import os import sys import json import time import openai import pickle import argparse import requests from tqdm import tqdm import torch from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizer from fastchat.model import load_model, get_conversation_template, add_model_args from nltk.tag.mapping import _UNIVERSAL_TAGS import gradio as gr uni_tags = list(_UNIVERSAL_TAGS) uni_tags[-1] = 'PUNC' bio_tags = ['B', 'I', 'O'] chunk_tags = ['ADJP', 'ADVP', 'CONJP', 'INTJ', 'LST', 'NP', 'O', 'PP', 'PRT', 'SBAR', 'UCP', 'VP'] syntags = ['NP', 'S', 'VP', 'ADJP', 'ADVP', 'SBAR', 'TOP', 'PP', 'POS', 'NAC', "''", 'SINV', 'PRN', 'QP', 'WHNP', 'RB', 'FRAG', 'WHADVP', 'NX', 'PRT', 'VBZ', 'VBP', 'MD', 'NN', 'WHPP', 'SQ', 'SBARQ', 'LST', 'INTJ', 'X', 'UCP', 'CONJP', 'NNP', 'CD', 'JJ', 'VBD', 'WHADJP', 'PRP', 'RRC', 'NNS', 'SYM', 'CC'] openai.api_key = "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" # determinant vs. determiner # https://wikidiff.com/determiner/determinant ents_prompt = ['Noun','Verb','Adjective','Adverb','Preposition/Subord','Coordinating Conjunction',# 'Cardinal Number', 'Determiner', 'Noun Phrase','Verb Phrase','Adjective Phrase','Adverb Phrase','Preposition Phrase','Conjunction Phrase','Coordinate Phrase','Quantitave Phrase','Complex Nominal', 'Clause','Dependent Clause','Fragment Clause','T-unit','Complex T-unit',# 'Fragment T-unit', ][7:] ents = ['NN', 'VB', 'JJ', 'RB', 'IN', 'CC', 'DT', 'NP', 'VP', 'ADJP', 'ADVP', 'PP', 'CONJP', 'CP', 'QP', 'CN', 'C', 'DC', 'FC', 'T', 'CT'][7:] ents_prompt_uni_tags = ['Verb', 'Noun', 'Pronoun', 'Adjective', 'Adverb', 'Preposition and Postposition', 'Coordinating Conjunction', 'Determiner', 'Cardinal Number', 'Particles or other function words', 'Words that cannot be assigned a POS tag', 'Punctuation'] ents = uni_tags + ents ents_prompt = ents_prompt_uni_tags + ents_prompt for i, j in zip(ents, ents_prompt): print(i, j) # raise model_mapping = { # 'gpt3': 'gpt-3', 'gpt3.5': 'gpt-3.5-turbo-0613', 'vicuna-7b': 'lmsys/vicuna-7b-v1.3', 'vicuna-13b': 'lmsys/vicuna-13b-v1.3', 'vicuna-33b': 'lmsys/vicuna-33b-v1.3', 'fastchat-t5': 'lmsys/fastchat-t5-3b-v1.0', # 'llama2-7b': 'meta-llama/Llama-2-7b-hf', # 'llama2-13b': 'meta-llama/Llama-2-13b-hf', # 'llama2-70b': 'meta-llama/Llama-2-70b-hf', 'llama-7b': './llama/hf/7B', 'llama-13b': './llama/hf/13B', 'llama-30b': './llama/hf/30B', # 'llama-65b': './llama/hf/65B', 'alpaca': './alpaca-7B', # 'koala-7b': 'koala-7b', # 'koala-13b': 'koala-13b', } for m in model_mapping.keys(): for eid, ent in enumerate(ents): os.makedirs(f'result/prompt1_qa/{m}/ptb/per_ent/{ent}', exist_ok=True) os.makedirs(f'result/prompt2_instruction/pos_tagging/{m}/ptb', exist_ok=True) os.makedirs(f'result/prompt2_instruction/chunking/{m}/ptb', exist_ok=True) os.makedirs(f'result/prompt2_instruction/parsing/{m}/ptb', exist_ok=True) os.makedirs(f'result/prompt3_structured_prompt/pos_tagging/{m}/ptb', exist_ok=True) os.makedirs(f'result/prompt3_structured_prompt/chunking/{m}/ptb', exist_ok=True) os.makedirs(f'result/prompt3_structured_prompt/parsing/{m}/ptb', exist_ok=True) #s = int(sys.argv[1]) #e = int(sys.argv[2]) #s = 0 #e = 1000 with open('sample_uniform_1k_2.txt', 'r') as f: selected_idx = f.readlines() selected_idx = [int(i.strip()) for i in selected_idx]#[s:e] ptb = [] with open('ptb.jsonl', 'r') as f: for l in f: ptb.append(json.loads(l)) ## Prompt 1 template_all = '''Please output the in the following sentence without any additional text in json format: "{}"''' template_single = '''Please output any <{}> in the following sentence one per line without any additional text: "{}"''' ## Prompt 2 prompt2_pos = '''Please pos tag the following sentence using Universal POS tag set without generating any additional text: {}''' prompt2_chunk = '''Please do sentence chunking for the following sentence as in CoNLL 2000 shared task without generating any addtional text: {}''' prompt2_parse = '''Generate textual representation of the constituency parse tree of the following sentence using Penn TreeBank tag set without outputing any additional text: {}''' prompt2_chunk = '''Please chunk the following sentence in CoNLL 2000 format with BIO tags without outputing any additional text: {}''' ## Prompt 3 with open('demonstration_3_42_pos.txt', 'r') as f: demon_pos = f.read() with open('demonstration_3_42_chunk.txt', 'r') as f: demon_chunk = f.read() with open('demonstration_3_42_parse.txt', 'r') as f: demon_parse = f.read() def para(m): c = 0 for n, p in m.named_parameters(): c += p.numel() return c def main(args=None): gid_list = selected_idx[args.start:args.end] if 'gpt3' in args.model_path: pass else: path = model_mapping[args.model_path] model, tokenizer = load_model( path, args.device, args.num_gpus, args.max_gpu_memory, args.load_8bit, args.cpu_offloading, revision=args.revision, debug=args.debug, ) whitelist_ids_pos = [tokenizer.encode(word)[1] for word in uni_tags] bad_words_ids_pos = [[ids] for ids in range(tokenizer.vocab_size) if ids not in whitelist_ids_pos] whitelist_ids_bio = [tokenizer.encode(word)[1] for word in bio_tags] bad_words_ids_bio = [[ids] for ids in range(tokenizer.vocab_size) if ids not in whitelist_ids_bio] whitelist_ids_chunk = [tokenizer.encode(word)[1] for word in chunk_tags] bad_words_ids_chunk = [[ids] for ids in range(tokenizer.vocab_size) if ids not in whitelist_ids_chunk] whitelist_ids_parse = [tokenizer.encode(word)[1] for word in syntags] bad_words_ids_parse = [[ids] for ids in range(tokenizer.vocab_size) if ids not in whitelist_ids_parse] if args.prompt == 1: for gid in tqdm(gid_list, desc='Query'): text = ptb[gid]['text'] for eid, ent in enumerate(ents): os.makedirs(f'result/prompt1_qa/{args.model_path}/ptb/per_ent/{ent}', exist_ok=True) if ent == 'NOUN' and not os.path.exists(f'result/prompt1_qa/{args.model_path}/ptb/per_ent/NOUN'): os.system(f'ln -sT ./NN result/prompt1_qa/{args.model_path}/ptb/per_ent/NOUN') if ent == 'VERB' and not os.path.exists(f'result/prompt1_qa/{args.model_path}/ptb/per_ent/VERB'): os.system(f'ln -sT ./VB result/prompt1_qa/{args.model_path}/ptb/per_ent/VERB') if ent == 'ADJ' and not os.path.exists(f'result/prompt1_qa/{args.model_path}/ptb/per_ent/ADJ'): os.system(f'ln -sT ./JJ result/prompt1_qa/{args.model_path}/ptb/per_ent/ADJ') if ent == 'ADV' and not os.path.exists(f'result/prompt1_qa/{args.model_path}/ptb/per_ent/ADV'): os.system(f'ln -sT ./RB result/prompt1_qa/{args.model_path}/ptb/per_ent/ADV') if ent == 'CONJ' and not os.path.exists(f'result/prompt1_qa/{args.model_path}/ptb/per_ent/CONJ'): os.system(f'ln -sT ./CC result/prompt1_qa/{args.model_path}/ptb/per_ent/CONJ') if ent == 'DET' and not os.path.exists(f'result/prompt1_qa/{args.model_path}/ptb/per_ent/DET'): os.system(f'ln -sT ./DT result/prompt1_qa/{args.model_path}/ptb/per_ent/DET') if ent == 'ADP' and not os.path.exists(f'result/prompt1_qa/{args.model_path}/ptb/per_ent/ADP'): os.system(f'ln -sT ./DT result/prompt1_qa/{args.model_path}/ptb/per_ent/IN') if os.path.exists(f'result/prompt1_qa/{args.model_path}/ptb/per_ent/{ent}/{gid}.txt'): print(gid, ent, 'skip') continue ## Get prompt msg = template_single.format(ents_prompt[eid], text) ## Run if 'gpt3' in args.model_path: if os.path.exists(f'result/prompt1_qa/{args.model_path}/ptb/per_ent/{ent}/{gid}.pkl'): print('Found cache') with open(f'result/prompt1_qa/{args.model_path}/ptb/per_ent/{ent}/{gid}.pkl', 'rb') as f: outputs = pickle.load(f) outputs = outputs['choices'][0]['message']['content'] else: outputs = gpt3(msg) if outputs is None: continue time.sleep(0.2) else: conv = get_conversation_template(args.model_path) conv.append_message(conv.roles[0], msg) conv.append_message(conv.roles[1], None) conv.system = '' prompt = conv.get_prompt().strip() outputs = fastchat(prompt, model, tokenizer) with open(f'result/prompt1_qa/{args.model_path}/ptb/per_ent/{ent}/{gid}.txt', 'w') as f: f.write(outputs) if args.prompt == 2: for gid in tqdm(gid_list, desc='Query'): text = ptb[gid]['text'] ## POS tagging if os.path.exists(f'result/prompt2_instruction/pos_tagging/{args.model_path}/ptb/{gid}.txt'): print(gid, 'skip') else: msg = prompt2_pos.format(text) if 'gpt3' in args.model_path: outputs = gpt3(msg) if outputs is None: continue time.sleep(0.2) else: conv = get_conversation_template(args.model_path) conv.append_message(conv.roles[0], msg) conv.append_message(conv.roles[1], None) conv.system = '' prompt = conv.get_prompt() outputs = fastchat(prompt, model, tokenizer) with open(f'result/prompt2_instruction/pos_tagging/{args.model_path}/ptb/{gid}.txt', 'w') as f: f.write(outputs) ## Sentence chunking if os.path.exists(f'result/prompt2_instruction/chunking/{args.model_path}/ptb/{gid}.txt'): print(gid, 'skip') if False: pass else: msg = prompt2_chunk.format(text) if 'gpt3' in args.model_path: outputs = gpt3(msg) if outputs is None: continue time.sleep(0.2) else: conv = get_conversation_template(args.model_path) conv.append_message(conv.roles[0], msg) conv.append_message(conv.roles[1], None) conv.system = '' prompt = conv.get_prompt() outputs = fastchat(prompt, model, tokenizer) print(args.model_path, gid, outputs) with open(f'result/prompt2_instruction/chunking/{args.model_path}/ptb/{gid}.txt', 'w') as f: f.write(outputs) ## Parsing if os.path.exists(f'result/prompt2_instruction/parsing/{args.model_path}/ptb/{gid}.txt'): print(gid, 'skip') else: msg = prompt2_parse.format(text) if 'gpt3' in args.model_path: outputs = gpt3(msg) if outputs is None: continue time.sleep(0.2) else: conv = get_conversation_template(args.model_path) conv.append_message(conv.roles[0], msg) conv.append_message(conv.roles[1], None) conv.system = '' prompt = conv.get_prompt() outputs = fastchat(prompt, model, tokenizer) with open(f'result/prompt2_instruction/parsing/{args.model_path}/ptb/{gid}.txt', 'w') as f: f.write(outputs) if args.prompt == 3: for gid in tqdm(gid_list, desc='Query'): text = ptb[gid]['text'] tokens = ptb[gid]['tokens'] poss = ptb[gid]['uni_poss'] ## POS tagging if os.path.exists(f'result/prompt3_structured_prompt/pos_tagging/{args.model_path}/ptb/{gid}.txt'): print(gid, 'skip') continue prompt = demon_pos + '\n' + 'C: ' + text + '\n' + 'T: ' if 'gpt3' in args.model_path: outputs = gpt3(prompt) if outputs is None: continue time.sleep(0.2) else: pred_poss = [] for _tok, _pos in zip(tokens, poss): prompt = prompt + ' ' + _tok + '_' outputs = structured_prompt(prompt, model, tokenizer, bad_words_ids_pos) prompt = prompt + outputs pred_poss.append(outputs) outputs = ' '.join(pred_poss) with open(f'result/prompt3_structured_prompt/pos_tagging/{args.model_path}/ptb/{gid}.txt', 'w') as f: f.write(outputs) ## Chunking if os.path.exists(f'result/prompt3_structured_prompt/chunking/{args.model_path}/ptb/{gid}.txt'): print(gid, 'skip') continue prompt = demon_chunk + '\n' + 'C: ' + text + '\n' + 'T: ' if 'gpt3' in args.model_path: outputs = gpt3(prompt) print(outputs) if outputs is None: continue time.sleep(0.2) else: pred_chunk = [] for _tok, _pos in zip(tokens, poss): prompt = prompt + ' ' + _tok + '_' # Generate BIO outputs_bio = structured_prompt(prompt, model, tokenizer, bad_words_ids_bio) prompt = prompt + outputs_bio + '-' # Generate tag outputs_chunk = structured_prompt(prompt, model, tokenizer, bad_words_ids_chunk) prompt = prompt + outputs_chunk pred_chunk.append((outputs_bio + '-' + outputs_chunk)) outputs = ' '.join(pred_chunk) with open(f'result/prompt3_structured_prompt/chunking/{args.model_path}/ptb/{gid}.txt', 'w') as f: f.write(outputs) ## Parsing if os.path.exists(f'result/prompt3_structured_prompt/parsing/{args.model_path}/ptb/{gid}.txt'): print(gid, 'skip') continue prompt = demon_parse + '\n' + 'C: ' + text + '\n' + 'T: ' if 'gpt3' in args.model_path: outputs = gpt3(prompt) if outputs is None: continue time.sleep(0.2) else: pred_syn = [] for _tok, _pos in zip(tokens, poss): prompt = prompt + _tok + '_' outputs = structured_prompt(prompt, model, tokenizer, bad_words_ids_parse) pred_syn.append(outputs) with open(f'result/prompt3_structured_prompt/parsing/{args.model_path}/ptb/{gid}.txt', 'w') as f: f.write(' '.join(pred_syn)) def structured_prompt(prompt, model, tokenizer, bad_words_ids): input_ids = tokenizer([prompt]).input_ids output_ids = model.generate( torch.as_tensor(input_ids).cuda(), max_new_tokens=1, bad_words_ids=bad_words_ids, ) if model.config.is_encoder_decoder: output_ids = output_ids[0] else: output_ids = output_ids[0][len(input_ids[0]) :] outputs = tokenizer.decode( output_ids, skip_special_tokens=True, spaces_between_special_tokens=False ) return outputs def fastchat(prompt, model, tokenizer): input_ids = tokenizer([prompt]).input_ids output_ids = model.generate( torch.as_tensor(input_ids).cuda(), do_sample=True, temperature=args.temperature, repetition_penalty=args.repetition_penalty, max_new_tokens=args.max_new_tokens, ) if model.config.is_encoder_decoder: output_ids = output_ids[0] else: output_ids = output_ids[0][len(input_ids[0]) :] outputs = tokenizer.decode( output_ids, skip_special_tokens=True, spaces_between_special_tokens=False ) #print('Empty system message') #print(f"{conv.roles[0]}: {msg}") #print(f"{conv.roles[1]}: {outputs}") return outputs def gpt3(prompt): try: response = openai.ChatCompletion.create( model=model_mapping[args.model_path], messages=[{"role": "user", "content": prompt}]) return response['choices'][0]['message']['content'] except Exception as err: print('Error') print(err) return None def run_llm_interface(model_path, prompt, sentence): import argparse from run_llm import main # Construct arguments args = argparse.Namespace( model_path=model_path, temperature=0.7, repetition_penalty=1.0, max_new_tokens=512, debug=False, message="Hello! Who are you?", start=0, end=1000, prompt=prompt, ) # Run the main function # For simplicity, assuming prompt values 1, 2, and 3 correspond to different strategies # You may need to adjust this based on your actual logic main(args=args) # Return dummy values for now, replace with actual outputs return "Strategy 1 Output", "Strategy 2 Output", "Strategy 3 Output" if __name__ == "__main__": parser = argparse.ArgumentParser() add_model_args(parser) parser.add_argument("--temperature", type=float, default=0.7) parser.add_argument("--repetition_penalty", type=float, default=1.0) parser.add_argument("--max-new-tokens", type=int, default=512) parser.add_argument("--debug", action="store_true") parser.add_argument("--message", type=str, default="Hello! Who are you?") parser.add_argument("--start", type=int, default=0) parser.add_argument("--end", type=int, default=1000) parser.add_argument("--prompt", required=True, type=int, default=None) # parser.add_argument("--system_msg", required=True, type=str, default='default_system_msg') args = parser.parse_args() # Reset default repetition penalty for T5 models. if "t5" in args.model_path and args.repetition_penalty == 1.0: args.repetition_penalty = 1.2 main(args)