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80300e5 | 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 | #!/usr/bin/env python3
"""Reproducible timing probe for the EXLLM 5M PyTorch checkpoint."""
import argparse
import json
import platform
import statistics
import sys
import time
from pathlib import Path
import torch
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from src.infer import bad_text # noqa: E402
from src.model import EXLLM, EXLLMConfig # noqa: E402
from src.tokenizer import HybridTokenizer, UTF8State # noqa: E402
def sync(device):
if device.type == "cuda":
torch.cuda.synchronize(device)
def load(checkpoint, device):
data = torch.load(checkpoint, map_location="cpu", weights_only=False)
model = EXLLM(EXLLMConfig(**data["config"]))
model.load_state_dict(data["model"])
return model.eval().to(device), HybridTokenizer.load(ROOT / "tokenizer.json")
@torch.inference_mode()
def generate_timed(model, tok, prompt, device, max_new=48):
ids = tok.encode_user(prompt)
reserve = min(max_new, model.cfg.max_seq_len // 2)
if len(ids) > model.cfg.max_seq_len - reserve:
keep = model.cfg.max_seq_len - reserve - 3
ids = [tok.BOS, tok.USER, *ids[2:-1][-max(1, keep):], tok.ASSIST]
out, state, token_ms = [], UTF8State(), []
sync(device)
start = time.perf_counter_ns()
ttft_ns = None
for _ in range(min(max_new, model.cfg.max_seq_len - len(ids))):
x = torch.tensor([ids + out], dtype=torch.long, device=device)
logits = model(x)[0, -1].clone()
logits[tok.PAD] = logits[tok.BOS] = logits[tok.USER] = logits[tok.ASSIST] = -1e30
valid = torch.zeros_like(logits, dtype=torch.bool)
if state.complete:
valid[tok.EOS] = True
for token in range(256):
if state.accepts_byte(token):
valid[token] = True
if tok.chars:
valid[256:256 + len(tok.chars)] = True
else:
for token in range(256):
if state.accepts_byte(token):
valid[token] = True
logits[~valid] = -1e30
token = int(torch.argmax(logits).item())
sync(device)
now = time.perf_counter_ns()
if ttft_ns is None:
ttft_ns = now - start
token_ms.append((now - start) / 1e6 if len(token_ms) == 0 else 0.0)
if token == tok.EOS:
break
if token < 256:
state.push(token)
elif not state.complete:
continue
out.append(token)
sync(device)
end = time.perf_counter_ns()
while out:
try:
text = tok.decode(out)
break
except UnicodeDecodeError:
out.pop()
else:
text = ""
total_s = (end - start) / 1e9
generated = len(out)
decode_s = max(0.0, total_s - (ttft_ns or 0) / 1e9)
return {
"prompt": prompt,
"input_tokens": len(ids),
"output_tokens": generated,
"ttft_ms": round((ttft_ns or 0) / 1e6, 3),
"total_ms": round(total_s * 1000, 3),
"decode_tokens_per_second": round(max(0, generated - 1) / decode_s, 3) if decode_s else None,
"text": text.strip(),
"bad_text": bad_text(text.strip()),
}
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--checkpoint", default=str(ROOT / "weights" / "EXLLM-v1.1-5m-release3.pt"))
ap.add_argument("--device", choices=("cpu", "cuda"), default="cuda" if torch.cuda.is_available() else "cpu")
ap.add_argument("--repeats", type=int, default=3)
args = ap.parse_args()
device = torch.device(args.device)
if device.type == "cuda" and not torch.cuda.is_available():
raise SystemExit("CUDA was requested but is unavailable")
started = time.perf_counter()
model, tok = load(args.checkpoint, device)
sync(device)
load_s = time.perf_counter() - started
prompts = ["こんにちは", "あなたは何というモデルですか？", "RAMとは何ですか？", "オフラインとは何ですか？"]
_ = generate_timed(model, tok, prompts[0], device, 16) # warm-up
runs = [generate_timed(model, tok, p, device) for p in prompts for _ in range(args.repeats)]
report = {
"schema": "exllm-benchmark-v1",
"checkpoint": Path(args.checkpoint).name,
"parameters": model.num_parameters(),
"device": str(device),
"torch": torch.__version__,
"python": platform.python_version(),
"load_seconds": round(load_s, 4),
"gpu": None,
"runs": runs,
"summary": {
"median_ttft_ms": round(statistics.median(x["ttft_ms"] for x in runs), 3),
"median_total_ms": round(statistics.median(x["total_ms"] for x in runs), 3),
"median_decode_tokens_per_second": round(statistics.median(x["decode_tokens_per_second"] for x in runs if x["decode_tokens_per_second"] is not None), 3),
},
}
if device.type == "cuda":
prop = torch.cuda.get_device_properties(device)
report["gpu"] = {
"name": prop.name,
"vram_bytes": prop.total_memory,
"compute_capability": f"{prop.major}.{prop.minor}",
"peak_allocated_bytes": torch.cuda.max_memory_allocated(device),
}
print(json.dumps(report, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
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