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import gradio as gr
import torch
import torch.nn as nn
import torch.functional as F
n_embd = 64
dropout = 0.0
block_size = 32
vocab_size = 65
n_head = 4
n_layer = 4
class Head(nn.Module):
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)))
self.dropout = nn.Dropout(dropout)
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
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, num_heads, head_size):
super().__init__()
self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
self.proj = 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.dropout(self.proj(out))
return out
class FeedFoward(nn.Module):
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 Block(nn.Module):
def __init__(self, n_embd, n_head):
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):
x = x + self.sa(self.ln1(x))
x = x + self.ffwd(self.ln2(x))
return x
class BigramLanguageModel(nn.Module):
def __init__(self):
super().__init__()
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)])
self.ln_f = nn.LayerNorm(n_embd)
self.lm_head = nn.Linear(n_embd, vocab_size)
def forward(self, idx, targets=None):
B, T = idx.shape
tok_emb = self.token_embedding_table(idx)
pos_emb = self.position_embedding_table(torch.arange(T))
x = tok_emb + pos_emb
x = self.blocks(x)
x = self.ln_f(x)
logits = self.lm_head(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)
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)
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
chars = "\n !$&',-.3:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
itos = { i:ch for i,ch in enumerate(chars) }
stoi = { ch:i for i,ch in enumerate(chars) }
decode = lambda l: ''.join([itos[i] for i in l])
encode = lambda s: [stoi[c] for c in s]
model = BigramLanguageModel()
def respond(
message,
history: list[tuple[str, str]],
):
messages = [{"role": "system", "content": "Cocaine"}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
input_txt = encode(message)
context = torch.tensor(input_txt).unsqueeze(0)
# response = decode(model.generate(context, max_new_tokens=2000)[0].tolist())
idx = context
for _ in range(2000):
idx_cond = idx[:, -block_size:]
logits = model(idx_cond).logits
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)
yield decode(idx_next[0].tolist())
demo = gr.ChatInterface(respond)
if __name__ == "__main__":
demo.launch() |