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import os
import sys
import torch
from modelscope_agent.agent_types import AgentType
from swift import Swift
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from modelscope import GenerationConfig, snapshot_download
from .base import LLM
class ModelScopeLLM(LLM):
def __init__(self, cfg):
super().__init__(cfg)
model_id = self.cfg.get('model_id', '')
self.model_id = model_id
model_revision = self.cfg.get('model_revision', None)
cache_dir = self.cfg.get('cache_dir', None)
if not os.path.exists(model_id):
model_dir = snapshot_download(
model_id, model_revision, cache_dir=cache_dir)
else:
model_dir = model_id
self.model_dir = model_dir
sys.path.append(self.model_dir)
self.model_cls = self.cfg.get('model_cls', AutoModelForCausalLM)
self.tokenizer_cls = self.cfg.get('tokenizer_cls', AutoTokenizer)
self.device_map = self.cfg.get('device_map', 'auto')
self.generation_cfg = GenerationConfig(
**self.cfg.get('generate_cfg', {}))
self.use_lora = self.cfg.get('use_lora', False)
self.lora_ckpt_dir = self.cfg.get('lora_ckpt_dir',
None) if self.use_lora else None
self.custom_chat = self.cfg.get('custom_chat', False)
self.end_token = self.cfg.get('end_token', '<|endofthink|>')
self.include_end = self.cfg.get('include_end', True)
self.setup()
self.agent_type = self.cfg.get('agent_type', AgentType.DEFAULT)
def setup(self):
model_cls = self.model_cls
tokenizer_cls = self.tokenizer_cls
self.model = model_cls.from_pretrained(
self.model_dir,
device_map=self.device_map,
# device='cuda:0',
torch_dtype=torch.float16,
trust_remote_code=True)
self.tokenizer = tokenizer_cls.from_pretrained(
self.model_dir, trust_remote_code=True)
self.model = self.model.eval()
if self.use_lora:
self.load_from_lora()
if self.cfg.get('use_raw_generation_config', False):
self.model.generation_config = GenerationConfig.from_pretrained(
self.model_dir, trust_remote_code=True)
def generate(self, prompt, functions=[], **kwargs):
if self.custom_chat and self.model.chat:
response = self.model.chat(
self.tokenizer, prompt, history=[], system='')[0]
else:
response = self.chat(prompt)
end_idx = response.find(self.end_token)
if end_idx != -1:
end_idx += len(self.end_token) if self.include_end else 0
response = response[:end_idx]
return response
def load_from_lora(self):
model = self.model.bfloat16()
# transform to lora
model = Swift.from_pretrained(model, self.lora_ckpt_dir)
self.model = model
def chat(self, prompt):
device = self.model.device
input_ids = self.tokenizer(
prompt, return_tensors='pt').input_ids.to(device)
input_len = input_ids.shape[1]
result = self.model.generate(
input_ids=input_ids, generation_config=self.generation_cfg)
result = result[0].tolist()[input_len:]
response = self.tokenizer.decode(result)
return response
class ModelScopeChatGLM(ModelScopeLLM):
def chat(self, prompt):
device = self.model.device
input_ids = self.tokenizer(
prompt, return_tensors='pt').input_ids.to(device)
input_len = input_ids.shape[1]
eos_token_id = [
self.tokenizer.eos_token_id,
self.tokenizer.get_command('<|user|>'),
self.tokenizer.get_command('<|observation|>')
]
result = self.model.generate(
input_ids=input_ids,
generation_config=self.generation_cfg,
eos_token_id=eos_token_id)
result = result[0].tolist()[input_len:]
response = self.tokenizer.decode(result)
# 遇到生成'<', '|', 'user', '|', '>'的case
response = response.split('<|user|>')[0].split('<|observation|>')[0]
return response
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