75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
import torch
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import torch.nn as nn
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import random
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from model.tokenizer import Tokenizer
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import torch.nn.functional as F
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from model.memory import save_dream
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import time
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recent_dreams = []
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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tokenizer = Tokenizer()
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VOCAB_SIZE = 10000 # Temporary cap, grows dynamically
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EMBED_DIM = 128
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class TinyTransformer(nn.Module):
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def __init__(self):
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super().__init__()
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self.embed = nn.Embedding(VOCAB_SIZE, EMBED_DIM)
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self.ln1 = nn.LayerNorm(EMBED_DIM)
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self.fc = nn.Linear(EMBED_DIM, VOCAB_SIZE)
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def forward(self, x):
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x = self.embed(x)
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x = self.ln1(x)
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return self.fc(x)
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model = TinyTransformer().to(DEVICE)
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
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loss_fn = nn.CrossEntropyLoss()
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def generate_response():
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seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE)
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output = model(seed.unsqueeze(0))
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pred = torch.argmax(output, dim=-1).squeeze().tolist()
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if not isinstance(pred, list):
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pred = [pred]
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return tokenizer.detokenize(pred)
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def score_sentence(sentence: str) -> float:
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words = sentence.strip().split()
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length = len(words)
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diversity = len(set(words)) / (length + 1)
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if length < 4:
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return 0.0
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return diversity * min(length, 20)
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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 > 0.3:
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save_dream(sentence, score)
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train_on_message(sentence)
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recent_dreams.append((score, sentence))
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if len(recent_dreams) > 10:
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recent_dreams.pop(0)
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