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#!/usr/bin/env python3
import json
from pathlib import Path
import re
import torch
from transformers import AutoTokenizer, Gemma3ForCausalLM
from nltk.translate.bleu_score import corpus_bleu, SmoothingFunction
from tqdm import tqdm
import os
import torch._dynamo
torch._dynamo.config.suppress_errors = True
torch.set_float32_matmul_precision('high') # Also address the warning you saw
# ─── CONFIG ─────────────────────────────────────────────────────────
SRC_LANG, TGT_LANG = "en", "kk"
MODEL_PATH = "/raid/srp_base_model_training/abai_workspace/models/sync_kk_en/checkpoint-final"
TEST_FILE = "/raid/srp_base_model_training/abai_workspace/data/flores/en_to_kk_formatted.jsonl" # JSONL with fields system,user,assistant
OUTPUT_JSON = f"eval_sync_KKEN_data_{SRC_LANG}_to_{TGT_LANG}.json"
MAX_NEW_TOKS = 64
os.environ["CUDA_VISIBLE_DEVICES"] = "2,3,4,5" # specify your GPU IDs here
DEVICE = "cuda" # or "cpu"
# ────────────────────────────────────────────────────────────────────
# Add this helper near the top
def clean_user_field(user_str: str) -> str:
"""
Remove leading <src=xx><tgt=yy> tags and any whitespace/newlines after them.
"""
# This pattern matches <src=..><tgt=..> plus any whitespace/newline
return re.sub(r'^<src=[^>]+><tgt=[^>]+>\s*', '', user_str)
def load_model_and_tokenizer():
print(f"Loading model/tokenizer from {MODEL_PATH} …")
tok = AutoTokenizer.from_pretrained(MODEL_PATH)
model = Gemma3ForCausalLM.from_pretrained(
MODEL_PATH,
torch_dtype=torch.bfloat16,
device_map="auto", # auto‐shard if you have multiple GPUs
)
model.eval()
return tok, model
def build_prompt(system: str, user: str) -> str:
return (
f"<start_of_turn>system\n{system}<end_of_turn>\n"
f"<start_of_turn>user\n{user}<end_of_turn>\n"
f"<start_of_turn>assistant"
)
def run_inference(tok, model, system: str, user: str) -> str:
prompt = build_prompt(system, user)
inputs = tok(prompt, return_tensors="pt", truncation=True).to(model.device)
input_len = inputs["input_ids"].shape[-1]
with torch.inference_mode():
out = model.generate(
**inputs,
max_new_tokens=MAX_NEW_TOKS,
do_sample=False,
eos_token_id=tok.convert_tokens_to_ids("<end_of_turn>"),
pad_token_id=tok.eos_token_id,
)
gen_ids = out[0][input_len:]
return tok.decode(gen_ids, skip_special_tokens=True).strip()
def load_test_examples(path: str):
examples = []
for line in open(path, encoding="utf-8"):
obj = json.loads(line)
examples.append((obj["system"].strip(),
obj["user"].strip(),
obj["assistant"].strip()))
return examples
def evaluate_bleu_nltk(hyps, refs):
"""
Compute corpus-level 4-gram BLEU using NLTK.
- hyps: list of hypothesis strings
- refs: list of reference strings
Returns BLEU in percentage (e.g. 27.53).
"""
# 1) tokenize on whitespace
tokenized_hyps = [hyp.split() for hyp in hyps]
# NLTK expects a list of references per hypothesis
tokenized_refs = [[ref.split()] for ref in refs]
# 2) smoothing to avoid zero scores on short examples
smoothing = SmoothingFunction().method1
# 3) compute corpus_bleu with uniform 4-gram weights
score = corpus_bleu(
tokenized_refs,
tokenized_hyps,
weights=(0.25, 0.25, 0.25, 0.25),
smoothing_function=smoothing,
)
# convert to percentage
return round(score, 4)
def main():
tok, model = load_model_and_tokenizer()
examples = load_test_examples(TEST_FILE)
hyps, refs, users = [], [], []
for system, user, assistant in tqdm(examples, desc="Translating"):
clean_user = clean_user_field(user)
hyp = run_inference(tok, model, system, clean_user)
hyps.append(hyp)
refs.append(assistant)
users.append(clean_user) # collect cleaned user texts
bleu_score = evaluate_bleu_nltk(hyps, refs)
# Save results, using cleaned user strings
out = {
"model": MODEL_PATH,
"bleu": bleu_score,
"examples": []
}
for (s, _, r), u_clean, h in zip(examples, users, hyps):
out["examples"].append({
"system": s,
"user": u_clean, # no tags here
"reference": r,
"hypothesis": h
})
with open(OUTPUT_JSON, "w", encoding="utf-8") as f:
json.dump(out, f, ensure_ascii=False, indent=2)
print(f"✅ Saved cleaned evaluation to {OUTPUT_JSON}")
if __name__ == "__main__":
main()
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