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[docs] device_map #11711

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stevhliu Jun 13, 2025
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50 changes: 47 additions & 3 deletions docs/source/en/optimization/memory.md
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Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,12 @@ pipeline = StableDiffusionXLPipeline.from_pretrained(
> [!WARNING]
> Device placement is an experimental feature and the API may change. Only the `balanced` strategy is supported at the moment. We plan to support additional mapping strategies in the future.

The `device_map` parameter controls how the model components in a pipeline are distributed across devices. The `balanced` device placement strategy evenly splits the pipeline across all available devices.
The `device_map` parameter controls how the model components in a pipeline or the layers in an individual model are distributed across devices.

<hfoptions id="device-map">
<hfoption id="pipeline level">

The `balanced` device placement strategy evenly splits the pipeline across all available devices.

```py
import torch
Expand All @@ -83,7 +88,10 @@ print(pipeline.hf_device_map)
{'unet': 1, 'vae': 1, 'safety_checker': 0, 'text_encoder': 0}
```

The `device_map` parameter also works on the model-level. This is useful for loading large models, such as the Flux diffusion transformer which has 12.5B parameters. Instead of `balanced`, set it to `"auto"` to automatically distribute a model across the fastest device first before moving to slower devices. Refer to the [Model sharding](../training/distributed_inference#model-sharding) docs for more details.
</hfoption>
<hfoption id="model level">

The `device_map` is useful for loading large models, such as the Flux diffusion transformer which has 12.5B parameters. Set it to `"auto"` to automatically distribute a model across the fastest device first before moving to slower devices. Refer to the [Model sharding](../training/distributed_inference#model-sharding) docs for more details.

```py
import torch
Expand All @@ -97,7 +105,43 @@ transformer = AutoModel.from_pretrained(
)
```

For more fine-grained control, pass a dictionary to enforce the maximum GPU memory to use on each device. If a device is not in `max_memory`, it is ignored and pipeline components won't be distributed to it.
You can inspect a model's device map with `hf_device_map`.

```py
print(transformer.hf_device_map)
```

</hfoption>
</hfoptions>

When designing your own `device_map`, it should be a dictionary of a model's specific module name or layer and a device identifier (an integer for GPUs, `cpu` for CPUs, and `disk` for disk).

Call `hf_device_map` on a model to see how model layers are distributed and then design your own.

```py
print(transformer.hf_device_map)
{'pos_embed': 0, 'time_text_embed': 0, 'context_embedder': 0, 'x_embedder': 0, 'transformer_blocks': 0, 'single_transformer_blocks.0': 0, 'single_transformer_blocks.1': 0, 'single_transformer_blocks.2': 0, 'single_transformer_blocks.3': 0, 'single_transformer_blocks.4': 0, 'single_transformer_blocks.5': 0, 'single_transformer_blocks.6': 0, 'single_transformer_blocks.7': 0, 'single_transformer_blocks.8': 0, 'single_transformer_blocks.9': 0, 'single_transformer_blocks.10': 'cpu', 'single_transformer_blocks.11': 'cpu', 'single_transformer_blocks.12': 'cpu', 'single_transformer_blocks.13': 'cpu', 'single_transformer_blocks.14': 'cpu', 'single_transformer_blocks.15': 'cpu', 'single_transformer_blocks.16': 'cpu', 'single_transformer_blocks.17': 'cpu', 'single_transformer_blocks.18': 'cpu', 'single_transformer_blocks.19': 'cpu', 'single_transformer_blocks.20': 'cpu', 'single_transformer_blocks.21': 'cpu', 'single_transformer_blocks.22': 'cpu', 'single_transformer_blocks.23': 'cpu', 'single_transformer_blocks.24': 'cpu', 'single_transformer_blocks.25': 'cpu', 'single_transformer_blocks.26': 'cpu', 'single_transformer_blocks.27': 'cpu', 'single_transformer_blocks.28': 'cpu', 'single_transformer_blocks.29': 'cpu', 'single_transformer_blocks.30': 'cpu', 'single_transformer_blocks.31': 'cpu', 'single_transformer_blocks.32': 'cpu', 'single_transformer_blocks.33': 'cpu', 'single_transformer_blocks.34': 'cpu', 'single_transformer_blocks.35': 'cpu', 'single_transformer_blocks.36': 'cpu', 'single_transformer_blocks.37': 'cpu', 'norm_out': 'cpu', 'proj_out': 'cpu'}
```

For example, the `device_map` below places `single_transformer_blocks.10` through `single_transformer_blocks.20` on a second GPU (`1`).

```py
import torch
from diffusers import AutoModel

device_map = {
'pos_embed': 0, 'time_text_embed': 0, 'context_embedder': 0, 'x_embedder': 0, 'transformer_blocks': 0, 'single_transformer_blocks.0': 0, 'single_transformer_blocks.1': 0, 'single_transformer_blocks.2': 0, 'single_transformer_blocks.3': 0, 'single_transformer_blocks.4': 0, 'single_transformer_blocks.5': 0, 'single_transformer_blocks.6': 0, 'single_transformer_blocks.7': 0, 'single_transformer_blocks.8': 0, 'single_transformer_blocks.9': 0, 'single_transformer_blocks.10': 1, 'single_transformer_blocks.11': 1, 'single_transformer_blocks.12': 1, 'single_transformer_blocks.13': 1, 'single_transformer_blocks.14': 1, 'single_transformer_blocks.15': 1, 'single_transformer_blocks.16': 1, 'single_transformer_blocks.17': 1, 'single_transformer_blocks.18': 1, 'single_transformer_blocks.19': 1, 'single_transformer_blocks.20': 1, 'single_transformer_blocks.21': 'cpu', 'single_transformer_blocks.22': 'cpu', 'single_transformer_blocks.23': 'cpu', 'single_transformer_blocks.24': 'cpu', 'single_transformer_blocks.25': 'cpu', 'single_transformer_blocks.26': 'cpu', 'single_transformer_blocks.27': 'cpu', 'single_transformer_blocks.28': 'cpu', 'single_transformer_blocks.29': 'cpu', 'single_transformer_blocks.30': 'cpu', 'single_transformer_blocks.31': 'cpu', 'single_transformer_blocks.32': 'cpu', 'single_transformer_blocks.33': 'cpu', 'single_transformer_blocks.34': 'cpu', 'single_transformer_blocks.35': 'cpu', 'single_transformer_blocks.36': 'cpu', 'single_transformer_blocks.37': 'cpu', 'norm_out': 'cpu', 'proj_out': 'cpu'
}

transformer = AutoModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
device_map=device_map,
torch_dtype=torch.bfloat16
)
```

Pass a dictionary mapping maximum memory usage to each device to enforce a limit. If a device is not in `max_memory`, it is ignored and pipeline components won't be distributed to it.

```py
import torch
Expand Down