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app.py
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app.py
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import argparse
from functools import partial
from typing import Any, Callable
import gradio as gr
import torch
import torch.nn.functional as F
from tqdm import tqdm
from transformers import GPTNeoXTokenizerFast
from torch_compatability.GPT2 import model_getter
def parse():
parser = argparse.ArgumentParser(description="Gradio Inference App")
parser.add_argument("--model-size", default="medium", type=str)
parser.add_argument("--share", default=False, action="store_true")
parser.add_argument("--model-path", default="medium", type=str)
args = parser.parse_args()
return args
if torch.cuda.is_available():
DEVICE = "cuda"
tokenizer = GPTNeoXTokenizerFast.from_pretrained("EleutherAI/gpt-neox-20b")
def model_creator(size: str, path: str) -> torch.nn.Module:
model = model_getter(size, model_checkpoint=path)
model.to(DEVICE)
model.half()
torch.cuda.empty_cache()
model.eval()
return model
@torch.no_grad()
def generate_from_prompt(
prompt: str,
model: Any,
tokenizer: Any,
sampling_func: Callable,
logit_processor: Callable,
sample: bool,
steps: int,
device: Any,
return_on_eos: bool,
):
tokens = torch.tensor(
tokenizer.encode(prompt.strip()),
dtype=torch.long,
)
x = tokens.view(1, -1).to(device)
if x.shape[1] > model.num_ctx:
x_cond = x[:, -model.num_ctx :]
else:
x_cond = x
layer_past = None
generated_tokens = []
for _ in tqdm(range(steps), disable=True):
with torch.cuda.amp.autocast(cache_enabled=False):
logits, layer_past = model(x_cond, use_cache=True, past_states=layer_past)
logits = logit_processor(logits, generated_tokens)
logits = sampling_func(logits)
probs = F.softmax(logits, dim=-1)
if sample:
x_cond = torch.multinomial(probs, num_samples=1)
if return_on_eos:
if x_cond.item() == tokenizer.eos_token_id:
return x
x = torch.cat((x[:, :], x_cond), axis=1)
if x_cond.item() not in generated_tokens:
generated_tokens.append(x_cond.item())
else:
x_cond = torch.topk(probs, k=1).indices
if return_on_eos:
if x_cond.item() == tokenizer.eos_token_id:
return x
x = torch.cat((x[:, :], x_cond), axis=1)
yield x_cond
def process_logits(
logits: torch.tensor, generated_tokens: list, rep_pen: float, temperature: float
) -> torch.tensor:
logits = logits[:, -1, :] / temperature
for prev_gen_token in generated_tokens:
if logits[:, prev_gen_token] < 0:
logits[:, prev_gen_token] *= rep_pen
else:
logits[:, prev_gen_token] /= rep_pen
return logits
def top_k_logits(logits: torch.Tensor, k: int) -> torch.Tensor:
v, ix = torch.topk(logits, k)
out = logits.clone()
out[out < v[:, [-1]]] = -float("Inf")
return out
def top_p_logits(
logits: torch.Tensor,
top_p: float = 0.0,
filter_value: float = -float("Inf"),
) -> torch.Tensor:
"""Filter a distribution of logits using nucleus (top-p) filtering
Args:
logits: logits distribution shape (vocabulary size)
top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
"""
if top_p > 0.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
indices_to_remove = sorted_indices[sorted_indices_to_remove]
logits[:, indices_to_remove] = filter_value
return logits
def generate_text(
prompt,
steps,
temperature,
top_k,
top_p,
repetition_penalty,
sampling_choice,
eos_return,
):
if sampling_choice == "Top-k":
sampling_func = partial(top_k_logits, k=top_k)
elif sampling_choice == "Nucleus":
sampling_func = partial(top_p_logits, top_p=top_p)
elif sampling_choice == "Greedy":
sampling_func = partial(top_k_logits, k=1)
processer_partial = partial(
process_logits, rep_pen=repetition_penalty, temperature=temperature
)
text_generator = generate_from_prompt(
prompt,
model,
tokenizer,
sampling_func=sampling_func,
logit_processor=processer_partial,
sample=True,
steps=steps,
device=DEVICE,
return_on_eos=eos_return,
)
text = []
for token in text_generator:
text.append(tokenizer.decode(token.tolist()[0]))
generated_text = "".join(text)
return [
(prompt, None),
(generated_text, "Generated Text"),
]
if __name__ == "__main__":
args = parse()
model = model_creator(args.model_size, args.model_path)
# model = torch.compile(model)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
input_txt = gr.Textbox(lines=10, label="Enter your text here")
token_slider = gr.Slider(
0, 1000, value=100, label="Number of tokens to generate"
)
with gr.Accordion("Generation Parameters", open=False):
temp_slider = gr.Slider(0, 2, value=0.80, label="Temperature")
topk_slider = gr.Slider(
0,
50,
value=40,
label="k (Top-k Sampling)",
)
topp_slider = gr.Slider(
0,
1,
value=0.96,
label="p (Nucleus Sampling)",
)
rep_slider = gr.Slider(
0.0,
1.3,
value=1.2,
label="Repetition Penalty",
)
radio = gr.Dropdown(
choices=["Top-k", "Nucleus", "Greedy"],
label="Sampling Method",
value="Nucleus",
)
eos_return = gr.Checkbox(
value=True, label="Terminate generation on EOS token."
)
with gr.Column():
output_txt = gr.HighlightedText(
label="Generated Text",
combine_adjacent=True,
color_map=["Generated Text", "blue"],
)
generate_btn = gr.Button("Generate Text")
generate_btn.click(
generate_text,
[
input_txt,
token_slider,
temp_slider,
topk_slider,
topp_slider,
rep_slider,
radio,
eos_return,
],
[output_txt],
)
demo.launch()