[Feature] Support HSDP for diffusion models (#1339)

Signed-off-by: gcanlin <canlinguosdu@gmail.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
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Canlin GuoandHongsheng Liu authored and GitHub committed 2026-02-26 21:31:10 +08:00
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@@ -16,6 +16,8 @@ The following parallelism methods are currently supported in vLLM-Omni:
5. [VAE Patch Parallelism](#vae-patch-parallelism): VAE patch parallelism shards VAE decode spatially across ranks. This can reduce the peak memory of VAE decode and (depending on resolution and communication overhead) speed up VAE decode.
6. [HSDP](#hsdp): Hybrid Sharded Data Parallel shards model weights across GPUs using PyTorch FSDP2. This reduces per-GPU memory usage, enabling inference of large models on GPUs with limited memory.
The following table shows which models are currently supported by parallelism method:
### ImageGen
@@ -49,9 +51,9 @@ The following table shows which models are currently supported by parallelism me
### VideoGen
| Model | Model Identifier | Ulysses-SP | Ring-Attention | Tensor-Parallel |
|-------|------------------|------------|---------|--------------------------|
| **Wan2.2** | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | ✅ | ✅ | ✅ |
| Model | Model Identifier | Ulysses-SP | Ring-Attention | Tensor-Parallel | HSDP |
|-------|------------------|:----------:|:--------------:|:---------------:|:----:|
| **Wan2.2** | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | ✅ | ✅ | ✅ | ✅ |
### Tensor Parallelism
@@ -330,3 +332,84 @@ You can enable CFG-Parallel in online serving for diffusion models via `--cfg-pa
```bash
vllm serve Qwen/Qwen-Image-Edit --omni --port 8091 --cfg-parallel-size 2
```
### HSDP
HSDP (Hybrid Sharded Data Parallel) shards model weights across GPUs to reduce per-GPU memory usage. This enables inference of large models (e.g., Wan2.2 14B) on GPUs with limited memory.
Unlike Tensor Parallelism which splits computation, HSDP uses PyTorch's FSDP2 to shard and redistribute weights at runtime. Each GPU only holds a fraction of the model weights, and weights are gathered on-demand during forward passes.
#### Configuration
HSDP is configured via `DiffusionParallelConfig`:
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `use_hsdp` | bool | False | Enable HSDP |
| `hsdp_shard_size` | int | -1 | Number of GPUs to shard weights across. -1 = auto (requires other parallelism > 1) |
| `hsdp_replicate_size` | int | 1 | Number of replica groups. Each group holds a full sharded copy |
**Constraints:**
- `hsdp_replicate_size × hsdp_shard_size == world_size`
- HSDP cannot be used with Tensor Parallelism (`tensor_parallel_size` must be 1)
#### Operating Modes
HSDP can work in two modes:
- **Standalone Mode**: HSDP alone without other parallelism. Must specify `hsdp_shard_size` explicitly.
- **Combined Mode**: HSDP overlays on top of other parallelism (Ulysses Sequence Parallel, CFG Parallel). HSDP dimensions must match world_size.
#### Offline Inference
**Standalone HSDP** (shard across 4 GPUs, no other parallelism):
```python
from vllm_omni import Omni
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
from vllm_omni.diffusion.data import DiffusionParallelConfig
omni = Omni(
model="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
parallel_config=DiffusionParallelConfig(
use_hsdp=True,
hsdp_shard_size=4, # Shard across 4 GPUs
),
)
outputs = omni.generate(
"A cat playing piano",
OmniDiffusionSamplingParams(num_inference_steps=50),
)
```
**Combined HSDP + Sequence Parallel**:
```python
omni = Omni(
model="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
parallel_config=DiffusionParallelConfig(
ulysses_degree=4, # Sequence parallel
use_hsdp=True, # HSDP overlays on SP
),
)
```
#### Online Serving
**Standalone HSDP** (shard model across 4 GPUs):
```bash
vllm serve Wan-AI/Wan2.2-T2V-A14B-Diffusers --omni --port 8091 --use-hsdp --hsdp-shard-size 4
```
**Combined with Sequence Parallel**:
```bash
vllm serve Wan-AI/Wan2.2-T2V-A14B-Diffusers --omni --port 8091 --use-hsdp --usp 4
```
#### Adding HSDP Support to New Models
For detailed instructions on adding HSDP support to new models, see the [HSDP Contributing Guide](../../design/feature/hsdp.md).