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4754707 | 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 | """Minimal CPU inference server for BitLMv47B (PyTorch path, no AVX-512).
For testing on hardware that can't run the optimized C binary
(e.g. 1-vCPU QEMU with no AVX). Loads a small .pt checkpoint, exposes a
streaming generation endpoint, and supports a built-in benchmark.
Run:
python _inf_server_simple.py --ckpt synth_5m.pt --port 5002
python _inf_server_simple.py --ckpt synth_5m.pt --bench # bench only
"""
import argparse, time, os, sys, math, json
import torch
import torch.nn.functional as F
from tokenizers import Tokenizer
from flask import Flask, Response, request, render_template_string
from model_v47b import BitLMv47B
import model_v16 as v16
def load_model(ckpt_path, device='cpu'):
ck = torch.load(ckpt_path, map_location=device, weights_only=False)
args = ck['args']
kw = {}
for k in ('vocab_size', 'd_model', 'n_layers', 'n_heads', 'd_ff', 'slope_groups'):
if k in args:
kw[k] = args[k]
if 'seq_len' in args:
kw['max_seq_len'] = args['seq_len']
elif 'max_seq_len' in args:
kw['max_seq_len'] = args['max_seq_len']
m = BitLMv47B(**kw)
sd = {k.replace('._orig_mod.', '.'): v for k, v in ck['model'].items()}
miss, unexp = m.load_state_dict(sd, strict=False)
print(f'load: missing={len(miss)} unexpected={len(unexp)} '
f'val_bpc={ck.get("val_bpc")}', flush=True)
m.eval().to(device)
return m, kw
@torch.no_grad()
def generate(m, tok, prompt, n_new=64, temp=0.0, top_k=0, device='cpu'):
v16.set_gumbel_tau(0.1)
enc = tok.encode(prompt)
ids = enc.ids
bos = tok.token_to_id('[BOS]')
if bos is not None and (not ids or ids[0] != bos):
ids = [bos] + ids
x = torch.tensor([ids], dtype=torch.long, device=device)
out_ids = list(ids)
max_seq = m.max_seq_len if hasattr(m, 'max_seq_len') else 2048
for _ in range(n_new):
if x.size(1) >= max_seq: break
logits, _ = m(x, None)
last = logits[0, -1]
if temp <= 0:
nxt = int(last.argmax().item())
else:
probs = F.softmax(last / temp, dim=-1)
if top_k > 0:
v, idx = probs.topk(top_k)
nxt = int(idx[torch.multinomial(v, 1).item()].item())
else:
nxt = int(torch.multinomial(probs, 1).item())
out_ids.append(nxt)
x = torch.cat([x, torch.tensor([[nxt]], device=device)], dim=1)
return out_ids
def benchmark(m, tok, n_new=64, prompt='The quick brown fox', warmup=2, runs=3):
print(f'Benchmark: prompt={prompt!r} n_new={n_new}', flush=True)
enc = tok.encode(prompt)
ids = enc.ids
print(f'prompt tokens: {len(ids)}', flush=True)
# Warmup
for _ in range(warmup):
_ = generate(m, tok, prompt, n_new=8)
# Time it
times = []
for r in range(runs):
t0 = time.time()
out = generate(m, tok, prompt, n_new=n_new)
elapsed = time.time() - t0
n_gen = len(out) - len(ids)
rate = n_gen / elapsed
times.append(rate)
print(f' run {r+1}: {n_gen} tok in {elapsed:.2f}s = {rate:.2f} tok/s',
flush=True)
avg = sum(times) / len(times)
print(f'\nAVG: {avg:.2f} tok/s ({n_new}-token generation, '
f'{m.d_model}-d {m.n_layers}-layer model)', flush=True)
return avg
HTML = """<!doctype html>
<html><head><title>BitLM CPU Inference</title>
<style>body{font-family:sans-serif;max-width:800px;margin:2em auto;}
textarea{width:100%;min-height:120px;}.gen{color:#0a0;}</style></head>
<body><h2>BitLM CPU Inference (synth_5m, no AVX)</h2>
<form id=f><textarea id=p>The quick brown fox</textarea>
<input type=number id=n value=64 min=1 max=512> tokens
<button>generate</button></form>
<pre id=o></pre>
<script>
document.getElementById('f').addEventListener('submit',async e=>{
e.preventDefault();let p=document.getElementById('p').value;
let n=document.getElementById('n').value;let o=document.getElementById('o');
o.innerText='';let r=await fetch('/gen',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({prompt:p,n_new:+n})});
let rd=r.body.getReader();while(1){let{done,value}=await rd.read();if(done)break;
o.innerText+=new TextDecoder().decode(value);}});
</script></body></html>"""
def make_app(ckpt_path, tokenizer_path):
m, kw = load_model(ckpt_path)
tok = Tokenizer.from_file(tokenizer_path)
app = Flask(__name__)
@app.route('/')
def root():
return render_template_string(HTML)
@app.route('/gen', methods=['POST'])
def gen():
d = request.get_json()
prompt = d.get('prompt', '')
n_new = int(d.get('n_new', 64))
def stream():
yield prompt
yield ' [GEN] '
t0 = time.time()
out = generate(m, tok, prompt, n_new=n_new)
elapsed = time.time() - t0
new_ids = out[len(tok.encode(prompt).ids) + (1 if tok.token_to_id('[BOS]') is not None else 0):]
yield tok.decode(new_ids) if new_ids else ''
yield f'\n\n[{len(new_ids)} tok in {elapsed:.2f}s = {len(new_ids)/max(elapsed,1e-3):.1f} tok/s]'
return Response(stream(), mimetype='text/plain')
return app
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--ckpt', required=True)
ap.add_argument('--tokenizer', default='rdv4_tokenizer.json')
ap.add_argument('--port', type=int, default=5002)
ap.add_argument('--bench', action='store_true')
ap.add_argument('--n-new', type=int, default=64)
args = ap.parse_args()
if args.bench:
m, _ = load_model(args.ckpt)
tok = Tokenizer.from_file(args.tokenizer)
benchmark(m, tok, n_new=args.n_new)
return
app = make_app(args.ckpt, args.tokenizer)
print(f'serving on http://0.0.0.0:{args.port}', flush=True)
app.run(host='0.0.0.0', port=args.port, debug=False, threaded=False)
if __name__ == '__main__':
main()
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