Add mixed precision training example

.azure/hpu-tests.yml

@@ -136,7 +136,9 @@ jobs:
 
     - bash: |
        export PYTHONPATH="${PYTHONPATH}:$(pwd)"
-       python mnist_sample.py
+       python mnist_trainer.py
+       LOWER_LIST=ops_fp32_mnist.txt FP32_LIST=ops_bf16_mnist.txt \
+        python mnist_trainer.py -r autocast
       workingDirectory: examples/pytorch/
       displayName: 'Testing HPU examples'
 

CHANGELOG.md

@@ -12,12 +12,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 
 - Added tests for mixed precision training ([#36](https://github.com/Lightning-AI/lightning-Habana/pull/36))
+- Example to include mixed precision training ([%54](https://github.com/Lightning-AI/lightning-Habana/pull/54))
 -
 
 ### Changed
 
 - Enabled skipped tests based on registered strategy, accelerator ([#46](https://github.com/Lightning-AI/lightning-Habana/pull/46))
--
 
 ### Fixed
 

examples/pytorch/mnist_sample.py

@@ -12,24 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import argparse
-
 import torch
 from lightning_utilities import module_available
 from torch.nn import functional as F  # noqa: N812
 
 if module_available("lightning"):
-    from lightning.pytorch import LightningModule, Trainer
-    from lightning.pytorch.demos.mnist_datamodule import MNISTDataModule
+    from lightning.pytorch import LightningModule
 elif module_available("pytorch_lightning"):
-    from pytorch_lightning import LightningModule, Trainer
-    from pytorch_lightning.demos.mnist_datamodule import MNISTDataModule
-
-from lightning_habana.pytorch.accelerator import HPUAccelerator
-from lightning_habana.pytorch.strategies import HPUParallelStrategy, SingleHPUStrategy
+    from pytorch_lightning import LightningModule
 
 
 class LitClassifier(LightningModule):
+    """Base model."""
+
     def __init__(self):
         super().__init__()
         self.l1 = torch.nn.Linear(28 * 28, 10)
@@ -61,20 +56,37 @@ def configure_optimizers(self):
         return torch.optim.Adam(self.parameters(), lr=0.02)
 
 
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="MNIST on HPU", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument("--hpus", default=1, type=int, help="Number of hpus to be used for training")
-    parser.add_argument("-b", "--batch-size", default=32, type=int)
-    args = parser.parse_args()
-    dm = MNISTDataModule(batch_size=args.batch_size)
-    model = LitClassifier()
+class LitAutocastClassifier(LitClassifier):
+    """Base Model with torch.autocast CM."""
 
-    hpus = args.hpus
-    _strategy = SingleHPUStrategy()
-    if hpus > 1:
-        parallel_hpus = [torch.device("hpu")] * hpus
-        _strategy = HPUParallelStrategy(parallel_devices=parallel_hpus)
-    trainer = Trainer(fast_dev_run=True, accelerator=HPUAccelerator(), devices=hpus, strategy=_strategy)
+    def __init__(self, op_override=False):
+        super().__init__()
+        self.op_override = op_override
+
+    def forward(self, x):
+        if self.op_override:
+            self.check_override(x)
+        return super().forward(x)
+
+    def check_override(self, x):
+        """Checks for op override."""
+        identity = torch.eye(x.shape[1], device=x.device, dtype=x.dtype)
+        y = torch.mm(x, identity)
+        z = torch.tan(x)
+        assert y.dtype == torch.float32
+        assert z.dtype == torch.bfloat16
 
-    trainer.fit(model, datamodule=dm)
-    trainer.test(model, datamodule=dm)
+    def training_step(self, batch, batch_idx):
+        """Training step."""
+        with torch.autocast(device_type="hpu", dtype=torch.bfloat16):
+            return super().training_step(batch, batch_idx)
+
+    def validation_step(self, batch, batch_idx):
+        """Validation step."""
+        with torch.autocast(device_type="hpu", dtype=torch.bfloat16):
+            return super().validation_step(batch, batch_idx)
+
+    def test_step(self, batch, batch_idx):
+        """Test step."""
+        with torch.autocast(device_type="hpu", dtype=torch.bfloat16):
+            return super().test_step(batch, batch_idx)

examples/pytorch/mnist_trainer.py

@@ -0,0 +1,112 @@
+# Copyright The Lightning AI team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import os
+import warnings
+
+from lightning_utilities import module_available
+from mnist_sample import LitAutocastClassifier, LitClassifier
+
+if module_available("lightning"):
+    from lightning.pytorch import Trainer, seed_everything
+    from lightning.pytorch.demos.mnist_datamodule import MNISTDataModule
+    from lightning.pytorch.plugins.precision import MixedPrecisionPlugin
+elif module_available("pytorch_lightning"):
+    from pytorch_lightning import Trainer, seed_everything
+    from pytorch_lightning.demos.mnist_datamodule import MNISTDataModule
+    from pytorch_lightning.plugins.precision import MixedPrecisionPlugin
+
+from lightning_habana import HPUAccelerator, SingleHPUStrategy
+
+RUN_TYPE = ["basic", "autocast"]
+
+
+def run_trainer(model, plugin):
+    """Run trainer.fit and trainer.test with given parameters."""
+    _data_module = MNISTDataModule(batch_size=32)
+    trainer = Trainer(
+        accelerator=HPUAccelerator(),
+        devices=1,
+        strategy=SingleHPUStrategy(),
+        plugins=plugin,
+        fast_dev_run=True,
+    )
+    trainer.fit(model, _data_module)
+    trainer.test(model, _data_module)
+
+
+def check_and_init_plugins(plugins, run_type, verbose):
+    """Initialise plugins with appropriate checks."""
+    _plugins = []
+    for plugin in plugins:
+        if verbose:
+            print(f"Initializing {plugin}")
+        if plugin == "MixedPrecisionPlugin":
+            warnings.warn("Operator overriding is not supported with MixedPrecisionPlugin on Habana devices.")
+            if run_type != "autocast":
+                _plugins.append(MixedPrecisionPlugin(device="hpu", precision="bf16-mixed"))
+            else:
+                warnings.warn("Skipping MixedPrecisionPlugin. Redundant with autocast run.")
+        else:
+            print(f"Unsupported or invalid plugin: {plugin}")
+    return _plugins
+
+
+def run_model(run_type, plugins, verbose):
+    """Picks appropriate model and plugins."""
+    # Initialise plugins
+    _plugins = check_and_init_plugins(plugins, run_type, verbose)
+    if run_type == "basic":
+        _model = LitClassifier()
+    elif run_type == "autocast":
+        if "LOWER_LIST" in os.environ or "FP32_LIST" in os.environ:
+            _model = LitAutocastClassifier(op_override=True)
+        else:
+            _model = LitAutocastClassifier()
+        warnings.warn(
+            "To override operators with autocast, set LOWER_LIST and FP32_LIST file paths as env variables."
+            "Example: LOWER_LIST=<path_to_bf16_ops> python example.py"
+            "https://docs.habana.ai/en/latest/PyTorch/PyTorch_Mixed_Precision/Autocast.html#override-options"
+        )
+
+    if verbose:
+        print(f"With run type: {run_type}, running model: {_model} with plugin: {_plugins}")
+    return run_trainer(_model, _plugins)
+
+
+def parse_args():
+    """Cmdline arguments parser."""
+    parser = argparse.ArgumentParser(description="Example to showcase mixed precision training with HPU.")
+
+    parser.add_argument("-v", "--verbose", action="store_true", help="Enable verbosity")
+    parser.add_argument(
+        "-r", "--run_types", nargs="+", choices=RUN_TYPE, default=RUN_TYPE, help="Select run type for example"
+    )
+    parser.add_argument(
+        "-p", "--plugins", nargs="+", default=[], choices=["MixedPrecisionPlugin"], help="Plugins for use in training"
+    )
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    # Get options
+    options = parse_args()
+    if options.verbose:
+        print(f"Running MNIST mixed precision training with options: {options}")
+
+    # Run model and print accuracy
+    for run_type in options.run_types:
+        seed_everything(42)
+        run_model(run_type, options.plugins, options.verbose)

examples/pytorch/ops_bf16_mnist.txt

@@ -1,2 +1,3 @@
 linear
 relu
+mm

examples/pytorch/ops_fp32_mnist.txt

@@ -1 +1,2 @@
 cross_entropy
+tan