Synchronize PyTorchLightning/pytorch-lightning (revision 7b283e3c@master) to...

Synchronize PyTorchLightning/pytorch-lightning (revision 7b283e3c@master) to github/third-party/PyTorchLightning/pytorch-lightning

Summary:
# Manual
 - remove fixme's in `model_checkpoint.py`, `parameter_monitor.py`, `test_quantization.py`, and `speed_monitor.py` now that `Trainer` is properly annotated.
- update `test_quantization.py` to `trainer.train_loop.global_step` instead of `trainer.global_step` which is a read-only.
- update `loop_callback.py` to read from `train_loop` for `batch_idx` (which is no longer available).

# Automatic
### New commit log messages
  7b283e3c Bugfix/Multiple dataloaders (#7433)
  d7c44cc6 Docs: sync chlog 1.3.1 (#7478)
  fdf50a5e Mark certain Trainer APIs as protected (#7420)
  ad9118f0 remove trainer hidden state | sanity refactor [1 / n] (#7437)
  4a1134db Log epoch metrics before firing the `on_evaluation_end` hook (#7272)
  b65ae794 Automatically check `DataModule.has_{setup,teardown,prepare_data}` [2/2] (#7238)
  8660d8cf [pre-commit.ci] pre-commit autoupdate (#7475)
  f6fe715e Fix Sphinx argument deprecation (#7464)

Reviewed By: shuyingsunshine21

Differential Revision: D28353491

fbshipit-source-id: 98b87d99e2f09b47b07270858fcbdb5d5299730b

tests/runner/test_runner_lightning_quantization.py

@@ -20,20 +20,18 @@ from d2go.utils.testing.helper import tempdir
 from d2go.utils.testing.lightning_test_module import TestModule
 from pytorch_lightning import Trainer, seed_everything
 from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
-from torch.quantization.quantize_fx import convert_fx, prepare_fx, prepare_qat_fx
-
-
 from torch.quantization import (  # @manual; @manual
     default_dynamic_qconfig,
     get_default_qconfig,
 )
+from torch.quantization.quantize_fx import convert_fx, prepare_fx, prepare_qat_fx
 
 
 class TestUtilities(unittest.TestCase):
-    """ Test some basic utilities we rely on. """
+    """Test some basic utilities we rely on."""
 
     def test_get_set_has(self):
-        """ Trivial test for generic behavior. Only support pre-existing deeply nested values."""
+        """Trivial test for generic behavior. Only support pre-existing deeply nested values."""
 
         class TestObject(object):
             def __init__(self):
@@ -54,10 +52,10 @@ class TestUtilities(unittest.TestCase):
 
 
 class TestModelTransform(unittest.TestCase):
-    """ Tests ModelTransforms. """
+    """Tests ModelTransforms."""
 
     def test_invalid_construction_type_error(self):
-        """ Validate construction of ModelTransforms. Always have fn, msg, and one of [step, interval]. """
+        """Validate construction of ModelTransforms. Always have fn, msg, and one of [step, interval]."""
         with self.assertRaises(TypeError):
             _ = ModelTransform()
         with self.assertRaises(TypeError):
@@ -73,7 +71,7 @@ class TestModelTransform(unittest.TestCase):
             )
 
     def test_positivity_value_error(self):
-        """ Validates ModelTransforms are constructed with only valid arguments. """
+        """Validates ModelTransforms are constructed with only valid arguments."""
 
         def identity(x):
             return x
@@ -88,7 +86,7 @@ class TestModelTransform(unittest.TestCase):
 
 class TestQuantizationAwareTraining(unittest.TestCase):
     def test_qat_misconfiguration(self):
-        """ Tests failure when misconfiguring the QAT Callback. """
+        """Tests failure when misconfiguring the QAT Callback."""
         invalid_params = [
             {"start_step": -1},
             {"enable_observer": (42, 42)},
@@ -101,7 +99,7 @@ class TestQuantizationAwareTraining(unittest.TestCase):
                 _ = QuantizationAwareTraining(**invalid_param)
 
     def test_qat_transforms(self):
-        """ Tests the appropropriate ModelTransforms are defined with QAT."""
+        """Tests the appropropriate ModelTransforms are defined with QAT."""
         qat = QuantizationAwareTraining(
             start_step=300, enable_observer=(350, 500), freeze_bn_step=550
         )
@@ -129,7 +127,7 @@ class TestQuantizationAwareTraining(unittest.TestCase):
                 0,
                 f"step={step}",
             )
-            trainer.global_step = step
+            trainer.train_loop.global_step = step
             qat.on_train_batch_start(
                 trainer, module, batch=None, batch_idx=0, dataloader_idx=0
             )
@@ -153,7 +151,7 @@ class TestQuantizationAwareTraining(unittest.TestCase):
 
     @tempdir
     def test_qat_interval_transform(self, root_dir):
-        """ Tests an interval transform is applied multiple times. """
+        """Tests an interval transform is applied multiple times."""
         seed_everything(100)
 
         def linear_fn_counter(mod):
@@ -182,7 +180,7 @@ class TestQuantizationAwareTraining(unittest.TestCase):
 
     @tempdir
     def test_module_quantized_during_train(self, root_dir):
-        """ Validate quantized aware training works as expected. """
+        """Validate quantized aware training works as expected."""
         seed_everything(100)
 
         model = TestModule()
@@ -219,7 +217,7 @@ class TestQuantizationAwareTraining(unittest.TestCase):
 
     @tempdir
     def test_quantization_without_train(self, root_dir):
-        """ Validate quantization occurs even without a call to .fit() first. """
+        """Validate quantization occurs even without a call to .fit() first."""
         seed_everything(100)
 
         model = TestModule()
@@ -240,7 +238,7 @@ class TestQuantizationAwareTraining(unittest.TestCase):
 
     @tempdir
     def test_attribute_preservation_qat(self, root_dir):
-        """ Validates we can preserve specified properties in module. """
+        """Validates we can preserve specified properties in module."""
         seed_everything(100)
 
         model = TestModule()
@@ -274,7 +272,7 @@ class TestQuantizationAwareTraining(unittest.TestCase):
 
     @tempdir
     def test_quantization_and_checkpointing(self, root_dir):
-        """ Validate written checkpoints can be loaded back as expected. """
+        """Validate written checkpoints can be loaded back as expected."""
         seed_everything(100)
 
         model = TestModule()
@@ -297,10 +295,10 @@ class TestQuantizationAwareTraining(unittest.TestCase):
 
     @tempdir
     def test_custom_qat(self, root_dir):
-        """ Tests that we can customize QAT by skipping certain layers. """
+        """Tests that we can customize QAT by skipping certain layers."""
 
         class _CustomQAT(QuantizationAwareTraining):
-            """ Only quantize TestModule.another_layer. """
+            """Only quantize TestModule.another_layer."""
 
             def prepare(self, model, configs, attrs):
                 model.another_layer = prepare_qat_fx(model.another_layer, configs[""])
@@ -345,7 +343,7 @@ class TestQuantizationAwareTraining(unittest.TestCase):
 
     @tempdir
     def test_submodule_qat(self, root_dir):
-        """ Tests that we can customize QAT through exposed API. """
+        """Tests that we can customize QAT through exposed API."""
         seed_everything(100)
 
         model = TestModule()
@@ -386,7 +384,7 @@ class TestQuantizationAwareTraining(unittest.TestCase):
 class TestPostTrainingQuantization(unittest.TestCase):
     @tempdir
     def test_post_training_static_quantization(self, root_dir):
-        """ Validate post-training static quantization. """
+        """Validate post-training static quantization."""
         seed_everything(100)
 
         model = TestModule()
@@ -423,7 +421,7 @@ class TestPostTrainingQuantization(unittest.TestCase):
 
     @tempdir
     def test_post_training_dynamic_quantization(self, root_dir):
-        """ Validates post-training dynamic quantization. """
+        """Validates post-training dynamic quantization."""
         seed_everything(100)
 
         model = TestModule()
@@ -460,10 +458,10 @@ class TestPostTrainingQuantization(unittest.TestCase):
 
     @tempdir
     def test_custom_post_training_static_quant(self, root_dir):
-        """ Tests that we can customize Post-Training static by skipping certain layers. """
+        """Tests that we can customize Post-Training static by skipping certain layers."""
 
         class _CustomStaticQuant(PostTrainingQuantization):
-            """ Only quantize TestModule.another_layer. """
+            """Only quantize TestModule.another_layer."""
 
             def prepare(self, model, configs, attrs):
                 model.another_layer = prepare_fx(model.another_layer, configs[""])