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import torch
import tiktoken
from model import GPT, GPTConfig
import gradio as gr
from torch.nn import functional as F
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
device = "mps"
# STOP
num_return_sequences = 1
# max_length = 100
model = GPT(GPTConfig())
model.to(device)
model.load_state_dict(torch.load('./checkpoints/final_model.pth', map_location=device))
# Set the model to evaluation mode
model.eval()
def generate(text, max_length):
enc = tiktoken.get_encoding("gpt2")
tokens = enc.encode(text)
tokens = torch.tensor(tokens)
tokens = torch.tensor(tokens, dtype= torch.long) # (len,) #check tiktoken app
tokens = tokens.unsqueeze(0).repeat(num_return_sequences, 1) # (1, len)
x = tokens.to(device)
while x.size(1) < max_length:
# forward the model to get the logits
with torch.no_grad():
logits = model(x)[0] # (B, T, vocab_size)
# take the logits at the last position
logits = logits[:, -1, :] # (B, vocab_size)
# get the probabilities
probs = F.softmax(logits, dim=-1)
# do top-k sampling of 50 (huggingface pipeline default)
# topk_probs here becomes (5, 50), topk_indices is (5, 50)
topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
# select a token from the top-k probabilities
# note: multinomial does not demand the input to sum to 1
ix = torch.multinomial(topk_probs, 1) # (B, 1)
# gather the corresponding indices
xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
# append to the sequence
x = torch.cat((x, xcol), dim=1)
# print the generated text
for i in range(num_return_sequences):
tokens = x[i, :max_length].tolist()
decoded = enc.decode(tokens)
return decoded
title = "Shakespeare Poem generation using GPT - 121M Model."
description = "A simple Gradio interface to demo genaration of shakespeare poem."
examples = [["Let us kill him, and we'll have corn at our own price."],
["Would you proceed especially against Caius Marcius?"],
["Nay, but speak not maliciously."]]
demo = gr.Interface(
generate,
inputs=[
gr.TextArea(label="Enter text"),
gr.Slider(10, 100, value = 10, step=1, label="Token Length"),
],
outputs=[
gr.TextArea(label="Generated Text")
],
title=title,
description=description,
examples=examples,
cache_examples=False,
live=True
)
demo.launch() |