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import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass
import tiktoken
@dataclass
class GPTConfig:
block_size: int = 256
vocab_size: int = 50257
n_layer: int = 8
n_head: int = 8
n_embd: int = 512
dropout: float = 0.05
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd)
self.attn = nn.MultiheadAttention(config.n_embd, config.n_head, dropout=config.dropout, batch_first=True)
self.ln_2 = nn.LayerNorm(config.n_embd)
self.mlp = nn.Sequential(
nn.Linear(config.n_embd, 4 * config.n_embd),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(4 * config.n_embd, config.n_embd),
nn.Dropout(config.dropout),
)
def forward(self, x):
x = x + self._attention_block(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
def _attention_block(self, x):
attn_output, _ = self.attn(x, x, x)
return attn_output
class GPT(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.transformer = nn.ModuleDict(
{
"wte": nn.Embedding(config.vocab_size, config.n_embd),
"wpe": nn.Embedding(config.block_size, config.n_embd),
"h": nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
"ln_f": nn.LayerNorm(config.n_embd),
}
)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
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, idx, targets=None):
B, T = idx.size()
assert T <= self.config.block_size, f"Sequence length {T} exceeds block size {self.config.block_size}."
pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
tok_emb = self.transformer.wte(idx)
pos_emb = self.transformer.wpe(pos)
x = tok_emb + pos_emb
for block in self.transformer.h:
x = block(x)
x = self.transformer.ln_f(x)
logits = self.lm_head(x)
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), label_smoothing=0.05)
return logits, loss
def load_model(model_path):
"""Load the trained model"""
try:
checkpoint = torch.load(model_path, map_location=torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
# Create config from the saved dictionary
config_dict = checkpoint['config']
if isinstance(config_dict, dict):
config = GPTConfig(**config_dict.__dict__) # Convert dataclass to dict
else:
config = config_dict # If config was already saved as GPTConfig instance
model = GPT(config)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
return model
except Exception as e:
print(f"Error loading model: {e}")
return None
def generate_text(model, prompt, max_new_tokens=50, temperature=0.8, top_k=40):
"""Generate text based on a prompt
Args:
model: The GPT model
prompt (str): Input text to continue from
max_new_tokens (int): Maximum number of tokens to generate
temperature (float): Higher values produce more diverse text (default: 0.8)
top_k (int): Number of highest probability tokens to consider (default: 40)
Returns:
str: Generated text including the original prompt
"""
try:
# Initialize tokenizer and encode prompt
enc = tiktoken.get_encoding("gpt2")
input_ids = torch.tensor(enc.encode(prompt)).unsqueeze(0)
# Move to same device as model
device = next(model.parameters()).device
input_ids = input_ids.to(device)
# Generate tokens
with torch.no_grad():
generated_tokens = []
for _ in range(max_new_tokens):
# Truncate if sequence length exceeds block size
if input_ids.size(1) > model.config.block_size:
input_ids = input_ids[:, -model.config.block_size:]
# Get predictions from model
logits, _ = model(input_ids)
logits = logits[:, -1, :] # Get last token's logits
# Apply temperature scaling
logits = logits / temperature
# Apply top-k filtering
if top_k > 0:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = float('-inf')
# Sample next token
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
# Append the token and continue generating
generated_tokens.append(next_token.item())
input_ids = torch.cat((input_ids, next_token), dim=1)
# Decode the generated tokens
output_text = prompt + enc.decode(generated_tokens)
return output_text
except Exception as e:
print(f"Error during text generation: {str(e)}")
return prompt
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