code_samples/training_configuration/pytorch_fsdp

##
Below is an example yaml for BF16 mixed-precision training using PyTorch Fully Sharded Data Parallism (FSDP) with CPU offloading on 8 GPUs.
<pre>
compute_environment: LOCAL_MACHINE
deepspeed_config: {}
+distributed_type: FSDP
downcast_bf16: 'no'
dynamo_backend: 'NO'
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_backward_prefetch_policy: BACKWARD_PRE
+  fsdp_offload_params: true
+  fsdp_sharding_strategy: 1
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_transformer_layer_cls_to_wrap: T5Block
machine_rank: 0
main_training_function: main
megatron_lm_config: {}
mixed_precision: bf16
num_machines: 1
+num_processes: 8
rdzv_backend: static
same_network: true
use_cpu: false
</pre>
##
<pre>
from accelerate import Accelerator

accelerator = Accelerator()
- model, optimizer, dataloader, scheduler = accelerator.prepare(
-  model, optimizer, dataloader, scheduler
-)
+model = accelerator.prepare(model)
+# Optimizer can be any PyTorch optimizer class
+optimizer = torch.optim.AdamW(params=model.parameters(), lr=lr)
+optimizer, dataloader, scheduler = accelerator.prepare(
+  optimizer, dataloader, scheduler
+)
</pre>
##
If the YAML was generated through the `accelerate config` command:
```
accelerate launch {script_name.py} {--arg1} {--arg2} ...
```

If the YAML is saved to a `~/config.yaml` file:
```
accelerate launch --config_file ~/config.yaml {script_name.py} {--arg1} {--arg2} ...
```

Or you can use `accelerate launch` with right configuration parameters and have no `config.yaml` file:
```
accelerate launch \
    --use_fsdp \
    --num_processes=8 \
    --mixed_precision=bf16 \
    --fsdp_sharding_strategy=1 \
    --fsdp_auto_wrap_policy=TRANSFORMER_BASED_WRAP \
    --fsdp_transformer_layer_cls_to_wrap=T5Block \
    --fsdp_offload_params=true \
    {script_name.py} {--arg1} {--arg2} ...
```

##
For PyTorch FDSP, you need to prepare the model first **before** preparing the optimizer since FSDP will shard parameters in-place and this will break any previously initialized optimizers.

For transformer models, please use `TRANSFORMER_BASED_WRAP` auto wrap policy as shown in the config above.

##
To learn more checkout the related documentation:
- <a href="https://huggingface.co/docs/accelerate/usage_guides/fsdp" target="_blank">How to use Fully Sharded Data Parallelism</a>
- <a href="https://huggingface.co/blog/pytorch-fsdp" target="_blank">Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel</a>