import torch import numpy as np import gradio as gr from random import sample from detoxify import Detoxify from datasets import load_dataset from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM from transformers import BloomTokenizerFast, BloomForCausalLM DATASET = "allenai/real-toxicity-prompts" CHECKPOINTS = { "DistilGPT2 by HuggingFace 🤗" : "distilgpt2", "GPT-Neo 125M by EleutherAI 🤖" : "EleutherAI/gpt-neo-125M", "BLOOM 560M by BigScience 🌸" : "bigscience/bloom-560m" } MODEL_CLASSES = { "DistilGPT2 by HuggingFace 🤗" : (GPT2LMHeadModel, GPT2Tokenizer), "GPT-Neo 125M by EleutherAI 🤖" : (GPTNeoForCausalLM, GPT2Tokenizer), "BLOOM 560M by BigScience 🌸" : (BloomForCausalLM, BloomTokenizerFast), } def load_model(model_name): model_class, tokenizer_class = MODEL_CLASSES[model_name] model_path = CHECKPOINTS[model_name] model = model_class.from_pretrained(model_path) tokenizer = tokenizer_class.from_pretrained(model_path) tokenizer.pad_token = tokenizer.eos_token model.config.pad_token_id = model.config.eos_token_id model.eval() return model, tokenizer MAX_LENGTH = int(10000) # Hardcoded max length to avoid infinite loop def set_seed(seed, n_gpu): np.random.seed(seed) torch.manual_seed(seed) if n_gpu > 0: torch.cuda.manual_seed_all(seed) def adjust_length_to_model(length, max_sequence_length): if length < 0 and max_sequence_length > 0: length = max_sequence_length elif 0 < max_sequence_length < length: length = max_sequence_length # No generation bigger than model size elif length < 0: length = MAX_LENGTH # avoid infinite loop return length def generate(model_name, input_sentence, length = 75, temperature = 0.7, top_k = 50, top_p = 0.95, seed = 42, no_cuda = False, num_return_sequences = 1, stop_token = '.' ): # load device #if not no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not no_cuda else "cpu") n_gpu = 0 if no_cuda else torch.cuda.device_count() # Set seed set_seed(seed, n_gpu) # Load model model, tokenizer = load_model(model_name) model.to(device) #length = adjust_length_to_model(length, max_sequence_length=model.config.max_position_embeddings) # Tokenize input encoded_prompt = tokenizer.encode(input_sentence, add_special_tokens=False, return_tensors='pt') encoded_prompt = encoded_prompt.to(device) input_ids = encoded_prompt # Generate output output_sequences = model.generate(input_ids=input_ids, max_length=length + len(encoded_prompt[0]), temperature=temperature, top_k=top_k, top_p=top_p, do_sample=True, num_return_sequences=num_return_sequences ) generated_sequences = list() for generated_sequence_idx, generated_sequence in enumerate(output_sequences): generated_sequence = generated_sequence.tolist() text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True) #remove prompt text = text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :] #remove all text after last occurence of stop_token text = text[:text.rfind(stop_token)+1] generated_sequences.append(text) return generated_sequences[0] def prepare_dataset(dataset): dataset = load_dataset(dataset, split='train') return dataset def load_prompts(dataset): prompts = [dataset[i]['prompt']['text'] for i in range(len(dataset))] return prompts def random_sample(prompt_list): random_sample = sample(prompt_list,10) return random_sample def show_dataset(dataset): raw_data = prepare_dataset(dataset) prompts = load_prompts(raw_data) return (gr.update(choices=random_sample(prompts), label='You can find below a random subset from the RealToxicityPrompts dataset', visible=True), gr.update(visible=True), prompts, ) def update_dropdown(prompts): return gr.update(choices=random_sample(prompts)) def show_text(text): new_text = "lol " + text return gr.update(visible = True, value=new_text) def process_user_input(model, input): warning = 'Please enter a valid prompt.' if input == None: input = warning generated = generate(model, input) return ( gr.update(visible = True, value=generated), gr.update(visible=True) ) def pass_to_textbox(input): return gr.update(value=input) def run_detoxify(text): results = Detoxify('original').predict(text) json_ready_results = {cat:float(score) for (cat,score) in results.items()} return gr.update(value=json_ready_results, visible=True) with gr.Blocks() as demo: gr.Markdown("# Project Interface proposal") dataset = gr.Variable(value=DATASET) prompts_var = gr.Variable(value=None) with gr.Row(equal_height=True): with gr.Column(): gr.Markdown("### 1. Select a prompt") input_text = gr.Textbox(label="Write your prompt below.", interactive=True) gr.Markdown("— or —") inspo_button = gr.Button('Click here if you need some inspiration') prompts_drop = gr.Dropdown(visible=False) prompts_drop.change(fn=pass_to_textbox, inputs=prompts_drop, outputs=input_text) randomize_button = gr.Button('Show another subset', visible=False) inspo_button.click(fn=show_dataset, inputs=dataset, outputs=[prompts_drop, randomize_button, prompts_var]) randomize_button.click(fn=update_dropdown, inputs=prompts_var, outputs=prompts_drop) with gr.Column(): gr.Markdown("### 2. Evaluate output") generate_button = gr.Button('Pick a model below and submit your prompt') model_radio = gr.Radio(choices=list(CHECKPOINTS.keys()), label='Model', interactive=True) model_choice = gr.Variable(value=None) model_radio.change(fn=lambda value: value, inputs=model_radio, outputs=model_choice) output_text = gr.Textbox(label="Generated prompt.", visible=False) toxi_button = gr.Button("Run a toxicity analysis of the model's output", visible=False) toxi_scores = gr.JSON(visible=False) generate_button.click(fn=process_user_input, inputs=[model_choice, input_text], outputs=[output_text,toxi_button]) toxi_button.click(fn=run_detoxify, inputs=output_text, outputs=toxi_scores) #demo.launch(debug=True) if __name__ == "__main__": demo.launch(enable_queue=False)