635 lines
23 KiB
Python
635 lines
23 KiB
Python
#!/usr/bin/env python3
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"""Train Qwen2.5-Omni Thinker LoRA + EgoSpeak 10x3 head from AV caches.
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This is a custom task runner intended to execute inside the ms-swift image.
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It uses the PEFT library shipped with ms-swift because Swift's normal SFT CLI
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optimizes generated text tokens, whereas this experiment reads one continuous
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Thinker hidden state and applies a multi-horizon classification loss.
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The cached audio and vision towers remain frozen. The reconstructed sequence
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is forwarded with ``use_cache=False`` so all retained 2-second blocks
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participate in autograd through the Thinker LoRA adapters.
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"""
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from __future__ import annotations
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import argparse
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import gc
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import importlib.metadata
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import json
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import math
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import random
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from pathlib import Path
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from typing import Any, Sequence
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import numpy as np
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import torch
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from torch import nn
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from torch.utils.data import DataLoader, Dataset
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from tqdm.auto import tqdm
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import streaming_turn_model as core
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from easycom_head import (
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NUM_CLASSES,
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NUM_HORIZONS,
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TurnPredictionHead,
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checkpoint_head_config,
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egospeak_multihorizon_loss,
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)
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from omni_cached_av import (
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SCHEMA_VERSION,
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CachedBlockStore,
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collate_cached_sequences,
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forward_last_state,
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load_json,
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reconstruct_open_stream_sequence,
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save_json,
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torch_load,
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)
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DEFAULT_LORA_TARGETS = (
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"q_proj",
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"k_proj",
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"v_proj",
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"o_proj",
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"gate_proj",
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"up_proj",
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"down_proj",
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)
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def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--model-id", required=True)
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parser.add_argument(
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"--cache-dir",
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type=Path,
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action="append",
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required=True,
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help="Cache directory from cache_omni_av_pieces.py; repeat for sessions",
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)
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parser.add_argument(
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"--target",
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type=Path,
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help="Override target path for a single --cache-dir",
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)
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parser.add_argument("--output-dir", type=Path, required=True)
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parser.add_argument("--head-checkpoint", type=Path)
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parser.add_argument("--max-examples", type=int, default=0)
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parser.add_argument("--max-steps", type=int, default=200)
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parser.add_argument("--batch-size", type=int, default=1)
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parser.add_argument("--gradient-accumulation-steps", type=int, default=1)
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parser.add_argument("--num-workers", type=int, default=0)
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parser.add_argument("--block-cache-size", type=int, default=16)
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parser.add_argument("--learning-rate", type=float, default=2e-5)
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parser.add_argument("--head-learning-rate", type=float, default=1e-4)
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parser.add_argument("--weight-decay", type=float, default=0.01)
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parser.add_argument("--warmup-steps", type=int, default=10)
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parser.add_argument("--max-grad-norm", type=float, default=1.0)
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parser.add_argument("--lora-rank", type=int, default=8)
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parser.add_argument("--lora-alpha", type=int, default=16)
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parser.add_argument("--lora-dropout", type=float, default=0.05)
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parser.add_argument(
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"--lora-target-modules",
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default=",".join(DEFAULT_LORA_TARGETS),
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)
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parser.add_argument("--head-dropout", type=float, default=0.1)
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parser.add_argument("--gradient-checkpointing", action="store_true")
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parser.add_argument("--seed", type=int, default=1337)
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parser.add_argument("--log-every", type=int, default=5)
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parser.add_argument("--eval-max-batches", type=int, default=4)
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parser.add_argument(
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"--dtype",
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choices=("bfloat16", "float16"),
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default="bfloat16",
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)
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parser.add_argument("--device", default="cuda:0")
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parser.add_argument("--flash-attention-2", action="store_true")
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parser.add_argument("--overwrite-output", action="store_true")
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parser.add_argument("--no-progress", action="store_true")
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return parser.parse_args(argv)
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def _seed_everything(seed: int) -> None:
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(seed)
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def _package_version(name: str) -> str | None:
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try:
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return importlib.metadata.version(name)
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except importlib.metadata.PackageNotFoundError:
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return None
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def _load_targets(path: Path) -> np.ndarray:
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targets = np.load(path, mmap_mode="r")
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if targets.ndim != 2 or targets.shape[1] != NUM_CLASSES:
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raise ValueError(f"Expected [T,{NUM_CLASSES}] target, got {targets.shape}: {path}")
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return targets
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class OmniTurnCacheDataset(Dataset[dict[str, Any]]):
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"""One sample per 200-ms anchor, reconstructed lazily on CPU."""
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def __init__(
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self,
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cache_dirs: Sequence[Path],
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*,
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target_override: Path | None,
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max_examples: int,
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seed: int,
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block_cache_size: int,
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) -> None:
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if target_override is not None and len(cache_dirs) != 1:
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raise ValueError("--target can override only one --cache-dir")
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self.sources: list[dict[str, Any]] = []
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self.examples: list[tuple[int, dict[str, Any]]] = []
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hidden_size: int | None = None
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reference_contract: tuple[Any, ...] | None = None
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for source_index, raw_cache_dir in enumerate(cache_dirs):
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cache_dir = raw_cache_dir.expanduser().resolve()
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manifest_path = cache_dir / "manifest.json"
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manifest = load_json(manifest_path)
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if int(manifest.get("schema_version", -1)) != SCHEMA_VERSION:
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raise ValueError(
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f"Unsupported cache schema in {manifest_path}: "
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f"{manifest.get('schema_version')}"
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)
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static = torch_load(cache_dir / manifest["static_path"])
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target_path = (
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target_override.expanduser().resolve()
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if target_override is not None
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else Path(manifest["target_path"])
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)
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targets = _load_targets(target_path)
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if len(targets) != int(manifest["target_steps"]):
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raise ValueError(
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f"Target length changed for {manifest_path}: "
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f"{len(targets)} vs {manifest['target_steps']}"
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)
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source_hidden = int(manifest["hidden_size"])
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hidden_size = source_hidden if hidden_size is None else hidden_size
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if source_hidden != hidden_size:
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raise ValueError("Cache directories have different hidden sizes")
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contract = (
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manifest["model_id"],
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manifest["step_seconds"],
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manifest["native_block_seconds"],
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manifest["memory_seconds"],
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manifest["video_fps"],
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manifest["positions_per_chunk"],
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)
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reference_contract = contract if reference_contract is None else reference_contract
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if contract != reference_contract:
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raise ValueError(
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"Cache directories do not share the same model/timing contract: "
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f"{manifest_path}"
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)
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self.sources.append(
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{
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"cache_dir": cache_dir,
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"manifest": manifest,
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"static": static,
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"targets": targets,
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"store": CachedBlockStore(cache_dir, max_blocks=block_cache_size),
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}
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)
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for anchor in manifest["anchors"]:
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target_index = int(anchor["target_index"])
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if not bool(anchor.get("has_future_target", True)):
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continue
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if target_index + NUM_HORIZONS >= len(targets):
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continue
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self.examples.append((source_index, anchor))
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if not self.examples:
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raise ValueError("No cache anchors have ten future target steps")
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if max_examples < 0:
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raise ValueError("--max-examples must be non-negative")
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if max_examples and max_examples < len(self.examples):
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generator = random.Random(seed)
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selected = generator.sample(range(len(self.examples)), max_examples)
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self.examples = [self.examples[index] for index in sorted(selected)]
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self.hidden_size = int(hidden_size or 0)
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def __len__(self) -> int:
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return len(self.examples)
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def __getitem__(self, index: int) -> dict[str, Any]:
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source_index, anchor = self.examples[index]
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source = self.sources[source_index]
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embeddings, positions = reconstruct_open_stream_sequence(
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store=source["store"],
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static=source["static"],
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anchor=anchor,
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)
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target_index = int(anchor["target_index"])
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targets = torch.from_numpy(
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np.array(
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source["targets"][
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target_index + 1 : target_index + NUM_HORIZONS + 1
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],
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dtype=np.float32,
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copy=True,
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)
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)
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if targets.shape != (NUM_HORIZONS, NUM_CLASSES):
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raise RuntimeError(
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f"Unexpected future target shape {tuple(targets.shape)} at {target_index}"
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)
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return {
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"inputs_embeds": embeddings,
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"position_ids": positions,
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"targets": targets,
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"step_index": target_index,
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}
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def _load_initial_head(
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checkpoint: Path | None,
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*,
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hidden_size: int,
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dropout: float,
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) -> TurnPredictionHead:
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if checkpoint is None:
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return TurnPredictionHead(hidden_size=hidden_size, dropout=dropout)
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try:
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payload = torch.load(checkpoint, map_location="cpu", weights_only=True)
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except (TypeError, RuntimeError):
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payload = torch.load(checkpoint, map_location="cpu")
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if not isinstance(payload, dict):
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raise ValueError(f"Expected dict head checkpoint: {checkpoint}")
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config = checkpoint_head_config(payload)
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if int(config["hidden_size"]) != hidden_size:
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raise ValueError(
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f"Head hidden size {config['hidden_size']} != cache hidden size {hidden_size}"
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)
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head = TurnPredictionHead(
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hidden_size=hidden_size,
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num_horizons=int(config["num_horizons"]),
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num_classes=int(config["num_classes"]),
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dropout=float(config["dropout"]),
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)
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head.load_state_dict(payload.get("head", payload))
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print(f"Initialized classification head from {checkpoint}")
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return head
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def _prepare_language_lora(
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model: torch.nn.Module,
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*,
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rank: int,
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alpha: int,
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dropout: float,
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target_modules: Sequence[str],
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gradient_checkpointing: bool,
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) -> torch.nn.Module:
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try:
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from peft import LoraConfig, get_peft_model
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except ImportError as exc:
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raise RuntimeError(
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"PEFT is required. The ms-swift 3.12.5 image should include it."
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) from exc
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model.requires_grad_(False)
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language_model = model.model
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matched = [
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name
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for name, module in language_model.named_modules()
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if isinstance(module, nn.Linear) and name.rsplit(".", 1)[-1] in target_modules
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]
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if not matched:
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raise RuntimeError(
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"No language-model linear layers matched LoRA targets "
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f"{list(target_modules)}"
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)
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config = LoraConfig(
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r=rank,
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lora_alpha=alpha,
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lora_dropout=dropout,
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target_modules=list(target_modules),
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bias="none",
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)
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language_model = get_peft_model(language_model, config)
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language_model.config.use_cache = False
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if gradient_checkpointing:
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try:
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language_model.gradient_checkpointing_enable(
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gradient_checkpointing_kwargs={"use_reentrant": False}
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)
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except TypeError:
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language_model.gradient_checkpointing_enable()
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language_model.print_trainable_parameters()
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print(
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f"LoRA matched {len(matched)} language layers; first={matched[0]}, "
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f"last={matched[-1]}"
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)
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return language_model
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def _lr_lambda(step: int, *, total_steps: int, warmup_steps: int) -> float:
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if warmup_steps > 0 and step < warmup_steps:
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return float(step + 1) / float(warmup_steps)
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remaining = max(1, total_steps - warmup_steps)
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progress = min(1.0, max(0.0, (step - warmup_steps) / remaining))
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return 0.5 * (1.0 + math.cos(math.pi * progress))
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@torch.inference_mode()
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def _evaluate_loss(
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language_model: torch.nn.Module,
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head: TurnPredictionHead,
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loader: DataLoader,
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*,
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device: torch.device,
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model_dtype: torch.dtype,
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max_batches: int,
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) -> float:
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language_was_training = language_model.training
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head_was_training = head.training
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language_model.eval()
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head.eval()
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losses: list[float] = []
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for batch_index, batch in enumerate(loader):
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if max_batches > 0 and batch_index >= max_batches:
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break
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inputs_embeds = batch["inputs_embeds"].to(
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device=device, dtype=model_dtype, non_blocking=True
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)
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position_ids = batch["position_ids"].to(device=device, non_blocking=True)
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attention_mask = batch["attention_mask"].to(device=device, non_blocking=True)
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readout_indices = batch["readout_indices"].to(device=device, non_blocking=True)
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targets = batch["targets"].to(device=device, dtype=torch.float32)
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states = forward_last_state(
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language_model,
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inputs_embeds=inputs_embeds,
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position_ids=position_ids,
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attention_mask=attention_mask,
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readout_indices=readout_indices,
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)
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logits = head(states.float())
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losses.append(float(egospeak_multihorizon_loss(logits, targets).item()))
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language_model.train(language_was_training)
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head.train(head_was_training)
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return float(np.mean(losses))
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def _jsonable_args(args: argparse.Namespace) -> dict[str, Any]:
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output: dict[str, Any] = {}
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for key, value in vars(args).items():
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if isinstance(value, Path):
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output[key] = str(value)
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elif isinstance(value, list) and value and isinstance(value[0], Path):
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output[key] = [str(item) for item in value]
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else:
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output[key] = value
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return output
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def main(argv: Sequence[str] | None = None) -> None:
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args = parse_args(argv)
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if args.max_steps <= 0:
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raise ValueError("--max-steps must be positive")
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if args.batch_size <= 0 or args.gradient_accumulation_steps <= 0:
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raise ValueError("Batch size and gradient accumulation must be positive")
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if args.num_workers != 0:
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print(
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"Warning: each worker owns an independent block-file LRU; use 0 for "
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"the 16-example pilot and increase only after profiling."
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)
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output_dir = args.output_dir.expanduser().resolve()
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known_outputs = (
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output_dir / "adapter_config.json",
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output_dir / "adapter",
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output_dir / "turn_head.pt",
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output_dir / "train_summary.json",
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)
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if not args.overwrite_output and any(path.exists() for path in known_outputs):
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raise FileExistsError(
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f"Training output already exists in {output_dir}; pass --overwrite-output"
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)
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output_dir.mkdir(parents=True, exist_ok=True)
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_seed_everything(args.seed)
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core._configure_qwen_logging()
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device = torch.device(args.device)
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if device.type != "cuda":
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raise ValueError("This LoRA pilot is intended for one CUDA GPU")
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dataset = OmniTurnCacheDataset(
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args.cache_dir,
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target_override=args.target,
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max_examples=args.max_examples,
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seed=args.seed,
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block_cache_size=args.block_cache_size,
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)
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generator = torch.Generator().manual_seed(args.seed)
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train_loader = DataLoader(
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dataset,
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batch_size=args.batch_size,
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shuffle=True,
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num_workers=args.num_workers,
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pin_memory=True,
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persistent_workers=args.num_workers > 0,
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collate_fn=collate_cached_sequences,
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generator=generator,
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)
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eval_loader = DataLoader(
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dataset,
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batch_size=args.batch_size,
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shuffle=False,
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num_workers=0,
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pin_memory=True,
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collate_fn=collate_cached_sequences,
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)
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print(
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f"Cached training examples: {len(dataset)}, hidden_size={dataset.hidden_size}, "
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f"batch={args.batch_size} x accum={args.gradient_accumulation_steps}"
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)
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base_model, _ = core.load_qwen_omni(
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args.model_id,
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dtype=args.dtype,
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device_map=args.device,
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use_flash_attention_2=args.flash_attention_2,
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)
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target_modules = tuple(
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value.strip() for value in args.lora_target_modules.split(",") if value.strip()
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)
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if not target_modules:
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raise ValueError("--lora-target-modules is empty")
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language_model = _prepare_language_lora(
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base_model,
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rank=args.lora_rank,
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alpha=args.lora_alpha,
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dropout=args.lora_dropout,
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target_modules=target_modules,
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gradient_checkpointing=args.gradient_checkpointing,
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)
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# Cached embeddings make both modality towers unnecessary during training.
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# Keeping only the language model materially reduces L20 memory use.
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del base_model
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gc.collect()
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torch.cuda.empty_cache()
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model_dtype = next(language_model.parameters()).dtype
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head = _load_initial_head(
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args.head_checkpoint,
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hidden_size=dataset.hidden_size,
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dropout=args.head_dropout,
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).to(device=device, dtype=torch.float32)
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lora_parameters = [parameter for parameter in language_model.parameters() if parameter.requires_grad]
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if not lora_parameters:
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raise RuntimeError("LoRA preparation produced no trainable parameters")
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optimizer = torch.optim.AdamW(
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[
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{"params": lora_parameters, "lr": args.learning_rate},
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|
{"params": head.parameters(), "lr": args.head_learning_rate},
|
|
],
|
|
weight_decay=args.weight_decay,
|
|
)
|
|
scheduler = torch.optim.lr_scheduler.LambdaLR(
|
|
optimizer,
|
|
lr_lambda=lambda step: _lr_lambda(
|
|
step, total_steps=args.max_steps, warmup_steps=args.warmup_steps
|
|
),
|
|
)
|
|
|
|
initial_loss = _evaluate_loss(
|
|
language_model,
|
|
head,
|
|
eval_loader,
|
|
device=device,
|
|
model_dtype=model_dtype,
|
|
max_batches=args.eval_max_batches,
|
|
)
|
|
print(f"Initial cached loss: {initial_loss:.6f}")
|
|
language_model.train()
|
|
head.train()
|
|
optimizer.zero_grad(set_to_none=True)
|
|
iterator = iter(train_loader)
|
|
history: list[dict[str, float | int]] = []
|
|
progress = tqdm(
|
|
total=args.max_steps,
|
|
desc="Thinker LoRA",
|
|
unit="update",
|
|
disable=args.no_progress,
|
|
)
|
|
for update_step in range(1, args.max_steps + 1):
|
|
accumulated_loss = 0.0
|
|
for _ in range(args.gradient_accumulation_steps):
|
|
try:
|
|
batch = next(iterator)
|
|
except StopIteration:
|
|
iterator = iter(train_loader)
|
|
batch = next(iterator)
|
|
inputs_embeds = batch["inputs_embeds"].to(
|
|
device=device, dtype=model_dtype, non_blocking=True
|
|
)
|
|
position_ids = batch["position_ids"].to(device=device, non_blocking=True)
|
|
attention_mask = batch["attention_mask"].to(device=device, non_blocking=True)
|
|
readout_indices = batch["readout_indices"].to(device=device, non_blocking=True)
|
|
targets = batch["targets"].to(device=device, dtype=torch.float32)
|
|
states = forward_last_state(
|
|
language_model,
|
|
inputs_embeds=inputs_embeds,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
readout_indices=readout_indices,
|
|
)
|
|
logits = head(states.float())
|
|
loss = egospeak_multihorizon_loss(logits, targets)
|
|
(loss / args.gradient_accumulation_steps).backward()
|
|
accumulated_loss += float(loss.detach()) / args.gradient_accumulation_steps
|
|
|
|
trainable_parameters = [*lora_parameters, *head.parameters()]
|
|
grad_norm = torch.nn.utils.clip_grad_norm_(
|
|
trainable_parameters, args.max_grad_norm
|
|
)
|
|
optimizer.step()
|
|
scheduler.step()
|
|
optimizer.zero_grad(set_to_none=True)
|
|
record = {
|
|
"step": update_step,
|
|
"loss": accumulated_loss,
|
|
"grad_norm": float(grad_norm),
|
|
"lora_lr": float(optimizer.param_groups[0]["lr"]),
|
|
"head_lr": float(optimizer.param_groups[1]["lr"]),
|
|
}
|
|
history.append(record)
|
|
progress.update(1)
|
|
progress.set_postfix(
|
|
loss=f"{accumulated_loss:.4f}",
|
|
lr=f"{optimizer.param_groups[0]['lr']:.2g}",
|
|
)
|
|
if update_step == 1 or update_step % args.log_every == 0:
|
|
print(
|
|
f"step={update_step} loss={accumulated_loss:.6f} "
|
|
f"grad_norm={float(grad_norm):.4f} "
|
|
f"lora_lr={optimizer.param_groups[0]['lr']:.3g}",
|
|
flush=True,
|
|
)
|
|
progress.close()
|
|
|
|
final_loss = _evaluate_loss(
|
|
language_model,
|
|
head,
|
|
eval_loader,
|
|
device=device,
|
|
model_dtype=model_dtype,
|
|
max_batches=args.eval_max_batches,
|
|
)
|
|
adapter_dir = output_dir / "adapter"
|
|
language_model.save_pretrained(adapter_dir)
|
|
torch.save(
|
|
{
|
|
"head": head.state_dict(),
|
|
"head_config": {
|
|
"hidden_size": dataset.hidden_size,
|
|
"num_horizons": NUM_HORIZONS,
|
|
"num_classes": NUM_CLASSES,
|
|
"dropout": head.dropout,
|
|
},
|
|
"initial_head_checkpoint": (
|
|
str(args.head_checkpoint.resolve()) if args.head_checkpoint else None
|
|
),
|
|
"training_step": args.max_steps,
|
|
},
|
|
output_dir / "turn_head.pt",
|
|
)
|
|
summary = {
|
|
"schema_version": 1,
|
|
"status": "pilot" if args.max_examples else "training",
|
|
"model_id": args.model_id,
|
|
"cache_dirs": [str(path.expanduser().resolve()) for path in args.cache_dir],
|
|
"num_examples": len(dataset),
|
|
"initial_loss": initial_loss,
|
|
"final_loss": final_loss,
|
|
"loss_reduction_ratio": (
|
|
(initial_loss - final_loss) / initial_loss if initial_loss else None
|
|
),
|
|
"peak_cuda_allocated_gib": torch.cuda.max_memory_allocated(device) / (1024 ** 3),
|
|
"packages": {
|
|
"torch": torch.__version__,
|
|
"transformers": _package_version("transformers"),
|
|
"peft": _package_version("peft"),
|
|
"ms-swift": _package_version("ms-swift"),
|
|
},
|
|
"args": _jsonable_args(args),
|
|
"history": history,
|
|
}
|
|
save_json(output_dir / "train_summary.json", summary)
|
|
print(f"Final cached loss: {final_loss:.6f} (initial {initial_loss:.6f})")
|
|
print(f"Saved LoRA adapter to {adapter_dir}")
|
|
print(f"Saved turn head to {output_dir / 'turn_head.pt'}")
|
|
print(f"Peak CUDA allocated: {summary['peak_cuda_allocated_gib']:.2f} GiB")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|