""" 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()