fix: ModelBuilder not passing HF_TOKEN to model.

src/sagemaker/serve/builder/jumpstart_builder.py

@@ -669,7 +669,6 @@ def _optimize_for_jumpstart(
         self,
         output_path: Optional[str] = None,
         instance_type: Optional[str] = None,
-        role_arn: Optional[str] = None,
         tags: Optional[Tags] = None,
         job_name: Optional[str] = None,
         accept_eula: Optional[bool] = None,
@@ -685,9 +684,7 @@ def _optimize_for_jumpstart(
 
         Args:
             output_path (Optional[str]): Specifies where to store the compiled/quantized model.
-            instance_type (Optional[str]): Target deployment instance type that
-                the model is optimized for.
-            role_arn (Optional[str]): Execution role. Defaults to ``None``.
+            instance_type (str): Target deployment instance type that the model is optimized for.
             tags (Optional[Tags]): Tags for labeling a model optimization job. Defaults to ``None``.
             job_name (Optional[str]): The name of the model optimization job. Defaults to ``None``.
             accept_eula (bool): For models that require a Model Access Config, specify True or
@@ -715,7 +712,7 @@ def _optimize_for_jumpstart(
                 f"Model '{self.model}' requires accepting end-user license agreement (EULA)."
             )
 
-        is_compilation = (quantization_config is None) and (
+        is_compilation = (not quantization_config) and (
             (compilation_config is not None) or _is_inferentia_or_trainium(instance_type)
         )
 
@@ -758,7 +755,6 @@ def _optimize_for_jumpstart(
             else None
         )
         self.instance_type = instance_type or deployment_config_instance_type or _get_nb_instance()
-        self.role_arn = role_arn or self.role_arn
 
         create_optimization_job_args = {
             "OptimizationJobName": job_name,
@@ -787,10 +783,10 @@ def _optimize_for_jumpstart(
                     "AcceptEula": True
                 }
 
+        optimization_env_vars = _update_environment_variables(optimization_env_vars, override_env)
+        if optimization_env_vars:
+            self.pysdk_model.env.update(optimization_env_vars)
         if quantization_config or is_compilation:
-            self.pysdk_model.env = _update_environment_variables(
-                optimization_env_vars, override_env
-            )
             return create_optimization_job_args
         return None
 

src/sagemaker/serve/builder/model_builder.py

@@ -73,7 +73,6 @@
     _generate_model_source,
     _extract_optimization_config_and_env,
     _is_s3_uri,
-    _normalize_local_model_path,
     _custom_speculative_decoding,
     _extract_speculative_draft_model_provider,
 )
@@ -833,6 +832,8 @@ def build(  # pylint: disable=R0911
         # until we deprecate HUGGING_FACE_HUB_TOKEN.
         if self.env_vars.get("HUGGING_FACE_HUB_TOKEN") and not self.env_vars.get("HF_TOKEN"):
             self.env_vars["HF_TOKEN"] = self.env_vars.get("HUGGING_FACE_HUB_TOKEN")
+        elif self.env_vars.get("HF_TOKEN") and not self.env_vars.get("HUGGING_FACE_HUB_TOKEN"):
+            self.env_vars["HUGGING_FACE_HUB_TOKEN"] = self.env_vars.get("HF_TOKEN")
 
         self.sagemaker_session.settings._local_download_dir = self.model_path
 
@@ -851,7 +852,9 @@ def build(  # pylint: disable=R0911
 
         self._build_validations()
 
-        if not self._is_jumpstart_model_id() and self.model_server:
+        if (
+            not (isinstance(self.model, str) and self._is_jumpstart_model_id())
+        ) and self.model_server:
             return self._build_for_model_server()
 
         if isinstance(self.model, str):
@@ -1216,18 +1219,15 @@ def _model_builder_optimize_wrapper(
             raise ValueError("Quantization config and compilation config are mutually exclusive.")
 
         self.sagemaker_session = sagemaker_session or self.sagemaker_session or Session()
-
         self.instance_type = instance_type or self.instance_type
         self.role_arn = role_arn or self.role_arn
 
-        self.build(mode=self.mode, sagemaker_session=self.sagemaker_session)
         job_name = job_name or f"modelbuilderjob-{uuid.uuid4().hex}"
-
         if self._is_jumpstart_model_id():
+            self.build(mode=self.mode, sagemaker_session=self.sagemaker_session)
             input_args = self._optimize_for_jumpstart(
                 output_path=output_path,
                 instance_type=instance_type,
-                role_arn=self.role_arn,
                 tags=tags,
                 job_name=job_name,
                 accept_eula=accept_eula,
@@ -1240,10 +1240,13 @@ def _model_builder_optimize_wrapper(
                 max_runtime_in_sec=max_runtime_in_sec,
             )
         else:
+            if self.model_server != ModelServer.DJL_SERVING:
+                logger.info("Overriding model server to DJL_SERVING.")
+                self.model_server = ModelServer.DJL_SERVING
+
+            self.build(mode=self.mode, sagemaker_session=self.sagemaker_session)
             input_args = self._optimize_for_hf(
                 output_path=output_path,
-                instance_type=instance_type,
-                role_arn=self.role_arn,
                 tags=tags,
                 job_name=job_name,
                 quantization_config=quantization_config,
@@ -1269,8 +1272,6 @@ def _model_builder_optimize_wrapper(
     def _optimize_for_hf(
         self,
         output_path: str,
-        instance_type: Optional[str] = None,
-        role_arn: Optional[str] = None,
         tags: Optional[Tags] = None,
         job_name: Optional[str] = None,
         quantization_config: Optional[Dict] = None,
@@ -1285,9 +1286,6 @@ def _optimize_for_hf(
 
         Args:
             output_path (str): Specifies where to store the compiled/quantized model.
-            instance_type (Optional[str]): Target deployment instance type that
-                the model is optimized for.
-            role_arn (Optional[str]): Execution role. Defaults to ``None``.
             tags (Optional[Tags]): Tags for labeling a model optimization job. Defaults to ``None``.
             job_name (Optional[str]): The name of the model optimization job. Defaults to ``None``.
             quantization_config (Optional[Dict]): Quantization configuration. Defaults to ``None``.
@@ -1305,13 +1303,6 @@ def _optimize_for_hf(
         Returns:
             Optional[Dict[str, Any]]: Model optimization job input arguments.
         """
-        if self.model_server != ModelServer.DJL_SERVING:
-            logger.info("Overwriting model server to DJL.")
-            self.model_server = ModelServer.DJL_SERVING
-
-        self.role_arn = role_arn or self.role_arn
-        self.instance_type = instance_type or self.instance_type
-
         self.pysdk_model = _custom_speculative_decoding(
             self.pysdk_model, speculative_decoding_config, False
         )
@@ -1371,13 +1362,12 @@ def _optimize_prepare_for_hf(self):
             )
         else:
             if not custom_model_path:
-                custom_model_path = f"/tmp/sagemaker/model-builder/{self.model}/code"
+                custom_model_path = f"/tmp/sagemaker/model-builder/{self.model}"
                 download_huggingface_model_metadata(
                     self.model,
-                    custom_model_path,
+                    os.path.join(custom_model_path, "code"),
                     self.env_vars.get("HUGGING_FACE_HUB_TOKEN"),
                 )
-            custom_model_path = _normalize_local_model_path(custom_model_path)
 
         self.pysdk_model.model_data, env = self._prepare_for_mode(
             model_path=custom_model_path,

src/sagemaker/serve/utils/optimize_utils.py

@@ -282,26 +282,6 @@ def _extract_optimization_config_and_env(
     return None, None
 
 
-def _normalize_local_model_path(local_model_path: Optional[str]) -> Optional[str]:
-    """Normalizes the local model path.
-
-    Args:
-        local_model_path (Optional[str]): The local model path.
-
-    Returns:
-        Optional[str]: The normalized model path.
-    """
-    if local_model_path is None:
-        return local_model_path
-
-    # Removes /code or /code/ path at the end of local_model_path,
-    # as it is appended during artifacts upload.
-    pattern = r"/code/?$"
-    if re.search(pattern, local_model_path):
-        return re.sub(pattern, "", local_model_path)
-    return local_model_path
-
-
 def _custom_speculative_decoding(
     model: Model,
     speculative_decoding_config: Optional[Dict],