clean AMP logic (#5994)

* clean AMP logic * cleaning * ... * ... * Even apex
Lightning-AI · Feb 16, 2021 · 936f42a · 936f42a
1 parent b40d414
commit 936f42a
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Showing 2 changed files with 25 additions and 40 deletions.
diff --git a/pytorch_lightning/accelerators/accelerator_connector.py b/pytorch_lightning/accelerators/accelerator_connector.py
@@ -298,46 +298,42 @@ def select_precision_plugin(self) -> PrecisionPlugin:
             if self.on_tpu:
                 return TPUHalfPrecisionPlugin()
 
-            if self.amp_type == "native":
-                if not _NATIVE_AMP_AVAILABLE:
-                    rank_zero_warn(
-                        "You have asked for native AMP but your PyTorch version does not support it."
-                        " Consider upgrading with `pip install torch>=1.6`."
-                        " We will attempt to use NVIDIA Apex for this session."
-                    )
-                    if not _APEX_AVAILABLE and self.on_cpu:
-                        raise MisconfigurationException(
-                            "You have asked for native AMP on CPU, but AMP is only available on GPU."
-                        )
-                    self.amp_type = "apex"
-                elif self.on_cpu:
+            self.amp_type = AMPType(self.amp_type)
+            if self.amp_type == AMPType.NATIVE:
+                if self.on_cpu:
                     raise MisconfigurationException(
                         "You have asked for native AMP on CPU, but AMP is only available on GPU."
                     )
+                elif not _NATIVE_AMP_AVAILABLE:
+                    msg = "You have asked for native AMP but your PyTorch version does not support it." \
+                          " Consider upgrading with `pip install torch>=1.6`."
+                    if _APEX_AVAILABLE:
+                        self.amp_type = AMPType.APEX
+                        msg += " We will attempt to use NVIDIA Apex for this session."
+                        rank_zero_warn(msg)
+                    else:
+                        raise MisconfigurationException(msg)
                 else:
                     log.info("Using native 16bit precision.")
                     if isinstance(self.training_type_plugin, (DDPShardedPlugin, DDPSpawnShardedPlugin)):
                         return ShardedNativeMixedPrecisionPlugin()
-                    self.amp_type = AMPType.NATIVE
                     return NativeMixedPrecisionPlugin()
 
-            if self.amp_type == "apex":
+            if self.amp_type == AMPType.APEX:
                 if not _APEX_AVAILABLE:
-                    rank_zero_warn(
+                    raise MisconfigurationException(
                         "You have asked for Apex AMP but you have not installed it yet."
                         " Install apex first using this guide: https://github.com/NVIDIA/apex#linux"
                     )
-                else:
-                    if isinstance(self.training_type_plugin, (DDPShardedPlugin, DDPSpawnShardedPlugin)):
-                        raise MisconfigurationException(
-                            "Sharded Plugin is not supported with Apex AMP, "
-                            "please using native AMP for 16-bit precision."
-                        )
-                    log.info("Using APEX 16bit precision.")
-                    self.amp_type = AMPType.APEX
-                    return ApexMixedPrecisionPlugin(self.amp_level)
-        else:
-            raise NotImplementedError("We only support precisions 32 and 16!")
+                if isinstance(self.training_type_plugin, (DDPShardedPlugin, DDPSpawnShardedPlugin)):
+                    raise MisconfigurationException(
+                        "Sharded Plugin is not supported with Apex AMP,"
+                        " please using native AMP for 16-bit precision."
+                    )
+                log.info("Using APEX 16bit precision.")
+                return ApexMixedPrecisionPlugin(self.amp_level)
+
+        raise NotImplementedError("We only support precisions 32 and 16!")
 
     def select_training_type_plugin(self) -> TrainingTypePlugin:
         if self.use_ddp2:

diff --git a/tests/models/test_amp.py b/tests/models/test_amp.py
@@ -18,7 +18,6 @@
 import torch
 from torch import optim
 
-import tests.helpers.pipelines as tpipes
 import tests.helpers.utils as tutils
 from pytorch_lightning import Trainer
 from pytorch_lightning.plugins.environments import SLURMEnvironment
@@ -155,21 +154,11 @@ def test_amp_gpu_ddp_slurm_managed(tmpdir):
     assert generated == 'abc23'
 
 
+@pytest.mark.skipif(torch.cuda.is_available(), reason="test is restricted only on CPU")
 def test_cpu_model_with_amp(tmpdir):
     """Make sure model trains on CPU."""
-    trainer_options = dict(
-        default_root_dir=tmpdir,
-        progress_bar_refresh_rate=0,
-        max_epochs=1,
-        limit_train_batches=0.4,
-        limit_val_batches=0.4,
-        precision=16,
-    )
-
-    model = BoringModel()
-
     with pytest.raises(MisconfigurationException, match="AMP is only available on GPU"):
-        tpipes.run_model_test(trainer_options, model, on_gpu=False)
+        Trainer(precision=16)
 
 
 @mock.patch.dict(os.environ, {"PL_DEV_DEBUG": "1"})