change: merge method inputs with class inputs

src/sagemaker/modules/train/model_trainer.py

@@ -580,7 +580,7 @@ def train(
         """Train a model using AWS SageMaker.
 
         Args:
-            input_data_config (Optional[Union[List[Channel], Dict[str, DataSourceType]]]):
+            input_data_config (Optional[List[Union[Channel, InputData]]]):
                 The input data config for the training job.
                 Takes a list of Channel objects or a dictionary of channel names to DataSourceType.
                 DataSourceType can be an S3 URI string, local file path string,
@@ -596,11 +596,23 @@ def train(
         current_training_job_name = _get_unique_name(self.base_job_name)
         input_data_key_prefix = f"{self.base_job_name}/{current_training_job_name}/input"
 
-        self.input_data_config = input_data_config or self.input_data_config or []
+        final_input_data_config = self.input_data_config.copy() if self.input_data_config else []
+
+        if input_data_config:
+            # merge the inputs with method parameter taking precedence
+            existing_channels = {input.channel_name: input for input in final_input_data_config}
+            new_channels = []
+            for new_input in input_data_config:
+                if new_input.channel_name in existing_channels:
+                    existing_channels[new_input.channel_name] = new_input
+                else:
+                    new_channels.append(new_input)
+
+            final_input_data_config = list(existing_channels.values()) + new_channels
 
-        if self.input_data_config:
-            self.input_data_config = self._get_input_data_config(
-                self.input_data_config, input_data_key_prefix
+        if final_input_data_config:
+            final_input_data_config = self._get_input_data_config(
+                final_input_data_config, input_data_key_prefix
             )
 
         if self.checkpoint_config and not self.checkpoint_config.s3_uri:
@@ -643,7 +655,7 @@ def train(
                     data_source=self.source_code.source_dir,
                     key_prefix=input_data_key_prefix,
                 )
-                self.input_data_config.append(source_code_channel)
+                final_input_data_config.append(source_code_channel)
 
             self._prepare_train_script(
                 tmp_dir=tmp_dir,
@@ -664,7 +676,7 @@ def train(
                 data_source=tmp_dir.name,
                 key_prefix=input_data_key_prefix,
             )
-            self.input_data_config.append(sm_drivers_channel)
+            final_input_data_config.append(sm_drivers_channel)
 
             # If source_code is provided, we will always use
             # the default container entrypoint and arguments
@@ -691,7 +703,7 @@ def train(
                 training_job_name=current_training_job_name,
                 algorithm_specification=algorithm_specification,
                 hyper_parameters=string_hyper_parameters,
-                input_data_config=self.input_data_config,
+                input_data_config=final_input_data_config,
                 resource_config=resource_config,
                 vpc_config=vpc_config,
                 # Public Instance Attributes
@@ -736,7 +748,7 @@ def train(
                 sagemaker_session=self.sagemaker_session,
                 container_entrypoint=algorithm_specification.container_entrypoint,
                 container_arguments=algorithm_specification.container_arguments,
-                input_data_config=self.input_data_config,
+                input_data_config=final_input_data_config,
                 hyper_parameters=string_hyper_parameters,
                 environment=self.environment,
             )

tests/unit/sagemaker/modules/train/test_model_trainer.py

@@ -1258,3 +1258,44 @@ def mock_upload_data(path, bucket, key_prefix):
 
     assert kwargs["tensor_board_output_config"].s3_output_path == default_base_path
     assert kwargs["tensor_board_output_config"].local_path == "/opt/ml/output/tensorboard"
+
+
+@patch("sagemaker.modules.train.model_trainer.TrainingJob")
+def test_input_merge(mock_training_job, modules_session):
+    model_input = InputData(channel_name="model", data_source="s3://bucket/model/model.tar.gz")
+    model_trainer = ModelTrainer(
+        training_image=DEFAULT_IMAGE,
+        role=DEFAULT_ROLE,
+        sagemaker_session=modules_session,
+        compute=DEFAULT_COMPUTE_CONFIG,
+        input_data_config=[model_input],
+    )
+
+    train_input = InputData(channel_name="train", data_source="s3://bucket/data/train")
+    model_trainer.train(input_data_config=[train_input])
+
+    mock_training_job.create.assert_called_once()
+    assert mock_training_job.create.call_args.kwargs["input_data_config"] == [
+        Channel(
+            channel_name="model",
+            data_source=DataSource(
+                s3_data_source=S3DataSource(
+                    s3_data_type="S3Prefix",
+                    s3_uri="s3://bucket/model/model.tar.gz",
+                    s3_data_distribution_type="FullyReplicated",
+                )
+            ),
+            input_mode="File",
+        ),
+        Channel(
+            channel_name="train",
+            data_source=DataSource(
+                s3_data_source=S3DataSource(
+                    s3_data_type="S3Prefix",
+                    s3_uri="s3://bucket/data/train",
+                    s3_data_distribution_type="FullyReplicated",
+                )
+            ),
+            input_mode="File",
+        ),
+    ]