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import torch
import torch.nn.functional as F
import gradio as gr
import tiktoken
from dataclasses import dataclass
import torch.nn as nn
import math
import inspect
# Configuration class (same as in training)
@dataclass
class GPTConfig:
block_size: int = 512
vocab_size: int = 50304
n_layer: int = 8
n_head: int = 8
n_embd: int = 384
# Model architecture classes (copied from training notebook)
class CausalSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
assert config.n_embd % config.n_head == 0
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
self.c_proj = nn.Linear(config.n_embd, config.n_embd)
self.c_proj.NANGPT_SCALE_INIT = 1
self.n_head = config.n_head
self.n_embd = config.n_embd
self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size)).view(1, 1, config.block_size, config.block_size))
def forward(self, x):
B, T, C = x.size()
qkv = self.c_attn(x)
q, k, v = qkv.split(self.n_embd, dim=2)
k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
y = y.transpose(1, 2).contiguous().view(B, T, C)
y = self.c_proj(y)
return y
class MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
self.gelu = nn.GELU(approximate='tanh')
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
self.c_proj.NANOGPT_SCALE_INIT = 1
def forward(self, x):
x = self.c_fc(x)
x = self.gelu(x)
x = self.c_proj(x)
return x
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd)
self.attn = CausalSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.n_embd)
self.mlp = MLP(config)
def forward(self, x):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class GPT(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.gradient_checkpointing = True
self.transformer = nn.ModuleDict(dict(
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.transformer.wte.weight = self.lm_head.weight
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
std = 0.02
if hasattr(module, 'NANGPT_SCALE_INIT'):
std *= (2 * self.config.n_layer) ** -0.5
torch.nn.init.normal_(module.weight, mean=0.0, std=std)
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"Cannot forward sequence of length {T}, block size is only {self.config.block_size}"
pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
pos_emb = self.transformer.wpe(pos)
tok_emb = self.transformer.wte(idx)
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)
return logits, None if targets is None else F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
# Initialize model and load weights
def load_model():
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = GPT(GPTConfig())
model.load_state_dict(torch.load('nano_gpt_model.pt', map_location=device))
model.to(device)
model.eval()
return model, device
# Text generation function
def generate_text(prompt, num_tokens, model, device, temperature=0.8):
enc = tiktoken.get_encoding('gpt2')
x = torch.tensor([enc.encode(prompt)], dtype=torch.long, device=device)
with torch.no_grad():
while x.size(1) < num_tokens:
with torch.autocast(device_type=device, dtype=torch.bfloat16):
logits = model(x)[0]
logits = logits[:, -1, :] / temperature
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
x = torch.cat([x, next_token], dim=1)
decoded = enc.decode(x[0].tolist())
return decoded
# Load the model globally
model, device = load_model()
# Gradio interface
def gradio_interface(prompt, num_tokens, temperature):
return generate_text(prompt, num_tokens, model, device, temperature)
# Create the Gradio interface
iface = gr.Interface(
fn=gradio_interface,
inputs=[
gr.Textbox(label="Enter your prompt", value="Once upon a time"),
gr.Slider(minimum=1, maximum=100, value=50, step=1, label="Number of tokens to generate"),
gr.Slider(minimum=0.1, maximum=2.0, value=0.8, step=0.1, label="Temperature (higher = more random)")
],
outputs=gr.Textbox(label="Generated Text"),
title="NanoGPT Text Generator",
description="Generate Shakespeare-style text using a trained NanoGPT model",
)
if __name__ == "__main__":
iface.launch() |