File size: 6,912 Bytes
db82745 | 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 | """Benchmark Bee against real, publicly available small LLMs.
Measures:
- Perplexity on TinyStories (lower = better)
- Forward latency (ms per token)
- Generation throughput (tok/s)
- Memory footprint
Models compared:
- Bee-Nano (random init)
- Bee-Nano (distilled, if available)
- GPT-2 124M
- SmolLM2-135M
- Qwen2.5-0.5B (if fits)
"""
import argparse
import json
import logging
import os
import sys
import time
from pathlib import Path
import torch
import torch.nn.functional as F
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from bee.register import register
from bee.config import BeeConfig
from bee.modeling_bee import BeeForCausalLM
register()
logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(name)s | %(message)s")
logger = logging.getLogger("bee.benchmark")
def count_params(model):
return sum(p.numel() for p in model.parameters())
def measure_perplexity(model, tokenizer, device, max_samples=100, max_length=256):
"""Measure perplexity on TinyStories validation."""
ds = load_dataset("roneneldan/TinyStories", split="validation", streaming=True)
ds = ds.take(max_samples)
total_nll = 0.0
total_tokens = 0
model = model.to(device).eval()
for ex in ds:
text = ex["text"]
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=max_length).to(device)
with torch.no_grad():
out = model(**inputs)
logits = out.logits if hasattr(out, "logits") else out[0]
shift_logits = logits[:, :-1, :].contiguous()
shift_labels = inputs["input_ids"][:, 1:].contiguous()
nll = F.cross_entropy(
shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1),
reduction="sum",
)
total_nll += nll.item()
total_tokens += shift_labels.numel()
perplexity = torch.exp(torch.tensor(total_nll / total_tokens)).item()
return perplexity
def measure_generation_speed(model, tokenizer, device, prompt="Once upon a time", max_new_tokens=64):
"""Measure generation throughput."""
inputs = tokenizer(prompt, return_tensors="pt").to(device)
model = model.to(device).eval()
# Warmup
with torch.no_grad():
_ = model.generate(**inputs, max_new_tokens=4, do_sample=False)
torch.cuda.synchronize() if device == "cuda" else None
t0 = time.perf_counter()
with torch.no_grad():
out = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
torch.cuda.synchronize() if device == "cuda" else None
t1 = time.perf_counter()
gen_time = t1 - t0
tok_per_sec = max_new_tokens / gen_time
return tok_per_sec, gen_time, out.shape[1]
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--device", type=str, default="mps" if torch.backends.mps.is_available() else "cpu")
parser.add_argument("--bee_checkpoint", type=str, default=None, help="Distilled Bee checkpoint")
parser.add_argument("--max_samples", type=int, default=50)
parser.add_argument("--output", type=str, default="benchmark_results.json")
args = parser.parse_args()
results = []
device = args.device
# Models to benchmark
models_to_test = []
# Bee-Nano (random init)
logger.info("Preparing Bee-Nano (random init)")
bee_cfg = BeeConfig(vocab_size=49152, hidden_size=512, num_hidden_layers=8,
num_attention_heads=8, intermediate_size=1024, max_position_embeddings=2048)
bee_random = BeeForCausalLM(bee_cfg)
models_to_test.append(("Bee-Nano (random)", bee_random, None))
# Bee-Nano (distilled, if exists)
if args.bee_checkpoint and os.path.exists(args.bee_checkpoint):
logger.info("Loading distilled Bee from %s", args.bee_checkpoint)
bee_distilled = BeeForCausalLM.from_pretrained(args.bee_checkpoint)
tok = AutoTokenizer.from_pretrained(args.bee_checkpoint)
models_to_test.append(("Bee-Nano (distilled)", bee_distilled, tok))
# GPT-2
try:
logger.info("Loading GPT-2")
gpt2 = AutoModelForCausalLM.from_pretrained("gpt2")
gpt2_tok = AutoTokenizer.from_pretrained("gpt2")
models_to_test.append(("GPT-2 124M", gpt2, gpt2_tok))
except Exception as e:
logger.warning("Failed to load GPT-2: %s", e)
# SmolLM2-135M
try:
logger.info("Loading SmolLM2-135M")
smol = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-135M", trust_remote_code=True)
smol_tok = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M", trust_remote_code=True)
models_to_test.append(("SmolLM2-135M", smol, smol_tok))
except Exception as e:
logger.warning("Failed to load SmolLM2: %s", e)
# Run benchmarks
for name, model, tok in models_to_test:
logger.info("=" * 50)
logger.info("Benchmarking: %s", name)
logger.info("=" * 50)
params = count_params(model)
logger.info("Parameters: %.2fM", params / 1e6)
# We need a tokenizer
if tok is None:
tok = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M", trust_remote_code=True)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
try:
ppl = measure_perplexity(model, tok, device, max_samples=args.max_samples)
logger.info("Perplexity: %.2f", ppl)
except Exception as e:
logger.error("Perplexity failed: %s", e)
ppl = None
try:
tps, gen_time, out_len = measure_generation_speed(model, tok, device, max_new_tokens=32)
logger.info("Generation: %.2f tok/s (%.2f ms for 32 tok)", tps, gen_time * 1000)
except Exception as e:
logger.error("Generation speed failed: %s", e)
tps = gen_time = out_len = None
results.append({
"model": name,
"params_M": params / 1e6,
"perplexity": ppl,
"gen_tok_per_sec": tps,
"gen_time_ms": gen_time * 1000 if gen_time else None,
"output_tokens": out_len,
})
# Save and print summary
with open(args.output, "w") as f:
json.dump(results, f, indent=2)
logger.info("\n" + "=" * 50)
logger.info("SUMMARY")
logger.info("=" * 50)
for r in results:
ppl_str = f"{r['perplexity']:.2f}" if r['perplexity'] else "N/A"
tps_str = f"{r['gen_tok_per_sec']:.1f}" if r['gen_tok_per_sec'] else "N/A"
logger.info("%-25s | %.1fM params | PPL: %s | Gen: %s tok/s",
r["model"], r["params_M"], ppl_str, tps_str)
logger.info("Results saved to %s", args.output)
if __name__ == "__main__":
main()
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