mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-09-16 14:32:41 +00:00
466 lines
17 KiB
Python
466 lines
17 KiB
Python
import ast
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import copy
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import html
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import pprint
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import random
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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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from transformers import (
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LogitsProcessorList,
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is_torch_npu_available,
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is_torch_xpu_available
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)
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import modules.shared as shared
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from modules import models
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from modules.cache_utils import process_llamacpp_cache
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from modules.callbacks import (
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Iteratorize,
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Stream,
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_StopEverythingStoppingCriteria
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)
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from modules.extensions import apply_extensions
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from modules.grammar.grammar_utils import initialize_grammar
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from modules.grammar.logits_process import GrammarConstrainedLogitsProcessor
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from modules.html_generator import generate_basic_html
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from modules.logging_colors import logger
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from modules.models import clear_torch_cache, load_model
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def generate_reply(*args, **kwargs):
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if shared.args.idle_timeout > 0 and shared.model is None and shared.model_name not in [None, 'None']:
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shared.model, shared.tokenizer = load_model(shared.model_name)
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shared.generation_lock.acquire()
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try:
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for result in _generate_reply(*args, **kwargs):
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yield result
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finally:
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models.last_generation_time = time.time()
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shared.generation_lock.release()
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def _generate_reply(question, state, stopping_strings=None, is_chat=False, escape_html=False, for_ui=False):
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# Find the appropriate generation function
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generate_func = apply_extensions('custom_generate_reply')
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if generate_func is None:
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if shared.model_name == 'None' or shared.model is None:
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logger.error("No model is loaded! Select one in the Model tab.")
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yield ''
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return
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'Exllamav2Model', 'TensorRTLLMModel']:
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generate_func = generate_reply_custom
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else:
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generate_func = generate_reply_HF
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if generate_func != generate_reply_HF and shared.args.verbose:
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logger.info("PROMPT=")
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print_prompt(question)
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# Prepare the input
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original_question = question
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if not is_chat:
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state = apply_extensions('state', state)
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question = apply_extensions('input', question, state)
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# Find the stopping strings
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all_stop_strings = []
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for st in (stopping_strings, state['custom_stopping_strings']):
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if type(st) is str:
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st = ast.literal_eval(f"[{st}]")
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if type(st) is list and len(st) > 0:
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all_stop_strings += st
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shared.stop_everything = False
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clear_torch_cache()
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seed = set_manual_seed(state['seed'])
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last_update = -1
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reply = ''
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is_stream = state['stream']
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if len(all_stop_strings) > 0 and not state['stream']:
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state = copy.deepcopy(state)
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state['stream'] = True
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min_update_interval = 0
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if state.get('max_updates_second', 0) > 0:
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min_update_interval = 1 / state['max_updates_second']
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# Generate
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for reply in generate_func(question, original_question, seed, state, stopping_strings, is_chat=is_chat):
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reply, stop_found = apply_stopping_strings(reply, all_stop_strings)
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if escape_html:
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reply = html.escape(reply)
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if is_stream:
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cur_time = time.time()
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# Limit number of tokens/second to make text readable in real time
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if state['max_tokens_second'] > 0:
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diff = 1 / state['max_tokens_second'] - (cur_time - last_update)
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if diff > 0:
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time.sleep(diff)
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last_update = time.time()
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yield reply
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# Limit updates to avoid lag in the Gradio UI
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# API updates are not limited
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else:
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if cur_time - last_update > min_update_interval:
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last_update = cur_time
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yield reply
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yield reply
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if stop_found or (state['max_tokens_second'] > 0 and shared.stop_everything):
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break
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if not is_chat:
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reply = apply_extensions('output', reply, state)
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yield reply
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def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None):
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if shared.tokenizer is None:
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raise ValueError('No tokenizer is loaded')
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'Exllamav2Model', 'TensorRTLLMModel']:
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input_ids = shared.tokenizer.encode(str(prompt))
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if shared.model.__class__.__name__ not in ['Exllamav2Model']:
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input_ids = np.array(input_ids).reshape(1, len(input_ids))
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else:
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input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', add_special_tokens=add_special_tokens)
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if hasattr(shared.tokenizer, 'bos_token_id') and shared.tokenizer.bos_token_id is not None:
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if add_bos_token:
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if (len(input_ids[0]) > 0 and input_ids[0][0] != shared.tokenizer.bos_token_id) or len(input_ids[0]) == 0:
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# Add a missing bos token (it may not have been added due to faulty model metadata)
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bos_tensor = torch.tensor([[shared.tokenizer.bos_token_id]])
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input_ids = torch.cat((bos_tensor, input_ids), 1)
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# Prevent double bos token due to jinja templates with <s> somewhere
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while len(input_ids[0]) > 1 and input_ids[0][0] == shared.tokenizer.bos_token_id and input_ids[0][1] == shared.tokenizer.bos_token_id:
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input_ids = input_ids[:, 1:]
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else:
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# Remove any bos token that may have been added
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while len(input_ids[0]) > 0 and input_ids[0][0] == shared.tokenizer.bos_token_id:
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input_ids = input_ids[:, 1:]
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# Handling truncation
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if truncation_length is not None:
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input_ids = input_ids[:, -truncation_length:]
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'Exllamav2Model', 'TensorRTLLMModel'] or shared.args.cpu:
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return input_ids
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elif shared.args.deepspeed:
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import deepspeed
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return input_ids.to(deepspeed.get_accelerator().current_device_name())
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elif torch.backends.mps.is_available():
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device = torch.device('mps')
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return input_ids.to(device)
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elif is_torch_xpu_available():
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return input_ids.to("xpu:0")
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elif is_torch_npu_available():
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return input_ids.to("npu:0")
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else:
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return input_ids.cuda()
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def decode(output_ids, skip_special_tokens=True):
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if shared.tokenizer is None:
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raise ValueError('No tokenizer is loaded')
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return shared.tokenizer.decode(output_ids, skip_special_tokens=skip_special_tokens)
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def get_encoded_length(prompt):
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length_after_extensions = apply_extensions('tokenized_length', prompt)
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if length_after_extensions is not None:
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return length_after_extensions
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return len(encode(prompt)[0])
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def get_token_ids(prompt):
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tokens = encode(prompt)[0]
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decoded_tokens = [shared.tokenizer.decode([i]) for i in tokens]
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output = ''
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for row in list(zip(tokens, decoded_tokens)):
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output += f"{str(int(row[0])).ljust(5)} - {repr(row[1])}\n"
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return output
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def get_max_prompt_length(state):
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return state['truncation_length'] - state['max_new_tokens']
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def generate_reply_wrapper(question, state, stopping_strings=None):
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"""
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Returns formatted outputs for the UI
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"""
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reply = question if not shared.is_seq2seq else ''
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yield formatted_outputs(reply, shared.model_name)
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for reply in generate_reply(question, state, stopping_strings, is_chat=False, escape_html=True, for_ui=True):
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if not shared.is_seq2seq:
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reply = question + reply
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yield formatted_outputs(reply, shared.model_name)
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def formatted_outputs(reply, model_name):
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return html.unescape(reply), generate_basic_html(reply)
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def set_manual_seed(seed):
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seed = int(seed)
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if seed == -1:
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seed = random.randint(1, 2**31)
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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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elif is_torch_xpu_available():
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torch.xpu.manual_seed_all(seed)
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elif is_torch_npu_available():
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torch.npu.manual_seed_all(seed)
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return seed
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def stop_everything_event():
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shared.stop_everything = True
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def apply_stopping_strings(reply, all_stop_strings):
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stop_found = False
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for string in all_stop_strings:
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idx = reply.find(string)
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if idx != -1:
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reply = reply[:idx]
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stop_found = True
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break
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if not stop_found:
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# If something like "\nYo" is generated just before "\nYou:"
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# is completed, trim it
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for string in all_stop_strings:
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for j in range(len(string) - 1, 0, -1):
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if reply[-j:] == string[:j]:
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reply = reply[:-j]
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break
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else:
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continue
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break
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return reply, stop_found
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def get_reply_from_output_ids(output_ids, state=None, starting_from=0):
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reply = decode(output_ids[starting_from:], state['skip_special_tokens'] if state else True)
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# Handle tokenizers that do not add the leading space for the first token
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if (hasattr(shared.tokenizer, 'convert_ids_to_tokens') and len(output_ids) > starting_from) and not reply.startswith(' '):
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first_token = shared.tokenizer.convert_ids_to_tokens(int(output_ids[starting_from]))
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if isinstance(first_token, (bytes,)):
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first_token = first_token.decode('utf8')
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if first_token.startswith('▁'):
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reply = ' ' + reply
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return reply
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def generate_reply_HF(question, original_question, seed, state, stopping_strings=None, is_chat=False):
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generate_params = {}
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for k in ['max_new_tokens', 'temperature', 'temperature_last', 'dynamic_temperature', 'dynatemp_low', 'dynatemp_high', 'dynatemp_exponent', 'smoothing_factor', 'smoothing_curve', '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', 'dry_multiplier', 'dry_base', 'dry_allowed_length', 'dry_sequence_breakers']:
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if k in state:
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generate_params[k] = state[k]
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if isinstance(state['sampler_priority'], list) and len(state['sampler_priority']) > 0:
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generate_params['sampler_priority'] = state['sampler_priority']
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elif isinstance(state['sampler_priority'], str) and state['sampler_priority'].strip() != '':
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generate_params['sampler_priority'] = [x.strip() for x in state['sampler_priority'].replace('\n', ',').split(',') if x.strip()]
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if state['negative_prompt'] != '':
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generate_params['negative_prompt_ids'] = encode(state['negative_prompt'])
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if state['prompt_lookup_num_tokens'] > 0:
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generate_params['prompt_lookup_num_tokens'] = state['prompt_lookup_num_tokens']
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for k in ['epsilon_cutoff', 'eta_cutoff']:
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if state[k] > 0:
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generate_params[k] = state[k] * 1e-4
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if state['ban_eos_token']:
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generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id]
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if state['custom_token_bans']:
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to_ban = [int(x) for x in state['custom_token_bans'].split(',')]
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if len(to_ban) > 0:
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if generate_params.get('suppress_tokens', None):
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generate_params['suppress_tokens'] += to_ban
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else:
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generate_params['suppress_tokens'] = to_ban
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generate_params.update({'use_cache': not shared.args.no_cache})
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if shared.args.deepspeed:
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generate_params.update({'synced_gpus': True})
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# Encode the input
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input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
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output = input_ids[0]
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cuda = not any((shared.args.cpu, shared.args.deepspeed))
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if state['auto_max_new_tokens']:
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generate_params['max_new_tokens'] = state['truncation_length'] - input_ids.shape[-1]
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# Add the encoded tokens to generate_params
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question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None)
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original_input_ids = input_ids
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generate_params.update({'inputs': input_ids})
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if inputs_embeds is not None:
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generate_params.update({'inputs_embeds': inputs_embeds})
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# Stopping criteria / eos token
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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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generate_params['eos_token_id'] = eos_token_ids
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generate_params['stopping_criteria'] = transformers.StoppingCriteriaList()
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generate_params['stopping_criteria'].append(_StopEverythingStoppingCriteria())
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# Logits processor
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processor = state.get('logits_processor', LogitsProcessorList([]))
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if not isinstance(processor, LogitsProcessorList):
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processor = LogitsProcessorList([processor])
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# Grammar
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if state['grammar_string'].strip() != '':
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grammar = initialize_grammar(state['grammar_string'])
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grammar_processor = GrammarConstrainedLogitsProcessor(grammar)
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processor.append(grammar_processor)
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apply_extensions('logits_processor', processor, input_ids)
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generate_params['logits_processor'] = processor
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if shared.args.verbose:
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logger.info("GENERATE_PARAMS=")
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filtered_params = {key: value for key, value in generate_params.items() if not isinstance(value, torch.Tensor)}
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pprint.PrettyPrinter(indent=4, sort_dicts=False).pprint(filtered_params)
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print()
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logger.info("PROMPT=")
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print_prompt(decode(input_ids[0], skip_special_tokens=False))
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# Handle StreamingLLM for llamacpp_HF
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if shared.model.__class__.__name__ == 'LlamacppHF' and shared.args.streaming_llm:
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tmp = process_llamacpp_cache(shared.model.model, input_ids[-1].tolist(), shared.model.model._input_ids.tolist())
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shared.model.past_seq = torch.tensor(tmp)
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shared.model.save_cache()
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t0 = time.time()
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try:
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if not is_chat and not shared.is_seq2seq:
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yield ''
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# Generate the entire reply at once.
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if not state['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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starting_from = 0 if shared.is_seq2seq else len(input_ids[0])
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yield get_reply_from_output_ids(output, state, starting_from=starting_from)
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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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else:
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def generate_with_callback(callback=None, *args, **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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with generate_with_streaming(**generate_params) as generator:
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cumulative_reply = ''
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starting_from = 0 if shared.is_seq2seq else len(input_ids[0])
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for output in generator:
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if output[-1] in eos_token_ids:
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break
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new_content = get_reply_from_output_ids(output, state, starting_from=starting_from)
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# check the partial unicode character
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if chr(0xfffd) in new_content:
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continue
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cumulative_reply += new_content
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starting_from = len(output)
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yield cumulative_reply
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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 if not shared.is_seq2seq else 0)
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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}, seed {seed})')
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return
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def generate_reply_custom(question, original_question, seed, state, stopping_strings=None, is_chat=False):
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"""
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For models that do not use the transformers library for sampling
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"""
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seed = set_manual_seed(state['seed'])
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t0 = time.time()
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reply = ''
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try:
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if not is_chat:
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yield ''
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if not state['stream']:
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reply = shared.model.generate(question, state)
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yield reply
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else:
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for reply in shared.model.generate_with_streaming(question, state):
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yield reply
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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(original_question + reply)[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}, seed {seed})')
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return
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def print_prompt(prompt, max_chars=2000):
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DARK_YELLOW = "\033[38;5;3m"
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RESET = "\033[0m"
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if len(prompt) > max_chars:
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half_chars = max_chars // 2
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hidden_len = len(prompt[half_chars:-half_chars])
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hidden_msg = f"{DARK_YELLOW}[...{hidden_len} characters hidden...]{RESET}"
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print(prompt[:half_chars] + hidden_msg + prompt[-half_chars:])
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else:
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print(prompt)
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print()
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