mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-22 16:17:57 +01:00
288 lines
12 KiB
Python
288 lines
12 KiB
Python
import gc
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import re
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import time
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import traceback
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import numpy as np
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import torch
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import transformers
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import modules.shared as shared
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from modules.callbacks import (Iteratorize, Stream,
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_SentinelTokenStoppingCriteria)
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from modules.extensions import apply_extensions
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from modules.html_generator import generate_4chan_html, generate_basic_html
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from modules.models import local_rank
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def get_max_prompt_length(tokens):
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max_length = 2048 - tokens
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if shared.soft_prompt:
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max_length -= shared.soft_prompt_tensor.shape[1]
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return max_length
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def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
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if any((shared.is_RWKV, shared.is_llamacpp)):
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input_ids = shared.tokenizer.encode(str(prompt))
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input_ids = np.array(input_ids).reshape(1, len(input_ids))
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return input_ids
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else:
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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)
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if type(shared.tokenizer) is transformers.LlamaTokenizer and input_ids[0][0] == 29871:
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input_ids = input_ids[:, 1:]
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if shared.args.cpu:
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return input_ids
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elif shared.args.flexgen:
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return input_ids.numpy()
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elif shared.args.deepspeed:
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return input_ids.to(device=local_rank)
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elif torch.has_mps:
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device = torch.device('mps')
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return input_ids.to(device)
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else:
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return input_ids.cuda()
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def decode(output_ids):
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# Open Assistant relies on special tokens like <|endoftext|>
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if re.match('.*(oasst|galactica)-*', shared.model_name.lower()):
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return shared.tokenizer.decode(output_ids, skip_special_tokens=False)
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else:
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reply = shared.tokenizer.decode(output_ids, skip_special_tokens=True)
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reply = reply.replace(r'<|endoftext|>', '')
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return reply
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def generate_softprompt_input_tensors(input_ids):
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inputs_embeds = shared.model.transformer.wte(input_ids)
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inputs_embeds = torch.cat((shared.soft_prompt_tensor, inputs_embeds), dim=1)
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filler_input_ids = torch.zeros((1, inputs_embeds.shape[1]), dtype=input_ids.dtype).to(shared.model.device)
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# filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens
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return inputs_embeds, filler_input_ids
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# Removes empty replies from gpt4chan outputs
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def fix_gpt4chan(s):
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for i in range(10):
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s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
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s = re.sub("--- [0-9]*\n *\n---", "---", s)
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s = re.sub("--- [0-9]*\n\n\n---", "---", s)
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return s
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# Fix the LaTeX equations in galactica
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def fix_galactica(s):
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s = s.replace(r'\[', r'$')
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s = s.replace(r'\]', r'$')
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s = s.replace(r'\(', r'$')
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s = s.replace(r'\)', r'$')
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s = s.replace(r'$$', r'$')
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s = re.sub(r'\n', r'\n\n', s)
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s = re.sub(r"\n{3,}", "\n\n", s)
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return s
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def formatted_outputs(reply, model_name):
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if not shared.is_chat():
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if 'galactica' in model_name.lower():
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reply = fix_galactica(reply)
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return reply, reply, generate_basic_html(reply)
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elif any((k in shared.model_name.lower() for k in ['gpt4chan', 'gpt-4chan'])):
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reply = fix_gpt4chan(reply)
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return reply, 'Only applicable for GALACTICA models.', generate_4chan_html(reply)
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else:
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return reply, 'Only applicable for GALACTICA models.', generate_basic_html(reply)
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else:
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return reply
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def clear_torch_cache():
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gc.collect()
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if not shared.args.cpu:
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torch.cuda.empty_cache()
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def set_manual_seed(seed):
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if seed != -1:
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(seed)
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def stop_everything_event():
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shared.stop_everything = True
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def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]):
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clear_torch_cache()
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set_manual_seed(generate_state['seed'])
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shared.stop_everything = False
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generate_params = {}
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t0 = time.time()
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original_question = question
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if not shared.is_chat():
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question = apply_extensions(question, 'input')
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if shared.args.verbose:
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print(f'\n\n{question}\n--------------------\n')
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# These models are not part of Hugging Face, so we handle them
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# separately and terminate the function call earlier
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if any((shared.is_RWKV, shared.is_llamacpp)):
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for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
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generate_params[k] = generate_state[k]
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generate_params['token_count'] = generate_state['max_new_tokens']
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try:
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if shared.args.no_stream:
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reply = shared.model.generate(context=question, **generate_params)
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output = original_question + reply
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if not shared.is_chat():
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reply = original_question + apply_extensions(reply, 'output')
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yield formatted_outputs(reply, shared.model_name)
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else:
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if not shared.is_chat():
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yield formatted_outputs(question, shared.model_name)
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# RWKV has proper streaming, which is very nice.
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# No need to generate 8 tokens at a time.
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for reply in shared.model.generate_with_streaming(context=question, **generate_params):
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output = original_question + reply
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if not shared.is_chat():
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reply = original_question + apply_extensions(reply, 'output')
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yield formatted_outputs(reply, shared.model_name)
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except Exception:
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traceback.print_exc()
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finally:
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t1 = time.time()
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original_tokens = len(encode(original_question)[0])
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new_tokens = len(encode(output)[0]) - original_tokens
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print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})')
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return
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input_ids = encode(question, generate_state['max_new_tokens'])
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original_input_ids = input_ids
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output = input_ids[0]
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cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
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eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
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if eos_token is not None:
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eos_token_ids.append(int(encode(eos_token)[0][-1]))
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stopping_criteria_list = transformers.StoppingCriteriaList()
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if type(stopping_strings) is list and len(stopping_strings) > 0:
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t = [encode(string, 0, add_special_tokens=False) for string in stopping_strings]
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stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
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if not shared.args.flexgen:
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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']:
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generate_params[k] = generate_state[k]
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generate_params['eos_token_id'] = eos_token_ids
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generate_params['stopping_criteria'] = stopping_criteria_list
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if shared.args.no_stream:
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generate_params['min_length'] = 0
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else:
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for k in ['max_new_tokens', 'do_sample', 'temperature']:
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generate_params[k] = generate_state[k]
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generate_params['stop'] = generate_state['eos_token_ids'][-1]
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if not shared.args.no_stream:
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generate_params['max_new_tokens'] = 8
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if shared.args.no_cache:
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generate_params.update({'use_cache': False})
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if shared.args.deepspeed:
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generate_params.update({'synced_gpus': True})
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if shared.soft_prompt:
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inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
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generate_params.update({'inputs_embeds': inputs_embeds})
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generate_params.update({'inputs': filler_input_ids})
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else:
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generate_params.update({'inputs': input_ids})
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try:
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# Generate the entire reply at once.
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if shared.args.no_stream:
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with torch.no_grad():
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output = shared.model.generate(**generate_params)[0]
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if cuda:
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output = output.cuda()
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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new_tokens = len(output) - len(input_ids[0])
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reply = decode(output[-new_tokens:])
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if not shared.is_chat():
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reply = original_question + apply_extensions(reply, 'output')
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yield formatted_outputs(reply, shared.model_name)
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# Stream the reply 1 token at a time.
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# This is based on the trick of using 'stopping_criteria' to create an iterator.
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elif not shared.args.flexgen:
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def generate_with_callback(callback=None, **kwargs):
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kwargs['stopping_criteria'].append(Stream(callback_func=callback))
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clear_torch_cache()
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with torch.no_grad():
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shared.model.generate(**kwargs)
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def generate_with_streaming(**kwargs):
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return Iteratorize(generate_with_callback, kwargs, callback=None)
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if not shared.is_chat():
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yield formatted_outputs(original_question, shared.model_name)
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with generate_with_streaming(**generate_params) as generator:
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for output in generator:
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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new_tokens = len(output) - len(input_ids[0])
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reply = decode(output[-new_tokens:])
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if not shared.is_chat():
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reply = original_question + apply_extensions(reply, 'output')
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if output[-1] in eos_token_ids:
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break
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yield formatted_outputs(reply, shared.model_name)
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# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
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else:
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for i in range(generate_state['max_new_tokens'] // 8 + 1):
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clear_torch_cache()
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with torch.no_grad():
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output = shared.model.generate(**generate_params)[0]
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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new_tokens = len(output) - len(original_input_ids[0])
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reply = decode(output[-new_tokens:])
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if not shared.is_chat():
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reply = original_question + apply_extensions(reply, 'output')
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if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
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break
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yield formatted_outputs(reply, shared.model_name)
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input_ids = np.reshape(output, (1, output.shape[0]))
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if shared.soft_prompt:
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inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
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generate_params.update({'inputs_embeds': inputs_embeds})
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generate_params.update({'inputs': filler_input_ids})
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else:
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generate_params.update({'inputs': input_ids})
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yield formatted_outputs(reply, shared.model_name)
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except Exception:
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traceback.print_exc()
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finally:
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t1 = time.time()
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original_tokens = len(original_input_ids[0])
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new_tokens = len(output) - original_tokens
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print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})')
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return
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