Upload handler.py

handler.py

@@ -0,0 +1,70 @@
+from typing import Dict, Any, List
+import logging
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftConfig, PeftModel
+import torch.cuda
+
+dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0]==8 else torch.float16
+
+# LOGGER = logging.getLogger(__name__)
+# logging.basicConfig(level=logging.INFO)
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
+        model = AutoModelForCausalLM.from_pretrained(
+            path,
+            return_dict=True,
+            device_map="auto",
+            load_in_8bit=True,
+            torch_dtype=dtype,
+            trust_remote_code=True,
+        )
+
+        generation_config = model.generation_config
+        generation_config.max_new_tokens=512
+        generation_config.temperation = 0
+        generation_config.num_return_sequences=1
+        generation_config.pad_token_id = tokenizer.eos_token_id
+        generation_config.eos_token_id = tokenizer.eos_token_id
+        self.generation_config = generation_config
+
+        self.pipeline = transformers.pipeline(
+            "text-generation",model=model,tokenizer=tokenizer
+        )
+
+
+        # config = PeftConfig.from_pretrained(path)
+        # model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map='auto')
+        # self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+        # # Load the Lora model
+        # self.model = PeftModel.from_pretrained(model, path)
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        # """
+        # Args:
+        #     data (Dict): The payload with the text prompt and generation parameters.
+        # """
+        # LOGGER.info(f"Received data: {data}")
+        # Get inputs
+        prompt = data.pop("inputs", None)
+        # parameters = data.pop("parameters", None)
+        # if prompt is None:
+        #     raise ValueError("Missing prompt.")
+        # # Preprocess
+        # input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+        # # Forward
+        # LOGGER.info(f"Start generation.")
+        # if parameters is not None:
+        #     output = self.model.generate(input_ids=input_ids, **parameters)
+        # else:
+        #     output = self.model.generate(input_ids=input_ids)
+        # # Postprocess
+        # prediction = self.tokenizer.decode(output[0])
+        # LOGGER.info(f"Generated text: {prediction}")
+        # return {"generated_text": prediction}
+        result = self.pipeline(prompt,generation_config=self.generation_config)
+        return result