add TRUST_REMOTE_CODE param to python backend.

backends/python/server/text_embeddings_server/models/__init__.py

@@ -1,3 +1,4 @@
+import os
 import torch
 
 from loguru import logger
@@ -13,6 +14,7 @@
 
 __all__ = ["Model"]
 
+TRUST_REMOTE_CODE = os.getenv("TRUST_REMOTE_CODE", "false").lower() in ["true", "1"]
 # Disable gradients
 torch.set_grad_enabled(False)
 
@@ -40,7 +42,7 @@ def get_model(model_path: Path, dtype: Optional[str], pool: str):
     device = get_device()
     logger.info(f"backend device: {device}")
 
-    config = AutoConfig.from_pretrained(model_path)
+    config = AutoConfig.from_pretrained(model_path, trust_remote_code=TRUST_REMOTE_CODE)
     if config.model_type == "bert":
         config: BertConfig
         if (
@@ -51,12 +53,22 @@ def get_model(model_path: Path, dtype: Optional[str], pool: str):
             and FLASH_ATTENTION
         ):
             if pool != "cls":
-                return DefaultModel(model_path, device, datatype, pool)
+                return DefaultModel(
+                    model_path, device, datatype, pool, trust_remote=TRUST_REMOTE_CODE
+                )
             return FlashBert(model_path, device, datatype)
         if config.architectures[0].endswith("Classification"):
-            return ClassificationModel(model_path, device, datatype)
+            return ClassificationModel(
+                model_path, device, datatype, trust_remote=TRUST_REMOTE_CODE
+            )
         else:
-            return DefaultModel(model_path, device, datatype, pool)
+            return DefaultModel(
+                model_path,
+                device,
+                datatype,
+                pool,
+                trust_remote=TRUST_REMOTE_CODE,
+            )
     else:
         if device.type == "hpu":
             from habana_frameworks.torch.hpu import wrap_in_hpu_graph
@@ -66,13 +78,35 @@ def get_model(model_path: Path, dtype: Optional[str], pool: str):
 
             adapt_transformers_to_gaudi()
             if config.architectures[0].endswith("Classification"):
-                model_handle = ClassificationModel(model_path, device, datatype)
+                model_handle = ClassificationModel(
+                    model_path,
+                    device,
+                    datatype,
+                    trust_remote=TRUST_REMOTE_CODE,
+                )
             else:
-                model_handle = DefaultModel(model_path, device, datatype, pool)
+                model_handle = DefaultModel(
+                    model_path,
+                    device,
+                    datatype,
+                    pool,
+                    trust_remote=TRUST_REMOTE_CODE,
+                )
             model_handle.model = wrap_in_hpu_graph(model_handle.model)
             return model_handle
         elif use_ipex():
             if config.architectures[0].endswith("Classification"):
-                return ClassificationModel(model_path, device, datatype)
+                return ClassificationModel(
+                    model_path,
+                    device,
+                    datatype,
+                    trust_remote=TRUST_REMOTE_CODE,
+                )
             else:
-                return DefaultModel(model_path, device, datatype, pool)
+                return DefaultModel(
+                    model_path,
+                    device,
+                    datatype,
+                    pool,
+                    trust_remote=TRUST_REMOTE_CODE,
+                )

backends/python/server/text_embeddings_server/models/classification_model.py

@@ -13,8 +13,16 @@
 
 
 class ClassificationModel(Model):
-    def __init__(self, model_path: Path, device: torch.device, dtype: torch.dtype):
-        model = AutoModelForSequenceClassification.from_pretrained(model_path)
+    def __init__(
+        self,
+        model_path: Path,
+        device: torch.device,
+        dtype: torch.dtype,
+        trust_remote: bool = False,
+    ):
+        model = AutoModelForSequenceClassification.from_pretrained(
+            model_path, trust_remote_code=trust_remote
+        )
         model = model.to(dtype).to(device)
 
         self.hidden_size = model.config.hidden_size

backends/python/server/text_embeddings_server/models/default_model.py

@@ -15,9 +15,18 @@
 
 class DefaultModel(Model):
     def __init__(
-        self, model_path: Path, device: torch.device, dtype: torch.dtype, pool: str
+        self,
+        model_path: Path,
+        device: torch.device,
+        dtype: torch.dtype,
+        pool: str,
+        trust_remote: bool = False,
     ):
-        model = AutoModel.from_pretrained(model_path).to(dtype).to(device)
+        model = (
+            AutoModel.from_pretrained(model_path, trust_remote_code=trust_remote)
+            .to(dtype)
+            .to(device)
+        )
         self.hidden_size = model.config.hidden_size
         self.pooling = Pooling(self.hidden_size, pooling_mode=pool)