wehub-resource-sync
109 lines
4.3 KiB
Markdown
109 lines
4.3 KiB
Markdown
# Online Quantization
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## Overview
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Online quantization means vLLM-Omni computes quantized weights and scales while
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loading the model. Use it when you want memory savings without preparing a
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separate quantized checkpoint.
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This mode is different from pre-quantized checkpoint formats such as GGUF,
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AutoRound, msModelSlim, or serialized Int8 checkpoints. Those formats are
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prepared before serving and are documented in their method-specific guides.
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For MXFP8 and MXFP4, use this page for load-time quantization from BF16
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checkpoints, and use the method-specific pages for offline checkpoints produced
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by msModelSlim and the merge tools.
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## Hardware Support
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| Device | FP8 W8A8 | Int8 W8A8 | MXFP8 W8A8 | MXFP4 W4A4 |
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|--------|----------|-----------|------------|------------|
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| NVIDIA Blackwell GPU (SM 100+) | ✅ | ✅ | ⭕ | ⭕ |
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| NVIDIA Ada/Hopper GPU (SM 89+) | ✅ | ✅ | ⭕ | ⭕ |
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| NVIDIA Ampere GPU (SM 80+) | ✅ | ✅ | ⭕ | ⭕ |
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| AMD ROCm | ⭕ | ⭕ | ⭕ | ⭕ |
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| Intel XPU | ⭕ | ⭕ | ✅ | ⭕ |
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| Ascend NPU | ❌ | ✅ | ✅ | ✅ |
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Legend: `✅` supported, `❌` unsupported, `⭕` not verified in this
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guide. FP8 on Ampere may use a weight-only path where available. MXFP8 and
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MXFP4 are documented for the Ascend NPU path.
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## Model Type Support
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### Diffusion Model (Qwen-Image, Wan2.2)
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| Method | Guide | Example models | Status |
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|--------|-------|----------------|--------|
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| FP8 W8A8 | [FP8](fp8.md) | Qwen-Image; Wan2.2 is not validated | Validated for Qwen-Image family and other DiT models |
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| Int8 W8A8 | [Int8](int8.md) | Qwen-Image; Wan2.2 is not validated | Validated for Qwen-Image and Z-Image |
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| MXFP8 W8A8 | [MXFP8](mxfp8.md) | Wan2.2-T2V-A14B, Wan2.2-I2V-A14B, Wan2.2-TI2V-5B | Validated on Ascend NPU and Intel XPU |
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| MXFP4 W4A4 | [MXFP4](mxfp4.md) | Wan2.2-T2V-A14B, Wan2.2-I2V-A14B | Ascend NPU only; TI2V-5B is not supported |
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### Multi-Stage Omni/TTS Model (Qwen3-Omni, Qwen3-TTS)
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Online quantization is not currently validated for the omni/TTS stages. For
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Qwen3-Omni and related models, prefer checkpoint-declared ModelOpt or
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AutoRound paths when available.
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### Multi-Stage Diffusion Model (BAGEL, GLM-Image)
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Online quantization must be routed to the intended stage. BAGEL and GLM-Image
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need model-specific validation before they are listed as supported targets.
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## Configuration
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Python API:
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```python
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from vllm_omni import Omni
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omni_fp8 = Omni(model="<your-model>", quantization="fp8")
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omni_int8 = Omni(model="<your-model>", quantization="int8")
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omni_mxfp8 = Omni(model="<your-model>", quantization="mxfp8")
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omni_mxfp4 = Omni(model="<your-model>", quantization="mxfp4")
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omni_mxfp4_dualscale = Omni(model="<your-model>", quantization="mxfp4_dualscale")
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```
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CLI:
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```bash
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vllm serve <your-model> --omni --quantization fp8
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vllm serve <your-model> --omni --quantization int8
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vllm serve <your-model> --omni --quantization mxfp8
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vllm serve <your-model> --omni --quantization mxfp4
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vllm serve <your-model> --omni --quantization mxfp4_dualscale
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```
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Per-component routing:
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```python
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from vllm_omni.quantization import build_quant_config
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config = build_quant_config({
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"transformer": {"method": "fp8"},
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"vae": None,
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})
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```
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## Parameters
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| Parameter | Methods | Description |
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|-----------|---------|-------------|
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| `method` | FP8, Int8, MXFP8, MXFP4 | Quantization method: `"fp8"`, `"int8"`, `"mxfp8"`, `"mxfp4"`, or `"mxfp4_dualscale"` |
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| `ignored_layers` | FP8, Int8, MXFP8, MXFP4 | Layer name patterns to keep in BF16/FP16 |
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| `activation_scheme` | FP8, Int8 | The runtime value `"dynamic"` selects online activation scaling |
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| `weight_block_size` | FP8 | Optional block-wise FP8 weight quantization size |
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| `num_bf16_fallback_layers` | MXFP4 DualScale | Leading transformer blocks to keep in BF16 for online `mxfp4_dualscale`; defaults to `5` |
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## Validation and Notes
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1. Compare the online-quantized output against a BF16 baseline with the same
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seed and generation parameters.
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2. Use `ignored_layers` for quality-sensitive MLPs or output projections.
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3. Document any required skipped layers in the method page before marking a new
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model as supported.
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4. If a model already ships quantized weights, use the matching pre-quantized
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method guide instead of online quantization.
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5. For Ascend MXFP4 deployments, prefer offline `mxfp4_dualscale` checkpoints
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when production quality is more important than avoiding preprocessing.
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