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| 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, is_chat=False): | |
| if shared.model_type == 'HF_seq2seq': | |
| reply = decode(output_ids, state['skip_special_tokens']) | |
| else: | |
| new_tokens = len(output_ids) - len(input_ids[0]) | |
| reply = decode(output_ids[-new_tokens:], state['skip_special_tokens']) | |
| # Prevent LlamaTokenizer from skipping a space | |
| if type(shared.tokenizer) is transformers.LlamaTokenizer and len(output_ids) > 0: | |
| if shared.tokenizer.convert_ids_to_tokens(int(output_ids[-new_tokens])).startswith('▁'): | |
| reply = ' ' + reply | |
| if not is_chat: | |
| reply = apply_extensions('output', reply) | |
| return reply | |
| def formatted_outputs(reply, model_name): | |
| 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) | |
| 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_wrapper(question, state, eos_token=None, stopping_strings=None): | |
| for reply in generate_reply(question, state, eos_token, stopping_strings, is_chat=False): | |
| if shared.model_type not in ['HF_seq2seq']: | |
| reply = question + reply | |
| yield formatted_outputs(reply, shared.model_name) | |
| def generate_reply(question, state, eos_token=None, stopping_strings=None, is_chat=False): | |
| 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 question | |
| 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 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, is_chat=is_chat): | |
| yield reply | |
| def generate_reply_HF(question, original_question, seed, state, eos_token=None, stopping_strings=None, is_chat=False): | |
| 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 is_chat and shared.model_type != 'HF_seq2seq': | |
| yield '' | |
| # 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, is_chat=is_chat) | |
| # 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, is_chat=is_chat) | |
| 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, is_chat=False): | |
| 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 is_chat: | |
| yield '' | |
| if not state['stream']: | |
| reply = shared.model.generate(context=question, **generate_params) | |
| if not is_chat: | |
| reply = apply_extensions('output', reply) | |
| yield reply | |
| else: | |
| for reply in shared.model.generate_with_streaming(context=question, **generate_params): | |
| if not is_chat: | |
| reply = 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(original_question + reply)[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, is_chat=False): | |
| 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 is_chat: | |
| yield '' | |
| # 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, is_chat=is_chat) | |
| # 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 | |