import ast import logging import random import re import time import traceback import numpy as np import torch import transformers import modules.shared as shared from modules.callbacks import (Iteratorize, Stream, _SentinelTokenStoppingCriteria) from modules.extensions import apply_extensions from modules.html_generator import generate_4chan_html, generate_basic_html from modules.models import clear_torch_cache, local_rank def get_max_prompt_length(state): max_length = state['truncation_length'] - state['max_new_tokens'] if shared.soft_prompt: max_length -= shared.soft_prompt_tensor.shape[1] return max_length def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None): if shared.model_type in ['rwkv', 'llamacpp']: input_ids = shared.tokenizer.encode(str(prompt)) input_ids = np.array(input_ids).reshape(1, len(input_ids)) return input_ids else: input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', add_special_tokens=add_special_tokens) # This is a hack for making replies more creative. if not add_bos_token and input_ids[0][0] == shared.tokenizer.bos_token_id: input_ids = input_ids[:, 1:] # Llama adds this extra token when the first character is '\n', and this # compromises the stopping criteria, so we just remove it if type(shared.tokenizer) is transformers.LlamaTokenizer and input_ids[0][0] == 29871: input_ids = input_ids[:, 1:] # Handling truncation if truncation_length is not None: input_ids = input_ids[:, -truncation_length:] if shared.model_type in ['rwkv', 'llamacpp'] or shared.args.cpu: return input_ids elif shared.args.flexgen: return input_ids.numpy() elif shared.args.deepspeed: return input_ids.to(device=local_rank) elif torch.has_mps: device = torch.device('mps') return input_ids.to(device) else: return input_ids.cuda() def get_encoded_length(prompt): length_after_extensions = apply_extensions('tokenized_length', prompt) if length_after_extensions is not None: return length_after_extensions return len(encode(prompt)[0]) def decode(output_ids, skip_special_tokens=True): return shared.tokenizer.decode(output_ids, skip_special_tokens) def generate_softprompt_input_tensors(input_ids): inputs_embeds = shared.model.transformer.wte(input_ids) inputs_embeds = torch.cat((shared.soft_prompt_tensor, inputs_embeds), dim=1) filler_input_ids = torch.zeros((1, inputs_embeds.shape[1]), dtype=input_ids.dtype).to(shared.model.device) # filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens return inputs_embeds, filler_input_ids # Removes empty replies from gpt4chan outputs def fix_gpt4chan(s): for i in range(10): s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s) s = re.sub("--- [0-9]*\n *\n---", "---", s) s = re.sub("--- [0-9]*\n\n\n---", "---", s) return s # Fix the LaTeX equations in galactica def fix_galactica(s): s = s.replace(r'\[', r'$') s = s.replace(r'\]', r'$') s = s.replace(r'\(', r'$') s = s.replace(r'\)', r'$') s = s.replace(r'$$', r'$') s = re.sub(r'\n', r'\n\n', s) s = re.sub(r"\n{3,}", "\n\n", s) return s def get_reply_from_output_ids(output_ids, input_ids, original_question, state): if shared.model_type == 'HF_seq2seq': reply = decode(output_ids, state['skip_special_tokens']) if not shared.is_chat(): reply = apply_extensions('output', reply) else: new_tokens = len(output_ids) - len(input_ids[0]) reply = decode(output_ids[-new_tokens:], state['skip_special_tokens']) if type(shared.tokenizer) is transformers.LlamaTokenizer: if len(original_question) > 0 and original_question[-1] not in [' ', '\n']: reply = ' ' + reply if not shared.is_chat(): reply = original_question + apply_extensions('output', reply) return reply def formatted_outputs(reply, model_name): if not shared.is_chat(): if shared.model_type == 'galactica': reply = fix_galactica(reply) return reply, reply, generate_basic_html(reply) elif shared.model_type == 'gpt4chan': reply = fix_gpt4chan(reply) return reply, 'Only applicable for GALACTICA models.', generate_4chan_html(reply) else: return reply, 'Only applicable for GALACTICA models.', generate_basic_html(reply) else: return reply def set_manual_seed(seed): seed = int(seed) if seed == -1: seed = random.randint(1, 2**31) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) return seed def stop_everything_event(): shared.stop_everything = True def generate_reply(question, state, eos_token=None, stopping_strings=None): state = apply_extensions('state', state) generate_func = apply_extensions('custom_generate_reply') if generate_func is None: if shared.model_name == 'None' or shared.model is None: logging.error("No model is loaded! Select one in the Model tab.") yield formatted_outputs(question, shared.model_name) return if shared.model_type in ['rwkv', 'llamacpp']: generate_func = generate_reply_custom elif shared.args.flexgen: generate_func = generate_reply_flexgen else: generate_func = generate_reply_HF # Preparing the input original_question = question if not shared.is_chat(): question = apply_extensions('input', question) if shared.args.verbose: print(f'\n\n{question}\n--------------------\n') shared.stop_everything = False clear_torch_cache() seed = set_manual_seed(state['seed']) for reply in generate_func(question, original_question, seed, state, eos_token, stopping_strings): yield formatted_outputs(reply, shared.model_name) def generate_reply_HF(question, original_question, seed, state, eos_token=None, stopping_strings=None): generate_params = {} for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']: generate_params[k] = state[k] if state['ban_eos_token']: generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id] if shared.args.no_cache: generate_params.update({'use_cache': False}) if shared.args.deepspeed: generate_params.update({'synced_gpus': True}) # Encode the input input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state)) output = input_ids[0] cuda = not any((shared.args.cpu, shared.args.deepspeed)) # Find the eos tokens eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else [] if eos_token is not None: eos_token_ids.append(int(encode(eos_token)[0][-1])) # Add the encoded tokens to generate_params if shared.soft_prompt: inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids) question, filler_input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, filler_input_ids, inputs_embeds) original_input_ids = input_ids generate_params.update({'inputs_embeds': inputs_embeds}) generate_params.update({'inputs': filler_input_ids}) else: question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None) original_input_ids = input_ids generate_params.update({'inputs': input_ids}) if inputs_embeds is not None: generate_params.update({'inputs_embeds': inputs_embeds}) # Create the StoppingCriteriaList with the stopping strings (needs to be done after tokenizer extensions) stopping_criteria_list = transformers.StoppingCriteriaList() for st in (stopping_strings, ast.literal_eval(f"[{state['custom_stopping_strings']}]")): if type(st) is list and len(st) > 0: sentinel_token_ids = [encode(string, add_special_tokens=False) for string in st] stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=sentinel_token_ids, starting_idx=len(input_ids[0]))) break # Update generate_params with the eos token and the stopping strings generate_params['eos_token_id'] = eos_token_ids generate_params['stopping_criteria'] = stopping_criteria_list t0 = time.time() try: if not shared.is_chat() and shared.model_type != 'HF_seq2seq': yield original_question # Generate the entire reply at once. if not state['stream']: with torch.no_grad(): output = shared.model.generate(**generate_params)[0] if cuda: output = output.cuda() if shared.soft_prompt: output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) yield get_reply_from_output_ids(output, input_ids, original_question, state) # Stream the reply 1 token at a time. # This is based on the trick of using 'stopping_criteria' to create an iterator. else: def generate_with_callback(callback=None, **kwargs): kwargs['stopping_criteria'].append(Stream(callback_func=callback)) clear_torch_cache() with torch.no_grad(): shared.model.generate(**kwargs) def generate_with_streaming(**kwargs): return Iteratorize(generate_with_callback, kwargs, callback=None) with generate_with_streaming(**generate_params) as generator: for output in generator: if shared.soft_prompt: output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) yield get_reply_from_output_ids(output, input_ids, original_question, state) if output[-1] in eos_token_ids: break except Exception: traceback.print_exc() finally: t1 = time.time() original_tokens = len(original_input_ids[0]) new_tokens = len(output) - (original_tokens if shared.model_type != 'HF_seq2seq' else 0) print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})') return def generate_reply_custom(question, original_question, seed, state, eos_token=None, stopping_strings=None): seed = set_manual_seed(state['seed']) generate_params = {'token_count': state['max_new_tokens']} for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']: generate_params[k] = state[k] t0 = time.time() try: if not shared.is_chat(): yield question if not state['stream']: reply = shared.model.generate(context=question, **generate_params) output = original_question + reply if not shared.is_chat(): reply = original_question + apply_extensions('output', reply) yield reply else: for reply in shared.model.generate_with_streaming(context=question, **generate_params): output = original_question + reply if not shared.is_chat(): reply = original_question + apply_extensions('output', reply) yield reply except Exception: traceback.print_exc() finally: t1 = time.time() original_tokens = len(encode(original_question)[0]) new_tokens = len(encode(output)[0]) - original_tokens print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})') return def generate_reply_flexgen(question, original_question, seed, state, eos_token=None, stopping_strings=None): generate_params = {} for k in ['max_new_tokens', 'do_sample', 'temperature']: generate_params[k] = state[k] if state['stream']: generate_params['max_new_tokens'] = 8 # Encode the input input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state)) output = input_ids[0] # Find the eos tokens eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else [] if eos_token is not None: eos_token_ids.append(int(encode(eos_token)[0][-1])) # Add the encoded tokens to generate_params question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None) original_input_ids = input_ids generate_params.update({'inputs': input_ids}) if inputs_embeds is not None: generate_params.update({'inputs_embeds': inputs_embeds}) # Update generate_params with the eos token and the stopping strings generate_params['stop'] = eos_token_ids[-1] t0 = time.time() try: if not shared.is_chat(): yield question # Generate the entire reply at once. if not state['stream']: with torch.no_grad(): output = shared.model.generate(**generate_params)[0] yield get_reply_from_output_ids(output, input_ids, original_question, state) # Stream the output naively for FlexGen since it doesn't support 'stopping_criteria' else: for i in range(state['max_new_tokens'] // 8 + 1): if shared.stop_everything: break clear_torch_cache() with torch.no_grad(): output = shared.model.generate(**generate_params)[0] if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)): break yield get_reply_from_output_ids(output, original_input_ids, original_question, state) input_ids = np.reshape(output, (1, output.shape[0])) generate_params.update({'inputs': input_ids}) except Exception: traceback.print_exc() finally: t1 = time.time() original_tokens = len(original_input_ids[0]) new_tokens = len(output) - (original_tokens if shared.model_type != 'HF_seq2seq' else 0) print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})') return