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import torch | |
from torch import nn | |
from torch.nn import functional as F | |
batch_size = 32 | |
block_size = 128 | |
max_iters = 1000 | |
learning_rate = 3e-4 | |
eval_steps = 200 | |
n_embd = 384 | |
n_head = 4 | |
n_layer = 4 | |
dropout = 0.2 | |
class Block(nn.Module): | |
"""Transformer block: communication followed by computation""" | |
def __init__(self, n_embd, n_head): | |
# n_embd: embedding dimension, n_head: the number of heads we'd like | |
super().__init__() | |
head_size = n_embd // n_head | |
self.sa = MultiHeadAttention(n_head, head_size) | |
self.ffwd = FeedFoward(n_embd) | |
self.ln1 = nn.LayerNorm(n_embd) | |
self.ln2 = nn.LayerNorm(n_embd) | |
def forward(self, x): | |
y = self.sa(x) | |
x = self.ln1(x + y) | |
y = self.ffwd(x) | |
x = self.ln2(x + y) | |
return x | |
class MultiHeadAttention(nn.Module): | |
"""multiple heads of self-attention in parallel""" | |
def __init__(self, num_heads, head_size): | |
super().__init__() | |
self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)]) | |
self.proj = nn.Linear(head_size * num_heads, n_embd) | |
self.dropout = nn.Dropout(dropout) | |
def forward(self, x): | |
# (B, T, F) -> (B, T, [h1, h1, h1, h1, h2, h2, h2, h2, h3, h3, h3, h3]) | |
out = torch.cat([h(x) for h in self.heads], dim=-1) | |
out = self.dropout(self.proj(out)) | |
return out | |
class Head(nn.Module): | |
"""one head of self-attention""" | |
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)) | |
) # noqa 5501 | |
self.dropout = nn.Dropout(dropout) | |
def forward(self, x): | |
# input of size (batch, time-step, channels) | |
# output of size (batch, time-step, head size) | |
B, T, C = x.shape | |
k = self.key(x) # (B,T,hs) | |
q = self.query(x) # (B,T,hs) | |
# compute attention scores ("affinities") | |
wei = ( | |
q @ k.transpose(-2, -1) * k.shape[-1] ** -0.5 | |
) # (B, T, hs) @ (B, hs, T) -> (B, T, T) # noqa 5501 | |
wei = wei.masked_fill( | |
self.tril[:T, :T] == 0, float("-inf") | |
) # (B, T, T) # noqa 5501 | |
wei = F.softmax(wei, dim=-1) # (B, T, T) | |
wei = self.dropout(wei) | |
# perform the weighted aggregation of the values | |
v = self.value(x) # (B,T,hs) | |
out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs) | |
return out | |
class FeedFoward(nn.Module): | |
"""Simple linear layer followed by non_linear layer""" | |
def __init__(self, n_embd): | |
super().__init__() | |
self.net = nn.Sequential( | |
nn.Linear(n_embd, 4 * n_embd), | |
nn.ReLU(), | |
nn.Linear(4 * n_embd, n_embd), | |
nn.Dropout(dropout), | |
) | |
def forward(self, x): | |
return self.net(x) | |
class GPTLanguageModel(nn.Module): | |
def __init__(self, vocab_size, device): | |
super().__init__() | |
self.device = device | |
self.token_embedding_table = nn.Embedding(vocab_size, n_embd) | |
self.position_embedding_table = nn.Embedding(block_size, n_embd) | |
self.blocks = nn.Sequential( | |
*[Block(n_embd, n_head=n_head) for _ in range(n_layer)] | |
) # noqa 5501 | |
self.ln_f = nn.LayerNorm(n_embd) | |
self.lm_head = nn.Linear(n_embd, vocab_size) | |
self.apply(self._init_weights) | |
def _init_weights(self, module): | |
if isinstance(module, nn.Linear): | |
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) | |
if module.bias is not None: | |
torch.nn.init.zeros_(module.bias) | |
elif isinstance(module, nn.Embedding): | |
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) | |
def forward(self, index, targets=None): | |
B, T = index.shape | |
# idx and targets are both (B,T) tensor of integers | |
tok_emb = self.token_embedding_table(index) # (B,T,C) | |
pos_emb = self.position_embedding_table( | |
torch.arange(T, device=self.device) | |
) # (T,C) # noqa 5501 | |
x = tok_emb + pos_emb # (B,T,C) | |
x = self.blocks(x) # (B,T,C) | |
x = self.ln_f(x) # (B,T,C) | |
logits = self.lm_head(x) # (B,T,vocab_size) | |
if targets is None: | |
loss = None | |
else: | |
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, index, max_new_tokens): | |
# index is (B, T) array of indices in the current context | |
for _ in range(max_new_tokens): | |
# crop idx to the last block_size tokens | |
index_cond = index[:, -block_size:] | |
# get the predictions | |
logits, loss = self.forward(index_cond) | |
# focus only on the last time step | |
logits = logits[:, -1, :] # becomes (B, C) | |
# apply softmax to get probabilities | |
probs = F.softmax(logits, dim=-1) # (B, C) | |
# sample from the distribution | |
index_next = torch.multinomial(probs, num_samples=1) # (B, 1) | |
# append sampled index to the running sequence | |
index = torch.cat((index, index_next), dim=1) # (B, T+1) | |
return index | |
def create_GPT_model(vocab_size, device): | |
model = GPTLanguageModel(vocab_size=vocab_size, device=device) | |
model = model.to(device) | |
return model | |