Replace _TORCH_GREATER_EQUAL_DEV_1_10 with _TORCH_GREATER_EQUAL_1_10

Author: carmocca

docs/source/advanced/mixed_precision.rst

@@ -50,14 +50,14 @@ BFloat16 Mixed precision is similar to FP16 mixed precision, however we maintain
 Since BFloat16 is more stable than FP16 during training, we do not need to worry about any gradient scaling or nan gradient values that comes with using FP16 mixed precision.
 
 .. testcode::
-    :skipif: not _TORCH_GREATER_EQUAL_DEV_1_10 or not torch.cuda.is_available()
+    :skipif: not _TORCH_GREATER_EQUAL_1_10 or not torch.cuda.is_available()
 
     Trainer(gpus=1, precision="bf16")
 
 It is also possible to use BFloat16 mixed precision on the CPU, relying on MKLDNN under the hood.
 
 .. testcode::
-    :skipif: not _TORCH_GREATER_EQUAL_DEV_1_10
+    :skipif: not _TORCH_GREATER_EQUAL_1_10
 
     Trainer(precision="bf16")
 

docs/source/conf.py

@@ -373,7 +373,7 @@ def package_list_from_file(file):
     _XLA_AVAILABLE,
     _TPU_AVAILABLE,
     _TORCHVISION_AVAILABLE,
-    _TORCH_GREATER_EQUAL_DEV_1_10,
+    _TORCH_GREATER_EQUAL_1_10,
     _module_available,
 )
 _JSONARGPARSE_AVAILABLE = _module_available("jsonargparse")

pytorch_lightning/callbacks/quantization.py

@@ -33,10 +33,10 @@
 
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks.base import Callback
-from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_DEV_1_10
+from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_10
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
-if _TORCH_GREATER_EQUAL_DEV_1_10:
+if _TORCH_GREATER_EQUAL_1_10:
     from torch.ao.quantization.qconfig import QConfig
 else:
     from torch.quantization import QConfig
@@ -245,7 +245,7 @@ def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -
                 # version=None corresponds to using FakeQuantize rather than
                 # FusedMovingAvgObsFakeQuantize which was introduced in PT1.10
                 # details in https://github.com/pytorch/pytorch/issues/64564
-                extra_kwargs = dict(version=None) if _TORCH_GREATER_EQUAL_DEV_1_10 else {}
+                extra_kwargs = dict(version=None) if _TORCH_GREATER_EQUAL_1_10 else {}
                 pl_module.qconfig = torch.quantization.get_default_qat_qconfig(self._qconfig, **extra_kwargs)
 
         elif isinstance(self._qconfig, QConfig):

pytorch_lightning/core/lightning.py

@@ -39,7 +39,7 @@
 from pytorch_lightning.trainer.connectors.logger_connector.fx_validator import _FxValidator
 from pytorch_lightning.utilities import (
     _IS_WINDOWS,
-    _TORCH_GREATER_EQUAL_DEV_1_10,
+    _TORCH_GREATER_EQUAL_1_10,
     GradClipAlgorithmType,
     rank_zero_deprecation,
     rank_zero_warn,
@@ -2043,7 +2043,7 @@ def _register_sharded_tensor_state_dict_hooks_if_available(self) -> None:
 
         These hooks ensure that ShardedTensors are included when saving, and are loaded the LightningModule correctly.
         """
-        if not _TORCH_GREATER_EQUAL_DEV_1_10 or _IS_WINDOWS:
+        if not _TORCH_GREATER_EQUAL_1_10 or _IS_WINDOWS:
             return
 
         from torch.distributed._sharded_tensor import pre_load_state_dict_hook, state_dict_hook

pytorch_lightning/plugins/precision/native_amp.py

@@ -21,10 +21,10 @@
 
 import pytorch_lightning as pl
 from pytorch_lightning.plugins.precision.mixed import MixedPrecisionPlugin
-from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_DEV_1_10, AMPType
+from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_10, AMPType
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
-if _TORCH_GREATER_EQUAL_DEV_1_10:
+if _TORCH_GREATER_EQUAL_1_10:
     from torch import autocast
 else:
     from torch.cuda.amp import autocast
@@ -47,7 +47,7 @@ def __init__(self, precision: Union[int, str] = 16, use_cpu: bool = False) -> No
 
     def _select_precision_dtype(self, precision: Union[int, str] = 16) -> torch.dtype:
         if precision == "bf16":
-            if not _TORCH_GREATER_EQUAL_DEV_1_10:
+            if not _TORCH_GREATER_EQUAL_1_10:
                 raise MisconfigurationException(
                     "To use bfloat16 with native amp you must install torch greater or equal to 1.10."
                 )
@@ -97,7 +97,7 @@ def optimizer_step(
             self.scaler.update()
 
     def autocast_context_manager(self) -> autocast:
-        if _TORCH_GREATER_EQUAL_DEV_1_10:
+        if _TORCH_GREATER_EQUAL_1_10:
             return autocast("cpu" if self.use_cpu else "cuda", dtype=self._dtype)
         return autocast()
 

pytorch_lightning/utilities/__init__.py

@@ -48,7 +48,6 @@
     _TORCH_GREATER_EQUAL_1_8,
     _TORCH_GREATER_EQUAL_1_9,
     _TORCH_GREATER_EQUAL_1_10,
-    _TORCH_GREATER_EQUAL_DEV_1_10,
     _TORCH_QUANTIZE_AVAILABLE,
     _TORCHTEXT_AVAILABLE,
     _TORCHVISION_AVAILABLE,

pytorch_lightning/utilities/imports.py

@@ -75,7 +75,7 @@ def _compare_version(package: str, op: Callable, version: str, use_base_version:
 _TORCH_GREATER_EQUAL_1_8_1 = _compare_version("torch", operator.ge, "1.8.1")
 _TORCH_GREATER_EQUAL_1_9 = _compare_version("torch", operator.ge, "1.9.0")
 _TORCH_GREATER_EQUAL_1_10 = _compare_version("torch", operator.ge, "1.10.0")
-_TORCH_GREATER_EQUAL_DEV_1_10 = _compare_version("torch", operator.ge, "1.10.0", use_base_version=True)
+# _TORCH_GREATER_EQUAL_DEV_1_11 = _compare_version("torch", operator.ge, "1.11.0", use_base_version=True)
 
 _APEX_AVAILABLE = _module_available("apex.amp")
 _DEEPSPEED_AVAILABLE = _module_available("deepspeed")

tests/core/test_lightning_module.py

@@ -21,7 +21,6 @@
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import TensorBoardLogger
-from pytorch_lightning.utilities import _IS_WINDOWS, _TORCH_GREATER_EQUAL_DEV_1_10
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers import BoringModel
 from tests.helpers.runif import RunIf
@@ -312,10 +311,7 @@ def __init__(self, spec):
         self.sharded_tensor.local_shards()[0].tensor.fill_(0)
 
 
-@pytest.mark.skipif(
-    not _TORCH_GREATER_EQUAL_DEV_1_10, reason="Test requires the torch version to support `ShardedTensor`"
-)
-@pytest.mark.skipif(_IS_WINDOWS, reason="Not supported on Windows")
+@RunIf(min_torch="1.10", skip_windows=True)
 def test_sharded_tensor_state_dict(tmpdir, single_process_pg):
     spec = dist._sharding_spec.ChunkShardingSpec(
         dim=0,

tests/models/test_amp.py

@@ -22,7 +22,6 @@
 import tests.helpers.utils as tutils
 from pytorch_lightning import Trainer
 from pytorch_lightning.plugins.environments import SLURMEnvironment
-from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_DEV_1_10
 from tests.helpers import BoringModel, RandomDataset
 from tests.helpers.runif import RunIf
 
@@ -68,7 +67,7 @@ def _assert_autocast_enabled(self):
             assert torch.is_autocast_enabled()
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_DEV_1_10, reason="Needs bfloat16 support")
+@RunIf(min_torch="1.10")
 @pytest.mark.parametrize(
     "strategy",
     [
@@ -95,8 +94,7 @@ def test_amp_cpus(tmpdir, strategy, precision, num_processes):
     assert trainer.state.finished, f"Training failed with {trainer.state}"
 
 
-@RunIf(min_gpus=2)
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_DEV_1_10, reason="Needs bfloat16 support")
+@RunIf(min_gpus=2, min_torch="1.10")
 @pytest.mark.parametrize("strategy", [None, "dp", "ddp_spawn"])
 @pytest.mark.parametrize("precision", [16, "bf16"])
 @pytest.mark.parametrize("gpus", [1, 2])

tests/plugins/test_amp_plugins.py

@@ -21,7 +21,6 @@
 from pytorch_lightning import Trainer
 from pytorch_lightning.plugins import ApexMixedPrecisionPlugin, NativeMixedPrecisionPlugin
 from pytorch_lightning.plugins.precision import MixedPrecisionPlugin
-from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_DEV_1_10
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers import BoringModel
 from tests.helpers.runif import RunIf
@@ -178,7 +177,7 @@ def test_amp_apex_ddp_spawn_fit(amp_level, tmpdir):
     trainer.fit(model)
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_DEV_1_10, reason="Torch CPU AMP is not available.")
+@RunIf(min_torch="1.10")
 def test_cpu_amp_precision_context_manager(tmpdir):
     """Test to ensure that the context manager correctly is set to CPU + bfloat16, and a scaler isn't set."""
     plugin = NativeMixedPrecisionPlugin(precision="bf16", use_cpu=True)
@@ -197,7 +196,7 @@ def test_precision_selection_raises(monkeypatch):
 
     import pytorch_lightning.plugins.precision.native_amp as amp
 
-    monkeypatch.setattr(amp, "_TORCH_GREATER_EQUAL_DEV_1_10", False)
+    monkeypatch.setattr(amp, "_TORCH_GREATER_EQUAL_1_10", False)
     with pytest.warns(
         UserWarning, match=r"precision=16\)` but native AMP is not supported on CPU. Using `precision='bf16"
     ), pytest.raises(MisconfigurationException, match="must install torch greater or equal to 1.10"):