Add torch 2.3 support

.github/workflows/daily.yaml

@@ -18,11 +18,6 @@ jobs:
     strategy:
       matrix:
         include:
-        - name: cpu-3.10-2.0
-          container: mosaicml/pytorch:2.0.1_cpu-python3.10-ubuntu20.04
-          markers: not daily and (remote or not remote) and not gpu and not doctest
-          pytest_command: coverage run -m pytest
-          composer_package_name: mosaicml
         - name: cpu-3.10-2.1
           container: mosaicml/pytorch:2.1.2_cpu-python3.10-ubuntu20.04
           markers: not daily and (remote or not remote) and not gpu and not doctest
@@ -34,7 +29,7 @@ jobs:
           pytest_command: coverage run -m pytest
           composer_package_name: composer
         - name: cpu-3.11-2.2
-          container: mosaicml/pytorch:2.2.0_cpu-python3.11-ubuntu20.04
+          container: mosaicml/pytorch:2.2.1_cpu-python3.11-ubuntu20.04
           markers: not daily and (remote or not remote) and not gpu and not doctest
           pytest_command: coverage run -m pytest
           composer_package_name: mosaicml
@@ -43,11 +38,6 @@ jobs:
           markers: not daily and (remote or not remote) and not gpu and doctest
           pytest_command: coverage run -m pytest tests/test_docs.py
           composer_package_name: mosaicml
-        - name: daily-cpu-3.10-2.0
-          container: mosaicml/pytorch:2.0.1_cpu-python3.10-ubuntu20.04
-          markers: daily and (remote or not remote) and not gpu and not doctest
-          pytest_command: coverage run -m pytest
-          composer_package_name: mosaicml
         - name: daily-cpu-3.10-2.1
           container: mosaicml/pytorch:2.1.2_cpu-python3.10-ubuntu20.04
           markers: daily and (remote or not remote) and not gpu and not doctest
@@ -59,12 +49,12 @@ jobs:
           pytest_command: coverage run -m pytest
           composer_package_name: composer
         - name: daily-cpu-3.11-2.2
-          container: mosaicml/pytorch:2.2.0_cpu-python3.11-ubuntu20.04
+          container: mosaicml/pytorch:2.2.1_cpu-python3.11-ubuntu20.04
           markers: daily and (remote or not remote) and not gpu and not doctest
           pytest_command: coverage run -m pytest
           composer_package_name: mosaicml
         - name: daily-cpu-doctest
-          container: mosaicml/pytorch:2.1.2_cpu-python3.10-ubuntu20.04
+          container: mosaicml/pytorch:2.2.1_cpu-python3.11-ubuntu20.04
           markers: daily and (remote or not remote) and not gpu and doctest
           pytest_command: coverage run -m pytest tests/test_docs.py
           composer_package_name: mosaicml
@@ -106,17 +96,12 @@ jobs:
         # Unlike CPU tests, we run daily tests together with GPU tests to minimize launch time
         # on MCLOUD and not eat up all GPUs at once
         include:
-        - name: "gpu-3.10-2.0"
-          container: mosaicml/pytorch_vision:2.0.1_cu117-python3.10-ubuntu20.04
-          markers: "(daily or not daily) and (remote or not remote) and gpu and (doctest or not doctest)"
-          pytest_command: "coverage run -m pytest"
-          composer_package_name: "mosaicml"
         - name: "gpu-3.10-2.1"
           container: mosaicml/pytorch:2.1.2_cu121-python3.10-ubuntu20.04
           markers: "(daily or not daily) and (remote or not remote) and gpu and (doctest or not doctest)"
           pytest_command: "coverage run -m pytest"
           composer_package_name: "mosaicml"
-        - name: "gpu-3.10-2.2"
+        - name: "gpu-3.11-2.2"
           container: mosaicml/pytorch:2.2.0_cu121-python3.11-ubuntu20.04
           markers: "(daily or not daily) and (remote or not remote) and gpu and (doctest or not doctest)"
           pytest_command: "coverage run -m pytest"

.github/workflows/pr-cpu.yaml

@@ -13,16 +13,16 @@ jobs:
     strategy:
       matrix:
         include:
-        - name: cpu-3.10-2.0
-          container: mosaicml/pytorch:2.0.1_cpu-python3.10-ubuntu20.04
-          markers: not daily and not remote and not gpu and not doctest
-          pytest_command: coverage run -m pytest
         - name: cpu-3.10-2.1
           container: mosaicml/pytorch:2.1.2_cpu-python3.10-ubuntu20.04
           markers: not daily and not remote and not gpu and not doctest
           pytest_command: coverage run -m pytest
+        - name: cpu-3.11-2.2
+          container: mosaicml/pytorch:2.2.1_cpu-python3.11-ubuntu20.04
+          markers: not daily and not remote and not gpu and not doctest
+          pytest_command: coverage run -m pytest
         - name: cpu-doctest
-          container: mosaicml/pytorch:2.1.2_cpu-python3.10-ubuntu20.04
+          container: mosaicml/pytorch:2.2.1_cpu-python3.11-ubuntu20.04
           markers: not daily and not remote and not gpu and doctest
           pytest_command: coverage run -m pytest tests/test_docs.py
     name: ${{ matrix.name }}

.github/workflows/pr-gpu.yaml

@@ -13,8 +13,8 @@ jobs:
     strategy:
       matrix:
         include:
-        - name: gpu-3.10-2.1
-          container: mosaicml/pytorch:2.1.2_cu121-python3.10-ubuntu20.04
+        - name: gpu-3.11-2.2
+          container: mosaicml/pytorch:2.2.1_cu121-python3.10-ubuntu20.04
           markers: not daily and not remote and gpu and (doctest or not doctest)
           pytest_command: coverage run -m pytest
           composer_package_name: mosaicml

composer/algorithms/utils/augmentation_common.py

@@ -43,7 +43,7 @@ def image_as_type(image: _InputImgT, typ: Type[_OutputImgT]) -> _OutputImgT:
         raise TypeError(f'Only typ={{torch.Tensor, Image}} is supported; got {typ}')
 
     if typ is torch.Tensor:
-        return cast(_OutputImgT, torchvision.transforms.functional.to_tensor(image))  # PIL -> Tensor
+        return cast(_OutputImgT, torchvision.transforms.functional.to_tensor(image))  # type: ignore PIL -> Tensor
     return cast(_OutputImgT, torchvision.transforms.functional.to_pil_image(image))  # Tensor -> PIL
 
 

composer/core/state.py

@@ -29,6 +29,11 @@
 from torch.utils.data import DataLoader, Dataset
 from torchmetrics import Metric
 
+if version.parse(torch.__version__) >= version.parse('2.3.0'):
+    from torch.amp.grad_scaler import GradScaler  # type: ignore
+else:
+    from torch.cuda.amp.grad_scaler import GradScaler  # type: ignore
+
 from composer.core.data_spec import DataSpec
 from composer.core.event import Event
 from composer.core.precision import Precision
@@ -242,7 +247,7 @@ class State(Serializable):
             train the model. Multiple optimizers are not currently supported.
         schedulers (LRScheduler | Sequence[LRScheduler], optional):
             The learning rate scheduler (can also be a list or tuple of schedulers).
-        scaler (torch.cuda.amp.GradScaler, optional): The gradient scaler in use for mixed precision training.
+        scaler (torch.amp.GradScaler, optional): The gradient scaler in use for mixed precision training.
         save_metrics (bool, optional): Whether to save metrics in state_dict.
         algorithms (Algorithm | Sequence[Algorithm], optional): The algorithms used for training.
         callbacks (Callback | Sequence[Callback], optional): The callbacks used for training.
@@ -326,7 +331,7 @@ class State(Serializable):
         profiler (Profiler): The profiler (if profiling is enabled), or ``None`` if not profiling.
         rank_zero_seed (int): The seed of the rank zero process.
         run_name (str): The name for this training run.
-        scaler (torch.cuda.amp.GradScaler): The gradient scaler if using mixed-precision training, or
+        scaler (torch.amp.GradScaler): The gradient scaler if using mixed-precision training, or
             ``None`` if not using mixed-precision training.
         serialized_attributes (List[str]): The names of the attribute which are serialized in a checkpoint.
 
@@ -404,7 +409,7 @@ def __init__(
         optimizers: Optional[Union[Optimizer, Sequence[Optimizer]]] = None,
 
         # scaler
-        scaler: Optional[torch.cuda.amp.grad_scaler.GradScaler] = None,
+        scaler: Optional[GradScaler] = None,
 
         # state_dict
         save_metrics: bool = False,
@@ -868,7 +873,7 @@ def get_model_state_dict(self) -> Dict[str, Any]:
         Returns:
             Dict[str, Any]: The state dict for the model.
         """
-        if version.parse(torch.__version__) > version.parse('2.2.9'):
+        if version.parse(torch.__version__) >= version.parse('2.3.0'):
             from torch.distributed.checkpoint.state_dict import StateDictOptions, get_model_state_dict
             if self.fsdp_state_dict_type not in [None, 'full', 'sharded']:
                 raise NotImplementedError(
@@ -906,7 +911,7 @@ def get_optim_state_dict(self) -> Dict[str, Any]:
         Returns:
             Dict[str, Any]: The state dict for the optimizer.
         """
-        if version.parse(torch.__version__) > version.parse('2.2.9'):
+        if version.parse(torch.__version__) >= version.parse('2.3.0'):
             from torch.distributed.checkpoint.state_dict import StateDictOptions, get_optimizer_state_dict
             if self.fsdp_state_dict_type not in [None, 'full', 'sharded']:
                 raise NotImplementedError(
@@ -1228,7 +1233,7 @@ def load_model_state(
         model_on_rank = state_dict['model'] is not None
 
         if model_on_rank:
-            if version.parse(torch.__version__) > version.parse('2.2.9'):
+            if version.parse(torch.__version__) >= version.parse('2.3.0'):
                 from torch.distributed.checkpoint.state_dict import StateDictOptions, set_model_state_dict
                 set_model_state_dict(
                     model=self.model,
@@ -1292,7 +1297,7 @@ def load_optim_state(self, state_dict: Dict[str, Any], strict: bool = True):
             strict (bool): Whether the keys (i.e., optimizer parameter names) in the optimizer
                 state dict should perfectly match the keys in the optimizer instance.
         """
-        if version.parse(torch.__version__) > version.parse('2.2.9'):
+        if version.parse(torch.__version__) >= version.parse('2.3.0'):
             from torch.distributed.checkpoint.state_dict import StateDictOptions, set_optimizer_state_dict
             optimizer = self.optimizers[0]
             set_optimizer_state_dict(

composer/trainer/_scaler.py

@@ -5,9 +5,15 @@
 from typing import Optional, Union
 
 import torch
-from torch.cuda.amp.grad_scaler import GradScaler, OptState, _refresh_per_optimizer_state
+from packaging import version
+from torch.cuda.amp.grad_scaler import GradScaler, OptState
 from torch.optim import Optimizer
 
+if version.parse(torch.__version__) >= version.parse('2.3.0'):
+    from torch.amp.grad_scaler import _refresh_per_optimizer_state  # type: ignore
+else:
+    from torch.cuda.amp.grad_scaler import _refresh_per_optimizer_state  # type: ignore
+
 from composer.utils import dist
 
 __all__ = ['ClosureGradScaler']
@@ -78,7 +84,7 @@ def _amp_closure(**kwargs):
         return optimizer.step(closure=_amp_closure)  # type: ignore
 
     # Mostly copied from original grad_scaler implementation
-    # See: https://pytorch.org/docs/stable/_modules/torch/cuda/amp/grad_scaler.html#GradScaler
+    # See: https://pytorch.org/docs/stable/_modules/torch/amp/grad_scaler.html#GradScaler
     def update(self, new_scale: Optional[Union[float, torch.FloatTensor]] = None):
         """Updates the scale factor.
 

composer/trainer/mosaic_fsdp.py

@@ -10,25 +10,11 @@
 import torch
 from packaging import version
 from torch.distributed._shard.sharding_spec import ChunkShardingSpec
-from torch.distributed.fsdp import FullyShardedDataParallel
 
 
 def patch_pytorch():
     """Monkey patches pytorch functions based on pytorch version."""
-    if version.parse(torch.__version__) < version.parse('2.0.2'):
-        # Monkey patch for torch == 2.0.1
-
-        # Monkey patch __init__ where __init__ calls the custom _auto_wrap fn
-        from composer.trainer.mosaic_fsdp_utils import init_fn_t2p0p1
-
-        FullyShardedDataParallel.__init__ = init_fn_t2p0p1  # type: ignore
-
-        # Monkey patch sharding method
-        from composer.trainer.mosaic_fsdp_utils import build_metadata
-
-        ChunkShardingSpec.build_metadata = build_metadata
-
-    elif version.parse(torch.__version__) < version.parse('2.1.1'):
+    if version.parse(torch.__version__) < version.parse('2.1.1'):
         # Monkey patch for torch < 2.1.1 ie torch == 2.1.0
 
         # Monkey patch sharding method
@@ -61,8 +47,8 @@ def patch_pytorch():
         from torch.distributed.fsdp import _runtime_utils
         _runtime_utils._validate_and_get_hybrid_shard_state = lambda *args, **kwargs: None
 
-    elif version.parse(torch.__version__) < version.parse('2.2.9'):
-        # Monkey patch for torch < 2.3.0 ie torch == 2.2.1/2.2.2 currently
+    elif version.parse(torch.__version__) < version.parse('2.2.3'):
+        # Monkey patch for torch < 2.2.3 ie torch == 2.2.1/2.2.2 currently
 
         # Fix memory leak for FSDP.optim_state_dict_to_load
         # https://github.com/pytorch/pytorch/issues/116553
@@ -73,35 +59,6 @@ def patch_pytorch():
 
     elif version.parse(torch.__version__) < version.parse('2.3.1'):
         # Monkey patch for torch < 2.3.1 ie torch == 2.3.0
-        # Note: this is the same patch as 2.2.0, we are just making a new if branch
-        # for clarity and modularity of changes.
-
-        # Allow 2D HSDP
-        from torch.distributed.fsdp import _runtime_utils
-        _runtime_utils._validate_and_get_hybrid_shard_state = lambda *args, **kwargs: None
-
-        # Monkeypatch state_dict
-        from composer.trainer.mosaic_fsdp_utils import init_fn_t2p3p0
-        FullyShardedDataParallel.__init__ = init_fn_t2p3p0
-
-        # Monkeypatch state_dict
-        from torch.distributed.checkpoint import state_dict  # type: ignore
-
-        from composer.trainer.mosaic_fsdp_utils import _verify_options_t2p3p0
-        state_dict._verify_options = _verify_options_t2p3p0
-
-        # Monkeypatch sharding optim state
-        from torch.distributed.fsdp import _optim_utils
-
-        from composer.trainer.mosaic_fsdp_utils import _shard_orig_param_state
-        _optim_utils._shard_orig_param_state = _shard_orig_param_state
-
-        # Monkeypatch checkpointing full state dict
-        from torch.distributed.fsdp import _state_dict_utils
-
-        from composer.trainer.mosaic_fsdp_utils import _full_pre_state_dict_hook, _set_use_dtensor
-        _state_dict_utils._full_pre_state_dict_hook = _full_pre_state_dict_hook
-        _state_dict_utils._set_use_dtensor = _set_use_dtensor
 
         # Monkeypatch _flat_param.py to fix 2D with SHARD_GRAD_OP
         # Issue: https://github.com/pytorch/pytorch/issues/123272