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chatllm.py
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chatllm.py
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import os
from typing import Dict, List, Optional, Tuple, Union
import torch
from fastchat.conversation import (compute_skip_echo_len,
get_default_conv_template)
from fastchat.serve.inference import load_model as load_fastchat_model
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
from config import *
os.environ["TOKENIZERS_PARALLELISM"] = "false"
DEVICE = LLM_DEVICE
DEVICE_ID = "0"
CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE
init_llm = init_llm
init_embedding_model = init_embedding_model
def torch_gc():
if torch.cuda.is_available():
with torch.cuda.device(CUDA_DEVICE):
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
num_trans_layers = 28
per_gpu_layers = 30 / num_gpus
device_map = {
'transformer.word_embeddings': 0,
'transformer.final_layernorm': 0,
'lm_head': 0
}
used = 2
gpu_target = 0
for i in range(num_trans_layers):
if used >= per_gpu_layers:
gpu_target += 1
used = 0
assert gpu_target < num_gpus
device_map[f'transformer.layers.{i}'] = gpu_target
used += 1
return device_map
class ChatLLM(LLM):
max_token: int = 10000
temperature: float = 0.1
top_p = 0.9
history = []
model_type: str = "chatglm"
model_name_or_path: str = init_llm,
tokenizer: object = None
model: object = None
def __init__(self):
super().__init__()
@property
def _llm_type(self) -> str:
return "ChatLLM"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
if self.model_type == 'vicuna':
conv = get_default_conv_template(self.model_name_or_path).copy()
conv.append_message(conv.roles[0], prompt)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
inputs = self.tokenizer([prompt])
output_ids = self.model.generate(
torch.as_tensor(inputs.input_ids).cuda(),
do_sample=True,
temperature=self.temperature,
max_new_tokens=self.max_token,
)
outputs = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
skip_echo_len = compute_skip_echo_len(self.model_name_or_path, conv, prompt)
response = outputs[skip_echo_len:]
torch_gc()
if stop is not None:
response = enforce_stop_tokens(response, stop)
self.history = [[None, response]]
elif self.model_type == 'belle':
prompt = "Human: "+ prompt +" \n\nAssistant: "
input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(DEVICE)
generate_ids = self.model.generate(input_ids, max_new_tokens=self.max_token, do_sample = True, top_k = 30, top_p = self.top_p, temperature = self.temperature, repetition_penalty=1., eos_token_id=2, bos_token_id=1, pad_token_id=0)
output = self.tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
response = output[len(prompt)+1:]
torch_gc()
if stop is not None:
response = enforce_stop_tokens(response, stop)
self.history = [[None, response]]
elif self.model_type == 'chatglm2':
response, _ = self.model.chat(
self.tokenizer,
prompt,
history=self.history,
max_length=self.max_token,
temperature=self.temperature,
top_p = self.top_p,
)
torch_gc()
if stop is not None:
response = enforce_stop_tokens(response, stop)
self.history = self.history + [[None, response]]
elif self.model_type == 'chatglm':
response, _ = self.model.chat(
self.tokenizer,
prompt,
history=self.history,
max_length=self.max_token,
temperature=self.temperature,
)
torch_gc()
if stop is not None:
response = enforce_stop_tokens(response, stop)
self.history = self.history + [[None, response]]
elif self.model_type == 'internlm':
response, _ = self.model.chat(self.tokenizer, prompt, history=self.history, max_length=self.max_token, temperature=self.temperature)
elif self.model_type == 'yuan2':
inputs = self.tokenizer(prompt, return_tensors="pt").to(DEVICE)
outputs = self.model.generate(inputs.input_ids, max_length=self.max_token, do_sample=True,
temperature=self.temperature)
output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
response = output[len(prompt)+1:]
torch_gc()
if stop is not None:
response = enforce_stop_tokens(response, stop)
self.history = self.history + [[None, response]]
return response
def load_llm(self,
llm_device=DEVICE,
num_gpus='auto',
device_map: Optional[Dict[str, int]] = None,
**kwargs):
if 'chatglm2' in self.model_name_or_path.lower():
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path,
trust_remote_code=True, cache_dir=os.path.join(MODEL_CACHE_PATH, self.model_name_or_path))
if torch.cuda.is_available() and llm_device.lower().startswith("cuda"):
num_gpus = torch.cuda.device_count()
if num_gpus < 2 and device_map is None:
self.model = (AutoModel.from_pretrained(
self.model_name_or_path, trust_remote_code=True, cache_dir=os.path.join(MODEL_CACHE_PATH, self.model_name_or_path),
**kwargs).half().cuda())
else:
from accelerate import dispatch_model
model = AutoModel.from_pretrained(self.model_name_or_path,
trust_remote_code=True, cache_dir=os.path.join(MODEL_CACHE_PATH, self.model_name_or_path),
**kwargs).half()
if device_map is None:
device_map = auto_configure_device_map(num_gpus)
self.model = dispatch_model(model, device_map=device_map)
else:
self.model = (AutoModel.from_pretrained(
self.model_name_or_path,
trust_remote_code=True, cache_dir=os.path.join(MODEL_CACHE_PATH, self.model_name_or_path)).float().to(llm_device))
self.model = self.model.eval()
elif 'chatglm' in self.model_name_or_path.lower():
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path,
trust_remote_code=True, cache_dir=os.path.join(MODEL_CACHE_PATH, self.model_name_or_path))
if torch.cuda.is_available() and llm_device.lower().startswith("cuda"):
num_gpus = torch.cuda.device_count()
if num_gpus < 2 and device_map is None:
self.model = (AutoModel.from_pretrained(
self.model_name_or_path, trust_remote_code=True, cache_dir=os.path.join(MODEL_CACHE_PATH, self.model_name_or_path),
**kwargs).half().cuda())
else:
from accelerate import dispatch_model
model = AutoModel.from_pretrained(self.model_name_or_path,
trust_remote_code=True, cache_dir=os.path.join(MODEL_CACHE_PATH, self.model_name_or_path),
**kwargs).half()
if device_map is None:
device_map = auto_configure_device_map(num_gpus)
self.model = dispatch_model(model, device_map=device_map)
else:
self.model = (AutoModel.from_pretrained(
self.model_name_or_path,
trust_remote_code=True, cache_dir=os.path.join(MODEL_CACHE_PATH, self.model_name_or_path)).float().to(llm_device))
self.model = self.model.eval()
elif 'internlm' in self.model_name_or_path.lower():
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, trust_remote_code=True)
self.model = AutoModelForCausalLM.from_pretrained(self.model_name_or_path, trust_remote_code=True).cuda()
self.model = self.model.eval()
elif 'yuan2' in self.model_name_or_path.lower():
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, trust_remote_code=True)
self.model = AutoModelForCausalLM.from_pretrained(self.model_name_or_path, device_map='auto',
torch_dtype=torch.bfloat16, trust_remote_code=True)
self.model = self.model.eval()
else:
self.model, self.tokenizer = load_fastchat_model(
model_path = self.model_name_or_path,
device = llm_device,
num_gpus = num_gpus
)