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import sys
import json
import fire
import torch
from peft import PeftModel
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
from utils.prompter import Prompter
if torch.cuda.is_available():
device = "cuda"
class Infer():
def __init__(
self,
load_8bit: bool = False,
base_model: str = "",
lora_weights: str = "",
prompt_template: str = "", # The prompt template to use, will default to alpaca.
):
prompter = Prompter(prompt_template)
tokenizer = LlamaTokenizer.from_pretrained(base_model)
model = LlamaForCausalLM.from_pretrained(
base_model,
load_in_8bit=load_8bit,
torch_dtype=torch.float16,
device_map="auto",
)
try:
print(f"Using lora {lora_weights}")
model = PeftModel.from_pretrained(
model,
lora_weights,
torch_dtype=torch.float16,
)
except:
print("*"*50, "\n Attention! No Lora Weights \n", "*"*50)
# unwind broken decapoda-research config
model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk
model.config.bos_token_id = 1
model.config.eos_token_id = 2
if not load_8bit:
model.half() # seems to fix bugs for some users.
model.eval()
if torch.__version__ >= "2" and sys.platform != "win32":
model = torch.compile(model)
self.base_model = base_model
self.lora_weights = lora_weights
self.model = model
self.prompter = prompter
self.tokenizer = tokenizer
def generate_output(
self,
instruction,
input=None,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=1,
max_new_tokens=256,
**kwargs,
):
prompt = self.prompter.generate_prompt(instruction, input)
inputs = self.tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
# repetition_penalty=10.0,
**kwargs,
)
with torch.no_grad():
generation_output = self.model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
)
s = generation_output.sequences[0]
output = self.tokenizer.decode(s)
return self.prompter.get_response(output)
def infer_from_file(self, infer_data_path):
with open(infer_data_path) as f:
for line in f:
data = json.loads(line)
instruction = data["instruction"]
output = data["output"]
print('=' * 100)
print(f"Base Model: {self.base_model} Lora Weights: {self.lora_weights}")
print("Instruction:\n", instruction)
model_output = self.generate_output(instruction)
print("Model Output:\n", model_output)
print("Ground Truth:\n", output)
print('=' * 100)
def main(
load_8bit: bool = False,
base_model: str = "",
lora_weights: str = "",
prompt_template: str = "", # The prompt template to use, will default to alpaca.
infer_data_path: str = "",
):
infer = Infer(
load_8bit=load_8bit,
base_model=base_model,
lora_weights=lora_weights,
prompt_template=prompt_template
)
try:
infer.infer_from_file(infer_data_path)
except Exception as e:
print(e, "Read infer_data_path Failed! Now Interactive Mode: ")
while True:
print('=' * 100)
instruction = input("请输入您的问题: ")
print("LaWGPT:")
print(infer.generate_output(instruction))
print('=' * 100)
if __name__ == "__main__":
fire.Fire(main) |