Workflow init (#645)

* Introduce @workflow.init decorator

README.md

@@ -575,6 +575,11 @@ Here are the decorators that can be applied:
   * The method's arguments are the workflow's arguments
   * The first parameter must be `self`, followed by positional arguments. Best practice is to only take a single
     argument that is an object/dataclass of fields that can be added to as needed.
+* `@workflow.init` - Specifies that the `__init__`  method accepts the workflow's arguments.
+  * If present, may only be applied to the `__init__` method, the parameters of which must then be identical to those of
+    the `@workflow.run` method.
+  * The purpose of this decorator is to allow operations involving workflow arguments to be performed in the `__init__`
+    method, before any signal or update handler has a chance to execute.
 * `@workflow.signal` - Defines a method as a signal
   * Can be defined on an `async` or non-`async` function at any hierarchy depth, but if decorated method is overridden,
     the override must also be decorated

temporalio/worker/_workflow_instance.py

@@ -201,6 +201,7 @@ def __init__(self, det: WorkflowInstanceDetails) -> None:
         self._payload_converter = det.payload_converter_class()
         self._failure_converter = det.failure_converter_class()
         self._defn = det.defn
+        self._workflow_input: Optional[ExecuteWorkflowInput] = None
         self._info = det.info
         self._extern_functions = det.extern_functions
         self._disable_eager_activity_execution = det.disable_eager_activity_execution
@@ -318,8 +319,9 @@ def get_thread_id(self) -> Optional[int]:
         return self._current_thread_id
 
     #### Activation functions ####
-    # These are in alphabetical order and besides "activate", all other calls
-    # are "_apply_" + the job field name.
+    # These are in alphabetical order and besides "activate", and
+    # "_make_workflow_input", all other calls are "_apply_" + the job field
+    # name.
 
     def activate(
         self, act: temporalio.bridge.proto.workflow_activation.WorkflowActivation
@@ -342,6 +344,7 @@ def activate(
         try:
             # Split into job sets with patches, then signals + updates, then
             # non-queries, then queries
+            start_job = None
             job_sets: List[
                 List[temporalio.bridge.proto.workflow_activation.WorkflowActivationJob]
             ] = [[], [], [], []]
@@ -351,10 +354,15 @@ def activate(
                 elif job.HasField("signal_workflow") or job.HasField("do_update"):
                     job_sets[1].append(job)
                 elif not job.HasField("query_workflow"):
+                    if job.HasField("start_workflow"):
+                        start_job = job.start_workflow
                     job_sets[2].append(job)
                 else:
                     job_sets[3].append(job)
 
+            if start_job:
+                self._workflow_input = self._make_workflow_input(start_job)
+
             # Apply every job set, running after each set
             for index, job_set in enumerate(job_sets):
                 if not job_set:
@@ -863,34 +871,41 @@ async def run_workflow(input: ExecuteWorkflowInput) -> None:
                     return
                 raise
 
+        if not self._workflow_input:
+            raise RuntimeError(
+                "Expected workflow input to be set. This is an SDK Python bug."
+            )
+        self._primary_task = self.create_task(
+            self._run_top_level_workflow_function(run_workflow(self._workflow_input)),
+            name="run",
+        )
+
+    def _apply_update_random_seed(
+        self, job: temporalio.bridge.proto.workflow_activation.UpdateRandomSeed
+    ) -> None:
+        self._random.seed(job.randomness_seed)
+
+    def _make_workflow_input(
+        self, start_job: temporalio.bridge.proto.workflow_activation.StartWorkflow
+    ) -> ExecuteWorkflowInput:
         # Set arg types, using raw values for dynamic
         arg_types = self._defn.arg_types
         if not self._defn.name:
             # Dynamic is just the raw value for each input value
-            arg_types = [temporalio.common.RawValue] * len(job.arguments)
-        args = self._convert_payloads(job.arguments, arg_types)
+            arg_types = [temporalio.common.RawValue] * len(start_job.arguments)
+        args = self._convert_payloads(start_job.arguments, arg_types)
         # Put args in a list if dynamic
         if not self._defn.name:
             args = [args]
 
-        # Schedule it
-        input = ExecuteWorkflowInput(
+        return ExecuteWorkflowInput(
             type=self._defn.cls,
             # TODO(cretz): Remove cast when https://github.com/python/mypy/issues/5485 fixed
             run_fn=cast(Callable[..., Awaitable[Any]], self._defn.run_fn),
             args=args,
-            headers=job.headers,
-        )
-        self._primary_task = self.create_task(
-            self._run_top_level_workflow_function(run_workflow(input)),
-            name="run",
+            headers=start_job.headers,
         )
 
-    def _apply_update_random_seed(
-        self, job: temporalio.bridge.proto.workflow_activation.UpdateRandomSeed
-    ) -> None:
-        self._random.seed(job.randomness_seed)
-
     #### _Runtime direct workflow call overrides ####
     # These are in alphabetical order and all start with "workflow_".
 
@@ -1617,6 +1632,14 @@ def _convert_payloads(
         except Exception as err:
             raise RuntimeError("Failed decoding arguments") from err
 
+    def _instantiate_workflow_object(self) -> Any:
+        if not self._workflow_input:
+            raise RuntimeError("Expected workflow input. This is a Python SDK bug.")
+        if hasattr(self._defn.cls.__init__, "__temporal_workflow_init"):
+            return self._defn.cls(*self._workflow_input.args)
+        else:
+            return self._defn.cls()
+
     def _is_workflow_failure_exception(self, err: BaseException) -> bool:
         # An exception is a failure instead of a task fail if it's already a
         # failure error or if it is an instance of any of the failure types in
@@ -1752,7 +1775,7 @@ def _run_once(self, *, check_conditions: bool) -> None:
             # We instantiate the workflow class _inside_ here because __init__
             # needs to run with this event loop set
             if not self._object:
-                self._object = self._defn.cls()
+                self._object = self._instantiate_workflow_object()
 
             # Run while there is anything ready
             while self._ready:

temporalio/workflow.py

@@ -143,10 +143,38 @@ def decorator(cls: ClassType) -> ClassType:
     return decorator
 
 
+def init(
+    init_fn: CallableType,
+) -> CallableType:
+    """Decorator for the workflow init method.
+
+    This may be used on the __init__ method of the workflow class to specify
+    that it accepts the same workflow input arguments as the ``@workflow.run``
+    method. It may not be used on any other method.
+
+    If used, the workflow will be instantiated as
+    ``MyWorkflow(**workflow_input_args)``. If not used, the workflow will be
+    instantiated as ``MyWorkflow()``.
+
+    Note that the ``@workflow.run`` method is always called as
+    ``my_workflow.my_run_method(**workflow_input_args)``. If you use the
+    ``@workflow.init`` decorator, the parameter list of your  __init__ and
+    ``@workflow.run`` methods must be identical.
+
+    Args:
+        init_fn: The __init__function to decorate.
+    """
+    if init_fn.__name__ != "__init__":
+        raise ValueError("@workflow.init may only be used on the __init__ method")
+
+    setattr(init_fn, "__temporal_workflow_init", True)
+    return init_fn
+
+
 def run(fn: CallableAsyncType) -> CallableAsyncType:
     """Decorator for the workflow run method.
 
-    This must be set on one and only one async method defined on the same class
+    This must be used on one and only one async method defined on the same class
     as ``@workflow.defn``. This can be defined on a base class method but must
     then be explicitly overridden and defined on the workflow class.
 
@@ -238,7 +266,7 @@ def signal(
 ):
     """Decorator for a workflow signal method.
 
-    This is set on any async or non-async method that you wish to be called upon
+    This is used on any async or non-async method that you wish to be called upon
     receiving a signal. If a function overrides one with this decorator, it too
     must be decorated.
 
@@ -309,7 +337,7 @@ def query(
 ):
     """Decorator for a workflow query method.
 
-    This is set on any non-async method that expects to handle a query. If a
+    This is used on any non-async method that expects to handle a query. If a
     function overrides one with this decorator, it too must be decorated.
 
     Query methods can only have positional parameters. Best practice for
@@ -983,7 +1011,7 @@ def update(
 ):
     """Decorator for a workflow update handler method.
 
-    This is set on any async or non-async method that you wish to be called upon
+    This is used on any async or non-async method that you wish to be called upon
     receiving an update. If a function overrides one with this decorator, it too
     must be decorated.
 
@@ -1307,13 +1335,13 @@ def _apply_to_class(
         issues: List[str] = []
 
         # Collect run fn and all signal/query/update fns
-        members = inspect.getmembers(cls)
+        init_fn: Optional[Callable[..., None]] = None
         run_fn: Optional[Callable[..., Awaitable[Any]]] = None
         seen_run_attr = False
         signals: Dict[Optional[str], _SignalDefinition] = {}
         queries: Dict[Optional[str], _QueryDefinition] = {}
         updates: Dict[Optional[str], _UpdateDefinition] = {}
-        for name, member in members:
+        for name, member in inspect.getmembers(cls):
             if hasattr(member, "__temporal_workflow_run"):
                 seen_run_attr = True
                 if not _is_unbound_method_on_cls(member, cls):
@@ -1354,6 +1382,8 @@ def _apply_to_class(
                     )
                 else:
                     queries[query_defn.name] = query_defn
+            elif name == "__init__" and hasattr(member, "__temporal_workflow_init"):
+                init_fn = member
             elif isinstance(member, UpdateMethodMultiParam):
                 update_defn = member._defn
                 if update_defn.name in updates:
@@ -1406,9 +1436,14 @@ def _apply_to_class(
 
         if not seen_run_attr:
             issues.append("Missing @workflow.run method")
-        if len(issues) == 1:
-            raise ValueError(f"Invalid workflow class: {issues[0]}")
-        elif issues:
+        if init_fn and run_fn:
+            if not _parameters_identical_up_to_naming(init_fn, run_fn):
+                issues.append(
+                    "@workflow.init and @workflow.run method parameters do not match"
+                )
+        if issues:
+            if len(issues) == 1:
+                raise ValueError(f"Invalid workflow class: {issues[0]}")
             raise ValueError(
                 f"Invalid workflow class for {len(issues)} reasons: {', '.join(issues)}"
             )
@@ -1444,6 +1479,19 @@ def __post_init__(self) -> None:
             object.__setattr__(self, "ret_type", ret_type)
 
 
+def _parameters_identical_up_to_naming(fn1: Callable, fn2: Callable) -> bool:
+    """Return True if the functions have identical parameter lists, ignoring parameter names."""
+
+    def params(fn: Callable) -> List[inspect.Parameter]:
+        # Ignore name when comparing parameters (remaining fields are kind,
+        # default, and annotation).
+        return [p.replace(name="x") for p in inspect.signature(fn).parameters.values()]
+
+    # We require that any type annotations present match exactly; i.e. we do
+    # not support any notion of subtype compatibility.
+    return params(fn1) == params(fn2)
+
+
 # Async safe version of partial
 def _bind_method(obj: Any, fn: Callable[..., Any]) -> Callable[..., Any]:
     # Curry instance on the definition function since that represents an

tests/test_workflow.py

@@ -1,4 +1,6 @@
-from typing import Any, Sequence
+import inspect
+import itertools
+from typing import Sequence
 
 import pytest
 
@@ -342,3 +344,68 @@ def test_workflow_defn_dynamic_handler_warnings():
     # We want to make sure they are reporting the right stacklevel
     warnings[0].filename.endswith("test_workflow.py")
     warnings[1].filename.endswith("test_workflow.py")
+
+
+class _TestParametersIdenticalUpToNaming:
+    def a1(self, a):
+        pass
+
+    def a2(self, b):
+        pass
+
+    def b1(self, a: int):
+        pass
+
+    def b2(self, b: int) -> str:
+        return ""
+
+    def c1(self, a1: int, a2: str) -> str:
+        return ""
+
+    def c2(self, b1: int, b2: str) -> int:
+        return 0
+
+    def d1(self, a1, a2: str) -> None:
+        pass
+
+    def d2(self, b1, b2: str) -> str:
+        return ""
+
+    def e1(self, a1, a2: str = "") -> None:
+        return None
+
+    def e2(self, b1, b2: str = "") -> str:
+        return ""
+
+    def f1(self, a1, a2: str = "a") -> None:
+        return None
+
+
+def test_parameters_identical_up_to_naming():
+    fns = [
+        f
+        for _, f in inspect.getmembers(_TestParametersIdenticalUpToNaming)
+        if inspect.isfunction(f)
+    ]
+    for f1, f2 in itertools.combinations(fns, 2):
+        name1, name2 = f1.__name__, f2.__name__
+        expect_equal = name1[0] == name2[0]
+        assert (
+            workflow._parameters_identical_up_to_naming(f1, f2) == (expect_equal)
+        ), f"expected {name1} and {name2} parameters{' ' if expect_equal else ' not '}to compare equal"
+
+
+@workflow.defn
+class BadWorkflowInit:
+    def not__init__(self):
+        pass
+
+    @workflow.run
+    async def run(self):
+        pass
+
+
+def test_workflow_init_not__init__():
+    with pytest.raises(ValueError) as err:
+        workflow.init(BadWorkflowInit.not__init__)
+    assert "@workflow.init may only be used on the __init__ method" in str(err.value)