File size: 2,742 Bytes
4bc8857 | 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 | """A very small GPT-style language model (character level)."""
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
"""One transformer block: self-attention, then a small feed-forward net."""
def __init__(self, n_embd, n_head, dropout):
super().__init__()
self.n_head = n_head
self.ln1 = nn.LayerNorm(n_embd)
self.qkv = nn.Linear(n_embd, 3 * n_embd)
self.proj = nn.Linear(n_embd, n_embd)
self.ln2 = nn.LayerNorm(n_embd)
self.mlp = nn.Sequential(
nn.Linear(n_embd, 4 * n_embd), nn.GELU(), nn.Linear(4 * n_embd, n_embd)
)
self.dropout = dropout
def forward(self, x):
B, T, C = x.shape
q, k, v = self.qkv(self.ln1(x)).split(C, dim=2)
q, k, v = (t.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) for t in (q, k, v))
# is_causal=True: each character may only look at the ones before it
a = F.scaled_dot_product_attention(
q, k, v, is_causal=True, dropout_p=self.dropout if self.training else 0.0
)
x = x + self.proj(a.transpose(1, 2).reshape(B, T, C))
return x + self.mlp(self.ln2(x))
class TinyGPT(nn.Module):
def __init__(self, vocab_size, block_size=128, n_layer=4, n_head=4, n_embd=128, dropout=0.1):
super().__init__()
self.block_size = block_size
self.tok_emb = nn.Embedding(vocab_size, n_embd)
self.pos_emb = nn.Embedding(block_size, n_embd)
self.blocks = nn.ModuleList(Block(n_embd, n_head, dropout) for _ in range(n_layer))
self.ln_f = nn.LayerNorm(n_embd)
self.head = nn.Linear(n_embd, vocab_size)
def forward(self, idx, targets=None):
T = idx.shape[1]
x = self.tok_emb(idx) + self.pos_emb(torch.arange(T, device=idx.device))
for block in self.blocks:
x = block(x)
logits = self.head(self.ln_f(x))
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
return logits, loss
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=0.8, top_k=None):
"""Yield one new token id at a time, feeding each back in as input."""
for _ in range(max_new_tokens):
logits, _ = self(idx[:, -self.block_size:])
logits = logits[:, -1, :] / max(temperature, 1e-5)
if top_k:
kth = torch.topk(logits, min(top_k, logits.size(-1))).values[:, [-1]]
logits[logits < kth] = -float("inf")
nxt = torch.multinomial(F.softmax(logits, dim=-1), 1)
idx = torch.cat([idx, nxt], dim=1)
yield nxt.item()
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