58c2ec1ebd* Studio: add shared Xet-primary download helper with HTTP stall fallback Xet is the fast default transport in huggingface_hub, but a stalled Xet transfer hangs with no progress and no exception, and a blocked native thread cannot be killed. The safetensors inference path already recovers (subprocess watchdog + respawn with HF_HUB_DISABLE_XET=1); the GGUF and training paths do not. Add a reusable helper that the in-process paths can adopt. utils/hf_xet_fallback.py: - DownloadStallError (moved here from core/inference/orchestrator.py, which now imports it; behavior unchanged, still a RuntimeError subclass). - get_hf_download_state / start_watchdog: a no-progress watchdog built on the sparse-aware hub.utils.hf_cache_state helpers; fires only while a .incomplete is present and the on-disk byte total is unchanged for stall_timeout. - hf_hub_download_with_xet_fallback: cached files short-circuit; otherwise the download runs in a spawn child (own process group) supervised by the watchdog. On a stall it kills the child, makes the partial safe for HTTP via prepare_cache_for_transport, and respawns once with HF_HUB_DISABLE_XET=1. Cancel and deterministic errors (auth/missing/disk) propagate without a fallback. Tests cover the watchdog state machine, the transport decision logic, and a regression lock that HF_HUB_DISABLE_XET is honored in a fresh interpreter. * Studio: route GGUF Chat-Mode downloads through the Xet->HTTP fallback The GGUF load path (_download_gguf main+shards, _download_companion_gguf for mmproj/MTP) called a bare blocking hf_hub_download with no recovery, so a Xet stall hung the Chat-Mode load with no fallback. Route those three calls through hf_hub_download_with_xet_fallback: Xet stays primary, HTTP is used only if Xet stalls, per-file so finished shards stay cached. The existing _cancel_event is threaded through, the Cancelled sentinel is preserved, and companions stay best-effort (a terminal stall is swallowed to None). Cached files short-circuit in the helper with no subprocess, so the fast path is unchanged. The two offline mmproj tests are repointed from huggingface_hub.hf_hub_download to the new call boundary (the helper) since the download now goes through it. * Studio: recover a stalled training model-load via Xet->HTTP respawn Training runs in a spawn subprocess and FastModel.from_pretrained downloads internally, so the download cannot be wrapped per-file like GGUF. Instead the worker now watches the HF cache during the model-load phase (emitting model_load_started / model_load_completed and a stall event), and the parent recovers a stall by terminating the worker and respawning it once with HF_HUB_DISABLE_XET=1. worker.py: set HF_HUB_DISABLE_XET=1 before any HF import when the parent passes disable_xet (respawn), and wrap trainer.load_model with start_watchdog. training.py: plumb disable_xet through the config; track the model-load window; on a first-load stall arm a one-shot respawn (handled on the exiting pump thread, so no pump self-join) that preserves the DB run row (history is not duplicated) and re-runs the load over HTTP. A second stall, or a stall outside model-load, surfaces as a normal error. W&B init happens after model-load, so a pre-load respawn cannot duplicate it; the dataset is re-formatted in the new worker. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: add gated test-only fault-injection hook for the Xet stall path UNSLOTH_HF_XET_FORCE_STALL=1 makes the Xet download attempt write a partial blob and hang, so the no-progress watchdog and the HTTP fallback can be exercised end to end against a real repo (never set in production). Used to verify recovery on real models: a forced Xet stall on a 5.37GB Qwen3.5-35B-A3B shard triggered the watchdog and the HTTP retry downloaded the correct file (sha256 verified). * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: tighten Xet-fallback comments and consolidate its tests Trim docstrings and inline comments across the Xet->HTTP fallback code to the non-obvious why (spawn-not-thread, killpg-not-getpgid, the sparse-partial HTTP-resume hazard); drop comments that merely restate the code. Verified comment-only with an AST signature check. Merge the three helper-level test files (watchdog, transport policy, and the HF_HUB_DISABLE_XET regression lock) into tests/test_hf_xet_fallback.py, and prefer the real structlog over a bare stub so test collection order cannot leak an incomplete module to others that log at import. Full backend suite: 3455 passed, 14 pre-existing flash-attn failures only. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
1179 lines
47 KiB
Python
1179 lines
47 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""
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Training backend — subprocess orchestrator.
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Each job runs in a fresh spawn subprocess (solving transformers version-switching);
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the in-process UnslothTrainer singleton is only used inside the worker. This file
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orchestrates the subprocess lifecycle, pumps events from the worker's mp.Queue, and
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exposes the same API to routes/training.py. Pattern follows data_recipe/jobs/manager.py.
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"""
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import json as _json
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import math
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import multiprocessing as mp
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import os
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import queue
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import re
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import shutil
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import threading
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import time
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import structlog
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from datetime import datetime, timezone
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from loggers import get_logger
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Optional, Tuple, Any
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import matplotlib.pyplot as plt
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from utils.hardware import prepare_gpu_selection
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from utils.native_path_leases import (
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native_path_secret_removed_for_child_start,
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run_without_native_path_secret,
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)
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from utils.paths import outputs_root
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logger = get_logger(__name__)
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def _coerce_seed(value, default = 3407) -> int:
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"""Normalize None / non-int to `default` (transformers.set_seed(None) raises)."""
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if value is None:
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return int(default)
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try:
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return int(value)
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except (TypeError, ValueError):
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return int(default)
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def _coerce_optional_bool(value, default: bool) -> bool:
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"""Treat explicit None as `default` instead of `bool(None) == False`."""
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if value is None:
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return bool(default)
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if isinstance(value, str):
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normalized = value.strip().lower()
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if normalized in ("true", "1", "yes", "on"):
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return True
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if normalized in ("false", "0", "no", "off", ""):
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return False
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return bool(value)
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def _coerce_optional_nonneg_float(name: str, value):
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"""Reject negatives; HTTP `ge=0` doesn't cover raw `**kwargs` callers."""
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if value is None:
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return None
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try:
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coerced = float(value)
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except (TypeError, ValueError):
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raise ValueError(f"Unsloth: {name}={value!r} must be a non-negative float or None.")
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if coerced < 0:
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raise ValueError(f"Unsloth: {name}={coerced} must be >= 0 (use 0 or None to disable).")
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return coerced
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_HF_TMP_CHECKPOINT_RE = re.compile(r"^tmp-checkpoint-\d+$")
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def _sanitize_db_config(config: dict[str, Any]) -> dict[str, Any]:
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db_config = {
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k: v for k, v in config.items() if k not in {"hf_token", "wandb_token", "s3_config"}
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}
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s3_config = config.get("s3_config")
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if hasattr(s3_config, "model_dump"):
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s3_config = s3_config.model_dump()
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if isinstance(s3_config, dict) and s3_config:
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db_config["dataset_source"] = "s3"
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db_config["s3_dataset"] = {
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"bucket": s3_config.get("bucket"),
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"region": s3_config.get("region"),
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"prefix": s3_config.get("prefix"),
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"use_iam_role": bool(s3_config.get("use_iam_role")),
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}
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return db_config
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def _s3_dataset_name(s3_dataset: Any) -> Optional[str]:
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if not isinstance(s3_dataset, dict):
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return None
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bucket = s3_dataset.get("bucket")
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if not bucket:
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return None
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prefix = s3_dataset.get("prefix")
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return f"s3://{bucket}/{prefix}" if prefix else f"s3://{bucket}"
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def _cleanup_cancelled_checkpoints(output_dir: str | os.PathLike) -> None:
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"""Remove only HF Trainer ``tmp-checkpoint-<step>/`` partials after a cancel.
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Completed ``checkpoint-<int>/`` dirs survive. Symlinked output_dir / children
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are skipped so containment can't be bypassed.
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"""
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out = Path(output_dir)
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if not out.exists() or not out.is_dir() or out.is_symlink():
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return
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try:
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out_real = out.resolve()
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out_root_real = Path(outputs_root()).resolve()
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except OSError:
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return
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try:
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out_real.relative_to(out_root_real)
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except ValueError:
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logger.warning(
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"Skipping checkpoint cleanup - %s is not under outputs_root %s",
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out_real,
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out_root_real,
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)
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return
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removed = 0
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for entry in out.iterdir():
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if not entry.is_dir() or entry.is_symlink():
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continue
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if not _HF_TMP_CHECKPOINT_RE.match(entry.name):
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continue
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try:
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shutil.rmtree(entry, ignore_errors = False)
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removed += 1
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except OSError as exc:
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logger.warning("Could not remove %s: %s", entry, exc)
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logger.info(
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"Cancelled-run cleanup removed %d in-flight tmp-checkpoint dir(s) under %s",
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removed,
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out,
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)
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_CTX = mp.get_context("spawn")
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# Plot styling constants
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PLOT_WIDTH = 8
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PLOT_HEIGHT = 3.5
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@dataclass
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class TrainingProgress:
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"""Mirror of trainer.TrainingProgress so the parent never imports heavy ML modules."""
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epoch: float = 0
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step: int = 0
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total_steps: int = 0
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loss: Optional[float] = None
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learning_rate: Optional[float] = None
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is_training: bool = False
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is_completed: bool = False
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error: Optional[str] = None
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status_message: str = "Ready to train"
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elapsed_seconds: Optional[float] = None
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eta_seconds: Optional[float] = None
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grad_norm: Optional[float] = None
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num_tokens: Optional[int] = None
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eval_loss: Optional[float] = None
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peak_memory_gb: Optional[float] = None
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class TrainingBackend:
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"""
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Training orchestration backend — subprocess-based.
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Launches a fresh subprocess per job, communicates via mp.Queue.
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"""
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FLUSH_THRESHOLD: int = 10
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def __init__(self):
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# Subprocess state
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self._proc: Optional[mp.Process] = None
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self._event_queue: Any = None
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self._stop_queue: Any = None
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self._pump_thread: Optional[threading.Thread] = None
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self._lock = threading.Lock()
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# Progress state (updated by pump thread from subprocess events)
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self._progress = TrainingProgress()
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self._should_stop = False
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self._cancel_requested = False # True only for stop(save=False)
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# Training metrics (consumed by routes for SSE and /metrics)
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self.loss_history: list = []
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self.lr_history: list = []
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self.step_history: list = []
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self.grad_norm_history: list = []
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self.grad_norm_step_history: list = []
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self.eval_loss_history: list = []
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self.eval_step_history: list = []
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self.eval_enabled: bool = False
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self.current_theme: str = "light"
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# Job metadata
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self.current_job_id: Optional[str] = None
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self._output_dir: Optional[str] = None
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# DB persistence
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self._metric_buffer: list[dict] = []
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self._run_finalized: bool = False
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self._db_run_created: bool = False
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self._db_total_steps_set: bool = False
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self._db_config: Optional[dict] = None
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self._db_started_at: Optional[str] = None
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# Xet -> HTTP model-load fallback state (config kept for the respawn).
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self._last_full_config: Optional[dict] = None
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self._in_model_load: bool = False
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self._xet_fallback_used: bool = False
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self._needs_xet_respawn: bool = False
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logger.info("TrainingBackend initialized (subprocess mode)")
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# ------------------------------------------------------------------
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# Public API (called by routes/training.py)
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# ------------------------------------------------------------------
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def start_training(self, job_id: str, **kwargs) -> bool:
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"""Spawn a subprocess to run the full training pipeline.
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All kwargs are serialized into a config dict and sent to the worker.
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Returns True if the subprocess started successfully.
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"""
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with self._lock:
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if self._proc is not None and self._proc.is_alive():
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logger.warning("Training subprocess already running")
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return False
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# Join prior pump thread — refuse to start if it won't die
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if self._pump_thread is not None and self._pump_thread.is_alive():
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self._pump_thread.join(timeout = 5.0)
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if self._pump_thread.is_alive():
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logger.warning("Previous pump thread did not exit within 5s — refusing to start")
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return False
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self._pump_thread = None
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# Build config dict for the subprocess
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config = {
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"model_name": kwargs["model_name"],
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"training_type": kwargs.get("training_type", "LoRA/QLoRA"),
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"hf_token": kwargs.get("hf_token", ""),
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"load_in_4bit": kwargs.get("load_in_4bit", True),
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"max_seq_length": kwargs.get("max_seq_length", 2048),
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"vision_image_size": kwargs.get("vision_image_size"),
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"hf_dataset": kwargs.get("hf_dataset", ""),
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"local_datasets": kwargs.get("local_datasets"),
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"local_eval_datasets": kwargs.get("local_eval_datasets"),
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"format_type": kwargs.get("format_type", ""),
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"subset": kwargs.get("subset"),
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"train_split": kwargs.get("train_split", "train"),
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"eval_split": kwargs.get("eval_split"),
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"eval_steps": kwargs.get("eval_steps", 0.00),
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"dataset_slice_start": kwargs.get("dataset_slice_start"),
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"dataset_slice_end": kwargs.get("dataset_slice_end"),
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"custom_format_mapping": kwargs.get("custom_format_mapping"),
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"is_dataset_image": kwargs.get("is_dataset_image", False),
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"is_dataset_audio": kwargs.get("is_dataset_audio", False),
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"is_embedding": kwargs.get("is_embedding", False),
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"num_epochs": kwargs.get("num_epochs", 3),
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"learning_rate": kwargs.get("learning_rate", "2e-4"),
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"embedding_learning_rate": kwargs.get("embedding_learning_rate"),
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"batch_size": kwargs.get("batch_size", 2),
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"gradient_accumulation_steps": kwargs.get("gradient_accumulation_steps", 4),
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"warmup_steps": kwargs.get("warmup_steps"),
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"warmup_ratio": kwargs.get("warmup_ratio"),
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"max_steps": kwargs.get("max_steps", 0),
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"save_steps": kwargs.get("save_steps", 0),
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"weight_decay": kwargs.get("weight_decay", 0.001),
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"max_grad_norm": kwargs.get("max_grad_norm", 0.0),
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"max_grad_value": _coerce_optional_nonneg_float(
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"max_grad_value", kwargs.get("max_grad_value")
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),
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"max_grad_leaf_norm": _coerce_optional_nonneg_float(
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"max_grad_leaf_norm", kwargs.get("max_grad_leaf_norm")
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),
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"cast_norm_output_to_input_dtype": _coerce_optional_bool(
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kwargs.get("cast_norm_output_to_input_dtype"), True
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),
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# MLX/CUDA/embedding workers need an int (transformers.set_seed(None) raises).
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"random_seed": _coerce_seed(kwargs.get("random_seed")),
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"packing": kwargs.get("packing", False),
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"optim": kwargs.get("optim", "adamw_8bit"),
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"lr_scheduler_type": kwargs.get("lr_scheduler_type", "linear"),
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"use_lora": kwargs.get("use_lora", True),
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"lora_r": kwargs.get("lora_r", 16),
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"lora_alpha": kwargs.get("lora_alpha", 16),
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"lora_dropout": kwargs.get("lora_dropout", 0.0),
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"target_modules": kwargs.get("target_modules"),
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"gradient_checkpointing": kwargs.get("gradient_checkpointing", "unsloth"),
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"use_rslora": kwargs.get("use_rslora", False),
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"use_loftq": kwargs.get("use_loftq", False),
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"train_on_completions": kwargs.get("train_on_completions", False),
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"finetune_vision_layers": kwargs.get("finetune_vision_layers", True),
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"finetune_language_layers": kwargs.get("finetune_language_layers", True),
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"finetune_attention_modules": kwargs.get("finetune_attention_modules", True),
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"finetune_mlp_modules": kwargs.get("finetune_mlp_modules", True),
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"enable_wandb": kwargs.get("enable_wandb", False),
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"wandb_token": kwargs.get("wandb_token"),
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"wandb_project": kwargs.get("wandb_project", "unsloth-training"),
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"enable_tensorboard": kwargs.get("enable_tensorboard", False),
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"tensorboard_dir": kwargs.get("tensorboard_dir", "runs"),
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"resume_from_checkpoint": kwargs.get("resume_from_checkpoint"),
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"trust_remote_code": kwargs.get("trust_remote_code", False),
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"gpu_ids": kwargs.get("gpu_ids"),
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"s3_config": kwargs.get("s3_config"),
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# Flipped to True only by the HTTP-fallback respawn after a stall.
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"disable_xet": kwargs.get("disable_xet", False),
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}
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# Full finetuning always runs in 16-bit; LoRA/QLoRA/CPT keep the request.
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if config["training_type"] == "Full Finetuning":
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config["load_in_4bit"] = False
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# Spawn into locals so state is untouched on failure.
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from utils.hardware import hardware as _hw
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if _hw.DEVICE == _hw.DeviceType.MLX:
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config["resolved_gpu_ids"] = None
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config["gpu_selection"] = None
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else:
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resolved_gpu_ids, gpu_selection = prepare_gpu_selection(
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kwargs.get("gpu_ids"),
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model_name = config["model_name"],
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hf_token = config["hf_token"] or None,
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training_type = config["training_type"],
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load_in_4bit = config["load_in_4bit"],
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batch_size = config.get("batch_size", 4),
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max_seq_length = config.get("max_seq_length", 2048),
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lora_rank = config.get("lora_r", 16),
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target_modules = config.get("target_modules"),
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gradient_checkpointing = config.get("gradient_checkpointing", "unsloth"),
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optimizer = config.get("optim", "adamw_8bit"),
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)
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config["resolved_gpu_ids"] = resolved_gpu_ids
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config["gpu_selection"] = gpu_selection
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from .worker import run_training_process
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try:
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with native_path_secret_removed_for_child_start():
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event_queue = _CTX.Queue()
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stop_queue = _CTX.Queue()
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proc = _CTX.Process(
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target = run_without_native_path_secret,
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args = (run_training_process,),
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kwargs = {
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"event_queue": event_queue,
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"stop_queue": stop_queue,
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"config": config,
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},
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daemon = True,
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)
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proc.start()
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except Exception:
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logger.error("Failed to start training subprocess", exc_info = True)
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return False
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logger.info("Training subprocess started (pid=%s)", proc.pid)
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# Reset state (old pump thread dead, proc.start() succeeded).
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self.current_job_id = job_id
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self._should_stop = False
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self._cancel_requested = False
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self._progress = TrainingProgress(
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is_training = True, status_message = "Initializing training..."
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)
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self.loss_history.clear()
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self.lr_history.clear()
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self.step_history.clear()
|
|
self.grad_norm_history.clear()
|
|
self.grad_norm_step_history.clear()
|
|
self.eval_loss_history.clear()
|
|
self.eval_step_history.clear()
|
|
self.eval_enabled = False
|
|
self._output_dir = None
|
|
self._metric_buffer.clear()
|
|
self._run_finalized = False
|
|
self._db_run_created = False
|
|
self._db_total_steps_set = False
|
|
self._db_config = _sanitize_db_config(config)
|
|
self._db_started_at = datetime.now(timezone.utc).isoformat()
|
|
# Start each job Xet-first; keep config so a stall can respawn over HTTP.
|
|
self._last_full_config = config
|
|
self._in_model_load = False
|
|
self._xet_fallback_used = False
|
|
self._needs_xet_respawn = False
|
|
|
|
# Assign subprocess handles after state reset.
|
|
self._event_queue = event_queue
|
|
self._stop_queue = stop_queue
|
|
self._proc = proc
|
|
|
|
# Eagerly create DB run row so it appears in history during model loading.
|
|
self._ensure_db_run_created()
|
|
|
|
self._pump_thread = threading.Thread(target = self._pump_loop, daemon = True)
|
|
self._pump_thread.start()
|
|
|
|
return True
|
|
|
|
def stop_training(self, save: bool = True) -> bool:
|
|
"""Send stop signal to the training subprocess."""
|
|
self._should_stop = True
|
|
if not save:
|
|
self._cancel_requested = True
|
|
with self._lock:
|
|
if self._stop_queue is not None:
|
|
try:
|
|
self._stop_queue.put({"type": "stop", "save": save})
|
|
except (OSError, ValueError):
|
|
pass
|
|
# Update progress immediately for responsive UI.
|
|
self._progress.status_message = (
|
|
"Stopping training and saving checkpoint..." if save else "Cancelling training..."
|
|
)
|
|
return True
|
|
|
|
def force_terminate(self) -> None:
|
|
"""Force-kill the training subprocess so state can be reset immediately."""
|
|
with self._lock:
|
|
if self._proc is not None and self._proc.is_alive():
|
|
logger.info("Force-terminating training subprocess (pid=%s)", self._proc.pid)
|
|
self._proc.terminate()
|
|
proc = self._proc
|
|
cancelled = self._cancel_requested
|
|
output_dir = self._output_dir
|
|
|
|
if proc is not None:
|
|
proc.join(timeout = 5.0)
|
|
if proc.is_alive():
|
|
proc.kill()
|
|
proc.join(timeout = 2.0)
|
|
|
|
# Wait for pump thread to finish DB finalization (8s covers SQLite's 5s lock timeout).
|
|
if self._pump_thread is not None and self._pump_thread.is_alive():
|
|
self._pump_thread.join(timeout = 8.0)
|
|
|
|
if cancelled and output_dir:
|
|
try:
|
|
_cleanup_cancelled_checkpoints(output_dir)
|
|
except Exception:
|
|
logger.exception(
|
|
"Failed to clean up cancelled-run checkpoints under %s",
|
|
output_dir,
|
|
)
|
|
|
|
def _handle_stall_event(self, event: dict) -> None:
|
|
"""A worker reported a no-progress download stall.
|
|
|
|
On the first model-load, terminate the worker so the pump loop respawns it
|
|
over HTTP. A later stall (already on HTTP, or outside model-load) surfaces
|
|
as an error instead.
|
|
"""
|
|
msg = event.get("message", "Download stalled")
|
|
with self._lock:
|
|
recover = self._in_model_load and not self._xet_fallback_used
|
|
proc = self._proc
|
|
if recover:
|
|
self._xet_fallback_used = True
|
|
self._needs_xet_respawn = True
|
|
self._progress.status_message = (
|
|
"Model download stalled on Xet; retrying over HTTP..."
|
|
)
|
|
else:
|
|
self._progress.error = self._progress.error or (
|
|
"Model download stalled even over HTTP -- check your network connection"
|
|
)
|
|
if recover:
|
|
logger.warning("Training model-load stalled on Xet; respawning over HTTP: %s", msg)
|
|
else:
|
|
logger.error("Training download stalled with no further fallback: %s", msg)
|
|
# Terminate either way so the pump loop proceeds (respawn or finalize).
|
|
if proc is not None and proc.is_alive():
|
|
proc.terminate()
|
|
|
|
def _respawn_worker_disable_xet(self) -> None:
|
|
"""Respawn the worker once with HF_HUB_DISABLE_XET=1 after a model-load
|
|
stall. Runs on the exiting pump thread, reaps the terminated worker, and
|
|
starts a fresh worker + pump. DB/progress run-state is preserved so the
|
|
history row is not duplicated; the new worker re-formats and loads over HTTP.
|
|
"""
|
|
config = self._last_full_config
|
|
if config is None:
|
|
logger.error("Cannot respawn training worker: no stored config")
|
|
return
|
|
|
|
with self._lock:
|
|
old_proc = self._proc
|
|
if old_proc is not None:
|
|
old_proc.join(timeout = 5.0)
|
|
if old_proc.is_alive():
|
|
old_proc.kill()
|
|
old_proc.join(timeout = 2.0)
|
|
|
|
config = {**config, "disable_xet": True}
|
|
self._last_full_config = config
|
|
logger.warning("Respawning training worker with HF_HUB_DISABLE_XET=1 after Xet stall")
|
|
|
|
from .worker import run_training_process
|
|
|
|
try:
|
|
with native_path_secret_removed_for_child_start():
|
|
event_queue = _CTX.Queue()
|
|
stop_queue = _CTX.Queue()
|
|
new_proc = _CTX.Process(
|
|
target = run_without_native_path_secret,
|
|
args = (run_training_process,),
|
|
kwargs = {
|
|
"event_queue": event_queue,
|
|
"stop_queue": stop_queue,
|
|
"config": config,
|
|
},
|
|
daemon = True,
|
|
)
|
|
new_proc.start()
|
|
except Exception:
|
|
logger.error("Failed to respawn training subprocess", exc_info = True)
|
|
with self._lock:
|
|
self._progress.is_training = False
|
|
self._progress.error = "Failed to recover stalled model download"
|
|
self._ensure_db_run_created()
|
|
self._finalize_run_in_db(
|
|
status = "error",
|
|
error_message = "Failed to recover stalled model download",
|
|
)
|
|
return
|
|
|
|
logger.info("Training subprocess respawned with Xet disabled (pid=%s)", new_proc.pid)
|
|
new_pump = threading.Thread(target = self._pump_loop, daemon = True)
|
|
with self._lock:
|
|
self._in_model_load = False
|
|
self._event_queue = event_queue
|
|
self._stop_queue = stop_queue
|
|
self._proc = new_proc
|
|
self._pump_thread = new_pump
|
|
new_pump.start()
|
|
|
|
def is_training_active(self) -> bool:
|
|
"""Check if training is currently active."""
|
|
with self._lock:
|
|
if self._proc is not None and self._proc.is_alive():
|
|
return True
|
|
|
|
if self._should_stop:
|
|
return False
|
|
|
|
p = self._progress
|
|
if p.is_training:
|
|
return True
|
|
if p.is_completed or p.error:
|
|
return False
|
|
|
|
# Infer activity from the status message.
|
|
status_lower = (p.status_message or "").lower()
|
|
if any(
|
|
k in status_lower
|
|
for k in [
|
|
"cancelled",
|
|
"canceled",
|
|
"stopped",
|
|
"completed",
|
|
"ready to train",
|
|
]
|
|
):
|
|
return False
|
|
if any(
|
|
k in status_lower
|
|
for k in [
|
|
"loading",
|
|
"preparing",
|
|
"training",
|
|
"configuring",
|
|
"tokenizing",
|
|
"starting",
|
|
"importing",
|
|
]
|
|
):
|
|
return True
|
|
|
|
return False
|
|
|
|
def get_training_status(self, theme: str = "light") -> Tuple:
|
|
"""Get current training status and loss plot."""
|
|
with self._lock:
|
|
progress = self._progress
|
|
|
|
if not (progress.is_training or progress.is_completed or progress.error):
|
|
return (None, progress)
|
|
|
|
plot = self._create_loss_plot(progress, theme)
|
|
return (plot, progress)
|
|
|
|
def refresh_plot_for_theme(self, theme: str) -> Optional[plt.Figure]:
|
|
"""Refresh plot with new theme."""
|
|
if theme and isinstance(theme, str) and theme in ["light", "dark"]:
|
|
self.current_theme = theme
|
|
if self.loss_history:
|
|
with self._lock:
|
|
progress = self._progress
|
|
return self._create_loss_plot(progress, self.current_theme)
|
|
return None
|
|
|
|
# ------------------------------------------------------------------
|
|
# Compatibility shims — routes/training.py accesses these
|
|
# ------------------------------------------------------------------
|
|
|
|
class _TrainerShim:
|
|
"""Minimal shim so routes that access backend.trainer.* still work."""
|
|
|
|
def __init__(self, backend: "TrainingBackend"):
|
|
self._backend = backend
|
|
self.should_stop = False
|
|
|
|
@property
|
|
def training_progress(self):
|
|
return self._backend._progress
|
|
|
|
@training_progress.setter
|
|
def training_progress(self, value):
|
|
self._backend._progress = value
|
|
|
|
def get_training_progress(self):
|
|
return self._backend._progress
|
|
|
|
def _update_progress(self, **kwargs):
|
|
with self._backend._lock:
|
|
for key, value in kwargs.items():
|
|
if hasattr(self._backend._progress, key):
|
|
setattr(self._backend._progress, key, value)
|
|
|
|
@property
|
|
def trainer(self):
|
|
"""Compatibility shim for routes that access backend.trainer.*"""
|
|
return self._TrainerShim(self)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Event pump (background thread)
|
|
# ------------------------------------------------------------------
|
|
|
|
def _pump_loop(self) -> None:
|
|
"""Background thread: consume events from subprocess → update state."""
|
|
while True:
|
|
if self._proc is None or self._event_queue is None:
|
|
return
|
|
|
|
event = self._read_queue(self._event_queue, timeout_sec = 0.25)
|
|
if event is not None:
|
|
self._handle_event(event)
|
|
continue
|
|
|
|
if self._proc.is_alive():
|
|
continue
|
|
|
|
# Process exited — drain remaining events.
|
|
for e in self._drain_queue(self._event_queue):
|
|
self._handle_event(e)
|
|
|
|
# Model-load stall: respawn over HTTP instead of finalizing as failure.
|
|
# Runs on THIS exiting pump thread and starts a fresh pump (never joins
|
|
# the current thread); DB run-state is preserved.
|
|
if self._needs_xet_respawn:
|
|
self._needs_xet_respawn = False
|
|
self._respawn_worker_disable_xet()
|
|
return
|
|
|
|
# Mark done if no explicit complete/error was received.
|
|
with self._lock:
|
|
if self._progress.is_training:
|
|
if self._should_stop:
|
|
self._progress.is_training = False
|
|
self._progress.status_message = "Training stopped."
|
|
else:
|
|
self._progress.is_training = False
|
|
self._progress.error = (
|
|
self._progress.error or "Training process exited unexpectedly"
|
|
)
|
|
|
|
self._ensure_db_run_created()
|
|
self._finalize_run_in_db(
|
|
status = "stopped" if self._should_stop else "error",
|
|
error_message = None
|
|
if self._should_stop
|
|
else "Training process terminated unexpectedly",
|
|
)
|
|
return
|
|
|
|
def _handle_event(self, event: dict) -> None:
|
|
"""Apply a subprocess event to local state.
|
|
|
|
State updates happen inside self._lock; DB I/O happens after releasing
|
|
it so status-polling endpoints aren't blocked by slow SQLite writes.
|
|
"""
|
|
etype = event.get("type")
|
|
db_action: Optional[str] = None
|
|
db_action_kwargs: dict = {}
|
|
|
|
# Model-load lifecycle + stall recovery (no DB metrics); handled first.
|
|
if etype == "model_load_started":
|
|
with self._lock:
|
|
self._in_model_load = True
|
|
return
|
|
if etype == "model_load_completed":
|
|
with self._lock:
|
|
self._in_model_load = False
|
|
return
|
|
if etype == "stall":
|
|
self._handle_stall_event(event)
|
|
return
|
|
|
|
with self._lock:
|
|
if etype == "progress":
|
|
self._progress.step = event.get("step", self._progress.step)
|
|
self._progress.epoch = event.get("epoch", self._progress.epoch)
|
|
# loss/lr sanitized below.
|
|
_raw_loss = event.get("loss")
|
|
_raw_lr = event.get("learning_rate")
|
|
try:
|
|
_safe_loss = float(_raw_loss) if _raw_loss is not None else None
|
|
except (TypeError, ValueError):
|
|
logger.debug("Could not convert loss to float: %s", _raw_loss)
|
|
_safe_loss = None
|
|
_loss_is_nonfinite = _safe_loss is not None and not math.isfinite(_safe_loss)
|
|
if _loss_is_nonfinite:
|
|
# Drop the value rather than laundering it back to the last
|
|
# finite loss; clients see loss=None at this step so the NaN
|
|
# is not hidden behind a stale value. Training continues.
|
|
_safe_loss = None
|
|
if not getattr(self._progress, "_nonfinite_loss_warned", False):
|
|
self._progress._nonfinite_loss_warned = True
|
|
logger.warning(
|
|
"Training produced non-finite loss at step %s; "
|
|
"loss field will report null until it recovers.",
|
|
event.get("step", "?"),
|
|
)
|
|
try:
|
|
_safe_lr = float(_raw_lr) if _raw_lr is not None else None
|
|
except (TypeError, ValueError):
|
|
logger.debug("Could not convert learning_rate to float: %s", _raw_lr)
|
|
_safe_lr = None
|
|
if _safe_lr is not None and not math.isfinite(_safe_lr):
|
|
_safe_lr = None
|
|
if _safe_loss is not None:
|
|
self._progress.loss = _safe_loss
|
|
elif _loss_is_nonfinite:
|
|
# Clear stale finite loss so the API doesn't keep
|
|
# reporting the last good value while NaN is happening.
|
|
self._progress.loss = None
|
|
if _safe_lr is not None:
|
|
self._progress.learning_rate = _safe_lr
|
|
self._progress.total_steps = event.get("total_steps", self._progress.total_steps)
|
|
self._progress.elapsed_seconds = event.get("elapsed_seconds")
|
|
self._progress.eta_seconds = event.get("eta_seconds")
|
|
self._progress.grad_norm = event.get("grad_norm")
|
|
self._progress.num_tokens = event.get("num_tokens")
|
|
self._progress.eval_loss = event.get("eval_loss")
|
|
_peak = event.get("peak_memory_gb")
|
|
if _peak is not None:
|
|
try:
|
|
self._progress.peak_memory_gb = float(_peak)
|
|
except (TypeError, ValueError):
|
|
pass
|
|
self._progress.is_training = True
|
|
status = event.get("status_message", "")
|
|
if status:
|
|
self._progress.status_message = status
|
|
|
|
# Update metric histories using sanitized values.
|
|
step = event.get("step", 0)
|
|
loss = _safe_loss
|
|
lr = _safe_lr
|
|
if step > 0 and loss is not None:
|
|
self.loss_history.append(loss)
|
|
self.lr_history.append(lr if lr is not None else 0.0)
|
|
self.step_history.append(step)
|
|
|
|
grad_norm = event.get("grad_norm")
|
|
gn = None
|
|
if grad_norm is not None:
|
|
try:
|
|
gn = float(grad_norm)
|
|
except (TypeError, ValueError):
|
|
gn = None
|
|
if step > 0 and gn is not None and math.isfinite(gn):
|
|
self.grad_norm_history.append(gn)
|
|
self.grad_norm_step_history.append(step)
|
|
else:
|
|
gn = None
|
|
|
|
eval_loss = event.get("eval_loss")
|
|
if eval_loss is not None:
|
|
try:
|
|
eval_loss = float(eval_loss)
|
|
except (TypeError, ValueError):
|
|
logger.debug("Could not convert eval_loss to float: %s", eval_loss)
|
|
eval_loss = None
|
|
if step > 0 and eval_loss is not None and math.isfinite(eval_loss):
|
|
self.eval_loss_history.append(eval_loss)
|
|
self.eval_step_history.append(step)
|
|
self.eval_enabled = True
|
|
else:
|
|
eval_loss = None
|
|
|
|
# Buffer metric for DB flush.
|
|
self._metric_buffer.append(
|
|
{
|
|
"step": step,
|
|
"loss": loss,
|
|
"learning_rate": lr,
|
|
"grad_norm": gn,
|
|
"eval_loss": eval_loss,
|
|
"epoch": event.get("epoch"),
|
|
"num_tokens": event.get("num_tokens"),
|
|
"elapsed_seconds": event.get("elapsed_seconds"),
|
|
}
|
|
)
|
|
|
|
# Pick the DB action to run after releasing the lock.
|
|
if not self._db_run_created and self.current_job_id and self._db_config:
|
|
db_action = "create_run"
|
|
db_action_kwargs = {
|
|
"job_id": self.current_job_id,
|
|
"model_name": self._db_config["model_name"],
|
|
"dataset_name": self._db_config.get("hf_dataset")
|
|
or next(iter(self._db_config.get("local_datasets") or []), "unknown"),
|
|
"config_json": _json.dumps(self._db_config),
|
|
"started_at": self._db_started_at or datetime.now(timezone.utc).isoformat(),
|
|
"total_steps": event.get("total_steps"),
|
|
}
|
|
elif (
|
|
event.get("total_steps")
|
|
and self._db_run_created
|
|
and not self._db_total_steps_set
|
|
):
|
|
db_action = "update_total_steps"
|
|
db_action_kwargs = {
|
|
"job_id": self.current_job_id,
|
|
"total_steps": event["total_steps"],
|
|
}
|
|
elif len(self._metric_buffer) >= self.FLUSH_THRESHOLD:
|
|
db_action = "flush"
|
|
|
|
elif etype == "eval_configured":
|
|
self.eval_enabled = True
|
|
|
|
elif etype == "status":
|
|
self._progress.status_message = event.get("message", "")
|
|
self._progress.is_training = True
|
|
|
|
elif etype == "complete":
|
|
self._progress.is_training = False
|
|
self._progress.is_completed = True
|
|
self._output_dir = event.get("output_dir")
|
|
msg = event.get("status_message", "Training completed")
|
|
self._progress.status_message = msg
|
|
if not self._db_run_created and self.current_job_id and self._db_config:
|
|
db_action = "create_and_finalize"
|
|
else:
|
|
db_action = "finalize"
|
|
db_action_kwargs = {
|
|
"status": "stopped" if self._should_stop else "completed",
|
|
"output_dir": self._output_dir,
|
|
}
|
|
|
|
elif etype == "error":
|
|
self._progress.is_training = False
|
|
self._progress.error = event.get("error", "Unknown error")
|
|
logger.error("Training error: %s", event.get("error"))
|
|
stack = event.get("stack", "")
|
|
if stack:
|
|
logger.error("Stack trace:\n%s", stack)
|
|
if not self._db_run_created and self.current_job_id and self._db_config:
|
|
db_action = "create_and_finalize"
|
|
else:
|
|
db_action = "finalize"
|
|
db_action_kwargs = {
|
|
"status": "stopped" if self._should_stop else "error",
|
|
"error_message": event.get("error", "Unknown error"),
|
|
}
|
|
|
|
# --- DB I/O outside the lock ---
|
|
if db_action == "create_run":
|
|
try:
|
|
from storage.studio_db import create_run
|
|
|
|
create_run(
|
|
id = db_action_kwargs["job_id"],
|
|
model_name = db_action_kwargs["model_name"],
|
|
dataset_name = db_action_kwargs["dataset_name"],
|
|
config_json = db_action_kwargs["config_json"],
|
|
started_at = db_action_kwargs["started_at"],
|
|
total_steps = db_action_kwargs["total_steps"],
|
|
)
|
|
self._db_run_created = True
|
|
if db_action_kwargs["total_steps"]:
|
|
self._db_total_steps_set = True
|
|
except Exception:
|
|
logger.warning("Failed to create DB run record", exc_info = True)
|
|
elif db_action == "create_and_finalize":
|
|
self._ensure_db_run_created()
|
|
self._finalize_run_in_db(**db_action_kwargs)
|
|
elif db_action == "update_total_steps":
|
|
try:
|
|
from storage.studio_db import update_run_total_steps
|
|
update_run_total_steps(db_action_kwargs["job_id"], db_action_kwargs["total_steps"])
|
|
self._db_total_steps_set = True
|
|
except Exception:
|
|
logger.warning("Failed to update total_steps in DB", exc_info = True)
|
|
elif db_action == "flush":
|
|
self._flush_metrics_to_db()
|
|
elif db_action == "finalize":
|
|
self._finalize_run_in_db(**db_action_kwargs)
|
|
|
|
def _ensure_db_run_created(self) -> None:
|
|
"""Create the DB row if it doesn't exist yet. Called outside the lock."""
|
|
if self._db_run_created or not self.current_job_id or not self._db_config:
|
|
return
|
|
try:
|
|
from storage.studio_db import create_run
|
|
|
|
dataset_name = (
|
|
self._db_config.get("hf_dataset")
|
|
or next(iter(self._db_config.get("local_datasets") or []), None)
|
|
or _s3_dataset_name(self._db_config.get("s3_dataset"))
|
|
or "unknown"
|
|
)
|
|
create_run(
|
|
id = self.current_job_id,
|
|
model_name = self._db_config["model_name"],
|
|
dataset_name = dataset_name,
|
|
config_json = _json.dumps(self._db_config),
|
|
started_at = self._db_started_at or datetime.now(timezone.utc).isoformat(),
|
|
total_steps = self._progress.total_steps or None,
|
|
)
|
|
self._db_run_created = True
|
|
except Exception:
|
|
logger.warning("Failed to create DB run record for early failure", exc_info = True)
|
|
|
|
def _finalize_run_in_db(
|
|
self,
|
|
status: str,
|
|
error_message: Optional[str] = None,
|
|
output_dir: Optional[str] = None,
|
|
) -> None:
|
|
"""Flush remaining metrics and mark a run as finished in the DB."""
|
|
if not self.current_job_id or not self._db_run_created or self._run_finalized:
|
|
return
|
|
self._flush_metrics_to_db()
|
|
try:
|
|
from storage.studio_db import finish_run
|
|
from utils.downsample import downsample
|
|
|
|
sparkline = downsample(self.loss_history, 50)
|
|
finish_run(
|
|
id = self.current_job_id,
|
|
status = status,
|
|
ended_at = datetime.now(timezone.utc).isoformat(),
|
|
final_step = self._progress.step,
|
|
final_loss = self._progress.loss
|
|
if (self._progress.loss is not None and math.isfinite(self._progress.loss))
|
|
else None,
|
|
duration_seconds = self._progress.elapsed_seconds,
|
|
loss_sparkline = _json.dumps(sparkline),
|
|
output_dir = output_dir,
|
|
error_message = error_message,
|
|
)
|
|
self._run_finalized = True
|
|
except Exception:
|
|
logger.warning("Failed to finalize run in DB (status=%s)", status, exc_info = True)
|
|
|
|
def _flush_metrics_to_db(self) -> None:
|
|
"""Flush buffered metrics to the database and update live progress."""
|
|
if not self._metric_buffer or not self.current_job_id or not self._db_run_created:
|
|
return
|
|
# Cap buffer to bound memory growth.
|
|
if len(self._metric_buffer) > 500:
|
|
logger.warning(
|
|
"Metric buffer exceeded 500 entries (%d) — trimming oldest",
|
|
len(self._metric_buffer),
|
|
)
|
|
self._metric_buffer = self._metric_buffer[-500:]
|
|
# Snapshot before insert so metrics arriving during the write survive.
|
|
batch = list(self._metric_buffer)
|
|
try:
|
|
from storage.studio_db import insert_metrics_batch, update_run_progress
|
|
|
|
insert_metrics_batch(self.current_job_id, batch)
|
|
del self._metric_buffer[: len(batch)]
|
|
update_run_progress(
|
|
id = self.current_job_id,
|
|
step = self._progress.step,
|
|
loss = self._progress.loss
|
|
if (self._progress.loss is not None and math.isfinite(self._progress.loss))
|
|
else None,
|
|
duration_seconds = self._progress.elapsed_seconds,
|
|
)
|
|
except Exception:
|
|
# Leave buffer intact for retry on next flush
|
|
logger.warning("Failed to flush metrics to DB", exc_info = True)
|
|
|
|
@staticmethod
|
|
def _read_queue(q: Any, timeout_sec: float) -> Optional[dict]:
|
|
try:
|
|
return q.get(timeout = timeout_sec)
|
|
except queue.Empty:
|
|
return None
|
|
except (EOFError, OSError, ValueError):
|
|
return None
|
|
|
|
@staticmethod
|
|
def _drain_queue(q: Any) -> list:
|
|
events = []
|
|
while True:
|
|
try:
|
|
events.append(q.get_nowait())
|
|
except queue.Empty:
|
|
return events
|
|
except (EOFError, OSError, ValueError):
|
|
return events
|
|
|
|
# ------------------------------------------------------------------
|
|
# Plot generation
|
|
# ------------------------------------------------------------------
|
|
|
|
def _create_loss_plot(
|
|
self,
|
|
progress: TrainingProgress,
|
|
theme: str = "light",
|
|
) -> plt.Figure:
|
|
"""Create training loss plot with theme-aware styling."""
|
|
plt.close("all")
|
|
|
|
LIGHT_STYLE = {
|
|
"facecolor": "#ffffff",
|
|
"grid_color": "#d1d5db",
|
|
"line": "#16b88a",
|
|
"text": "#1f2937",
|
|
"empty_text": "#6b7280",
|
|
}
|
|
DARK_STYLE = {
|
|
"facecolor": "#292929",
|
|
"grid_color": "#404040",
|
|
"line": "#4ade80",
|
|
"text": "#e5e7eb",
|
|
"empty_text": "#9ca3af",
|
|
}
|
|
|
|
style = LIGHT_STYLE if theme == "light" else DARK_STYLE
|
|
|
|
fig, ax = plt.subplots(figsize = (PLOT_WIDTH, PLOT_HEIGHT))
|
|
fig.patch.set_facecolor(style["facecolor"])
|
|
ax.set_facecolor(style["facecolor"])
|
|
|
|
if self.loss_history:
|
|
steps = self.step_history
|
|
losses = self.loss_history
|
|
scatter_color = "#60a5fa"
|
|
ax.scatter(
|
|
steps,
|
|
losses,
|
|
s = 16,
|
|
alpha = 0.6,
|
|
color = scatter_color,
|
|
linewidths = 0,
|
|
label = "Training Loss (raw)",
|
|
)
|
|
|
|
MA_WINDOW = 20
|
|
window = min(MA_WINDOW, len(losses))
|
|
|
|
if window >= 2:
|
|
cumsum = [0.0]
|
|
for v in losses:
|
|
cumsum.append(cumsum[-1] + float(v))
|
|
|
|
ma = []
|
|
for i in range(len(losses)):
|
|
start = max(0, i - window + 1)
|
|
denom = i - start + 1
|
|
ma.append((cumsum[i + 1] - cumsum[start]) / denom)
|
|
|
|
ax.plot(
|
|
steps,
|
|
ma,
|
|
color = style["line"],
|
|
linewidth = 2.5,
|
|
alpha = 0.95,
|
|
label = f"Moving Avg ({ma[-1]:.4f})",
|
|
)
|
|
|
|
leg = ax.legend(frameon = False, fontsize = 9)
|
|
for t in leg.get_texts():
|
|
t.set_color(style["text"])
|
|
|
|
ax.set_xlabel("Steps", fontsize = 10, color = style["text"])
|
|
ax.set_ylabel("Loss", fontsize = 10, color = style["text"])
|
|
|
|
if progress.error:
|
|
title = f"Error: {progress.error}"
|
|
elif progress.is_completed:
|
|
loss_str = f"{progress.loss:.4f}" if progress.loss is not None else "--"
|
|
title = f"Training completed! Final loss: {loss_str}"
|
|
elif progress.status_message:
|
|
title = progress.status_message
|
|
elif progress.step > 0:
|
|
loss_str = f"{progress.loss:.4f}" if progress.loss is not None else "--"
|
|
title = f"Epoch: {progress.epoch} | Step: {progress.step}/{progress.total_steps} | Loss: {loss_str}"
|
|
else:
|
|
title = "Training Loss"
|
|
|
|
ax.set_title(title, fontsize = 11, fontweight = "bold", pad = 10, color = style["text"])
|
|
ax.grid(True, alpha = 0.4, linestyle = "--", color = style["grid_color"])
|
|
ax.tick_params(colors = style["text"], which = "both")
|
|
ax.spines["top"].set_visible(False)
|
|
ax.spines["right"].set_visible(False)
|
|
ax.spines["bottom"].set_color(style["text"])
|
|
ax.spines["left"].set_color(style["text"])
|
|
else:
|
|
display_msg = (
|
|
progress.status_message
|
|
if progress.status_message
|
|
else "Waiting for training data..."
|
|
)
|
|
ax.text(
|
|
0.5,
|
|
0.5,
|
|
display_msg,
|
|
ha = "center",
|
|
va = "center",
|
|
fontsize = 16,
|
|
color = style["empty_text"],
|
|
transform = ax.transAxes,
|
|
)
|
|
ax.set_xticks([])
|
|
ax.set_yticks([])
|
|
for spine in ax.spines.values():
|
|
spine.set_visible(False)
|
|
|
|
fig.tight_layout()
|
|
return fig
|
|
|
|
def _transfer_to_inference_backend(self) -> bool:
|
|
"""Transfer model to inference backend.
|
|
|
|
No-op: with subprocess training the model is freed on exit, so inference
|
|
must load from the saved checkpoint on disk.
|
|
"""
|
|
logger.info(
|
|
"_transfer_to_inference_backend: subprocess training — "
|
|
"model must be loaded from disk (output_dir=%s)",
|
|
self._output_dir,
|
|
)
|
|
return False
|
|
|
|
|
|
# ========== GLOBAL INSTANCE ==========
|
|
_training_backend = None
|
|
|
|
|
|
def get_training_backend() -> TrainingBackend:
|
|
"""Get global training backend instance"""
|
|
global _training_backend
|
|
if _training_backend is None:
|
|
_training_backend = TrainingBackend()
|
|
return _training_backend
|