[MNT] Fix infer scripts

infer.py

@@ -12,43 +12,53 @@
     device = "cuda"
 
 
-def main(
-    load_8bit: bool = False,
-    base_model: str = "",
-    lora_weights: str = "",
-    infer_data_path: 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,
+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",
         )
-    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.
+
+        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()
+        model.eval()
 
-    if torch.__version__ >= "2" and sys.platform != "win32":
-        model = torch.compile(model)
+        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 evaluate(
+    def generate_output(
+        self,
         instruction,
         input=None,
         temperature=0.1,
@@ -58,8 +68,8 @@ def evaluate(
         max_new_tokens=256,
         **kwargs,
     ):
-        prompt = prompter.generate_prompt(instruction, input)
-        inputs = tokenizer(prompt, return_tensors="pt")
+        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,
@@ -70,42 +80,57 @@ def evaluate(
             **kwargs,
         )
         with torch.no_grad():
-            generation_output = model.generate(
+            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 = tokenizer.decode(s)
-        return prompter.get_response(output)
+        output = self.tokenizer.decode(s)
+        return self.prompter.get_response(output)
 
-    def infer_from_file():
+    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: {base_model}    Lora Weights: {lora_weights}")
+                print(f"Base Model: {self.base_model}    Lora Weights: {self.lora_weights}")
                 print("Instruction:\n", instruction)
-                model_output = evaluate(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_from_file()
-    except:
-        print("Read infer_data_path Failed! Now Interactive Mode: ")
+        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(evaluate(instruction))
+            print(infer.generate_output(instruction))
             print('=' * 100)
 
 
 if __name__ == "__main__":
-    fire.Fire(main)
+    fire.Fire(main)

scripts/infer.sh

@@ -3,5 +3,5 @@ python infer.py \
     --load_8bit True \
     --base_model 'minlik/chinese-llama-7b-merged' \
     --lora_weights 'entity303/lawgpt-lora-7b' \
-    --infer_data_path './resources/example_infer_data.json' \
-    --prompt_template 'law_template'
+    --prompt_template 'law_template' \
+    --infer_data_path './resources/example_infer_data.json'