44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
import torch
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import threading
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from model.brain_architecture import TinyTransformer
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from model.brain_state import model, tokenizer, DEVICE
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
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_last_vocab_size = 0
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_expand_lock = threading.Lock()
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def get_optimizer():
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return optimizer
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def expand_model_if_needed():
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global model, optimizer, _last_vocab_size
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with _expand_lock:
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current_vocab_size = len(tokenizer.vocab) + 10
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if current_vocab_size - _last_vocab_size < 10:
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return # Expand only after 10 new words collected
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old_vocab_size = model.head.out_features
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if current_vocab_size <= old_vocab_size:
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return
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# print(f"Expanding model from {old_vocab_size} -> {current_vocab_size}")
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old_state = model.state_dict()
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new_model = TinyTransformer(vocab_size=current_vocab_size).to(DEVICE)
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with torch.no_grad():
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for name, param in new_model.named_parameters():
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if name in old_state and old_state[name].shape == param.shape:
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param.copy_(old_state[name])
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model = new_model
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
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_last_vocab_size = current_vocab_size
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# print("Expansion complete.") |