diffusers/pr_14039/en/api/parallel.md

# Parallelism

Parallelism strategies help speed up diffusion transformers by distributing computations across multiple devices, allowing for faster inference/training times. Refer to the [Distributed inferece](../training/distributed_inference) guide to learn more.

## ParallelConfig[[diffusers.ParallelConfig]]

#### diffusers.ParallelConfig[[diffusers.ParallelConfig]]

```python
diffusers.ParallelConfig(context_parallel_config: diffusers.models._modeling_parallel.ContextParallelConfig | None = None, tensor_parallel_config: diffusers.models._modeling_parallel.TensorParallelConfig | None = None, _rank: int = None, _world_size: int = None, _device: device = None, _mesh: DeviceMesh = None)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14039/src/diffusers/models/_modeling_parallel.py#L197)

**Parameters:**

context_parallel_config (`ContextParallelConfig`, *optional*) : Configuration for context parallelism.

tensor_parallel_config (`TensorParallelConfig`, *optional*) : Configuration for tensor parallelism.

Configuration for applying different parallelisms.

## ContextParallelConfig[[diffusers.ContextParallelConfig]]

#### diffusers.ContextParallelConfig[[diffusers.ContextParallelConfig]]

```python
diffusers.ContextParallelConfig(ring_degree: int | None = None, ulysses_degree: int | None = None, convert_to_fp32: bool = True, rotate_method: typing.Literal['allgather', 'alltoall'] = 'allgather', mesh: typing.Optional[torch.distributed.device_mesh.DeviceMesh] = None, ulysses_anything: bool = False, ring_anything: bool = False, _rank: int = None, _world_size: int = None, _device: device = None, _mesh: DeviceMesh = None, _flattened_mesh: DeviceMesh = None, _ring_mesh: DeviceMesh = None, _ulysses_mesh: DeviceMesh = None, _ring_local_rank: int = None, _ulysses_local_rank: int = None)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14039/src/diffusers/models/_modeling_parallel.py#L41)

**Parameters:**

ring_degree (`int`, *optional*, defaults to `1`) : Number of devices to use for Ring Attention. Sequence is split across devices. Each device computes attention between its local Q and KV chunks passed sequentially around ring. Lower memory (only holds 1/N of KV at a time), overlaps compute with communication, but requires N iterations to see all tokens. Best for long sequences with limited memory/bandwidth. Number of devices to use for ring attention within a context parallel region. Must be a divisor of the total number of devices in the context parallel mesh.

ulysses_degree (`int`, *optional*, defaults to `1`) : Number of devices to use for Ulysses Attention. Sequence split is across devices. Each device computes local QKV, then all-gathers all KV chunks to compute full attention in one pass. Higher memory (stores all KV), requires high-bandwidth all-to-all communication, but lower latency. Best for moderate sequences with good interconnect bandwidth.

convert_to_fp32 (`bool`, *optional*, defaults to `True`) : Whether to convert output and LSE to float32 for ring attention numerical stability.

rotate_method (`str`, *optional*, defaults to `"allgather"`) : Method to use for rotating key/value states across devices in ring attention. Currently, only `"allgather"` is supported.

ulysses_anything (`bool`, *optional*, defaults to `False`) : Whether to enable "Ulysses Anything" mode, which supports arbitrary sequence lengths and head counts that are not evenly divisible by `ulysses_degree`. When enabled, `ulysses_degree` must be greater than 1 and `ring_degree` must be 1.

ring_anything (`bool`, *optional*, defaults to `False`) : Whether to enable "Ring Anything" mode, which supports arbitrary sequence lengths. When enabled, `ring_degree` must be greater than 1 and `ulysses_degree` must be 1.

mesh (`torch.distributed.device_mesh.DeviceMesh`, *optional*) : A custom device mesh to use for context parallelism. If provided, this mesh will be used instead of creating a new one. This is useful when combining context parallelism with other parallelism strategies (e.g., FSDP, tensor parallelism) that share the same device mesh. The mesh must have both "ring" and "ulysses" dimensions. Use size 1 for dimensions not being used (e.g., `mesh_shape=(2, 1, 4)` with `mesh_dim_names=("ring", "ulysses", "fsdp")` for ring attention only with FSDP).

Configuration for context parallelism.

#### diffusers.hooks.apply_context_parallel[[diffusers.hooks.apply_context_parallel]]

```python
diffusers.hooks.apply_context_parallel(module: Module, parallel_config: ContextParallelConfig, plan: dict)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14039/src/diffusers/hooks/context_parallel.py#L80)

Apply context parallel on a model.

## TensorParallelConfig[[diffusers.TensorParallelConfig]]

#### diffusers.TensorParallelConfig[[diffusers.TensorParallelConfig]]

```python
diffusers.TensorParallelConfig(tp_degree: int = 1, mesh: typing.Optional[torch.distributed.device_mesh.DeviceMesh] = None, _rank: int = None, _world_size: int = None, _device: device = None, _mesh: DeviceMesh = None, _tp_degree: int = None)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14039/src/diffusers/models/_modeling_parallel.py#L157)

**Parameters:**

tp_degree (`int`, defaults to `1`) : Number of devices to shard across. Must be a divisor of the number of attention heads (and FFN hidden dimensions) of the model being parallelised.

mesh (`torch.distributed.device_mesh.DeviceMesh`, *optional*) : A custom device mesh to use. If provided, `tp_degree` is inferred from `mesh.size()` and the argument is ignored. Useful when combining TP with other parallelism strategies (e.g. CP) that share the same mesh.

Configuration for tensor parallelism.

Tensor parallelism shards weight matrices (column-wise and row-wise) across devices. Each device computes a partial
result; an AllReduce/AllGather at layer boundaries reconstructs the full output. Uses
`torch.distributed.tensor.parallelize_module` with `ColwiseParallel` / `RowwiseParallel` sharding styles. Supported
device types are `"cuda"` and `"neuron"`.

#### diffusers.hooks.apply_tensor_parallel[[diffusers.hooks.apply_tensor_parallel]]

```python
diffusers.hooks.apply_tensor_parallel(model: Module, config: TensorParallelConfig, tp_plan: dict)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14039/src/diffusers/hooks/tensor_parallel.py#L243)

Apply tensor parallel on a model from its flat `_tp_plan`.

## TPU

### `enable_tpu_compile`[[diffusers.DiffusionPipeline.enable_tpu_compile]]

#### diffusers.DiffusionPipeline.enable_tpu_compile[[diffusers.DiffusionPipeline.enable_tpu_compile]]

```python
diffusers.DiffusionPipeline.enable_tpu_compile(model_names: typing.Optional[typing.List[str]] = None, **compile_kwargs)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14039/src/diffusers/pipelines/pipeline_utils.py#L2292)

**Parameters:**

model_names (*list[str]*, *optional*) : Names of pipeline components to compile. Defaults to all `torch.nn.Module` components currently resident on a TPU device.

- ****compile_kwargs** : Extra keyword arguments forwarded to `torch.compile`. `backend` defaults to `TpuBackend()` and `dynamic` defaults to `False` (required for static tracing).

Compile pipeline components that are on TPU using `torch.compile` with the `TpuBackend`.

Before compiling, each component that exposes `set_attn_processor` has `AttnProcessor` applied. This
replaces `AttnProcessor2_0` (SDP-based) which triggers XLA fusion-emitter crashes in eager/lazy mode.
`TpuBackend` handles the resulting `torch.cat` layout internally during static tracing, so no additional
wrapper is needed at compile time.

Example:
```python
import torch
import torch_tpu  # noqa: F401

pipe.transformer.to("tpu")
pipe.vae.to("tpu")
pipe.enable_tpu_compile()
```

### `tpu_warmup`[[diffusers.DiffusionPipeline.tpu_warmup]]

#### diffusers.DiffusionPipeline.tpu_warmup[[diffusers.DiffusionPipeline.tpu_warmup]]

```python
diffusers.DiffusionPipeline.tpu_warmup(*args, **kwargs)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14039/src/diffusers/pipelines/pipeline_utils.py#L2349)

**Parameters:**

- ***args** : Positional arguments forwarded to the pipeline `__call__`.

- ****kwargs** : Keyword arguments forwarded to the pipeline `__call__`.

Run a single forward pass to trigger XLA / `TpuBackend` compilation.

Call this after `enable_tpu_compile` and before timed inference. The warmup pass compiles the static
computation graphs; subsequent calls reuse the compiled graphs and run at full speed.

Example:
```python
pipe.tpu_warmup(
    prompt="warmup",
    height=1024,
    width=1024,
    num_inference_steps=4,
    guidance_scale=0.0,
)
```