Add testing for PyTorch 2.4 (Fabric)

.azure/gpu-tests-fabric.yml

@@ -62,6 +62,9 @@ jobs:
         "Lightning | latest":
           image: "pytorchlightning/pytorch_lightning:base-cuda-py3.11-torch2.3-cuda12.1.0"
           PACKAGE_NAME: "lightning"
+        "Lightning | future":
+          image: "pytorchlightning/pytorch_lightning:base-cuda-py3.11-torch2.4-cuda12.1.0"
+          PACKAGE_NAME: "lightning"
     workspace:
       clean: all
     steps:
@@ -72,9 +75,12 @@ jobs:
           echo "##vso[task.setvariable variable=TORCH_URL]https://download.pytorch.org/whl/cu${cuda_ver}/torch_stable.html"
           scope=$(python -c 'n = "$(PACKAGE_NAME)" ; print(dict(fabric="lightning_fabric").get(n, n))')
           echo "##vso[task.setvariable variable=COVERAGE_SOURCE]$scope"
+          python_ver=$(python -c "import sys; print(f'{sys.version_info.major}{sys.version_info.minor}')")
+          echo "##vso[task.setvariable variable=PYTHON_VERSION_MM]$python_ver"
         displayName: "set env. vars"
       - bash: |
-          echo "##vso[task.setvariable variable=TORCH_URL]https://download.pytorch.org/whl/test/cu${CUDA_VERSION_MM}/torch_test.html"
+          echo "##vso[task.setvariable variable=TORCH_URL]https://download.pytorch.org/whl/test/cu${CUDA_VERSION_MM}"
+          echo "##vso[task.setvariable variable=TORCHVISION_URL]https://download.pytorch.org/whl/test/cu124/torchvision-0.19.0%2Bcu124-cp${PYTHON_VERSION_MM}-cp${PYTHON_VERSION_MM}-linux_x86_64.whl"
         condition: endsWith(variables['Agent.JobName'], 'future')
         displayName: "set env. vars 4 future"
 
@@ -103,7 +109,7 @@ jobs:
 
       - bash: |
           extra=$(python -c "print({'lightning': 'fabric-'}.get('$(PACKAGE_NAME)', ''))")
-          pip install -e ".[${extra}dev]" pytest-timeout -U --find-links="${TORCH_URL}"
+          pip install -e ".[${extra}dev]" pytest-timeout -U --find-links="${TORCH_URL}" --find-links="${TORCHVISION_URL}"
         displayName: "Install package & dependencies"
 
       - bash: |

.github/workflows/ci-tests-fabric.yml

@@ -49,6 +49,10 @@ jobs:
           - { os: "macOS-14", pkg-name: "lightning", python-version: "3.10", pytorch-version: "2.3" }
           - { os: "ubuntu-20.04", pkg-name: "lightning", python-version: "3.11", pytorch-version: "2.3" }
           - { os: "windows-2022", pkg-name: "lightning", python-version: "3.11", pytorch-version: "2.3" }
+          - { os: "macOS-14", pkg-name: "lightning", python-version: "3.11", pytorch-version: "2.4" }
+          - { os: "ubuntu-20.04", pkg-name: "lightning", python-version: "3.11", pytorch-version: "2.4" }
+          # TODO: PyTorch 2.4 on Windows not yet working with `torch.distributed` (not compiled with libuv support)
+          # - { os: "windows-2022", pkg-name: "lightning", python-version: "3.11", pytorch-version: "2.4" }
           # only run PyTorch latest with Python latest, use Fabric scope to limit dependency issues
           - { os: "macOS-12", pkg-name: "fabric", python-version: "3.11", pytorch-version: "2.1" }
           - { os: "ubuntu-22.04", pkg-name: "fabric", python-version: "3.11", pytorch-version: "2.1" }
@@ -79,7 +83,7 @@ jobs:
       FREEZE_REQUIREMENTS: ${{ ! (github.ref == 'refs/heads/master' || startsWith(github.ref, 'refs/heads/release/')) }}
       PYPI_CACHE_DIR: "_pip-wheels"
       TORCH_URL_STABLE: "https://download.pytorch.org/whl/cpu/torch_stable.html"
-      TORCH_URL_TEST: "https://download.pytorch.org/whl/test/cpu/torch_test.html"
+      TORCH_URL_TEST: "https://download.pytorch.org/whl/test/cpu/torch"
       # TODO: Remove this - Enable running MPS tests on this platform
       DISABLE_MPS: ${{ matrix.os == 'macOS-14' && '1' || '0' }}
     steps:
@@ -118,7 +122,7 @@ jobs:
       - name: Env. variables
         run: |
           # Switch PyTorch URL
-          python -c "print('TORCH_URL=' + str('${{env.TORCH_URL_TEST}}' if '${{ matrix.pytorch-version }}' == '2.3' else '${{env.TORCH_URL_STABLE}}'))" >> $GITHUB_ENV
+          python -c "print('TORCH_URL=' + str('${{env.TORCH_URL_TEST}}' if '${{ matrix.pytorch-version }}' == '2.4' else '${{env.TORCH_URL_STABLE}}'))" >> $GITHUB_ENV
           # Switch coverage scope
           python -c "print('COVERAGE_SCOPE=' + str('lightning' if '${{matrix.pkg-name}}' == 'lightning' else 'lightning_fabric'))" >> $GITHUB_ENV
           # if you install mono-package set dependency only for this subpackage

requirements/fabric/base.txt

@@ -2,7 +2,7 @@
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
 numpy >=1.21.0, <1.27.0
-torch >=2.1.0, <2.4.0
+torch >=2.1.0, <2.5.0
 fsspec[http] >=2022.5.0, <2024.4.0
 packaging >=20.0, <=23.1
 typing-extensions >=4.4.0, <4.10.0

requirements/fabric/examples.txt

@@ -1,6 +1,6 @@
 # NOTE: the upper bound for the package version is only set for CI stability, and it is dropped while installing this package
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
-torchvision >=0.16.0, <0.19.0
+torchvision >=0.16.0, <0.20.0
 torchmetrics >=0.10.0, <1.3.0
 lightning-utilities >=0.8.0, <0.12.0

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

@@ -22,6 +22,7 @@
 
 from lightning.fabric.plugins.precision.precision import Precision
 from lightning.fabric.plugins.precision.utils import _convert_fp_tensor
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_4
 from lightning.fabric.utilities.types import Optimizable
 
 
@@ -39,7 +40,7 @@ def __init__(
         self,
         precision: Literal["16-mixed", "bf16-mixed"],
         device: str,
-        scaler: Optional[torch.cuda.amp.GradScaler] = None,
+        scaler: Optional["torch.cuda.amp.GradScaler"] = None,
     ) -> None:
         if precision not in ("16-mixed", "bf16-mixed"):
             raise ValueError(
@@ -49,7 +50,7 @@ def __init__(
 
         self.precision = precision
         if scaler is None and self.precision == "16-mixed":
-            scaler = torch.cuda.amp.GradScaler()
+            scaler = torch.amp.GradScaler(device=device) if _TORCH_GREATER_EQUAL_2_4 else torch.cuda.amp.GradScaler()
         if scaler is not None and self.precision == "bf16-mixed":
             raise ValueError(f"`precision='bf16-mixed'` does not use a scaler, found {scaler}.")
         self.device = device

src/lightning/fabric/strategies/fsdp.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import shutil
+import warnings
 from contextlib import ExitStack, nullcontext
 from datetime import timedelta
 from functools import partial
@@ -83,6 +84,9 @@
 
 _FSDP_ALIASES = ("fsdp", "fsdp_cpu_offload")
 
+# TODO: Switch to new state-dict APIs
+warnings.filterwarnings("ignore", category=FutureWarning, message=".*FSDP.state_dict_type.*")  # from torch >= 2.4
+
 
 class FSDPStrategy(ParallelStrategy, _Sharded):
     r"""Strategy for Fully Sharded Data Parallel provided by torch.distributed.

src/lightning/fabric/strategies/model_parallel.py

@@ -70,7 +70,7 @@ class ModelParallelStrategy(ParallelStrategy):
 
     Currently supports up to 2D parallelism. Specifically, it supports the combination of
     Fully Sharded Data-Parallel 2 (FSDP2) with Tensor Parallelism (DTensor). These PyTorch APIs are currently still
-    experimental in PyTorch. Requires PyTorch 2.3 or newer.
+    experimental in PyTorch. Requires PyTorch 2.4 or newer.
 
     Arguments:
         parallelize_fn: A function that applies parallelisms to a module. The strategy will provide the
@@ -95,8 +95,8 @@ def __init__(
         timeout: Optional[timedelta] = default_pg_timeout,
     ) -> None:
         super().__init__()
-        if not _TORCH_GREATER_EQUAL_2_3:
-            raise ImportError(f"{type(self).__name__} requires PyTorch 2.3 or higher.")
+        if not _TORCH_GREATER_EQUAL_2_4:
+            raise ImportError(f"{type(self).__name__} requires PyTorch 2.4 or higher.")
         self._parallelize_fn = parallelize_fn
         self._data_parallel_size = data_parallel_size
         self._tensor_parallel_size = tensor_parallel_size
@@ -178,7 +178,7 @@ def setup_module(self, module: TModel) -> TModel:
         if any(isinstance(mod, FullyShardedDataParallel) for mod in module.modules()):
             raise TypeError(
                 "Found modules that are wrapped with `torch.distributed.fsdp.FullyShardedDataParallel`."
-                f" The `{self.__class__.__name__}` only supports the new FSDP2 APIs in PyTorch >= 2.3."
+                f" The `{self.__class__.__name__}` only supports the new FSDP2 APIs in PyTorch >= 2.4."
             )
 
         module = self._parallelize_fn(module, self.device_mesh)
@@ -329,10 +329,10 @@ def __init__(self, module: Module, enabled: bool) -> None:
         self._enabled = enabled
 
     def _set_requires_grad_sync(self, requires_grad_sync: bool) -> None:
-        from torch.distributed._composable.fsdp import FSDP
+        from torch.distributed._composable.fsdp import FSDPModule
 
         for mod in self._module.modules():
-            if isinstance(mod, FSDP):
+            if isinstance(mod, FSDPModule):
                 mod.set_requires_gradient_sync(requires_grad_sync, recurse=False)
 
     def __enter__(self) -> None:
@@ -458,9 +458,6 @@ def _load_checkpoint(
         return metadata
 
     if _is_full_checkpoint(path):
-        if not _TORCH_GREATER_EQUAL_2_4:
-            raise ImportError("Loading a non-distributed checkpoint into a distributed model requires PyTorch >= 2.4.")
-
         checkpoint = torch.load(path, mmap=True, map_location="cpu")
         _load_raw_module_state(checkpoint.pop(module_key), module, strict=strict)
 
@@ -546,9 +543,6 @@ def _load_raw_module_state(
     from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
 
     if _has_dtensor_modules(module):
-        if not _TORCH_GREATER_EQUAL_2_4:
-            raise ImportError("Loading a non-distributed checkpoint into a distributed model requires PyTorch >= 2.4.")
-
         from torch.distributed.checkpoint.state_dict import StateDictOptions, set_model_state_dict
 
         state_dict_options = StateDictOptions(

src/lightning/fabric/utilities/imports.py

@@ -28,7 +28,7 @@
 
 _TORCH_GREATER_EQUAL_2_2 = compare_version("torch", operator.ge, "2.2.0")
 _TORCH_GREATER_EQUAL_2_3 = compare_version("torch", operator.ge, "2.3.0")
-_TORCH_GREATER_EQUAL_2_4 = compare_version("torch", operator.ge, "2.4.0", use_base_version=True)
+_TORCH_GREATER_EQUAL_2_4 = compare_version("torch", operator.ge, "2.4.0")
 
 _PYTHON_GREATER_EQUAL_3_8_0 = (sys.version_info.major, sys.version_info.minor) >= (3, 8)
 _PYTHON_GREATER_EQUAL_3_10_0 = (sys.version_info.major, sys.version_info.minor) >= (3, 10)

src/lightning/fabric/utilities/testing/_runif.py

@@ -24,6 +24,7 @@
 from lightning.fabric.accelerators.cuda import num_cuda_devices
 from lightning.fabric.accelerators.mps import MPSAccelerator
 from lightning.fabric.strategies.deepspeed import _DEEPSPEED_AVAILABLE
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_4
 
 
 def _runif_reasons(
@@ -111,7 +112,9 @@ def _runif_reasons(
             reasons.append("Standalone execution")
         kwargs["standalone"] = True
 
-    if deepspeed and not (_DEEPSPEED_AVAILABLE and RequirementCache(module="deepspeed.utils")):
+    if deepspeed and not (
+        _DEEPSPEED_AVAILABLE and not _TORCH_GREATER_EQUAL_2_4 and RequirementCache(module="deepspeed.utils")
+    ):
         reasons.append("Deepspeed")
 
     if dynamo:

tests/tests_fabric/conftest.py

@@ -104,6 +104,7 @@ def thread_police_duuu_daaa_duuu_daaa():
         elif (
             thread.name == "QueueFeederThread"  # tensorboardX
             or thread.name == "QueueManagerThread"  # torch.compile
+            or "(_read_thread)" in thread.name  # torch.compile
         ):
             thread.join(timeout=20)
         elif (

tests/tests_fabric/plugins/precision/test_amp.py

@@ -17,11 +17,13 @@
 import pytest
 import torch
 from lightning.fabric.plugins.precision.amp import MixedPrecision
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_4
 
 
 def test_amp_precision_default_scaler():
     precision = MixedPrecision(precision="16-mixed", device=Mock())
-    assert isinstance(precision.scaler, torch.cuda.amp.GradScaler)
+    scaler_cls = torch.amp.GradScaler if _TORCH_GREATER_EQUAL_2_4 else torch.cuda.amp.GradScaler
+    assert isinstance(precision.scaler, scaler_cls)
 
 
 def test_amp_precision_scaler_with_bf16():
@@ -36,7 +38,8 @@ def test_amp_precision_forward_context():
     """Test to ensure that the context manager correctly is set to bfloat16 on CPU and CUDA."""
     precision = MixedPrecision(precision="16-mixed", device="cuda")
     assert precision.device == "cuda"
-    assert isinstance(precision.scaler, torch.cuda.amp.GradScaler)
+    scaler_cls = torch.amp.GradScaler if _TORCH_GREATER_EQUAL_2_4 else torch.cuda.amp.GradScaler
+    assert isinstance(precision.scaler, scaler_cls)
     assert torch.get_default_dtype() == torch.float32
     with precision.forward_context():
         assert torch.get_autocast_gpu_dtype() == torch.float16

tests/tests_fabric/plugins/precision/test_amp_integration.py

@@ -17,6 +17,7 @@
 import torch
 import torch.nn as nn
 from lightning.fabric import Fabric, seed_everything
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_4
 
 from tests_fabric.helpers.runif import RunIf
 
@@ -82,7 +83,8 @@ def run(fused=False):
         optimizer = torch.optim.Adam(model.parameters(), lr=1.0, fused=fused)
 
         model, optimizer = fabric.setup(model, optimizer)
-        assert isinstance(fabric._precision.scaler, torch.cuda.amp.GradScaler)
+        scaler_cls = torch.amp.GradScaler if _TORCH_GREATER_EQUAL_2_4 else torch.cuda.amp.GradScaler
+        assert isinstance(fabric._precision.scaler, scaler_cls)
 
         data = torch.randn(10, 10, device="cuda")
         target = torch.randn(10, 10, device="cuda")

tests/tests_fabric/plugins/precision/test_bitsandbytes.py

@@ -93,7 +93,7 @@ def __init__(self):
         precision.convert_module(model)
 
 
-@RunIf(min_cuda_gpus=1)
+@RunIf(min_cuda_gpus=1, max_torch="2.4")
 @pytest.mark.skipif(not _BITSANDBYTES_AVAILABLE, reason="bitsandbytes unavailable")
 @pytest.mark.parametrize(
     ("args", "expected"),
@@ -232,7 +232,7 @@ def __init__(self):
     assert model.l.weight.dtype == expected
 
 
-@RunIf(min_cuda_gpus=1)
+@RunIf(min_cuda_gpus=1, max_torch="2.4")
 @pytest.mark.skipif(not _BITSANDBYTES_AVAILABLE, reason="bitsandbytes unavailable")
 def test_load_quantized_checkpoint(tmp_path):
     """Test that a checkpoint saved from a quantized model can be loaded back into a quantized model."""

tests/tests_fabric/strategies/test_dp.py

@@ -74,6 +74,7 @@ def __instancecheck__(self, instance):
         assert strategy.get_module_state_dict(wrapped_module).keys() == original_module.state_dict().keys()
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @pytest.mark.parametrize(
     "precision",
     [

tests/tests_fabric/strategies/test_fsdp_integration.py

@@ -118,7 +118,7 @@ def get_model(self):
         return model
 
 
-@RunIf(min_cuda_gpus=2, standalone=True)
+@RunIf(min_cuda_gpus=2, standalone=True, max_torch="2.4")
 @pytest.mark.parametrize("precision", ["16-mixed", pytest.param("bf16-mixed", marks=RunIf(bf16_cuda=True))])
 @pytest.mark.parametrize("manual_wrapping", [True, False])
 def test_train_save_load(tmp_path, manual_wrapping, precision):
@@ -173,6 +173,7 @@ def test_train_save_load(tmp_path, manual_wrapping, precision):
     assert state["coconut"] == 11
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=2, standalone=True)
 def test_save_full_state_dict(tmp_path):
     """Test that FSDP saves the full state into a single file with `state_dict_type="full"`."""
@@ -287,6 +288,7 @@ def test_save_full_state_dict(tmp_path):
     trainer.run()
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=2, standalone=True)
 def test_load_full_state_dict_into_sharded_model(tmp_path):
     """Test that the strategy can load a full-state checkpoint into a FSDP sharded model."""
@@ -469,6 +471,7 @@ def _run_setup_assertions(empty_init, expected_device):
     _run_setup_assertions(empty_init=True, expected_device=torch.device("meta"))
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=2, standalone=True)
 def test_save_filter(tmp_path):
     fabric = Fabric(accelerator="cuda", strategy=FSDPStrategy(state_dict_type="full"), devices=2)
@@ -602,6 +605,7 @@ def test_clip_gradients(clip_type, precision):
     optimizer.zero_grad()
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=2, standalone=True, min_torch="2.3.0")
 def test_save_sharded_and_consolidate_and_load(tmp_path):
     """Test the consolidation of a FSDP-sharded checkpoint into a single file."""