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"""Aligned 8K HELMET-ICL and OpenAI-MRCR pilot for Qwen checkpoints.
This is deliberately a small diagnostic runner. HELMET prompt construction
matches the ICL schedule used by the local OmniServe reproduction; MRCR uses
the official OpenAI rows selected by that reproduction, but renders the
messages with the Qwen chat template.
"""
from __future__ import annotations
import argparse
import csv
import hashlib
import json
import math
import random
import re
import string
from collections import defaultdict
from difflib import SequenceMatcher
from pathlib import Path
import pandas as pd
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer
HELMET_SPECS = {
"trec_coarse": ("icl_trec_coarse_400shot_balance", 6),
"trec_fine": ("icl_trec_fine_400shot_balance", 50),
"banking77": ("icl_banking77_360shot_balance", 77),
"clinic150": ("icl_clinic150_440shot_balance", 151),
"nlu": ("icl_nlu_510shot_balance", 68),
}
def balanced(data, shots: int, label_field: str, seed: int):
rng = random.Random(seed)
by_label = defaultdict(list)
for item in data:
by_label[item[label_field]].append(item)
rounds = math.ceil(shots / len(by_label))
selected_rounds = [[] for _ in range(rounds)]
for examples in by_label.values():
indices = rng.sample(range(len(examples)), rounds % len(examples))
while len(indices) < rounds:
indices += rng.sample(range(len(examples)), min(rounds - len(indices), len(examples)))
for index, example_index in enumerate(indices):
selected_rounds[index].append(examples[example_index])
for examples in selected_rounds:
rng.shuffle(examples)
return [item for group in selected_rounds for item in group][:shots]
def helmet_source(family: str, seed: int):
if family == "trec_coarse":
source = load_dataset("CogComp/trec", trust_remote_code=True)
return source["train"], source["test"], "text", "coarse_label"
if family == "trec_fine":
source = load_dataset("CogComp/trec", trust_remote_code=True)
return source["train"], source["test"], "text", "fine_label"
if family == "banking77":
source = load_dataset("PolyAI/banking77", trust_remote_code=True)
return source["train"], source["test"], "text", "label"
if family == "clinic150":
source = load_dataset("clinc/clinc_oos", "plus")
return source["train"], source["validation"], "text", "intent"
if family == "nlu":
source = load_dataset("xingkunliuxtracta/nlu_evaluation_data", trust_remote_code=True)["train"]
split = source.train_test_split(test_size=0.1, seed=seed)
return split["train"], split["test"], "text", "label"
raise ValueError(family)
def build_helmet(seed: int, limit: int):
rows = []
user_template = (
'Use the provided mapping from the text to label to assign a label to the text. '
'Only output "label: {{label}}" and nothing else. \n\n{context}\n\n{question}'
)
for family, (dataset_name, num_labels) in HELMET_SPECS.items():
shots = int(dataset_name.split("shot")[0].split("_")[-1])
train, test, text_field, label_field = helmet_source(family, seed)
samples = balanced(test, limit, label_field, seed)
for sample_index, sample in enumerate(samples):
local_seed = (int(hashlib.sha256(sample[text_field].encode()).hexdigest(), 16) + seed) % 2**31
demos = balanced(train, shots, label_field, local_seed)
mapping = list(range(num_labels))
random.Random(local_seed).shuffle(mapping)
context = "\n\n".join(
f"{demo[text_field]}\nlabel: {mapping[int(demo[label_field])]}" for demo in demos
)
rows.append({
"row_id": f"helmet_icl:8k:{family}:{sample_index}",
"benchmark": "helmet_icl",
"config": family,
"prompt": user_template.format(context=context, question=sample[text_field]) + "\nlabel:",
"answer": str(mapping[int(sample[label_field])]),
"max_new_tokens": 20,
"metadata": {"dataset": dataset_name, "shots": shots},
})
return rows
def build_mrcr(tokenizer, selection_path: Path, limit: int):
selection = [json.loads(line) for line in selection_path.read_text(encoding="utf-8").splitlines() if line.strip()]
chosen = sorted(
(row for row in selection if int(row["context_k"]) == 8),
key=lambda row: int(row["sample_index"]),
)[:limit]
if len(chosen) < limit:
raise ValueError(f"MRCR selection contains {len(chosen)} 8K rows, requested {limit}")
files = [
hf_hub_download("openai/mrcr", filename=f"2needle/2needle_{shard}.parquet", repo_type="dataset")
for shard in (0, 1)
]
frame = pd.concat([pd.read_parquet(path) for path in files], ignore_index=True)
rows = []
for selected in chosen:
source = frame.iloc[int(selected["source_index"])]
messages = json.loads(source["prompt"])
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False,
)
answer = str(source["answer"])
sample_index = int(selected["sample_index"])
rows.append({
"row_id": f"mrcr:8k:2needle:{sample_index}",
"benchmark": "mrcr",
"config": "2needle",
"prompt": prompt,
"answer": answer,
"prefix": str(source["random_string_to_prepend"]),
"max_new_tokens": min(768, len(tokenizer(answer, add_special_tokens=False).input_ids) + 64),
"metadata": {"source_index": int(selected["source_index"]), "official_tokens": int(selected["official_tokens"])},
})
return rows
def normalize(text: str) -> str:
text = text.lower()
text = re.sub(r"\b(a|an|the)\b", " ", text)
text = "".join(ch for ch in text if ch not in string.punctuation)
return " ".join(text.split())
def score(row, prediction: str) -> float:
if row["benchmark"] == "helmet_icl":
first = prediction.strip().splitlines()[0] if prediction.strip() else ""
parsed = re.sub(r"^label:", "", first, flags=re.I).strip()
return float(normalize(parsed) == normalize(row["answer"]))
prefix = row["prefix"]
if not prediction.startswith(prefix):
return 0.0
response = prediction.removeprefix(prefix).strip()
reference = row["answer"].removeprefix(prefix).strip()
return float(SequenceMatcher(None, response, reference).ratio())
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--benchmark", choices=("helmet_icl", "mrcr"), required=True)
parser.add_argument("--model-path", required=True)
parser.add_argument("--tokenizer-path", required=True)
parser.add_argument(
"--attn-implementation",
default="flash_attention_2",
choices=("eager", "sdpa", "flash_attention_2"),
help="Transformers attention backend; AHA 4-D local masks require SDPA or eager.",
)
parser.add_argument("--selection", type=Path)
parser.add_argument("--rows", type=Path, help="Frozen aligned prompt rows (JSONL)")
parser.add_argument("--method", required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--seed", type=int, default=20260710)
parser.add_argument("--limit", type=int, default=1, help="Samples per benchmark config")
parser.add_argument(
"--stop-new-line",
action="store_true",
help="Use HELMET's newline stop-token policy during generation.",
)
parser.add_argument(
"--duo-sink-size",
type=int,
help="Optional inference-only override for the AHA sink token count.",
)
parser.add_argument(
"--duo-recent-size",
type=int,
help="Optional inference-only override for the AHA recent-window token count.",
)
args = parser.parse_args()
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path, trust_remote_code=True, use_fast=False)
if args.rows:
rows = [json.loads(line) for line in args.rows.read_text(encoding="utf-8").splitlines() if line.strip()]
else:
rows = (
build_helmet(args.seed, args.limit)
if args.benchmark == "helmet_icl"
else build_mrcr(tokenizer, args.selection, args.limit)
)
model = AutoModelForCausalLM.from_pretrained(
args.model_path,
trust_remote_code=True,
torch_dtype=torch.bfloat16,
attn_implementation=args.attn_implementation,
)
for field, value in (
("duo_sink_size", args.duo_sink_size),
("duo_recent_size", args.duo_recent_size),
):
if value is not None:
if value < 0:
parser.error(f"--{field.replace('_', '-')} must be non-negative")
previous = getattr(model.config, field, None)
setattr(model.config, field, value)
print(f"[eval] {field} override: {previous!r} -> {value!r}", flush=True)
model = model.to("cuda").eval()
stop_token_ids = model.generation_config.eos_token_id
stop_token_ids = list(stop_token_ids) if isinstance(stop_token_ids, list) else [stop_token_ids]
if args.stop_new_line:
newline_tokens = ["\n", "Ċ", "ĊĊ", "<0x0A>"]
stop_token_ids += [tokenizer.convert_tokens_to_ids(token) for token in newline_tokens]
stop_token_ids = sorted(
{
token_id
for token_id in stop_token_ids
if token_id is not None and token_id != tokenizer.unk_token_id
}
)
print(f"[eval] stop_token_ids={stop_token_ids}", flush=True)
args.output.parent.mkdir(parents=True, exist_ok=True)
outputs = []
for ordinal, row in enumerate(rows, 1):
encoded = tokenizer(row["prompt"], return_tensors="pt", add_special_tokens=False)
prompt_tokens = int(encoded.input_ids.shape[1])
print(f"[{args.benchmark}] {ordinal}/{len(rows)} {row['config']} tokens={prompt_tokens}", flush=True)
encoded = {key: value.to("cuda") for key, value in encoded.items()}
with torch.inference_mode():
generated = model.generate(
**encoded,
do_sample=False,
max_new_tokens=row["max_new_tokens"],
use_cache=True,
eos_token_id=stop_token_ids,
pad_token_id=tokenizer.pad_token_id,
)
prediction = tokenizer.decode(generated[0, prompt_tokens:], skip_special_tokens=True).strip()
outputs.append({
**row,
"method": args.method,
"prompt_tokens": prompt_tokens,
"prompt_sha256": hashlib.sha256(row["prompt"].encode()).hexdigest(),
"prediction": prediction,
"score": score(row, prediction),
})
args.output.write_text("".join(json.dumps(row, ensure_ascii=False) + "\n" for row in outputs))
summary = args.output.with_suffix(".summary.csv")
with summary.open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(handle, fieldnames=["method", "benchmark", "config", "n", "score", "prompt_tokens"])
writer.writeheader()
for row in outputs:
writer.writerow({"method": args.method, "benchmark": row["benchmark"], "config": row["config"], "n": 1, "score": row["score"], "prompt_tokens": row["prompt_tokens"]})
print(summary)
if __name__ == "__main__":
main()
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