pytorch plugin implementation

README.md

@@ -58,6 +58,14 @@ If `@sidecar_task` is to be used, one should install the `sidecar` plugin.
 pip install flytekit[sidecar]
 ```
 
+### Pytorch
+
+If `@pytorch_task` is to be used, one should install the `pytorch` plugin.
+
+```bash
+pip install flytekit[pytorch]
+```
+
 ### Full Installation
 
 To install all or multiple available plugins, one can specify them individually:

flytekit/common/constants.py

@@ -21,6 +21,7 @@ class SdkTaskType(object):
     SIDECAR_TASK = "sidecar"
     SENSOR_TASK = "sensor-task"
     PRESTO_TASK = "presto"
+    PYTORCH_TASK = "pytorch"
 
 GLOBAL_INPUT_NODE_ID = ''
 

flytekit/common/tasks/pytorch_task.py

@@ -0,0 +1,80 @@
+from __future__ import absolute_import
+
+try:
+    from inspect import getfullargspec as _getargspec
+except ImportError:
+    from inspect import getargspec as _getargspec
+
+import six as _six
+from flytekit.common import constants as _constants
+from flytekit.common.exceptions import scopes as _exception_scopes
+from flytekit.common.tasks import output as _task_output, sdk_runnable as _sdk_runnable
+from flytekit.common.types import helpers as _type_helpers
+from flytekit.models import literals as _literal_models, task as _task_models
+from google.protobuf.json_format import MessageToDict as _MessageToDict
+
+
+class SdkRunnablePytorchContainer(_sdk_runnable.SdkRunnableContainer):
+
+    @property
+    def args(self):
+        """
+        Override args to remove the injection of command prefixes
+        :rtype: list[Text]
+        """
+        return self._args
+
+class SdkPyTorchTask(_sdk_runnable.SdkRunnableTask):
+    def __init__(
+            self,
+            task_function,
+            task_type,
+            discovery_version,
+            retries,
+            interruptible,
+            deprecated,
+            discoverable,
+            timeout,
+            workers_count,
+            instance_storage_request,
+            instance_cpu_request,
+            instance_gpu_request,
+            instance_memory_request,
+            instance_storage_limit,
+            instance_cpu_limit,
+            instance_gpu_limit,
+            instance_memory_limit,
+            environment
+    ):
+        pytorch_job = _task_models.PyTorchJob(
+            workers_count=workers_count
+        ).to_flyte_idl()
+        super(SdkPyTorchTask, self).__init__(
+            task_function=task_function,
+            task_type=task_type,
+            discovery_version=discovery_version,
+            retries=retries,
+            interruptible=interruptible,
+            deprecated=deprecated,
+            storage_request=instance_storage_request,
+            cpu_request=instance_cpu_request,
+            gpu_request=instance_gpu_request,
+            memory_request=instance_memory_request,
+            storage_limit=instance_storage_limit,
+            cpu_limit=instance_cpu_limit,
+            gpu_limit=instance_gpu_limit,
+            memory_limit=instance_memory_limit,
+            discoverable=discoverable,
+            timeout=timeout,
+            environment=environment,
+            custom=_MessageToDict(pytorch_job)
+        )
+
+    def _get_container_definition(
+            self,
+            **kwargs
+    ):
+        """
+        :rtype: SdkRunnablePytorchContainer
+        """
+        return super(SdkPyTorchTask, self)._get_container_definition(cls=SdkRunnablePytorchContainer, **kwargs)

flytekit/models/task.py

@@ -6,6 +6,7 @@
 from flyteidl.admin import task_pb2 as _admin_task
 from flyteidl.core import tasks_pb2 as _core_task, literals_pb2 as _literals_pb2, compiler_pb2 as _compiler
 from flyteidl.plugins import spark_pb2 as _spark_task
+from flyteidl.plugins import pytorch_pb2 as _pytorch_task
 from flytekit.plugins import flyteidl as _lazy_flyteidl
 from google.protobuf import json_format as _json_format, struct_pb2 as _struct
 from flytekit.sdk.spark_types import SparkType as _spark_type
@@ -804,3 +805,24 @@ def from_flyte_idl(cls, pb2_object):
             pod_spec=pb2_object.pod_spec,
             primary_container_name=pb2_object.primary_container_name,
         )
+
+
+class PyTorchJob(_common.FlyteIdlEntity):
+
+    def __init__(self, workers_count):
+        self._workers_count = workers_count
+
+    @property
+    def workers_count(self):
+        return self._workers_count
+
+    def to_flyte_idl(self):
+        return _pytorch_task.DistributedPyTorchTrainingTask(
+            workers=self.workers_count,
+        )
+
+    @classmethod
+    def from_flyte_idl(cls, pb2_object):
+        return cls(
+            workers_count=pb2_object.workers,
+        )

flytekit/plugins/__init__.py

@@ -17,6 +17,8 @@
 hmsclient = _lazy_loader.lazy_load_module("hmsclient")  # type: types.ModuleType
 type(hmsclient).add_sub_module("genthrift.hive_metastore.ttypes")
 
+torch = _lazy_loader.lazy_load_module("torch")  # type: types.ModuleType
+
 _lazy_loader.LazyLoadPlugin(
     "spark",
     ["pyspark>=2.4.0,<3.0.0"],
@@ -46,3 +48,9 @@
     ],
     [hmsclient]
 )
+
+_lazy_loader.LazyLoadPlugin(
+    "pytorch",
+    ["torch>=1.0.0,<2.0.0"],
+    [torch]
+)

flytekit/sdk/tasks.py

@@ -6,7 +6,8 @@
 from flytekit.common import constants as _common_constants
 from flytekit.common.exceptions import user as _user_exceptions
 from flytekit.common.tasks import sdk_runnable as _sdk_runnable_tasks, sdk_dynamic as _sdk_dynamic, \
-    spark_task as _sdk_spark_tasks, generic_spark_task as _sdk_generic_spark_task, hive_task as _sdk_hive_tasks, sidecar_task as _sdk_sidecar_tasks
+    spark_task as _sdk_spark_tasks, generic_spark_task as _sdk_generic_spark_task, hive_task as _sdk_hive_tasks, \
+    sidecar_task as _sdk_sidecar_tasks, pytorch_task as _sdk_pytorch_tasks
 from flytekit.common.tasks import task as _task
 from flytekit.common.types import helpers as _type_helpers
 from flytekit.sdk.spark_types import  SparkType as _spark_type
@@ -1000,3 +1001,156 @@ def wrapper(fn):
         return wrapper(_task_function)
     else:
         return wrapper
+
+
+def pytorch_task(
+        _task_function=None,
+        cache_version='',
+        retries=0,
+        interruptible=False,
+        deprecated='',
+        cache=False,
+        timeout=None,
+        workers_count=1,
+        instance_storage_request="",
+        instance_cpu_request="",
+        instance_gpu_request="",
+        instance_memory_request="",
+        instance_storage_limit="",
+        instance_cpu_limit="",
+        instance_gpu_limit="",
+        instance_memory_limit="",
+        environment=None,
+        cls=None
+):
+    """
+    Decorator to create a Pytorch Task definition. This task will submit PyTorchJob (see https://github.com/kubeflow/pytorch-operator)
+        defined by the code within the _task_function to k8s cluster.
+
+    .. code-block:: python
+
+        @inputs(int_list=[Types.Integer])
+        @outputs(result=Types.Integer
+        @pytorch_task(
+            workers_count=2,
+            instance_cpu_request="500m",
+            instance_memory_request="4Gi",
+            instance_memory_limit="8Gi",
+            instance_gpu_limit="1",
+        )
+        def my_pytorch_job(wf_params, int_list, result):
+            pass
+
+    :param _task_function: this is the decorated method and shouldn't be declared explicitly.  The function must
+        take a first argument, and then named arguments matching those defined in @inputs and @outputs.  No keyword
+        arguments are allowed for wrapped task functions.
+
+    :param Text cache_version: [optional] string representing logical version for discovery.  This field should be
+        updated whenever the underlying algorithm changes.
+
+        .. note::
+
+            This argument is required to be a non-empty string if `cache` is True.
+
+    :param int retries: [optional] integer determining number of times task can be retried on
+        :py:exc:`flytekit.sdk.exceptions.RecoverableException` or transient platform failures.  Defaults
+        to 0.
+
+        .. note::
+
+            If retries > 0, the task must be able to recover from any remote state created within the user code.  It is
+            strongly recommended that tasks are written to be idempotent.
+
+    :param bool interruptible: [optional] boolean describing if the task is interruptible.
+
+    :param Text deprecated: [optional] string that should be provided if this task is deprecated.  The string
+        will be logged as a warning so it should contain information regarding how to update to a newer task.
+
+    :param bool cache: [optional] boolean describing if the outputs of this task should be cached and
+        re-usable.
+
+    :param datetime.timedelta timeout: [optional] describes how long the task should be allowed to
+        run at max before triggering a retry (if retries are enabled).  By default, tasks are allowed to run
+        indefinitely.  If a null timedelta is passed (i.e. timedelta(seconds=0)), the task will not timeout.
+
+    :param int workers_count: integer determining the number of worker replicas spawned in the cluster for this job.
+
+    :param Text instance_storage_request: [optional] Kubernetes resource string for lower-bound of disk storage space
+        for each instance spawned for this job (i.e. both for master and workers).  Default is set by platform-level configuration.
+
+        .. note::
+
+            This is currently not supported by the platform.
+
+    :param Text instance_cpu_request: [optional] Kubernetes resource string for lower-bound of cores for each instance
+        spawned for this job (i.e. both for master and workers).
+        This can be set to a fractional portion of a CPU. Default is set by platform-level configuration.
+
+        TODO: Add links to resource string documentation for Kubernetes
+
+    :param Text instance_gpu_request: [optional] Kubernetes resource string for lower-bound of desired GPUs for each
+        instance spawned for this job (i.e. both for master and workers).
+        Default is set by platform-level configuration.
+
+        TODO: Add links to resource string documentation for Kubernetes
+
+    :param Text instance_memory_request: [optional]  Kubernetes resource string for lower-bound of physical memory
+        necessary for each instance spawned for this job (i.e. both for master and workers).  Default is set by platform-level configuration.
+
+        TODO: Add links to resource string documentation for Kubernetes
+
+    :param Text instance_storage_limit: [optional] Kubernetes resource string for upper-bound of disk storage space
+        for each instance spawned for this job (i.e. both for master and workers).
+        This amount is not guaranteed!  If not specified, it is set equal to storage_request.
+
+        .. note::
+
+            This is currently not supported by the platform.
+
+    :param Text instance_cpu_limit: [optional] Kubernetes resource string for upper-bound of cores for each instance
+        spawned for this job (i.e. both for master and workers).
+        This can be set to a fractional portion of a CPU. This amount is not guaranteed!  If not specified,
+        it is set equal to cpu_request.
+
+    :param Text instance_gpu_limit: [optional] Kubernetes resource string for upper-bound of desired GPUs for each
+        instance spawned for this job (i.e. both for master and workers).
+        This amount is not guaranteed!  If not specified, it is set equal to gpu_request.
+
+    :param Text instance_memory_limit: [optional]  Kubernetes resource string for upper-bound of physical memory
+        necessary for each instance spawned for this job (i.e. both for master and workers).
+        This amount is not guaranteed!  If not specified, it is set equal to memory_request.
+
+    :param dict[Text,Text] environment: [optional] environment variables to set when executing this task.
+
+    :param cls: This can be used to override the task implementation with a user-defined extension. The class
+        provided must be a subclass of flytekit.common.tasks.sdk_runnable.SdkRunnableTask.  A user can use this to
+        inject bespoke logic into the base Flyte programming model.
+
+    :rtype: flytekit.common.tasks.sdk_runnable.SdkRunnableTask
+    """
+    def wrapper(fn):
+        return (cls or _sdk_pytorch_tasks.SdkPyTorchTask)(
+            task_function=fn,
+            task_type=_common_constants.SdkTaskType.PYTORCH_TASK,
+            discovery_version=cache_version,
+            retries=retries,
+            interruptible=interruptible,
+            deprecated=deprecated,
+            discoverable=cache,
+            timeout=timeout or _datetime.timedelta(seconds=0),
+            workers_count=workers_count,
+            instance_storage_request=instance_storage_request,
+            instance_cpu_request=instance_cpu_request,
+            instance_gpu_request=instance_gpu_request,
+            instance_memory_request=instance_memory_request,
+            instance_storage_limit=instance_storage_limit,
+            instance_cpu_limit=instance_cpu_limit,
+            instance_gpu_limit=instance_gpu_limit,
+            instance_memory_limit=instance_memory_limit,
+            environment=environment or {}
+        )
+
+    if _task_function:
+        return wrapper(_task_function)
+    else:
+        return wrapper

setup.py

@@ -30,7 +30,7 @@
         ]
     },
     install_requires=[
-        "flyteidl>=0.17.27,<1.0.0",
+        "flyteidl>=0.17.32,<1.0.0",
         "click>=6.6,<8.0",
         "croniter>=0.3.20,<4.0.0",
         "deprecation>=2.0,<3.0",

tests/flytekit/unit/sdk/tasks/test_pytorch_task.py

@@ -0,0 +1,45 @@
+from __future__ import absolute_import
+from flytekit.sdk.tasks import pytorch_task, inputs, outputs
+from flytekit.sdk.types import Types
+from flytekit.common import constants as _common_constants
+from flytekit.common.tasks import sdk_runnable as _sdk_runnable, pytorch_task as _pytorch_task
+from flytekit.models import types as _type_models
+from flytekit.models.core import identifier as _identifier
+import datetime as _datetime
+
+
+@inputs(in1=Types.Integer)
+@outputs(out1=Types.String)
+@pytorch_task(workers_count=1)
+def simple_pytorch_task(wf_params, sc, in1, out1):
+    pass
+
+
+simple_pytorch_task._id = _identifier.Identifier(_identifier.ResourceType.TASK, "project", "domain", "name", "version")
+
+
+def test_simple_pytorch_task():
+    assert isinstance(simple_pytorch_task, _pytorch_task.SdkPyTorchTask)
+    assert isinstance(simple_pytorch_task, _sdk_runnable.SdkRunnableTask)
+    assert simple_pytorch_task.interface.inputs['in1'].description == ''
+    assert simple_pytorch_task.interface.inputs['in1'].type == \
+           _type_models.LiteralType(simple=_type_models.SimpleType.INTEGER)
+    assert simple_pytorch_task.interface.outputs['out1'].description == ''
+    assert simple_pytorch_task.interface.outputs['out1'].type == \
+           _type_models.LiteralType(simple=_type_models.SimpleType.STRING)
+    assert simple_pytorch_task.type == _common_constants.SdkTaskType.PYTORCH_TASK
+    assert simple_pytorch_task.task_function_name == 'simple_pytorch_task'
+    assert simple_pytorch_task.task_module == __name__
+    assert simple_pytorch_task.metadata.timeout == _datetime.timedelta(seconds=0)
+    assert simple_pytorch_task.metadata.deprecated_error_message == ''
+    assert simple_pytorch_task.metadata.discoverable is False
+    assert simple_pytorch_task.metadata.discovery_version == ''
+    assert simple_pytorch_task.metadata.retries.retries == 0
+    assert len(simple_pytorch_task.container.resources.limits) == 0
+    assert len(simple_pytorch_task.container.resources.requests) == 0
+    assert simple_pytorch_task.custom['workers'] == 1
+    # Should strip out the venv component of the args.
+    assert simple_pytorch_task._get_container_definition().args[0] == 'pyflyte-execute'
+
+    pb2 = simple_pytorch_task.to_flyte_idl()
+    assert pb2.custom['workers'] == 1