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91a78ba | 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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | import torch
import torch.nn as nn
import torch.nn.functional as F
class CausalSelfAttention(nn.Module):
def __init__(self, n_embd, n_head):
super().__init__()
assert n_embd % n_head == 0
self.n_head = n_head
self.head_dim = n_embd // n_head
self.c_attn = nn.Linear(n_embd, 3 * n_embd, bias=False)
self.c_proj = nn.Linear(n_embd, n_embd, bias=False)
def forward(self, x):
B, T, C = x.size()
q, k, v = self.c_attn(x).split(self.head_dim * self.n_head, dim=2)
q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
att = F.scaled_dot_product_attention(q, k, v, is_causal=True)
att = att.transpose(1, 2).contiguous().view(B, T, C)
return self.c_proj(att)
class MLP(nn.Module):
def __init__(self, n_embd, n_inner):
super().__init__()
self.c_fc = nn.Linear(n_embd, n_inner, bias=False)
self.c_proj = nn.Linear(n_inner, n_embd, bias=False)
def forward(self, x):
return self.c_proj(F.gelu(self.c_fc(x)))
class Block(nn.Module):
def __init__(self, n_embd, n_head):
super().__init__()
self.ln_1 = nn.LayerNorm(n_embd)
self.attn = CausalSelfAttention(n_embd, n_head)
self.ln_2 = nn.LayerNorm(n_embd)
self.mlp = MLP(n_embd, 4 * n_embd)
def forward(self, x):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class CharGPT(nn.Module):
"""Character-level causal transformer (nanoGPT-style). No bias in
attention / FFN / lm_head; LayerNorm carries the affine bias."""
def __init__(self, vocab_size, block_size, n_layer, n_head, n_embd):
super().__init__()
self.block_size = block_size
self.transformer = nn.ModuleDict({
"wte": nn.Embedding(vocab_size, n_embd),
"wpe": nn.Embedding(block_size, n_embd),
"drop": nn.Dropout(0.0),
"h": nn.ModuleList([Block(n_embd, n_head) for _ in range(n_layer)]),
"ln_f": nn.LayerNorm(n_embd),
})
self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)
def forward(self, idx, targets=None):
B, T = idx.size()
assert T <= self.block_size, f"block size {self.block_size} < {T}"
pos = torch.arange(0, T, device=idx.device)
x = self.transformer["drop"](
self.transformer["wte"](idx) + self.transformer["wpe"](pos))
for block in self.transformer["h"]:
x = block(x)
x = self.transformer["ln_f"](x)
logits = self.lm_head(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=40):
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.block_size:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / temperature
if top_k:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = float("-inf")
probs = F.softmax(logits, dim=-1)
idx = torch.cat((idx, torch.multinomial(probs, num_samples=1)), dim=1)
return idx
def from_config(config):
return CharGPT(
vocab_size=config["vocab_size"],
block_size=config["block_size"],
n_layer=config["n_layer"],
n_head=config["n_head"],
n_embd=config["n_embd"],
)
if __name__ == "__main__":
import json
cfg = json.load(open("config.json"))
m = from_config(cfg)
print("params:", sum(p.numel() for p in m.parameters()))
# tiny smoke test
x = torch.randint(0, cfg["vocab_size"], (1, 32))
logits, loss = m(x, x)
print("smoke loss:", loss.item()) |