fix amp/apex misconfiguration error for cpu (#6107)

* fix weird test

* fix apex plugin test

* fix raise

* cpu test

* fix type

* add changelog

Author: awaelchli

CHANGELOG.md

@@ -15,11 +15,14 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed priority of plugin/accelerator when setting distributed mode ([#6089](https://github.com/PyTorchLightning/pytorch-lightning/pull/6089))
 
 
+- Fixed error message for AMP + CPU incompatibility ([#6107](https://github.com/PyTorchLightning/pytorch-lightning/pull/6107))
+
+
 ## [1.2.0] - 2021-02-18
 
 ### Added
 
-- Added `DataType`, `AverageMethod` and `MDMCAverageMethod` enum in metrics ([#5657](https://github.com/PyTorchLightning/pytorch-lightning/pull/5689)
+- Added `DataType`, `AverageMethod` and `MDMCAverageMethod` enum in metrics ([#5657](https://github.com/PyTorchLightning/pytorch-lightning/pull/5689))
 - Added support for summarized model total params size in megabytes ([#5590](https://github.com/PyTorchLightning/pytorch-lightning/pull/5590))
 - Added support for multiple train loaders ([#1959](https://github.com/PyTorchLightning/pytorch-lightning/pull/1959))
 - Added `Accuracy` metric now generalizes to Top-k accuracy for (multi-dimensional) multi-class inputs using the `top_k` parameter ([#4838](https://github.com/PyTorchLightning/pytorch-lightning/pull/4838))

pytorch_lightning/accelerators/cpu.py

@@ -7,7 +7,7 @@ class CPUAccelerator(Accelerator):
 
     def setup(self, trainer, model):
         if isinstance(self.precision_plugin, MixedPrecisionPlugin):
-            MisconfigurationException("amp + cpu is not supported.  Please use a GPU option")
+            raise MisconfigurationException("amp + cpu is not supported. Please use a GPU option")
 
         if "cpu" not in str(self.root_device):
             raise MisconfigurationException(f"Device should be CPU, got {self.root_device} instead")

tests/accelerators/test_cpu.py

@@ -0,0 +1,21 @@
+from unittest.mock import Mock
+
+import pytest
+import torch
+
+from pytorch_lightning.accelerators import CPUAccelerator
+from pytorch_lightning.plugins import SingleDevicePlugin
+from pytorch_lightning.plugins.precision import MixedPrecisionPlugin
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
+
+
+def test_unsupported_precision_plugins():
+    """ Test error messages are raised for unsupported precision plugins with CPU. """
+    trainer = Mock()
+    model = Mock()
+    accelerator = CPUAccelerator(
+        training_type_plugin=SingleDevicePlugin(torch.device("cpu")),
+        precision_plugin=MixedPrecisionPlugin()
+    )
+    with pytest.raises(MisconfigurationException, match=r"amp \+ cpu is not supported."):
+        accelerator.setup(trainer=trainer, model=model)

tests/plugins/test_amp_plugin.py

@@ -5,10 +5,8 @@
 import torch
 
 from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.plugins import NativeMixedPrecisionPlugin
 from pytorch_lightning.utilities import _NATIVE_AMP_AVAILABLE
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers.boring_model import BoringModel
 
 
@@ -25,78 +23,21 @@
 )
 @mock.patch('torch.cuda.device_count', return_value=2)
 @pytest.mark.parametrize(
-    ['ddp_backend', 'gpus', 'num_processes'],
-    [('ddp_cpu', None, 2), ('ddp', 2, 0), ('ddp2', 2, 0), ('ddp_spawn', 2, 0)],
+    ['ddp_backend', 'gpus'],
+    [('ddp', 2), ('ddp2', 2), ('ddp_spawn', 2)],
 )
-def on_fit_start(tmpdir, ddp_backend, gpus, num_processes):
-
-    class CB(Callback):
-
-        def on_fit_start(self, trainer, pl_module):
-            assert isinstance(trainer.precision_plugin, NativeMixedPrecisionPlugin)
-            raise SystemExit()
-
-    def train():
-        model = BoringModel()
-        trainer = Trainer(
-            fast_dev_run=True,
-            precision=16,
-            amp_backend='native',
-            gpus=gpus,
-            num_processes=num_processes,
-            accelerator=ddp_backend,
-            callbacks=[CB()],
-        )
-        trainer.fit(model)
-
-    if ddp_backend == "ddp_cpu":
-        with pytest.raises(MisconfigurationException, match="MP is only available on GPU"):
-            train()
-    else:
-        with pytest.raises(SystemExit):
-            train()
-
-
-@pytest.mark.skipif(not _NATIVE_AMP_AVAILABLE, reason="Minimal PT version is set to 1.6")
-@mock.patch.dict(
-    os.environ, {
-        "CUDA_VISIBLE_DEVICES": "0,1",
-        "SLURM_NTASKS": "2",
-        "SLURM_JOB_NAME": "SOME_NAME",
-        "SLURM_NODEID": "0",
-        "LOCAL_RANK": "0",
-        "SLURM_LOCALID": "0"
-    }
-)
-@mock.patch('torch.cuda.device_count', return_value=2)
-@pytest.mark.parametrize(
-    ['ddp_backend', 'gpus', 'num_processes'],
-    [('ddp_cpu', None, 2), ('ddp', 2, 0), ('ddp2', 2, 0), ('ddp_spawn', 2, 0)],
-)
-def test_amp_choice_custom_ddp_cpu(tmpdir, ddp_backend, gpus, num_processes):
+def test_amp_choice_custom_ddp_cpu(device_count_mock, ddp_backend, gpus):
 
     class MyNativeAMP(NativeMixedPrecisionPlugin):
         pass
 
-    class CB(Callback):
-
-        def on_fit_start(self, trainer, pl_module):
-            assert isinstance(trainer.precision_plugin, MyNativeAMP)
-            raise SystemExit()
-
-    model = BoringModel()
     trainer = Trainer(
-        fast_dev_run=True,
         precision=16,
         amp_backend='native',
-        num_processes=num_processes,
         accelerator=ddp_backend,
         plugins=[MyNativeAMP()],
-        callbacks=[CB()],
     )
-
-    with pytest.raises(SystemExit):
-        trainer.fit(model)
+    assert isinstance(trainer.precision_plugin, MyNativeAMP)
 
 
 class GradientUnscaleBoringModel(BoringModel):

tests/plugins/test_apex_plugin.py

@@ -4,10 +4,8 @@
 import pytest
 
 from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.plugins import ApexMixedPrecisionPlugin
 from pytorch_lightning.utilities import _APEX_AVAILABLE
-from tests.helpers.boring_model import BoringModel
 
 
 @pytest.mark.skipif(not _APEX_AVAILABLE, reason="test requires apex")
@@ -23,30 +21,19 @@
 )
 @mock.patch('torch.cuda.device_count', return_value=2)
 @pytest.mark.parametrize(
-    ['ddp_backend', 'gpus', 'num_processes'],
-    [('ddp_cpu', None, 2), ('ddp', 2, 0), ('ddp2', 2, 0), ('ddp_spawn', 2, 0)],
+    ['ddp_backend', 'gpus'],
+    [('ddp', 2), ('ddp2', 2), ('ddp_spawn', 2)],
 )
-def test_amp_choice_default_ddp_cpu(tmpdir, ddp_backend, gpus, num_processes):
+def test_amp_choice_default_ddp(mocked_device_count, ddp_backend, gpus):
 
-    class CB(Callback):
-
-        def on_fit_start(self, trainer, pl_module):
-            assert isinstance(trainer.precision_plugin, ApexMixedPrecisionPlugin)
-            raise SystemExit()
-
-    model = BoringModel()
     trainer = Trainer(
         fast_dev_run=True,
         precision=16,
         amp_backend='apex',
         gpus=gpus,
-        num_processes=num_processes,
         accelerator=ddp_backend,
-        callbacks=[CB()],
     )
-
-    with pytest.raises(SystemExit):
-        trainer.fit(model)
+    assert isinstance(trainer.precision_plugin, ApexMixedPrecisionPlugin)
 
 
 @pytest.mark.skipif(not _APEX_AVAILABLE, reason="test requires apex")
@@ -62,31 +49,20 @@ def on_fit_start(self, trainer, pl_module):
 )
 @mock.patch('torch.cuda.device_count', return_value=2)
 @pytest.mark.parametrize(
-    ['ddp_backend', 'gpus', 'num_processes'],
-    [('ddp_cpu', None, 2), ('ddp', 2, 0), ('ddp2', 2, 0), ('ddp_spawn', 2, 0)],
+    ['ddp_backend', 'gpus'],
+    [('ddp', 2), ('ddp2', 2), ('ddp_spawn', 2)],
 )
-def test_amp_choice_custom_ddp_cpu(tmpdir, ddp_backend, gpus, num_processes):
+def test_amp_choice_custom_ddp(mocked_device_count, ddp_backend, gpus):
 
     class MyApexPlugin(ApexMixedPrecisionPlugin):
         pass
 
-    class CB(Callback):
-
-        def on_fit_start(self, trainer, pl_module):
-            assert isinstance(trainer.precision_plugin, MyApexPlugin)
-            raise SystemExit()
-
-    model = BoringModel()
     trainer = Trainer(
         fast_dev_run=True,
         precision=16,
         amp_backend='apex',
         gpus=gpus,
-        num_processes=num_processes,
         accelerator=ddp_backend,
         plugins=[MyApexPlugin(amp_level="O2")],
-        callbacks=[CB()],
     )
-
-    with pytest.raises(SystemExit):
-        trainer.fit(model)
+    assert isinstance(trainer.precision_plugin, MyApexPlugin)