Spaces:
Sleeping
Sleeping
File size: 6,032 Bytes
0c94512 921561a 0c94512 37bc9d5 0c94512 100d65e 0c94512 972725f 921561a 0c94512 921561a 0c94512 37bc9d5 0c94512 f1387d1 921561a 0c94512 921561a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
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
import spaces
# Configuration class (same as in training)
@dataclass
class GPTConfig:
block_size: int = 1024
vocab_size: int = 50257
n_layer: int = 12
n_head: int = 12
n_embd: int = 768
# 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())
# Load the state dict with weights_only=True and remove the _orig_mod prefix
state_dict = torch.load('nano_gpt_model.pt', map_location=device, weights_only=True)
new_state_dict = {}
for key in state_dict.keys():
new_key = key.replace('_orig_mod.', '')
new_state_dict[new_key] = state_dict[key]
model.load_state_dict(new_state_dict)
model.to(device)
model.eval()
return model, device
# Text generation function
@spaces.GPU(enable_queue=True)
def generate_text(prompt, num_tokens, 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()
# Create the Gradio interface
demo = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(label="Enter your prompt", value="Thou shalt"),
gr.Slider(minimum=1, maximum=1024, value=100, 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="GPT Text Generator",
description="Generate Shakespeare-style text using a trained GPT model",
allow_flagging="never",
cache_examples=True
)
if __name__ == "__main__":
demo.launch() |