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import os
import sys
import os.path as osp
import torch
from ..smp import *
def get_gpu_num(model_name):
model_name = model_name.lower()
kws = {
8: ['65b', '70b'],
4: ['30b', '33b', '35b', '40b'],
2: ['13b', '14b', '20b', '8b'],
1: ['6b', '7b', 'moss'],
}
for k in [8, 4, 2, 1]:
for keyword in kws[k]:
if keyword in model_name:
return k
return 8
validated_llms = [
'internlm/internlm-chat-7b', 'internlm/internlm-chat-7b-8k', 'internlm/internlm-chat-20b',
'Qwen/Qwen-7B-Chat', 'Qwen/Qwen-14B-Chat',
'THUDM/chatglm2-6b', 'THUDM/chatglm2-6b-32k', 'THUDM/chatglm3-6b', 'THUDM/chatglm3-6b-32k',
'baichuan-inc/Baichuan2-7B-Chat', 'baichuan-inc/Baichuan2-13B-Chat',
'lmsys/vicuna-7b-v1.5', 'lmsys/vicuna-13b-v1.5',
'meta-llama/Llama-2-7b-chat-hf',
'meta-llama/Llama-3.1-8B-Instruct'
]
Auto_model = ['chatglm']
class HFChatModel:
def _get_context_length(self, model, model_path):
# By default, we use model.config.seq_length
model_path = model_path.lower()
if 'baichuan' in model_path:
context_window = model.config.model_max_length
elif 'internlm' in model_path or 'llama' in model_path:
context_window = model.config.max_position_embeddings
elif 'vicuna' in model_path:
context_window = model.generation_config.max_length
else:
# chatglm & qwen
context_window = model.config.seq_length
return context_window
def _get_context_length_robust(self, model, model_path):
try:
context_window = self._get_context_length(model, model_path)
return context_window
except Exception as err:
self.logger.critical(f'{type(err)}: {err}')
self.logger.critical(
'Failed to extract context_window information from config / generation_config. '
'Please read the above code and check if the logic works for you model path'
)
raise NotImplementedError
def __init__(self,
model_path,
system_prompt: str = None,
**kwargs):
self.logger = get_logger('HFChatModel')
if 'vicuna' in model_path.lower() or 'llama' in model_path.lower():
try:
from fastchat.model import get_conversation_template
except Exception as err:
self.logger.critical('Please install fastchat first to use vicuna. ')
raise err
self.explicit_device = kwargs.pop('device', None)
if self.explicit_device is None:
# If CUDA_VISIBLE_DEVICES is not properly set
if 'CUDA_VISIBLE_DEVICES' not in os.environ or os.environ['CUDA_VISIBLE_DEVICES'] == '0,1,2,3,4,5,6,7':
num_gpu = get_gpu_num(model_path)
gpu_offset = kwargs.pop('gpu_offset', 0)
cuda_visible_devices = ','.join([str(i) for i in range(gpu_offset, gpu_offset + num_gpu)])
os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
from transformers.generation import GenerationConfig
if model_path not in validated_llms:
self.logger.warning(f'{model_path} not in validated LLMs, may have inference troubles. ')
self.model_path = model_path
if listinstr(Auto_model, model_path):
LoadModel = AutoModel
else:
LoadModel = AutoModelForCausalLM
assert osp.exists(model_path) or len(model_path.split('/')) == 2
device = self.explicit_device if self.explicit_device else 'auto'
precision = {}
if 'internlm-chat-7b' in model_path:
precision = {'torch_dtype': torch.float16}
elif 'internlm-chat-20b' in model_path:
precision = {'torch_dtype': torch.bfloat16}
self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
cuda_devices = os.environ.get('CUDA_VISIBLE_DEVICES', '0')
if ',' in cuda_devices:
device_ids = [int(x) for x in cuda_devices.split(',')]
device_map = {i: i for i in range(len(device_ids))}
else:
device_map = {'': 0}
if 'llama' in self.model_path.lower():
from lmdeploy import pipeline, GenerationConfig, TurbomindEngineConfig
print(f"Loading model {model_path} with {num_gpu} GPUs")
backend_config = TurbomindEngineConfig(tp=num_gpu)
self.gen_config = GenerationConfig(max_new_tokens=256)
model = pipeline(model_path,
backend_config=backend_config)
else:
model = LoadModel.from_pretrained(model_path, trust_remote_code=True, device_map='cpu', **precision)
model = model.eval()
if device != 'cpu':
model = model.to(f'cuda:{device}' if isinstance(device, int) else 'cuda')
try:
model.generation_config = GenerationConfig.from_pretrained(
model_path, trust_remote_code=True, device_map=device)
except Exception as err:
self.logger.warning(f'{type(err)}: {err}')
self.context_length = self._get_context_length_robust(model=model, model_path=model_path)
torch.cuda.empty_cache()
self.model = model
self.answer_buffer = 192
self.system_prompt = system_prompt
for k, v in kwargs.items():
self.logger.info(f'Following args will be used for generation (If not set specifically), {k}: {v}. ')
self.kwargs = kwargs
def generate_str(self, input, **kwargs):
if 'baichuan' in self.model_path.lower():
messages = []
messages.append({'role': 'user', 'content': input})
resp = self.model.chat(self.tokenizer, messages, **kwargs)
elif 'vicuna' in self.model_path.lower():
from fastchat.model import get_conversation_template
conv = get_conversation_template('vicuna')
conv.append_message(conv.roles[0], input)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
inputs = self.tokenizer([prompt], return_tensors='pt')
if torch.cuda.is_available():
for k in inputs:
inputs[k] = inputs[k].cuda()
params = dict(do_sample=True, temperature=0.7, repetition_penalty=1.0, max_new_tokens=512)
params.update(self.kwargs)
params.update(kwargs)
outputs = self.model.generate(**inputs, **params)
resp = self.tokenizer.decode(
outputs[0][len(inputs['input_ids'][0]):],
skip_special_tokens=True,
spaces_between_special_tokens=False)
elif 'llama' in self.model_path.lower():
prompt = [{'role': 'system', 'content': self.system_prompt}, {'role': 'user', 'content': input}]
resp = self.model(prompt, gen_config=self.gen_config).text
else:
params = self.kwargs
params.update(kwargs)
resp, _ = self.model.chat(self.tokenizer, input, history=[], **params)
return resp
def length_ok(self, inputs):
tot = len(self.tokenizer.encode(self.system_prompt)) if self.system_prompt is not None else 0
for s in inputs:
tot += len(self.tokenizer.encode(s))
return tot + self.answer_buffer < self.context_length
def generate_list(self, full_inputs, offset=0, **kwargs):
assert isinstance(full_inputs, list)
inputs = full_inputs[offset:]
if not self.length_ok(inputs):
return self.chat(full_inputs, offset + 1)
model_path = self.model_path.lower()
if sum([x in model_path for x in ['baichuan']]):
input_msgs = []
if self.system_prompt is not None:
input_msgs.append(dict(role='user', content=self.system_prompt))
if len(inputs):
assert isinstance(inputs, list) and isinstance(inputs[0], str)
roles = ['user', 'assistant'] if len(inputs) % 2 == 1 else ['assistant', 'user']
roles = roles * len(inputs)
for role, msg in zip(roles, inputs):
input_msgs.append(dict(role=role, content=msg))
response = self.model.chat(self.tokenizer, input_msgs)
elif sum([x in model_path for x in ['vicuna']]):
from fastchat.model import get_conversation_template
conv = get_conversation_template('vicuna')
assert isinstance(inputs, list) and isinstance(inputs[0], str)
if len(inputs) % 2 == 1:
if self.system_prompt is not None:
conv.append_message(conv.roles[0], self.system_prompt)
for i in range(len(inputs) // 2):
conv.append_message(conv.roles[0], inputs[2 * i])
conv.append_message(conv.roles[1], inputs[2 * i + 1])
else:
assert self.system_prompt is not None
conv.append_message(conv.roles[0], self.system_prompt)
conv.append_message(conv.roles[1], inputs[0])
for i in range(len(inputs) // 2 - 1):
conv.append_message(conv.roles[0], inputs[2 * i + 1])
conv.append_message(conv.roles[1], inputs[2 * i + 2])
conv.append_message(conv.roles[0], inputs[-1])
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
inputs = self.tokenizer([prompt], return_tensors='pt')
if torch.cuda.is_available():
for k in inputs:
inputs[k] = inputs[k].cuda()
params = dict(do_sample=True, temperature=0.7, repetition_penalty=1.0, max_new_tokens=512)
params.update(self.kwargs)
params.update(kwargs)
outputs = self.model.generate(**inputs, **params)
response = self.tokenizer.decode(
outputs[0][len(inputs['input_ids'][0]):],
skip_special_tokens=True,
spaces_between_special_tokens=False)
response = response.lstrip('\n')
else:
# The default option, support internlm, chatglm, qwen
history, msg = [], None
if len(inputs) % 2 == 1:
if self.system_prompt is not None:
history = [(self.system_prompt, '')]
for i in range(len(inputs) // 2):
history.append((inputs[2 * i], inputs[2 * i + 1]))
else:
assert self.system_prompt is not None
history = [(self.system_prompt, inputs[0])]
for i in range(len(inputs) // 2 - 1):
history.append((inputs[2 * i + 1], inputs[2 * i + 2]))
msg = inputs[-1]
params = self.kwargs
params.update(kwargs)
response, _ = self.model.chat(self.tokenizer, msg, history=history, **params)
return response, offset
def generate(self, inputs, **kwargs):
if isinstance(inputs, str):
return self.generate_str(inputs, **kwargs)
elif isinstance(inputs, list):
return self.generate_list(inputs, **kwargs)
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