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Train H4 attention model for extractive question-answering.
Uses the autoresearch pattern: modify -> run (2 min) -> measure -> keep/discard.
Metric: F1 score on validation QA pairs (not bpb).
The model learns to generate answer text given [context | question |] as input.
This is extractive QA — the answer is a span from the context.
Architecture: same H4LanguageModel from Phase 5/6, trained on QA-formatted text.
"""
import os
import math
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import sys
import re
from collections import Counter
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
from h4_language_model import H4LanguageModel
from rag.prepare_qa import generate_sample_qa, prepare_training_data, format_qa_for_training, download_squad_dev
from rag.tokenizer import BPETokenizer
# ---------------------------------------------------------------------------
# Hyperparameters (AGENT MAY MODIFY THESE)
# ---------------------------------------------------------------------------
TIME_BUDGET = 600 # 10 minutes for QA fine-tuning
# Model (must match pre-trained checkpoint)
D_MODEL = 64
N_HEADS = 8
N_LAYERS = 2
D_VALUE = 16
D_FFN = 256
DROPOUT = 0.0
USE_BITLINEAR = True
# Pre-trained checkpoint (set to None to train from scratch)
PRETRAINED_CKPT = os.path.join(os.path.dirname(__file__), '..', '..', 'checkpoints', 'lm_pretrained.pt')
# Optimizer (lower LR for fine-tuning from pre-trained)
LR = 3e-3
WEIGHT_DECAY = 0.01
WARMUP_STEPS = 30
GRAD_CLIP = 1.0
# Training
BATCH_SIZE = 2
MAX_SEQ_LEN = 512
EVAL_INTERVAL = 500
# ---------------------------------------------------------------------------
# QA-specific metrics
# ---------------------------------------------------------------------------
def normalize_answer(s: str) -> str:
"""Lower case, strip whitespace and punctuation."""
s = s.lower().strip()
s = re.sub(r'[^\w\s]', '', s)
s = re.sub(r'\s+', ' ', s)
return s
def compute_f1(prediction: str, gold: str) -> float:
"""Token-level F1 between prediction and gold answer."""
pred_tokens = normalize_answer(prediction).split()
gold_tokens = normalize_answer(gold).split()
if not gold_tokens:
return 1.0 if not pred_tokens else 0.0
if not pred_tokens:
return 0.0
common = Counter(pred_tokens) & Counter(gold_tokens)
n_common = sum(common.values())
if n_common == 0:
return 0.0
precision = n_common / len(pred_tokens)
recall = n_common / len(gold_tokens)
f1 = 2 * precision * recall / (precision + recall)
return f1
def compute_exact_match(prediction: str, gold: str) -> float:
"""Exact match after normalization."""
return 1.0 if normalize_answer(prediction) == normalize_answer(gold) else 0.0
# ---------------------------------------------------------------------------
# Training
# ---------------------------------------------------------------------------
def main():
t_start = time.time()
torch.manual_seed(42)
np.random.seed(42)
# Load QA data — use SQuAD if available, fall back to sample
squad_qa = download_squad_dev()
if len(squad_qa) > 100:
print(f"Using SQuAD 2.0: {len(squad_qa)} QA pairs")
all_qa = squad_qa
else:
print("SQuAD not available, using sample QA pairs")
all_qa = generate_sample_qa()
# Build BPE tokenizer from training data
tokenizer = BPETokenizer(max_vocab=4096)
all_texts = [qa['context'] + ' ' + qa['question'] + ' ' + qa['answer'] for qa in all_qa[:2000]]
tokenizer.build_vocab(all_texts)
vocab_size = tokenizer.vocab_size
# Prepare training sequences using BPE
# Format: [context SEP question SEP answer]
# With BPE, seq_len=512 covers ~2500 chars — virtually all SQuAD contexts
all_seqs = []
for qa in all_qa:
input_ids, answer_ids = tokenizer.encode_qa(qa['context'], qa['question'], qa['answer'])
full_ids = input_ids + answer_ids
if len(full_ids) <= MAX_SEQ_LEN and len(full_ids) > 5:
all_seqs.append((full_ids, len(input_ids), qa))
print(f"BPE sequences that fit seq_len={MAX_SEQ_LEN}: {len(all_seqs)} "
f"(from {len(all_qa)} total QA pairs)")
# Split into train/val
rng = np.random.RandomState(42)
indices = list(range(len(all_seqs)))
rng.shuffle(indices)
n_val = min(30, max(20, int(len(all_seqs) * 0.05)))
val_indices = set(indices[:n_val])
train_seqs = []
val_pairs = []
for i, (full_ids, input_len, qa) in enumerate(all_seqs):
if i in val_indices:
val_pairs.append((full_ids[:input_len], qa['answer'], qa))
else:
train_seqs.append(torch.tensor(full_ids, dtype=torch.long))
rng.shuffle(train_seqs)
avg_len = sum(len(s) for s in train_seqs) / len(train_seqs) if train_seqs else 0
print(f"Train: {len(train_seqs)} seqs (avg {avg_len:.0f} BPE tokens), Val: {len(val_pairs)} pairs")
# Create model
model = H4LanguageModel(
vocab_size=vocab_size,
d_model=D_MODEL,
n_heads=N_HEADS,
n_layers=N_LAYERS,
d_value=D_VALUE,
d_ffn=D_FFN,
max_seq_len=MAX_SEQ_LEN,
dropout=DROPOUT,
use_bitlinear=USE_BITLINEAR,
)
params = model.count_params()
print(f"Model: {params['trainable']:,} trainable params")
# Note: pre-trained checkpoint skipped because vocab size changed with BPE.
# The model trains from scratch but BPE makes this much more efficient.
optimizer = torch.optim.AdamW(
model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY, betas=(0.9, 0.95))
def lr_schedule(step):
if step < WARMUP_STEPS:
return step / max(WARMUP_STEPS, 1)
progress = (step - WARMUP_STEPS) / max(1, 5000 - WARMUP_STEPS)
return 0.1 + 0.9 * 0.5 * (1 + math.cos(math.pi * min(progress, 1.0)))
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_schedule)
# Training loop
step = 0
total_training_time = 0.0
best_f1 = 0.0
model.train()
print(f"\nTraining for {TIME_BUDGET}s, metric=F1")
print(f"{'step':>6} {'loss':>8} {'val_f1':>8} {'val_em':>8} {'lr':>10}")
print("-" * 48)
while True:
t0 = time.time()
# Sample a training sequence
seq = train_seqs[step % len(train_seqs)]
x = seq[:-1].unsqueeze(0) # (1, T-1)
y = seq[1:].unsqueeze(0) # (1, T-1)
# Pad/truncate to consistent length
if x.shape[1] > MAX_SEQ_LEN:
x = x[:, :MAX_SEQ_LEN]
y = y[:, :MAX_SEQ_LEN]
logits = model(x, use_tree=False)
loss = F.cross_entropy(logits.view(-1, vocab_size), y.view(-1))
optimizer.zero_grad()
loss.backward()
if GRAD_CLIP > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
optimizer.step()
scheduler.step()
dt = time.time() - t0
if step > 2:
total_training_time += dt
# Evaluate
if step % EVAL_INTERVAL == 0:
model.eval()
f1_scores = []
em_scores = []
with torch.no_grad():
for inp_ids, gold_answer, qa in val_pairs:
# Generate answer
inp_tensor = torch.tensor([inp_ids], dtype=torch.long)
max_answer_len = min(len(tokenizer.encode(gold_answer)) + 10, 50)
generated = model.generate(
inp_tensor,
max_new_tokens=max_answer_len,
temperature=0.5,
top_k_sample=10,
)
gen_ids = generated[0, len(inp_ids):]
pred_answer = tokenizer.decode(gen_ids.tolist())
f1 = compute_f1(pred_answer, gold_answer)
em = compute_exact_match(pred_answer, gold_answer)
f1_scores.append(f1)
em_scores.append(em)
avg_f1 = sum(f1_scores) / len(f1_scores)
avg_em = sum(em_scores) / len(em_scores)
if avg_f1 > best_f1:
best_f1 = avg_f1
current_lr = scheduler.get_last_lr()[0]
print(f"{step:6d} {loss.item():8.4f} {avg_f1:8.3f} {avg_em:8.3f} {current_lr:10.6f}")
model.train()
step += 1
if step > 2 and total_training_time >= TIME_BUDGET:
break
# Final evaluation with sample outputs
model.eval()
print("\n" + "=" * 60)
print("SAMPLE QA RESULTS:")
final_f1_scores = []
final_em_scores = []
with torch.no_grad():
for inp_ids, gold_answer, qa in val_pairs:
inp_tensor = torch.tensor([inp_ids], dtype=torch.long)
max_answer_len = min(len(gold_answer) + 20, 100)
generated = model.generate(
inp_tensor, max_new_tokens=max_answer_len,
temperature=0.3, top_k_sample=5,
)
gen_ids = generated[0, len(inp_ids):]
pred_answer = tokenizer.decode(gen_ids.tolist())
f1 = compute_f1(pred_answer, gold_answer)
em = compute_exact_match(pred_answer, gold_answer)
final_f1_scores.append(f1)
final_em_scores.append(em)
print(f" Q: {qa['question']}")
print(f" Gold: {gold_answer}")
print(f" Pred: {pred_answer[:80]}")
print(f" F1={f1:.3f} EM={em:.1f}")
print()
avg_f1 = sum(final_f1_scores) / len(final_f1_scores) if final_f1_scores else 0
avg_em = sum(final_em_scores) / len(final_em_scores) if final_em_scores else 0
print("=" * 60)
print("\n---")
print(f"val_f1: {avg_f1:.4f}")
print(f"val_em: {avg_em:.4f}")
print(f"best_f1: {best_f1:.4f}")
print(f"training_seconds: {total_training_time:.1f}")
print(f"total_seconds: {time.time() - t_start:.1f}")
print(f"num_steps: {step}")
print(f"num_params: {params['trainable']}")
print(f"ternary: {'yes' if USE_BITLINEAR else 'no'}")
if __name__ == '__main__':
main()
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