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import torch
from transformers import PreTrainedModel, AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification, TextClassificationPipeline
from configuration_kraken_lora import KrakenConfig
import tokenizer_template_switch
from peft import PeftModel, PeftConfig # Import necessary modules for LoRA
class KrakenForCausalLM(PreTrainedModel):
config_class = KrakenConfig
def __init__(self, config):
super().__init__(config)
self.tokenizers = {key: AutoTokenizer.from_pretrained(name, device_map="auto") for key, name in config.config_dict['tokenizers'].items()}
self.model = self.load_base_model(config.config_dict['models']['base'], config.config_dict['quantization']['base']) # Load only expert1 as the base model
self.lora_adapters = config.config_dict['lora_adapters'] # Load LoRA adapter paths
self.router_model = AutoModelForSequenceClassification.from_pretrained(config.config_dict['router'], trust_remote_code=True, device_map="auto")
self.tokenizer = AutoTokenizer.from_pretrained(config.config_dict['router'], trust_remote_code=True, device_map="auto")
self.router = TextClassificationPipeline(model=self.router_model, tokenizer=self.tokenizer)
self.models_indices = config.config_dict['class_indices']
def load_base_model(self, model_name, quantization):
if quantization == "8bit":
return AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="auto", load_in_8bit=True, torch_dtype="auto")
elif quantization == "4bit":
return AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="auto", load_in_4bit=True, torch_dtype="auto")
elif quantization == "awq":
return self.load_awq_model(model_name)
else:
return AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="auto", torch_dtype="auto")
def load_awq_model(self, name):
return AutoModelForCausalLM.from_pretrained(name, trust_remote_code=True, device_map="auto")
def load_lora_adapter(self, base_model, adapter_path):
print("Loading adapter: "+adapter_path)
return PeftModel.from_pretrained(base_model, adapter_path)
def tokenize_inputs(self, text, adapter_key):
return self.tokenizers[adapter_key](text, return_tensors="pt")
def determine_adapter(self, text):
prediction = self.router(text)[0]["label"]
model_decision_index = self.models_indices[prediction]
adapter_keys = ['lora_expert1', 'lora_expert2', 'lora_expert3', 'lora_expert4', 'lora_expert5']
return adapter_keys[model_decision_index]
def generate(self, input_ids, **generate_kwargs):
# Tokenize the input_ids
text = self.tokenizer.batch_decode(input_ids, skip_special_tokens=False)[0]
msgs = tokenizer_template_switch.recover_chat_messages(text, self.tokenizer)
if msgs and msgs[0]['role'] == 'system' and msgs[0]['content']=='<|im_start|>system':
# Delete the first element
msgs.pop(0)
# Check if the last element has the role 'assistant'
if msgs and msgs[-1]['role'] == 'assistant':
# Delete the last element
msgs.pop()
# Determine the appropriate LoRA adapter
adapter_key = self.determine_adapter(text)
print(f"Choosing LoRA adapter for {adapter_key} ..")
# Load and apply the LoRA adapter to the base model (expert1)
lora_adapter_path = self.lora_adapters[adapter_key]
model_with_lora = self.load_lora_adapter(self.model, lora_adapter_path)
# Use the tokenizer for the selected expert to tokenize the inputs
mod_txt = self.tokenizers[adapter_key].apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
current_device = input_ids.device if isinstance(input_ids, torch.Tensor) else 'cpu'
# Tokenize accordingly to the best model
tok = self.tokenizers[adapter_key](mod_txt, return_tensors="pt")
tok_input_ids = tok.input_ids.to(current_device)
tok_attention_mask = tok.attention_mask.to(current_device)
# Generate text using the modified model
output_ids = model_with_lora.generate(tok_input_ids, attention_mask=tok_attention_mask, **generate_kwargs)
# Decode the output using the expert tokenizer
decoded_text = self.tokenizers[adapter_key].decode(output_ids[0], skip_special_tokens=True)
# Retokenize the decoded text using the base tokenizer for external compatibility
retokenized_ids = self.tokenizer(decoded_text, return_tensors="pt").input_ids.to(current_device)
return retokenized_ids
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