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import gradio as gr
import torch
import tiktoken
import math
class LayerNorm(torch.nn.Module):
def __init__(self, ndim, bias):
super().__init__()
self.weight = torch.nn.Parameter(torch.ones(ndim))
self.bias = torch.nn.Parameter(torch.zeros(ndim)) if bias else None
def forward(self, input):
return torch.nn.functional.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
class CausalSelfAttention(torch.nn.Module):
def __init__(self, config):
super().__init__()
assert config["emb_dim"] % config["n_heads"] == 0
self.c_attn = torch.nn.Linear(config["emb_dim"], 3 * config["emb_dim"], bias=config["qkv_bias"])
self.c_proj = torch.nn.Linear(config["emb_dim"], config["emb_dim"], bias=True)
self.attn_dropout = torch.nn.Dropout(config["drop_rate"])
self.resid_dropout = torch.nn.Dropout(config["drop_rate"])
self.n_heads = config["n_heads"]
self.n_embd = config["emb_dim"]
self.dropout = config["drop_rate"]
self.register_buffer("bias", torch.tril(torch.ones(config["context_length"], config["context_length"]))
.view(1, 1, config["context_length"], config["context_length"]))
def forward(self, x):
B, T, C = x.size()
q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
k = k.view(B, T, self.n_heads, C // self.n_heads).transpose(1, 2)
q = q.view(B, T, self.n_heads, C // self.n_heads).transpose(1, 2)
v = v.view(B, T, self.n_heads, C // self.n_heads).transpose(1, 2)
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
att = torch.nn.functional.softmax(att, dim=-1)
att = self.attn_dropout(att)
y = att @ v
y = y.transpose(1, 2).contiguous().view(B, T, C)
y = self.resid_dropout(self.c_proj(y))
return y
class MLP(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.c_fc = torch.nn.Linear(config["emb_dim"], 4 * config["emb_dim"], bias=True)
self.gelu = torch.nn.GELU()
self.c_proj = torch.nn.Linear(4 * config["emb_dim"], config["emb_dim"], bias=True)
self.dropout = torch.nn.Dropout(config["drop_rate"])
def forward(self, x):
x = self.c_fc(x)
x = self.gelu(x)
x = self.c_proj(x)
x = self.dropout(x)
return x
class Block(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = LayerNorm(config["emb_dim"], bias=True)
self.attn = CausalSelfAttention(config)
self.ln_2 = LayerNorm(config["emb_dim"], bias=True)
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 GPTModel(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.transformer = torch.nn.ModuleDict(dict(
wte = torch.nn.Embedding(config["vocab_size"], config["emb_dim"]),
wpe = torch.nn.Embedding(config["context_length"], config["emb_dim"]),
drop = torch.nn.Dropout(config["drop_rate"]),
h = torch.nn.ModuleList([Block(config) for _ in range(config["n_layers"])]),
ln_f = LayerNorm(config["emb_dim"], bias=True)
))
self.lm_head = torch.nn.Linear(config["emb_dim"], 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, torch.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, torch.nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, idx, targets=None):
device = idx.device
b, t = idx.size()
pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0)
tok_emb = self.transformer.wte(idx)
pos_emb = self.transformer.wpe(pos)
x = self.transformer.drop(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 = torch.nn.functional.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
return logits, loss
def generate_text_simple(model, idx, max_new_tokens, context_size):
for _ in range(max_new_tokens):
idx_cond = idx[:, -context_size:]
logits, _ = model(idx_cond)
logits = logits[:, -1, :]
probs = torch.nn.functional.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, idx_next), dim=1)
return idx
# Load model configuration
GPT_CONFIG_124M = {
"vocab_size": 50257,
"context_length": 1024,
"emb_dim": 768,
"n_heads": 12,
"n_layers": 12,
"drop_rate": 0.1,
"qkv_bias": False
}
# Initialize model
model = GPTModel(GPT_CONFIG_124M)
# Load the trained weights
model.load_state_dict(torch.load("my_gpt_model.pth", map_location=torch.device('cpu')))
model.eval()
tokenizer = tiktoken.get_encoding("gpt2")
def generate(prompt, max_new_tokens):
token_ids = tokenizer.encode(prompt)
input_ids = torch.tensor(token_ids).unsqueeze(0)
output_ids = generate_text_simple(
model=model,
idx=input_ids,
max_new_tokens=max_new_tokens,
context_size=GPT_CONFIG_124M["context_length"]
)
return tokenizer.decode(output_ids.squeeze(0).tolist())
iface = gr.Interface(
fn=generate,
inputs=[
gr.Textbox(label="Prompt"),
gr.Slider(minimum=1, maximum=100, value=20, step=1, label="Max New Tokens")
],
outputs=gr.Textbox(label="Generated Text"),
title="SamGPT Text Generation",
description="Enter a prompt to generate text with the custom language model."
)
iface.launch() |