wehub-resource-sync
108 lines
3.4 KiB
Markdown
108 lines
3.4 KiB
Markdown
# Int8 Quantization
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## Overview
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Int8 quantization supports W8A8 diffusion transformer inference on CUDA and
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Ascend NPU. It can quantize BF16/FP16 weights at load time, or load serialized
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Int8 checkpoints that already contain quantized weights and scales.
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Only online activation scaling is currently supported.
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## Hardware Support
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| Device | Support |
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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.
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## Model Type Support
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### Diffusion Model (Qwen-Image, Wan2.2)
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| Model | HF models | CUDA | Ascend NPU | Mode | Recommendation |
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|-------|-----------|:----:|:----------:|------|----------------|
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| Qwen-Image | `Qwen/Qwen-Image`, `Qwen/Qwen-Image-2512` | Yes | Yes | Online W8A8 | All layers |
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| Wan2.2 | Wan2.2 diffusion pipelines | Not validated | Not validated | Online W8A8 | Validate before enabling in docs |
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| Z-Image | `Tongyi-MAI/Z-Image-Turbo` | Yes | Yes | Online W8A8 | All layers |
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Other diffusion models may work if their transformer uses supported linear
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layers, but they are not validated in this guide.
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### Multi-Stage Omni/TTS Model (Qwen3-Omni, Qwen3-TTS)
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| Model | Scope | Status | Notes |
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|-------|-------|--------|-------|
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| Qwen3-Omni | Thinker language-model stage | Not validated | Prefer checkpoint-supported ModelOpt FP8 or AutoRound paths |
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| Qwen3-TTS | TTS language-model stage | Not validated | No Int8 TTS stage support is documented |
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### Multi-Stage Diffusion Model (BAGEL, GLM-Image)
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| Model | Scope | Status | Notes |
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|-------|-------|--------|-------|
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| BAGEL | Stage-specific transformer or DiT module | Not validated | Requires explicit stage routing |
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| GLM-Image | Stage-specific transformer or DiT module | Not validated | Requires quality comparison with BF16 |
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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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from vllm_omni.inputs.data import OmniDiffusionSamplingParams
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omni = Omni(model="<your-model>", quantization="int8")
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omni_with_skips = Omni(
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model="<your-model>",
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quantization_config={
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"method": "int8",
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"ignored_layers": ["<layer-name>"],
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},
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)
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outputs = omni.generate(
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"A cat sitting on a windowsill",
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OmniDiffusionSamplingParams(num_inference_steps=50),
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)
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```
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CLI:
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```bash
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python text_to_image.py --model <your-model> --quantization int8
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python text_to_image.py --model <your-model> --quantization int8 --ignored-layers "img_mlp"
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vllm serve <your-model> --omni --quantization int8
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```
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## Parameters
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `method` | str | - | Quantization method (`"int8"`) |
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| `activation_scheme` | str | `"dynamic"` | `"dynamic"` selects online activation scaling; static is not supported |
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| `ignored_layers` | list[str] | `[]` | Layer name patterns to keep in BF16/FP16 |
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| `is_checkpoint_int8_serialized` | bool | `False` | Set by checkpoint config when loading serialized Int8 weights |
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## Validation and Notes
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Int8 quantization can be combined with cache acceleration:
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```python
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omni = Omni(
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model="<your-model>",
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quantization="int8",
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cache_backend="tea_cache",
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cache_config={"rel_l1_thresh": 0.2},
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)
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```
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Only add a new model to the supported table after comparing the Int8 output
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against a BF16 baseline and documenting any required `ignored_layers`.
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