File size: 6,013 Bytes
e2bfccc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | """Probe residual activation scale for a saved TaoTrain checkpoint."""
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
from typing import Any
import torch
REPO_ROOT = Path(__file__).resolve().parents[2]
SRC_ROOT = REPO_ROOT / "src"
if str(SRC_ROOT) not in sys.path:
sys.path.insert(0, str(SRC_ROOT))
from taoTrain.checkpointing.checkpoint import CheckpointManager
from taoTrain.config import ModelConfig
from taoTrain.models import get_model
def load_sentencepiece(path: Path):
import sentencepiece as spm
processor = spm.SentencePieceProcessor()
processor.load(str(path))
return processor
def load_tokens(args: argparse.Namespace) -> tuple[torch.Tensor, int]:
tokenizer = load_sentencepiece(Path(args.tokenizer_path))
tokens: list[int] = []
with Path(args.data_path).open("r", encoding="utf-8", errors="replace") as handle:
for line in handle:
if len(tokens) >= args.max_tokens:
break
line = line.strip()
if not line:
continue
try:
record = json.loads(line)
except json.JSONDecodeError:
continue
text = record.get(args.text_field)
if not isinstance(text, str) or not text:
continue
ids = list(tokenizer.encode(text, out_type=int))
eos_id = tokenizer.eos_id()
if eos_id >= 0:
ids.append(eos_id)
tokens.extend(ids)
if len(tokens) < args.seq_len + 2:
raise ValueError(f"Need at least {args.seq_len + 2} tokens, got {len(tokens)}")
return torch.tensor(tokens[: args.max_tokens], dtype=torch.long), int(tokenizer.vocab_size())
def sample_batch(tokens: torch.Tensor, *, batch_size: int, seq_len: int, device: torch.device) -> tuple[torch.Tensor, torch.Tensor]:
max_start = tokens.numel() - seq_len - 1
starts = torch.linspace(0, max_start - 1, steps=batch_size).long()
rows = [tokens[int(start) : int(start) + seq_len + 1] for start in starts]
batch = torch.stack(rows, dim=0).to(device=device)
return batch[:, :-1].contiguous(), batch[:, 1:].contiguous()
def tensor_stats(value: torch.Tensor) -> dict[str, float | int]:
data = value.detach().float()
finite = torch.isfinite(data)
finite_count = int(finite.sum().cpu())
numel = data.numel()
if finite_count:
finite_data = data[finite]
rms = float(torch.sqrt(torch.mean(finite_data * finite_data)).cpu())
max_abs = float(finite_data.abs().max().cpu())
else:
rms = float("inf")
max_abs = float("inf")
return {
"numel": numel,
"finite": finite_count,
"rms": rms,
"max_abs": max_abs,
}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", required=True)
parser.add_argument("--tokenizer-path", required=True)
parser.add_argument("--data-path", required=True)
parser.add_argument("--text-field", default="text")
parser.add_argument("--output", required=True)
parser.add_argument("--batch-size", type=int, default=2)
parser.add_argument("--seq-len", type=int, default=512)
parser.add_argument("--max-tokens", type=int, default=200_000)
parser.add_argument("--device", default="cuda")
parser.add_argument("--dtype", choices=["float32", "bfloat16", "float16"], default="bfloat16")
args = parser.parse_args()
device = torch.device(args.device if args.device == "cpu" or torch.cuda.is_available() else "cpu")
dtype = {
"float32": torch.float32,
"bfloat16": torch.bfloat16,
"float16": torch.float16,
}[args.dtype]
tokens, _ = load_tokens(args)
input_ids, labels = sample_batch(tokens, batch_size=args.batch_size, seq_len=args.seq_len, device=device)
attention_mask = torch.ones_like(input_ids)
checkpoint_path = Path(args.checkpoint)
checkpoint = CheckpointManager(checkpoint_path.parent).load(checkpoint_path, device=device)
config_dict = checkpoint.get("config", {})
model_config = ModelConfig(**config_dict.get("model", {}))
model = get_model(model_config, device=device)
model.load_state_dict(checkpoint["model_state"], strict=False)
model.eval()
layer_stats: dict[str, dict[str, float | int]] = {}
handles = []
layer_pattern = re.compile(r"^(?:model\.)?(?:layers|blocks)\.\d+$")
def make_hook(name: str):
def hook(_module, _inputs, output):
value = output[0] if isinstance(output, tuple) else output
if torch.is_tensor(value):
layer_stats[name] = tensor_stats(value)
return hook
for name, module in model.named_modules():
if layer_pattern.match(name):
handles.append(module.register_forward_hook(make_hook(name)))
device_type = "cuda" if device.type == "cuda" else "cpu"
autocast_enabled = device.type == "cuda" and dtype in {torch.float16, torch.bfloat16}
with torch.no_grad(), torch.autocast(device_type=device_type, dtype=dtype, enabled=autocast_enabled):
outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
for handle in handles:
handle.remove()
result: dict[str, Any] = {
"checkpoint": str(checkpoint_path),
"loss": float(outputs["loss"].detach().cpu()),
"batch_size": args.batch_size,
"seq_len": args.seq_len,
"device": str(device),
"dtype": str(dtype),
"layers": layer_stats,
}
output_path = Path(args.output)
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(json.dumps(result, indent=2), encoding="utf-8")
print(json.dumps(result, indent=2))
if __name__ == "__main__":
main()
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