Spaces:
Sleeping
Sleeping
File size: 2,307 Bytes
d00fb47 |
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 |
import torch, torch.nn as nn, torch.nn.functional as F
batch_size = 64
max_len = 256
d_model = 384
n_layer = 6 # 6 blocks in the decoder
n_head = 6
d_q = int(d_model / n_head)
dropout = 0.2
vocab_size = 65
from block import Block
class Model(nn.Module):
def __init__(self):
super().__init__()
self.token_embedding_table = nn.Embedding(vocab_size, d_model) # Embedding matrix size: (65, 384)
self.positional_embedding_table = nn.Embedding(max_len, d_model) # Position matrix size: (256, 384)
self.blocks = nn.Sequential(*[Block(d_model, n_head) for _ in range(n_layer)])
self.ln = nn.LayerNorm(d_model)
self.unembedding_matrix_calc = nn.Linear(d_model, vocab_size)
def forward(self, idx, targets=None):
B, S = idx.shape
tok_emb = self.token_embedding_table(idx) # Size of embedding: (B, S, 384)
pos_emb = self.positional_embedding_table(torch.arange(S, device=idx.device)) # Shape: (S, 384)
x = tok_emb + pos_emb
x = self.blocks(x) # Pass through all 6 blocks each of all 6 heads
x = self.ln(x)
logits = self.unembedding_matrix_calc(x) # --> (B, S, 384) * (384, 65) --> (B, S, 65)
if targets is None:
loss = None
else:
B, S, V = logits.shape
logits = logits.view(-1, V) # (B, S, V) --> (B*S, V)
targets = targets.view(-1) # --> (B, S) --> (B*S)
loss = F.cross_entropy(logits, targets) # Handles softmax interally as well (better because it does log addition which reduces errors instead of log multi)
return logits, loss
def generate(self, idx, max_new_tokens):
for _ in range(max_new_tokens):
idx_cond = idx[:, -max_len:]
logits, loss = self(idx_cond)
logits = logits[:, -1, :]
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, idx_next), dim = 1)
return idx
if __name__ == "__main__":
model = Model()
idx = torch.zeros((batch_size, max_len), dtype=torch.long)
logits, loss = model(idx, idx)
print("Input shape:", idx.shape)
print("Output logits shape:", logits.shape)
print("Calculated loss:", loss.item()) |