docs: align quantization overview

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>

docs/user_guide/quantization/overview.md

@@ -12,20 +12,20 @@ For the internal architecture and backend extension points, see the
 
 | Mode | Guide | Description | Methods |
 |------|-------|-------------|---------|
-| Online quantization | [Online Quantization](online.md) | vLLM-Omni computes quantized weights and scales while loading the model. | FP8 W8A8, Int8 W8A8, MXFP8 W8A8, MXFP4 W4A4 |
+| Online quantization | [Online Quantization](online.md) | vLLM-Omni computes quantized weights and scales while loading the model. | FP8 W8A8, Int8 W8A8, BitsAndBytes W4, MXFP8 W8A8, MXFP4 W4A4 |
 | Runtime attention quantization | [Quantized KV Cache](quantized_kvcache.md) | vLLM-Omni dynamically quantizes eligible diffusion Flash Attention tensors during inference. | FP8 FA |
-| Pre-quantized checkpoints | Method-specific guides | The checkpoint or an offline quantizer provides quantized weights and scales before serving. | ModelOpt, GGUF, AutoRound, msModelSlim, serialized Int8, offline MXFP8, offline MXFP4 DualScale |
+| Pre-quantized checkpoints | Method-specific guides | The checkpoint or an offline quantizer provides quantized weights and scales before serving. | ModelOpt, AutoRound, msModelSlim, serialized Int8, offline MXFP8, offline MXFP4 DualScale |
 
 ## Hardware Support
 
-| Device | FP8 W8A8 | Int8 W8A8 | ModelOpt | MXFP8 W8A8 | MXFP4 W4A4 | GGUF | AutoRound | msModelSlim |
-|--------|----------|-----------|----------|------------|------------|------|-----------|-------------|
-| NVIDIA Blackwell GPU (SM 100+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
-| NVIDIA Ada/Hopper GPU (SM 89+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
-| NVIDIA Ampere GPU (SM 80+) | ✅ | ✅ | ⭕ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
-| AMD ROCm | ⭕ | ⭕ | ⭕ | ⭕ | ✅ | ⭕ | ⭕ | ❌ |
-| Intel XPU | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ✅ | ❌ |
-| Ascend NPU | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
+| Device | FP8 W8A8 | Int8 W8A8 | BitsAndBytes W4 | ModelOpt | MXFP8 W8A8 | MXFP4 W4A4 | AutoRound | msModelSlim |
+|--------|----------|-----------|------------------|----------|------------|------------|-----------|-------------|
+| NVIDIA Blackwell GPU (SM 100+) | ✅ | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ❌ |
+| NVIDIA Ada/Hopper GPU (SM 89+) | ✅ | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ❌ |
+| NVIDIA Ampere GPU (SM 80+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ⭕ | ✅ | ❌ |
+| AMD ROCm | ⭕ | ⭕ | ❌ | ⭕ | ⭕ | ✅ | ⭕ | ❌ |
+| Intel XPU | ⭕ | ⭕ | ❌ | ⭕ | ⭕ | ⭕ | ✅ | ❌ |
+| Ascend NPU | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ |
 
 Legend: `✅` supported, `❌` unsupported, `⭕` not verified in this
 guide. FP8 on Ampere may use a weight-only path where available.
@@ -43,10 +43,10 @@ otherwise.
 |--------|-------|------|----------------|--------|
 | FP8 W8A8 | [FP8](fp8.md) | Online W8A8 or checkpoint FP8 | Qwen-Image; Wan2.2 is not validated | Validated for Qwen-Image family and other DiT models |
 | Int8 W8A8 | [Int8](int8.md) | Online or serialized W8A8 | Qwen-Image; Wan2.2 is not validated | Validated for Qwen-Image and Z-Image |
+| BitsAndBytes W4 | [BitsAndBytes](bitsandbytes.md) | Online W4 weight-only | Z-Image-Turbo; Qwen-Image/Wan2.2 not validated | Validated for Z-Image-Turbo |
 | ModelOpt | [ModelOpt](modelopt.md) | Pre-quantized FP8 checkpoints | Qwen-Image, Z-Image, FLUX.2, HunyuanImage-3.0 | Validated for ModelOpt FP8 diffusion checkpoints |
 | MXFP8 W8A8 | [MXFP8](mxfp8.md) | Online W8A8 or offline pre-quantized | Wan2.2-T2V-A14B, I2V-A14B, TI2V-5B | Ascend NPU only; validated for Wan2.2 |
 | MXFP4 W4A4 | [MXFP4](mxfp4.md) | `mxfp4`: online single-scale only; `mxfp4_dualscale`: online or offline dual-scale (offline recommended) | Wan2.2-T2V-A14B, I2V-A14B | Ascend NPU only; validated for Wan2.2 A14B cascade models; TI2V-5B not supported; offline `mxfp4_dualscale` uses calibrated `mul_scale` for best accuracy |
-| GGUF | [GGUF](gguf.md) | Pre-quantized transformer weights | Qwen-Image | Validated where a model-specific GGUF adapter exists |
 | AutoRound | [AutoRound](autoround.md) | Pre-quantized W4A16 checkpoints | FLUX.1-dev; Qwen-Image/Wan2.2 not validated | Checkpoint-driven |
 | msModelSlim | [msModelSlim](msmodelslim.md) | Pre-quantized Ascend checkpoints | Wan2.2 recipe; HunyuanImage-3.0 inference target | Ascend/NPU path |
 
@@ -61,9 +61,9 @@ in BF16 unless the model guide explicitly adds support.
 |--------|-------|-------|----------------|--------|
 | ModelOpt | [ModelOpt](modelopt.md) | Thinker or language-model checkpoint config | Qwen3-Omni thinker | ModelOpt checkpoint path |
 | Int8 | [Int8](int8.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
+| BitsAndBytes | [BitsAndBytes](bitsandbytes.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
 | MXFP8 | [MXFP8](mxfp8.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
 | MXFP4 | [MXFP4](mxfp4.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
-| GGUF | [GGUF](gguf.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
 | AutoRound | [AutoRound](autoround.md) | Thinker or language-model checkpoint config | Qwen2.5-Omni, Qwen3-Omni | Supported through AutoRound checkpoints |
 | msModelSlim | [msModelSlim](msmodelslim.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
 
@@ -76,10 +76,10 @@ attached to the intended stage rather than applied globally.
 |--------|-------|-------|----------------|--------|
 | FP8 | [FP8](fp8.md) | Stage-specific DiT or transformer module | BAGEL, GLM-Image | Requires model-specific validation |
 | Int8 | [Int8](int8.md) | Stage-specific DiT or transformer module | BAGEL, GLM-Image | Requires model-specific validation |
+| BitsAndBytes | [BitsAndBytes](bitsandbytes.md) | Stage-specific transformer or DiT module | BAGEL, GLM-Image | Not validated |
 | ModelOpt | [ModelOpt](modelopt.md) | Checkpoint-defined diffusion stage | BAGEL, GLM-Image | Requires model-specific validation |
 | MXFP8 | [MXFP8](mxfp8.md) | Stage-specific DiT or transformer module | BAGEL, GLM-Image | Not validated |
 | MXFP4 | [MXFP4](mxfp4.md) | Stage-specific DiT or transformer module | BAGEL, GLM-Image | Not validated |
-| GGUF | [GGUF](gguf.md) | Stage-specific transformer weights | BAGEL, GLM-Image | No validated adapter listed |
 | AutoRound | [AutoRound](autoround.md) | Checkpoint-defined stage | BAGEL, GLM-Image | No validated checkpoint listed |
 | msModelSlim | [msModelSlim](msmodelslim.md) | Ascend-generated stage weights | GLM-Image | Requires model-specific adaptation |
 
@@ -106,7 +106,7 @@ config = build_quant_config({
 
 | Component | Default quantized? | Notes |
 |-----------|--------------------|-------|
-| Diffusion transformer | Yes | Primary target for FP8, Int8, ModelOpt, MXFP8, MXFP4, GGUF, AutoRound, and msModelSlim |
+| Diffusion transformer | Yes | Primary target for FP8, Int8, BitsAndBytes, ModelOpt, MXFP8, MXFP4, AutoRound, and msModelSlim |
 | Text encoder | No | Keep BF16 unless a method-specific guide documents support |
 | VAE | No | Keep BF16; storage-only paths are method-specific |
 | Scheduler/tokenizer | No | Loaded from the base model repository |
@@ -139,8 +139,8 @@ from vllm_omni.quantization import build_quant_config
 
 build_quant_config("fp8")
 build_quant_config({"method": "fp8", "activation_scheme": "static"})
+build_quant_config("bitsandbytes")
 build_quant_config("auto-round", bits=4, group_size=128)
-build_quant_config({"method": "gguf", "gguf_model": "/path/to/model.gguf"})
 build_quant_config({"transformer": {"method": "fp8"}, "vae": None})
 build_quant_config(None)
 ```

tests/diffusion/test_compile.py

@@ -6,7 +6,6 @@ import torch.nn as nn
 
 import vllm_omni.diffusion.compile as compile_module
 from vllm_omni.diffusion.compile import regionally_compile
-from vllm_omni.diffusion.hooks import HookRegistry, ModelHook
 
 pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
 
@@ -77,37 +76,6 @@ def test_regionally_compile_does_not_partially_mutate_on_setup_failure(monkeypat
     assert [block.forward.__func__ for block in model.transformer_blocks] == original_forwards
 
 
-def test_regionally_compile_keeps_hook_dispatch_outside_compiled_graph(monkeypatch):
-    model = _ModelWithWrappedRepeatedBlocks()
-    block = model.transformer_blocks[0]
-    registry = HookRegistry.get_or_create(block)
-    hook = ModelHook()
-    registry.register_hook("test", hook)
-
-    wrapped_forward = block.forward
-    original_forward = block._omni_original_forward
-    compile_calls = []
-
-    def _compile(fn, *args, **kwargs):
-        compile_calls.append(fn)
-
-        def _compiled(*fn_args, **fn_kwargs):
-            return f"compiled:{fn(*fn_args, **fn_kwargs)}"
-
-        return _compiled
-
-    monkeypatch.setattr(compile_module.torch, "compile", _compile)
-
-    regionally_compile(model)
-
-    assert compile_calls[0] is original_forward
-    assert compile_calls[0] is not wrapped_forward
-    assert block.forward is wrapped_forward
-    assert block._omni_original_forward is not original_forward
-    assert hook.fn_ref.original_forward is block._omni_original_forward
-    assert block("ok") == "compiled:ok"
-
-
 def test_compiled_block_preserves_forward_signature_for_inspection(monkeypatch):
     """cache-dit matches blocks via inspect.signature(block.forward).
 

vllm_omni/diffusion/compile.py

@@ -56,50 +56,26 @@ def regionally_compile(
     # Build all compiled callables before mutating the model. This keeps setup
     # failures atomic: callers can safely continue with the uncompiled model if
     # torch.compile raises synchronously for any repeated block.
-    # Keep the pre-hook callable separately.  DLO and the ordinary layerwise
-    # offloader install a HookRegistry wrapper in ``module.forward``.  That
-    # wrapper performs stream/event synchronization and storage rebinding, so
-    # compiling it pulls offload control flow into the graph.  Compile the
-    # original block compute instead and leave the wrapper outside the graph.
-    compiled_forwards: list[tuple[nn.Module, Any | None, Any]] = []
+    compiled_forwards: list[tuple[nn.Module, Any]] = []
     for name, submod in model.named_modules():
         if _matches_repeated_block(name, submod, repeated_blocks, repeated_block_attrs):
             # Compile the block compute while keeping nn.Module.__call__ hooks
-            # outside the compiled graph. If a HookRegistry is already
-            # installed, ``submod.forward`` is the hook dispatcher and the
-            # original callable is stored in ``_omni_original_forward``.
-            # NOTE: anything that wraps this callable must stay
-            # signature-transparent — cache-dit's BlockAdapter matches blocks
-            # by inspecting forward's parameter names and return annotation,
-            # which torch.compile preserves.
-            original_forward = getattr(submod, "_omni_original_forward", None)
-            forward_to_compile = original_forward if original_forward is not None else submod.forward
+            # outside the compiled graph. NOTE: anything that wraps this
+            # callable must stay signature-transparent — cache-dit's
+            # BlockAdapter matches blocks by inspecting forward's parameter
+            # names and return annotation, which torch.compile preserves.
             compiled_forwards.append(
                 (
                     submod,
-                    original_forward,
-                    torch.compile(forward_to_compile, *compile_args, **compile_kwargs),
+                    torch.compile(submod.forward, *compile_args, **compile_kwargs),
                 )
             )
 
     if not compiled_forwards:
         logger.warning(f"Regional compilation skipped because {repeated_blocks} classes are not found in the model.")
     else:
-        for submod, original_forward, compiled_forward in compiled_forwards:
-            if original_forward is None:
-                submod.forward = compiled_forward
-                continue
-
-            # Keep HookRegistry's dispatcher as ``forward`` and replace only
-            # the callable it dispatches to.  Hooks such as MagCache retain a
-            # reference to the original callable as well, so update those
-            # references when they still point at the pre-compile function.
-            submod._omni_original_forward = compiled_forward
-            registry = getattr(submod, "_hook_registry", None)
-            for hook in getattr(registry, "_hooks", {}).values():
-                fn_ref = getattr(hook, "fn_ref", None)
-                if fn_ref is not None and fn_ref.original_forward is original_forward:
-                    fn_ref.original_forward = compiled_forward
+        for submod, compiled_forward in compiled_forwards:
+            submod.forward = compiled_forward
         logger.info(
             "Regional compilation applied to %d module(s) for repeated blocks %s.",
             len(compiled_forwards),