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import torch
import torch.nn as nn
from torch.nn import functional as F
import gradio as gr
n_emb = 64
block_size = 32
# head_size = 4
n_x = 4
num_heads = 4
eval_iteration = 250
max_iters = 5000
batch_size = 32
device = 'cuda' if torch.cuda.is_available() else 'cpu'
class Head(nn.Module):
"""
one head in self attention
"""
def __init__(self, head_size):
super().__init__()
self.key = nn.Linear(n_emb, head_size)
self.query = nn.Linear(n_emb, head_size)
self.value = nn.Linear(n_emb, head_size)
self.dropout = nn.Dropout(0.0)
# tril: lower-triangular
self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
def forward(self, x):
batch, blocks, X = x.shape
query = self.query(x) # batch, block_size, X -- shape
key = self.key(x) # batch, block_size, X -- shape
weight = query @ key.transpose(-2, -1) * X ** -0.5 # batch, block_size, X @ batch, X, blocl_size ---> batch, block_size, block_size
weight = weight.masked_fill(self.tril[:blocks, :blocks] == 0,float('-inf'))
weight = F.softmax(weight, dim=-1)
weight = self.dropout(weight)
out = weight @ self.value(x)
return out
class MultiHeadAttention(nn.Module):
"""
multi head in self attention
"""
# nnum_head = 6
# head_size
def __init__(self, head_size, num_heads):
super().__init__()
self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
self.layer = nn.Linear(n_emb, n_emb)
self.dropout = nn.Dropout(0.0)
def forward(self, x):
out = torch.cat([h(x) for h in self.heads], dim=-1)
return self.dropout(self.layer(out))
class FeedForward(nn.Module):
def __init__(self, n_emb):
super().__init__()
self.dff = nn.Sequential(
nn.Linear(n_emb, n_emb*4),
nn.ReLU(),
nn.Linear(4*n_emb, n_emb),
nn.Dropout(0.0)
)
def forward(self, x):
return self.dff(x)
class BlockSeq(nn.Module):
def __init__(self, n_emb, num_heads):
super().__init__()
head_size = int(n_emb / num_heads)
self.mh_att = MultiHeadAttention(head_size, num_heads)
self.ff_lay = FeedForward(n_emb)
self.ln1 = nn.LayerNorm(n_emb)
self.ln2 = nn.LayerNorm(n_emb)
def forward(self, x):
x = x + self.mh_att(self.ln1(x))
x = x + self.ff_lay(self.ln2(x))
return x
class TextGenerator(nn.Module):
def __init__(self):
super().__init__()
# x = [1, 25, 89, 65,63,64]
self.lookup_token_emd_table = nn.Embedding(vocab_size, n_emb)
self.postional_encoding = nn.Embedding(block_size, n_emb)
self.blocks = nn.Sequential(*[BlockSeq(n_emb, num_heads) for _ in range(n_x)])
self.layer_norm = nn.LayerNorm(n_emb)
self.model_head = nn.Linear(n_emb, vocab_size)
def forward(self, x, y=None): #
batches, block_size_x = x.shape
out = self.lookup_token_emd_table(x) # 2, 7, 90 , x: 1,2 3
pos_enc = self.postional_encoding(torch.arange(block_size_x, device=device))
out = out + pos_enc
out = self.blocks(out)
out = self.layer_norm(out)
out = self.model_head(out)
if y is None:
loss = None
else:
batches, block_size, X = out.shape
loss = F.cross_entropy(out.view(batches*block_size, X), y.view(batches*block_size))
return out, loss
def generate(self, x, max_tokens=200):
for _ in range(max_tokens):
logits, _ = self(x[:, -block_size:])
logits = logits[:, -1, :]
# print(logits.shape)
probilities = F.softmax(logits, dim=-1) # 1, 90
next_x = torch.multinomial(probilities, num_samples=1)
x = torch.cat((x, next_x), dim=1) # [hi, ] 1 2 3
return x
model = torch.load('entire_model.pth')
import pickle
with open('meta.pkl', 'rb') as f:
meta = pickle.load(f)
stoi, itos = meta['stoi'], meta['itos']
encode = lambda s: [stoi[c] for c in s]
decode = lambda l: [itos[i] for i in l]
def reply(message, history):
# encode the beginning of the prompt
start = message
start_ids = encode(start)
x = (torch.tensor(start_ids, dtype=torch.long, device='cpu')[None, ...])
print(x)
replied = []
# run generation
with torch.no_grad():
for k in range(3):
y = model.generate(x, 200)
replied.append(''.join(decode(y[0].tolist())))
return '\n'.join(replied)
gr.Interface(reply, "text", "text", title="Poet Demo").launch() |