Make run_with_accelerate a pythonic decorator

src/zenml/integrations/huggingface/steps/accelerate_runner.py

@@ -17,15 +17,15 @@
 """Step function to run any ZenML step using Accelerate."""
 
 import functools
-import inspect
-from typing import Any, Callable, Dict, TypeVar, cast
+from typing import Any, Callable, Dict, Optional, TypeVar, Union, cast
 
 import cloudpickle as pickle
 from accelerate.commands.launch import (  # type: ignore[import-untyped]
     launch_command,
     launch_command_parser,
 )
 
+from zenml import get_pipeline_context
 from zenml.logger import get_logger
 from zenml.steps import BaseStep
 from zenml.utils.function_utils import _cli_arg_name, create_cli_wrapped_script
@@ -35,28 +35,31 @@
 
 
 def run_with_accelerate(
-    step_function: BaseStep,
+    step_function_top_level: Optional[BaseStep] = None,
     **accelerate_launch_kwargs: Any,
-) -> BaseStep:
+) -> Union[Callable[[BaseStep], BaseStep], BaseStep]:
     """Run a function with accelerate.
 
     Accelerate package: https://huggingface.co/docs/accelerate/en/index
     Example:
         ```python
         from zenml import step, pipeline
         from zenml.integrations.hugginface.steps import run_with_accelerate
+
+        @run_with_accelerate(num_processes=4, multi_gpu=True)
         @step
         def training_step(some_param: int, ...):
             # your training code is below
             ...
 
         @pipeline
         def training_pipeline(some_param: int, ...):
-            run_with_accelerate(training_step, num_processes=4)(some_param, ...)
+            training_step(some_param, ...)
         ```
 
     Args:
-        step_function: The step function to run.
+        step_function_top_level: The step function to run with accelerate [optional].
+            Used in functional calls like `run_with_accelerate(some_func,foo=bar)()`.
         accelerate_launch_kwargs: A dictionary of arguments to pass along to the
             `accelerate launch` command, including hardware selection, resource
             allocation, and training paradigm options. Visit
@@ -65,100 +68,99 @@ def training_pipeline(some_param: int, ...):
 
     Returns:
         The accelerate-enabled version of the step.
-
-    Raises:
-        RuntimeError: If the decorator is misused.
-
     """
 
-    def _decorator(
-        entrypoint: F, accelerate_launch_kwargs: Dict[str, Any]
-    ) -> F:
-        @functools.wraps(entrypoint)
-        def inner(*args: Any, **kwargs: Any) -> Any:
-            if args:
-                raise ValueError(
-                    "Accelerated steps do not support positional arguments."
-                )
-
-            with create_cli_wrapped_script(
-                entrypoint, flavor="accelerate"
-            ) as (
-                script_path,
-                output_path,
-            ):
-                commands = [str(script_path.absolute())]
-                for k, v in kwargs.items():
-                    k = _cli_arg_name(k)
-                    if isinstance(v, bool):
-                        if v:
-                            commands.append(f"--{k}")
-                    elif type(v) in (list, tuple, set):
-                        for each in v:
-                            commands += [f"--{k}", f"{each}"]
-                    else:
-                        commands += [f"--{k}", f"{v}"]
-                logger.debug(commands)
-
-                parser = launch_command_parser()
-                args = parser.parse_args(commands)
-                for k, v in accelerate_launch_kwargs.items():
-                    if k in args:
-                        setattr(args, k, v)
-                    else:
-                        logger.warning(
-                            f"You passed in `{k}` as an `accelerate launch` argument, but it was not accepted. "
-                            "Please check https://huggingface.co/docs/accelerate/en/package_reference/cli#accelerate-launch "
-                            "to find out more about supported arguments and retry."
-                        )
-                try:
-                    launch_command(args)
-                except Exception as e:
-                    logger.error(
-                        "Accelerate training job failed... See error message for details."
+    def _decorator(step_function: BaseStep) -> BaseStep:
+        def _wrapper(
+            entrypoint: F, accelerate_launch_kwargs: Dict[str, Any]
+        ) -> F:
+            @functools.wraps(entrypoint)
+            def inner(*args: Any, **kwargs: Any) -> Any:
+                if args:
+                    raise ValueError(
+                        "Accelerated steps do not support positional arguments."
                     )
-                    raise RuntimeError(
-                        "Accelerate training job failed."
-                    ) from e
-                else:
-                    logger.info(
-                        "Accelerate training job finished successfully."
-                    )
-                    return pickle.load(open(output_path, "rb"))
-
-        return cast(F, inner)
-
-    import __main__
 
-    if __main__.__file__ == inspect.getsourcefile(step_function.entrypoint):
-        raise RuntimeError(
-            f"`{run_with_accelerate.__name__}` decorator cannot be used "
-            "with steps defined inside the entrypoint script, please move "
-            f"your step `{step_function.name}` code to another file and retry."
+                with create_cli_wrapped_script(
+                    entrypoint, flavor="accelerate"
+                ) as (
+                    script_path,
+                    output_path,
+                ):
+                    commands = [str(script_path.absolute())]
+                    for k, v in kwargs.items():
+                        k = _cli_arg_name(k)
+                        if isinstance(v, bool):
+                            if v:
+                                commands.append(f"--{k}")
+                        elif type(v) in (list, tuple, set):
+                            for each in v:
+                                commands += [f"--{k}", f"{each}"]
+                        else:
+                            commands += [f"--{k}", f"{v}"]
+                    logger.debug(commands)
+
+                    parser = launch_command_parser()
+                    args = parser.parse_args(commands)
+                    for k, v in accelerate_launch_kwargs.items():
+                        if k in args:
+                            setattr(args, k, v)
+                        else:
+                            logger.warning(
+                                f"You passed in `{k}` as an `accelerate launch` argument, but it was not accepted. "
+                                "Please check https://huggingface.co/docs/accelerate/en/package_reference/cli#accelerate-launch "
+                                "to find out more about supported arguments and retry."
+                            )
+                    try:
+                        launch_command(args)
+                    except Exception as e:
+                        logger.error(
+                            "Accelerate training job failed... See error message for details."
+                        )
+                        raise RuntimeError(
+                            "Accelerate training job failed."
+                        ) from e
+                    else:
+                        logger.info(
+                            "Accelerate training job finished successfully."
+                        )
+                        return pickle.load(open(output_path, "rb"))
+
+            return cast(F, inner)
+
+        try:
+            get_pipeline_context()
+        except RuntimeError:
+            pass
+        else:
+            raise RuntimeError(
+                f"`{run_with_accelerate.__name__}` decorator cannot be used "
+                "in a functional way with steps, please apply decoration "
+                "directly to a step instead. This behavior will be also "
+                "allowed in future, but now it faces technical limitations.\n"
+                "Example (allowed):\n"
+                f"@{run_with_accelerate.__name__}(...)\n"
+                f"def {step_function.name}(...):\n"
+                "    ...\n"
+                "Example (not allowed):\n"
+                "def my_pipeline(...):\n"
+                f"    run_with_accelerate({step_function.name},...)(...)\n"
+            )
+
+        setattr(
+            step_function, "unwrapped_entrypoint", step_function.entrypoint
         )
-    if f"@{run_with_accelerate.__name__}" in inspect.getsource(
-        step_function.entrypoint
-    ):
-        raise RuntimeError(
-            f"`{run_with_accelerate.__name__}` decorator cannot be used "
-            "directly on steps using '@' syntax, please use a functional "
-            "decoration in your pipeline script instead.\n"
-            "Example (not allowed):\n"
-            f"@{run_with_accelerate.__name__}\n"
-            f"def {step_function.name}(...):\n"
-            "    ...\n"
-            "Example (allowed):\n"
-            "def my_pipeline(...):\n"
-            f"    run_with_accelerate({step_function.name})(...)\n"
+        setattr(
+            step_function,
+            "entrypoint",
+            _wrapper(
+                step_function.entrypoint,
+                accelerate_launch_kwargs=accelerate_launch_kwargs,
+            ),
         )
 
-    setattr(
-        step_function,
-        "entrypoint",
-        _decorator(
-            step_function.entrypoint,
-            accelerate_launch_kwargs=accelerate_launch_kwargs,
-        ),
-    )
+        return step_function
 
-    return step_function
+    if step_function_top_level:
+        return _decorator(step_function_top_level)
+    return _decorator

src/zenml/utils/function_utils.py

@@ -34,12 +34,12 @@
 import sys
 sys.path.append(r"{func_path}")
 
-from {func_module} import {func_name} as func_to_wrap
+from {func_module} import {func_name} as step_function
 
-if entrypoint:=getattr(func_to_wrap, "entrypoint", None):
-    func = _cli_wrapped_function(entrypoint)
+if unwrapped_entrypoint:=getattr(step_function, "unwrapped_entrypoint", None):
+    func = _cli_wrapped_function(unwrapped_entrypoint)
 else:
-    func = _cli_wrapped_function(func_to_wrap)
+    func = _cli_wrapped_function(step_function.entrypoint)
 """
 _CLI_WRAPPED_MAINS = {
     "accelerate": """

tests/integration/integrations/huggingface/steps/test_accelerate_runner.py

@@ -16,6 +16,7 @@
 import shutil
 from pathlib import Path
 
+import pytest
 import transformers
 from accelerate import Accelerator
 from datasets import load_from_disk
@@ -74,17 +75,20 @@ def get_full_path(folder: str):
     return str(ft_model_dir)
 
 
-@pipeline(enable_cache=False)
-def train_pipe():
-    model_dir = run_with_accelerate(train, num_processes=2, use_cpu=True)()
-    # if it is StepArtifact, we are still composing the pipeline
-    if not isinstance(model_dir, StepArtifact):
-        assert isinstance(model_dir, str)
-        assert model_dir == "model_dir"
+train_accelerated = run_with_accelerate(train, num_processes=2, use_cpu=True)
 
 
 def test_accelerate_runner_on_cpu_with_toy_model(clean_client):
     """Tests whether the run_with_accelerate wrapper works as expected."""
+
+    @pipeline(enable_cache=False)
+    def train_pipe():
+        model_dir = train_accelerated()
+        # if it is StepArtifact, we are still composing the pipeline
+        if not isinstance(model_dir, StepArtifact):
+            assert isinstance(model_dir, str)
+            assert model_dir == "model_dir"
+
     try:
         prev_files = os.listdir()
         response = train_pipe()
@@ -93,3 +97,14 @@ def test_accelerate_runner_on_cpu_with_toy_model(clean_client):
         cur_files = os.listdir()
         for each in set(cur_files) - set(prev_files):
             shutil.rmtree(each)
+
+
+def test_accelerate_runner_fails_on_functional_use(clean_client):
+    """Tests whether the run_with_accelerate wrapper works as expected."""
+
+    @pipeline(enable_cache=False)
+    def train_pipe():
+        _ = run_with_accelerate(train, num_processes=2, use_cpu=True)
+
+    with pytest.raises(RuntimeError):
+        train_pipe()