import ast import copy import html import pprint import random import re import time import traceback import numpy as np import torch import transformers from transformers import LogitsProcessorList, is_torch_xpu_available import modules.shared as shared from modules.callbacks import ( Iteratorize, Stream, _StopEverythingStoppingCriteria ) from modules.extensions import apply_extensions from modules.grammar.grammar_utils import initialize_grammar from modules.grammar.logits_process import GrammarConstrainedLogitsProcessor from modules.html_generator import generate_4chan_html, generate_basic_html from modules.logging_colors import logger from modules.models import clear_torch_cache, local_rank def generate_reply(*args, **kwargs): shared.generation_lock.acquire() try: for result in _generate_reply(*args, **kwargs): yield result finally: shared.generation_lock.release() def _generate_reply(question, state, stopping_strings=None, is_chat=False, escape_html=False, for_ui=False): # Find the appropriate generation function generate_func = apply_extensions('custom_generate_reply') if generate_func is None: if shared.model_name == 'None' or shared.model is None: logger.error("No model is loaded! Select one in the Model tab.") yield '' return if shared.model.__class__.__name__ in ['LlamaCppModel', 'Exllamav2Model', 'CtransformersModel']: generate_func = generate_reply_custom else: generate_func = generate_reply_HF if generate_func != generate_reply_HF and shared.args.verbose: logger.info("PROMPT=") print(question) print() # Prepare the input original_question = question if not is_chat: state = apply_extensions('state', state) question = apply_extensions('input', question, state) # Find the stopping strings all_stop_strings = [] for st in (stopping_strings, state['custom_stopping_strings']): if type(st) is str: st = ast.literal_eval(f"[{st}]") if type(st) is list and len(st) > 0: all_stop_strings += st shared.stop_everything = False clear_torch_cache() seed = set_manual_seed(state['seed']) last_update = -1 reply = '' is_stream = state['stream'] if len(all_stop_strings) > 0 and not state['stream']: state = copy.deepcopy(state) state['stream'] = True min_update_interval = 0 if state.get('max_updates_second', 0) > 0: min_update_interval = 1 / state['max_updates_second'] # Generate for reply in generate_func(question, original_question, seed, state, stopping_strings, is_chat=is_chat): reply, stop_found = apply_stopping_strings(reply, all_stop_strings) if escape_html: reply = html.escape(reply) if is_stream: cur_time = time.time() # Maximum number of tokens/second if state['max_tokens_second'] > 0: diff = 1 / state['max_tokens_second'] - (cur_time - last_update) if diff > 0: time.sleep(diff) last_update = time.time() yield reply # Limit updates to avoid lag in the Gradio UI # API updates are not limited else: if cur_time - last_update > min_update_interval: last_update = cur_time yield reply if stop_found or (state['max_tokens_second'] > 0 and shared.stop_everything): break if not is_chat: reply = apply_extensions('output', reply, state) yield reply def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None): if shared.tokenizer is None: raise ValueError('No tokenizer is loaded') if shared.model.__class__.__name__ in ['LlamaCppModel', 'CtransformersModel', 'Exllamav2Model']: input_ids = shared.tokenizer.encode(str(prompt)) if shared.model.__class__.__name__ not in ['Exllamav2Model']: input_ids = np.array(input_ids).reshape(1, len(input_ids)) else: input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', add_special_tokens=add_special_tokens) if not add_bos_token: while len(input_ids[0]) > 0 and input_ids[0][0] == shared.tokenizer.bos_token_id: input_ids = input_ids[:, 1:] # Handling truncation if truncation_length is not None: input_ids = input_ids[:, -truncation_length:] if shared.model.__class__.__name__ in ['LlamaCppModel', 'Exllamav2Model', 'CtransformersModel'] or shared.args.cpu: return input_ids elif shared.args.deepspeed: return input_ids.to(device=local_rank) elif torch.backends.mps.is_available(): device = torch.device('mps') return input_ids.to(device) elif is_torch_xpu_available(): return input_ids.to("xpu:0") else: return input_ids.cuda() def decode(output_ids, skip_special_tokens=True): if shared.tokenizer is None: raise ValueError('No tokenizer is loaded') return shared.tokenizer.decode(output_ids, skip_special_tokens=skip_special_tokens) 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 get_token_ids(prompt): tokens = encode(prompt)[0] decoded_tokens = [shared.tokenizer.decode([i]) for i in tokens] output = '' for row in list(zip(tokens, decoded_tokens)): output += f"{str(int(row[0])).ljust(5)} - {repr(row[1])}\n" return output def get_max_prompt_length(state): return state['truncation_length'] - state['max_new_tokens'] def generate_reply_wrapper(question, state, stopping_strings=None): """ Returns formatted outputs for the UI """ reply = question if not shared.is_seq2seq else '' yield formatted_outputs(reply, shared.model_name) for reply in generate_reply(question, state, stopping_strings, is_chat=False, escape_html=True, for_ui=True): if not shared.is_seq2seq: reply = question + reply yield formatted_outputs(reply, shared.model_name) def formatted_outputs(reply, model_name): if any(s in model_name for s in ['gpt-4chan', 'gpt4chan']): reply = fix_gpt4chan(reply) return html.unescape(reply), generate_4chan_html(reply) else: return html.unescape(reply), generate_basic_html(reply) def fix_gpt4chan(s): """ Removes empty replies from gpt4chan outputs """ 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 def fix_galactica(s): """ Fix the LaTeX equations in GALACTICA """ 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 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) elif is_torch_xpu_available(): torch.xpu.manual_seed_all(seed) return seed def stop_everything_event(): shared.stop_everything = True def apply_stopping_strings(reply, all_stop_strings): stop_found = False for string in all_stop_strings: idx = reply.find(string) if idx != -1: reply = reply[:idx] stop_found = True break if not stop_found: # If something like "\nYo" is generated just before "\nYou:" # is completed, trim it for string in all_stop_strings: for j in range(len(string) - 1, 0, -1): if reply[-j:] == string[:j]: reply = reply[:-j] break else: continue break return reply, stop_found def get_reply_from_output_ids(output_ids, state=None, starting_from=0): reply = decode(output_ids[starting_from:], state['skip_special_tokens'] if state else True) # Handle tokenizers that do not add the leading space for the first token if (hasattr(shared.tokenizer, 'convert_ids_to_tokens') and len(output_ids) > starting_from) and not reply.startswith(' '): first_token = shared.tokenizer.convert_ids_to_tokens(int(output_ids[starting_from])) if isinstance(first_token, (bytes,)): first_token = first_token.decode('utf8') if first_token.startswith('▁'): reply = ' ' + reply return reply def generate_reply_HF(question, original_question, seed, state, stopping_strings=None, is_chat=False): generate_params = {} for k in ['max_new_tokens', 'temperature', 'temperature_last', 'dynamic_temperature', 'dynatemp_low', 'dynatemp_high', 'dynatemp_exponent', 'smoothing_factor', 'top_p', 'min_p', 'top_k', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'repetition_penalty_range', 'typical_p', 'tfs', 'top_a', 'guidance_scale', 'penalty_alpha', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'do_sample', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'num_beams', 'length_penalty', 'early_stopping']: if k in state: generate_params[k] = state[k] if isinstance(state['sampler_priority'], list) and len(state['sampler_priority']) > 0: generate_params['sampler_priority'] = state['sampler_priority'] elif isinstance(state['sampler_priority'], str) and state['sampler_priority'].strip() != '': generate_params['sampler_priority'] = [x.strip() for x in state['sampler_priority'].replace('\n', ',').split(',') if x.strip()] if state['negative_prompt'] != '': generate_params['negative_prompt_ids'] = encode(state['negative_prompt']) if state['prompt_lookup_num_tokens'] > 0: generate_params['prompt_lookup_num_tokens'] = state['prompt_lookup_num_tokens'] for k in ['epsilon_cutoff', 'eta_cutoff']: if state[k] > 0: generate_params[k] = state[k] * 1e-4 if state['ban_eos_token']: generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id] if state['custom_token_bans']: to_ban = [int(x) for x in state['custom_token_bans'].split(',')] if len(to_ban) > 0: if generate_params.get('suppress_tokens', None): generate_params['suppress_tokens'] += to_ban else: generate_params['suppress_tokens'] = to_ban generate_params.update({'use_cache': not shared.args.no_cache}) 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)) if state['auto_max_new_tokens']: generate_params['max_new_tokens'] = state['truncation_length'] - input_ids.shape[-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}) # Stopping criteria / eos token eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else [] generate_params['eos_token_id'] = eos_token_ids generate_params['stopping_criteria'] = transformers.StoppingCriteriaList() generate_params['stopping_criteria'].append(_StopEverythingStoppingCriteria()) # Logits processor processor = state.get('logits_processor', LogitsProcessorList([])) if not isinstance(processor, LogitsProcessorList): processor = LogitsProcessorList([processor]) # Grammar if state['grammar_string'].strip() != '': grammar = initialize_grammar(state['grammar_string']) grammar_processor = GrammarConstrainedLogitsProcessor(grammar) processor.append(grammar_processor) apply_extensions('logits_processor', processor, input_ids) generate_params['logits_processor'] = processor if shared.args.verbose: logger.info("GENERATE_PARAMS=") filtered_params = {key: value for key, value in generate_params.items() if not isinstance(value, torch.Tensor)} pprint.PrettyPrinter(indent=4, sort_dicts=False).pprint(filtered_params) print() logger.info("PROMPT=") print(decode(input_ids[0], skip_special_tokens=False)) print() t0 = time.time() try: if not is_chat and not shared.is_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() starting_from = 0 if shared.is_seq2seq else len(input_ids[0]) yield get_reply_from_output_ids(output, state, starting_from=starting_from) # 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, *args, **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: cumulative_reply = '' starting_from = 0 if shared.is_seq2seq else len(input_ids[0]) for output in generator: if output[-1] in eos_token_ids: break new_content = get_reply_from_output_ids(output, state, starting_from=starting_from) # check the partial unicode character if chr(0xfffd) in new_content: continue cumulative_reply += new_content starting_from = len(output) yield cumulative_reply except Exception: traceback.print_exc() finally: t1 = time.time() original_tokens = len(original_input_ids[0]) new_tokens = len(output) - (original_tokens if not shared.is_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, stopping_strings=None, is_chat=False): """ For models that do not use the transformers library for sampling """ seed = set_manual_seed(state['seed']) t0 = time.time() reply = '' try: if not is_chat: yield '' if not state['stream']: reply = shared.model.generate(question, state) yield reply else: for reply in shared.model.generate_with_streaming(question, state): 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