Accelerator: HPU Training¶

This document offers instructions to Gaudi chip users who want to conserve memory and scale models using mixed-precision training.

Enable Mixed Precision¶

With Lightning, you can leverage mixed precision training on HPUs. By default, HPU training uses 32-bit precision. To enable mixed precision, set the precision flag.

from lightning_habana.pytorch.accelerator import HPUAccelerator

trainer = Trainer(devices=1, accelerator=HPUAccelerator(), precision="bf16-mixed")

Customize Mixed Precision¶

Internally, HPUPrecisionPlugin uses the Habana Mixed Precision (HMP) package to enable mixed precision training.

You can execute the ops in FP32 or BF16 precision. The HMP package modifies the Python operators to add the appropriate cast operations for the arguments before execution. With the default settings, you can easily enable mixed precision training with minimal code.

In addition to the default settings in HMP, you can choose to override these defaults and provide your own BF16 and FP32 operator lists by passing them as parameters to HPUPrecisionPlugin.

The following is an excerpt from an MNIST example implemented on a single HPU.

import pytorch_lightning as pl
from lightning_habana.pytorch.accelerator import HPUAccelerator
from lightning_habana.pytorch.plugins.precision import HPUPrecisionPlugin

# Initialize a trainer with HPU accelerator for HPU strategy for single device,
# with mixed precision using overidden HMP settings
trainer = pl.Trainer(
    accelerator=HPUAccelerator(),
    devices=1,
    # Optional Habana mixed precision params to be set
    # Checkout `examples/pl_hpu/ops_bf16_mnist.txt` for the format
    plugins=[
        HPUPrecisionPlugin(
            precision="bf16-mixed",
            opt_level="O1",
            verbose=False,
            bf16_file_path="ops_bf16_mnist.txt",
            fp32_file_path="ops_fp32_mnist.txt",
        )
    ],
)

# Init our model
model = LitClassifier()
# Init the data
dm = MNISTDataModule(batch_size=batch_size)

# Train the model ⚡
trainer.fit(model, datamodule=dm)

For more details, please refer to PyTorch Mixed Precision Training on Gaudi.

Enabling DeviceStatsMonitor with HPUs¶

DeviceStatsMonitor is a callback that automatically monitors and logs device stats during the training stage. This callback can be passed for training with HPUs. It returns a map of the following metrics with their values in bytes of type uint64:

Metric	Value
Limit	Amount of total memory on HPU.
InUse	Amount of allocated memory at any instance.
MaxInUse	Amount of total active memory allocated.
NumAllocs	Number of allocations.
NumFrees	Number of freed chunks.
ActiveAllocs	Number of active allocations.
MaxAllocSize	Maximum allocated size.
TotalSystemAllocs	Total number of system allocations.
TotalSystemFrees	Total number of system frees.
TotalActiveAllocs	Total number of active allocations.

The below shows how DeviceStatsMonitor can be enabled.

from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import DeviceStatsMonitor
from lightning_habana.pytorch.accelerator import HPUAccelerator

device_stats = DeviceStatsMonitor()
trainer = Trainer(accelerator=HPUAccelerator(), callbacks=[device_stats])

For more details, please refer to Memory Stats APIs.