Support parallelfor operations(re-push)

Signed-off-by: typhoonzero <[email protected]>

elyra/kfp/bootstrapper.py

@@ -50,6 +50,79 @@
 operation_name = None  # global used in formatted logging
 
 
+def set_dist_train_config(rank, nranks, step_name, port=9888):
+    """
+    Set distributed training envs for general uses.
+    For Tensorflow: TF_CONFIG is configured.
+    For Pytorch: MASTER_ADDR and MASTER_PORT is configured.
+    For general use cases: NRANKS and RANK is configured.
+
+    TODO: this function is Argo specific, should add Tekton support.
+    """
+    from kubernetes import client, config
+
+    wf_id = os.getenv("WORKFLOW_ID")
+    ns = os.getenv("KFP_NAMESPACE")
+    if not wf_id or not ns:
+        raise ValueError("WORKFLOW_ID and KFP_NAMESPACE env must be set in the workflow pod!")
+
+    config.load_incluster_config()
+    api = client.CustomObjectsApi()
+
+    worker_started = 0
+    while worker_started != nranks:
+        resource = api.get_namespaced_custom_object(
+            group="argoproj.io",
+            version="v1alpha1",
+            name=wf_id,
+            namespace=ns,
+            plural="workflows",
+        )
+        if not resource.get("status"):
+            time.sleep(2)
+            continue
+        if not resource["status"].get("nodes"):
+            time.sleep(2)
+            continue
+
+        nodes = resource["status"]["nodes"]
+        workers_spec = []
+        for nk in nodes:
+            node_info = nodes[nk]
+            OpUtil.log_operation_info(
+                "kfpdist: searching for {}, curr node: {}, templateName: {}, type: {}".format(
+                    step_name, nk, node_info["templateName"], node_info["type"]
+                )
+            )
+            if node_info["templateName"] == step_name and node_info["type"] == "Pod":
+                podid = node_info["id"]
+                for input_param in node_info["inputs"]["parameters"]:
+                    if input_param["name"].find("loop-item") >= 0:
+                        # FIXME: argo parameter with "loop-item" is the rank.
+                        curr_rank = int(input_param["value"])
+                        break
+                v1 = client.CoreV1Api()
+                podinfo = v1.read_namespaced_pod(podid, ns)
+                if podinfo.status.pod_ip:
+                    workers_spec.append((curr_rank, "%s:%d" % (podinfo.status.pod_ip, port)))
+        worker_started = len(workers_spec)
+        time.sleep(2)
+
+    workers_spec.sort(key=lambda item: item[0])
+    workers_spec_list = [i[1] for i in workers_spec]
+    # set TF_CONFIG env for tf dist train
+    os.environ["TF_CONFIG"] = json.dumps(
+        {"cluster": {"worker": workers_spec_list}, "task": {"type": "worker", "index": rank}}
+    )
+    OpUtil.log_operation_info("Setting TF_CONFIG: %s" % os.environ["TF_CONFIG"])
+    os.environ["MASTER_ADDR"] = workers_spec[0][1].split(":")[0]
+    os.environ["MASTER_PORT"] = workers_spec[0][1].split(":")[1]
+    OpUtil.log_operation_info(
+        "Setting MASTER_ADDR: {}, MASTER_PORT: {}".format(os.environ["MASTER_ADDR"], os.environ["MASTER_PORT"])
+    )
+    OpUtil.log_operation_info("Setting RANK: {}, NRANKS: {}".format(os.environ["RANK"], os.environ["NRANKS"]))
+
+
 class FileOpBase(ABC):
     """Abstract base class for file-based operations"""
 
@@ -724,6 +797,22 @@ def main():
     )
     # Setup packages and gather arguments
     input_params = OpUtil.parse_arguments(sys.argv[1:])
+
+    if os.getenv("RANK"):
+        op_name = os.getenv("ELYRA_OP_NAME")
+        if not op_name:
+            raise ValueError(
+                "env ELYRA_OP_NAME is not set. please check whether elyra version is matching bootstrapper.py"
+            )
+
+        # FIXME: operation name will be updated by kfp, replace these chars for matching.
+        op_name = op_name.replace("_", "-")
+        rank = int(os.getenv("RANK"))
+        nranks = int(os.getenv("NRANKS"))
+        if not nranks:
+            raise ValueError("rank argument setted but no NRANKS env found!")
+        set_dist_train_config(rank, nranks, op_name, port=9888)
+
     OpUtil.log_operation_info("starting operation")
     t0 = time.time()
     OpUtil.package_install(user_volume_path=input_params.get("user-volume-path"))

elyra/pipeline/kfp/processor_kfp.py

@@ -794,6 +794,7 @@ def _generate_workflow_tasks(
                 }
 
                 component_definition = generic_component_template.render(
+                    op_name=sanitize_label_value(operation.name),
                     container_image=operation.runtime_image,
                     task_parameters=task_parameters,
                     command_args=self._compose_container_command_args(
@@ -847,6 +848,7 @@ def _generate_workflow_tasks(
                 if operation.gpu_vendor:
                     gpu_vendor = operation.gpu_vendor
                 workflow_task["task_modifiers"]["gpu_limit"] = {"size": operation.gpu, "vendor": gpu_vendor}
+                workflow_task["task_modifiers"]["parallel_count"] = operation.parallel_count
 
                 if is_crio_runtime:
                     # Attach empty dir volume
@@ -880,6 +882,8 @@ def _generate_workflow_tasks(
                 )
                 # Pipeline node name
                 workflow_task["task_modifiers"]["pod_labels"]["elyra/node-name"] = sanitize_label_value(operation.name)
+                # Original operation name for runtime lookups
+                workflow_task["task_modifiers"]["env_variables"]["ELYRA_OP_NAME"] = operation.name
 
                 # Add non-identifying metadata
                 if workflow_task["task_modifiers"].get("pod_annotations") is None:

elyra/pipeline/pipeline.py

@@ -249,6 +249,7 @@ def __init__(
                 gpu: number of gpus requested to run the operation
                 parameters: a list of names of pipeline parameters that should be passed to this operation
                 gpu_vendor: gpu resource type, eg. nvidia.com/gpu, amd.com/gpu etc.
+                parallel_count: operation parallel count to run parallelfor steps.
         Entries for other (non-built-in) component types are a function of the respective component.
 
         :param elyra_props: dictionary of property key:value pairs that are owned by Elyra
@@ -276,6 +277,7 @@ def __init__(
         self._component_props["memory"] = component_props.get("memory")
         self._component_props["gpu"] = component_props.get("gpu")
         self._component_props["gpu_vendor"] = component_props.get("gpu_vendor")
+        self._component_props["parallel_count"] = component_props.get("parallel_count")
         self._component_props["parameters"] = component_props.get(PIPELINE_PARAMETERS, [])
 
         if not elyra_props:
@@ -332,6 +334,10 @@ def parameters(self) -> Optional[List[str]]:
     def gpu_vendor(self) -> Optional[str]:
         return self._component_props.get("gpu_vendor")
 
+    @property
+    def parallel_count(self) -> Optional[str]:
+        return self._component_props.get("parallel_count")
+
     def __eq__(self, other: GenericOperation) -> bool:
         if isinstance(self, other.__class__):
             return super().__eq__(other)

elyra/templates/components/generic_properties_template.jinja2

@@ -61,6 +61,13 @@
             "ui:placeholder": "nvidia.com/gpu"
           }
         },
+        "parallel_count": {
+          "type": "integer",
+          "title": "ParallelCount",
+          "description": "Each component can be run as parallel step, set this >1 to do parallelfor-like operation.",
+          "minimum": 1,
+          "default": 1
+        },
         "pipeline_parameters": {
           "type": "array",
           "title": "Pipeline Parameters",

elyra/templates/kubeflow/v1/generic_component_definition_template.jinja2

@@ -1,4 +1,4 @@
-name: Run a file
+name: {{ op_name }}
 description: Run a Jupyter notebook or Python/R script
 {% if task_parameters %}
 inputs:

elyra/templates/kubeflow/v1/python_dsl_template.jinja2

@@ -33,6 +33,14 @@ def generated_pipeline(
 {% for workflow_task in workflow_tasks.values() %}
     {% set task_name = "task_" + workflow_task.escaped_task_id %}
     # Task for node '{{ workflow_task.name }}'
+    {% set parallel_indent = 0 %}
+{%  if workflow_task.task_modifiers.parallel_count > 1 %}
+    {% set parallel_indent = 4 %}
+    parallel_count = {{workflow_task.task_modifiers.parallel_count}}
+    with kfp.dsl.ParallelFor(list(range(parallel_count))) as rank:
+{%  endif %}
+
+{%  filter indent(width=parallel_indent) %}
     {{ task_name }} = factory_{{ workflow_task.component_definition_hash | python_safe }}(
 {%  for task_input_name, task_input_spec in workflow_task.task_inputs.items() %}
 {%     if task_input_spec.task_output_reference %}
@@ -73,6 +81,44 @@ def generated_pipeline(
 {%    for env_var_name, env_var_value in workflow_task.task_modifiers.env_variables.items() %}
     {{ task_name }}.add_env_variable(V1EnvVar(name="{{ env_var_name }}", value="{{ env_var_value | string_delimiter_safe }}"))
 {%    endfor %}
+{%    if workflow_task.task_modifiers.parallel_count > 1 %}
+    {{ task_name }}.add_env_variable(V1EnvVar(name="NRANKS", value=str(parallel_count)))
+    {{ task_name }}.add_env_variable(V1EnvVar(name="RANK", value=str(rank)))
+{%    endif %}
+{%    if workflow_engine == "argo" %}
+    {{ task_name }}.add_env_variable(V1EnvVar(
+        name="WORKFLOW_ID",
+        value_from=V1EnvVarSource(
+            field_ref=V1ObjectFieldSelector(
+                api_version="v1", field_path="metadata.labels['workflows.argoproj.io/workflow']"
+            )
+        ),
+    ))
+    {{ task_name }}.add_env_variable(V1EnvVar(
+        name="KFP_NAMESPACE",
+        value_from=V1EnvVarSource(
+            field_ref=V1ObjectFieldSelector(api_version="v1", field_path="metadata.namespace")
+        ),
+    ))
+    {{ task_name }}.add_env_variable(V1EnvVar(
+        name="KFP_POD_NAME",
+        value_from=V1EnvVarSource(
+            field_ref=V1ObjectFieldSelector(api_version="v1", field_path="metadata.name")
+        ),
+    ))
+    {{ task_name }}.add_env_variable(V1EnvVar(
+        name="KFP_POD_UID",
+        value_from=V1EnvVarSource(
+            field_ref=V1ObjectFieldSelector(api_version="v1", field_path="metadata.uid")
+        ),
+    ))
+    {{ task_name }}.add_env_variable(V1EnvVar(
+        name="KFP_RUN_ID",
+        value_from=V1EnvVarSource(
+            field_ref=V1ObjectFieldSelector(api_version="v1", field_path="metadata.labels['pipeline/runid']")
+        ),
+    ))
+{%    endif %}
 {%  endif %}
 {%  if workflow_task.task_modifiers.set_run_name %}
 {%   if workflow_engine == "tekton" %}
@@ -163,6 +209,8 @@ def generated_pipeline(
     {{ task_name }}.after(task_{{ upstream_workflow_task_id | python_safe }})
 {%    endfor %}
 {%  endif %}
+{%  endfilter %}
+
 {% endfor %}
 
 if __name__ == "__main__":

elyra/tests/kfp/test_bootstrapper.py

@@ -192,6 +192,21 @@ def test_main_method(monkeypatch, s3_setup, tmpdir):
     main_method_setup_execution(monkeypatch, s3_setup, tmpdir, argument_dict)
 
 
+def test_main_method_with_parallel_count(monkeypatch, s3_setup, tmpdir):
+    argument_dict = {
+        "cos-endpoint": "http://" + MINIO_HOST_PORT,
+        "cos-bucket": "test-bucket",
+        "cos-directory": "test-directory",
+        "cos-dependencies-archive": "test-archive.tgz",
+        "filepath": os.path.join(RESOURCES_DIR, "test-notebookA.ipynb"),
+        "inputs": "test-file.txt;test,file.txt",
+        "outputs": "test-file/test-file-copy.txt;test-file/test,file/test,file-copy.txt",
+        "user-volume-path": None,
+        "parallel_count": 2,
+    }
+    main_method_setup_execution(monkeypatch, s3_setup, tmpdir, argument_dict)
+
+
 def test_main_method_with_wildcard_outputs(monkeypatch, s3_setup, tmpdir):
     argument_dict = {
         "cos-endpoint": "http://" + MINIO_HOST_PORT,

packages/pipeline-editor/src/FileSubmissionDialog.tsx

@@ -113,6 +113,10 @@ export const FileSubmissionDialog: React.FC<IProps> = ({
         </div>
       </div>
       <br />
+
+      <label htmlFor="parallel_count"> Parallel Count:</label>
+      <input id="parallel_count" type="number" name="parallel_count" />
+
       <input
         type="checkbox"
         className="elyra-Dialog-checkbox"