examples/eval_qwen_baseline.py

"""
Qwen3-4B baseline evaluation on BABILong QA1 (32k).

This script evaluates the pretrained Qwen model WITHOUT any training,
using the same chunk-based streaming approach as the Titans training script.

Purpose: Establish a baseline to compare with Titans memory-augmented models.
"""

import os
import json
import math
import argparse
import logging
from dataclasses import dataclass
from typing import Optional, Dict, List

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)


@dataclass
class EvalConfig:
    # paths - same as training config
    model_path: str = "/data/huangyifei/huggingface_cache/hub/models--Qwen--Qwen3-4B-Instruct-2507/snapshots/cdbee75f17c01a7cc42f958dc650907174af0554"
    data_path: str = "/data/yty/BABILong/babilong-train-5k-samples/data/qa1/32k.json"
    output_dir: str = "./outputs/qwen_baseline_eval"

    # streaming settings - same as training
    chunk_size: int = 8192
    max_length: int = 32768
    answer_reserve_tokens: int = 64

    # evaluation
    batch_size: int = 1  # use 1 for simplicity in baseline eval
    max_samples: Optional[int] = 500  # same as training default
    print_examples: int = 20

    # precision
    bf16: bool = True
    fp16: bool = False
    use_tf32: bool = True

    seed: int = 42


class BABILongDataset(Dataset):
    """Same dataset class as training script for consistency."""

    def __init__(
        self,
        data_path: str,
        tokenizer,
        max_length: int = 32768,
        answer_reserve_tokens: int = 64,
        max_samples: Optional[int] = None,
    ):
        self.tokenizer = tokenizer
        self.max_length = max_length
        self.answer_reserve_tokens = answer_reserve_tokens

        logger.info(f"Loading dataset: {data_path}")
        with open(data_path, "r") as f:
            self.data = json.load(f)

        if max_samples:
            self.data = self.data[:max_samples]

        logger.info(f"Dataset size: {len(self.data)}")

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        item = self.data[idx]
        text = f"{item['input']}\n\nQuestion: {item['question']}\nAnswer:"
        target = item["target"]

        pad_id = self.tokenizer.pad_token_id or 0
        reserve = int(self.answer_reserve_tokens)

        prompt_ids = self.tokenizer(
            text,
            max_length=max(self.max_length - reserve, 1),
            truncation=True,
            add_special_tokens=True,
            return_tensors="pt",
        ).input_ids.squeeze(0)

        answer_ids = self.tokenizer(
            f" {target}",
            add_special_tokens=False,
            return_tensors="pt",
        ).input_ids.squeeze(0)

        available = max(self.max_length - prompt_ids.numel(), 0)
        answer_ids = answer_ids[:available]

        input_ids = torch.cat([prompt_ids, answer_ids], dim=0)[: self.max_length]

        labels = torch.full_like(input_ids, fill_value=-100)
        if answer_ids.numel() > 0:
            start = prompt_ids.numel()
            end = min(start + answer_ids.numel(), labels.numel())
            labels[start:end] = input_ids[start:end]

        seq_len = input_ids.numel()
        if seq_len < self.max_length:
            pad_len = self.max_length - seq_len
            input_ids = F.pad(input_ids, (0, pad_len), value=int(pad_id))
            labels = F.pad(labels, (0, pad_len), value=-100)
            attention_mask = torch.cat(
                [torch.ones(seq_len, dtype=torch.long), torch.zeros(pad_len, dtype=torch.long)],
                dim=0,
            )
        else:
            attention_mask = torch.ones(self.max_length, dtype=torch.long)

        return {
            "input_ids": input_ids.to(dtype=torch.long),
            "labels": labels.to(dtype=torch.long),
            "attention_mask": attention_mask,
            "target_text": target,  # keep original target for comparison
        }


def collate_fn(batch):
    # separate target_text from tensor fields
    target_texts = [b.pop("target_text") for b in batch]
    tensor_batch = {k: torch.stack([b[k] for b in batch], dim=0) for k in batch[0].keys()}
    tensor_batch["target_texts"] = target_texts
    return tensor_batch


class QwenChunkwiseEvaluator:
    """
    Evaluates Qwen model using chunk-wise streaming (same as training).
    
    Key difference from training: NO memory module, just pure Qwen forward pass.
    Each chunk is processed independently with KV cache reset between samples.
    """

    def __init__(self, model, tokenizer, config: EvalConfig, device: torch.device):
        self.model = model
        self.tokenizer = tokenizer
        self.config = config
        self.device = device
        self.hidden_size = model.config.hidden_size

    def _split_into_chunks(self, seq_len: int, chunk_size: int):
        """Split sequence into chunks, same as training."""
        chunks = []
        for start in range(0, seq_len, chunk_size):
            end = min(start + chunk_size, seq_len)
            chunks.append((start, end))
        return chunks

    @torch.no_grad()
    def evaluate_sample(
        self,
        input_ids: torch.Tensor,
        attention_mask: torch.Tensor,
        labels: torch.Tensor,
    ) -> Dict:
        """
        Evaluate a single sample using chunk-wise streaming.
        
        Process:
        1. Split input into chunks
        2. Process each chunk through Qwen (with overlap for next-token prediction)
        3. Collect predictions only for answer tokens (labels != -100)
        4. Compute loss, token accuracy, and EM accuracy
        """
        batch_size, seq_len = input_ids.shape
        chunk_size = self.config.chunk_size
        chunks = self._split_into_chunks(seq_len, chunk_size)

        loss_fct_sum = nn.CrossEntropyLoss(reduction="sum")
        total_loss_sum = 0.0
        total_loss_tokens = 0

        pred_tokens: List[int] = []
        target_tokens: List[int] = []

        for start, end in chunks:
            # Include 1 overlap token for next-token prediction at chunk boundaries
            proc_start = max(0, start - 1)
            chunk_ids = input_ids[:, proc_start:end]
            chunk_labels = labels[:, proc_start:end]
            chunk_mask = attention_mask[:, proc_start:end]

            # Forward pass through Qwen
            outputs = self.model(
                input_ids=chunk_ids,
                attention_mask=chunk_mask,
                use_cache=False,
                output_hidden_states=False,
                return_dict=True,
            )
            logits = outputs.logits  # [batch, seq, vocab]

            # Compute loss and predictions for answer tokens
            if chunk_labels is not None and (chunk_labels != -100).any():
                # Shift for next-token prediction
                shift_logits = logits[:, :-1, :].contiguous()
                shift_labels = chunk_labels[:, 1:].contiguous()

                valid = shift_labels != -100
                if valid.any():
                    valid_logits = shift_logits[valid]
                    valid_targets = shift_labels[valid]

                    # Compute loss
                    chunk_loss = loss_fct_sum(valid_logits.float(), valid_targets)
                    total_loss_sum += chunk_loss.item()
                    total_loss_tokens += valid_targets.numel()

                    # Collect predictions
                    pred_ids = torch.argmax(valid_logits, dim=-1)
                    pred_tokens.extend(pred_ids.cpu().tolist())
                    target_tokens.extend(valid_targets.cpu().tolist())

        # Compute metrics
        if total_loss_tokens > 0:
            avg_loss = total_loss_sum / total_loss_tokens
        else:
            avg_loss = 0.0

        # Token accuracy
        if len(pred_tokens) > 0:
            tok_correct = sum(p == t for p, t in zip(pred_tokens, target_tokens))
            tok_acc = tok_correct / len(pred_tokens)
        else:
            tok_acc = 0.0

        # EM accuracy (exact match of decoded strings)
        if len(pred_tokens) > 0:
            pred_text = self.tokenizer.decode(pred_tokens, skip_special_tokens=True).strip()
            target_text = self.tokenizer.decode(target_tokens, skip_special_tokens=True).strip()
            em_match = (pred_text == target_text)
        else:
            pred_text = ""
            target_text = ""
            em_match = False

        return {
            "loss": avg_loss,
            "tok_acc": tok_acc,
            "em_match": em_match,
            "pred_text": pred_text,
            "target_text": target_text,
            "num_tokens": len(pred_tokens),
        }

    @torch.no_grad()
    def evaluate_dataset(self, dataloader: DataLoader, print_examples: int = 10) -> Dict:
        """Evaluate entire dataset."""
        self.model.eval()

        total_loss = 0.0
        total_batches = 0
        total_tok_correct = 0
        total_tok_total = 0
        total_em_correct = 0
        total_em_total = 0
        printed = 0

        pbar = tqdm(dataloader, desc="Evaluating", dynamic_ncols=True)
        for batch in pbar:
            input_ids = batch["input_ids"].to(self.device)
            attention_mask = batch["attention_mask"].to(self.device)
            labels = batch["labels"].to(self.device)
            target_texts = batch["target_texts"]

            # Process each sample in batch
            for i in range(input_ids.shape[0]):
                result = self.evaluate_sample(
                    input_ids[i:i+1],
                    attention_mask[i:i+1],
                    labels[i:i+1],
                )

                if result["num_tokens"] > 0:
                    total_loss += result["loss"]
                    total_batches += 1
                    total_tok_correct += int(result["tok_acc"] * result["num_tokens"])
                    total_tok_total += result["num_tokens"]
                    total_em_correct += int(result["em_match"])
                    total_em_total += 1

                    # Print examples
                    if printed < print_examples:
                        logger.info(
                            f"[EVAL SAMPLE {printed + 1}] "
                            f"pred={repr(result['pred_text'])} | "
                            f"label={repr(result['target_text'])} | "
                            f"match={result['em_match']}"
                        )
                        printed += 1

                # Update progress bar
                if total_em_total > 0:
                    pbar.set_postfix({
                        "em_acc": f"{total_em_correct / total_em_total * 100:.1f}%",
                        "tok_acc": f"{total_tok_correct / max(total_tok_total, 1) * 100:.1f}%",
                    })

        # Compute final metrics
        avg_loss = total_loss / max(total_batches, 1)
        tok_acc = total_tok_correct / max(total_tok_total, 1)
        em_acc = total_em_correct / max(total_em_total, 1)

        return {
            "loss": avg_loss,
            "tok_acc": tok_acc,
            "em_acc": em_acc,
            "total_samples": total_em_total,
            "total_tokens": total_tok_total,
        }


def main():
    from transformers import AutoModelForCausalLM, AutoTokenizer

    parser = argparse.ArgumentParser(description="Evaluate Qwen baseline on BABILong")
    parser.add_argument("--model_path", type=str, default=None, help="Path to Qwen model")
    parser.add_argument("--data_path", type=str, default=None, help="Path to BABILong data")
    parser.add_argument("--output_dir", type=str, default=None, help="Output directory")
    parser.add_argument("--max_samples", type=int, default=None, help="Max samples to evaluate")
    parser.add_argument("--chunk_size", type=int, default=None, help="Chunk size for streaming")
    parser.add_argument("--batch_size", type=int, default=1, help="Batch size")
    parser.add_argument("--print_examples", type=int, default=20, help="Number of examples to print")
    parser.add_argument("--eval_split", type=str, default="eval", choices=["train", "eval", "all"],
                        help="Which split to evaluate: train (90%), eval (10%), or all")
    args = parser.parse_args()

    config = EvalConfig()
    if args.model_path:
        config.model_path = args.model_path
    if args.data_path:
        config.data_path = args.data_path
    if args.output_dir:
        config.output_dir = args.output_dir
    if args.max_samples is not None:
        config.max_samples = args.max_samples
    if args.chunk_size is not None:
        config.chunk_size = args.chunk_size
    if args.batch_size:
        config.batch_size = args.batch_size
    if args.print_examples is not None:
        config.print_examples = args.print_examples

    torch.manual_seed(config.seed)

    # Device setup
    if torch.cuda.is_available():
        device = torch.device("cuda")
    else:
        device = torch.device("cpu")

    # TF32 settings
    if torch.cuda.is_available() and config.use_tf32:
        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.allow_tf32 = True
        try:
            torch.set_float32_matmul_precision("high")
        except Exception:
            pass

    logger.info("=" * 60)
    logger.info("Qwen3-4B Baseline Evaluation (NO TRAINING)")
    logger.info("=" * 60)
    logger.info(f"model_path: {config.model_path}")
    logger.info(f"data_path: {config.data_path}")
    logger.info(f"output_dir: {config.output_dir}")
    logger.info(f"max_samples: {config.max_samples}")
    logger.info(f"max_length: {config.max_length}")
    logger.info(f"chunk_size: {config.chunk_size}")
    logger.info(f"eval_split: {args.eval_split}")
    logger.info("=" * 60)

    # Load tokenizer
    logger.info("Loading tokenizer...")
    tokenizer = AutoTokenizer.from_pretrained(config.model_path, trust_remote_code=True)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    # Disable flash-attn checks
    try:
        import transformers
        from transformers.utils import import_utils as _import_utils

        def _disabled(*args, **kwargs):
            return False

        _import_utils.is_flash_attn_2_available = _disabled
        if hasattr(transformers, "utils") and hasattr(transformers.utils, "is_flash_attn_2_available"):
            transformers.utils.is_flash_attn_2_available = _disabled
        _import_utils.is_torchao_available = _disabled
        if hasattr(transformers, "utils") and hasattr(transformers.utils, "is_torchao_available"):
            transformers.utils.is_torchao_available = _disabled
    except Exception as e:
        logger.warning(f"Disable checks failed (ignored): {e}")

    # Load model
    logger.info("Loading model...")
    torch_dtype = torch.bfloat16 if config.bf16 else (torch.float16 if config.fp16 else torch.float32)
    model = AutoModelForCausalLM.from_pretrained(
        config.model_path,
        torch_dtype=torch_dtype,
        device_map=None,
        trust_remote_code=True,
        attn_implementation="sdpa",
        low_cpu_mem_usage=True,
    )
    model.to(device)
    model.config.use_cache = False
    model.eval()
    logger.info(f"Model loaded: {model.config.hidden_size} hidden size, {model.config.num_hidden_layers} layers")

    # Load dataset
    logger.info("Loading dataset...")
    full_dataset = BABILongDataset(
        config.data_path,
        tokenizer,
        max_length=config.max_length,
        answer_reserve_tokens=config.answer_reserve_tokens,
        max_samples=config.max_samples,
    )

    # Split dataset same as training (90% train, 10% eval)
    train_size = int(0.9 * len(full_dataset))
    eval_size = len(full_dataset) - train_size
    train_dataset, eval_dataset = torch.utils.data.random_split(
        full_dataset,
        [train_size, eval_size],
        generator=torch.Generator().manual_seed(config.seed),
    )

    # Select which split to evaluate
    if args.eval_split == "train":
        dataset = train_dataset
        split_name = "train"
    elif args.eval_split == "eval":
        dataset = eval_dataset
        split_name = "eval"
    else:  # all
        dataset = full_dataset
        split_name = "all"

    logger.info(f"Evaluating on '{split_name}' split: {len(dataset)} samples")

    dataloader = DataLoader(
        dataset,
        batch_size=config.batch_size,
        shuffle=False,
        collate_fn=collate_fn,
        num_workers=0,
    )

    # Create evaluator
    evaluator = QwenChunkwiseEvaluator(model, tokenizer, config, device)

    # Run evaluation
    logger.info("Starting evaluation...")
    results = evaluator.evaluate_dataset(dataloader, print_examples=config.print_examples)

    # Print results
    ppl = math.exp(min(20.0, results["loss"]))
    logger.info("=" * 60)
    logger.info("EVALUATION RESULTS (Qwen Baseline - NO TRAINING)")
    logger.info("=" * 60)
    logger.info(f"Split: {split_name}")
    logger.info(f"Total samples: {results['total_samples']}")
    logger.info(f"Total answer tokens: {results['total_tokens']}")
    logger.info(f"Loss: {results['loss']:.4f}")
    logger.info(f"Perplexity: {ppl:.3f}")
    logger.info(f"Token Accuracy: {results['tok_acc'] * 100:.2f}%")
    logger.info(f"EM Accuracy: {results['em_acc'] * 100:.2f}%")
    logger.info("=" * 60)

    # Save results
    os.makedirs(config.output_dir, exist_ok=True)
    results_path = os.path.join(config.output_dir, f"baseline_results_{split_name}.json")
    with open(results_path, "w") as f:
        json.dump({
            "split": split_name,
            "total_samples": int(results["total_samples"]),
            "total_tokens": int(results["total_tokens"]),
            "loss": float(results["loss"]),
            "perplexity": float(ppl),
            "tok_acc_pct": float(results["tok_acc"] * 100),
            "em_acc_pct": float(results["em_acc"] * 100),
            "config": {
                "model_path": config.model_path,
                "data_path": config.data_path,
                "max_samples": config.max_samples,
                "max_length": config.max_length,
                "chunk_size": config.chunk_size,
            }
        }, f, indent=2)
    logger.info(f"Results saved to: {results_path}")


if __name__ == "__main__":
    main()