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inference_hf.py
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inference_hf.py
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import argparse
import json, os
parser = argparse.ArgumentParser()
parser.add_argument('--base_model', default=None, type=str, required=True)
parser.add_argument('--lora_model', default=None, type=str,help="If None, perform inference on the base model")
parser.add_argument('--tokenizer_path',default=None,type=str)
parser.add_argument('--data_file',default=None, type=str,help="A file that contains instructions (one instruction per line)")
parser.add_argument('--with_prompt',action='store_true',help="wrap the input with the prompt automatically")
parser.add_argument('--interactive',action='store_true',help="run in the instruction mode (single-turn)")
parser.add_argument('--predictions_file', default='./predictions.json', type=str)
parser.add_argument('--gpus', default="0", type=str)
parser.add_argument('--only_cpu',action='store_true',help='only use CPU for inference')
parser.add_argument('--alpha',type=str,default="1.0", help="The scaling factor of NTK method, can be a float or 'auto'. ")
parser.add_argument('--load_in_8bit',action='store_true', help="Load the LLM in the 8bit mode")
args = parser.parse_args()
if args.only_cpu is True:
args.gpus = ""
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
from peft import PeftModel
from patches import apply_attention_patch, apply_ntk_scaling_patch
apply_attention_patch(use_memory_efficient_attention=True)
apply_ntk_scaling_patch(args.alpha)
generation_config = dict(
temperature=0.2,
top_k=40,
top_p=0.9,
do_sample=True,
num_beams=1,
repetition_penalty=1.1,
max_new_tokens=400
)
# The prompt template below is taken from llama.cpp
# and is slightly different from the one used in training.
# But we find it gives better results
prompt_input = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n\n{instruction}\n\n### Response:\n\n"
)
sample_data = ["为什么要减少污染，保护环境？"]
def generate_prompt(instruction, input=None):
if input:
instruction = instruction + '\n' + input
return prompt_input.format_map({'instruction': instruction})
if __name__ == '__main__':
load_type = torch.float16
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
if args.tokenizer_path is None:
args.tokenizer_path = args.lora_model
if args.lora_model is None:
args.tokenizer_path = args.base_model
tokenizer = LlamaTokenizer.from_pretrained(args.tokenizer_path)
base_model = LlamaForCausalLM.from_pretrained(
args.base_model,
load_in_8bit=args.load_in_8bit,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
)
model_vocab_size = base_model.get_input_embeddings().weight.size(0)
tokenzier_vocab_size = len(tokenizer)
print(f"Vocab of the base model: {model_vocab_size}")
print(f"Vocab of the tokenizer: {tokenzier_vocab_size}")
if model_vocab_size!=tokenzier_vocab_size:
assert tokenzier_vocab_size > model_vocab_size
print("Resize model embeddings to fit tokenizer")
base_model.resize_token_embeddings(tokenzier_vocab_size)
if args.lora_model is not None:
print("loading peft model")
model = PeftModel.from_pretrained(base_model, args.lora_model,torch_dtype=load_type,device_map='auto',)
else:
model = base_model
if device==torch.device('cpu'):
model.float()
# test data
if args.data_file is None:
examples = sample_data
else:
with open(args.data_file,'r') as f:
examples = [l.strip() for l in f.readlines()]
print("first 10 examples:")
for example in examples[:10]:
print(example)
model.eval()
with torch.no_grad():
if args.interactive:
print("Start inference with instruction mode.")
print('='*85)
print("+ 该模式下仅支持单轮问答，无多轮对话能力。\n"
"+ 如要进行多轮对话，请使用llama.cpp或llamachat工具。")
print('-'*85)
print("+ This mode only supports single-turn QA.\n"
"+ If you want to experience multi-turn dialogue, please use llama.cpp or llamachat.")
print('='*85)
while True:
raw_input_text = input("Input:")
if len(raw_input_text.strip())==0:
break
if args.with_prompt:
input_text = generate_prompt(instruction=raw_input_text)
else:
input_text = raw_input_text
inputs = tokenizer(input_text,return_tensors="pt") #add_special_tokens=False ?
generation_output = model.generate(
input_ids = inputs["input_ids"].to(device),
attention_mask = inputs['attention_mask'].to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
**generation_config
)
s = generation_output[0]
output = tokenizer.decode(s,skip_special_tokens=True)
if args.with_prompt:
response = output.split("### Response:")[1].strip()
else:
response = output
print("Response: ",response)
print("\n")
else:
print("Start inference.")
results = []
for index, example in enumerate(examples):
if args.with_prompt is True:
input_text = generate_prompt(instruction=example)
else:
input_text = example
inputs = tokenizer(input_text,return_tensors="pt") #add_special_tokens=False ?
generation_output = model.generate(
input_ids = inputs["input_ids"].to(device),
attention_mask = inputs['attention_mask'].to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
**generation_config
)
s = generation_output[0]
output = tokenizer.decode(s,skip_special_tokens=True)
if args.with_prompt:
response = output.split("### Response:")[1].strip()
else:
response = output
print(f"======={index}=======")
print(f"Input: {example}\n")
print(f"Output: {response}\n")
results.append({"Input":input_text,"Output":response})
dirname = os.path.dirname(args.predictions_file)
os.makedirs(dirname,exist_ok=True)
with open(args.predictions_file,'w') as f:
json.dump(results,f,ensure_ascii=False,indent=2)
with open(dirname+'/generation_config.json','w') as f:
json.dump(generation_config,f,ensure_ascii=False,indent=2)