import gc 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 local_rank def get_max_prompt_length(tokens): max_length = 2048-tokens if shared.soft_prompt: max_length -= shared.soft_prompt_tensor.shape[1] return max_length def encode(prompt, tokens_to_generate=0, add_special_tokens=True): if shared.is_RWKV: 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', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens) if 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 decode(output_ids): # Open Assistant relies on special tokens like <|endoftext|> if re.match('(oasst|galactica)-*', shared.model_name.lower()): return shared.tokenizer.decode(output_ids, skip_special_tokens=False) else: reply = shared.tokenizer.decode(output_ids, skip_special_tokens=True) reply = reply.replace(r'<|endoftext|>', '') return reply 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 formatted_outputs(reply, model_name): if not (shared.args.chat or shared.args.cai_chat): if model_name.lower().startswith('galactica'): reply = fix_galactica(reply) return reply, reply, generate_basic_html(reply) elif model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')): 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 clear_torch_cache(): gc.collect() if not shared.args.cpu: torch.cuda.empty_cache() def set_manual_seed(seed): if seed != -1: torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) def generate_reply(question, 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, seed, eos_token=None, stopping_strings=[]): clear_torch_cache() set_manual_seed(seed) t0 = time.time() original_question = question if not (shared.args.chat or shared.args.cai_chat): question = apply_extensions(question, "input") if shared.args.verbose: print(f"\n\n{question}\n--------------------\n") # These models are not part of Hugging Face, so we handle them # separately and terminate the function call earlier if shared.is_RWKV: try: if shared.args.no_stream: reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k) if not (shared.args.chat or shared.args.cai_chat): reply = original_question + apply_extensions(reply, "output") yield formatted_outputs(reply, shared.model_name) else: if not (shared.args.chat or shared.args.cai_chat): yield formatted_outputs(question, shared.model_name) # RWKV has proper streaming, which is very nice. # No need to generate 8 tokens at a time. for reply in shared.model.generate_with_streaming(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k): if not (shared.args.chat or shared.args.cai_chat): reply = original_question + apply_extensions(reply, "output") yield formatted_outputs(reply, shared.model_name) except Exception: traceback.print_exc() finally: t1 = time.time() output = encode(reply)[0] input_ids = encode(question) print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)") return input_ids = encode(question, max_new_tokens) original_input_ids = input_ids output = input_ids[0] cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen)) 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])) stopping_criteria_list = transformers.StoppingCriteriaList() if type(stopping_strings) is list and len(stopping_strings) > 0: t = [encode(string, 0, add_special_tokens=False) for string in stopping_strings] stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0]))) generate_params = {} if not shared.args.flexgen: generate_params.update({ "max_new_tokens": max_new_tokens, "eos_token_id": eos_token_ids, "stopping_criteria": stopping_criteria_list, "do_sample": do_sample, "temperature": temperature, "top_p": top_p, "typical_p": typical_p, "repetition_penalty": repetition_penalty, "encoder_repetition_penalty": encoder_repetition_penalty, "top_k": top_k, "min_length": min_length if shared.args.no_stream else 0, "no_repeat_ngram_size": no_repeat_ngram_size, "num_beams": num_beams, "penalty_alpha": penalty_alpha, "length_penalty": length_penalty, "early_stopping": early_stopping, }) else: generate_params.update({ "max_new_tokens": max_new_tokens if shared.args.no_stream else 8, "do_sample": do_sample, "temperature": temperature, "stop": eos_token_ids[-1], }) if shared.args.no_cache: generate_params.update({"use_cache": False}) if shared.args.deepspeed: generate_params.update({"synced_gpus": True}) if shared.soft_prompt: inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids) generate_params.update({"inputs_embeds": inputs_embeds}) generate_params.update({"inputs": filler_input_ids}) else: generate_params.update({"inputs": input_ids}) try: # Generate the entire reply at once. if shared.args.no_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]:])) new_tokens = len(output) - len(input_ids[0]) reply = decode(output[-new_tokens:]) if not (shared.args.chat or shared.args.cai_chat): reply = original_question + apply_extensions(reply, "output") yield formatted_outputs(reply, shared.model_name) # Stream the reply 1 token at a time. # This is based on the trick of using 'stopping_criteria' to create an iterator. elif not shared.args.flexgen: 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) if not (shared.args.chat or shared.args.cai_chat): yield formatted_outputs(original_question, shared.model_name) 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]:])) new_tokens = len(output) - len(input_ids[0]) reply = decode(output[-new_tokens:]) if not (shared.args.chat or shared.args.cai_chat): reply = original_question + apply_extensions(reply, "output") if output[-1] in eos_token_ids: break yield formatted_outputs(reply, shared.model_name) yield formatted_outputs(reply, shared.model_name) # Stream the output naively for FlexGen since it doesn't support 'stopping_criteria' else: for i in range(max_new_tokens//8+1): clear_torch_cache() with torch.no_grad(): output = shared.model.generate(**generate_params)[0] if shared.soft_prompt: output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) new_tokens = len(output) - len(original_input_ids[0]) reply = decode(output[-new_tokens:]) if not (shared.args.chat or shared.args.cai_chat): reply = original_question + apply_extensions(reply, "output") if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)): break yield formatted_outputs(reply, shared.model_name) input_ids = np.reshape(output, (1, output.shape[0])) if shared.soft_prompt: inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids) generate_params.update({"inputs_embeds": inputs_embeds}) generate_params.update({"inputs": filler_input_ids}) else: generate_params.update({"inputs": input_ids}) yield formatted_outputs(reply, shared.model_name) except Exception: traceback.print_exc() finally: t1 = time.time() print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(original_input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(original_input_ids[0])} tokens, context {len(original_input_ids[0])})") return