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5e5c72f | 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 | #!/usr/bin/env python3
"""
peer_run.py -- generate completions for peer-model NIAH prompts.
Backends, tried in this order unless --backend forces one:
vllm fast, batched. Cannot load Spark-X2.5 (model_type spark2_5 is
custom_code and has no vLLM backend), so it is skipped automatically.
hf transformers with trust_remote_code. Works for everything, slower.
Prompts are fed as TOKEN IDS, never text: peer_gen.py guarantees an exact token
count and a decode->re-encode round trip would silently break it.
Greedy decoding, matching our own harness (CP_GEN_GREEDY=1). Generation stops at
--max-new tokens; no early stop, because the peer models have no shared
end-of-answer convention and truncation is harmless -- scoring is substring
match, so trailing chatter costs nothing.
usage:
peer_run.py --model Qwen/Qwen3-4B-Base --prompts out/niah_single_prompts.jsonl \
--out gen/qwen3-4b_niah_single.jsonl --max-new 24
"""
import argparse
import json
import os
import sys
import time
def load_prompts(p):
rows = []
with open(p) as f:
for line in f:
line = line.strip()
if line:
rows.append(json.loads(line))
return rows
def run_vllm(model, rows, max_new, gpu_frac, max_len):
from vllm import LLM, SamplingParams
llm = LLM(model=model, dtype="bfloat16", trust_remote_code=True,
gpu_memory_utilization=gpu_frac, max_model_len=max_len,
enforce_eager=False)
sp = SamplingParams(temperature=0.0, max_tokens=max_new)
outs = llm.generate(prompt_token_ids=[r["ids"] for r in rows],
sampling_params=sp)
return [o.outputs[0].text for o in outs]
def run_hf(model, rows, max_new, batch):
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained(model, trust_remote_code=True)
m = AutoModelForCausalLM.from_pretrained(
model, dtype=torch.bfloat16, device_map="cuda", trust_remote_code=True)
m.eval()
texts = []
# batch=1 by default: these are 16k-token prompts and padding a ragged batch
# would change each sequence's positions, which is exactly what NIAH measures.
for s in range(0, len(rows), batch):
chunk = rows[s:s + batch]
ids = torch.tensor([r["ids"] for r in chunk], device="cuda")
with torch.no_grad():
out = m.generate(input_ids=ids, max_new_tokens=max_new,
do_sample=False, num_beams=1,
pad_token_id=getattr(tok, "pad_token_id", None)
or getattr(tok, "eos_token_id", 0))
for j in range(len(chunk)):
texts.append(tok.decode(out[j][ids.shape[1]:], skip_special_tokens=True))
done = min(s + batch, len(rows))
print(f" {done}/{len(rows)}", flush=True)
return texts
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--model", required=True)
ap.add_argument("--prompts", required=True)
ap.add_argument("--out", required=True)
ap.add_argument("--max-new", type=int, default=24)
ap.add_argument("--backend", default="auto", choices=["auto", "vllm", "hf"])
ap.add_argument("--gpu-frac", type=float, default=0.90)
ap.add_argument("--batch", type=int, default=1, help="hf backend only")
args = ap.parse_args()
rows = load_prompts(args.prompts)
if not rows:
sys.exit(f"no prompts in {args.prompts}")
plen = len(rows[0]["ids"])
max_len = plen + args.max_new + 8
# Never ask vLLM for more than the checkpoint's trained window: Qwen3-Base
# ships max_position_embeddings=32768, so a 32,744-token prompt + 24 new + 8
# slack would exceed it and vLLM refuses to start. Clamp, and say so.
try:
from transformers import AutoConfig
mp = getattr(AutoConfig.from_pretrained(args.model, trust_remote_code=True),
"max_position_embeddings", None)
if mp and max_len > mp:
print(f" clamping max_model_len {max_len} -> {mp} (model's native window)")
max_len = mp
if plen + args.max_new > mp:
sys.exit(f" FATAL: prompt {plen} + max_new {args.max_new} exceeds "
f"the model's window {mp}; lower --prompt-len")
except Exception as e:
print(f" (could not read max_position_embeddings: {type(e).__name__})")
print(f" {args.model} {len(rows)} prompts x {plen} tok max_new={args.max_new}")
backend = args.backend
if backend == "auto":
backend = "hf"
try:
import vllm # noqa: F401
# spark2_5 is custom_code with no vLLM backend; do not waste a load
backend = "hf" if "spark" in args.model.lower() else "vllm"
except ImportError:
pass
print(f" backend: {backend}")
t0 = time.time()
if backend == "vllm":
try:
texts = run_vllm(args.model, rows, args.max_new, args.gpu_frac, max_len)
except Exception as e:
print(f" vLLM failed ({type(e).__name__}: {str(e)[:140]}) -- falling back to hf")
texts = run_hf(args.model, rows, args.max_new, args.batch)
else:
texts = run_hf(args.model, rows, args.max_new, args.batch)
dt = time.time() - t0
os.makedirs(os.path.dirname(args.out) or ".", exist_ok=True)
with open(args.out, "w") as f:
for r, t in zip(rows, texts):
f.write(json.dumps({"i": r["i"], "generated": t}) + "\n")
print(f" wrote {args.out} ({len(texts)} rows, {dt:.0f}s, "
f"{dt/max(1,len(texts)):.2f}s/prompt)")
if __name__ == "__main__":
main()
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