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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | #!/usr/bin/env python3
import argparse
import csv
import json
import os
import sys
from typing import Iterable
import numpy as np
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
class IndexDataset(Dataset):
def __init__(self, tensors: torch.Tensor):
self.tensors = tensors
def __getitem__(self, index: int) -> torch.Tensor:
return self.tensors[index]
def __len__(self) -> int:
return len(self.tensors)
def get_dataset(name: str):
if name == "wikitext2":
train_data = load_dataset("wikitext", "wikitext-2-raw-v1", split="train")
test_data = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")
return train_data, test_data, "text"
if name == "ptb":
train_data = load_dataset("ptb_text_only", "penn_treebank", split="train")
test_data = load_dataset("ptb_text_only", "penn_treebank", split="validation")
return train_data, test_data, "sentence"
raise ValueError(f"Unsupported dataset: {name}")
def process_data(samples, tokenizer, seq_len: int, field_name: str, add_bos_to_every: bool) -> IndexDataset:
test_ids = tokenizer(
"\n\n".join(samples[field_name]),
return_tensors="pt",
add_special_tokens=False,
).input_ids[0]
if not add_bos_to_every and tokenizer.bos_token_id is not None:
test_ids = torch.cat((torch.LongTensor([tokenizer.bos_token_id]), test_ids), dim=0)
batches = []
num_samples = test_ids.numel() // seq_len
for index in range(num_samples):
batch = test_ids[(index * seq_len) : ((index + 1) * seq_len)]
if add_bos_to_every and tokenizer.bos_token_id is not None:
batch = torch.cat((torch.LongTensor([tokenizer.bos_token_id]), batch), dim=0)
batches.append(batch)
return IndexDataset(tensors=torch.stack(batches))
def get_loader(name: str, tokenizer, seq_len: int, batch_size: int, add_bos_to_every: bool):
_, test_data, field_name = get_dataset(name)
dataset = process_data(test_data, tokenizer, seq_len, field_name, add_bos_to_every)
return DataLoader(dataset, batch_size=batch_size, shuffle=False)
@torch.no_grad()
def evaluate_ppl(model, test_loader, device: str) -> float:
nlls = []
for batch in tqdm(test_loader, desc="Running PPL", dynamic_ncols=True):
batch = batch.to(device)
outputs = model(batch)
shift_logits = outputs.logits[:, :-1, :].contiguous()
shift_labels = batch[:, 1:].contiguous()
loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
loss = loss_fct(
shift_logits.reshape(-1, shift_logits.size(-1)),
shift_labels.view(-1),
)
nlls.append(loss.cpu())
return float(np.exp(torch.cat(nlls, dim=-1).mean().item()))
def resolve_dtype(args) -> torch.dtype:
if args.use_bfloat:
return torch.bfloat16
dtype_name = args.dtype if args.dtype is not None else args.torch_dtype
if dtype_name is None:
dtype_name = "float16"
dtype_map = {
"float16": torch.float16,
"fp16": torch.float16,
"bfloat16": torch.bfloat16,
"bf16": torch.bfloat16,
"float32": torch.float32,
"fp32": torch.float32,
}
if dtype_name not in dtype_map:
raise ValueError(f"Unsupported dtype: {dtype_name}")
return dtype_map[dtype_name]
def normalize_datasets(datasets: Iterable[str]) -> list[str]:
normalized = []
for dataset in datasets:
normalized.append("wikitext2" if dataset == "wikitext" else dataset)
return normalized
def build_arg_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="Shared perplexity evaluation for abprune.")
parser.add_argument("--base_model", "--model-path", dest="model_path", required=True)
parser.add_argument("--output_dir", type=str, default=None)
parser.add_argument("--dataset", nargs="+", default=["wikitext2", "ptb"])
parser.add_argument("--max_seq_len", "--seq-len", dest="seq_len", type=int, default=1024)
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument("--device", default="cuda")
parser.add_argument(
"--dtype",
default=None,
choices=["float16", "fp16", "bfloat16", "bf16", "float32", "fp32"],
)
parser.add_argument(
"--torch_dtype",
default=None,
choices=["float16", "fp16", "bfloat16", "bf16", "float32", "fp32"],
)
parser.add_argument("--use_bfloat", action="store_true")
parser.add_argument("--add_bos_to_every", action="store_true")
parser.add_argument("--fix_decapoda_config", action="store_true")
parser.add_argument("--local_files_only", action="store_true")
return parser
def maybe_fix_decapoda_config(tokenizer, enabled: bool) -> None:
if not enabled:
return
if tokenizer.bos_token_id is None and tokenizer.eos_token_id is not None:
tokenizer.bos_token = tokenizer.eos_token
if tokenizer.pad_token is None and tokenizer.eos_token is not None:
tokenizer.pad_token = tokenizer.eos_token
def ensure_llmpruner_on_path() -> None:
repo_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
llmpruner_root = os.path.join(repo_root, "compare_model", "LLM-Pruner")
if os.path.isdir(llmpruner_root) and llmpruner_root not in sys.path:
sys.path.insert(0, llmpruner_root)
def load_model_and_tokenizer(model_path: str, *, torch_dtype: torch.dtype, local_files_only: bool):
if os.path.isfile(model_path) and model_path.endswith(".bin"):
ensure_llmpruner_on_path()
checkpoint = torch.load(model_path, map_location="cpu", weights_only=False)
if not isinstance(checkpoint, dict) or "model" not in checkpoint or "tokenizer" not in checkpoint:
raise ValueError(
"Expected an LLM-Pruner checkpoint dict with `model` and `tokenizer` entries."
)
model = checkpoint["model"]
tokenizer = checkpoint["tokenizer"]
if torch_dtype is not None:
model = model.to(dtype=torch_dtype)
return model, tokenizer
tokenizer = AutoTokenizer.from_pretrained(
model_path,
local_files_only=local_files_only,
)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch_dtype,
local_files_only=local_files_only,
)
return model, tokenizer
def main() -> None:
parser = build_arg_parser()
args = parser.parse_args()
datasets = normalize_datasets(args.dataset)
torch_dtype = resolve_dtype(args)
model, tokenizer = load_model_and_tokenizer(
args.model_path,
torch_dtype=torch_dtype,
local_files_only=args.local_files_only,
)
maybe_fix_decapoda_config(tokenizer, args.fix_decapoda_config)
if tokenizer.pad_token is None and tokenizer.eos_token is not None:
tokenizer.pad_token = tokenizer.eos_token
model.eval()
model.to(args.device)
metrics = {}
for dataset in datasets:
test_loader = get_loader(
dataset,
tokenizer,
seq_len=args.seq_len,
batch_size=args.batch_size,
add_bos_to_every=args.add_bos_to_every,
)
metrics[dataset] = evaluate_ppl(model, test_loader, args.device)
print(f"PPL-{dataset}: {metrics[dataset]} | add_bos_to_every: {args.add_bos_to_every} | seq_len: {args.seq_len}")
mem = None
if torch.cuda.is_available() and args.device.startswith("cuda"):
mem = torch.cuda.memory_allocated(args.device) / 1024 / 1024
result = {
"model_path": os.path.abspath(args.model_path),
"datasets": datasets,
"seq_len": args.seq_len,
"batch_size": args.batch_size,
"device": args.device,
"dtype": str(torch_dtype).replace("torch.", ""),
"add_bos_to_every": args.add_bos_to_every,
"metrics": metrics,
"params": int(sum(parameter.numel() for parameter in model.parameters())),
"mem_mib": mem,
}
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
filename = "ppl_bos.csv" if args.add_bos_to_every else "ppl.csv"
csv_path = os.path.join(args.output_dir, filename)
with open(csv_path, "w", newline="", encoding="utf-8") as handle:
writer = csv.writer(handle)
writer.writerow([*(f"ppl_{dataset}" for dataset in datasets), "params", "mem"])
writer.writerow([*(metrics[dataset] for dataset in datasets), result["params"], mem])
print(json.dumps(result, ensure_ascii=True))
if __name__ == "__main__":
main()
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