129 lines
4.7 KiB
Markdown
129 lines
4.7 KiB
Markdown
# BitsAndBytes W4 Quantization
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## Overview
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BitsAndBytes 4-bit quantization supports weight-only NF4/FP4 diffusion transformer
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inference on CUDA GPUs. It quantizes BF16/FP16 weights at load time; activations
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stay in BF16/FP16. No pre-quantized checkpoint is required.
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This is an online (dynamic) path: load the normal HuggingFace checkpoint and
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quantize during model loading via the `bitsandbytes` CUDA kernels.
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The validation terms on this page follow the
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[support levels](overview.md#support-levels). BitsAndBytes kernel support does
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not imply that every diffusion transformer has been quality-validated.
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## Hardware Support
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| Device | Support |
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|--------|---------|
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| NVIDIA CUDA GPU (SM 75+) | ✅ |
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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: `✅` backend available, `❌` unsupported. Non-CUDA platforms raise in
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`get_quant_method()`; CUDA requires compute capability 7.5+ (`SM 75+`).
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Requires the optional `bitsandbytes` package (`pip install bitsandbytes`).
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## Model Type Support
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### Diffusion Model (Qwen-Image, Wan2.2)
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| Model | HF models | Scope | Level | Recommendation |
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|-------|-----------|-------|-------|----------------|
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| Z-Image | `Tongyi-MAI/Z-Image-Turbo` | Diffusion transformer | Validated | All heavy linear layers; sensitive embedders stay BF16 |
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| Qwen-Image | `Qwen/Qwen-Image`, `Qwen/Qwen-Image-2512` | Diffusion transformer | Not validated | Compare with BF16 before enabling |
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| Wan2.2 | Wan2.2 diffusion pipelines | One or both diffusion transformers | Not validated | Validate quality and cascade behavior before enabling |
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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 BitsAndBytes 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="Tongyi-MAI/Z-Image-Turbo", quantization="bitsandbytes")
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omni_with_skips = Omni(
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model="Tongyi-MAI/Z-Image-Turbo",
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quantization_config={
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"method": "bitsandbytes",
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"ignored_layers": ["to_out"],
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},
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)
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outputs = omni.generate(
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"A cup of coffee on the table",
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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 Tongyi-MAI/Z-Image-Turbo --quantization bitsandbytes
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python text_to_image.py --model Tongyi-MAI/Z-Image-Turbo --quantization bitsandbytes --ignored-layers "to_out"
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vllm serve Tongyi-MAI/Z-Image-Turbo --omni --quantization bitsandbytes
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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 (`"bitsandbytes"`) |
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| `quant_type` | str | `"nf4"` | 4-bit data type: `"nf4"` (recommended) or `"fp4"` |
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| `compress_statistics` | bool | `True` | Double-quantize block scaling statistics for better accuracy |
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| `ignored_layers` | list[str] | `[]` | Layer name patterns to keep in BF16/FP16 |
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## Validation and Notes
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On Z-Image-Turbo (single GPU, 1024×1024, 50 steps), BitsAndBytes W4 typically
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reduces peak VRAM from roughly 24.5 GiB (BF16) to roughly 17 GiB. Compare output
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quality against a BF16 baseline before enabling on new models.
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Multi-GPU tensor parallelism (`tensor_parallel_size` > 1) is not validated for
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BitsAndBytes in diffusion models; each rank quantizes its own weight shard
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independently.
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Z-Image-Turbo is validated outside scheduled full-model BitsAndBytes CI. The
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real CUDA kernel smoke tests cover quantization and matrix multiplication, but
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do not establish model-level quality for unlisted architectures.
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If quality regresses, use `ignored_layers` to keep sensitive projections in BF16
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(for example `to_out` or `w2`).
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```python
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omni = Omni(
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model="Tongyi-MAI/Z-Image-Turbo",
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quantization="bitsandbytes",
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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 BitsAndBytes
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output against a BF16 baseline and documenting any required `ignored_layers`.
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