812 lines
29 KiB
Python
812 lines
29 KiB
Python
"""CPU-side Hugging Face snapshot staging for Modal LLM profiles."""
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from __future__ import annotations
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import json
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import logging
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import math
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import os
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import shutil
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import time
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from contextlib import contextmanager
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from dataclasses import asdict, dataclass, replace
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from pathlib import Path
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from typing import Any, Callable, Iterator, Mapping
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from uuid import uuid4
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if __package__:
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from .llm_profiles import LLMModelProfile, get_llm_profile, load_llm_profiles
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from .snapshot_lease import (
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_DEFAULT_LEASE_HEARTBEAT_STALE_SECONDS,
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_local_lease_owner_is_alive,
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_process_start_identity,
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_snapshot_lease as _generic_snapshot_lease,
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)
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else: # pragma: no cover - the stable cloud entrypoint imports top-level modules.
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from llm_profiles import LLMModelProfile, get_llm_profile, load_llm_profiles
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from snapshot_lease import (
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_DEFAULT_LEASE_HEARTBEAT_STALE_SECONDS,
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_local_lease_owner_is_alive,
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_process_start_identity,
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_snapshot_lease as _generic_snapshot_lease,
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)
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logger = logging.getLogger(__name__)
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_COMPLETE_MARKER_FILENAME = ".comfy-modal-llm-complete.json"
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_DEFAULT_MINIMUM_FREE_DISK_BYTES = 8 * 1024**3
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_SNAPSHOT_ALLOW_PATTERNS = (
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"*.safetensors",
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"*.safetensors.index.json",
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"*.model",
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"*.tiktoken",
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"added_tokens.json",
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"chat_template*",
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"config.json",
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"generation_config.json",
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"hf_quant_config.json",
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"merges.txt",
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"preprocessor_config.json",
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"processor*",
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"special_tokens_map.json",
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"tokenizer*",
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"video_preprocessor_config.json",
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"vocab*",
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)
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_TOKENIZER_ALLOW_PATTERNS = tuple(
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pattern for pattern in _SNAPSHOT_ALLOW_PATTERNS if "safetensors" not in pattern
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)
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@dataclass(frozen=True)
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class StagedModelSnapshot:
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"""Describe a model snapshot made visible on the shared Modal Volume."""
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profile_id: str
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repository: str
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revision: str
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path: str
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downloaded: bool
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elapsed_seconds: float
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@dataclass(frozen=True)
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class LLMStagingProgress:
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"""Describe one numeric Hugging Face snapshot-staging update."""
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stage: str
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message: str
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value: float | None = None
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maximum: float | None = None
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unit: str | None = None
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indeterminate: bool = False
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model_reference: str | None = None
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StagingProgressCallback = Callable[[LLMStagingProgress], None]
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@dataclass(frozen=True)
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class ResolvedStagedModelProfile:
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"""Describe one resolved immutable profile and its staged snapshot."""
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requested_reference: str
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profile_id: str
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repository: str
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revision: str
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backend: str
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quantization_method: str
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artifact_bytes: int
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manifest_path: str | None
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manifest_created: bool
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security_scan_complete: bool
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path: str
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downloaded: bool
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resolve_elapsed_seconds: float
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elapsed_seconds: float
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def to_dict(self) -> dict[str, Any]:
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"""Return JSON-compatible staging metadata."""
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return asdict(self)
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def _snapshot_tqdm_class(
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progress_callback: StagingProgressCallback,
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) -> type[Any]:
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"""Build a tqdm subclass that forwards Hugging Face file progress."""
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from tqdm.auto import tqdm
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class SnapshotProgressTqdm(tqdm):
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"""Mirror snapshot_download's aggregate file bar to the caller."""
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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"""Initialize the Hugging Face bar and report its initial state."""
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super().__init__(*args, **kwargs)
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self._report_progress()
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def update(self, n: float | None = 1) -> bool | None:
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"""Forward every aggregate file-count update."""
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updated = super().update(n)
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self._report_progress()
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return updated
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def _report_progress(self) -> None:
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"""Publish the current tqdm count with compact user-facing copy."""
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maximum = float(self.total) if self.total is not None else None
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value = float(self.n)
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description = str(
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getattr(self, "desc", None) or "Downloading model snapshot"
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).strip()
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progress_callback(
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LLMStagingProgress(
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stage="download",
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message=description,
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value=value,
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maximum=maximum,
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unit=str(getattr(self, "unit", None) or "files"),
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indeterminate=maximum is None,
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)
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)
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return SnapshotProgressTqdm
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def model_snapshot_path(storage_root: str | Path, profile: LLMModelProfile) -> Path:
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"""Return the shared path, reusing a valid legacy profile path in place."""
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resolved_root = Path(storage_root).resolve()
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canonical_path = resolved_root / profile.storage_relative_path()
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if canonical_path.exists():
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return canonical_path
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model_root = resolved_root / "llm_models"
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for legacy_path in sorted(model_root.glob(f"*/{profile.revision}")):
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if legacy_path != canonical_path and _legacy_snapshot_matches(
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legacy_path,
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profile,
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):
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logger.info(
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"Reusing legacy Modal LLM weight path repository=%s revision=%s "
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"path=%s.",
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profile.repository,
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profile.revision,
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legacy_path,
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)
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return legacy_path
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return canonical_path
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def _marker_path(snapshot_path: Path) -> Path:
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"""Return the completion marker path for one staged snapshot."""
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return snapshot_path / _COMPLETE_MARKER_FILENAME
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def _required_model_paths(
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snapshot_path: Path,
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profile: LLMModelProfile,
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) -> tuple[Path, ...]:
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"""Return every required model artifact inside one snapshot."""
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model_filename = profile.backend_option("model_filename")
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if model_filename is None:
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return (snapshot_path / "config.json",)
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filenames = [str(model_filename)]
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mmproj_filename = profile.backend_option("mmproj_filename")
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if mmproj_filename is not None:
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filenames.append(str(mmproj_filename))
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return tuple(snapshot_path / filename for filename in filenames)
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def _tokenizer_source(profile: LLMModelProfile) -> tuple[str, str] | None:
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"""Return an optional separately pinned tokenizer snapshot source."""
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repository = profile.backend_option("tokenizer_repository")
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revision = profile.backend_option("tokenizer_revision")
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if repository is None and revision is None:
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return None
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normalized_repository = str(repository or "").strip()
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normalized_revision = str(revision or "").strip().lower()
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if "/" not in normalized_repository or len(normalized_revision) != 40:
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raise ValueError(
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f"Modal LLM profile {profile.profile_id!r} has an invalid separately "
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"pinned tokenizer source."
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)
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return normalized_repository, normalized_revision
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def _snapshot_has_required_artifacts(
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snapshot_path: Path,
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profile: LLMModelProfile,
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) -> bool:
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"""Return whether model and optional tokenizer artifacts are complete."""
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if not all(
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required_path.is_file()
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for required_path in _required_model_paths(snapshot_path, profile)
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):
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return False
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return (
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_tokenizer_source(profile) is None
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or (snapshot_path / "tokenizer_config.json").is_file()
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)
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def _model_allow_patterns(profile: LLMModelProfile) -> tuple[str, ...]:
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"""Return a bounded allowlist for this profile's primary repository."""
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model_filename = profile.backend_option("model_filename")
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if model_filename is None:
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return _SNAPSHOT_ALLOW_PATTERNS
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selected_filenames = [str(model_filename)]
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mmproj_filename = profile.backend_option("mmproj_filename")
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if mmproj_filename is not None:
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selected_filenames.append(str(mmproj_filename))
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return (*_TOKENIZER_ALLOW_PATTERNS, *selected_filenames)
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def _read_marker(snapshot_path: Path) -> dict[str, Any] | None:
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"""Return a valid completion marker, if present."""
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marker_path = _marker_path(snapshot_path)
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try:
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payload = json.loads(marker_path.read_text(encoding="utf-8"))
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except (FileNotFoundError, json.JSONDecodeError, OSError):
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return None
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return payload if isinstance(payload, dict) else None
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def is_model_snapshot_staged(
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storage_root: str | Path, profile: LLMModelProfile
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) -> bool:
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"""Return whether the requested immutable snapshot is completely staged."""
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snapshot_path = model_snapshot_path(storage_root, profile)
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marker = _read_marker(snapshot_path)
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return bool(
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marker
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and marker.get("repository") == profile.repository
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and marker.get("revision") == profile.revision
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and _snapshot_has_required_artifacts(snapshot_path, profile)
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)
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def _write_marker(snapshot_path: Path, profile: LLMModelProfile) -> None:
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"""Atomically record that an immutable model snapshot is complete."""
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marker_path = _marker_path(snapshot_path)
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temporary_path = marker_path.with_suffix(".tmp")
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marker_payload = {
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"marker_version": 2,
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"profile_id": profile.profile_id,
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"repository": profile.repository,
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"revision": profile.revision,
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"completed_at_unix": time.time(),
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}
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temporary_path.write_text(
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json.dumps(marker_payload, sort_keys=True, separators=(",", ":")),
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encoding="utf-8",
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)
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os.replace(temporary_path, marker_path)
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def _legacy_snapshot_matches(
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snapshot_path: Path,
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profile: LLMModelProfile,
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) -> bool:
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"""Return whether an old profile-keyed directory has these exact weights."""
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marker = _read_marker(snapshot_path)
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return bool(
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marker
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and marker.get("repository") == profile.repository
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and marker.get("revision") == profile.revision
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and _snapshot_has_required_artifacts(snapshot_path, profile)
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)
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@contextmanager
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def _snapshot_lease(
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snapshot_path: Path,
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*,
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progress_callback: StagingProgressCallback | None = None,
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model_label: str,
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owner_id: str,
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) -> Iterator[None]:
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"""Adapt generic lease wait messages to LLM staging progress events."""
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def report_wait(message: str) -> None:
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"""Publish one generic lease wait through the staging callback."""
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if progress_callback is not None:
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progress_callback(
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LLMStagingProgress(
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stage="waiting_for_download",
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message=message,
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indeterminate=True,
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)
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)
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with _generic_snapshot_lease(
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snapshot_path,
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progress_callback=report_wait if progress_callback is not None else None,
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model_label=model_label,
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owner_id=owner_id,
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):
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yield
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def _snapshot_existing_bytes(snapshot_path: Path) -> int:
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"""Return bytes already present for a resumable model snapshot."""
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total = 0
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if not snapshot_path.exists():
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return total
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for candidate in snapshot_path.rglob("*"):
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try:
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if candidate.is_file() and not candidate.name.endswith(".lock"):
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total += candidate.stat().st_size
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except (FileNotFoundError, OSError):
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continue
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return total
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def _minimum_free_disk_bytes() -> int:
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"""Return the configured free-space reserve retained after staging."""
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raw_value = os.getenv("COMFY_MODAL_LLM_MIN_FREE_DISK_GB")
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if raw_value is None:
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return _DEFAULT_MINIMUM_FREE_DISK_BYTES
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value_gb = float(raw_value)
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if not math.isfinite(value_gb) or value_gb < 0:
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raise ValueError("COMFY_MODAL_LLM_MIN_FREE_DISK_GB must not be negative.")
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return int(value_gb * 1024**3)
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def _preflight_snapshot_capacity(
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snapshot_path: Path,
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profile: LLMModelProfile,
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) -> tuple[int, int, int]:
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"""Require room for missing artifacts plus a post-download safety reserve."""
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snapshot_path.parent.mkdir(parents=True, exist_ok=True)
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free_bytes = shutil.disk_usage(snapshot_path.parent).free
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existing_bytes = _snapshot_existing_bytes(snapshot_path)
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remaining_bytes = max(0, profile.artifact_bytes - existing_bytes)
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required_bytes = remaining_bytes + _minimum_free_disk_bytes()
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if free_bytes < required_bytes:
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raise RuntimeError(
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"Insufficient disk space to stage LLM profile "
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f"{profile.profile_id!r}: {free_bytes / 1024**3:.2f} GiB free, "
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f"{remaining_bytes / 1024**3:.2f} GiB still required, and "
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f"{_minimum_free_disk_bytes() / 1024**3:.2f} GiB must remain free."
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)
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return free_bytes, remaining_bytes, required_bytes
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def stage_model_profile(
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profile_id: str,
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storage_root: str | Path,
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*,
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profile: LLMModelProfile | None = None,
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snapshot_download: Callable[..., str] | None = None,
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progress_callback: StagingProgressCallback | None = None,
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model_reference: str | None = None,
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owner_id: str | None = None,
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) -> StagedModelSnapshot:
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"""Download one pinned curated or generated snapshot on a CPU worker."""
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profile = profile or get_llm_profile(profile_id, storage_root=storage_root)
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if profile.profile_id != profile_id:
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raise ValueError(
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f"Stage request id {profile_id!r} does not match profile "
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f"{profile.profile_id!r}."
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)
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model_label = model_reference or profile.profile_id
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def report(progress: LLMStagingProgress) -> None:
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"""Attach the user-facing model reference to one staging event."""
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if progress_callback is not None:
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progress_callback(replace(progress, model_reference=model_label))
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snapshot_path = model_snapshot_path(storage_root, profile)
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started_at = time.perf_counter()
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report(
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LLMStagingProgress(
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stage="snapshot_check",
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message=f"Checking staged snapshot for {model_label}",
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indeterminate=True,
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)
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)
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if is_model_snapshot_staged(storage_root, profile):
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logger.info(
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"Reusing staged Modal LLM profile=%s revision=%s path=%s.",
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profile.profile_id,
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profile.revision,
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snapshot_path,
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)
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report(
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LLMStagingProgress(
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stage="cached",
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message=f"Using staged snapshot for {model_label}",
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value=1,
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maximum=1,
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unit="model",
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)
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)
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return StagedModelSnapshot(
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profile_id=profile.profile_id,
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repository=profile.repository,
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revision=profile.revision,
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path=str(snapshot_path),
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downloaded=False,
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elapsed_seconds=time.perf_counter() - started_at,
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)
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with _snapshot_lease(
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snapshot_path,
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progress_callback=report,
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model_label=model_label,
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owner_id=owner_id or f"pid-{os.getpid()}",
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):
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if is_model_snapshot_staged(storage_root, profile):
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report(
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LLMStagingProgress(
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stage="cached",
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message=(
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f"Using snapshot staged by another worker for {model_label}"
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),
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value=1,
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maximum=1,
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unit="model",
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)
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)
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return StagedModelSnapshot(
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profile_id=profile.profile_id,
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repository=profile.repository,
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revision=profile.revision,
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path=str(snapshot_path),
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downloaded=False,
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elapsed_seconds=time.perf_counter() - started_at,
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)
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free_bytes, remaining_bytes, _required_bytes = _preflight_snapshot_capacity(
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snapshot_path,
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profile,
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)
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report(
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LLMStagingProgress(
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stage="disk_check",
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message=(
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f"Storage ready for {model_label}: "
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f"{free_bytes / 1024**3:.1f} GiB free, "
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f"{remaining_bytes / 1024**3:.1f} GiB remaining"
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),
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value=free_bytes,
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maximum=free_bytes,
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unit="bytes",
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)
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)
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if snapshot_download is None:
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from huggingface_hub import (
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snapshot_download as huggingface_snapshot_download,
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)
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snapshot_download = huggingface_snapshot_download
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report(
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LLMStagingProgress(
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stage="download_preparing",
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message=f"Preparing download for {model_label}",
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indeterminate=True,
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)
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)
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snapshot_path.mkdir(parents=True, exist_ok=True)
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logger.info(
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"Staging Modal LLM profile=%s repository=%s revision=%s to %s.",
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profile.profile_id,
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profile.repository,
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profile.revision,
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snapshot_path,
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)
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download_options: dict[str, Any] = {
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"repo_id": profile.repository,
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"revision": profile.revision,
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"local_dir": str(snapshot_path),
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"token": os.getenv("HF_TOKEN") or os.getenv("HUGGING_FACE_HUB_TOKEN"),
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"allow_patterns": _model_allow_patterns(profile),
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}
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if progress_callback is not None:
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download_options["tqdm_class"] = _snapshot_tqdm_class(report)
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resolved_path = Path(snapshot_download(**download_options)).resolve()
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if resolved_path != snapshot_path.resolve():
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raise RuntimeError(
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f"Hugging Face staged {profile.profile_id!r} at unexpected path "
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f"{resolved_path}; expected {snapshot_path.resolve()}."
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)
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tokenizer_source = _tokenizer_source(profile)
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if tokenizer_source is not None:
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tokenizer_repository, tokenizer_revision = tokenizer_source
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tokenizer_options: dict[str, Any] = {
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"repo_id": tokenizer_repository,
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"revision": tokenizer_revision,
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|
"local_dir": str(snapshot_path),
|
|
"token": download_options["token"],
|
|
"allow_patterns": _TOKENIZER_ALLOW_PATTERNS,
|
|
}
|
|
if progress_callback is not None:
|
|
tokenizer_options["tqdm_class"] = _snapshot_tqdm_class(
|
|
report
|
|
)
|
|
tokenizer_path = Path(snapshot_download(**tokenizer_options)).resolve()
|
|
if tokenizer_path != snapshot_path.resolve():
|
|
raise RuntimeError(
|
|
f"Hugging Face staged tokenizer for {profile.profile_id!r} at "
|
|
f"unexpected path {tokenizer_path}; expected "
|
|
f"{snapshot_path.resolve()}."
|
|
)
|
|
if not _snapshot_has_required_artifacts(snapshot_path, profile):
|
|
raise RuntimeError(
|
|
f"Staged Modal LLM profile {profile.profile_id!r} is missing its "
|
|
"required model or tokenizer artifacts."
|
|
)
|
|
_write_marker(snapshot_path, profile)
|
|
report(
|
|
LLMStagingProgress(
|
|
stage="staged",
|
|
message=f"Model snapshot ready for {model_label}",
|
|
value=1,
|
|
maximum=1,
|
|
unit="model",
|
|
)
|
|
)
|
|
result = StagedModelSnapshot(
|
|
profile_id=profile.profile_id,
|
|
repository=profile.repository,
|
|
revision=profile.revision,
|
|
path=str(snapshot_path),
|
|
downloaded=True,
|
|
elapsed_seconds=time.perf_counter() - started_at,
|
|
)
|
|
logger.info("Completed Modal LLM staging: %s", asdict(result))
|
|
return result
|
|
|
|
|
|
def _persist_supplied_profile(
|
|
model_reference: str,
|
|
storage_root: str | Path,
|
|
supplied: Mapping[str, Any],
|
|
) -> tuple[LLMModelProfile, str | None, bool, bool]:
|
|
"""Validate planner metadata and persist a generated manifest remotely."""
|
|
raw_profile = supplied.get("profile", supplied)
|
|
if not isinstance(raw_profile, Mapping):
|
|
raise ValueError(
|
|
f"Resolved LLM profile for {model_reference!r} must contain an object."
|
|
)
|
|
profile = LLMModelProfile.from_mapping(raw_profile)
|
|
_validate_supplied_profile_reference(model_reference, profile)
|
|
if profile.source != "generated":
|
|
return profile, None, False, True
|
|
if __package__:
|
|
from .llm_profiles import generated_profile_manifest_path
|
|
else: # pragma: no cover - stable cloud entrypoint imports top-level modules.
|
|
from llm_profiles import generated_profile_manifest_path
|
|
|
|
manifest_path = generated_profile_manifest_path(storage_root, profile.profile_id)
|
|
manifest_payload = _supplied_profile_manifest_payload(
|
|
model_reference,
|
|
profile,
|
|
supplied,
|
|
)
|
|
existing_scan = _existing_supplied_profile_scan(
|
|
manifest_path,
|
|
raw_profile,
|
|
)
|
|
if existing_scan is not None:
|
|
return profile, str(manifest_path), False, existing_scan
|
|
manifest_path.parent.mkdir(parents=True, exist_ok=True)
|
|
temporary_path = manifest_path.with_suffix(f".{uuid4().hex}.tmp")
|
|
temporary_path.write_text(
|
|
json.dumps(manifest_payload, sort_keys=True, separators=(",", ":")),
|
|
encoding="utf-8",
|
|
)
|
|
os.replace(temporary_path, manifest_path)
|
|
return profile, str(manifest_path), True, bool(
|
|
manifest_payload["security_scan_complete"]
|
|
)
|
|
|
|
|
|
def _supplied_profile_manifest_payload(
|
|
model_reference: str,
|
|
profile: LLMModelProfile,
|
|
supplied: Mapping[str, Any],
|
|
) -> dict[str, Any]:
|
|
"""Build the generated manifest persisted from trusted planner metadata."""
|
|
security_scan_complete = supplied.get("security_scan_complete", False)
|
|
if not isinstance(security_scan_complete, bool):
|
|
raise ValueError(
|
|
f"Resolved LLM profile for {model_reference!r} has an invalid "
|
|
"security scan state."
|
|
)
|
|
return {
|
|
"schema_version": profile.schema_version,
|
|
"compatibility_policy_version": profile.compatibility_policy_version,
|
|
"requested_reference": model_reference,
|
|
"resolved_at_unix": time.time(),
|
|
"security_scan_complete": security_scan_complete,
|
|
"profile": profile.to_mapping(),
|
|
}
|
|
|
|
|
|
def _existing_supplied_profile_scan(
|
|
manifest_path: Path,
|
|
raw_profile: Mapping[str, Any],
|
|
) -> bool | None:
|
|
"""Return an existing matching manifest's scan state, or None if absent."""
|
|
if manifest_path.is_file():
|
|
try:
|
|
existing = json.loads(manifest_path.read_text(encoding="utf-8"))
|
|
except (json.JSONDecodeError, OSError) as exc:
|
|
raise RuntimeError(
|
|
f"Resolved LLM manifest {manifest_path} is unreadable: {exc}"
|
|
) from exc
|
|
if not isinstance(existing, Mapping) or existing.get("profile") != raw_profile:
|
|
raise RuntimeError(
|
|
f"Resolved LLM profile collision at {manifest_path}."
|
|
)
|
|
return bool(existing.get("security_scan_complete", False))
|
|
return None
|
|
|
|
|
|
def _validate_supplied_profile_reference(
|
|
model_reference: str,
|
|
profile: LLMModelProfile,
|
|
) -> None:
|
|
"""Reject planner metadata that does not describe its requested reference."""
|
|
curated_profile = load_llm_profiles().get(model_reference)
|
|
if curated_profile is not None:
|
|
if profile != curated_profile:
|
|
raise ValueError(
|
|
f"Resolved metadata for curated profile {model_reference!r} does "
|
|
"not match the checked-in profile."
|
|
)
|
|
return
|
|
if model_reference.startswith("hf-"):
|
|
if profile.profile_id != model_reference:
|
|
raise ValueError(
|
|
f"Resolved metadata profile id {profile.profile_id!r} does not "
|
|
f"match requested id {model_reference!r}."
|
|
)
|
|
return
|
|
if profile.source != "generated":
|
|
raise ValueError(
|
|
f"Resolved metadata for Hugging Face reference {model_reference!r} "
|
|
"must contain a generated profile."
|
|
)
|
|
if __package__:
|
|
from .llm_resolver import HuggingFaceModelReference
|
|
else: # pragma: no cover - stable cloud entrypoint imports top-level modules.
|
|
from llm_resolver import HuggingFaceModelReference
|
|
|
|
parsed = HuggingFaceModelReference.parse(model_reference)
|
|
if profile.repository != parsed.repository:
|
|
raise ValueError(
|
|
f"Resolved metadata repository {profile.repository!r} does not match "
|
|
f"requested repository {parsed.repository!r}."
|
|
)
|
|
requested_revision = (parsed.requested_revision or "").lower()
|
|
if len(requested_revision) == 40 and requested_revision != profile.revision:
|
|
raise ValueError(
|
|
f"Resolved metadata revision {profile.revision!r} does not match "
|
|
f"requested revision {requested_revision!r}."
|
|
)
|
|
|
|
|
|
def _resolve_profile_for_staging(
|
|
model_reference: str,
|
|
storage_root: str | Path,
|
|
supplied_profile: Mapping[str, Any] | None = None,
|
|
) -> tuple[LLMModelProfile, str | None, bool, bool]:
|
|
"""Resolve one curated, generated, or Hugging Face model reference."""
|
|
if supplied_profile is not None:
|
|
return _persist_supplied_profile(
|
|
model_reference,
|
|
storage_root,
|
|
supplied_profile,
|
|
)
|
|
curated_profiles = load_llm_profiles()
|
|
if model_reference in curated_profiles:
|
|
return curated_profiles[model_reference], None, False, True
|
|
if model_reference.startswith("hf-"):
|
|
return (
|
|
get_llm_profile(model_reference, storage_root=storage_root),
|
|
None,
|
|
False,
|
|
True,
|
|
)
|
|
if __package__:
|
|
from .llm_resolver import resolve_model_profile
|
|
else: # pragma: no cover - stable cloud entrypoint imports top-level modules.
|
|
from llm_resolver import resolve_model_profile
|
|
|
|
resolved = resolve_model_profile(model_reference, storage_root)
|
|
return (
|
|
resolved.profile,
|
|
resolved.manifest_path,
|
|
resolved.manifest_created,
|
|
resolved.security_scan_complete,
|
|
)
|
|
|
|
|
|
def resolve_and_stage_model_references(
|
|
model_references: list[str],
|
|
storage_root: str | Path,
|
|
*,
|
|
progress_callback: StagingProgressCallback | None = None,
|
|
resolved_profiles: Mapping[str, Mapping[str, Any]] | None = None,
|
|
owner_id: str | None = None,
|
|
) -> list[ResolvedStagedModelProfile]:
|
|
"""Resolve and stage model references on any CPU-backed remote worker."""
|
|
results: list[ResolvedStagedModelProfile] = []
|
|
for model_reference in model_references:
|
|
supplied_profile = (resolved_profiles or {}).get(model_reference)
|
|
if progress_callback is not None:
|
|
is_hugging_face_reference = (
|
|
"/" in model_reference and not model_reference.startswith("hf-")
|
|
)
|
|
progress_callback(
|
|
LLMStagingProgress(
|
|
stage=(
|
|
"resolved_metadata"
|
|
if supplied_profile is not None
|
|
else "metadata"
|
|
if is_hugging_face_reference
|
|
else "profile_resolution"
|
|
),
|
|
message=(
|
|
f"Using planner-resolved metadata for {model_reference}"
|
|
if supplied_profile is not None
|
|
else f"Inspecting Hugging Face metadata for {model_reference}"
|
|
if is_hugging_face_reference
|
|
else f"Resolving model profile {model_reference}"
|
|
),
|
|
indeterminate=True,
|
|
model_reference=model_reference,
|
|
)
|
|
)
|
|
resolve_started_at = time.perf_counter()
|
|
(
|
|
profile,
|
|
manifest_path,
|
|
manifest_created,
|
|
scan_complete,
|
|
) = (
|
|
_resolve_profile_for_staging(
|
|
model_reference,
|
|
storage_root,
|
|
supplied_profile,
|
|
)
|
|
if supplied_profile is not None
|
|
else _resolve_profile_for_staging(model_reference, storage_root)
|
|
)
|
|
resolve_elapsed_seconds = time.perf_counter() - resolve_started_at
|
|
staged = stage_model_profile(
|
|
profile.profile_id,
|
|
storage_root,
|
|
profile=profile,
|
|
progress_callback=progress_callback,
|
|
model_reference=model_reference,
|
|
owner_id=owner_id,
|
|
)
|
|
results.append(
|
|
ResolvedStagedModelProfile(
|
|
requested_reference=model_reference,
|
|
profile_id=staged.profile_id,
|
|
repository=staged.repository,
|
|
revision=staged.revision,
|
|
backend=profile.backend,
|
|
quantization_method=profile.quantization_method,
|
|
artifact_bytes=profile.artifact_bytes,
|
|
manifest_path=manifest_path,
|
|
manifest_created=manifest_created,
|
|
security_scan_complete=scan_complete,
|
|
path=staged.path,
|
|
downloaded=staged.downloaded,
|
|
resolve_elapsed_seconds=resolve_elapsed_seconds,
|
|
elapsed_seconds=staged.elapsed_seconds,
|
|
)
|
|
)
|
|
return results
|
|
|
|
|
|
__all__ = [
|
|
"LLMStagingProgress",
|
|
"ResolvedStagedModelProfile",
|
|
"StagedModelSnapshot",
|
|
"is_model_snapshot_staged",
|
|
"model_snapshot_path",
|
|
"resolve_and_stage_model_references",
|
|
"stage_model_profile",
|
|
]
|