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import torch
import torch.nn as nn
import torch.nn.functional as F
# hyperparameters
batch_size = 16 # how many independent sequences will we process in parallel?
block_size = 64 # what is the maximum context length for predictions?
max_iters = 5000
eval_interval = 100
learning_rate = 1e-3
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 200
n_embd = 128
n_head = 8
n_layer = 4
dropout = 0.0
vocab = 101
# ------------
class Head(nn.Module):
def __init__(self, head_size):
super(Head,self).__init__()
self.head_size = head_size
self.dropout = nn.Dropout(dropout)
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)))
def forward(self,x):
k = self.key(x)
q = self.query(x)
wei = q @ k.transpose(-2,-1) * (self.head_size ** -0.5)
wei = wei.masked_fill(self.tril == 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_head, head_size):
super(MultiHeadAttention,self).__init__()
self.head_size = head_size
self.n_head = n_head
self.heads = nn.ModuleList([Head(head_size) for _ in range(n_head)])
self.out = nn.Linear(n_embd, n_embd)
self.dropout = nn.Dropout(dropout)
def forward(self,x):
out = torch.cat([h(x) for h in self.heads], dim=-1)
out = self.out(out)
out = self.dropout(out)
return out
class FeedForwardLayer(nn.Module):
def __init__(self, n_embd):
super(FeedForwardLayer, self).__init__()
self.n_embd = n_embd
self.fc1 = nn.Linear(n_embd, 4*n_embd)
self.fc2 = nn.Linear(4*n_embd,n_embd)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
out = self.fc1(x)
out = F.gelu(out)
out = self.fc2(out)
out = self.dropout(out)
return out
class Block(nn.Module):
def __init__(self):
super(Block, self).__init__()
self.attn = MultiHeadAttention(n_head, n_embd // n_head)
self.ff = FeedForwardLayer(n_embd)
self.ln1 = nn.LayerNorm(n_embd)
self.ln2 = nn.LayerNorm(n_embd)
def forward(self,x):
x = x + self.attn(self.ln1(x))
x = x + self.ff(self.ln2(x))
return x
class Transformer(nn.Module):
def __init__(self, n_embd, n_layer):
super(Transformer, self).__init__()
self.n_embd = n_embd
self.n_layer = n_layer
self.token_embedding = nn.Embedding(vocab, n_embd)
self.position_embedding = nn.Embedding(block_size,n_embd)
self.blocks = nn.Sequential(*[Block() for _ in range(n_layer)])
self.ln_f = nn.LayerNorm(n_embd) # final layer norm
self.ffwd = nn.Linear(n_embd, vocab)
def forward(self, idx, targets=None):
B,T = idx.shape
x = self.token_embedding(idx) + self.position_embedding(torch.arange(T, device=idx.device))
x = self.blocks(x)
x = self.ln_f(x)
logits = self.ffwd(x)
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, ignore_index=0)
return logits,loss
def generate(self, idx, max_tokens):
for _ in range(max_tokens):
idx_cond = idx[:, -block_size:]
logits, _ = 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
print(torch. __version__ ) |