customer handler

handler.py

@@ -0,0 +1,35 @@
+from typing import Dict, List, Any
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import torch
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        tokenizer = AutoTokenizer.from_pretrained(path)
+        model = AutoModelForMaskedLM.from_pretrained(path)
+        self.tokenizer = tokenizer
+        self.model = model
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj: `str`)
+            date (:obj: `str`)
+      Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+        # get inputs
+        tokens = self.tokenizer(text, return_tensors='pt')
+        output = self.model(**tokens)
+        vec = torch.max(
+            torch.log(
+                1 + torch.relu(output.logits)
+            ) * tokens.attention_mask.unsqueeze(-1),
+            dim=1)[0].squeeze()
+        instruction = data.pop("instruction", data)
+        cols = vec.nonzero().squeeze().cpu().tolist()
+        # extract the non-zero values
+        weights = vec[cols].cpu().tolist()
+        # use to create a dictionary of token ID to weight
+        sparse_dict = dict(zip(cols, weights))
+        return sparse_dict