| |
| |
| @@ -440,21 +440,17 @@ |
| name='pair_layer_norm')( |
| representations['pair']) |
| |
| - outputs = [] |
| - safe_keys = safe_key.split(c.num_layer) |
| - for sub_key in safe_keys: |
| - activations, output = fold_iteration( |
| - activations, |
| - initial_act=initial_act, |
| - static_feat_2d=act_2d, |
| - safe_key=sub_key, |
| - sequence_mask=sequence_mask, |
| - update_affine=True, |
| - is_training=is_training, |
| - aatype=batch['aatype']) |
| - outputs.append(output) |
| + def fold_iter(act, key): |
| + return fold_iteration(act, initial_act=initial_act, |
| + static_feat_2d=act_2d, |
| + safe_key=prng.SafeKey(key), |
| + sequence_mask=sequence_mask, |
| + update_affine=True, |
| + is_training=is_training, |
| + aatype=batch['aatype']) |
| + keys = jax.random.split(safe_key.get(), c.num_layer) |
| + activations, output = hk.scan(fold_iter, activations, keys) |
| |
| - output = jax.tree_map(lambda *x: jnp.stack(x), *outputs) |
| # Include the activations in the output dict for use by the LDDT-Head. |
| output['act'] = activations['act'] |
| |
|
|
