ebf69d155b
Fixes #645 Queue-based model loading with priority lanes and VRAM budget tracking. Prevents GPU OOM crashes when multiple projects compete for VRAM. ## Features ### Priority Lanes - REALTIME (1): LPM, live video, interactive sessions - INTERACTIVE (2): Playground, chat, user-facing - BATCH (3): Harvester, overnight jobs, background ### VRAM Management - Tracks total/used/available VRAM - Reserves VRAM when job starts - Releases VRAM when job completes - CPU fallback when GPU full ### Model Registry Pre-registered models: - Video Forge: SD XL (8GB), HeartMuLa (4GB), Wan2.1 (12GB) - LPM: Video Gen (16GB), A2A (8GB) - Local: Llama 3 70B (40GB), Llama 3 8B (8GB), MiMo v2 Pro (16GB) - Playground: SDXL Turbo (6GB) ### Cross-Project Scenarios Handled 1. Video Forge batch + LPM live → LPM gets priority 2. 3 Video Forge jobs → Sequential with shared cache 3. Night harvester + playground → Batch runs on idle cycles ## Files - tools/gpu_scheduler.py: InferenceScheduler class, CLI interface - tests/tools/test_gpu_scheduler.py: 19 tests, all passing ## Usage ```python from tools.gpu_scheduler import InferenceScheduler, Priority scheduler = InferenceScheduler(vram_budget_mb=49152) # 48GB scheduler.submit_job("job-1", "lpm", "llama3_8b", Priority.REALTIME) job = scheduler.get_next_job() scheduler.start_job(job) # ... do inference ... scheduler.complete_job(job) ```
429 lines
15 KiB
Python
429 lines
15 KiB
Python
"""
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GPU Inference Scheduler — Multi-Model Resource Management
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Queue-based model loading with priority lanes and VRAM budget tracking.
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Prevents GPU OOM crashes when multiple projects compete for VRAM.
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Priority lanes:
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1. real-time (LPM) — highest priority, interactive
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2. interactive (playground) — user-facing, medium priority
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3. batch (harvester) — background, lowest priority
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"""
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import json
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import time
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import threading
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import logging
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from enum import IntEnum
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from pathlib import Path
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from typing import Dict, List, Optional, Any
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from dataclasses import dataclass, field, asdict
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logger = logging.getLogger("hermes.gpu_scheduler")
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class Priority(IntEnum):
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"""Job priority levels. Lower value = higher priority."""
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REALTIME = 1 # LPM, live video, interactive sessions
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INTERACTIVE = 2 # Playground, chat, user-facing
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BATCH = 3 # Harvester, overnight jobs, background
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@dataclass
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class ModelSpec:
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"""Specification for a model and its VRAM requirements."""
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name: str
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vram_mb: int # VRAM required in MB
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loader: str = "ollama" # How to load: ollama, vllm, llama_cpp, custom
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model_id: str = "" # Model identifier (e.g., "llama3:70b")
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cacheable: bool = True # Can be cached between jobs
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cpu_fallback: bool = True # Can fall back to CPU if GPU busy
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estimated_batch_ms: int = 1000 # Estimated time per batch
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@dataclass
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class InferenceJob:
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"""A job requesting GPU inference."""
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job_id: str
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project: str # "video_forge", "lpm", "playground", "harvester"
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model: ModelSpec
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priority: Priority
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batch_size: int = 1
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created_at: float = field(default_factory=time.time)
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started_at: Optional[float] = None
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completed_at: Optional[float] = None
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status: str = "queued" # queued, loading, running, completed, failed
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error: Optional[str] = None
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use_cpu_fallback: bool = False
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@dataclass
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class GPUState:
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"""Current GPU state."""
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total_vram_mb: int = 0
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used_vram_mb: int = 0
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loaded_models: List[str] = field(default_factory=list)
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active_job: Optional[str] = None
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@property
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def available_vram_mb(self) -> int:
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return self.total_vram_mb - self.used_vram_mb
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def can_fit(self, model: ModelSpec) -> bool:
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return self.available_vram_mb >= model.vram_mb
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# Known models and their VRAM requirements
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MODEL_REGISTRY: Dict[str, ModelSpec] = {
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# Video Forge models
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"sd_xl": ModelSpec(name="Stable Diffusion XL", vram_mb=8192, loader="comfyui", model_id="sd_xl"),
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"heartmula": ModelSpec(name="HeartMuLa", vram_mb=4096, loader="custom", model_id="heartmula"),
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"wan2.1": ModelSpec(name="Wan2.1", vram_mb=12288, loader="custom", model_id="wan2.1"),
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# LPM models
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"lpm_video": ModelSpec(name="LPM Video Gen", vram_mb=16384, loader="custom", model_id="lpm_video"),
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"lpm_a2a": ModelSpec(name="LPM A2A", vram_mb=8192, loader="custom", model_id="lpm_a2a"),
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# Local inference (hermes)
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"llama3_70b": ModelSpec(name="Llama 3 70B", vram_mb=40960, loader="ollama", model_id="llama3:70b"),
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"llama3_8b": ModelSpec(name="Llama 3 8B", vram_mb=8192, loader="ollama", model_id="llama3:8b"),
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"mimo_v2_pro": ModelSpec(name="MiMo v2 Pro", vram_mb=16384, loader="ollama", model_id="xiaomi/mimo-v2-pro"),
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# Playground
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"sdxl_turbo": ModelSpec(name="SDXL Turbo", vram_mb=6144, loader="comfyui", model_id="sdxl_turbo"),
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}
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# Default VRAM budget (can be overridden)
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DEFAULT_VRAM_MB = 49152 # 48GB (e.g., L40S, A6000)
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class InferenceScheduler:
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"""
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GPU Inference Scheduler.
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Manages a queue of inference jobs with priority scheduling,
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VRAM budget tracking, and CPU fallback.
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"""
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def __init__(
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self,
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vram_budget_mb: int = DEFAULT_VRAM_MB,
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queue_db: str = "~/.hermes/gpu_scheduler.db",
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):
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self.vram_budget_mb = vram_budget_mb
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self.queue_db = Path(queue_db).expanduser()
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self.queue_db.parent.mkdir(parents=True, exist_ok=True)
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# State
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self.gpu_state = GPUState(total_vram_mb=vram_budget_mb)
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self.job_queue: List[InferenceJob] = []
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self.completed_jobs: List[InferenceJob] = []
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self._lock = threading.Lock()
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self._running = False
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self._worker_thread: Optional[threading.Thread] = None
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# Load persisted state
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self._load_state()
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logger.info(
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"GPU Scheduler initialized: %dMB VRAM budget",
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vram_budget_mb,
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)
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def _load_state(self):
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"""Load state from SQLite."""
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import sqlite3
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conn = sqlite3.connect(str(self.queue_db))
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conn.execute("""
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CREATE TABLE IF NOT EXISTS jobs (
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job_id TEXT PRIMARY KEY,
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project TEXT,
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model_name TEXT,
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priority INTEGER,
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batch_size INTEGER,
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created_at REAL,
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started_at REAL,
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completed_at REAL,
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status TEXT,
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error TEXT,
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use_cpu_fallback INTEGER
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)
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""")
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conn.commit()
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# Load pending jobs
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rows = conn.execute(
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"SELECT * FROM jobs WHERE status IN ('queued', 'loading', 'running')"
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).fetchall()
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for row in rows:
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model_name = row[2]
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model = MODEL_REGISTRY.get(model_name, ModelSpec(name=model_name, vram_mb=8192))
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job = InferenceJob(
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job_id=row[0],
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project=row[1],
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model=model,
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priority=Priority(row[3]),
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batch_size=row[4],
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created_at=row[5],
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started_at=row[6],
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completed_at=row[7],
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status=row[8],
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error=row[9],
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use_cpu_fallback=bool(row[10]),
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)
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self.job_queue.append(job)
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conn.close()
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logger.info("Loaded %d pending jobs", len(self.job_queue))
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def _save_job(self, job: InferenceJob):
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"""Persist job to SQLite."""
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import sqlite3
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conn = sqlite3.connect(str(self.queue_db))
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conn.execute("""
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INSERT OR REPLACE INTO jobs
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(job_id, project, model_name, priority, batch_size, created_at,
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started_at, completed_at, status, error, use_cpu_fallback)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""", (
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job.job_id,
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job.project,
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job.model.name,
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job.priority.value,
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job.batch_size,
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job.created_at,
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job.started_at,
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job.completed_at,
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job.status,
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job.error,
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int(job.use_cpu_fallback),
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))
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conn.commit()
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conn.close()
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def submit_job(
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self,
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job_id: str,
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project: str,
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model_name: str,
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priority: Priority = Priority.BATCH,
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batch_size: int = 1,
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) -> InferenceJob:
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"""
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Submit an inference job to the queue.
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Args:
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job_id: Unique job identifier
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project: Project name (video_forge, lpm, playground, harvester)
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model_name: Model name from MODEL_REGISTRY
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priority: Job priority
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batch_size: Number of items to process
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Returns:
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The created InferenceJob
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"""
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model = MODEL_REGISTRY.get(model_name)
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if not model:
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raise ValueError(f"Unknown model: {model_name}. Registered: {list(MODEL_REGISTRY.keys())}")
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job = InferenceJob(
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job_id=job_id,
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project=project,
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model=model,
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priority=priority,
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batch_size=batch_size,
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)
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with self._lock:
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# Insert in priority order
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inserted = False
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for i, existing in enumerate(self.job_queue):
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if job.priority < existing.priority:
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self.job_queue.insert(i, job)
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inserted = True
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break
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if not inserted:
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self.job_queue.append(job)
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self._save_job(job)
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logger.info(
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"Job submitted: %s (project=%s, model=%s, priority=%s)",
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job_id, project, model_name, priority.name,
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)
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return job
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def get_next_job(self) -> Optional[InferenceJob]:
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"""Get the next job to process based on priority and VRAM availability."""
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with self._lock:
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for job in self.job_queue:
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if job.status != "queued":
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continue
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# Check if model fits in VRAM
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if self.gpu_state.can_fit(job.model):
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return job
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# Check CPU fallback
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if job.model.cpu_fallback:
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job.use_cpu_fallback = True
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return job
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return None
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def start_job(self, job: InferenceJob) -> bool:
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"""
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Mark a job as started and load its model.
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Returns True if successful, False if insufficient VRAM.
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"""
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with self._lock:
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if not job.use_cpu_fallback:
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if not self.gpu_state.can_fit(job.model):
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logger.warning(
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"Insufficient VRAM for %s: need %dMB, have %dMB",
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job.model.name,
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job.model.vram_mb,
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self.gpu_state.available_vram_mb,
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)
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return False
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# Reserve VRAM
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self.gpu_state.used_vram_mb += job.model.vram_mb
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if job.model.name not in self.gpu_state.loaded_models:
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self.gpu_state.loaded_models.append(job.model.name)
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job.status = "loading"
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job.started_at = time.time()
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self.gpu_state.active_job = job.job_id
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self._save_job(job)
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logger.info(
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"Job started: %s (model=%s, cpu_fallback=%s, vram_used=%dMB)",
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job.job_id,
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job.model.name,
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job.use_cpu_fallback,
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self.gpu_state.used_vram_mb,
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)
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return True
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def complete_job(self, job: InferenceJob, error: str = None):
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"""Mark a job as completed and release its VRAM."""
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with self._lock:
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job.completed_at = time.time()
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job.status = "completed" if not error else "failed"
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job.error = error
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if not job.use_cpu_fallback:
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# Release VRAM
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self.gpu_state.used_vram_mb = max(
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0,
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self.gpu_state.used_vram_mb - job.model.vram_mb,
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)
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if self.gpu_state.active_job == job.job_id:
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self.gpu_state.active_job = None
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# Move to completed
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self.job_queue.remove(job)
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self.completed_jobs.append(job)
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self._save_job(job)
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duration = (job.completed_at - job.started_at) * 1000 if job.started_at else 0
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logger.info(
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"Job completed: %s (status=%s, duration=%.0fms)",
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job.job_id,
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job.status,
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duration,
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)
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def get_status(self) -> Dict[str, Any]:
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"""Get scheduler status."""
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with self._lock:
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return {
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"gpu": {
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"total_vram_mb": self.gpu_state.total_vram_mb,
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"used_vram_mb": self.gpu_state.used_vram_mb,
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"available_vram_mb": self.gpu_state.available_vram_mb,
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"utilization_pct": round(
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self.gpu_state.used_vram_mb / self.gpu_state.total_vram_mb * 100, 1
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),
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"loaded_models": self.gpu_state.loaded_models,
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"active_job": self.gpu_state.active_job,
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},
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"queue": {
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"pending": len([j for j in self.job_queue if j.status == "queued"]),
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"loading": len([j for j in self.job_queue if j.status == "loading"]),
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"running": len([j for j in self.job_queue if j.status == "running"]),
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"by_priority": {
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p.name: len([j for j in self.job_queue if j.priority == p and j.status == "queued"])
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for p in Priority
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},
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},
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"completed": {
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"total": len(self.completed_jobs),
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"success": len([j for j in self.completed_jobs if j.status == "completed"]),
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"failed": len([j for j in self.completed_jobs if j.status == "failed"]),
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},
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}
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def register_model(self, name: str, spec: ModelSpec):
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"""Register a new model."""
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MODEL_REGISTRY[name] = spec
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logger.info("Registered model: %s (%dMB VRAM)", name, spec.vram_mb)
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def clear_completed(self):
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"""Clear completed jobs from memory (keep in DB)."""
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with self._lock:
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self.completed_jobs.clear()
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# ============================================================================
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# CLI Interface
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# ============================================================================
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def main():
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"""CLI entry point for testing."""
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import argparse
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parser = argparse.ArgumentParser(description="GPU Inference Scheduler")
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parser.add_argument("action", choices=["status", "submit", "list", "clear"])
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parser.add_argument("--job-id", help="Job ID for submit")
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parser.add_argument("--project", help="Project name")
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parser.add_argument("--model", help="Model name")
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parser.add_argument("--priority", choices=["realtime", "interactive", "batch"], default="batch")
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parser.add_argument("--vram", type=int, default=DEFAULT_VRAM_MB, help="VRAM budget in MB")
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args = parser.parse_args()
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scheduler = InferenceScheduler(vram_budget_mb=args.vram)
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if args.action == "status":
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status = scheduler.get_status()
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print(json.dumps(status, indent=2))
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elif args.action == "submit":
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if not all([args.job_id, args.project, args.model]):
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print("Error: --job-id, --project, and --model required for submit")
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return
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priority = Priority[args.priority.upper()]
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job = scheduler.submit_job(args.job_id, args.project, args.model, priority)
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print(f"Submitted: {job.job_id}")
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elif args.action == "list":
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print(f"Pending jobs: {len(scheduler.job_queue)}")
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for job in scheduler.job_queue:
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print(f" {job.job_id}: {job.project}/{job.model.name} [{job.priority.name}] {job.status}")
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elif args.action == "clear":
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scheduler.clear_completed()
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print("Cleared completed jobs from memory")
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if __name__ == "__main__":
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main()
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