请问训练完毕，如何进行generation about loramoe HOT 3 OPEN

hi, I have modified the model.generate() function for loramoe, and it seems it is working.
You can check the code here : https://github.com/mok0102/LoRAMoE.git -> Try run_test.sh
Still the double check is needed for my modification. Please anyone check my modification and tell me if there is any error.

from loramoe.

@mok0102 @fzp0424 Thank you for your modified code. Because we provide the default value (i.e., None) for the task_type, it can be inferred without modifying. Additionally, we use opencompass to evaluate the fine-tuned models, and we've included profile details in the README.

from loramoe.

我看repo并没有修改generate函数，是在评测时不需要task_type吗？这样会导致报错吧 希望能有eval的代码

os.environ["CUDA_VISIBLE_DEVICES"] = "2"
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM, LlamaTokenizer

output_dir = "./output/0308_debug_format_for_opensource/checkpoint-561"
peft_model_path = os.path.join(output_dir, "sft_lora_model")  # change checkpoint path

peftconfig = PeftConfig.from_pretrained(peft_model_path)

model_base = LlamaForCausalLM.from_pretrained(peftconfig.base_model_name_or_path,
                                             device_map = "auto",
                                            )

tokenizer = LlamaTokenizer.from_pretrained(peftconfig.base_model_name_or_path,
                                          add_bos_token = True,
                                          add_eos_token = False  # always False for inference
                                          )

new_model = PeftModel.from_pretrained(model_base, peft_model_path)

print("Peft model loaded")

import torch
def generate_response(prompt, model):
  encoded_input = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
  task_types=0
  task_types = torch.tensor(data = task_types)
  encoded_input["task_types"]=task_types
  model_inputs = encoded_input.to('cuda')
  print(model_inputs)
  generated_ids = model.generate(**model_inputs,
                                 max_new_tokens=20,
                                 min_new_tokens=1,
                                 do_sample=False,
                                 pad_token_id=tokenizer.eos_token_id)

  decoded_output = tokenizer.batch_decode(generated_ids)

  return decoded_output[0].replace(prompt, "")

prompt = """Spanish: Período de validez después de abierto el envase: 10 horas.
English:"""

generate_response(prompt, new_model)

Error info:

{'input_ids': tensor([[    1, 10432, 29901,  2431, 29983,  8144,   316,   659,   680, 29920,
         11006,   316,   633, 25449,   560,  8829,   559, 29901, 29871, 29896,
         29900,  4029,   294, 29889,    13, 24636, 29901]], device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1]], device='cuda:0'), 'task_types': tensor(0, device='cuda:0')}
Output exceeds the [size limit](command:workbench.action.openSettings?[). Open the full output data [in a text editor](command:workbench.action.openLargeOutput?4edebf64-8b8d-479e-88d4-531631ec5757)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[4], line 22
     17   return decoded_output[0].replace(prompt, "")
     19 prompt = """Spanish: Período de validez después de abierto el envase: 10 horas.
     20 English:"""
---> 22 generate_response(prompt, new_model)

Cell In[4], line 9, in generate_response(prompt, model)
      7 model_inputs = encoded_input.to('cuda')
      8 print(model_inputs)
----> 9 generated_ids = model.generate(**model_inputs,
     10                                max_new_tokens=20,
     11                                min_new_tokens=1,
     12                                do_sample=False,
     13                                pad_token_id=tokenizer.eos_token_id)
     15 decoded_output = tokenizer.batch_decode(generated_ids)
     17 return decoded_output[0].replace(prompt, "")

File ~/LoRAMoE/peft/peft_model.py:587, in PeftModelForCausalLM.generate(self, **kwargs)
    585 self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
    586 try:
--> 587     outputs = self.base_model.generate(**kwargs)
    588 except:
    589     self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
...
-> 2349 next_token_logits = outputs.logits[:, -1, :]
   2351 # pre-process distribution
   2352 next_tokens_scores = logits_processor(input_ids, next_token_logits)

AttributeError: 'str' object has no attribute 'logits'

同学，请问你能顺利generate了嘛，想请教一下~谢谢

from loramoe.