Fix incorrect precision="mixed" being used with DeepSpeedStrategy and IPUStrategy

src/pytorch_lightning/CHANGELOG.md

@@ -67,6 +67,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed MPS device being unrecognized ([#13992](https://github.com/Lightning-AI/lightning/pull/13992))
 
 
+- Fixed incorrect `precision="mixed"` being used with `DeepSpeedStrategy` and `IPUStrategy` ([#14041](https://github.com/Lightning-AI/lightning/pull/14041))
+
+
 - Fixed dtype inference during gradient norm computation ([#14051](https://github.com/Lightning-AI/lightning/pull/14051))
 
 

src/pytorch_lightning/plugins/precision/deepspeed.py

@@ -60,7 +60,7 @@ def __init__(self, precision: Union[str, int], amp_type: str, amp_level: Optiona
 
             amp_level = amp_level or "O2"
 
-        supported_precision = (PrecisionType.HALF, PrecisionType.FLOAT, PrecisionType.BFLOAT, PrecisionType.MIXED)
+        supported_precision = (PrecisionType.HALF, PrecisionType.FLOAT, PrecisionType.BFLOAT)
         if precision not in supported_precision:
             raise ValueError(
                 f"`Trainer(strategy='deepspeed', precision={precision!r})` is not supported."

src/pytorch_lightning/plugins/precision/ipu.py

@@ -19,6 +19,7 @@
 import pytorch_lightning as pl
 from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
 from pytorch_lightning.utilities import GradClipAlgorithmType
+from pytorch_lightning.utilities.enums import PrecisionType
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.warnings import WarningCache
@@ -35,7 +36,7 @@ class IPUPrecisionPlugin(PrecisionPlugin):
     """
 
     def __init__(self, precision: int) -> None:
-        supported_precision_values = (16, 32)
+        supported_precision_values = (PrecisionType.HALF, PrecisionType.FLOAT)
         if precision not in supported_precision_values:
             raise ValueError(
                 f"`Trainer(accelerator='ipu', precision={precision!r})` is not supported."

src/pytorch_lightning/strategies/deepspeed.py

@@ -696,7 +696,7 @@ def _auto_select_batch_size(self) -> int:
 
     def _format_precision_config(self) -> None:
         assert isinstance(self.config, dict)
-        if self.precision_plugin.precision in (PrecisionType.HALF, PrecisionType.MIXED):
+        if self.precision_plugin.precision == PrecisionType.HALF:
             if "fp16" not in self.config and self.precision_plugin.amp_type == AMPType.NATIVE:
                 # FP16 is a DeepSpeed standalone AMP implementation
                 rank_zero_info("Enabling DeepSpeed FP16.")

src/pytorch_lightning/strategies/ipu.py

@@ -58,7 +58,7 @@ def __init__(
         self.precision = precision
 
     def forward(self, *inputs: Any, **kwargs: Any) -> Any:
-        if self.precision in (PrecisionType.MIXED, PrecisionType.HALF):
+        if self.precision == PrecisionType.HALF:
             inputs = self._move_float_tensors_to_half(inputs)
 
         return super().forward(*inputs, **kwargs)

src/pytorch_lightning/strategies/utils.py

@@ -24,7 +24,7 @@ def on_colab_kaggle() -> bool:
 
 def _fp_to_half(tensor: torch.Tensor, precision: PrecisionType) -> torch.Tensor:
     if torch.is_floating_point(tensor):
-        if precision in (PrecisionType.MIXED, PrecisionType.HALF):
+        if precision == PrecisionType.HALF:
             return tensor.half()
         if precision == PrecisionType.BFLOAT:
             return tensor.bfloat16()

tests/tests_pytorch/accelerators/test_ipu.py

@@ -185,7 +185,7 @@ def test_optimization(tmpdir):
 
 
 @RunIf(ipu=True)
-def test_mixed_precision(tmpdir):
+def test_half_precision(tmpdir):
     class TestCallback(Callback):
         def setup(self, trainer: Trainer, pl_module: LightningModule, stage: Optional[str] = None) -> None:
             assert trainer.strategy.model.precision == 16

tests/tests_pytorch/strategies/test_deepspeed_strategy.py

@@ -171,12 +171,11 @@ def test_deepspeed_strategy_env(tmpdir, monkeypatch, deepspeed_config):
 
 @RunIf(deepspeed=True)
 @mock.patch("pytorch_lightning.utilities.device_parser.num_cuda_devices", return_value=1)
-@pytest.mark.parametrize("precision", [16, "mixed"])
 @pytest.mark.parametrize(
     "amp_backend",
     ["native", pytest.param("apex", marks=RunIf(amp_apex=True))],
 )
-def test_deepspeed_precision_choice(_, amp_backend, precision, tmpdir):
+def test_deepspeed_precision_choice(_, amp_backend, tmpdir):
     """Test to ensure precision plugin is also correctly chosen.
 
     DeepSpeed handles precision via Custom DeepSpeedPrecisionPlugin
@@ -188,16 +187,16 @@ def test_deepspeed_precision_choice(_, amp_backend, precision, tmpdir):
         accelerator="gpu",
         strategy="deepspeed",
         amp_backend=amp_backend,
-        precision=precision,
+        precision=16,
     )
 
     assert isinstance(trainer.strategy, DeepSpeedStrategy)
     assert isinstance(trainer.strategy.precision_plugin, DeepSpeedPrecisionPlugin)
-    assert trainer.strategy.precision_plugin.precision == precision
+    assert trainer.strategy.precision_plugin.precision == 16
 
 
 @RunIf(deepspeed=True)
-def test_deepspeed_with_invalid_config_path(tmpdir):
+def test_deepspeed_with_invalid_config_path():
     """Test to ensure if we pass an invalid config path we throw an exception."""
 
     with pytest.raises(
@@ -218,7 +217,7 @@ def test_deepspeed_with_env_path(tmpdir, monkeypatch, deepspeed_config):
 
 
 @RunIf(deepspeed=True)
-def test_deepspeed_defaults(tmpdir):
+def test_deepspeed_defaults():
     """Ensure that defaults are correctly set as a config for DeepSpeed if no arguments are passed."""
     strategy = DeepSpeedStrategy()
     assert strategy.config is not None
@@ -663,7 +662,7 @@ def training_step(self, batch, batch_idx):
 
 
 @RunIf(min_cuda_gpus=2, standalone=True, deepspeed=True)
-def test_deepspeed_multigpu_stage_3(tmpdir, deepspeed_config):
+def test_deepspeed_multigpu_stage_3(tmpdir):
     """Test to ensure ZeRO Stage 3 works with a parallel model."""
     model = ModelParallelBoringModel()
     trainer = Trainer(