File size: 14,386 Bytes
388fd6e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 | """Run lm-eval-harness on the packaged TaoNet-mini-T2 checkpoint.
This is a lightweight adapter around the custom TaoTrain checkpoint format.
By default it uses the fast full-sequence SSM path for benchmark scoring:
ssm_finite_tail_correction = True
ssm_kernel_mode = conv
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
from typing import Any
ROOT = Path(__file__).resolve().parent
TAOTRAIN_SRC = ROOT / "code" / "TaoTrain" / "src"
SSM_SRC = ROOT / "code" / "Taotern_SSM"
for path in (TAOTRAIN_SRC, SSM_SRC):
if str(path) not in sys.path:
sys.path.insert(0, str(path))
import torch
import torch.nn.functional as F
from tqdm import tqdm
from lm_eval import evaluator
from lm_eval.api.model import LM
from taoTrain.checkpointing.checkpoint import CheckpointManager
from taoTrain.config import ModelConfig
from taoTrain.inference.inferencer import Inferencer
from taoTrain.models import get_model
def apply_ssm_overrides(model: torch.nn.Module, *, kernel_mode: str, finite_tail: bool) -> int:
count = 0
for module in model.modules():
changed = False
if hasattr(module, "kernel_mode"):
module.kernel_mode = kernel_mode
changed = True
if hasattr(module, "finite_tail_correction"):
module.finite_tail_correction = finite_tail
changed = True
clear = getattr(module, "clear_kernel_cache", None)
if callable(clear):
clear()
if changed:
count += 1
return count
class TaoTrainLM(LM):
def __init__(
self,
checkpoint: str,
tokenizer: str,
device: str = "cuda",
dtype: str = "bfloat16",
max_length: int = 512,
ssm_kernel_mode: str = "conv",
finite_tail: bool = True,
eval_batch_size: int = 8,
) -> None:
super().__init__()
self._device = torch.device(device if device == "cpu" or torch.cuda.is_available() else "cpu")
self.dtype = {
"float32": torch.float32,
"bfloat16": torch.bfloat16,
"float16": torch.float16,
}[dtype]
self.max_length = max_length
self.ssm_kernel_mode = ssm_kernel_mode
self.finite_tail = finite_tail
self.eval_batch_size = eval_batch_size
self.checkpoint_path = Path(checkpoint)
self.tokenizer_path = Path(tokenizer)
checkpoint_obj = CheckpointManager(self.checkpoint_path.parent).load(
self.checkpoint_path,
device=self._device,
)
config_dict = checkpoint_obj.get("config", {})
model_config_dict = dict(config_dict.get("model", {}))
model_config_dict["ssm_finite_tail_correction"] = finite_tail
model_config_dict["ssm_kernel_mode"] = ssm_kernel_mode
self.tokenizer = Inferencer._load_tokenizer(self.tokenizer_path)
self.model = get_model(ModelConfig(**model_config_dict), device=self._device)
self.model.load_state_dict(checkpoint_obj["model_state"], strict=False)
self.model.to(self._device)
self.model.eval()
self.override_count = apply_ssm_overrides(
self.model,
kernel_mode=ssm_kernel_mode,
finite_tail=finite_tail,
)
@property
def tokenizer_name(self) -> str:
return str(self.tokenizer_path)
def tok_encode(self, text: str) -> list[int]:
return list(self.tokenizer.encode(text))
def tok_decode(self, tokens: list[int]) -> str:
return self.tokenizer.decode(tokens, skip_special_tokens=True)
def _prepare_ids(self, full_ids: list[int], continuation_start: int) -> tuple[list[int], int]:
bos = getattr(self.tokenizer, "bos_token_id", None)
if len(full_ids) < 2:
if bos is None:
return full_ids, continuation_start
full_ids = [bos] + full_ids
continuation_start += 1
if len(full_ids) > self.max_length:
drop = len(full_ids) - self.max_length
full_ids = full_ids[drop:]
continuation_start -= drop
continuation_start = max(1, continuation_start)
return full_ids, continuation_start
def _score_batch(self, batch: list[tuple[list[int], int]]) -> list[tuple[float, bool]]:
if not batch:
return []
pad_id = getattr(self.tokenizer, "pad_token_id", 0)
if pad_id is None or pad_id < 0:
pad_id = 0
max_len = max(len(ids) for ids, _ in batch)
if max_len < 2:
return [(0.0, True) for _ in batch]
rows = [ids + [pad_id] * (max_len - len(ids)) for ids, _ in batch]
input_ids = torch.tensor([row[:-1] for row in rows], dtype=torch.long, device=self._device)
target_ids = torch.tensor([row[1:] for row in rows], dtype=torch.long, device=self._device)
device_type = "cuda" if self._device.type == "cuda" else "cpu"
autocast_enabled = self._device.type == "cuda" and self.dtype in {torch.float16, torch.bfloat16}
with torch.inference_mode(), torch.autocast(
device_type=device_type,
dtype=self.dtype,
enabled=autocast_enabled,
):
apply_ssm_overrides(
self.model,
kernel_mode=self.ssm_kernel_mode,
finite_tail=self.finite_tail,
)
outputs = self.model(
input_ids=input_ids,
attention_mask=torch.ones_like(input_ids),
labels=None,
)
log_probs = F.log_softmax(outputs["logits"].float(), dim=-1)
results = []
for row_idx, (ids, continuation_start) in enumerate(batch):
continuation_start = max(1, continuation_start)
if continuation_start >= len(ids):
results.append((0.0, True))
continue
score_start = continuation_start - 1
score_end = len(ids) - 1
row_log_probs = log_probs[row_idx, score_start:score_end]
row_targets = target_ids[row_idx, score_start:score_end]
token_log_probs = row_log_probs.gather(1, row_targets.unsqueeze(1)).squeeze(1)
greedy = row_log_probs.argmax(dim=-1)
is_greedy = bool(torch.equal(greedy, row_targets))
results.append((float(token_log_probs.sum().item()), is_greedy))
return results
def _score_tokens(self, full_ids: list[int], continuation_start: int) -> tuple[float, bool]:
full_ids, continuation_start = self._prepare_ids(full_ids, continuation_start)
if continuation_start >= len(full_ids):
return 0.0, True
input_ids = torch.tensor(full_ids[:-1], dtype=torch.long, device=self._device).unsqueeze(0)
target_ids = torch.tensor(full_ids[1:], dtype=torch.long, device=self._device)
score_start = continuation_start - 1
device_type = "cuda" if self._device.type == "cuda" else "cpu"
autocast_enabled = self._device.type == "cuda" and self.dtype in {torch.float16, torch.bfloat16}
with torch.inference_mode(), torch.autocast(
device_type=device_type,
dtype=self.dtype,
enabled=autocast_enabled,
):
apply_ssm_overrides(
self.model,
kernel_mode=self.ssm_kernel_mode,
finite_tail=self.finite_tail,
)
outputs = self.model(
input_ids=input_ids,
attention_mask=torch.ones_like(input_ids),
labels=None,
)
logits = outputs["logits"][0]
log_probs = F.log_softmax(logits.float(), dim=-1)
cont_targets = target_ids[score_start:]
cont_log_probs = log_probs[score_start : score_start + len(cont_targets)]
token_log_probs = cont_log_probs.gather(1, cont_targets.unsqueeze(1)).squeeze(1)
greedy = cont_log_probs.argmax(dim=-1)
is_greedy = bool(torch.equal(greedy, cont_targets))
return float(token_log_probs.sum().item()), is_greedy
def loglikelihood(self, requests: list[Any]) -> list[tuple[float, bool]]:
prepared = []
for req in tqdm(requests, desc="loglikelihood"):
context, continuation = req.args
context_ids = self.tok_encode(context)
continuation_ids = self.tok_encode(continuation)
full_ids = self.tok_encode(context + continuation)
if len(full_ids) < len(continuation_ids):
full_ids = context_ids + continuation_ids
continuation_start = max(0, len(full_ids) - len(continuation_ids))
prepared.append(self._prepare_ids(full_ids, continuation_start))
results = []
for start in tqdm(range(0, len(prepared), self.eval_batch_size), desc="score_batches"):
results.extend(self._score_batch(prepared[start : start + self.eval_batch_size]))
return results
def loglikelihood_rolling(self, requests: list[Any]) -> list[float]:
results = []
for req in tqdm(requests, desc="rolling"):
(text,) = req.args
ids = self.tok_encode(text)
total = 0.0
step = max(1, self.max_length - 1)
bos = getattr(self.tokenizer, "bos_token_id", None)
prefix = [bos] if bos is not None else []
for start in range(0, len(ids), step):
chunk = prefix + ids[start : start + step]
score, _ = self._score_tokens(chunk, 1 if prefix else 0)
total += score
results.append(total)
return results
def generate_until(self, requests: list[Any]) -> list[str]:
from chat_ssm_fixed import generate
outputs = []
for req in tqdm(requests, desc="generate_until"):
context, gen_kwargs = req.args
max_gen_toks = int(gen_kwargs.get("max_gen_toks", 64))
until = gen_kwargs.get("until", [])
if isinstance(until, str):
until = [until]
text = generate(
self.model,
self.tokenizer,
context,
device=self._device,
dtype=self.dtype,
max_new_tokens=max_gen_toks,
temperature=0.7,
top_p=0.85,
repetition_penalty=1.2,
greedy=True,
)
for stop in until:
if stop and stop in text:
text = text.split(stop)[0]
outputs.append(text)
return outputs
def metric_value(task_result: dict[str, Any], preferred: list[str]) -> float | None:
for key in preferred:
if key in task_result:
value = task_result[key]
if isinstance(value, (int, float)):
return float(value)
return None
def json_safe(obj: Any) -> Any:
if isinstance(obj, dict):
return {str(k): json_safe(v) for k, v in obj.items()}
if isinstance(obj, list):
return [json_safe(v) for v in obj]
if isinstance(obj, tuple):
return [json_safe(v) for v in obj]
if isinstance(obj, (str, int, float, bool)) or obj is None:
return obj
return str(obj)
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", default=str(ROOT / "model" / "pretrain_final_model.pt"))
parser.add_argument("--tokenizer", default=str(ROOT / "tokenizer" / "tokenizer.model"))
parser.add_argument(
"--tasks",
default="mmlu,hellaswag,arc_easy,arc_challenge,piqa,winogrande",
)
parser.add_argument("--num-fewshot", type=int, default=0)
parser.add_argument("--limit", type=float, default=None)
parser.add_argument("--device", default="cuda")
parser.add_argument("--dtype", choices=["float32", "bfloat16", "float16"], default="bfloat16")
parser.add_argument("--ssm-kernel-mode", choices=["conv", "recurrent"], default="conv")
parser.add_argument("--finite-tail", action=argparse.BooleanOptionalAction, default=True)
parser.add_argument("--eval-batch-size", type=int, default=8)
parser.add_argument("--output", default=str(ROOT / "artifacts" / "lm_eval_results.json"))
args = parser.parse_args()
model = TaoTrainLM(
checkpoint=args.checkpoint,
tokenizer=args.tokenizer,
device=args.device,
dtype=args.dtype,
ssm_kernel_mode=args.ssm_kernel_mode,
finite_tail=args.finite_tail,
eval_batch_size=args.eval_batch_size,
)
print(f"device={model.device}")
print(f"ssm_overrides={model.override_count}")
print(f"ssm_kernel_mode={args.ssm_kernel_mode}")
print(f"finite_tail={args.finite_tail}")
task_names = [item.strip() for item in args.tasks.split(",") if item.strip()]
results = evaluator.simple_evaluate(
model=model,
tasks=task_names,
num_fewshot=args.num_fewshot,
limit=args.limit,
batch_size=1,
log_samples=False,
verbosity="INFO",
)
if results is None:
raise RuntimeError("lm-eval returned no results")
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(json.dumps(json_safe(results), indent=2, ensure_ascii=False), encoding="utf-8")
preferred = {
"mmlu": ["acc,none", "acc"],
"hellaswag": ["acc_norm,none", "acc_norm", "acc,none", "acc"],
"arc_easy": ["acc_norm,none", "acc_norm", "acc,none", "acc"],
"arc_challenge": ["acc_norm,none", "acc_norm", "acc,none", "acc"],
"piqa": ["acc_norm,none", "acc_norm", "acc,none", "acc"],
"winogrande": ["acc,none", "acc"],
}
print("\nTask benchmark:")
values = []
for task in task_names:
task_result = results["results"].get(task, {})
value = metric_value(task_result, preferred.get(task, ["acc_norm,none", "acc,none", "acc_norm", "acc"]))
if value is not None:
values.append(value)
print(f" {task}: {value:.4f}")
if values:
print(f" mean_primary_score: {sum(values) / len(values):.4f}")
print(f" num_fewshot: {args.num_fewshot}")
if args.limit is not None:
print(f" limit: {args.limit}")
if __name__ == "__main__":
main()
|