Accommodate FSDP full-precision param_dtype training with PyTorch <…

… 2.0 (#18278)

(cherry picked from commit c081b48)

src/lightning/fabric/CHANGELOG.md

@@ -30,6 +30,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- Fixed FSDP full-precision `param_dtype` training (`16-mixed`, `bf16-mixed` and `32-true` configurations) to avoid FSDP assertion errors with PyTorch < 2.0 ([#18278](https://github.com/Lightning-AI/lightning/pull/18278))
+
+
 - Fixed issue where DDP subprocesses that used Hydra would set hydra's working directory to current directory ([#18145](https://github.com/Lightning-AI/lightning/pull/18145))
 - Fixed an issue that would prevent the user to set the multiprocessing start method after importing lightning ([#18177](https://github.com/Lightning-AI/lightning/pull/18177))
 - Fixed an issue with `Fabric.all_reduce()` not performing an inplace operation for all backends consistently ([#18235](https://github.com/Lightning-AI/lightning/pull/18235))

src/lightning/fabric/plugins/precision/fsdp.py

@@ -16,7 +16,7 @@
 import torch
 
 from lightning.fabric.plugins.precision.amp import MixedPrecision
-from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_1_12
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_1_12, _TORCH_GREATER_EQUAL_2_0
 
 if TYPE_CHECKING:
     from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision as TorchMixedPrecision
@@ -48,11 +48,14 @@ def __init__(
     def mixed_precision_config(self) -> "TorchMixedPrecision":
         from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision as TorchMixedPrecision
 
+        # With PyTorch < 2.0, FSDP uses the noneness of `param_dtype` as a proxy for the `_uses_param_mixed_precision`
+        # property. In order to avoid FSDP assertion failures, we therefore avoid setting `param_dtype` to
+        # `torch.float32` here with PyTorch < 2.0.
         if self.precision == "16-mixed":
-            param_dtype = torch.float32
+            param_dtype = None if not _TORCH_GREATER_EQUAL_2_0 else torch.float32
             reduce_dtype = buffer_dtype = torch.float16
         elif self.precision == "bf16-mixed":
-            param_dtype = torch.float32
+            param_dtype = None if not _TORCH_GREATER_EQUAL_2_0 else torch.float32
             reduce_dtype = buffer_dtype = torch.bfloat16
         elif self.precision == "16-true":
             param_dtype = reduce_dtype = buffer_dtype = torch.float16

src/lightning/fabric/strategies/deepspeed.py

@@ -33,8 +33,7 @@
 from lightning.fabric.strategies.registry import _StrategyRegistry
 from lightning.fabric.strategies.strategy import _Sharded
 from lightning.fabric.utilities.distributed import log
-from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
-from lightning.fabric.utilities.rank_zero import rank_zero_info, rank_zero_only, rank_zero_warn
+from lightning.fabric.utilities.rank_zero import rank_zero_info, rank_zero_warn
 from lightning.fabric.utilities.seed import reset_seed
 from lightning.fabric.utilities.types import _PATH
 

src/lightning/pytorch/CHANGELOG.md

@@ -12,6 +12,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - On XLA, avoid setting the global rank before processes have been launched as this will initialize the PJRT computation client in the main process ([#16966](https://github.com/Lightning-AI/lightning/pull/16966))
 
 
+- Fixed FSDP full-precision `param_dtype` training (`16-mixed`, `bf16-mixed` and `32-true` configurations) to avoid FSDP assertion errors with PyTorch < 2.0 ([#18278](https://github.com/Lightning-AI/lightning/pull/18278))
+
+
 ## [2.0.7] - 2023-08-14
 
 ### Added

src/lightning/pytorch/plugins/precision/fsdp.py

@@ -15,7 +15,7 @@
 
 import torch
 
-from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_1_12
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_1_12, _TORCH_GREATER_EQUAL_2_0
 from lightning.pytorch.plugins.precision.amp import MixedPrecisionPlugin
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
 
@@ -57,11 +57,14 @@ def clip_grad_by_norm(self, *_: Any, **__: Any) -> None:
     def mixed_precision_config(self) -> Optional[MixedPrecision]:
         assert MixedPrecision is not None
 
+        # With PyTorch < 2.0, FSDP uses the noneness of `param_dtype` as a proxy for the `_uses_param_mixed_precision`
+        # property. In order to avoid FSDP assertion failures, we therefore avoid setting `param_dtype` to
+        # `torch.float32` here with PyTorch < 2.0.
         if self.precision == "16-mixed":
-            param_dtype = torch.float32
+            param_dtype = None if not _TORCH_GREATER_EQUAL_2_0 else torch.float32
             reduce_dtype = buffer_dtype = torch.float16
         elif self.precision == "bf16-mixed":
-            param_dtype = torch.float32
+            param_dtype = None if not _TORCH_GREATER_EQUAL_2_0 else torch.float32
             reduce_dtype = buffer_dtype = torch.bfloat16
         elif self.precision == "16-true":
             param_dtype = reduce_dtype = buffer_dtype = torch.float16

tests/tests_fabric/plugins/precision/test_fsdp.py

@@ -30,9 +30,21 @@ def test_fsdp_precision_support(*_):
 @pytest.mark.parametrize(
     ("precision", "expected"),
     [
-        ("16-mixed", (torch.float32, torch.float16, torch.float16)),
-        ("bf16-mixed", (torch.float32, torch.bfloat16, torch.bfloat16)),
-        # TODO: add 16-true and bf16-true once supported
+        pytest.param(
+            "16-mixed", (torch.float32, torch.float16, torch.float16), marks=RunIf(min_torch="2.0"), id="16-mixed-ge2_0"
+        ),
+        pytest.param(
+            "16-mixed", (None, torch.float16, torch.float16), marks=RunIf(max_torch="2.0"), id="16-mixed-lt2_0"
+        ),
+        pytest.param(
+            "bf16-mixed",
+            (torch.float32, torch.bfloat16, torch.bfloat16),
+            marks=RunIf(min_torch="2.0"),
+            id="bf16-mixed-ge2_0",
+        ),
+        pytest.param(
+            "bf16-mixed", (None, torch.bfloat16, torch.bfloat16), marks=RunIf(max_torch="2.0"), id="bf16-mixed-lt2_0"
+        ),
     ],
 )
 def test_fsdp_precision_config(precision, expected):

tests/tests_pytorch/strategies/test_fsdp.py

@@ -74,10 +74,10 @@ def _assert_layer_fsdp_instance(self) -> None:
         assert isinstance(self.trainer.strategy.precision_plugin, FSDPMixedPrecisionPlugin)
 
         if self.trainer.precision == "16-mixed":
-            param_dtype = torch.float32
+            param_dtype = None if not _TORCH_GREATER_EQUAL_2_0 else torch.float32
             reduce_dtype = buffer_dtype = torch.float16
         elif self.trainer.precision == "bf16-mixed":
-            param_dtype = torch.float32
+            param_dtype = None if not _TORCH_GREATER_EQUAL_2_0 else torch.float32
             reduce_dtype = buffer_dtype = torch.bfloat16
         elif self.trainer.precision == "16-true":
             param_dtype = reduce_dtype = buffer_dtype = torch.float16
@@ -122,10 +122,10 @@ def _assert_layer_fsdp_instance(self) -> None:
         assert isinstance(self.trainer.strategy.precision_plugin, FSDPMixedPrecisionPlugin)
 
         if self.trainer.precision == "16-mixed":
-            param_dtype = torch.float32
+            param_dtype = None if not _TORCH_GREATER_EQUAL_2_0 else torch.float32
             reduce_dtype = buffer_dtype = torch.float16
         elif self.trainer.precision == "bf16-mixed":
-            param_dtype = torch.float32
+            param_dtype = None if not _TORCH_GREATER_EQUAL_2_0 else torch.float32
             reduce_dtype = buffer_dtype = torch.bfloat16
         elif self.trainer.precision == "16-true":
             param_dtype = reduce_dtype = buffer_dtype = torch.float16