[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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@@ -16,6 +16,8 @@ The following parallelism methods are currently supported in vLLM-Omni:
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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.
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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.
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The following table shows which models are currently supported by parallelism method:
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### ImageGen
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@@ -49,9 +51,9 @@ The following table shows which models are currently supported by parallelism me
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### VideoGen
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| Model | Model Identifier | Ulysses-SP | Ring-Attention | Tensor-Parallel |
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|-------|------------------|------------|---------|--------------------------|
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| **Wan2.2** | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | ✅ | ✅ | ✅ |
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| Model | Model Identifier | Ulysses-SP | Ring-Attention | Tensor-Parallel | HSDP |
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|-------|------------------|:----------:|:--------------:|:---------------:|:----:|
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| **Wan2.2** | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | ✅ | ✅ | ✅ | ✅ |
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### Tensor Parallelism
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@@ -330,3 +332,84 @@ You can enable CFG-Parallel in online serving for diffusion models via `--cfg-pa
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```bash
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vllm serve Qwen/Qwen-Image-Edit --omni --port 8091 --cfg-parallel-size 2
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```
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### HSDP
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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.
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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.
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#### Configuration
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HSDP is configured via `DiffusionParallelConfig`:
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `use_hsdp` | bool | False | Enable HSDP |
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| `hsdp_shard_size` | int | -1 | Number of GPUs to shard weights across. -1 = auto (requires other parallelism > 1) |
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| `hsdp_replicate_size` | int | 1 | Number of replica groups. Each group holds a full sharded copy |
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**Constraints:**
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- `hsdp_replicate_size × hsdp_shard_size == world_size`
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- HSDP cannot be used with Tensor Parallelism (`tensor_parallel_size` must be 1)
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#### Operating Modes
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HSDP can work in two modes:
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- **Standalone Mode**: HSDP alone without other parallelism. Must specify `hsdp_shard_size` explicitly.
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- **Combined Mode**: HSDP overlays on top of other parallelism (Ulysses Sequence Parallel, CFG Parallel). HSDP dimensions must match world_size.
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#### Offline Inference
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**Standalone HSDP** (shard across 4 GPUs, no other parallelism):
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```python
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from vllm_omni import Omni
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams
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from vllm_omni.diffusion.data import DiffusionParallelConfig
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omni = Omni(
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model="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
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parallel_config=DiffusionParallelConfig(
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use_hsdp=True,
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hsdp_shard_size=4, # Shard across 4 GPUs
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),
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)
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outputs = omni.generate(
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"A cat playing piano",
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OmniDiffusionSamplingParams(num_inference_steps=50),
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)
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```
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**Combined HSDP + Sequence Parallel**:
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```python
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omni = Omni(
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model="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
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parallel_config=DiffusionParallelConfig(
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ulysses_degree=4, # Sequence parallel
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use_hsdp=True, # HSDP overlays on SP
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),
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)
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```
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#### Online Serving
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**Standalone HSDP** (shard model across 4 GPUs):
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```bash
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vllm serve Wan-AI/Wan2.2-T2V-A14B-Diffusers --omni --port 8091 --use-hsdp --hsdp-shard-size 4
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```
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**Combined with Sequence Parallel**:
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```bash
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vllm serve Wan-AI/Wan2.2-T2V-A14B-Diffusers --omni --port 8091 --use-hsdp --usp 4
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```
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#### Adding HSDP Support to New Models
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For detailed instructions on adding HSDP support to new models, see the [HSDP Contributing Guide](../../design/feature/hsdp.md).
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