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"""
J-lens v2: simpler gradient approach that works reliably.
Uses register_full_backward_hook to capture gradients from a dummy loss.
"""
import torch
import torch.nn as nn
import numpy as np
import pickle
import os
import os, sys
# jlens_v2.py is in src/, model.py is in repo root
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from collections import defaultdict
def load_model(checkpoint_path, device='cuda'):
checkpoint = torch.load(checkpoint_path, map_location=device)
model_args = checkpoint['model_args']
from model import GPT, GPTConfig
config = GPTConfig(**model_args)
model = GPT(config)
state_dict = checkpoint['model']
unwanted = '_orig_mod.'
for k in list(state_dict.keys()):
if k.startswith(unwanted):
state_dict[k[len(unwanted):]] = state_dict.pop(k)
model.load_state_dict(state_dict)
model.to(device)
model.eval()
return model, config
def compute_jlens_layer(model, layer_idx, data, device='cuda'):
"""
Compute J-lens for all tokens at one layer.
For each token k in vocab, J-lens = E_x[ grad of log p(k|x) w.r.t. residual at layer ]
Efficient approach: use torch.autograd.grad on log_softmax(logits)[:,:,k].sum()
"""
n_embd = model.config.n_embd
vocab_size = model.config.vocab_size
# Accumulators
accum = torch.zeros(vocab_size, n_embd, device='cpu')
counts = torch.zeros(vocab_size, device='cpu')
print(f" Layer {layer_idx}: processing {len(data)} batches...")
for batch_idx, (x, y) in enumerate(data):
x, y = x.to(device), y.to(device)
B, T = x.shape
# Store intermediate activations by splitting the forward pass
# Run model up to the target layer, capture output,
# then run the rest
# Approach: forward hook captures block output
resid_captured = {}
def hook(module, input, output):
resid_captured['val'] = output
target_block = model.transformer.h[layer_idx]
handle = target_block.register_forward_hook(hook)
# Full forward pass
logits, loss = model(x, y)
handle.remove()
# log_softmax for per-token log-probs
log_probs = torch.nn.functional.log_softmax(logits, dim=-1) # (B, T, V)
# For each token, compute gradient w.r.t. captured residual
for token_id in range(vocab_size):
token_log_prob = log_probs[:, :, token_id].sum()
grad = torch.autograd.grad(
token_log_prob,
resid_captured['val'],
retain_graph=(token_id < vocab_size - 1)
)[0] # (B, T, n_embd)
# Sum over batch and sequence positions, accumulate on CPU
accum[token_id] += grad.detach().cpu().reshape(-1, n_embd).sum(dim=0)
counts[token_id] += B * T
# Cleanup
del logits, loss, log_probs
del grad # noqa: F821
resid_captured.clear()
torch.cuda.empty_cache()
if (batch_idx + 1) % 5 == 0:
print(f" Batch {batch_idx + 1}/{len(data)} done")
# Average
jlens = {}
for token_id in range(vocab_size):
if counts[token_id] > 0:
jlens[token_id] = accum[token_id] / counts[token_id]
else:
jlens[token_id] = torch.zeros(n_embd)
return jlens
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', type=str, required=True)
parser.add_argument('--data_dir', type=str, default='data/shakespeare_char')
parser.add_argument('--output_dir', type=str, default='outputs/jlens')
parser.add_argument('--max_batches', type=int, default=20)
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--device', type=str, default='cuda')
args = parser.parse_args()
print(f"Loading model from {args.checkpoint}")
model, config = load_model(args.checkpoint, args.device)
print(f"Model: {config.n_layer} layers, {config.n_embd} dim, "
f"{config.n_head} heads, {config.vocab_size} vocab")
# Load data
from pathlib import Path
data_dir = Path(args.data_dir)
train_data = np.memmap(data_dir / 'train.bin', dtype=np.uint16, mode='r')
meta_path = data_dir / 'meta.pkl'
with open(meta_path, 'rb') as f:
meta = pickle.load(f)
itos = meta['itos']
print(f"Vocabulary size: {len(itos)}")
# Build batched data
block_size = config.block_size
batches = []
for _ in range(args.max_batches):
ix = torch.randint(len(train_data) - block_size, (args.batch_size,))
x = torch.stack([torch.from_numpy(
train_data[i:i+block_size].astype(np.int64)) for i in ix])
y = torch.stack([torch.from_numpy(
train_data[i+1:i+1+block_size].astype(np.int64)) for i in ix])
batches.append((x, y))
print(f"Processing {len(batches)} batches of size {args.batch_size}")
# Compute J-lens for all layers
all_jlens = {}
for layer_idx in range(config.n_layer):
print(f"\n=== Layer {layer_idx}/{config.n_layer} ===")
all_jlens[layer_idx] = compute_jlens_layer(model, layer_idx, batches, args.device)
# Save
os.makedirs(args.output_dir, exist_ok=True)
save_path = os.path.join(args.output_dir, 'jlens_vectors.pkl')
output = {
'metadata': {
'n_layers': config.n_layer,
'n_embd': config.n_embd,
'vocab_size': config.vocab_size,
'num_batches': len(batches),
'batch_size': args.batch_size,
},
'vectors': {
str(layer): {str(tid): v.numpy() for tid, v in tokens.items()}
for layer, tokens in all_jlens.items()
}
}
with open(save_path, 'wb') as f:
pickle.dump(output, f)
# Quick analysis
print(f"\n{'='*60}")
print("J-LENS ANALYSIS")
print(f"{'='*60}")
for layer_idx in range(config.n_layer):
norms = {tid: v.norm().item() for tid, v in all_jlens[layer_idx].items()}
sorted_tokens = sorted(norms.items(), key=lambda x: x[1], reverse=True)
print(f"\nLayer {layer_idx} — Top 10 tokens:")
for tid, norm in sorted_tokens[:10]:
tok = itos[tid].replace('\n', '\\n')
print(f" '{tok}': norm={norm:.4f}")
threshold = np.median(list(norms.values())) * 2
active = sum(1 for n in norms.values() if n > threshold)
print(f" Active tokens (norm > {threshold:.2f}): {active}/{len(norms)}")
print(f"\nSaved to {save_path}")
if __name__ == '__main__':
main()
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