Create handler.py

handler.py

@@ -0,0 +1,47 @@
+from typing import Any, Dict
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model and processor from path
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        # try:
+        # config = AutoConfig.from_pretrained(path)
+        model = AutoModelForCausalLM.from_pretrained(
+            path,
+            # return_dict=True,
+            # load_in_8bit=True,
+            device_map="auto",
+            torch_dtype=torch.float16,
+            trust_remote_code=True,
+        )
+        # model.resize_token_embeddings(len(self.tokenizer))
+        # model = PeftModel.from_pretrained(model, path)
+        # except Exception:
+        #     model = AutoModelForCausalLM.from_pretrained(
+        #         path, device_map="auto", load_in_8bit=True, torch_dtype=torch.float16, trust_remote_code=True
+        #     )
+        self.model = model
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        # process input
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+
+        # preprocess
+        inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device)
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            outputs = self.model.generate(**inputs, **parameters)
+        else:
+            outputs = self.model.generate(**inputs)
+
+        # postprocess the prediction
+        prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        return [{"generated_text": prediction}]