwiched_local_node_id_set = set(summary.sandwiched_local_node_ids) for component in components: logger.info( "Rewriting remote component %s covering nodes %s.", component.representative_node_id, component.node_ids, ) assignment = assignments_by_component_id[component.representative_node_id] component_custom_nodes_bundle = summary.custom_nodes_bundles_by_environment.get( assignment.environment_id ) uploaded_volume_paths = _component_uploaded_volume_paths( component_prompt=synced_component_prompts[component.representative_node_id], synced_assets=synced_assets_by_component_id[ component.representative_node_id ], custom_nodes_bundle=component_custom_nodes_bundle, ) payload = _build_component_payload( component=component, component_prompt=synced_component_prompts[component.representative_node_id], signature_prompt=prompt, extra_data=extra_data, settings=resolved_settings, requires_volume_reload=( assignment.provider is ExecutionProvider.MODAL and bool(uploaded_volume_paths) ), volume_reload_marker=volume_reload_marker, custom_nodes_bundle=component_custom_nodes_bundle, uploaded_volume_paths=uploaded_volume_paths, terminate_container_on_error=resolved_settings.terminate_container_on_error, nodes_module=resolved_nodes_module, remote_session=remote_sessions_by_component_id.get( component.representative_node_id ), ) _stamp_execution_assignment( payload, assignment, summary.execution_worker_indices_by_representative.get( component.representative_node_id, 0, ), _component_execution_signature(component, prompt), _execution_location_for_assignment( assignment, ssh_hosts_by_id, vast_leases_by_environment, ), _configured_provider_metadata( execution_plan=execution_plan, assignment=assignment, vast_leases_by_environment=vast_leases_by_environment, ), ) implicitly_mapped_output_sources = _implicitly_mapped_boundary_output_sources( component=component, original_prompt=prompt, rewritten_prompt=rewritten_prompt, nodes_module=resolved_nodes_module, ) _mark_payload_scheduler_list_outputs( payload, implicitly_mapped_output_sources, ) proxy_node_ids = _rewrite_component_into_proxy( component=component, rewritten_prompt=rewritten_prompt, payload=payload, nodes_module=resolved_nodes_module, ) for proxy_node_id in proxy_node_ids: summary.execution_assignments_by_representative[proxy_node_id] = assignment summary.execution_worker_indices_by_representative[proxy_node_id] = ( summary.execution_worker_indices_by_representative.get( component.representative_node_id, 0, ) ) split_proxy_payloads = payload.get("split_proxy_payloads") if isinstance(split_proxy_payloads, dict): static_proxy_node_id, mapped_proxy_node_id = proxy_node_ids summary.remote_component_ids.extend(proxy_node_ids) summary.component_node_ids_by_representative[static_proxy_node_id] = ( _workflow_visible_remote_node_ids(component.static_node_ids) ) summary.component_node_ids_by_representative[mapped_proxy_node_id] = ( _workflow_visible_remote_node_ids(component.mapped_node_ids) ) for node_id in _workflow_visible_remote_node_ids( component.static_node_ids ): summary.rewritten_node_id_map[node_id] = static_proxy_node_id for node_id in _workflow_visible_remote_node_ids( component.mapped_node_ids ): summary.rewritten_node_id_map[node_id] = mapped_proxy_node_id mapped_proxy_component_ids.add(mapped_proxy_node_id) continue if isinstance(split_proxy_payloads, list): summary.remote_component_ids.extend(proxy_node_ids) mapped_node_id_set = set(component.mapped_node_ids) for phase_payload in split_proxy_payloads: phase_proxy_node_id = str(phase_payload["component_id"]) phase_component_node_ids = [ str(node_id) for node_id in phase_payload["component_node_ids"] ] summary.component_node_ids_by_representative[ phase_proxy_node_id ] = _workflow_visible_remote_node_ids(phase_component_node_ids) for node_id in _workflow_visible_remote_node_ids( phase_component_node_ids ): summary.rewritten_node_id_map[node_id] = phase_proxy_node_id if mapped_node_id_set and mapped_node_id_set.intersection( phase_component_node_ids ): mapped_proxy_component_ids.add(phase_proxy_node_id) continue summary.remote_component_ids.extend(proxy_node_ids) summary.component_node_ids_by_representative[proxy_node_ids[0]] = ( _workflow_visible_remote_node_ids(component.node_ids) ) for node_id in _workflow_visible_remote_node_ids(component.node_ids): summary.rewritten_node_id_map[node_id] = proxy_node_ids[0] if component.mapped_boundary_input_name or implicitly_mapped_output_sources: mapped_proxy_component_ids.add(proxy_node_ids[0]) _configure_local_gap_keepalive_payloads( rewritten_prompt=rewritten_prompt, remote_component_ids=summary.remote_component_ids, sandwiched_local_node_ids=sandwiched_local_node_id_set, ) summary.parallel_local_branch_node_ids = _parallelize_non_returning_local_branches( rewritten_prompt=rewritten_prompt, remote_component_ids=summary.remote_component_ids, nodes_module=resolved_nodes_module, ) summary.artifact_finalizer_node_id = _attach_modal_artifact_finalizer( rewritten_prompt=rewritten_prompt, remote_component_ids=summary.remote_component_ids, nodes_module=resolved_nodes_module, ) proxy_component_groups = { component_id: {component_id} for component_id in summary.remote_component_ids } _, dependency_edges, _ = _component_dependency_graph( rewritten_prompt, proxy_component_groups ) execution_stages = _component_execution_stages( rewritten_prompt, proxy_component_groups ) _configure_speculative_affinity_prewarm_payloads( rewritten_prompt=rewritten_prompt, execution_stages=execution_stages, ) summary.component_dependency_ids_by_representative = { representative_node_id: sorted( upstream_component_id for upstream_component_id, downstream_component_ids in dependency_edges.items() if representative_node_id in downstream_component_ids ) for representative_node_id in sorted(proxy_component_groups) } summary.component_execution_stages = [list(stage) for stage in execution_stages] summary.mapped_component_ids = sorted(mapped_proxy_component_ids) summary.estimated_max_parallel_requests = _estimated_stage_parallelism( execution_stages, mapped_proxy_component_ids, mapped_component_weight=1, ) if execution_plan.configuration_set is not None: configured_capacity = sum( configuration.capacity_limit for configuration in execution_plan.configuration_set.configurations ) summary.max_parallel_requests_upper_bound = min( summary.estimated_max_parallel_requests, configured_capacity, ) elif resolved_settings.max_containers is not None: summary.max_parallel_requests_upper_bound = min( summary.estimated_max_parallel_requests, resolved_settings.max_containers, ) else: summary.max_parallel_requests_upper_bound = ( summary.estimated_max_parallel_requests ) logger.info( "Estimated remote parallelism after proxy rewrite: known_max_parallel_requests=%d max_parallel_requests_upper_bound=%s mapped_components=%s execution_stages=%s", summary.estimated_max_parallel_requests, summary.max_parallel_requests_upper_bound, summary.mapped_component_ids, summary.component_execution_stages, ) _log_modal_rewritten_prompt_diagnostics( prompt_id=( str(extra_data.get("prompt_id")) if isinstance(extra_data, Mapping) and extra_data.get("prompt_id") else None ), prompt=rewritten_prompt, summary=summary, reason="post_rewrite", ) return rewritten_prompt, summary async def rewrite_prompt_for_modal_async( 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, status_callback: SetupStatusCallback | None = None, plan_callback: ExecutionPlanStatusCallback | None = None, cancellation_check: Callable[[], bool] | None = None, ) -> tuple[dict[str, Any], RewriteSummary]: """Prepare one Modal prompt without blocking ComfyUI's event loop.""" return await asyncio.to_thread( rewrite_prompt_for_modal, prompt=prompt, workflow=workflow, sync_engine=sync_engine, settings=settings, nodes_module=nodes_module, extra_data=extra_data, status_callback=status_callback, plan_callback=plan_callback, cancellation_check=cancellation_check, ) def _execution_assignments_payload( summary: RewriteSummary, settings: ModalSyncSettings, ) -> dict[str, dict[str, Any]]: """Return planner assignments with member nodes and safe runtime labels.""" ssh_hosts_by_id = { host.environment_id: host for host in _configured_ssh_hosts(settings) } vast_leases_by_environment: dict[str, Any] = {} vast_registry = _vast_registry(settings) if vast_registry is not None: try: vast_leases_by_environment = { lease.environment_id: lease for lease in vast_registry.load().leases } except ValueError as exc: logger.warning("Unable to load Vast lease labels for UI status: %s", exc) return { component_id: { "provider": assignment.provider.value, "environment_id": assignment.environment_id, "configuration_id": assignment.configuration_id, "execution_location": _execution_location_for_assignment( assignment, ssh_hosts_by_id, vast_leases_by_environment ) or summary.execution_locations_by_environment.get( assignment.environment_id ), "node_ids": list( summary.component_node_ids_by_representative.get(component_id, []) ), "predicted_cost_usd": assignment.predicted_cost_usd, "predicted_completion_seconds": assignment.predicted_completion_seconds, "worker_index": summary.execution_worker_indices_by_representative.get( component_id, 0, ), "reasons": list(assignment.reasons), } for component_id, assignment in sorted( summary.execution_assignments_by_representative.items() ) } def _prompt_uses_remote_execution_configurator(prompt: Mapping[str, Any]) -> bool: """Return whether the serialized prompt opts into workflow-scoped capacity.""" return _remote_execution_configurator_node_id(prompt) is not None def _remote_execution_configurator_node_id( prompt: Mapping[str, Any], ) -> str | None: """Return the serialized identity of the workflow's sole configurator node.""" configurator_node_ids = [ str(node_id) for node_id, prompt_node in prompt.items() if isinstance(prompt_node, Mapping) and str(prompt_node.get("class_type") or "") == REMOTE_EXECUTION_CONFIGURATOR_NODE_ID ] return configurator_node_ids[0] if len(configurator_node_ids) == 1 else None def _selected_modal_gpus( summary: RewriteSummary, legacy_modal_gpu: str, ) -> list[str]: """Return distinct Modal GPUs selected by the completed execution plan.""" modal_assignments = [ assignment for assignment in summary.execution_assignments_by_representative.values() if assignment.provider is ExecutionProvider.MODAL ] if not modal_assignments: return [] configured_gpus = { str(configuration.get("configuration_id") or ""): str( configuration.get("gpu_type") or "" ) for configuration in summary.remote_configurations if configuration.get("provider") == ExecutionProvider.MODAL.value and configuration.get("configuration_id") and configuration.get("gpu_type") } selected_gpus = { configured_gpus.get(str(assignment.configuration_id or ""), "") for assignment in modal_assignments } selected_gpus.discard("") if not selected_gpus: selected_gpus.add(legacy_modal_gpu) return sorted(selected_gpus) async def _queue_prompt_json( prompt_server: Any, json_data: dict[str, Any], modal_response_payload: dict[str, Any] | None = None, ) -> 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") extra_data = dict(json_data.get("extra_data", {})) if "client_id" in json_data: extra_data["client_id"] = json_data["client_id"] valid = await execution.validate_prompt( prompt_id, prompt, partial_execution_targets ) if not valid[0]: modal_extra = extra_data.get("modal") if isinstance(modal_extra, Mapping) and modal_extra.get("remote_component_ids"): logger.warning( "ComfyUI rejected rewritten Modal prompt prompt_id=%s error=%s node_errors=%s", prompt_id, valid[1], valid[3], ) _log_modal_rewritten_prompt_diagnostics( prompt_id=prompt_id, prompt=prompt, reason="comfy_validation_failure", level=logging.WARNING, ) else: 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) ) response_payload: dict[str, Any] = { "prompt_id": prompt_id, "number": number, "node_errors": valid[3], } if modal_response_payload: response_payload.update(modal_response_payload) return web.json_response(response_payload) 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 _progress_state_route_path(route_path: str) -> str: """Return the sibling HTTP route used for Modal UI event replay.""" if route_path.endswith("/queue_prompt"): return f"{route_path.removesuffix('/queue_prompt')}/progress_state" return f"{route_path.rstrip('/')}/progress_state" def _container_status_route_path(route_path: str) -> str: """Return the sibling HTTP route used for active Modal container status.""" if route_path.endswith("/queue_prompt"): return f"{route_path.removesuffix('/queue_prompt')}/container_status" return f"{route_path.rstrip('/')}/container_status" def _delete_modal_caches_route_path(route_path: str) -> str: """Return the sibling HTTP route used to delete persistent Modal cache Dicts.""" if route_path.endswith("/queue_prompt"): return f"{route_path.removesuffix('/queue_prompt')}/delete_caches" return f"{route_path.rstrip('/')}/delete_caches" def _delete_modal_volume_route_path(route_path: str) -> str: """Return the sibling HTTP route used to delete the configured Modal Volume.""" if route_path.endswith("/queue_prompt"): return f"{route_path.removesuffix('/queue_prompt')}/delete_volume" return f"{route_path.rstrip('/')}/delete_volume" def _cancel_preparation_route_path(route_path: str) -> str: """Return the sibling route used to cancel queue-time remote preparation.""" if route_path.endswith("/queue_prompt"): return f"{route_path.removesuffix('/queue_prompt')}/cancel_preparation" return f"{route_path.rstrip('/')}/cancel_preparation" def _remote_environments_route_path(route_path: str) -> str: """Return the provider-neutral environment management route.""" del route_path return "/remote/environments" def _remote_environment_probe_route_path(route_path: str) -> str: """Return the provider-neutral host capability probe route.""" del route_path return "/remote/environments/probe" def _remote_environment_bootstrap_route_path(route_path: str) -> str: """Return the provider-neutral worker bootstrap route.""" del route_path return "/remote/environments/bootstrap" def _remote_environment_status_route_path(route_path: str) -> str: """Return the provider-neutral worker status route.""" del route_path return "/remote/environments/status" def _remote_environment_stop_route_path(route_path: str) -> str: """Return the provider-neutral worker shutdown route.""" del route_path return "/remote/environments/stop" def _vast_registry(settings: ModalSyncSettings) -> VastLeaseRegistry | None: """Return persistent credential-free Vast lease state when available.""" user_directory = discover_comfyui_user_directory(settings) if user_directory is None: return None return VastLeaseRegistry.for_user_directory(user_directory) def _modal_not_found_error_types() -> tuple[type[BaseException], ...]: """Return Modal SDK exception classes that mean a named object is absent.""" if modal is None: return () exception_namespace = getattr(modal, "exception", None) candidates = [ getattr(exception_namespace, "NotFoundError", None), getattr(exception_namespace, "InvalidError", None), ] return tuple( candidate for candidate in candidates if isinstance(candidate, type) and issubclass(candidate, BaseException) ) def _modal_cache_dict_names(settings: ModalSyncSettings) -> list[str]: """Return the configured persistent Modal Dict names used as local-reset caches.""" return [ settings.interrupt_dict_name, settings.node_output_cache_dict_name, settings.session_bridge_dict_name, settings.sync_index_dict_name, settings.snapshot_profile_dict_name, ] async def _call_modal_sdk(method: Any, *args: Any, **kwargs: Any) -> Any: """Call a Modal SDK method without blocking the aiohttp event loop.""" async_method = getattr(method, "aio", None) if callable(async_method): return await async_method(*args, **kwargs) return await asyncio.to_thread(method, *args, **kwargs) async def _delete_modal_named_object( namespace: Any, name: str, *, object_label: str ) -> None: """Delete a named Modal object using the non-deprecated manager API when available.""" objects_manager = getattr(namespace, "objects", None) manager_delete = getattr(objects_manager, "delete", None) if callable(manager_delete): await _call_modal_sdk(manager_delete, name, allow_missing=True) return instance = await _call_modal_sdk(namespace.from_name, name, create_if_missing=False) delete_method = getattr(instance, "delete", None) if not callable(delete_method): raise RuntimeError(f"Modal {object_label} {name!r} does not expose delete().") await _call_modal_sdk(delete_method, name) async def delete_modal_cache_dicts(settings: ModalSyncSettings) -> dict[str, Any]: """Delete all configured Modal Dict caches and return a reset summary.""" if modal is None: raise RuntimeError("Modal SDK is unavailable; cannot delete Modal caches.") modal_dict = getattr(modal, "Dict", None) if modal_dict is None: raise RuntimeError( "Modal SDK does not expose modal.Dict; cannot delete Modal caches." ) deleted: list[str] = [] skipped: list[str] = [] not_found_errors = _modal_not_found_error_types() for dict_name in _modal_cache_dict_names(settings): try: await _call_modal_sdk( modal_dict.from_name, dict_name, create_if_missing=False, ) except not_found_errors: skipped.append(dict_name) continue await _delete_modal_named_object(modal_dict, dict_name, object_label="Dict") deleted.append(dict_name) logger.info("Deleted Modal cache Dicts deleted=%s skipped=%s.", deleted, skipped) return {"deleted": deleted, "skipped": skipped} async def delete_modal_volume(settings: ModalSyncSettings) -> dict[str, Any]: """Delete the configured Modal Volume and return a reset summary.""" if modal is None: raise RuntimeError("Modal SDK is unavailable; cannot delete Modal volume.") modal_volume = getattr(modal, "Volume", None) if modal_volume is None: raise RuntimeError( "Modal SDK does not expose modal.Volume; cannot delete Modal volume." ) try: await _call_modal_sdk( modal_volume.from_name, settings.volume_name, create_if_missing=False, ) except _modal_not_found_error_types(): logger.info("Skipped deleting missing Modal Volume %s.", settings.volume_name) return {"deleted": [], "skipped": [settings.volume_name]} await _delete_modal_named_object( modal_volume, settings.volume_name, object_label="Volume" ) logger.info("Deleted Modal Volume %s.", settings.volume_name) return {"deleted": [settings.volume_name], "skipped": []} def _install_modal_interrupt_queue_bridge(prompt_server: Any) -> None: """Expose active remote work through every ComfyUI queue-state view.""" prompt_queue = getattr(prompt_server, "prompt_queue", None) if prompt_queue is None or getattr( prompt_queue, _MODAL_INTERRUPT_QUEUE_BRIDGE_ATTR, False ): return original_get_current_queue = getattr(prompt_queue, "get_current_queue", None) original_get_current_queue_volatile = getattr( prompt_queue, "get_current_queue_volatile", None ) original_get_tasks_remaining = getattr(prompt_queue, "get_tasks_remaining", None) if not any( callable(method) for method in ( original_get_current_queue, original_get_current_queue_volatile, original_get_tasks_remaining, ) ): logger.debug( "Prompt queue does not expose queue-state methods; skipping remote queue bridge." ) return preparation_prompts: dict[str, tuple[Any, ...]] = {} preparation_cancellations: dict[str, threading.Event] = {} preparation_lock = threading.RLock() setattr(prompt_queue, _REMOTE_PREPARATION_PROMPTS_ATTR, preparation_prompts) setattr( prompt_queue, _REMOTE_PREPARATION_CANCELLATIONS_ATTR, preparation_cancellations, ) setattr(prompt_queue, _REMOTE_PREPARATION_LOCK_ATTR, preparation_lock) def preparation_items() -> list[tuple[Any, ...]]: """Return a stable snapshot of prompts still preparing remote capacity.""" with preparation_lock: return list(preparation_prompts.values()) def append_missing_preparations( running: Iterable[Any], queued: Iterable[Any], ) -> tuple[list[Any], Any]: """Add preparation entries that are not already in ComfyUI's native queue.""" running_items = list(running) queued_items = list(queued) native_prompt_ids = _queue_item_prompt_ids((*running_items, *queued_items)) running_items.extend( item for item in preparation_items() if str(item[1]) not in native_prompt_ids ) return running_items, queued if callable(original_get_current_queue): def remote_get_current_queue() -> tuple[list[Any], Any]: """Return native work plus preparation and active remote prompt entries.""" running, queued = original_get_current_queue() running_items, queued = append_missing_preparations(running, queued) running_prompt_ids = _queue_item_prompt_ids(running_items) try: from .remote.modal_app import active_remote_modal_prompt_ids except ImportError: return running_items, queued for prompt_id in sorted( active_remote_modal_prompt_ids() - running_prompt_ids ): running_items.append((0, prompt_id, {}, {}, [], {})) return running_items, queued setattr(prompt_queue, "get_current_queue", remote_get_current_queue) if callable(original_get_current_queue_volatile): def remote_get_current_queue_volatile() -> tuple[list[Any], Any]: """Include remote preparation in ComfyUI's public `/queue` response.""" running, queued = original_get_current_queue_volatile() return append_missing_preparations(running, queued) setattr( prompt_queue, "get_current_queue_volatile", remote_get_current_queue_volatile, ) if callable(original_get_tasks_remaining): def remote_get_tasks_remaining() -> int: """Count remote preparation as work in websocket queue status.""" remaining = int(original_get_tasks_remaining()) native_prompt_ids: set[str] = set() native_queue_method = ( original_get_current_queue_volatile if callable(original_get_current_queue_volatile) else original_get_current_queue ) if callable(native_queue_method): running, queued = native_queue_method() native_prompt_ids = _queue_item_prompt_ids((*running, *queued)) return remaining + sum( str(item[1]) not in native_prompt_ids for item in preparation_items() ) setattr(prompt_queue, "get_tasks_remaining", remote_get_tasks_remaining) setattr(prompt_queue, _MODAL_INTERRUPT_QUEUE_BRIDGE_ATTR, True) logger.info("Installed remote preparation bridge on ComfyUI prompt queue.") def _queue_item_prompt_ids(items: Iterable[Any]) -> set[str]: """Return prompt IDs from well-formed native or synthetic queue items.""" return { str(item[1]) for item in items if isinstance(item, (list, tuple)) and len(item) > 1 } def _set_remote_preparation( prompt_server: Any, *, prompt_id: str, prompt: Mapping[str, Any], extra_data: Mapping[str, Any], cancellation_event: threading.Event | None = None, ) -> bool: """Register one pre-queue remote workflow and publish its active queue state.""" prompt_queue = getattr(prompt_server, "prompt_queue", None) preparations = getattr(prompt_queue, _REMOTE_PREPARATION_PROMPTS_ATTR, None) preparation_lock = getattr(prompt_queue, _REMOTE_PREPARATION_LOCK_ATTR, None) if not isinstance(preparations, dict) or preparation_lock is None: return False with preparation_lock: preparations[prompt_id] = ( 0, prompt_id, copy.deepcopy(dict(prompt)), copy.deepcopy(dict(extra_data)), [], {}, ) cancellations = getattr( prompt_queue, _REMOTE_PREPARATION_CANCELLATIONS_ATTR, None, ) if isinstance(cancellations, dict) and cancellation_event is not None: cancellations[prompt_id] = cancellation_event queue_updated = getattr(prompt_server, "queue_updated", None) if callable(queue_updated): queue_updated() return True def _clear_remote_preparation(prompt_server: Any, prompt_id: str) -> None: """Remove one pre-queue remote workflow and publish the resulting queue state.""" prompt_queue = getattr(prompt_server, "prompt_queue", None) preparations = getattr(prompt_queue, _REMOTE_PREPARATION_PROMPTS_ATTR, None) preparation_lock = getattr(prompt_queue, _REMOTE_PREPARATION_LOCK_ATTR, None) if not isinstance(preparations, dict) or preparation_lock is None: return with preparation_lock: removed = preparations.pop(prompt_id, None) cancellations = getattr( prompt_queue, _REMOTE_PREPARATION_CANCELLATIONS_ATTR, None, ) if isinstance(cancellations, dict): cancellations.pop(prompt_id, None) if removed is not None: queue_updated = getattr(prompt_server, "queue_updated", None) if callable(queue_updated): queue_updated() 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) progress_state_route_path = _progress_state_route_path(resolved_settings.route_path) container_status_route_path = _container_status_route_path( resolved_settings.route_path ) delete_caches_route_path = _delete_modal_caches_route_path( resolved_settings.route_path ) delete_volume_route_path = _delete_modal_volume_route_path( resolved_settings.route_path ) cancel_preparation_route_path = _cancel_preparation_route_path( resolved_settings.route_path ) remote_environments_route_path = _remote_environments_route_path( resolved_settings.route_path ) remote_environment_probe_route_path = _remote_environment_probe_route_path( resolved_settings.route_path ) remote_environment_bootstrap_route_path = _remote_environment_bootstrap_route_path( resolved_settings.route_path ) remote_environment_status_route_path = _remote_environment_status_route_path( resolved_settings.route_path ) remote_environment_stop_route_path = _remote_environment_stop_route_path( resolved_settings.route_path ) remote_host_registry = _ssh_host_registry(resolved_settings) vast_registry = _vast_registry(resolved_settings) _install_modal_interrupt_queue_bridge(prompt_server) @prompt_server.routes.post(cancel_preparation_route_path) async def cancel_remote_preparation(request: web.Request) -> web.Response: """Cancel one prompt while it is still preparing remote execution.""" payload = await request.json() prompt_id = str(payload.get("prompt_id") or "").strip() prompt_queue = getattr(prompt_server, "prompt_queue", None) cancellations = getattr( prompt_queue, _REMOTE_PREPARATION_CANCELLATIONS_ATTR, None, ) cancellation_event = ( cancellations.get(prompt_id) if isinstance(cancellations, dict) and prompt_id else None ) if cancellation_event is None: return web.json_response({"cancelled": False, "prompt_id": prompt_id}) cancellation_event.set() logger.info("Cancelled remote preparation for prompt %s.", prompt_id) return web.json_response({"cancelled": True, "prompt_id": prompt_id}) if hasattr(prompt_server.routes, "get"): @prompt_server.routes.get(remote_environments_route_path) async def remote_environments(request: web.Request) -> web.Response: """Return credential-free SSH host configuration and discovered state.""" del request if remote_host_registry is None: return web.json_response( { "error": "The ComfyUI user directory could not be resolved.", "hosts": [], }, status=503, ) try: config = await asyncio.to_thread(remote_host_registry.load) except ValueError as exc: return web.json_response({"error": str(exc), "hosts": []}, status=500) return web.json_response(config.to_dict()) @prompt_server.routes.get(progress_state_route_path) async def modal_progress_state(request: web.Request) -> web.Response: """Return recent Modal UI events for the requesting ComfyUI client.""" client_id = request.query.get("client_id") return web.json_response({"events": modal_ui_events_for_client(client_id)}) @prompt_server.routes.get(container_status_route_path) async def modal_container_status(request: web.Request) -> web.Response: """Return active containers and hourly billing for one selected GPU app.""" from .remote.modal_app import ( ModalBillingStatusError, ModalContainerStatusError, get_hourly_modal_app_billing, list_active_modal_containers, ) requested_modal_gpu = request.query.get( "modal_gpu", resolved_settings.modal_gpu, ) try: selected_settings = settings_for_modal_gpu( resolved_settings, requested_modal_gpu, ) except ValueError as exc: return web.json_response( {"containers": [], "error": str(exc), "polled_at": time.time()}, status=400, ) containers_task = asyncio.create_task( list_active_modal_containers(resolved_settings) ) billing_task = asyncio.create_task( get_hourly_modal_app_billing( selected_settings.modal_gpu, resolved_settings, ) ) try: containers = await containers_task except ModalContainerStatusError as exc: billing_task.cancel() await asyncio.gather(billing_task, return_exceptions=True) logger.warning("Unable to refresh Modal container status: %s", exc) return web.json_response( {"containers": [], "error": str(exc), "polled_at": time.time()}, status=502, ) billing = None billing_error = None try: billing = await billing_task except ModalBillingStatusError as exc: billing_error = str(exc) logger.warning("Unable to refresh Modal hourly billing: %s", exc) return web.json_response( { "containers": [container.as_dict() for container in containers], "billing": billing.as_dict() if billing is not None else None, "billing_error": billing_error, "polled_at": time.time(), } ) @prompt_server.routes.get("/remote/vast/status") async def vast_status(request: web.Request) -> web.Response: """Return credential-free managed lease state and controller readiness.""" del request if vast_registry is None: return web.json_response( {"configured": False, "leases": [], "error": "ComfyUI user directory unavailable."}, status=503, ) try: state = await asyncio.to_thread(vast_registry.load) except ValueError as exc: return web.json_response( {"configured": bool(os.getenv("VAST_API_KEY")), "leases": [], "error": str(exc)}, status=500, ) return web.json_response( { "configured": bool(os.getenv("VAST_API_KEY")), "image_configured": bool(os.getenv("COMFY_MODAL_VAST_IMAGE")), "leases": [lease.to_dict() for lease in state.leases], } ) if hasattr(prompt_server.routes, "put"): @prompt_server.routes.put(remote_environments_route_path) async def remote_environments_update(request: web.Request) -> web.Response: """Validate and atomically replace SSH host configuration.""" if remote_host_registry is None: return web.json_response( {"error": "The ComfyUI user directory could not be resolved."}, status=503, ) try: payload = await request.json() if not isinstance(payload, Mapping): raise ValueError( "Remote environment configuration must be a JSON object." ) config = RemoteExecutionConfig.from_dict(payload) await asyncio.to_thread(remote_host_registry.save, config) except (TypeError, ValueError, json.JSONDecodeError) as exc: return web.json_response({"error": str(exc)}, status=400) return web.json_response(config.to_dict()) @prompt_server.routes.post(remote_environment_probe_route_path) async def remote_environment_probe(request: web.Request) -> web.Response: """Probe one configured SSH host and persist its discovered capabilities.""" if remote_host_registry is None: return web.json_response( {"error": "The ComfyUI user directory could not be resolved."}, status=503, ) try: payload = await request.json() environment_id = str(payload.get("environment_id") or "").strip() host = await asyncio.to_thread( remote_host_registry.get_host, environment_id ) capabilities = await asyncio.to_thread( SshDockerController(host).probe_capabilities ) updated_host = await asyncio.to_thread( remote_host_registry.update_probe_result, environment_id, capabilities=capabilities, health=EnvironmentHealth.READY, last_error=None, ) except (KeyError, TypeError, ValueError, RuntimeError) as exc: environment_id = str(locals().get("environment_id") or "").strip() if environment_id: try: await asyncio.to_thread( remote_host_registry.update_probe_result, environment_id, capabilities=None, health=EnvironmentHealth.UNAVAILABLE, last_error=str(exc), ) except (KeyError, ValueError): pass return web.json_response({"error": str(exc)}, status=502) return web.json_response(updated_host.to_dict()) @prompt_server.routes.post("/remote/vast/verify") async def vast_verify(request: web.Request) -> web.Response: """Verify the configured Vast credential without returning it.""" del request api_key = str(os.getenv("VAST_API_KEY") or "").strip() if not api_key: return web.json_response( {"verified": False, "error": "Set VAST_API_KEY first."}, status=400, ) base_url = str(os.getenv("COMFY_MODAL_VAST_API_BASE_URL") or "").strip() try: client = VastApiClient(api_key, **({"base_url": base_url} if base_url else {})) account = await client.verify_credentials() except (OSError, RuntimeError, ValueError) as exc: return web.json_response( {"verified": False, "error": str(exc)}, status=502, ) return web.json_response({"verified": True, "account": account}) @prompt_server.routes.post("/remote/vast/reap") async def vast_reap(request: web.Request) -> web.Response: """Destroy only owned idle leases whose configured deadline has expired.""" del request try: service = VastService.from_environment( resolved_settings, repo_root=Path(__file__).resolve().parent, ) destroyed = await service.lease_manager.destroy_expired() except (OSError, RuntimeError, ValueError) as exc: return web.json_response({"error": str(exc)}, status=502) return web.json_response({"destroyed_instance_ids": list(destroyed)}) @prompt_server.routes.post("/remote/vast/destroy") async def vast_destroy(request: web.Request) -> web.Response: """Destroy one exact idle registry-owned lease after server-side label checks.""" try: payload = await request.json() instance_id = int(payload.get("instance_id")) service = VastService.from_environment( resolved_settings, repo_root=Path(__file__).resolve().parent, ) destroyed = await service.lease_manager.destroy_owned_lease(instance_id) except (OSError, RuntimeError, TypeError, ValueError) as exc: return web.json_response({"error": str(exc)}, status=502) return web.json_response({"instance_id": instance_id, "destroyed": destroyed}) @prompt_server.routes.post(remote_environment_bootstrap_route_path) async def remote_environment_bootstrap(request: web.Request) -> web.Response: """Build the current runtime and start one compatible warm SSH worker.""" if remote_host_registry is None: return web.json_response( {"error": "The ComfyUI user directory could not be resolved."}, status=503, ) environment_id = "" try: from .ssh_runtime import SshRuntimeManager payload = await request.json() environment_id = str(payload.get("environment_id") or "").strip() worker_index = int(payload.get("worker_index", 0)) host = await asyncio.to_thread( remote_host_registry.get_host, environment_id ) manager = SshRuntimeManager( controller=SshDockerController(host), repo_root=Path(__file__).resolve().parent, settings=resolved_settings, ) spec = await asyncio.to_thread(manager.ensure_worker, worker_index) except (KeyError, TypeError, ValueError, RuntimeError) as exc: if remote_host_registry is not None and environment_id: try: await asyncio.to_thread( remote_host_registry.update_probe_result, environment_id, capabilities=host.capabilities if "host" in locals() else None, health=EnvironmentHealth.UNAVAILABLE, last_error=str(exc), ) except (KeyError, ValueError): pass return web.json_response({"error": str(exc)}, status=502) await asyncio.to_thread( remote_host_registry.update_probe_result, environment_id, capabilities=host.capabilities, health=EnvironmentHealth.READY, last_error=None, ) return web.json_response( { "environment_id": environment_id, "worker_index": worker_index, "container_name": spec.container_name, "image_tag": spec.image_tag, "runtime_fingerprint": spec.identity.fingerprint, } ) @prompt_server.routes.post(remote_environment_status_route_path) async def remote_environment_status(request: web.Request) -> web.Response: """Return managed worker state for one SSH execution environment.""" if remote_host_registry is None: return web.json_response( {"error": "The ComfyUI user directory could not be resolved."}, status=503, ) try: payload = await request.json() environment_id = str(payload.get("environment_id") or "").strip() host = await asyncio.to_thread( remote_host_registry.get_host, environment_id ) workers = await asyncio.to_thread( SshDockerController(host).list_managed_workers ) except (KeyError, TypeError, ValueError, RuntimeError) as exc: return web.json_response({"error": str(exc)}, status=502) return web.json_response( { "environment_id": environment_id, "workers": [worker.to_dict() for worker in workers], } ) @prompt_server.routes.post(remote_environment_stop_route_path) async def remote_environment_stop(request: web.Request) -> web.Response: """Stop all node-pack-managed workers on one configured SSH host.""" if remote_host_registry is None: return web.json_response( {"error": "The ComfyUI user directory could not be resolved."}, status=503, ) try: from .ssh_runtime import SshRuntimeManager payload = await request.json() environment_id = str(payload.get("environment_id") or "").strip() host = await asyncio.to_thread( remote_host_registry.get_host, environment_id ) manager = SshRuntimeManager( controller=SshDockerController(host), repo_root=Path(__file__).resolve().parent, settings=resolved_settings, ) removed = await asyncio.to_thread(manager.stop_all_workers) except (KeyError, TypeError, ValueError, RuntimeError) as exc: return web.json_response({"error": str(exc)}, status=502) return web.json_response( {"environment_id": environment_id, "removed_containers": list(removed)} ) @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, "sandwiched_local_node_ids": analysis.sandwiched_local_node_ids, "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(delete_caches_route_path) async def modal_delete_caches(request: web.Request) -> web.Response: """Delete persistent Modal cache Dicts for the active configuration.""" del request logger.info("Received Modal cache deletion request.") try: return web.json_response(await delete_modal_cache_dicts(resolved_settings)) except RuntimeError as exc: logger.warning("Modal cache deletion request failed: %s", exc) return web.json_response({"error": str(exc), "node_errors": []}, status=400) @prompt_server.routes.post(delete_volume_route_path) async def modal_delete_volume(request: web.Request) -> web.Response: """Delete the configured Modal Volume for the active configuration.""" del request logger.info("Received Modal volume deletion request.") try: return web.json_response(await delete_modal_volume(resolved_settings)) except RuntimeError as exc: logger.warning("Modal volume deletion request failed: %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] = [] request_modal_gpu: str | None = None summary = RewriteSummary() preparation_prompt_id: str | None = None preparation_cancellation = threading.Event() configurator_node_id: str | None = None 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"] 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 ) extra_pnginfo = (json_data.get("extra_data") or {}).get( "extra_pnginfo" ) or {} workflow = extra_pnginfo.get("workflow") remote_node_ids = sorted( requested_remote_node_ids( prompt=json_data.get("prompt", {}), workflow=workflow, settings=resolved_settings, ) ) configurator_node_id = _remote_execution_configurator_node_id( json_data.get("prompt", {}) ) if "prompt" in json_data and not remote_node_ids: logger.info( "No workflow nodes are marked for Modal execution; forwarding prompt without Modal status or rewrite." ) 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 if prompt_id is not None: extra_pnginfo[MODAL_PROMPT_ID_EXTRA_PNGINFO_KEY] = prompt_id json_data["extra_data"]["extra_pnginfo"] = extra_pnginfo logger.debug( "Attached prompt-scoped Modal execution metadata prompt_id=%s.", prompt_id, ) if _set_remote_preparation( prompt_server, prompt_id=prompt_id, prompt=json_data.get("prompt", {}), extra_data=json_data.get("extra_data", {}), cancellation_event=preparation_cancellation, ): preparation_prompt_id = prompt_id try: request_settings = settings_for_modal_gpu( resolved_settings, modal_gpu_from_workflow(workflow, resolved_settings.modal_gpu), ) except ValueError as exc: raise ModalPromptValidationError(str(exc)) from exc logger.info( "Resolved workflow Modal GPU selection gpu=%s prompt_id=%s.", request_settings.modal_gpu, prompt_id, ) status_modal_gpu = ( None if _prompt_uses_remote_execution_configurator( json_data.get("prompt", {}) ) else request_settings.modal_gpu ) request_modal_gpu = status_modal_gpu def emit_setup_status( message: str, current: int | None = None, total: int | None = None, ) -> None: """Forward one queue-time Modal setup update into the websocket stream.""" if preparation_cancellation.is_set(): raise SyncCancelledError( "Remote workflow preparation was cancelled." ) _emit_modal_status( prompt_server=prompt_server, phase="setup", client_id=client_id, prompt_id=prompt_id, node_ids=remote_node_ids, configurator_node_id=configurator_node_id, modal_gpu=status_modal_gpu, component_node_ids_by_representative=( summary.component_node_ids_by_representative or None ), status_message=message, status_current=current, status_total=total, ) def emit_execution_plan( assignments: dict[str, dict[str, Any]], configurations: list[dict[str, Any]], ) -> None: """Publish scheduler choices before capacity acquisition can block.""" component_nodes = { component_id: list(assignment.get("node_ids", [])) for component_id, assignment in assignments.items() } _emit_modal_status( prompt_server=prompt_server, phase="setup", client_id=client_id, prompt_id=prompt_id, node_ids=remote_node_ids, configurator_node_id=configurator_node_id, modal_gpu=status_modal_gpu, component_node_ids_by_representative=component_nodes, status_message="Remote execution plan ready", remote_execution_assignments=assignments, remote_execution_configurations=configurations, ) if "prompt" in json_data: emit_setup_status("Preparing remote workflow") rewrite_started_at = time.perf_counter() rewritten_prompt, summary = await rewrite_prompt_for_modal_async( prompt=json_data["prompt"], workflow=workflow, sync_engine=resolved_sync_engine, settings=request_settings, extra_data=json_data.get("extra_data"), status_callback=emit_setup_status, plan_callback=emit_execution_plan, cancellation_check=preparation_cancellation.is_set, ) selected_modal_gpus = _selected_modal_gpus( summary, request_settings.modal_gpu, ) status_modal_gpu = ( selected_modal_gpus[0] if len(selected_modal_gpus) == 1 else None ) request_modal_gpu = status_modal_gpu 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"]["gpu"] = request_settings.modal_gpu 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"][ "component_dependency_ids_by_representative" ] = summary.component_dependency_ids_by_representative json_data["extra_data"]["modal"][ "component_execution_stages" ] = summary.component_execution_stages json_data["extra_data"]["modal"][ "mapped_component_ids" ] = summary.mapped_component_ids json_data["extra_data"]["modal"][ "estimated_max_parallel_requests" ] = summary.estimated_max_parallel_requests json_data["extra_data"]["modal"][ "max_parallel_requests_upper_bound" ] = summary.max_parallel_requests_upper_bound json_data["extra_data"]["modal"]["synced_assets"] = [ asset.remote_path for asset in summary.synced_assets ] json_data["extra_data"]["remote_execution"] = { "assignments": _execution_assignments_payload( summary, request_settings, ), "configurations": list(summary.remote_configurations), } 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=client_id, prompt_id=prompt_id, node_ids=remote_node_ids, configurator_node_id=configurator_node_id, modal_gpu=status_modal_gpu, component_node_ids_by_representative=summary.component_node_ids_by_representative, status_message="Submitting remote workflow", ) response = await _queue_prompt_json( prompt_server, json_data, modal_response_payload=( { "modal_gpu": status_modal_gpu, "remote_execution_configurator_node_id": configurator_node_id, "remote_execution_modal_gpus": selected_modal_gpus, "modal_remote_node_ids": list(summary.remote_node_ids), "modal_sandwiched_local_node_ids": list( summary.sandwiched_local_node_ids ), "modal_parallel_local_branch_node_ids": list( summary.parallel_local_branch_node_ids ), "remote_execution_assignments": _execution_assignments_payload( summary, request_settings, ), "remote_execution_configurations": list( summary.remote_configurations ), "modal_components": [ { "representative_node_id": representative_node_id, "node_ids": list(component_node_ids), } for representative_node_id, component_node_ids in sorted( summary.component_node_ids_by_representative.items() ) ], } if summary.remote_node_ids else None ), ) logger.info( "Modal queue request completed in %.3fs.", time.perf_counter() - request_started_at, ) return response except SyncCancelledError as exc: logger.info("Remote workflow preparation cancelled: %s", exc) if json_data is not None: _emit_modal_status( prompt_server=prompt_server, phase="execution_interrupted", 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, configurator_node_id=configurator_node_id, modal_gpu=request_modal_gpu, status_message=str(exc), ) return web.json_response( {"error": str(exc), "node_errors": [], "cancelled": True}, status=409, ) 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, configurator_node_id=configurator_node_id, modal_gpu=request_modal_gpu, 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, configurator_node_id=configurator_node_id, modal_gpu=request_modal_gpu, 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, configurator_node_id=configurator_node_id, modal_gpu=request_modal_gpu, error_message=str(exc), ) return web.json_response({"error": str(exc), "node_errors": []}, status=500) finally: if preparation_prompt_id is not None: _clear_remote_preparation(prompt_server, preparation_prompt_id) _ROUTE_REGISTERED = True logger.info( "Registered Modal queue route at %s, analysis route at %s, progress state route at %s, container status route at %s, cache deletion route at %s, and volume deletion route at %s", resolved_settings.route_path, analysis_route_path, progress_state_route_path, container_status_route_path, delete_caches_route_path, delete_volume_route_path, )