# DeepSpeed for HPUs

[DeepSpeed](https://www.deepspeed.ai/) enables you to fit and train larger models on HPUs thanks to various optimizations described in the [ZeRO paper](https://arxiv.org/abs/1910.02054).
In particular, you can use the two following ZeRO configurations that have been validated to be fully functioning with Gaudi:
- **ZeRO-1**: partitions the optimizer states across processes.
- **ZeRO-2**: partitions the optimizer states + gradients across processes.
- **ZeRO-3**: ZeRO-2 + full model state is partitioned across the processes.

These configurations are fully compatible with Intel Gaudi Mixed Precision and can thus be used to train your model in *bf16* precision.

You can find more information about DeepSpeed Gaudi integration [here](https://docs.habana.ai/en/latest/PyTorch/DeepSpeed/DeepSpeed_User_Guide/DeepSpeed_User_Guide.html#deepspeed-user-guide).


## Setup

To use DeepSpeed on Gaudi, you need to install Optimum for Intel Gaudi and [DeepSpeed fork for Intel Gaudi](https://github.com/HabanaAI/DeepSpeed) with:
```bash
pip install optimum[habana]
pip install git+https://github.com/HabanaAI/DeepSpeed.git@1.22.0
```


## Using DeepSpeed with Optimum for Intel Gaudi

The `GaudiTrainer` allows using DeepSpeed as easily as the [Transformers Trainer](https://huggingface.co/docs/transformers/main_classes/trainer).
This can be done in 3 steps:
1. A DeepSpeed configuration has to be defined.
2. The `deepspeed` training argument enables to specify the path to the DeepSpeed configuration.
3. The `deepspeed` launcher must be used to run your script.

These steps are detailed below.
A comprehensive guide about how to use DeepSpeed with the Transformers Trainer is also available [here](https://huggingface.co/docs/transformers/main_classes/deepspeed).


### DeepSpeed configuration

The DeepSpeed configuration to use is passed through a JSON file and enables you to choose the optimizations to apply.
Here is an example for applying ZeRO-2 optimizations and *bf16* precision:
```json
{
    "steps_per_print": 64,
    "train_batch_size": "auto",
    "train_micro_batch_size_per_gpu": "auto",
    "gradient_accumulation_steps": "auto",
    "bf16": {
        "enabled": true
    },
    "gradient_clipping": 1.0,
    "zero_optimization": {
        "stage": 2,
        "overlap_comm": false,
        "reduce_scatter": false,
        "contiguous_gradients": false
    }
}
```

<Tip>

The special value `"auto"` enables to automatically get the correct or most efficient value.
You can also specify the values yourself but, if you do so, you should be careful not to have conflicting values with your training arguments.
It is strongly advised to read [this section](https://huggingface.co/docs/transformers/main_classes/deepspeed#shared-configuration) in the Transformers documentation to completely understand how this works.

</Tip>

Other examples of configurations for HPUs are proposed [here](https://github.com/HabanaAI/Model-References/tree/1.22.0/PyTorch/nlp/DeepSpeedExamples/deepspeed-bert/scripts) by Intel.

The [Transformers documentation](https://huggingface.co/docs/transformers/main_classes/deepspeed#configuration) explains how to write a configuration from scratch very well.
A more complete description of all configuration possibilities is available [here](https://www.deepspeed.ai/docs/config-json/).


### The `deepspeed` training argument

To use DeepSpeed, you must specify `deespeed=path_to_my_deepspeed_configuration` in your `GaudiTrainingArguments` instance:
```python
training_args = GaudiTrainingArguments(
    # my usual training arguments...
    use_habana=True,
    use_lazy_mode=True,
    gaudi_config_name=path_to_my_gaudi_config,
    deepspeed=path_to_my_deepspeed_config,
)
```

This argument both indicates that DeepSpeed should be used and points to your DeepSpeed configuration.


### Launching your script

Finally, there are two possible ways to launch your script:

1. Using the [gaudi_spawn.py](https://github.com/huggingface/optimum-habana/blob/main/examples/gaudi_spawn.py) script:

```bash
python gaudi_spawn.py \
    --world_size number_of_hpu_you_have --use_deepspeed \
    path_to_script.py --args1 --args2 ... --argsN \
    --deepspeed path_to_deepspeed_config
```
where `--argX` is an argument of the script to run with DeepSpeed.

2. Using the `DistributedRunner` directly in code:

```python
from optimum.habana.distributed import DistributedRunner
from optimum.utils import logging

world_size=8 # Number of HPUs to use (1 or 8)

# define distributed runner
distributed_runner = DistributedRunner(
    command_list=["scripts/train.py --args1 --args2 ... --argsN --deepspeed path_to_deepspeed_config"],
    world_size=world_size,
    use_deepspeed=True,
)

# start job
ret_code = distributed_runner.run()
```

<Tip warning={true}>

You should set `"use_fused_adam": false` in your Gaudi configuration because it is not compatible with DeepSpeed yet.

</Tip>


<EditOnGithub source="https://github.com/huggingface/optimum-habana/blob/main/docs/source/usage_guides/deepspeed.mdx" />