fix(sdk): fix output type of importer (#8172)

* change output type of importer to artifact_class.TYPE_NAME

* use the same logic as component_factory._annotation_to_type_struct for determining type_name

* format using yapf

* fix type_name

* format using yapf

* add yaml file

* changes with regards to PR comments

* format pipeline_with_importer_and_gcpc_types.py

* remove unused imports

* change name of test component

* add pipeline_with_importer_and_gcpc_types to config

* update yaml file

sdk/python/kfp/compiler/_read_write_test_config.py

@@ -16,6 +16,7 @@ CONFIG = {
     'pipelines': {
         'test_cases': [
             'pipeline_with_importer',
+            'pipeline_with_importer_and_gcpc_types',
             'pipeline_with_ontology',
             'pipeline_with_if_placeholder',
             'pipeline_with_concat_placeholder',

sdk/python/kfp/compiler/test_data/pipelines/pipeline_with_importer_and_gcpc_types.py

@@ -0,0 +1,56 @@
+# Copyright 2022 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Pipeline using dsl.importer and GCPC types."""
+
+from kfp import compiler
+from kfp import components
+from kfp import dsl
+from kfp.dsl import importer
+
+
+class VertexDataset(dsl.Artifact):
+    """An artifact representing a GCPC Vertex Dataset."""
+    TYPE_NAME = 'google.VertexDataset'
+
+
+consumer_op = components.load_component_from_text("""
+name: consumer_op
+inputs:
+  - {name: dataset, type: google.VertexDataset}
+implementation:
+  container:
+    image: dummy
+    command:
+    - cmd
+    args:
+    - {inputPath: dataset}
+""")
+
+
+@dsl.pipeline(
+    name='pipeline-with-importer-and-gcpc-type', pipeline_root='dummy_root')
+def my_pipeline():
+
+    importer1 = importer(
+        artifact_uri='gs://ml-pipeline-playground/shakespeare1.txt',
+        artifact_class=VertexDataset,
+        reimport=False,
+        metadata={'key': 'value'})
+    consume1 = consumer_op(dataset=importer1.output)
+
+
+if __name__ == '__main__':
+    compiler.Compiler().compile(
+        pipeline_func=my_pipeline,
+        package_path=__file__.replace('.py', '.yaml'))

sdk/python/kfp/compiler/test_data/pipelines/pipeline_with_importer_and_gcpc_types.yaml

@@ -0,0 +1,74 @@
+components:
+  comp-consumer-op:
+    executorLabel: exec-consumer-op
+    inputDefinitions:
+      artifacts:
+        dataset:
+          artifactType:
+            schemaTitle: google.VertexDataset
+            schemaVersion: 0.0.1
+  comp-importer:
+    executorLabel: exec-importer
+    inputDefinitions:
+      parameters:
+        uri:
+          parameterType: STRING
+    outputDefinitions:
+      artifacts:
+        artifact:
+          artifactType:
+            schemaTitle: google.VertexDataset
+            schemaVersion: 0.0.1
+defaultPipelineRoot: dummy_root
+deploymentSpec:
+  executors:
+    exec-consumer-op:
+      container:
+        args:
+        - '{{$.inputs.artifacts[''dataset''].path}}'
+        command:
+        - cmd
+        image: dummy
+    exec-importer:
+      importer:
+        artifactUri:
+          constant: gs://ml-pipeline-playground/shakespeare1.txt
+        metadata:
+          key: value
+        typeSchema:
+          schemaTitle: google.VertexDataset
+          schemaVersion: 0.0.1
+pipelineInfo:
+  name: pipeline-with-importer-and-gcpc-type
+root:
+  dag:
+    tasks:
+      consumer-op:
+        cachingOptions:
+          enableCache: true
+        componentRef:
+          name: comp-consumer-op
+        dependentTasks:
+        - importer
+        inputs:
+          artifacts:
+            dataset:
+              taskOutputArtifact:
+                outputArtifactKey: artifact
+                producerTask: importer
+        taskInfo:
+          name: consumer-op
+      importer:
+        cachingOptions:
+          enableCache: true
+        componentRef:
+          name: comp-importer
+        inputs:
+          parameters:
+            uri:
+              runtimeValue:
+                constant: gs://ml-pipeline-playground/shakespeare1.txt
+        taskInfo:
+          name: importer
+schemaVersion: 2.1.0
+sdkVersion: kfp-2.0.0-beta.3

sdk/python/kfp/components/importer_node.py

@@ -54,6 +54,14 @@ def importer(
               reimport=False)
           train(dataset=importer1.output)
     """
+    if issubclass(artifact_class, artifact_types.Artifact
+                 ) and not artifact_class.TYPE_NAME.startswith('system.'):
+        # For artifact classes not under the `system` namespace,
+        # use its TYPE_NAME as-is.
+        type_name = artifact_class.TYPE_NAME
+    else:
+        type_name = artifact_class.__name__
+
     component_spec = structures.ComponentSpec(
         name='importer',
         implementation=structures.Implementation(
@@ -64,9 +72,7 @@ def importer(
                 reimport=reimport,
                 metadata=metadata)),
         inputs={INPUT_KEY: structures.InputSpec(type='String')},
-        outputs={
-            OUTPUT_KEY: structures.OutputSpec(type=artifact_class.__name__)
-        },
+        outputs={OUTPUT_KEY: structures.OutputSpec(type=type_name)},
     )
 
     importer = importer_component.ImporterComponent(