File size: 2,583 Bytes
a4829b4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import torch
import torch.nn as nn
from torch.nn import functional as F
# Hyperparameters for ZewAI 4
batch_size = 32
block_size = 256 # How many characters/tokens it looks at once
max_iters = 5000
learning_rate = 3e-4
n_embd = 384 # Embedding dimension
n_head = 6 # Number of "Attention Heads" (scanning eyes)
n_layer = 6 # Number of processing layers
dropout = 0.2
class ZewAI4_Attention(nn.Module):
""" The 'Scanning Eye' of zqai1 """
def __init__(self, head_size):
super().__init__()
self.key = nn.Linear(n_embd, head_size, bias=False)
self.query = nn.Linear(n_embd, head_size, bias=False)
self.value = nn.Linear(n_embd, head_size, bias=False)
self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
self.dropout = nn.Dropout(dropout)
def forward(self, x):
B, T, C = x.shape
k = self.key(x)
q = self.query(x)
# Compute attention scores ("affinities")
wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5
wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
wei = F.softmax(wei, dim=-1)
wei = self.dropout(wei)
# Perform the weighted aggregation of the values
v = self.value(x)
out = wei @ v
return out
class ZewBlock(nn.Module):
""" One layer of the ZewAI 4 brain """
def __init__(self, n_embd, n_head):
super().__init__()
head_size = n_embd // n_head
self.sa = MultiHeadAttention(n_head, head_size)
self.ffwd = FeedForward(n_embd)
self.ln1 = nn.LayerNorm(n_embd)
self.ln2 = nn.LayerNorm(n_embd)
def forward(self, x):
x = x + self.sa(self.ln1(x))
x = x + self.ffwd(self.ln2(x))
return x
class ZewAI4Model(nn.Module):
""" The full ZewAI 4 Model for zqai1 """
def __init__(self, vocab_size):
super().__init__()
self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
self.position_embedding_table = nn.Embedding(block_size, n_embd)
self.blocks = nn.Sequential(*[ZewBlock(n_embd, n_head=n_head) for _ in range(n_layer)])
self.ln_f = nn.LayerNorm(n_embd)
self.lm_head = nn.Linear(n_embd, vocab_size)
def forward(self, idx, targets=None):
B, T = idx.shape
tok_emb = self.token_embedding_table(idx)
pos_emb = self.position_embedding_table(torch.arange(T))
x = tok_emb + pos_emb
x = self.blocks(x)
x = self.ln_f(x)
logits = self.lm_head(x)
return logits
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