src/lmr/glue_benchmark/glue_benchmark_1-20.py

# lmr/glue_benchmark.py
"""
Standalone GLUE benchmark runner for your project.

Updated fixes:
- task-aware example extraction (fixes MNLI/QQP/other empty-text problems)
- robust tokenizer handling + debug empty-sample logging
- robust wrapper and postprocessing for predictions (fixes STS-B / QQP metric mismatches)
- auto-train per-task (full fine-tune) saving under out_dir/checkpoints/<task>
"""

import os
import json
import re
import time
from pathlib import Path
from typing import Optional, List, Tuple

import torch
import numpy as np
import pandas as pd
from datasets import load_dataset
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm

import evaluate

# Project imports (adjust if your package layout differs)
from lmr.checkpointing import Checkpointing
from lmr.ddp import unwrap_model

# ---------------------------------------------------------------------
# GLUE config
# ---------------------------------------------------------------------
GLUE_TASKS = {
    "cola": {"type": "classification", "num_labels": 2, "hf_name": "cola"},
    "sst2": {"type": "classification", "num_labels": 2, "hf_name": "sst2"},
    "mrpc": {"type": "classification", "num_labels": 2, "hf_name": "mrpc"},
    "stsb": {"type": "regression",     "num_labels": 1, "hf_name": "stsb"},
    "qqp": {"type": "classification", "num_labels": 2, "hf_name": "qqp"},
    "mnli": {"type": "classification", "num_labels": 3, "hf_name": "mnli"},
    "qnli": {"type": "classification", "num_labels": 2, "hf_name": "qnli"},
    "rte":  {"type": "classification", "num_labels": 2, "hf_name": "rte"},
    "wnli": {"type": "classification", "num_labels": 2, "hf_name": "wnli"},
}

# ---------------------------------------------------------------------
# Task-aware example field extraction (fixes empty-text issues)
# ---------------------------------------------------------------------
def _get_text_pair_from_example(task: str, ex: dict):
    """
    Robustly extract (s1, s2) from a HF GLUE example dict `ex` depending on task.
    Returns (s1:str, s2:Optional[str]) where s2 can be None for single-sentence tasks.
    """
    # Known per-task fields
    task_field_map = {
        "cola": ("sentence", None),
        "sst2": ("sentence", None),
        "mrpc": ("sentence1", "sentence2"),
        "stsb": ("sentence1", "sentence2"),
        "qqp": ("question1", "question2"),
        "mnli": ("premise", "hypothesis"),
        "qnli": ("question", "sentence"),
        "rte": ("sentence1", "sentence2"),
        "wnli": ("sentence1", "sentence2"),
    }
    f1, f2 = task_field_map.get(task, (None, None))

    def _try_keys(keys):
        for k in keys:
            if k in ex and ex.get(k) is not None:
                return ex.get(k)
        return None

    # Compose candidate key lists for robustness
    s1_candidates = []
    s2_candidates = []

    if f1:
        s1_candidates.append(f1)
    s1_candidates += ["sentence1", "premise", "question", "sentence", "text", "question1"]

    if f2:
        s2_candidates.append(f2)
    s2_candidates += ["sentence2", "hypothesis", "question2", "question1", "text2"]

    s1 = _try_keys(s1_candidates)
    s2 = _try_keys(s2_candidates)

    # Final fallback: try fields common in some GLUE subsets
    if s1 is None:
        s1 = ex.get("sentence") or ex.get("premise") or ex.get("question") or ex.get("text")
    if s2 is None:
        # allow None for single-sentence tasks
        s2 = ex.get("sentence2") or ex.get("hypothesis") or ex.get("question2")

    # Normalize types
    s1 = "" if s1 is None else (s1 if isinstance(s1, str) else str(s1))
    s2 = None if s2 is None else (s2 if isinstance(s2, str) else str(s2))

    return s1, s2

# ---------------------------------------------------------------------
# Tokenization helpers (robust to different tokenizer APIs)
# ---------------------------------------------------------------------
def _pad_and_tensorize(input_ids_list, attention_mask_list, pad_token_id: int):
    max_len = max(len(x) for x in input_ids_list) if input_ids_list else 0
    ids_padded = [ x + [pad_token_id]*(max_len - len(x)) for x in input_ids_list ]
    mask_padded = [ m + [0]*(max_len - len(m)) for m in attention_mask_list ]
    input_ids = torch.tensor(ids_padded, dtype=torch.long)
    attention_mask = torch.tensor(mask_padded, dtype=torch.long)
    return input_ids, attention_mask

def _batch_tokenize(tokenizer, texts: List[Tuple[Optional[str], Optional[str]]], max_length: int = 128):
    """
    Robust batch tokenization for a variety of tokenizer APIs.
    - texts: list of (s1, s2) where s2 may be None.
    - Try HF tokenizer(...) first, then various batch methods, then per-example fallback.
    Returns dict with 'input_ids' (list of lists) and 'attention_mask'.
    """
    sanitized = []
    for a, b in texts:
        a_s = "" if a is None else (a if isinstance(a, str) else str(a))
        b_s = None if b is None else (b if isinstance(b, str) else str(b))
        sanitized.append((a_s, b_s))

    # 1) Try HF-like tokenizer(...) first
    try:
        flat = [ (a if b is None else (a, b)) for a, b in sanitized ]
        enc = tokenizer(flat, truncation=True, padding=False, max_length=max_length)
        # normalize to python lists
        if isinstance(enc.get("input_ids", None), torch.Tensor):
            enc["input_ids"] = enc["input_ids"].tolist()
        if isinstance(enc.get("attention_mask", None), torch.Tensor):
            enc["attention_mask"] = enc["attention_mask"].tolist()
        # debug tag
        # print("[tokenizer] used hf-style batch tokenizer")
        return enc
    except Exception:
        pass

    # 2) Try other batch-like methods
    for method_name in ("batch_encode", "encode_batch", "batch_encode_plus", "encode_batch_pair", "encode_batch_items"):
        fn = getattr(tokenizer, method_name, None)
        if fn is None:
            continue
        try:
            try:
                enc = fn(sanitized, max_length=max_length, truncation=True, padding=False)
            except TypeError:
                enc = fn(sanitized)
            if isinstance(enc.get("input_ids", None), torch.Tensor):
                enc["input_ids"] = enc["input_ids"].tolist()
            if isinstance(enc.get("attention_mask", None), torch.Tensor):
                enc["attention_mask"] = enc["attention_mask"].tolist()
            return enc
        except Exception:
            continue

    # 3) Fallback per-example
    input_ids_list = []
    attention_mask_list = []
    for a, b in sanitized:
        try:
            if b is None:
                try:
                    single = tokenizer.encode(a)
                except TypeError:
                    single = tokenizer.encode([a])
            else:
                # try pair
                single = None
                try:
                    single = tokenizer.encode((a, b))
                except Exception:
                    try:
                        single = tokenizer.encode(a, b)
                    except Exception:
                        single = tokenizer(a if b is None else (a, b))
            # interpret return
            if isinstance(single, dict):
                ids = single.get("input_ids") or single.get("ids") or []
                mask = single.get("attention_mask") or single.get("mask") or [1]*len(ids)
            elif isinstance(single, torch.Tensor):
                ids = single.tolist()
                mask = [1] * len(ids)
            elif isinstance(single, list):
                ids = single
                mask = [1] * len(ids)
            else:
                # try tokenizer(...) convenience
                tmp = tokenizer(a if b is None else (a, b))
                if isinstance(tmp, dict):
                    ids = tmp.get("input_ids") or tmp.get("ids") or []
                    mask = tmp.get("attention_mask") or tmp.get("mask") or [1]*len(ids)
                elif torch.is_tensor(tmp):
                    ids = tmp.tolist()
                    mask = [1] * len(ids)
                else:
                    ids = list(tmp)
                    mask = [1] * len(ids)

            # truncation
            if len(ids) > max_length:
                ids = ids[:max_length]
                mask = mask[:max_length]

            input_ids_list.append(ids)
            attention_mask_list.append(mask)
        except Exception as e:
            snippet = (a[:80] + "...") if a else "<empty>"
            raise RuntimeError(f"Tokenizer fallback encode failed for example '{snippet}': {e}")

    return {"input_ids": input_ids_list, "attention_mask": attention_mask_list}

# ---------------------------------------------------------------------
# Postprocess preds to the right shapes/types (fixes metric mismatches)
# ---------------------------------------------------------------------
def _postprocess_predictions(task: str, logits_np: np.ndarray, cfg_task: dict):
    """
    Take logits (N, C) or (N,) or (N,1) and produce preds array ready for evaluate.compute:
      - classification -> 1D ints (class indices or binary 0/1)
      - regression  -> 1D floats (for stsb typically 0..5)
    """
    ttype = cfg_task["type"]
    num_labels = cfg_task["num_labels"]

    if logits_np is None or logits_np.size == 0:
        return np.array([])

    # If logits are shape (N, ) -> treat as single score per example (binary/regression)
    if logits_np.ndim == 1:
        if ttype == "classification":
            # binary: threshold at 0.5 for scores in [0,1] or sign threshold
            preds = (logits_np > 0.5).astype(int)
        else:
            preds = logits_np.astype(float)
        return preds

    # If logits shape (N, 1)
    if logits_np.ndim == 2 and logits_np.shape[1] == 1:
        col = logits_np[:, 0]
        if ttype == "classification":
            preds = (col > 0.5).astype(int)
        else:
            preds = col.astype(float)
        return preds

    # If logits shape (N, C)
    if logits_np.ndim == 2 and logits_np.shape[1] >= 1:
        if ttype == "classification":
            # argmax -> class index
            preds = np.argmax(logits_np, axis=-1).astype(int)
            return preds
        else:
            # regression: if multiple dims, average or take first
            if logits_np.shape[1] == 1:
                preds = logits_np[:, 0].astype(float)
            else:
                preds = logits_np.mean(axis=1).astype(float)
            # clamp STS-B to 0..5 if it's that task (defensive)
            if task == "stsb":
                preds = np.clip(preds, 0.0, 5.0)
            return preds

    # fallback
    return logits_np.ravel()

# ---------------------------------------------------------------------
# Model wrapping helper (robust)
# ---------------------------------------------------------------------
def make_wrapped_model_if_needed(model, hidden_size: Optional[int], num_labels: int, force_num_labels: Optional[int] = None):
    """
    Robust wrapper factory with resilient hidden_size inference.
    Returns (model_or_wrapper, wrapped_flag)
    """
    import torch.nn as nn
    import re

    base_model = model

    def _detect_head_dim(m):
        try:
            if hasattr(m, "classifier") and isinstance(getattr(m, "classifier"), nn.Linear):
                return getattr(m, "classifier").out_features
            if hasattr(m, "lm_head") and isinstance(getattr(m, "lm_head"), nn.Linear):
                return getattr(m, "lm_head").out_features
            if hasattr(m, "get_output_embeddings"):
                out_emb = m.get_output_embeddings()
                if out_emb is not None:
                    if isinstance(out_emb, nn.Embedding):
                        return out_emb.embedding_dim if hasattr(out_emb, "embedding_dim") else out_emb.num_embeddings
                    if isinstance(out_emb, nn.Linear):
                        return out_emb.out_features
        except Exception:
            pass
        return None

    # If existing head already matches desired num_labels -> reuse
    if force_num_labels is None:
        head_dim = _detect_head_dim(base_model)
        if head_dim is not None and head_dim == num_labels:
            return base_model, False

    # infer hidden_size if not provided
    inferred_hidden = hidden_size
    if inferred_hidden is None:
        try:
            cand = getattr(base_model, "config", None)
            if cand is not None and hasattr(cand, "hidden_size"):
                inferred_hidden = int(cand.hidden_size)
        except Exception:
            inferred_hidden = None

    # try unwrap and inspect params/state_dict shapes
    if inferred_hidden is None:
        try:
            un = unwrap_model(base_model)
            sd = un.state_dict()
            # search for embedding weight shapes
            for k, v in sd.items():
                if re.search(r"embed|embedding|word_embeddings|token_embedding|embed_tokens", k, re.I):
                    if hasattr(v, "shape") and len(v.shape) == 2:
                        inferred_hidden = int(v.shape[1])
                        break
                if re.search(r"q_proj|k_proj|v_proj|o_proj|dense|fc|linear|proj", k, re.I):
                    if hasattr(v, "shape") and len(v.shape) == 2:
                        cand = max(v.shape)
                        if cand > 1 and cand < 1000000:
                            inferred_hidden = int(cand)
                            break
        except Exception:
            inferred_hidden = None

    if inferred_hidden is None:
        raise RuntimeError(
            "Cannot infer hidden_size for wrapped classifier head. "
            "Please set `model.config.hidden_size` (e.g. model.config.hidden_size = 1024) "
            "or pass `hidden_size` explicitly when calling make_wrapped_model_if_needed."
        )

    class _WrappedModel(nn.Module):
        def __init__(self, base, hidden_size, num_labels):
            super().__init__()
            self.base = base
            self.classifier = nn.Linear(hidden_size, num_labels)
            self.logits_projector = None

        def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
            # try several base calling conventions
            try:
                out = self.base(input_ids=input_ids, attention_mask=attention_mask, **kwargs)
            except TypeError:
                out = self.base(input_ids)
            # last_hidden_state
            last_hidden = getattr(out, "last_hidden_state", None)
            if last_hidden is not None:
                pooled = last_hidden[:, 0, :]
                logits = self.classifier(pooled)
                return type("Out", (), {"logits": logits, "loss": None})
            # tuple/list case
            if isinstance(out, (tuple, list)) and len(out) > 0:
                cand = out[0]
                if torch.is_tensor(cand):
                    if cand.ndim == 3:
                        pooled = cand[:, 0, :]
                        logits = self.classifier(pooled)
                        return type("Out", (), {"logits": logits, "loss": None})
                    if cand.ndim == 2 and cand.shape[1] == num_labels:
                        return type("Out", (), {"logits": cand, "loss": None})
            # out.logits present
            logits = getattr(out, "logits", None)
            if logits is not None:
                if logits.ndim == 2 and logits.shape[1] == num_labels:
                    return type("Out", (), {"logits": logits, "loss": getattr(out, "loss", None)})
                # project if dims mismatch
                exist_dim = logits.shape[1]
                if self.logits_projector is None or self.logits_projector.weight.shape[1] != exist_dim:
                    self.logits_projector = nn.Linear(exist_dim, num_labels).to(logits.device)
                projected = self.logits_projector(logits)
                return type("Out", (), {"logits": projected, "loss": getattr(out, "loss", None)})
            # hidden_states attribute
            hidden_states = getattr(out, "hidden_states", None)
            if hidden_states is not None:
                if isinstance(hidden_states, (list, tuple)):
                    last_hidden = hidden_states[-1]
                else:
                    last_hidden = hidden_states
                if torch.is_tensor(last_hidden) and last_hidden.ndim == 3:
                    pooled = last_hidden[:, 0, :]
                    logits = self.classifier(pooled)
                    return type("Out", (), {"logits": logits, "loss": None})
            raise RuntimeError("Wrapped base model did not return recognizable hidden states or logits")

    return _WrappedModel(base_model, inferred_hidden, num_labels), True

# ---------------------------------------------------------------------
# Tokenize HF dataset split into tensors (used for finetune)
# ---------------------------------------------------------------------
def _tokenize_hf_split_to_tensors(task: str, tokenizer, raw_split, cfg_task, max_length=128, batch_tokenize_size=512):
    """
    Convert HF dataset split to tensors (input_ids tensor, attention_mask tensor, labels tensor)
    """
    texts = []
    labels = []
    empty_s1 = 0
    empty_s2 = 0

    for ex in raw_split:
        s1, s2 = _get_text_pair_from_example(task, ex)
        texts.append((s1, s2))
        labels.append(ex.get("label") if "label" in ex else -100)
        if not s1 or (isinstance(s1, str) and s1.strip() == ""):
            empty_s1 += 1
        if s2 is not None and (not s2 or (isinstance(s2, str) and s2.strip() == "")):
            empty_s2 += 1

    total = len(texts)
    print(f"[tokenize] task={task} samples={total} empty_s1={empty_s1} empty_s2={empty_s2} "
          f"({(empty_s1/total if total>0 else 0):.2%}, {(empty_s2/total if total>0 else 0):.2%})")

    input_ids_all = []
    attention_all = []
    for i in range(0, len(texts), batch_tokenize_size):
        batch_texts = texts[i:i+batch_tokenize_size]
        enc = _batch_tokenize(tokenizer, batch_texts, max_length=max_length)
        ids = enc.get("input_ids")
        masks = enc.get("attention_mask") or enc.get("mask") or enc.get("masks")
        if isinstance(ids, torch.Tensor):
            ids = ids.tolist()
        if isinstance(masks, torch.Tensor):
            masks = masks.tolist()
        input_ids_all.extend(ids)
        attention_all.extend(masks)

    pad_id = getattr(tokenizer, "pad_token_id", None)
    if pad_id is None:
        try:
            pad_id = tokenizer.token_to_id("[PAD]")
        except Exception:
            pad_id = 0
    input_ids_t, attention_mask_t = _pad_and_tensorize(input_ids_all, attention_all, pad_id)
    labels_t = torch.tensor(labels, dtype=torch.long if cfg_task["type"]=="classification" else torch.float)
    return input_ids_t, attention_mask_t, labels_t

# ---------------------------------------------------------------------
# Train full fine-tune (entire model) for a GLUE task
# ---------------------------------------------------------------------
def train_full_finetune(task: str, tokenizer, model, raw_train, raw_val,
                        device: str = "cuda", epochs: int = 3, batch_size: int = 32,
                        lr: float = 2e-5, weight_decay: float = 0.01, warmup_steps: int = 100,
                        max_length: int = 128, grad_accum_steps: int = 1, out_checkpoint_dir: Optional[str] = None):
    """
    Fine-tune the full model on the task train set, validate on raw_val.
    Saves final state_dict to out_checkpoint_dir/finetuned.pt if provided.
    Returns the fine-tuned model (in-place) and a dict with final validation metrics.
    """
    cfg_task = GLUE_TASKS[task]
    device = torch.device(device if torch.cuda.is_available() else "cpu")

    # Wrap/create classifier if necessary (force correct output dim)
    hidden_size = None
    if hasattr(model, "config") and hasattr(model.config, "hidden_size"):
        try:
            hidden_size = int(model.config.hidden_size)
        except Exception:
            hidden_size = None

    wrapped_model, wrapped_flag = make_wrapped_model_if_needed(model, hidden_size, cfg_task["num_labels"], force_num_labels=cfg_task["num_labels"])
    model = wrapped_model
    model.to(device)

    # Prepare tensors (task-aware)
    train_ids, train_mask, train_labels = _tokenize_hf_split_to_tensors(task, tokenizer, raw_train, cfg_task, max_length=max_length)
    val_ids, val_mask, val_labels = _tokenize_hf_split_to_tensors(task, tokenizer, raw_val, cfg_task, max_length=max_length)

    train_ds = TensorDataset(train_ids, train_mask, train_labels)
    val_ds = TensorDataset(val_ids, val_mask, val_labels)

    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, pin_memory=True)
    val_loader = DataLoader(val_ds, batch_size=max(64, batch_size), shuffle=False, pin_memory=True)

    # Optimizer & scheduler
    optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)
    total_steps = max(1, (len(train_loader) // max(1, grad_accum_steps)) * epochs)
    try:
        from transformers import get_cosine_schedule_with_warmup
        scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps)
    except Exception:
        scheduler = None

    loss_fn = torch.nn.CrossEntropyLoss() if cfg_task["type"]=="classification" else torch.nn.MSELoss()

    model.train()
    global_step = 0
    final_metric_res = {}
    for epoch in range(epochs):
        running_loss = 0.0
        for step, batch in enumerate(tqdm(train_loader, desc=f"Train {task} epoch {epoch+1}")):
            ids_b, mask_b, labs_b = batch
            ids_b = ids_b.to(device); mask_b = mask_b.to(device); labs_b = labs_b.to(device)
            out = model(input_ids=ids_b, attention_mask=mask_b, labels=None)
            logits = getattr(out, "logits", None)
            if logits is None:
                if isinstance(out, (tuple, list)):
                    logits = out[0]
                else:
                    raise RuntimeError("Model did not return logits during finetune")
            # compute loss
            if cfg_task["type"] == "classification":
                # logits shape (B, C)
                loss = loss_fn(logits, labs_b.long())
            else:
                # regression
                if logits.ndim == 2 and logits.shape[1] == 1:
                    preds = logits.squeeze(1)
                elif logits.ndim == 2:
                    preds = logits.mean(dim=1)
                else:
                    preds = logits
                loss = loss_fn(preds, labs_b.float())

            loss = loss / max(1, grad_accum_steps)
            loss.backward()

            if (step + 1) % max(1, grad_accum_steps) == 0:
                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
                optimizer.step()
                if scheduler is not None:
                    scheduler.step()
                optimizer.zero_grad()
                global_step += 1
            running_loss += loss.item() * ids_b.size(0)

        # validation at epoch end
        model.eval()
        tot_val_loss = 0.0
        all_logits = []
        all_labels = []
        with torch.no_grad():
            for ids_b, mask_b, labs_b in tqdm(val_loader, desc=f"Validate {task} epoch {epoch+1}", leave=False):
                ids_b = ids_b.to(device); mask_b = mask_b.to(device); labs_b = labs_b.to(device)
                out = model(input_ids=ids_b, attention_mask=mask_b, labels=None)
                logits = getattr(out, "logits", None)
                if logits is None:
                    if isinstance(out, (tuple, list)):
                        logits = out[0]
                    else:
                        raise RuntimeError("Model did not return logits during validation")
                if cfg_task["type"] == "classification":
                    l = loss_fn(logits, labs_b.long())
                else:
                    if logits.ndim == 2 and logits.shape[1] == 1:
                        preds = logits.squeeze(1)
                    elif logits.ndim == 2:
                        preds = logits.mean(dim=1)
                    else:
                        preds = logits
                    l = loss_fn(preds, labs_b.float())
                tot_val_loss += l.item() * ids_b.size(0)
                all_logits.append(logits.detach().cpu().numpy())
                all_labels.append(labs_b.detach().cpu().numpy())
        model.train()

        all_logits = np.concatenate(all_logits, axis=0) if all_logits else np.zeros((0, cfg_task["num_labels"]))
        all_labels = np.concatenate(all_labels, axis=0) if all_labels else np.zeros((0,))

        preds = _postprocess_predictions(task, all_logits, cfg_task)

        # compute metric with resilient handling
        metric = evaluate.load("glue", cfg_task["hf_name"])
        try:
            metric_res = metric.compute(predictions=preds.tolist(), references=all_labels.tolist())
        except Exception:
            try:
                metric_res = metric.compute(predictions=preds, references=all_labels)
            except Exception as e:
                metric_res = {"error": str(e)}
        print(f"[FT] {task} epoch {epoch+1} val_loss={tot_val_loss/len(val_ds):.6f} metric={metric_res}")

        final_metric_res = metric_res

    # Save final full model state_dict if desired
    if out_checkpoint_dir:
        outp = Path(out_checkpoint_dir)
        outp.mkdir(parents=True, exist_ok=True)
        fname = outp / "finetuned.pt"
        try:
            sd = unwrap_model(model).state_dict()
        except Exception:
            sd = model.state_dict()
        torch.save(sd, str(fname))
        print(f"[FT] Saved finetuned model to: {fname}")

    return model, final_metric_res

# ---------------------------------------------------------------------
# Main per-task runner (evaluation only)
# ---------------------------------------------------------------------
def run_glue_task(task: str, tokenizer, model, checkpointing: Optional[Checkpointing] = None,
                  device: str = "cuda", batch_size: int = 64, max_length: int = 128,
                  output_dir: str = "glue_output"):
    """
    Run a single GLUE task evaluation. Returns metric dict per split.
    """
    assert task in GLUE_TASKS, f"Unknown GLUE task: {task}"
    cfg = GLUE_TASKS[task]

    # load HF dataset
    hf = load_dataset("glue", cfg["hf_name"])
    # choose split(s): mnli has two val splits
    if task == "mnli":
        val_splits = ["validation_matched", "validation_mismatched"]
    else:
        val_splits = ["validation"]

    results_by_split = {}
    for split in val_splits:
        raw = hf[split]
        print(f"[GLUE] Task={task} split={split} samples={len(raw)}")

        # prepare texts and labels (task-aware)
        texts = []
        labels = []
        for ex in raw:
            s1, s2 = _get_text_pair_from_example(task, ex)
            texts.append((s1, s2))
            labels.append(ex.get("label") if "label" in ex else -100)

        # batch tokenize robustly
        BATCH = 512
        input_ids_all = []
        attention_all = []
        for i in range(0, len(texts), BATCH):
            batch_texts = texts[i:i+BATCH]
            enc = _batch_tokenize(tokenizer, batch_texts, max_length=max_length)
            ids = enc.get("input_ids")
            masks = enc.get("attention_mask") or enc.get("mask") or enc.get("masks")
            if isinstance(ids, torch.Tensor):
                ids = ids.tolist()
            if isinstance(masks, torch.Tensor):
                masks = masks.tolist()
            input_ids_all.extend(ids)
            attention_all.extend(masks)

        # pad and tensorize
        pad_id = getattr(tokenizer, "pad_token_id", None)
        if pad_id is None:
            try:
                pad_id = tokenizer.token_to_id("[PAD]")
            except Exception:
                pad_id = 0
        input_ids, attention_mask = _pad_and_tensorize(input_ids_all, attention_all, pad_id)
        labels_t = torch.tensor(labels, dtype=torch.long if cfg["type"]=="classification" else torch.float)

        ds = TensorDataset(input_ids, attention_mask, labels_t)
        loader = DataLoader(ds, batch_size=batch_size, shuffle=False, pin_memory=True)

        # optionally load checkpoint (recent pretrained) if provided
        if checkpointing is not None:
            try:
                checkpointing.load_model_states("recent")
            except Exception:
                pass

        device = torch.device(device if torch.cuda.is_available() else "cpu")
        model.to(device)
        model.eval()

        hidden_size = None
        if hasattr(model, "config") and hasattr(model.config, "hidden_size"):
            try:
                hidden_size = int(model.config.hidden_size)
            except Exception:
                hidden_size = None

        # decide wrapper force logic
        force = None
        if cfg["type"] == "regression":
            force = 1
        else:
            try:
                import torch.nn as nn
                existing_dim = None
                if hasattr(model, "classifier") and isinstance(getattr(model, "classifier"), nn.Linear):
                    existing_dim = getattr(model, "classifier").out_features
                elif hasattr(model, "lm_head") and isinstance(getattr(model, "lm_head"), nn.Linear):
                    existing_dim = getattr(model, "lm_head").out_features
                if existing_dim is not None and existing_dim != cfg["num_labels"]:
                    force = cfg["num_labels"]
            except Exception:
                force = cfg["num_labels"]

        wrapped_model, wrapped = make_wrapped_model_if_needed(model, hidden_size, cfg["num_labels"], force_num_labels=force)
        wrapped_model.to(device)
        wrapped_model.eval()

        all_logits = []
        all_labels = []
        with torch.no_grad():
            for batch in tqdm(loader, desc=f"Eval {task}:{split}"):
                ids_b, mask_b, labels_b = batch
                ids_b = ids_b.to(device)
                mask_b = mask_b.to(device)
                out = wrapped_model(input_ids=ids_b, attention_mask=mask_b, labels=None)
                logits = getattr(out, "logits", None)
                if logits is None:
                    if isinstance(out, (tuple, list)):
                        logits = out[0]
                    else:
                        raise RuntimeError("Model forward did not return logits")
                logits_np = logits.detach().cpu().numpy()
                all_logits.append(logits_np)
                all_labels.append(labels_b.detach().cpu().numpy())

        all_logits = np.concatenate(all_logits, axis=0) if all_logits else np.zeros((0, cfg["num_labels"]))
        all_labels = np.concatenate(all_labels, axis=0) if all_labels else np.zeros((0,))

        preds = _postprocess_predictions(task, all_logits, cfg)

        # ensure preds shape compatible with evaluate
        if cfg["type"] == "classification":
            preds_out = preds.astype(int).tolist()
            refs_out = all_labels.astype(int).tolist()
        else:
            preds_out = preds.astype(float).tolist()
            refs_out = all_labels.astype(float).tolist()

        metric = evaluate.load("glue", cfg["hf_name"])
        try:
            metric_res = metric.compute(predictions=preds_out, references=refs_out)
        except Exception:
            # second attempt with numpy arrays (some metrics accept np)
            try:
                metric_res = metric.compute(predictions=np.array(preds_out), references=np.array(refs_out))
            except Exception as e:
                metric_res = {"error": str(e)}

        os.makedirs(output_dir, exist_ok=True)
        out_json = Path(output_dir) / f"{task}_{split}_results.json"
        with open(out_json, "w", encoding="utf-8") as f:
            json.dump({"task": task, "split": split, "metrics": metric_res}, f, indent=2)

        csv_p = Path(output_dir) / f"{task}_{split}_preds.csv"
        pd.DataFrame({"pred": preds_out, "label": refs_out}).to_csv(csv_p, index=False)

        results_by_split[split] = metric_res

    return results_by_split

# ---------------------------------------------------------------------
# Top-level runner: multiple tasks + (optional) auto-train
# ---------------------------------------------------------------------
def run_glue_benchmark(
    config,
    tokenizer,
    model,
    checkpointing: Optional[Checkpointing] = None,
    out_dir: str = "glue_outputs",
):
    """
    config: object/dict with fields:
      - glue_tasks: list of task names (e.g. ["sst2","mnli"])
      - batch_size: int
      - max_length: int
      - device: "cuda" or "cpu"

      - auto_train: bool

      - train_epochs: int (default epochs)
      - train_epochs_per_task: dict (optional, per-task override)

      - train_batch_size
      - train_lr
      - train_warmup_steps
      - train_weight_decay
      - train_grad_accum_steps
    """

    # -------------------------
    # Tasks
    # -------------------------
    tasks = getattr(
        config,
        "glue_tasks",
        ["wnli", "rte", "stsb", "mrpc", "cola", "sst2", "qnli", "qqp", "mnli"],
    )
    # ["wnli", "rte", "stsb", "mrpc", "cola", "sst2", "qnli", "qqp", "mnli"]
    # -------------------------
    # Eval params
    # -------------------------
    batch_size = getattr(config, "batch_size", 64)
    max_length = getattr(config, "max_length", 128)
    device = getattr(config, "device", "cuda")

    # -------------------------
    # Training params
    # -------------------------
    auto_train = getattr(config, "auto_train", True)

    train_epochs = getattr(config, "train_epochs", 3)
    train_epochs_per_task = getattr(config, "train_epochs_per_task", {})

    train_epochs_per_task = {
        # Small / hard tasks
        "cola": 5,     # 8.5k samples, MCC, needs a bit more fitting
        "mrpc": 10,    # 3.7k, paraphrase, often underfits with few epochs
        "rte": 10,     # 2.5k, very small dataset

        # Regression
        "stsb": 20,    # regression task, converges slowly

        # Medium / large classification
        "sst2": 3,     # 67k
        "qqp": 3,      # 364k
        "qnli": 3,     # 108k
        "mnli": 3,     # 393k

        # Mostly sanity-check
        "wnli": 3,
    }

    train_lrs_per_task = {
        # Large datasets
        "mnli": 2e-6,
        "qqp": 2e-6,
        "qnli": 2e-6,

        # Medium
        "sst2": 3e-6,

        # Small datasets
        "cola": 1e-5,
        "mrpc": 1e-5,
        "rte": 1e-5,

        # Regression
        "stsb": 1e-5,

        # Sanity
        "wnli": 3e-6,
    }

    train_batch_size = getattr(config, "train_batch_size", 32)
    train_lr = getattr(config, "train_lr", 2e-6)
    train_warmup_steps = getattr(config, "train_warmup_steps", 100)
    train_weight_decay = getattr(config, "train_weight_decay", 0.01)
    train_grad_accum_steps = getattr(config, "train_grad_accum_steps", 1)

    # -------------------------
    # Output dirs
    # -------------------------
    out_dir = Path(out_dir)
    out_dir.mkdir(parents=True, exist_ok=True)

    checkpoint_out_root = out_dir / "checkpoints"
    checkpoint_out_root.mkdir(parents=True, exist_ok=True)

    rows = []

    # -------------------------
    # Save original pretrained state
    # -------------------------
    original_state = None
    try:
        original_state = unwrap_model(model).state_dict()
    except Exception:
        try:
            original_state = model.state_dict()
        except Exception:
            original_state = None

    # =========================
    # Loop over tasks
    # =========================
    for task in tasks:
        print(f"\n==== Running GLUE task: {task} ====")

        # ⭐ 核心：per-task epoch（没配就 fallback）
        epochs_this_task = train_epochs_per_task.get(task, train_epochs)
        train_lr_per_task = train_lrs_per_task.get(task, train_lr)
        print(f"[GLUE] Epochs for task '{task}': {epochs_this_task}")

        # -------------------------
        # Load dataset
        # -------------------------
        hf = load_dataset("glue", GLUE_TASKS[task]["hf_name"])
        train_raw = hf["train"]

        if task == "mnli":
            val_raw = hf["validation_matched"]
        else:
            val_raw = hf["validation"]

        # -------------------------
        # Train
        # -------------------------
        if auto_train:
            print(
                f"[GLUE] Auto-training enabled. "
                f"Fine-tuning task '{task}' from pretrained checkpoint."
            )

            # reset model so each task starts from same pretrained state
            if original_state is not None:
                try:
                    unwrap_model(model).load_state_dict(
                        original_state, strict=False
                    )
                except Exception:
                    try:
                        model.load_state_dict(original_state, strict=False)
                    except Exception:
                        pass

            # optional checkpoint reload
            if checkpointing is not None:
                try:
                    checkpointing.load_model_states("recent")
                except Exception:
                    pass

            task_ckpt_dir = checkpoint_out_root / task
            task_ckpt_dir.mkdir(parents=True, exist_ok=True)

            model, metric_res = train_full_finetune(
                task=task,
                tokenizer=tokenizer,
                model=model,
                raw_train=train_raw,
                raw_val=val_raw,
                device=device,
                epochs=epochs_this_task,          # ⭐ 用 per-task epoch
                batch_size=train_batch_size,
                lr=train_lr_per_task,
                weight_decay=train_weight_decay,
                warmup_steps=train_warmup_steps,
                max_length=max_length,
                grad_accum_steps=train_grad_accum_steps,
                out_checkpoint_dir=str(task_ckpt_dir),
            )

            print(
                f"[GLUE] Finished fine-tuning for task '{task}'. "
                f"Val metric: {metric_res}"
            )

        else:
            if checkpointing is not None:
                try:
                    checkpointing.load_model_states("recent")
                except Exception:
                    pass

        # -------------------------
        # Evaluate
        # -------------------------
        task_out_dir = out_dir / task
        task_out_dir.mkdir(parents=True, exist_ok=True)

        res = run_glue_task(
            task=task,
            tokenizer=tokenizer,
            model=model,
            checkpointing=None,
            device=device,
            batch_size=batch_size,
            max_length=max_length,
            output_dir=str(task_out_dir)
        )

        for split, metrics in res.items():
            rows.append(
                {
                    "task": task,
                    "split": split,
                    "epochs": epochs_this_task,
                    "metrics": json.dumps(metrics),
                }
            )

    # -------------------------
    # Save summary
    # -------------------------
    summary_csv = out_dir / "glue_summary.csv"
    pd.DataFrame(rows).to_csv(summary_csv, index=False)
    print(f"\n[GLUE] Summary saved to: {summary_csv}")

    return pd.DataFrame(rows)


# ---------------------------------------------------------------------
# CLI for quick testing (optional)
# ---------------------------------------------------------------------
if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--tasks", type=str, default="sst2", help="comma separated glue tasks")
    parser.add_argument("--batch_size", type=int, default=64)
    parser.add_argument("--max_length", type=int, default=128)
    parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--out_dir", type=str, default="glue_outputs")
    args = parser.parse_args()
    print("This module is intended to be invoked from your project's main, which provides tokenizer/model/checkpointing.")
    print(f"Example usage in your main: run_glue_benchmark(config.benchmark, tokenizer, model, checkpointing, out_dir={args.out_dir})")