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import torch
from torch import nn
import torch.nn.functional as F
batch_size = 64
block_size = 256
max_iters = 5000
eval_interval = 500
learning_rate = 3e-4
device = "cuda:1" if torch.cuda.is_available() else "cpu"
eval_iters = 200
n_embeds = 384
n_heads = 6
n_layers = 6
dropout = 0.2
torch.manual_seed(1123)
with open("input.txt") as f:
text = f.read()
chars = sorted(list(set(text)))
vocab_size = len(chars)
stoi = {ch: i for i, ch in enumerate(chars)}
itos = {i: ch for i, ch in enumerate(chars)}
def encode(s):
return [stoi[c] for c in s]
def decode(l):
return "".join([itos[i] for i in l])
data = torch.tensor(encode(text), dtype=torch.long)
n = int(0.9 * len(data))
train_data = data[:n]
val_data = data[n:]
def get_batch(split):
data = train_data if split == "train" else val_data
ix = torch.randint(len(data) - block_size, (batch_size,))
x = torch.stack([data[i : i + block_size] for i in ix])
y = torch.stack([data[i + 1 : i + block_size + 1] for i in ix])
return x, y
@torch.no_grad()
def estimate_loss(model: nn.Module):
out = {}
model.eval()
for split in ["train", "val"]:
losses = torch.zeros(eval_iters)
for k in range(eval_iters):
X, Y = get_batch(split)
X, Y = X.to(device), Y.to(device)
logits, loss = model(X, Y)
losses[k] = loss.item()
out[split] = losses.mean()
model.train()
return out
class Head(nn.Module):
def __init__(self, n_embed, head_size) -> None:
super().__init__()
self.key = nn.Linear(n_embed, head_size, bias=False)
self.query = nn.Linear(n_embed, head_size, bias=False)
self.value = nn.Linear(n_embed, head_size, bias=False)
self.dropout = nn.Dropout(dropout)
self.register_buffer("tril", torch.tril(torch.ones(block_size, block_size)))
def forward(self, x):
B, T, C = x.shape
k = self.key(x)
q = self.query(x)
wei = q @ k.transpose(-2, -1) * (C**-0.5) # (B,T,16) @ (B,16,T) --> (B,T,T)
wei = wei.masked_fill(self.tril[:T, :T] == 0, float("-inf"))
wei = F.softmax(wei, dim=-1)
wei = self.dropout(wei)
v = self.value(x)
out = wei @ v
return out
class MultiHeadAttention(nn.Module):
def __init__(self, n_heads, n_embeds, head_size):
super().__init__()
self.heads = nn.ModuleList([Head(n_embeds, head_size) for _ in range(n_heads)])
self.proj = nn.Linear(n_embeds, n_embeds)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x = torch.cat([h(x) for h in self.heads], dim=-1)
x = self.proj(x)
x = self.dropout(x)
return x
class FeedForward(nn.Module):
def __init__(self, n_embeds):
super().__init__()
self.net = nn.Sequential(
nn.Linear(n_embeds, 4 * n_embeds),
nn.ReLU(),
nn.Linear(4 * n_embeds, n_embeds),
nn.Dropout(dropout),
)
def forward(self, x):
return self.net(x)
class Block(nn.Module):
def __init__(self, n_embeds, n_heads):
super().__init__()
head_size = n_embeds // n_heads
self.sa_heads = MultiHeadAttention(n_heads, n_embeds, head_size)
self.ffwd = FeedForward(n_embeds)
self.ln1 = nn.LayerNorm(n_embeds)
self.ln2 = nn.LayerNorm(n_embeds)
def forward(self, x):
x = x + self.sa_heads(self.ln1(x))
x = x + self.ffwd(self.ln2(x))
return x
class BigramLanguageModel(nn.Module):
def __init__(self, vocab_size, n_embeds, block_size):
super().__init__()
self.token_embedding_table = nn.Embedding(vocab_size, n_embeds)
self.position_embedding_table = nn.Embedding(block_size, n_embeds)
self.blocks = nn.Sequential(
*[Block(n_embeds, n_heads) for _ in range(n_layers)]
)
self.lnf = nn.LayerNorm(n_embeds)
self.lm_head = nn.Linear(n_embeds, vocab_size)
def forward(self, idx, targets=None):
B, T = idx.shape
tok_embeds = self.token_embedding_table(idx) # BxTxNemb
pos_embeds = self.position_embedding_table(
torch.arange(T, device=device)
) # TXNemb
x = tok_embeds + pos_embeds # BxTxNemb
x = self.blocks(x)
x = self.lnf(x)
logits = self.lm_head(x) # BxTxVocabSize
loss = None
if targets is not None:
B, T, C = logits.shape
logits = logits.view(B * T, C)
targets = targets.view(B * T)
loss = F.cross_entropy(logits, targets)
return logits, loss
def generate(self, idx, max_new_tokens):
for _ in range(max_new_tokens):
idx_cond = idx[:, -block_size:]
logits, loss = self(idx_cond) # BxTxC
logits = logits[:, -1, :] # BxC
probs = F.softmax(logits, dim=-1) # BxC
idx_next = torch.multinomial(probs, num_samples=1) # Bx1
idx = torch.cat((idx, idx_next), dim=1) # BxT+1
return idx
model = BigramLanguageModel(vocab_size, n_embeds, block_size)
model = model.to(device)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3)
for iter in range(max_iters):
if iter % eval_interval == 0:
losses = estimate_loss(model)
print(
f"Step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}"
)
xb, yb = get_batch("train")
xb, yb = xb.to(device), yb.to(device)
logits, loss = model(xb, yb)
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
context = torch.zeros((1, 1), dtype=torch.long, device=device)
results = decode(model.generate(context, max_new_tokens=100)[0].tolist())
print(results)
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