File size: 4,063 Bytes
3c3eabb 5204c67 3c3eabb 92dde49 3c3eabb f026dba ac2cf21 5204c67 ac2cf21 92dde49 ac2cf21 5204c67 ac2cf21 92dde49 ac2cf21 edce2eb ac2cf21 5204c67 ac2cf21 5204c67 edce2eb 5204c67 ac2cf21 5204c67 ac2cf21 5204c67 ac2cf21 5204c67 e2ed84a 5204c67 ade3ffb ac2cf21 ade3ffb 92dde49 5204c67 ade3ffb ac2cf21 3c3eabb 5204c67 |
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 |
import gradio as gr
from transformers import GPT2Tokenizer, AutoModelForCausalLM
import numpy as np
from transformers_cfg.grammar_utils import IncrementalGrammarConstraint
from transformers_cfg.generation.logits_process import GrammarConstrainedLogitsProcessor
MODEL_NAME = "gpt2"
if __name__ == "__main__":
# Define your model and your tokenizer
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = tokenizer.eos_token_id
model.config.pad_token_id = model.config.eos_token_id
# Load json grammar
with open("json_minimal.ebnf", "r") as file:
grammar_str = file.read()
grammar = IncrementalGrammarConstraint(grammar_str, "root", tokenizer)
grammar_processor = GrammarConstrainedLogitsProcessor(grammar)
# Define your color-coding labels; if prob > x, then label = y; Sorted in descending probability order!
probs_to_label = [
(0.1, "p >= 10%"),
(0.01, "p >= 1%"),
(1e-20, "p < 1%"),
]
label_to_color = {
"p >= 10%": "green",
"p >= 1%": "yellow",
"p < 1%": "red"
}
def get_tokens_and_labels(prompt):
"""
Given the prompt (text), return a list of tuples (decoded_token, label)
"""
inputs = tokenizer([prompt], return_tensors="pt")
outputs = model.generate(
**inputs, max_new_tokens=20, return_dict_in_generate=True, output_scores=True, logits_processor=[grammar_processor]
)
# Important: don't forget to set `normalize_logits=True` to obtain normalized probabilities (i.e. sum(p) = 1)
transition_scores = model.compute_transition_scores(outputs.sequences, outputs.scores, normalize_logits=True)
transition_proba = np.exp(transition_scores)
# We only have scores for the generated tokens, so pop out the prompt tokens
input_length = 1 if model.config.is_encoder_decoder else inputs.input_ids.shape[1]
generated_tokens = outputs.sequences[:, input_length:]
# Initialize the highlighted output with the prompt, which will have no color label
highlighted_out = [(tokenizer.decode(token), None) for token in inputs.input_ids]
# Get the (decoded_token, label) pairs for the generated tokens
for token, proba in zip(generated_tokens[0], transition_proba[0]):
this_label = None
assert 0. <= proba <= 1.0
for min_proba, label in probs_to_label:
if proba >= min_proba:
this_label = label
break
highlighted_out.append((tokenizer.decode(token), this_label))
return highlighted_out
demo = gr.Blocks()
with demo:
gr.Markdown(
"""
# 🌈 Color Coded Text Generation 🌈
This is a demo of how you can obtain the probabilities of each generated token, and use them to
color code the model output.
Feel free to clone this demo and modify it to your needs 🤗
Internally, it relies on [`compute_transition_scores`](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.compute_transition_scores),
which was added in `transformers` v4.26.0.
"""
)
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", lines=3, value="This is a valid json string for http request:")
button = gr.Button(f"Generate with {MODEL_NAME}, using sampling!")
with gr.Column():
highlighted_text = gr.HighlightedText(
label="Highlighted generation",
combine_adjacent=True,
show_legend=True,
).style(color_map=label_to_color)
button.click(get_tokens_and_labels, inputs=prompt, outputs=highlighted_text)
if __name__ == "__main__":
demo.launch()
|