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"""Evaluate Step 5 adapters on an aligned FLORES-200 inventory.
This evaluator uses a common set of row indices across all languages, then
compares the frozen base model against the final routed adapter for each
language using normalized negative log-likelihood metrics.
Important caveat:
- For the FLORES-2K training runs, this is in-domain evaluation because the
training setup already consumed almost all available FLORES rows.
"""
from __future__ import annotations
import argparse
import csv
import json
import math
import random
from contextlib import nullcontext
from pathlib import Path
from typing import Any, Dict, List, Tuple
import matplotlib.pyplot as plt
import pandas as pd
import torch
from datasets import Dataset, concatenate_datasets, load_dataset
from peft import PeftModel
from torch.utils.data import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer
LANGUAGE_NAMES = {
"ben_Beng": "Bengali",
"cmn_Hans": "Chinese",
"eng_Latn": "English",
"fra_Latn": "French",
"hin_Deva": "Hindi",
"mar_Deva": "Marathi",
"nld_Latn": "Dutch",
"pol_Latn": "Polish",
"urd_Arab": "Urdu",
}
FLORES_MIRROR_LANG_MAP = {
"ben_Beng": "ben_Beng",
"cmn_Hans": "zho_Hans",
"eng_Latn": "eng_Latn",
"fra_Latn": "fra_Latn",
"hin_Deva": "hin_Deva",
"mar_Deva": "mar_Deva",
"nld_Latn": "nld_Latn",
"pol_Latn": "pol_Latn",
"urd_Arab": "urd_Arab",
}
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser(description="Evaluate Step 5 adapters on aligned FLORES with normalized NLL metrics")
p.add_argument("--run-dir", type=str, required=True, help="Step 5 output directory containing final/ and run_summary.json")
p.add_argument("--output-dir", type=str, default="", help="Directory to write eval artifacts. Defaults to <run-dir>/aligned_eval_flores_norm")
p.add_argument("--model-id", type=str, default="", help="Override base model id")
p.add_argument("--eval-samples", type=int, default=300)
p.add_argument("--max-length", type=int, default=512)
p.add_argument("--batch-size", type=int, default=2)
p.add_argument("--num-workers", type=int, default=0)
p.add_argument("--seed", type=int, default=42)
p.add_argument("--use-bf16", action="store_true")
p.add_argument("--trust-remote-code", action="store_true")
return p.parse_args()
def write_json(path: Path, payload: Any) -> None:
with open(path, "w", encoding="utf-8") as f:
json.dump(payload, f, indent=2, ensure_ascii=False)
def write_csv(path: Path, rows: List[Dict[str, Any]], fieldnames: List[str]) -> None:
with open(path, "w", encoding="utf-8", newline="") as f:
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
for row in rows:
writer.writerow(row)
def load_run_summary(run_dir: Path) -> Dict[str, Any]:
path = run_dir / "run_summary.json"
if not path.exists():
raise FileNotFoundError(f"Missing run summary: {path}")
return json.loads(path.read_text(encoding="utf-8"))
def find_latest_checkpoint_dir(run_dir: Path) -> Path | None:
checkpoints = []
for p in run_dir.iterdir():
if p.is_dir() and p.name.startswith("checkpoint_step_"):
try:
step = int(p.name.replace("checkpoint_step_", "", 1))
except ValueError:
continue
checkpoints.append((step, p))
if not checkpoints:
return None
checkpoints.sort(key=lambda x: x[0], reverse=True)
return checkpoints[0][1]
def resolve_adapter_dir(adapter_dir: Path) -> Path:
if (adapter_dir / "adapter_config.json").exists():
return adapter_dir
for child in adapter_dir.iterdir():
if child.is_dir() and (child / "adapter_config.json").exists():
return child
return adapter_dir
def adapter_root_is_valid(root: Path) -> bool:
if not root.exists() or not root.is_dir():
return False
adapter_dirs = [p for p in root.iterdir() if p.is_dir() and p.name.startswith("adapter_")]
if not adapter_dirs:
return False
return all((resolve_adapter_dir(p) / "adapter_config.json").exists() for p in adapter_dirs)
def resolve_artifact_root(run_dir: Path) -> Path:
final_dir = run_dir / "final"
if adapter_root_is_valid(final_dir):
return final_dir
latest_ckpt = find_latest_checkpoint_dir(run_dir)
if latest_ckpt and adapter_root_is_valid(latest_ckpt):
return latest_ckpt
raise FileNotFoundError(
"Could not find a valid adapter artifact root. Checked "
f"'{final_dir}' and latest checkpoint under '{run_dir}'."
)
def load_model_and_adapters(
run_dir: Path,
model_id: str,
trust_remote_code: bool,
use_bf16: bool,
) -> Tuple[PeftModel, AutoTokenizer, torch.device, List[str]]:
artifact_root = resolve_artifact_root(run_dir)
adapter_dirs = sorted(p for p in artifact_root.iterdir() if p.is_dir() and p.name.startswith("adapter_"))
if not adapter_dirs:
raise FileNotFoundError(f"No adapter directories found under {artifact_root}")
tokenizer_dir = artifact_root / "tokenizer"
if not tokenizer_dir.exists():
tokenizer_dir = run_dir / "final" / "tokenizer"
tokenizer = AutoTokenizer.from_pretrained(str(tokenizer_dir), trust_remote_code=trust_remote_code)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
dtype = torch.bfloat16 if (use_bf16 and torch.cuda.is_available()) else torch.float16
if not torch.cuda.is_available():
dtype = torch.float32
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
base = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=dtype,
trust_remote_code=trust_remote_code,
device_map="auto" if torch.cuda.is_available() else None,
)
if not torch.cuda.is_available():
base.to(device)
first = resolve_adapter_dir(adapter_dirs[0])
first_name = adapter_dirs[0].name.replace("adapter_", "", 1)
model = PeftModel.from_pretrained(base, str(first), adapter_name=first_name)
adapter_names = [first_name]
for adapter_dir in adapter_dirs[1:]:
resolved = resolve_adapter_dir(adapter_dir)
name = adapter_dir.name.replace("adapter_", "", 1)
model.load_adapter(str(resolved), adapter_name=name)
adapter_names.append(name)
return model, tokenizer, device, adapter_names
def build_flores_concat() -> Dataset:
ds = load_dataset("yash9439/flores200")
return concatenate_datasets([ds["dev"], ds["devtest"]])
def build_common_index_set(
ds: Dataset,
tokenizer,
lang_codes: List[str],
eval_samples: int,
max_length: int,
seed: int,
) -> Tuple[List[int], Dict[str, Any]]:
valid_indices: List[int] = []
per_lang_valid = {lc: 0 for lc in lang_codes}
for idx in range(len(ds)):
ok = True
for lc in lang_codes:
col = FLORES_MIRROR_LANG_MAP[lc]
text = ds[idx][col]
if not isinstance(text, str) or not text.strip():
ok = False
break
ids = tokenizer(text.strip(), truncation=False, padding=False)["input_ids"]
if len(ids) <= 8 or len(ids) > max_length:
ok = False
break
if ok:
valid_indices.append(idx)
for lc in lang_codes:
per_lang_valid[lc] += 1
if len(valid_indices) < eval_samples:
raise RuntimeError(f"Only found {len(valid_indices)} common valid FLORES rows; need {eval_samples}")
rng = random.Random(seed)
chosen = sorted(rng.sample(valid_indices, eval_samples))
meta = {
"dataset": "yash9439/flores200::dev+devtest",
"n_total_rows": len(ds),
"n_common_valid_rows": len(valid_indices),
"selected_indices": chosen,
"per_language_valid_rows": per_lang_valid,
}
return chosen, meta
def build_eval_rows(ds: Dataset, lang_code: str, indices: List[int]) -> List[Dict[str, Any]]:
col = FLORES_MIRROR_LANG_MAP[lang_code]
rows: List[Dict[str, Any]] = []
for idx in indices:
text = str(ds[idx][col]).strip()
rows.append(
{
"row_idx": idx,
"text": text,
"n_chars": len(text),
"n_bytes": len(text.encode("utf-8")),
}
)
return rows
def tokenize_eval_rows(tokenizer, rows: List[Dict[str, Any]], max_length: int) -> Dataset:
raw = Dataset.from_list(rows)
def _tok(batch):
tok = tokenizer(batch["text"], truncation=True, max_length=max_length, padding=False)
tok["labels"] = [ids[:] for ids in tok["input_ids"]]
tok["n_chars"] = batch["n_chars"]
tok["n_bytes"] = batch["n_bytes"]
tok["row_idx"] = batch["row_idx"]
return tok
return raw.map(_tok, batched=True, remove_columns=["text"])
def make_collator(tokenizer):
pad_id = tokenizer.pad_token_id
def _collate(features: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
max_len = max(len(f["input_ids"]) for f in features)
input_ids = []
attention_mask = []
labels = []
n_chars = []
n_bytes = []
row_idx = []
for f in features:
ids = list(f["input_ids"])
mask = list(f["attention_mask"])
labs = list(f["labels"])
pad_len = max_len - len(ids)
input_ids.append(ids + [pad_id] * pad_len)
attention_mask.append(mask + [0] * pad_len)
labels.append(labs + [-100] * pad_len)
n_chars.append(int(f["n_chars"]))
n_bytes.append(int(f["n_bytes"]))
row_idx.append(int(f["row_idx"]))
return {
"input_ids": torch.tensor(input_ids, dtype=torch.long),
"attention_mask": torch.tensor(attention_mask, dtype=torch.long),
"labels": torch.tensor(labels, dtype=torch.long),
"n_chars": torch.tensor(n_chars, dtype=torch.long),
"n_bytes": torch.tensor(n_bytes, dtype=torch.long),
"row_idx": torch.tensor(row_idx, dtype=torch.long),
}
return _collate
def make_loader(ds: Dataset, tokenizer, batch_size: int, num_workers: int) -> DataLoader:
return DataLoader(
ds,
batch_size=batch_size,
shuffle=False,
collate_fn=make_collator(tokenizer),
drop_last=False,
num_workers=num_workers,
)
def evaluate_loader(model, loader: DataLoader, device: torch.device, adapter_name: str | None) -> Dict[str, float]:
model.eval()
total_nll = 0.0
total_pred_tokens = 0
total_chars = 0
total_bytes = 0
if adapter_name is None:
adapter_ctx = model.disable_adapter()
else:
model.set_adapter(adapter_name)
adapter_ctx = nullcontext()
with adapter_ctx:
with torch.no_grad():
for batch in loader:
n_chars = int(batch["n_chars"].sum().item())
n_bytes = int(batch["n_bytes"].sum().item())
labels = batch["labels"]
valid = (labels != -100).sum(dim=1)
pred_tokens = int((valid - 1).clamp(min=0).sum().item())
forward_batch = {k: v.to(device) for k, v in batch.items() if k in {"input_ids", "attention_mask", "labels"}}
out = model(**forward_batch)
total_nll += float(out.loss.detach().float().item()) * pred_tokens
total_pred_tokens += pred_tokens
total_chars += n_chars
total_bytes += n_bytes
nll_per_token = total_nll / max(total_pred_tokens, 1)
nll_per_char = total_nll / max(total_chars, 1)
nll_per_byte = total_nll / max(total_bytes, 1)
return {
"nll_per_token": nll_per_token,
"ppl": float(math.exp(nll_per_token)),
"nll_per_char": nll_per_char,
"nll_per_byte": nll_per_byte,
"bits_per_char": nll_per_char / math.log(2.0),
"bits_per_byte": nll_per_byte / math.log(2.0),
"pred_tokens": total_pred_tokens,
"chars": total_chars,
"bytes": total_bytes,
}
def plot_summary(output_dir: Path, summary_df: pd.DataFrame) -> None:
ordered = summary_df.sort_values("adapted_bits_per_byte")
plt.figure(figsize=(11, 6))
y = range(len(ordered))
plt.barh([i + 0.18 for i in y], ordered["base_bits_per_byte"], height=0.35, label="Base")
plt.barh([i - 0.18 for i in y], ordered["adapted_bits_per_byte"], height=0.35, label="Final Adapter")
plt.yticks(list(y), ordered["language"])
plt.xlabel("Bits per Byte")
plt.title("Aligned FLORES Eval: Base vs Final Adapter")
plt.legend()
plt.tight_layout()
plt.savefig(output_dir / "base_vs_adapter_bits_per_byte.png", dpi=180)
plt.close()
plt.figure(figsize=(11, 6))
plt.barh(ordered["language"], ordered["byte_improvement_pct"])
plt.xlabel("Improvement over Base (%)")
plt.title("Aligned FLORES Eval: Relative Improvement (Bits per Byte)")
plt.tight_layout()
plt.savefig(output_dir / "bits_per_byte_improvement.png", dpi=180)
plt.close()
def main() -> None:
args = parse_args()
run_dir = Path(args.run_dir)
output_dir = Path(args.output_dir) if args.output_dir else run_dir / "aligned_eval_flores_norm"
output_dir.mkdir(parents=True, exist_ok=True)
summary = load_run_summary(run_dir)
model_id = args.model_id or summary["model_id"]
lang_codes = [lang for lang in summary["languages"] if lang in FLORES_MIRROR_LANG_MAP]
model, tokenizer, device, adapter_names = load_model_and_adapters(
run_dir=run_dir,
model_id=model_id,
trust_remote_code=args.trust_remote_code,
use_bf16=args.use_bf16,
)
adapter_set = set(adapter_names)
missing = [lang for lang in lang_codes if lang not in adapter_set]
if missing:
raise RuntimeError(f"Missing adapters for languages: {missing}")
ds = build_flores_concat()
indices, eval_meta = build_common_index_set(
ds=ds,
tokenizer=tokenizer,
lang_codes=lang_codes,
eval_samples=args.eval_samples,
max_length=args.max_length,
seed=args.seed,
)
loaders: Dict[str, DataLoader] = {}
for lang in lang_codes:
rows = build_eval_rows(ds, lang, indices)
tok_ds = tokenize_eval_rows(tokenizer, rows, args.max_length)
loaders[lang] = make_loader(tok_ds, tokenizer, args.batch_size, args.num_workers)
summary_rows: List[Dict[str, Any]] = []
for lang in lang_codes:
base_metrics = evaluate_loader(model, loaders[lang], device, adapter_name=None)
adapted_metrics = evaluate_loader(model, loaders[lang], device, adapter_name=lang)
row = {
"lang": lang,
"language": LANGUAGE_NAMES.get(lang, lang),
"n_eval": args.eval_samples,
"base_ppl": base_metrics["ppl"],
"adapted_ppl": adapted_metrics["ppl"],
"base_nll_per_char": base_metrics["nll_per_char"],
"adapted_nll_per_char": adapted_metrics["nll_per_char"],
"base_nll_per_byte": base_metrics["nll_per_byte"],
"adapted_nll_per_byte": adapted_metrics["nll_per_byte"],
"base_bits_per_char": base_metrics["bits_per_char"],
"adapted_bits_per_char": adapted_metrics["bits_per_char"],
"base_bits_per_byte": base_metrics["bits_per_byte"],
"adapted_bits_per_byte": adapted_metrics["bits_per_byte"],
"char_improvement_pct": (
(base_metrics["nll_per_char"] - adapted_metrics["nll_per_char"]) / base_metrics["nll_per_char"] * 100.0
if base_metrics["nll_per_char"] > 0
else 0.0
),
"byte_improvement_pct": (
(base_metrics["nll_per_byte"] - adapted_metrics["nll_per_byte"]) / base_metrics["nll_per_byte"] * 100.0
if base_metrics["nll_per_byte"] > 0
else 0.0
),
"ppl_improvement_pct": (
(base_metrics["ppl"] - adapted_metrics["ppl"]) / base_metrics["ppl"] * 100.0
if base_metrics["ppl"] > 0
else 0.0
),
}
summary_rows.append(row)
summary_df = pd.DataFrame(summary_rows).sort_values("adapted_bits_per_byte")
summary_df.to_csv(output_dir / "aligned_flores_base_vs_adapter.csv", index=False)
write_json(output_dir / "aligned_flores_eval_meta.json", eval_meta)
write_json(output_dir / "aligned_flores_base_vs_adapter.json", summary_rows)
plot_summary(output_dir, summary_df)
print(
json.dumps(
{
"run_dir": str(run_dir),
"output_dir": str(output_dir),
"eval_samples": args.eval_samples,
"languages": lang_codes,
"n_common_valid_rows": eval_meta["n_common_valid_rows"],
},
indent=2,
)
)
if __name__ == "__main__":
<<<<<<< HEAD
main()
=======
main()
>>>>>>> 6fefa02 (smth)
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