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fix: wikinews scorer imports modal-free #109

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Merged
ronaldtse merged 1 commit into from
Oct 7, 2026
Merged

fix: wikinews scorer imports modal-free #109

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133 changes: 71 additions & 62 deletions eval_wikinews_multiref.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,24 +22,32 @@
import re
from pathlib import Path

import modal
try:
import modal
except ImportError: # pure-function import (HF Jobs, local harnesses)
modal = None


datasets_volume = modal.Volume.from_name("rababa-datasets", create_if_missing=True)
checkpoints_volume = modal.Volume.from_name("rababa-checkpoints", create_if_missing=True)

MODEL_DIR = "/checkpoints/rababa_arabic_byt5/run-003-domain/best"
TAG = "r3"
BENCH = "WikiNews_2024_Multi_Ref.txt.diac"

DIACRITICS_RE = re.compile("[ؐ-ًؚ-ٰٟۖ-ۜ۟-۪ۨ-ۭ]")

image = (
modal.Image.debian_slim(python_version="3.11")
.pip_install("torch==2.5.1", "transformers==4.46.3", "pandas", "tqdm")
.env({"PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True"})
)

app = modal.App("rababa-wikinews-multiref", image=image)


if modal is not None:
import modal
datasets_volume = modal.Volume.from_name("rababa-datasets", create_if_missing=True)
checkpoints_volume = modal.Volume.from_name("rababa-checkpoints", create_if_missing=True)
image = (
modal.Image.debian_slim(python_version="3.11")
.pip_install("torch==2.5.1", "transformers==4.46.3", "pandas", "tqdm")
.env({"PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True"})
)
app = modal.App("rababa-wikinews-multiref", image=image)

# ---- Java removeDefaultDiac port (order matters) ----
_DEFAULT_RULES = [
Expand Down Expand Up @@ -152,56 +160,57 @@ def score(preds: list[str], refs: list[str], skip_last: bool) -> dict:
}


@app.function(
gpu="A100",
timeout=2 * 60 * 60,
volumes={"/datasets": datasets_volume, "/checkpoints": checkpoints_volume},
)
def run(model_dir: str = MODEL_DIR, tag: str = TAG, bench: str = BENCH) -> dict:
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

datasets_volume.reload()
checkpoints_volume.reload()

lines = [
l.strip()
for l in Path(f"/datasets/wikinews/{bench}").read_text(encoding="utf-8").splitlines()
if l.strip()
]
inputs = [DIACRITICS_RE.sub("", l) for l in lines]
print(f"[data] {bench}: {len(lines)} lines", flush=True)

tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSeq2SeqLM.from_pretrained(model_dir).to("cuda").eval()
device = next(model.parameters()).device

preds: list[str] = []
with torch.no_grad():
for i in range(0, len(inputs), 8):
batch = inputs[i : i + 8]
enc = tokenizer(batch, return_tensors="pt", padding=True, truncation=True,
max_length=1024).to(device)
with torch.autocast("cuda", torch.bfloat16):
gen = model.generate(**enc, max_new_tokens=2048, num_beams=1)
preds.extend(tokenizer.batch_decode(gen, skip_special_tokens=True))
if (i // 8) % 20 == 0:
print(f"[gen] {i + len(batch)}/{len(inputs)}", flush=True)

out: dict = {"model": tag, "bench": bench}
for skip_last, mode in ((False, "full"), (True, "no_case_ending")):
out[mode] = score(preds, lines, skip_last)
print(json.dumps(out, ensure_ascii=False, indent=1), flush=True)

out_dir = Path("/checkpoints") / "/".join(model_dir.strip("/").split("/")[1:-1])
(out_dir / f"wikinews_preds_{tag}_{bench}.txt").write_text(
"\n".join(preds), encoding="utf-8")
(out_dir / f"wikinews_multiref_{tag}.json").write_text(
json.dumps(out, ensure_ascii=False, indent=1), encoding="utf-8")
checkpoints_volume.commit()
return out


@app.local_entrypoint()
def main(model_dir: str = MODEL_DIR, tag: str = TAG, bench: str = BENCH):
run.remote(model_dir=model_dir, tag=tag, bench=bench)
if modal is not None:
@app.function(
gpu="A100",
timeout=2 * 60 * 60,
volumes={"/datasets": datasets_volume, "/checkpoints": checkpoints_volume},
)
def run(model_dir: str = MODEL_DIR, tag: str = TAG, bench: str = BENCH) -> dict:
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

datasets_volume.reload()
checkpoints_volume.reload()

lines = [
l.strip()
for l in Path(f"/datasets/wikinews/{bench}").read_text(encoding="utf-8").splitlines()
if l.strip()
]
inputs = [DIACRITICS_RE.sub("", l) for l in lines]
print(f"[data] {bench}: {len(lines)} lines", flush=True)

tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSeq2SeqLM.from_pretrained(model_dir).to("cuda").eval()
device = next(model.parameters()).device

preds: list[str] = []
with torch.no_grad():
for i in range(0, len(inputs), 8):
batch = inputs[i : i + 8]
enc = tokenizer(batch, return_tensors="pt", padding=True, truncation=True,
max_length=1024).to(device)
with torch.autocast("cuda", torch.bfloat16):
gen = model.generate(**enc, max_new_tokens=2048, num_beams=1)
preds.extend(tokenizer.batch_decode(gen, skip_special_tokens=True))
if (i // 8) % 20 == 0:
print(f"[gen] {i + len(batch)}/{len(inputs)}", flush=True)

out: dict = {"model": tag, "bench": bench}
for skip_last, mode in ((False, "full"), (True, "no_case_ending")):
out[mode] = score(preds, lines, skip_last)
print(json.dumps(out, ensure_ascii=False, indent=1), flush=True)

out_dir = Path("/checkpoints") / "/".join(model_dir.strip("/").split("/")[1:-1])
(out_dir / f"wikinews_preds_{tag}_{bench}.txt").write_text(
"\n".join(preds), encoding="utf-8")
(out_dir / f"wikinews_multiref_{tag}.json").write_text(
json.dumps(out, ensure_ascii=False, indent=1), encoding="utf-8")
checkpoints_volume.commit()
return out


@app.local_entrypoint()
def main(model_dir: str = MODEL_DIR, tag: str = TAG, bench: str = BENCH):
run.remote(model_dir=model_dir, tag=tag, bench=bench)
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