Merge branch 'master' into docs/csv-logger

Author: bhimrazy

docs/source-fabric/api/fabric_methods.rst

@@ -40,13 +40,17 @@ Moves the model and optimizer to the correct device automatically.
 
     model = nn.Linear(32, 64)
     optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
+    scheduler = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=1.0, end_factor=0.3, total_iters=10)
 
     # Set up model and optimizer for accelerated training
     model, optimizer = fabric.setup(model, optimizer)
 
     # If you don't want Fabric to set the device
     model, optimizer = fabric.setup(model, optimizer, move_to_device=False)
 
+    # If you want to additionally register a learning rate scheduler with compatible strategies such as DeepSpeed
+    model, optimizer, scheduler = fabric.setup(model, optimizer, scheduler)
+
 
 The setup method also prepares the model for the selected precision choice so that operations during ``forward()`` get
 cast automatically. Advanced users should read :doc:`the notes on models wrapped by Fabric <../api/wrappers>`.

docs/source-fabric/api/wrappers.rst

@@ -124,7 +124,7 @@ If you were to run this model in Fabric with multiple devices (DDP or FSDP), you
     # OK: Calling the model directly
     output = model(torch.randn(10))
 
-    # OK: Calling the model's forward (equivalent to the abvoe)
+    # OK: Calling the model's forward (equivalent to the above)
     output = model.forward(torch.randn(10))
 
     # ERROR: Calling another method that calls forward indirectly

docs/source-fabric/conf.py

@@ -287,6 +287,7 @@
     ("py:class", "torch.distributed.fsdp.wrap.ModuleWrapPolicy"),
     ("py:class", "torch.distributed.fsdp.sharded_grad_scaler.ShardedGradScaler"),
     ("py:class", "torch.amp.grad_scaler.GradScaler"),
+    ("py:class", "torch.optim.lr_scheduler._LRScheduler"),
     # Mocked optional packages
     ("py:class", "deepspeed.*"),
     ("py:.*", "torch_xla.*"),

docs/source-pytorch/versioning.rst

@@ -79,10 +79,16 @@ The table below indicates the coverage of tested versions in our CI. Versions ou
      - ``torch``
      - ``torchmetrics``
      - Python
+   * - 2.5
+     - 2.5
+     - 2.5
+     - ≥2.1, ≤2.7
+     - ≥0.7.0
+     - ≥3.9, ≤3.12
    * - 2.4
      - 2.4
      - 2.4
-     - ≥2.1, ≤2.4
+     - ≥2.1, ≤2.6
      - ≥0.7.0
      - ≥3.9, ≤3.12
    * - 2.3

src/lightning/fabric/fabric.py

@@ -18,6 +18,7 @@
 from functools import partial
 from pathlib import Path
 from typing import (
+    TYPE_CHECKING,
     Any,
     Callable,
     Optional,
@@ -74,6 +75,9 @@
     _unwrap_objects,
 )
 
+if TYPE_CHECKING:
+    from torch.optim.lr_scheduler import _LRScheduler
+
 
 def _do_nothing(*_: Any) -> None:
     pass
@@ -206,6 +210,7 @@ def setup(
         self,
         module: nn.Module,
         *optimizers: Optimizer,
+        scheduler: Optional["_LRScheduler"] = None,
         move_to_device: bool = True,
         _reapply_compile: bool = True,
     ) -> Any:  # no specific return because the way we want our API to look does not play well with mypy
@@ -214,6 +219,7 @@ def setup(
         Args:
             module: A :class:`torch.nn.Module` to set up
             *optimizers: The optimizer(s) to set up (no optimizers is also possible)
+            scheduler: The learning rate scheduler to set up (no learning rate scheduler is also possible)
             move_to_device: If set ``True`` (default), moves the model to the correct device. Set this to ``False``
                 and alternatively use :meth:`to_device` manually.
             _reapply_compile: If ``True`` (default), and the model was ``torch.compile``d before, the
@@ -222,7 +228,8 @@ def setup(
                 FSDP etc.). Set it to ``False`` if compiling DDP/FSDP is causing issues.
 
         Returns:
-            The tuple containing wrapped module and the optimizers, in the same order they were passed in.
+            The tuple containing wrapped module, optimizers, and an optional learning rate scheduler,
+            in the same order they were passed in.
 
         """
         self._validate_setup(module, optimizers)
@@ -236,8 +243,8 @@ def setup(
 
         # Let accelerator/plugin wrap and connect the models and optimizers
         if optimizers:
-            module, optimizers = self._strategy.setup_module_and_optimizers(  # type: ignore[assignment]
-                module, list(optimizers)
+            module, optimizers, scheduler = self._strategy.setup_module_and_optimizers(  # type: ignore[assignment]
+                module, list(optimizers), scheduler
             )
         else:
             module = self._strategy.setup_module(module)
@@ -266,7 +273,7 @@ def setup(
 
         if optimizers:
             # join both types in a tuple for API convenience
-            return (module, *optimizers)
+            return (module, *optimizers, scheduler) if scheduler is not None else (module, *optimizers)
         return module
 
     def setup_module(

src/lightning/fabric/strategies/deepspeed.py

@@ -44,6 +44,7 @@
 
 if TYPE_CHECKING:
     from deepspeed import DeepSpeedEngine
+    from torch.optim.lr_scheduler import _LRScheduler
 
 _DEEPSPEED_AVAILABLE = RequirementCache("deepspeed")
 _DEEPSPEED_GREATER_EQUAL_0_14_1 = RequirementCache("deepspeed>=0.14.1")
@@ -316,25 +317,24 @@ def model(self) -> "DeepSpeedEngine":
 
     @override
     def setup_module_and_optimizers(
-        self, module: Module, optimizers: list[Optimizer]
-    ) -> tuple["DeepSpeedEngine", list[Optimizer]]:
-        """Set up a model and multiple optimizers together.
-
-        Currently, only a single optimizer is supported.
+        self, module: Module, optimizers: list[Optimizer], scheduler: Optional["_LRScheduler"] = None
+    ) -> tuple["DeepSpeedEngine", list[Optimizer], Any]:
+        """Set up a model and multiple optimizers together, along with an optional learning rate scheduler. Currently,
+        only a single optimizer is supported.
 
         Return:
-            The model wrapped into a :class:`deepspeed.DeepSpeedEngine` and a list with a single
-            deepspeed optimizer.
+            The model wrapped into a :class:`deepspeed.DeepSpeedEngine`, a list with a single
+            deepspeed optimizer, and an optional learning rate scheduler.
 
         """
         if len(optimizers) != 1:
             raise ValueError(
                 f"Currently only one optimizer is supported with DeepSpeed. Got {len(optimizers)} optimizers instead."
             )
 
-        self._deepspeed_engine, optimizer = self._initialize_engine(module, optimizers[0])
+        self._deepspeed_engine, optimizer, scheduler = self._initialize_engine(module, optimizers[0], scheduler)
         self._set_deepspeed_activation_checkpointing()
-        return self._deepspeed_engine, [optimizer]
+        return self._deepspeed_engine, [optimizer], scheduler
 
     @override
     def setup_module(self, module: Module) -> "DeepSpeedEngine":
@@ -343,7 +343,7 @@ def setup_module(self, module: Module) -> "DeepSpeedEngine":
         For training, see :meth:`setup_module_and_optimizers`.
 
         """
-        self._deepspeed_engine, _ = self._initialize_engine(module)
+        self._deepspeed_engine, _, _ = self._initialize_engine(module)
         return self._deepspeed_engine
 
     @override
@@ -596,10 +596,8 @@ def register_strategies(cls, strategy_registry: _StrategyRegistry) -> None:
         )
 
     def _initialize_engine(
-        self,
-        model: Module,
-        optimizer: Optional[Optimizer] = None,
-    ) -> tuple["DeepSpeedEngine", Optimizer]:
+        self, model: Module, optimizer: Optional[Optimizer] = None, scheduler: Optional["_LRScheduler"] = None
+    ) -> tuple["DeepSpeedEngine", Optimizer, Any]:
         """Initialize one model and one optimizer with an optional learning rate scheduler.
 
         This calls ``deepspeed.initialize`` internally.
@@ -608,15 +606,16 @@ def _initialize_engine(
         import deepspeed
 
         model_parameters = filter(lambda p: p.requires_grad, model.parameters())
-        deepspeed_engine, deepspeed_optimizer, _, _ = deepspeed.initialize(
+        deepspeed_engine, deepspeed_optimizer, _, deepspeed_scheduler = deepspeed.initialize(
             args=argparse.Namespace(device_rank=self.root_device.index),
             config=self.config,
             model=model,
             model_parameters=model_parameters,
             optimizer=optimizer,
+            lr_scheduler=scheduler,
             dist_init_required=False,
         )
-        return deepspeed_engine, deepspeed_optimizer
+        return deepspeed_engine, deepspeed_optimizer, deepspeed_scheduler
 
     @override
     def setup_environment(self) -> None:

src/lightning/fabric/strategies/fsdp.py

@@ -71,6 +71,7 @@
     from torch.distributed.device_mesh import DeviceMesh
     from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, MixedPrecision, ShardingStrategy
     from torch.distributed.fsdp.wrap import ModuleWrapPolicy
+    from torch.optim.lr_scheduler import _LRScheduler
 
     _POLICY = Union[set[type[Module]], Callable[[Module, bool, int], bool], ModuleWrapPolicy]
     _SHARDING_STRATEGY = Union[ShardingStrategy, Literal["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD"]]
@@ -261,8 +262,8 @@ def setup_environment(self) -> None:
 
     @override
     def setup_module_and_optimizers(
-        self, module: Module, optimizers: list[Optimizer]
-    ) -> tuple[Module, list[Optimizer]]:
+        self, module: Module, optimizers: list[Optimizer], scheduler: Optional["_LRScheduler"] = None
+    ) -> tuple[Module, list[Optimizer], Optional["_LRScheduler"]]:
         """Wraps the model into a :class:`~torch.distributed.fsdp.fully_sharded_data_parallel.FullyShardedDataParallel`
         module and sets `use_orig_params=True` to keep the reference to the original parameters in the optimizer."""
         use_orig_params = self._fsdp_kwargs.get("use_orig_params")
@@ -274,7 +275,7 @@ def setup_module_and_optimizers(
                 " call `setup_optimizer`."
             )
         module = self.setup_module(module)
-        return module, optimizers
+        return module, optimizers, scheduler
 
     @override
     def setup_module(self, module: Module) -> Module:

src/lightning/fabric/strategies/strategy.py

@@ -15,7 +15,7 @@
 from abc import ABC, abstractmethod
 from collections.abc import Iterable
 from contextlib import AbstractContextManager, ExitStack
-from typing import Any, Callable, Optional, TypeVar, Union
+from typing import TYPE_CHECKING, Any, Callable, Optional, TypeVar, Union
 
 import torch
 from torch import Tensor
@@ -33,6 +33,9 @@
 from lightning.fabric.utilities.init import _EmptyInit
 from lightning.fabric.utilities.types import _PATH, Optimizable, ReduceOp, _Stateful
 
+if TYPE_CHECKING:
+    from torch.optim.lr_scheduler import _LRScheduler
+
 TBroadcast = TypeVar("TBroadcast")
 TReduce = TypeVar("TReduce")
 
@@ -145,8 +148,8 @@ def module_init_context(self, empty_init: Optional[bool] = None) -> AbstractCont
         return stack
 
     def setup_module_and_optimizers(
-        self, module: Module, optimizers: list[Optimizer]
-    ) -> tuple[Module, list[Optimizer]]:
+        self, module: Module, optimizers: list[Optimizer], scheduler: Optional["_LRScheduler"] = None
+    ) -> tuple[Module, list[Optimizer], Optional["_LRScheduler"]]:
         """Set up a model and multiple optimizers together.
 
         The returned objects are expected to be in the same order they were passed in. The default implementation will
@@ -155,7 +158,7 @@ def setup_module_and_optimizers(
         """
         module = self.setup_module(module)
         optimizers = [self.setup_optimizer(optimizer) for optimizer in optimizers]
-        return module, optimizers
+        return module, optimizers, scheduler
 
     def setup_module(self, module: Module) -> Module:
         """Performs setup for the model, e.g., by wrapping it by another class."""

src/lightning/fabric/strategies/xla_fsdp.py

@@ -44,6 +44,7 @@
 from lightning.fabric.utilities.types import _PATH, Optimizable, ReduceOp
 
 if TYPE_CHECKING:
+    from torch.optim.lr_scheduler import _LRScheduler
     from torch_xla.distributed.parallel_loader import MpDeviceLoader
 
 _POLICY_SET = set[type[Module]]
@@ -196,8 +197,8 @@ def setup_environment(self) -> None:
 
     @override
     def setup_module_and_optimizers(
-        self, module: Module, optimizers: list[Optimizer]
-    ) -> tuple[Module, list[Optimizer]]:
+        self, module: Module, optimizers: list[Optimizer], scheduler: Optional["_LRScheduler"] = None
+    ) -> tuple[Module, list[Optimizer], Optional["_LRScheduler"]]:
         """Returns NotImplementedError since for XLAFSDP optimizer setup must happen after module setup."""
         raise NotImplementedError(
             f"The `{type(self).__name__}` does not support the joint setup of module and optimizer(s)."

tests/tests_fabric/strategies/test_deepspeed.py

@@ -137,6 +137,7 @@ def test_deepspeed_setup_module(init_mock):
         model=model,
         model_parameters=ANY,
         optimizer=None,
+        lr_scheduler=None,
         dist_init_required=False,
     )