File size: 7,148 Bytes
e0a700d | 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 | from __future__ import annotations
import argparse
import json
import math
import time
from pathlib import Path
import torch
import torch.nn.functional as F
import yaml
from datasets import get_dataset_config_names, load_dataset
from litgpt import Tokenizer
from litgpt.config import Config
from litgpt.model import GPT
def load_model(checkpoint_dir: Path, device: torch.device) -> GPT:
config = Config.from_file(checkpoint_dir / "model_config.yaml")
model = GPT(config)
hyperparameters_path = checkpoint_dir / "hyperparameters.yaml"
if hyperparameters_path.is_file():
hyperparameters = yaml.safe_load(hyperparameters_path.read_text(encoding="utf-8")) or {}
if hyperparameters.get("train", {}).get("tie_embeddings"):
model.transformer.wte.weight = model.lm_head.weight
checkpoint = torch.load(checkpoint_dir / "lit_model.pth", map_location="cpu")
state_dict = checkpoint["model"] if "model" in checkpoint else checkpoint
model.load_state_dict(state_dict)
model.to(device)
model.eval()
return model
def encode_pair(tokenizer: Tokenizer, prompt: str, continuation: str, block_size: int) -> tuple[torch.Tensor, int]:
prompt_ids = tokenizer.encode(prompt, bos=True, eos=False).long()
continuation_ids = tokenizer.encode(continuation, bos=False, eos=False).long()
ids = torch.cat([prompt_ids, continuation_ids])
if ids.numel() > block_size:
keep = min(block_size, continuation_ids.numel() + min(32, prompt_ids.numel()))
ids = ids[-keep:]
continuation_len = min(continuation_ids.numel(), ids.numel() - 1)
else:
continuation_len = continuation_ids.numel()
return ids, continuation_len
@torch.no_grad()
def continuation_nll(
model: GPT,
tokenizer: Tokenizer,
prompt: str,
continuation: str,
device: torch.device,
) -> float:
ids, continuation_len = encode_pair(tokenizer, prompt, continuation, model.max_seq_length)
if continuation_len <= 0 or ids.numel() <= 1:
return float("inf")
inputs = ids[:-1].unsqueeze(0).to(device)
targets = ids[1:].to(device)
use_autocast = device.type == "cuda"
with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=use_autocast):
logits = model(inputs)[0].float()
start = max(0, targets.numel() - continuation_len)
loss = F.cross_entropy(logits[start:], targets[start:], reduction="sum")
return float(loss.cpu()) / continuation_len
def normalize_answer_key(answer_key: str, labels: list[str], texts: list[str]) -> int | None:
if answer_key in labels:
return labels.index(answer_key)
for index, text in enumerate(texts):
if answer_key.strip().lower() == text.strip().lower():
return index
return None
def benchmark_arc_easy(model: GPT, tokenizer: Tokenizer, device: torch.device, limit: int) -> dict[str, float | int]:
dataset = load_dataset("ai2_arc", "ARC-Easy", split=f"validation[:{limit}]")
correct = 0
total = 0
start = time.perf_counter()
for row in dataset:
question = row["question"].strip()
labels = [str(label) for label in row["choices"]["label"]]
texts = [str(text) for text in row["choices"]["text"]]
target = normalize_answer_key(str(row["answerKey"]), labels, texts)
if target is None:
continue
prompt = f"Question: {question}\nAnswer:"
scores = [continuation_nll(model, tokenizer, prompt, " " + choice, device) for choice in texts]
prediction = min(range(len(scores)), key=scores.__getitem__)
correct += int(prediction == target)
total += 1
elapsed = time.perf_counter() - start
return {
"arc_easy_validation_examples": total,
"arc_easy_accuracy": correct / total if total else math.nan,
"arc_easy_seconds": elapsed,
}
def benchmark_blimp(
model: GPT,
tokenizer: Tokenizer,
device: torch.device,
configs: list[str],
examples_per_config: int,
) -> dict[str, float | int | dict[str, float]]:
per_config: dict[str, float] = {}
correct = 0
total = 0
start = time.perf_counter()
for config in configs:
dataset = load_dataset("nyu-mll/blimp", config, split=f"train[:{examples_per_config}]")
config_correct = 0
config_total = 0
for row in dataset:
good = str(row["sentence_good"]).strip()
bad = str(row["sentence_bad"]).strip()
good_score = continuation_nll(model, tokenizer, "", good, device)
bad_score = continuation_nll(model, tokenizer, "", bad, device)
config_correct += int(good_score < bad_score)
config_total += 1
per_config[config] = config_correct / config_total if config_total else math.nan
correct += config_correct
total += config_total
elapsed = time.perf_counter() - start
return {
"blimp_examples": total,
"blimp_configs": len(configs),
"blimp_accuracy": correct / total if total else math.nan,
"blimp_per_config": per_config,
"blimp_seconds": elapsed,
}
def default_blimp_configs(limit: int) -> list[str]:
preferred = [
"adjunct_island",
"anaphor_number_agreement",
"determiner_noun_agreement_1",
"irregular_past_participle_adjectives",
"subject_verb_agreement_simple",
]
available = set(get_dataset_config_names("nyu-mll/blimp"))
configs = [name for name in preferred if name in available]
if len(configs) < limit:
configs.extend(name for name in sorted(available) if name not in configs)
return configs[:limit]
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint-dir", type=Path, required=True)
parser.add_argument("--tokenizer-dir", type=Path, required=True)
parser.add_argument("--arc-limit", type=int, default=100)
parser.add_argument("--blimp-configs", type=int, default=5)
parser.add_argument("--blimp-examples", type=int, default=50)
parser.add_argument("--out", type=Path)
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = load_model(args.checkpoint_dir, device)
tokenizer = Tokenizer(args.tokenizer_dir)
metrics: dict[str, object] = {
"checkpoint_dir": str(args.checkpoint_dir),
"tokenizer_dir": str(args.tokenizer_dir),
"device": str(device),
"model_name": model.config.name,
"parameters": sum(parameter.numel() for parameter in model.parameters()),
}
metrics.update(benchmark_arc_easy(model, tokenizer, device, args.arc_limit))
metrics.update(
benchmark_blimp(
model,
tokenizer,
device,
default_blimp_configs(args.blimp_configs),
args.blimp_examples,
)
)
text = json.dumps(metrics, indent=2, sort_keys=True)
print(text)
if args.out:
args.out.parent.mkdir(parents=True, exist_ok=True)
args.out.write_text(text + "\n", encoding="utf-8")
if __name__ == "__main__":
main()
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