Expose DeepSpeed FP16 parameters due to loss instability (#6115)

* Expose deepspeed config parameters to init function due to instability in parameters

* See if tests can run on normal CI, without special tests

* Add changelog

* Update pytorch_lightning/plugins/training_type/deepspeed.py

Co-authored-by: Carlos Mocholí <[email protected]>

Co-authored-by: Carlos Mocholí <[email protected]>

Author: SeanNaren

CHANGELOG.md

@@ -33,6 +33,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Move lightning module to correct device type when using LightningDistributedWrapper ([#6070](https://github.com/PyTorchLightning/pytorch-lightning/pull/6070)
 
 
+- Expose DeepSpeed loss parameters to allow users to fix loss instability ([#6115](https://github.com/PyTorchLightning/pytorch-lightning/pull/6115)
+
+
 ## [1.2.0] - 2021-02-18
 
 ### Added

pytorch_lightning/plugins/training_type/deepspeed.py

@@ -79,6 +79,11 @@ def __init__(
         num_nodes: int = 1,
         parallel_devices: Optional[List[torch.device]] = None,
         cluster_environment: Optional[ClusterEnvironment] = None,
+        loss_scale: float = 0,
+        initial_scale_power: int = 32,
+        loss_scale_window: int = 1000,
+        hysteresis: int = 2,
+        min_loss_scale: int = 1
     ) -> None:
         """
 
@@ -127,6 +132,18 @@ def __init__(
 
             logging_level: Set logging level for deepspeed. (Default: ``logging.WARN``)
 
+            loss_scale: Loss scaling value for FP16 training.
+                0.0 results in dynamic loss scaling, otherwise static (Default: 0)
+
+            initial_scale_power: Power of the initial dynamic loss scale value. Loss scale is computed
+                by ``2^initial_scale_power`` (Default: 32)
+
+            loss_scale_window: Window in which to raise/lower the dynamic FP16 loss scaling value (Default: 1000)
+
+            hysteresis: FP16 Delay shift in Dynamic Loss scaling (Default: 2)
+
+            min_loss_scale: The minimum FP16 dynamic loss scaling value (Default: 1000)
+
         """
         if not _DEEPSPEED_AVAILABLE:
             raise MisconfigurationException(
@@ -154,6 +171,13 @@ def __init__(
         self._config_initialized = False
         deepspeed.utils.logging.logger.setLevel(logging_level)
 
+        # default FP16 parameters.
+        self.loss_scale = loss_scale
+        self.initial_scale_power = initial_scale_power
+        self.loss_scale_window = loss_scale_window
+        self.hysteresis = hysteresis
+        self.min_loss_scale = min_loss_scale
+
     def _load_config(self, config):
         if config is None and self.DEEPSPEED_ENV_VAR in os.environ:
             rank_zero_info(f"Loading DeepSpeed config from set {self.DEEPSPEED_ENV_VAR} environment variable")
@@ -297,9 +321,19 @@ def _format_precision_config(self):
         amp_level = self.lightning_module.trainer.accelerator_connector.amp_level
         precision = self.lightning_module.trainer.accelerator_connector.precision
         if precision == 16:
-            if "amp" not in self.config and amp_type == AMPType.NATIVE:
-                self.config["fp16"] = {"enabled": True}
-            elif "apex" not in self.config and amp_type == AMPType.APEX:
+            if "fp16" not in self.config and amp_type == AMPType.NATIVE:
+                # FP16 is a DeepSpeed standalone AMP implementation
+                rank_zero_info("Enabling DeepSpeed FP16.")
+                self.config["fp16"] = {
+                    "enabled": True,
+                    "loss_scale": self.loss_scale,
+                    "initial_scale_power": self.initial_scale_power,
+                    "loss_scale_window": self.loss_scale_window,
+                    "hysteresis": self.hysteresis,
+                    "min_loss_scale": self.min_loss_scale
+                }
+            elif "amp" not in self.config and amp_type == AMPType.APEX:
+                rank_zero_only("Enabling DeepSpeed APEX Implementation.")
                 self.config["amp"] = {
                     "enabled": True,
                     "opt_level": amp_level,

tests/plugins/test_deepspeed_plugin.py

@@ -211,9 +211,6 @@ def test_invalid_deepspeed_defaults_no_precision(tmpdir):
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU machine")
 @pytest.mark.skipif(not _DEEPSPEED_AVAILABLE, reason="DeepSpeed not available.")
-@pytest.mark.skipif(
-    not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1', reason="test should be run outside of pytest"
-)
 def test_warn_deepspeed_override_backward(tmpdir):
     """
         Test to ensure that if the backward hook in the LightningModule is overridden, we throw a warning.
@@ -232,9 +229,6 @@ def backward(self, loss: Tensor, optimizer: Optimizer, optimizer_idx: int, *args
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU machine")
 @pytest.mark.skipif(not _DEEPSPEED_AVAILABLE, reason="DeepSpeed not available.")
-@pytest.mark.skipif(
-    not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1', reason="test should be run outside of pytest"
-)
 def test_deepspeed_run_configure_optimizers(tmpdir):
     """
         Test end to end that deepspeed works with defaults (without ZeRO as that requires compilation),
@@ -268,9 +262,6 @@ def on_train_start(self) -> None:
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU machine")
 @pytest.mark.skipif(not _DEEPSPEED_AVAILABLE, reason="DeepSpeed not available.")
-@pytest.mark.skipif(
-    not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1', reason="test should be run outside of pytest"
-)
 def test_deepspeed_config(tmpdir, deepspeed_zero_config):
     """
         Test to ensure deepspeed works correctly when passed a DeepSpeed config object including optimizers/schedulers
@@ -304,6 +295,58 @@ def on_train_start(self) -> None:
     _assert_save_model_is_equal(model, tmpdir, trainer)
 
 
+@pytest.mark.skipif(not _DEEPSPEED_AVAILABLE, reason="DeepSpeed not available.")
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU machine")
+def test_deepspeed_custom_precision_params(tmpdir):
+    """
+        Ensure if we modify the FP16 parameters via the DeepSpeedPlugin, the deepspeed config contains these changes.
+    """
+
+    class TestModel(BoringModel):
+
+        def on_train_start(self) -> None:
+            assert self.trainer.training_type_plugin.config['fp16']['loss_scale'] == 10
+            assert self.trainer.training_type_plugin.config['fp16']['initial_scale_power'] == 10
+            assert self.trainer.training_type_plugin.config['fp16']['loss_scale_window'] == 10
+            assert self.trainer.training_type_plugin.config['fp16']['hysteresis'] == 10
+            assert self.trainer.training_type_plugin.config['fp16']['min_loss_scale'] == 10
+            raise SystemExit()
+
+    model = TestModel()
+    trainer = Trainer(
+        plugins=[
+            DeepSpeedPlugin(
+                loss_scale=10, initial_scale_power=10, loss_scale_window=10, hysteresis=10, min_loss_scale=10
+            )
+        ],
+        precision=16,
+        gpus=1
+    )
+    with pytest.raises(SystemExit):
+        trainer.fit(model)
+
+
+@pytest.mark.skipif(not _DEEPSPEED_AVAILABLE, reason="DeepSpeed not available.")
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU machine")
+def test_deepspeed_assert_config_zero_offload_disabled(tmpdir, deepspeed_zero_config):
+    """
+        Ensure if we use a config and turn off cpu_offload, that this is set to False within the config.
+    """
+
+    deepspeed_zero_config['zero_optimization']['cpu_offload'] = False
+
+    class TestModel(BoringModel):
+
+        def on_train_start(self) -> None:
+            assert self.trainer.training_type_plugin.config['zero_optimization']['cpu_offload'] is False
+            raise SystemExit()
+
+    model = TestModel()
+    trainer = Trainer(plugins=[DeepSpeedPlugin(config=deepspeed_zero_config)], precision=16, gpus=1)
+    with pytest.raises(SystemExit):
+        trainer.fit(model)
+
+
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU machine")
 @pytest.mark.skipif(not _DEEPSPEED_AVAILABLE, reason="DeepSpeed not available.")
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")

tests/special_tests.sh

@@ -17,9 +17,6 @@ export PL_RUNNING_SPECIAL_TESTS=1
 DEFAULTS="-m coverage run --source pytorch_lightning -a -m pytest --verbose --capture=no"
 python ${DEFAULTS} tests/trainer/optimization/test_manual_optimization.py::test_step_with_optimizer_closure_with_different_frequencies_ddp
 python ${DEFAULTS} tests/models/test_sync_batchnorm.py::test_sync_batchnorm_ddp
-python ${DEFAULTS} tests/plugins/test_deepspeed_plugin.py::test_warn_deepspeed_override_backward
-python ${DEFAULTS} tests/plugins/test_deepspeed_plugin.py::test_deepspeed_run_configure_optimizers
-python ${DEFAULTS} tests/plugins/test_deepspeed_plugin.py::test_deepspeed_config
 python ${DEFAULTS} tests/plugins/test_deepspeed_plugin.py::test_deepspeed_multigpu
 python ${DEFAULTS} tests/plugins/test_rpc_plugin.py::test_rpc_function_calls_ddp
 python ${DEFAULTS} tests/plugins/test_rpc_sequential_plugin.py::test_rpc_sequential_plugin_manual