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import torch
import gradio as gr
import torch.nn as nn
import pickle
import time
# Load precomputed text or read from file if it doesn't exist
try:
# Try to load precomputed text and encoded tensor
with open('/kaggle/input/precomputed-stories/precomputed_text.pkl', 'rb') as f:
text = pickle.load(f)
data = torch.load('/kaggle/input/precomputed-stories/precomputed_data.pt')
print("Loaded precomputed data.")
except FileNotFoundError:
# If the precomputed data doesn't exist, read the text and encode it
print("Precomputed data not found. Reading and processing the text file...")
start_time = time.time()
# Read text file
with open('/kaggle/input/long-discord/messages.txt', 'r', encoding='utf-8') as f:
text = f.read()
# Save precomputed text
with open('precomputed_text.pkl', 'wb') as f:
pickle.dump(text, f)
# Encode text
chars = sorted(set(text))
vocab_size = len(chars)
string_to_int = {ch: i for i, ch in enumerate(chars)}
encode = lambda s: [string_to_int[c] for c in s]
encoded_text = encode(text)
# Convert to tensor
data = torch.tensor(encoded_text, dtype=torch.long)
# Save the tensor for future use
torch.save(data, 'precomputed_data.pt')
end_time = time.time()
print(f"Processed and saved data in {end_time - start_time:.4f} seconds.")
# Split data for training and validation
n = int(0.8 * len(data))
train_data = data[:n]
val_data = data[n:]
print("Data is ready for model initialization.")
import torch.nn as nn
from torch.nn import functional as F
chars = sorted(set(text))
vocab_size = len(chars)
n_embd = 384
n_head = 4
n_layer = 4
block_size = 128
dropout = 0.2
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
string_to_int = { ch:i for i,ch in enumerate(chars) }
int_to_string = { i:ch for i,ch in enumerate(chars) }
encode = lambda s: [string_to_int[c] for c in s]
decode = lambda l: ''.join([int_to_string[i] for i in l])
class FeedFoward(nn.Module):
""" a simple linear layer followed by a non-linearity """
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 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)))
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)
wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
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
# [1, 0, 0]
# [1, 0.6, 0]
# [1, 0.6, 0.4]
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):
out = torch.cat([h(x) for h in self.heads], dim=-1) # (B, T, F) -> (B, T, [h1, h1, h1, h1, h2, h2, h2, h2, h3, h3, h3, h3])
out = self.dropout(self.proj(out))
return out
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 GPTLanguageModel(nn.Module):
def __init__(self, vocab_size):
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) # final layer norm
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=device)) # (T,C)
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
model = GPTLanguageModel(vocab_size)
model.load_state_dict(torch.load( "/kaggle/input/longtext/transformers/default/1/longtext.pth", weights_only=True, map_location=device)["modelState"])
model.to(device)
print('loaded successfully!')
prompt = 'i look around and the world is strange'
context = torch.tensor(encode(prompt), dtype=torch.long, device=device)
generated_chars = decode(model.generate(context.unsqueeze(0), max_new_tokens=200)[0].tolist())
print(generated_chars)
if __name__ == "__main__":
gr.Interface(fn=main, inputs=[gr.Textbox(label='Starting context'), gr.Number(label="Maximum output tokens")], outputs=[gr.Textbox(label="Response:")], title="mattGPT", article="I TELL STORIES").launch()
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