model_name = "InternLM"
cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`"
from transformers import AutoModel, AutoTokenizer
import time
import threading
import importlib
from toolbox import update_ui, get_conf
from multiprocessing import Process, Pipe
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM
# ------------------------------------------------------------------------------------------------------------------------
# ππ» Local Model Utils
# ------------------------------------------------------------------------------------------------------------------------
def try_to_import_special_deps():
import sentencepiece
def combine_history(prompt, hist):
user_prompt = "<|User|>:{user}<eoh>\n"
robot_prompt = "<|Bot|>:{robot}<eoa>\n"
cur_query_prompt = "<|User|>:{user}<eoh>\n<|Bot|>:"
messages = hist
total_prompt = ""
for message in messages:
cur_content = message
cur_prompt = user_prompt.replace("{user}", cur_content[0])
total_prompt += cur_prompt
cur_prompt = robot_prompt.replace("{robot}", cur_content[1])
total_prompt += cur_prompt
total_prompt = total_prompt + cur_query_prompt.replace("{user}", prompt)
return total_prompt
# ------------------------------------------------------------------------------------------------------------------------
# ππ» Local Model
# ------------------------------------------------------------------------------------------------------------------------
@SingletonLocalLLM
class GetInternlmHandle(LocalLLMHandle):
def load_model_info(self):
# πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘
self.model_name = model_name
self.cmd_to_install = cmd_to_install
def try_to_import_special_deps(self, **kwargs):
"""
import something that will raise error if the user does not install requirement_*.txt
"""
import sentencepiece
def load_model_and_tokenizer(self):
# πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device, = get_conf('LOCAL_MODEL_DEVICE')
if self._model is None:
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
if device=='cpu':
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16)
else:
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16).cuda()
model = model.eval()
return model, tokenizer
def llm_stream_generator(self, **kwargs):
import torch
import logging
import copy
import warnings
import torch.nn as nn
from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
# πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘
def adaptor():
model = self._model
tokenizer = self._tokenizer
prompt = kwargs['query']
max_length = kwargs['max_length']
top_p = kwargs['top_p']
temperature = kwargs['temperature']
history = kwargs['history']
real_prompt = combine_history(prompt, history)
return model, tokenizer, real_prompt, max_length, top_p, temperature
model, tokenizer, prompt, max_length, top_p, temperature = adaptor()
prefix_allowed_tokens_fn = None
logits_processor = None
stopping_criteria = None
additional_eos_token_id = 103028
generation_config = None
# πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘
# πββοΈπββοΈπββοΈ https://github.com/InternLM/InternLM/blob/efbf5335709a8c8faeac6eaf07193973ff1d56a1/web_demo.py#L25
inputs = tokenizer([prompt], padding=True, return_tensors="pt")
input_length = len(inputs["input_ids"][0])
for k, v in inputs.items():
inputs[k] = v.cuda()
input_ids = inputs["input_ids"]
batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
if generation_config is None:
generation_config = model.generation_config
generation_config = copy.deepcopy(generation_config)
model_kwargs = generation_config.update(**kwargs)
bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
if additional_eos_token_id is not None:
eos_token_id.append(additional_eos_token_id)
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
if has_default_max_length and generation_config.max_new_tokens is None:
warnings.warn(
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
" recommend using `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif generation_config.max_new_tokens is not None:
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
if not has_default_max_length:
logging.warn(
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
"Please refer to the documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
UserWarning,
)
if input_ids_seq_length >= generation_config.max_length:
input_ids_string = "input_ids"
logging.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
" increasing `max_new_tokens`."
)
# 2. Set generation parameters if not already defined
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
logits_processor = model._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_seq_length,
encoder_input_ids=input_ids,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
logits_processor=logits_processor,
)
stopping_criteria = model._get_stopping_criteria(
generation_config=generation_config, stopping_criteria=stopping_criteria
)
logits_warper = model._get_logits_warper(generation_config)
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
scores = None
while True:
model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)
# forward pass to get next token
outputs = model(
**model_inputs,
return_dict=True,
output_attentions=False,
output_hidden_states=False,
)
next_token_logits = outputs.logits[:, -1, :]
# pre-process distribution
next_token_scores = logits_processor(input_ids, next_token_logits)
next_token_scores = logits_warper(input_ids, next_token_scores)
# sample
probs = nn.functional.softmax(next_token_scores, dim=-1)
if generation_config.do_sample:
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
else:
next_tokens = torch.argmax(probs, dim=-1)
# update generated ids, model inputs, and length for next step
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
model_kwargs = model._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=False
)
unfinished_sequences = unfinished_sequences.mul((min(next_tokens != i for i in eos_token_id)).long())
output_token_ids = input_ids[0].cpu().tolist()
output_token_ids = output_token_ids[input_length:]
for each_eos_token_id in eos_token_id:
if output_token_ids[-1] == each_eos_token_id:
output_token_ids = output_token_ids[:-1]
response = tokenizer.decode(output_token_ids)
yield response
# stop when each sentence is finished, or if we exceed the maximum length
if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
return
# ------------------------------------------------------------------------------------------------------------------------
# ππ» GPT-Academic Interface
# ------------------------------------------------------------------------------------------------------------------------
predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetInternlmHandle, model_name)