bump lightning dev sha, minor refactor of fsdp imports, update docker…

… image to use PT 2.6.0-rc3

dockers/base-cuda/Dockerfile

@@ -88,9 +88,9 @@ RUN \
         # ... pytorch patch version
         # pip install torch==1.11.1+cu113 torchvision==0.11.3+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html; \
         # ... pytorch nightly dev version
-        pip install --pre torch==2.6.0.dev20241121 torchvision==0.20.0.dev20241121 --index-url https://download.pytorch.org/whl/nightly/cu126; \
+        #pip install --pre torch==2.6.0.dev20241121 torchvision==0.20.0.dev20241121 --index-url https://download.pytorch.org/whl/nightly/cu126; \
         # ... test channel
-        #pip install --pre torch==2.6.0 torchvision --index-url https://download.pytorch.org/whl/test/cu126; \
+        pip install --pre torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/test/cu126; \
     fi && \
     # Install all requirements
     pip install -r requirements/devel.txt --no-cache-dir && \

requirements/base.txt

@@ -1,4 +1,4 @@
 #lightning>=2.6.0,<2.6.1
 # the below is uncommented when master is targeting a specific pl dev master commit
-git+https://github.com/Lightning-AI/lightning.git@110d62185161cd0b11d8619336ddd139e5ee09dd#egg=lightning
+git+https://github.com/Lightning-AI/lightning.git@efe311cd46a372aeb5912ea5adfeef573a5d64ca#egg=lightning
 torch>=2.3.0

requirements/standalone_base.txt

@@ -1,4 +1,4 @@
 #pytorch-lightning>=2.6.0,<2.6.1
 # the below is uncommented when master is targeting a specific pl dev master commit
-git+https://github.com/Lightning-AI/pytorch-lightning.git@110d62185161cd0b11d8619336ddd139e5ee09dd#egg=pytorch-lightning
+git+https://github.com/Lightning-AI/pytorch-lightning.git@efe311cd46a372aeb5912ea5adfeef573a5d64ca#egg=pytorch-lightning
 torch>=2.3.0

setup.py

@@ -131,15 +131,15 @@ def _setup_args(standalone: bool = False) -> Dict[str, Any]:
     )
 
     base_reqs = "standalone_base.txt" if standalone else "base.txt"
-    install_requires = setup_tools._load_requirements(
-    _INSTALL_PATHS["require"], file_name=base_reqs, standalone=standalone
-    )
     # install_requires = setup_tools._load_requirements(
-    #     _INSTALL_PATHS["require"],
-    #     file_name=base_reqs,
-    #     standalone=standalone,
-    #     pl_commit="110d62185161cd0b11d8619336ddd139e5ee09dd",
+    # _INSTALL_PATHS["require"], file_name=base_reqs, standalone=standalone
     # )
+    install_requires = setup_tools._load_requirements(
+        _INSTALL_PATHS["require"],
+        file_name=base_reqs,
+        standalone=standalone,
+        pl_commit="efe311cd46a372aeb5912ea5adfeef573a5d64ca",
+    )
     base_setup["install_requires"] = install_requires
     return base_setup
 

src/finetuning_scheduler/strategy_adapters/_mp_imports.py

@@ -7,19 +7,19 @@
 # ruff: noqa: F401
 # we require torch 2.5 or higher for composable distributed API support so until torch 2.5.0 is the minimum version,
 # supported, we conditionally import indirectly to avoid duplicating import logic in several different modules
+
 if _TORCH_GREATER_EQUAL_2_5:
-    from torch.distributed._composable import checkpoint
-    from torch.distributed._composable.fsdp._fsdp_api import CPUOffloadPolicy
-    from torch.nn.attention import SDPBackend, sdpa_kernel
     from torch.distributed.device_mesh import DeviceMesh
     from torch.distributed.tensor import DTensor, Replicate, Shard
-    from torch.distributed._tools.fsdp2_mem_tracker import FSDPMemTracker
     from torch.distributed.tensor.experimental import implicit_replication
-    from torch.distributed._composable.fsdp import FSDPModule, fully_shard
-    from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (checkpoint_wrapper, offload_wrapper,
-                                                                             ActivationWrapper)
     from torch.distributed.tensor.parallel import (ColwiseParallel, PrepareModuleInput, RowwiseParallel,
                                                    SequenceParallel, parallelize_module, loss_parallel)
+    from torch.nn.attention import SDPBackend, sdpa_kernel
+    from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (checkpoint_wrapper, offload_wrapper,
+                                                                             ActivationWrapper)
+    from torch.distributed._composable import checkpoint
+    from torch.distributed._composable.fsdp import CPUOffloadPolicy, FSDPModule, fully_shard
+    from torch.distributed._tools.fsdp2_mem_tracker import FSDPMemTracker
 else:
     for mp_obj in ["SDPBackend", "DeviceMesh", "DTensor", "Replicate", "Shard", "ColwiseParallel", "PrepareModuleInput",
                 "RowwiseParallel", "SequenceParallel", "implicit_replication", "parallelize_module", "loss_parallel",