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2c44909 | 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 | #!/usr/bin/env python3
"""Evaluate perplexity for a progressive-pruned model assembled from cycles."""
import argparse
import torch
try:
import ppl_eval
except Exception as exc: # pragma: no cover - optional dependency
raise SystemExit("ppl_eval.py is required (missing or invalid)") from exc
try:
from transformers import AutoTokenizer
except Exception as exc: # pragma: no cover - fail early with clear error
raise SystemExit("transformers is required: pip install transformers") from exc
from progressive_loader import load_progressive_model
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Evaluate PPL for a model reconstructed from progressive cycles."
)
parser.add_argument("--base_model", required=True, help="Base HF model id or path")
parser.add_argument(
"--progressive_dir",
required=True,
help="Output directory from progressive pruning",
)
parser.add_argument(
"--cycle",
type=int,
default=None,
help="Cycle to load (default: final)",
)
parser.add_argument(
"--dataset",
action="append",
default=[],
help="Evaluation dataset name (repeatable). Defaults to wikitext.",
)
parser.add_argument(
"--dataset_config",
action="append",
default=[],
help="Evaluation dataset config (repeatable or single shared config).",
)
parser.add_argument(
"--dataset_split",
default="test",
help="Evaluation dataset split (default: test)",
)
parser.add_argument(
"--dataset_text_field",
default=None,
help="Evaluation text field override (default: auto-detect)",
)
parser.add_argument(
"--num_samples",
type=int,
default=0,
help="Number of token sequences per dataset (0 = all)",
)
parser.add_argument(
"--seq_len",
type=int,
default=2048,
help="Sequence length for eval",
)
parser.add_argument(
"--batch_size",
type=int,
default=4,
help="Batch size for eval",
)
parser.add_argument(
"--device",
default="cuda" if torch.cuda.is_available() else "cpu",
help="Device for eval",
)
parser.add_argument("--seed", type=int, default=0, help="Random seed")
parser.add_argument(
"--model_family",
type=str,
choices=["auto", "llama", "qwen"],
default="auto",
help="Model family for BOS handling",
)
parser.add_argument(
"--add_bos",
type=str,
choices=["auto", "always", "never"],
default="auto",
help="Whether to prepend BOS to each sample",
)
parser.add_argument(
"--max_batches",
type=int,
default=None,
help="Optional max number of eval batches per dataset",
)
parser.add_argument(
"--cache_dir",
default=None,
help="Optional datasets cache dir for eval",
)
parser.add_argument(
"--num_workers",
type=int,
default=0,
help="Eval DataLoader workers",
)
parser.add_argument(
"--dtype",
default="auto",
choices=["auto", "float32", "float16", "bfloat16"],
help="Model dtype",
)
parser.add_argument(
"--trust_remote_code",
action="store_true",
help="Allow custom model code from hub",
)
parser.add_argument(
"--layer_path",
default=None,
help="Override layer attribute path if needed",
)
return parser.parse_args()
def main() -> None:
args = parse_args()
torch.manual_seed(args.seed)
datasets = args.dataset or ["wikitext"]
configs = args.dataset_config or ["wikitext-2-raw-v1"]
configs = ppl_eval._expand_dataset_configs(datasets, configs)
model = load_progressive_model(
args.base_model,
args.progressive_dir,
cycle=args.cycle,
device=args.device,
dtype=args.dtype,
trust_remote_code=args.trust_remote_code,
layer_path=args.layer_path,
)
tokenizer = AutoTokenizer.from_pretrained(
args.base_model, trust_remote_code=args.trust_remote_code
)
if tokenizer.pad_token is None and tokenizer.eos_token is not None:
tokenizer.pad_token = tokenizer.eos_token
results = ppl_eval.evaluate_ppl_datasets(
model,
tokenizer,
datasets=datasets,
configs=configs,
split=args.dataset_split,
text_field=args.dataset_text_field,
num_samples=args.num_samples,
seq_len=args.seq_len,
batch_size=args.batch_size,
device=args.device,
seed=args.seed,
shuffle=False,
model_family=args.model_family,
add_bos=args.add_bos,
max_batches=args.max_batches,
cache_dir=args.cache_dir,
num_workers=args.num_workers,
)
print("Perplexity results:")
for name, ppl in results.items():
print(f"{name}: {ppl:.4f}")
if __name__ == "__main__":
main()
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