feat: enforce DeepSpeed version requirement for PyTorch >= 2.6 and update checkpoint loading behavior

Author: deependujha

src/lightning/fabric/strategies/deepspeed.py

@@ -37,6 +37,7 @@
 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_6
 from lightning.fabric.utilities.load import _move_state_into
 from lightning.fabric.utilities.rank_zero import rank_zero_info, rank_zero_warn
 from lightning.fabric.utilities.seed import reset_seed
@@ -47,6 +48,7 @@
     from torch.optim.lr_scheduler import _LRScheduler
 
 _DEEPSPEED_AVAILABLE = RequirementCache("deepspeed")
+_DEEPSPEED_GREATER_EQUAL_0_16 = RequirementCache("deepspeed>=0.16.0")
 
 
 # TODO(fabric): Links in the docstrings to PL-specific deepspeed user docs need to be replaced.
@@ -239,6 +241,19 @@ def __init__(
                 " Install it by running `pip install -U deepspeed`."
             )
 
+        if _TORCH_GREATER_EQUAL_2_6 and not _DEEPSPEED_GREATER_EQUAL_0_16:
+            # Starting with PyTorch 2.6, `torch.load` defaults to `weights_only=True` when loading full checkpoints.
+            # DeepSpeed added support for this behavior in version 0.16.0.
+            import deepspeed
+
+            deepspeed_version = deepspeed.__version__
+
+            raise ImportError(
+                f"PyTorch >= 2.6 requires DeepSpeed >= 0.16.0. "
+                f"Detected DeepSpeed version: {deepspeed_version}. "
+                "Please upgrade by running `pip install -U 'deepspeed>=0.16.0'`."
+            )
+
         super().__init__(
             accelerator=accelerator,
             parallel_devices=parallel_devices,

src/lightning/fabric/utilities/imports.py

@@ -36,5 +36,6 @@
 _TORCH_GREATER_EQUAL_2_4_1 = compare_version("torch", operator.ge, "2.4.1")
 _TORCH_GREATER_EQUAL_2_5 = compare_version("torch", operator.ge, "2.5.0")
 _TORCH_LESS_EQUAL_2_6 = compare_version("torch", operator.le, "2.6.0")
+_TORCH_GREATER_EQUAL_2_6 = compare_version("torch", operator.ge, "2.6.0")
 _TORCHMETRICS_GREATER_EQUAL_1_0_0 = compare_version("torchmetrics", operator.ge, "1.0.0")
 _PYTHON_GREATER_EQUAL_3_10_0 = (sys.version_info.major, sys.version_info.minor) >= (3, 10)

src/lightning/pytorch/strategies/deepspeed.py

@@ -35,10 +35,12 @@
 from lightning.fabric.strategies import _StrategyRegistry
 from lightning.fabric.strategies.deepspeed import (
     _DEEPSPEED_AVAILABLE,
+    _DEEPSPEED_GREATER_EQUAL_0_16,
     _format_precision_config,
     _validate_checkpoint_directory,
     _validate_device_index_selection,
 )
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_6
 from lightning.fabric.utilities.optimizer import _optimizers_to_device
 from lightning.fabric.utilities.seed import reset_seed
 from lightning.fabric.utilities.types import _PATH
@@ -262,6 +264,19 @@ def __init__(
                 " Install it by running `pip install -U deepspeed`."
             )
 
+        if _TORCH_GREATER_EQUAL_2_6 and not _DEEPSPEED_GREATER_EQUAL_0_16:
+            # Starting with PyTorch 2.6, `torch.load` defaults to `weights_only=True` when loading full checkpoints.
+            # DeepSpeed added support for this behavior in version 0.16.0.
+            import deepspeed
+
+            deepspeed_version = deepspeed.__version__
+
+            raise ImportError(
+                f"PyTorch >= 2.6 requires DeepSpeed >= 0.16.0. "
+                f"Detected DeepSpeed version: {deepspeed_version}. "
+                "Please upgrade by running `pip install -U 'deepspeed>=0.16.0'`."
+            )
+
         super().__init__(
             accelerator=accelerator,
             parallel_devices=parallel_devices,

src/lightning/pytorch/utilities/deepspeed.py

@@ -93,10 +93,10 @@ def convert_zero_checkpoint_to_fp32_state_dict(
     ]
     checkpoint_dir = ds_checkpoint_dir(checkpoint_dir)
     optim_files = get_optim_files(checkpoint_dir)
-    optim_state = torch.load(optim_files[0], map_location=CPU_DEVICE)
+    optim_state = torch.load(optim_files[0], map_location=CPU_DEVICE, weights_only=False)
     zero_stage = optim_state["optimizer_state_dict"]["zero_stage"]
     model_file = get_model_state_file(checkpoint_dir, zero_stage)
-    client_state = torch.load(model_file, map_location=CPU_DEVICE)
+    client_state = torch.load(model_file, map_location=CPU_DEVICE, weights_only=False)
     client_state = {key: value for key, value in client_state.items() if key not in deepspeed_states}
     # State dict keys will include reference to wrapper _LightningModuleWrapperBase in old checkpoints created in
     # Lightning version < 2.1. Delete the `_forward_module` prefix before saving.