import spaces import torch import gradio as gr from transformers import AutoTokenizer, AutoModelForCausalLM # Load models implicit_cot_model_name = 'yuntian-deng/gpt2-implicit-cot-multiplication-20-digits' implicit_cot_model = AutoModelForCausalLM.from_pretrained(implicit_cot_model_name) tokenizer = AutoTokenizer.from_pretrained(implicit_cot_model_name) no_cot_model_name = 'yuntian-deng/gpt2-no-cot-multiplication' no_cot_model = AutoModelForCausalLM.from_pretrained(no_cot_model_name) explicit_cot_model_name = 'yuntian-deng/gpt2-explicit-cot-multiplication-20-digits' explicit_cot_model = AutoModelForCausalLM.from_pretrained(explicit_cot_model_name) models = {'implicit': implicit_cot_model, 'no': no_cot_model, 'explicit': explicit_cot_model} [model.to('cuda' if torch.cuda.is_available() else 'cpu') for model in models.values()] [model.eval() for model in models.values()] # Constants #MAX_PRODUCT_DIGITS_PER_MODEL = {'implicit': 100, 'no': 100, 'explicit': 960} MAX_PRODUCT_DIGITS_PER_MODEL = {'implicit': 100, 'no': 100, 'explicit': 1070} def preprocess(num): num = str(num).strip().replace(' ', '') reversed_num = ' '.join(num[::-1]) return reversed_num def postprocess(raw_output): prediction = raw_output.replace(' ', '')[::-1] return prediction @spaces.GPU def predict_product(num1, num2): input_text = f'{preprocess(num1)} * {preprocess(num2)} =' inputs = tokenizer(input_text, return_tensors='pt').to('cuda' if torch.cuda.is_available() else 'cpu') [model.to('cuda' if torch.cuda.is_available() else 'cpu') for model in models.values()] input_ids = inputs['input_ids'] input_len = input_ids.shape[-1] prediction = "" ground_truth_product = "" valid_input = True try: num1_int = int(num1) num2_int = int(num2) ground_truth_product = str(num1_int * num2_int) ground_truth_digits_reversed = list(ground_truth_product)[::-1] except ValueError: valid_input = False generated_ids_per_model = {model_name: inputs['input_ids'].data.clone() for model_name in models} finished_per_model = {model_name: False for model_name in models} past_key_values_per_model = {model_name: None for model_name in models} predicted_annotations_per_model = {} try: for step in range(max(MAX_PRODUCT_DIGITS_PER_MODEL.values())): # Set a maximum limit to prevent infinite loops # Ground Truth if not valid_input: ground_truth_annotations = [('Invalid Input!', None)] else: ground_truth_annotations = [(ground_truth_digit, None) for ground_truth_digit in ground_truth_digits_reversed[:step+1]] ground_truth_annotations = ground_truth_annotations[::-1] # Predicted for model_name in models: model = models[model_name] if finished_per_model[model_name]: continue if step >= MAX_PRODUCT_DIGITS_PER_MODEL[model_name]: continue generation_kwargs = { 'input_ids': generated_ids_per_model[model_name], 'max_new_tokens': 1, 'do_sample': False, 'past_key_values': past_key_values_per_model[model_name], 'return_dict_in_generate': True, 'use_cache': True } if step == 0: del generation_kwargs['past_key_values'] outputs = model.generate(**generation_kwargs) generated_ids = outputs.sequences next_token_id = generated_ids[0, -1] #print (next_token_id) if next_token_id.item() == tokenizer.eos_token_id: finished_per_model[model_name] = True if valid_input: if len([item for item in predicted_annotations_per_model[model_name] if item[1] is not None]) < len(ground_truth_digits_reversed): predicted_annotations_per_model[model_name].insert(0, ('⠀', 'wrong')) continue generated_ids_per_model[model_name] = generated_ids past_key_values_per_model[model_name] = outputs.past_key_values output_text = tokenizer.decode(generated_ids[0, input_len:], skip_special_tokens=True) predicted_digits_reversed = output_text.strip().split(' ') predicted_annotations = [] is_correct_sofar = True if model_name == 'explicit': if '=' not in predicted_digits_reversed: predicted_annotations = [(predicted_digit, None) for predicted_digit in predicted_digits_reversed] predicted_digits_reversed = [] else: equal_sign_position = predicted_digits_reversed.index('=') predicted_annotations = [(predicted_digit, None) for predicted_digit in predicted_digits_reversed[:equal_sign_position+1]] predicted_digits_reversed = predicted_digits_reversed[equal_sign_position+1:] for i in range(len(predicted_digits_reversed)): predicted_digit = predicted_digits_reversed[i] if not valid_input: is_correct_digit = None elif i >= len(ground_truth_digits_reversed): if predicted_digit == '0' and is_correct_sofar: is_correct_digit = True else: is_correct_digit = False else: ground_truth_digit = ground_truth_digits_reversed[i] if predicted_digit == ground_truth_digit: is_correct_digit = True else: is_correct_digit = False if not is_correct_digit: is_correct_sofar = False if is_correct_digit is None: predicted_annotations.append((predicted_digit, None)) elif is_correct_digit: predicted_annotations.append((predicted_digit, "correct")) else: predicted_annotations.append((predicted_digit, "wrong")) predicted_annotations = predicted_annotations[::-1] predicted_annotations_per_model[model_name] = predicted_annotations predicted_annotations_implicit_cot = predicted_annotations_per_model['implicit'] predicted_annotations_nocot = predicted_annotations_per_model['no'] predicted_annotations_explicit_cot = predicted_annotations_per_model['explicit'] yield ground_truth_annotations, predicted_annotations_implicit_cot, predicted_annotations_nocot, predicted_annotations_explicit_cot except Exception as e: pass color_map = {"correct": "green", "wrong": "red"} demo = gr.Interface( fn=predict_product, inputs=[ gr.Textbox(label='First Number (up to 20 digits)', value='12345678912345678912'), gr.Textbox(label='Second Number (up to 20 digits)', value='98765432198765432198'), ], outputs=[ gr.HighlightedText(label='Ground Truth Product', combine_adjacent=False, show_legend=False, color_map=color_map), gr.HighlightedText(label='Implicit CoT Prediction (Ours)', combine_adjacent=False, show_legend=False, color_map=color_map, show_inline_category=False), gr.HighlightedText(label='No CoT Prediction', combine_adjacent=False, show_legend=False, color_map=color_map, show_inline_category=False), gr.HighlightedText(label='Explicit CoT Steps & Prediction', combine_adjacent=False, show_legend=False, color_map=color_map, show_inline_category=False), ], title='Predicting Multiplication with GPT-2: Implicit vs. Explicit CoT', description='This demo showcases GPT-2\'s ability to directly predict the product of two large numbers without intermediate steps, using our stepwise internalization method. Compare the performance of implicit CoT (our method), no CoT, and explicit CoT. Implicit CoT offers accuracy and speed, while explicit CoT provides detailed reasoning but is slower.', article=""" - [Paper 1: Implicit Chain of Thought Reasoning via Knowledge Distillation](https://arxiv.org/pdf/2311.01460) - [Paper 2: From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step](https://arxiv.org/pdf/2405.14838) - [Code Repository](https://github.com/da03/Internalize_CoT_Step_by_Step) - [Tweet Announcement](https://twitter.com/yuntiandeng/status/1795854740879774036) """, clear_btn=None, submit_btn="Multiply!", live=False, concurrency_limit=1 ) demo.queue(max_size=20).launch()