Dani
0674d51471
Updated brain.py to be a tad more aggressive, and added a cleanup function for the brainmap to cleanup.py
92 lines
2.5 KiB
Python
92 lines
2.5 KiB
Python
import random
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import torch
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import torch.nn.functional as F
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from model.memory import save_dream
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from model.brain_state import model, tokenizer, DEVICE
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from model.journal import record_to_journal
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from model.trainer import train_on_message
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from context.context import get_recent_context
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recent_dreams = []
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@torch.inference_mode()
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def generate_response(max_tokens: int = 50, temperature: float = 1.0):
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model.eval()
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input_ids = torch.tensor([tokenizer.token_to_id("<start>")], device=DEVICE).unsqueeze(0)
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generated = []
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forbidden_tokens = {
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tokenizer.token_to_id("<unk>"),
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tokenizer.token_to_id("<start>"),
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tokenizer.token_to_id("<pad>"),
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tokenizer.token_to_id("<end>"),
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tokenizer.token_to_id("<sep>"),
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}
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for _ in range(max_tokens):
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output = model(input_ids)
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if torch.isnan(output).any():
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print("[Brain] Detected NaN in output, restarting generation.")
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return "..."
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next_token_logits = output[:, -1, :]
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probs = torch.softmax(next_token_logits / temperature, dim=-1)
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next_token = torch.multinomial(probs, num_samples=1)
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# Resample if forbidden token
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while next_token.item() in forbidden_tokens:
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next_token = torch.multinomial(probs, num_samples=1)
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token_id = next_token.item()
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if tokenizer.reverse_vocab.get(token_id, "") == "<end>":
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break
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generated.append(token_id)
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input_ids = torch.cat([input_ids, next_token], dim=1)
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return tokenizer.detokenize(generated)
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def score_sentence(sentence: str) -> float:
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words = sentence.strip().split()
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unique = set(words)
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length = len(words)
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unique_ratio = len(unique) / (length + 1)
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if length < 5:
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return 0.0
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if unique_ratio < 0.5:
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return 0.0
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return unique_ratio * min(length / 20.0, 1.0)
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def daydream():
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model.eval()
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seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)
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dream = []
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for _ in range(12):
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out = model(seed)
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logits = out[:, -1, :]
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probs = F.softmax(logits, dim=-1)
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token = torch.multinomial(probs, num_samples=1)
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dream.append(token.item())
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seed = torch.cat([seed, token], dim=1)
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sentence = tokenizer.detokenize(dream)
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score = score_sentence(sentence)
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if score > 0.5:
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save_dream(sentence, score)
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record_to_journal(sentence)
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train_on_message(sentence)
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if len(recent_dreams) > 10:
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recent_dreams.pop(0)
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