[Bugfix][Quant] Gate quack FP8 to datacenter Blackwell (sm_100.x) only (#4817)

Signed-off-by: wangrzneu <wangrzneu@gmail.com>

docs/user_guide/quantization/fp8.md

@@ -44,10 +44,16 @@ pip install vllm-omni[quack]
 pip install 'quack-kernels[cu13]' --extra-index-url https://download.pytorch.org/whl/cu130
 ```
 
-It is enabled automatically once installed (no flag needed) and is **Blackwell-only**:
-on Hopper/Ada the CUTLASS FP8 kernel already fuses bias, so quack is not used there.
-Set `VLLM_OMNI_USE_QUACK_FP8=0` to force the FlashInfer path. If `quack-kernels` is
-not installed, FP8 still works — it just keeps the unfused FlashInfer path.
+It is enabled automatically once installed (no flag needed) and is **datacenter
+Blackwell only** (`sm_100` / `sm_101` / `sm_103`, compute capability `10.x`, e.g.
+B200): quack's CuteDSL GEMM uses the 5th-gen `tcgen05` tensor-core MMA, which exists
+only on those parts. On Hopper/Ada the CUTLASS FP8 kernel already fuses bias, and on
+workstation/consumer Blackwell (`sm_120` / `sm_121`, compute capability `12.x`, e.g.
+RTX PRO 6000 / RTX 50-series) `tcgen05` is absent — so quack is **not** auto-enabled
+there and FlashInfer's native FP8 path is used instead. Set
+`VLLM_OMNI_USE_QUACK_FP8=1` to force quack on, or `VLLM_OMNI_USE_QUACK_FP8=0` to force
+the FlashInfer path. If `quack-kernels` is not installed, FP8 still works — it just
+keeps the unfused FlashInfer path.
 
 #### Compile cache and warmup
 

tests/quantization/test_quack_fp8_gate.py

@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for the quack FP8 auto-enable gate.
+
+quack's CuteDSL FP8 GEMM uses the 5th-gen ``tcgen05`` tensor-core MMA, which
+exists only on datacenter Blackwell (``sm_100a`` / ``sm_101a`` / ``sm_103a``,
+compute capability ``10.x``). It must NOT auto-enable on workstation/consumer
+Blackwell (``sm_120`` / ``sm_121``, cc ``12.x``, e.g. RTX PRO 6000 / RTX
+50-series), where ``tcgen05`` is absent and every FP8 GEMM would fall back to
+FlashInfer one call at a time (catastrophically slow). See
+``vllm_omni/quantization/quack_fp8.py``.
+
+Regression guard for the ``>= 10`` gate that matched cc ``12.x`` too. These
+tests are hardware-free: the ``torch.cuda`` probes are monkeypatched, so no GPU
+is required.
+"""
+
+import pytest
+import torch
+
+from vllm_omni.quantization import quack_fp8
+
+_ENV = "VLLM_OMNI_USE_QUACK_FP8"
+
+
+def _fake_cuda(monkeypatch: pytest.MonkeyPatch, capability: tuple[int, int]) -> None:
+    """Pretend a CUDA device with the given (major, minor) capability is present."""
+    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
+    monkeypatch.setattr(torch.cuda, "get_device_capability", lambda *a, **k: capability)
+
+
+@pytest.mark.parametrize(
+    "capability, expected",
+    [
+        ((10, 0), True),  # sm_100 datacenter Blackwell — tcgen05 present
+        ((10, 3), True),  # sm_103 datacenter Blackwell
+        ((12, 0), False),  # sm_120 workstation Blackwell — the regression
+        ((12, 1), False),  # sm_121 consumer Blackwell
+        ((9, 0), False),  # Hopper — CUTLASS already fuses bias, quack unused
+        ((8, 9), False),  # Ada
+    ],
+)
+def test_auto_enable_only_on_datacenter_blackwell(
+    monkeypatch: pytest.MonkeyPatch,
+    capability: tuple[int, int],
+    expected: bool,
+) -> None:
+    monkeypatch.delenv(_ENV, raising=False)
+    _fake_cuda(monkeypatch, capability)
+    assert quack_fp8._is_quack_capable() is expected
+    assert quack_fp8.quack_enabled() is expected
+
+
+def test_no_cuda_disables_quack(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.delenv(_ENV, raising=False)
+    monkeypatch.setattr(torch.cuda, "is_available", lambda: False)
+    assert quack_fp8._is_quack_capable() is False
+    assert quack_fp8.quack_enabled() is False
+
+
+@pytest.mark.parametrize("value", ["1", "true", "yes", "on", "TRUE", "On"])
+def test_env_override_forces_on_even_on_sm120(monkeypatch: pytest.MonkeyPatch, value: str) -> None:
+    # sm_120 auto-disables, but an explicit truthy override forces quack on
+    # (e.g. once CuteDSL ships sm_120a support).
+    _fake_cuda(monkeypatch, (12, 0))
+    monkeypatch.setenv(_ENV, value)
+    assert quack_fp8.quack_enabled() is True
+
+
+@pytest.mark.parametrize("value", ["0", "false", "no", "off", ""])
+def test_env_override_forces_off_even_on_datacenter_blackwell(monkeypatch: pytest.MonkeyPatch, value: str) -> None:
+    # sm_100 auto-enables, but an explicit non-truthy override forces quack off.
+    _fake_cuda(monkeypatch, (10, 0))
+    monkeypatch.setenv(_ENV, value)
+    assert quack_fp8.quack_enabled() is False

vllm_omni/quantization/quack_fp8.py

@@ -15,11 +15,21 @@ _TRUTHY = {"1", "true", "yes", "on"}
 _gemm_interface = None
 
 
-def _is_blackwell() -> bool:
+def _is_quack_capable() -> bool:
+    """quack's CuteDSL FP8 / block-scaled MMA is built on the 5th-gen tensor-core
+    ``tcgen05`` instruction family, which is datacenter-Blackwell only
+    (``sm_100a`` / ``sm_101a`` / ``sm_103a``, compute capability ``10.x``).
+
+    Workstation/consumer Blackwell (``sm_120`` / ``sm_121``, compute capability
+    ``12.x``, e.g. RTX PRO 6000 / RTX 50-series) lacks ``tcgen05``, so quack can
+    never run there — CuteDSL rejects the arch and every GEMM falls back to
+    FlashInfer one call at a time, which is catastrophically slow. Those GPUs
+    have working native FlashInfer FP8 kernels, so default quack off for them.
+    """
     try:
         if not torch.cuda.is_available():
             return False
-        return torch.cuda.get_device_capability()[0] >= 10
+        return torch.cuda.get_device_capability()[0] == 10
     except Exception:  # noqa: BLE001
         return False
 
@@ -28,7 +38,7 @@ def quack_enabled() -> bool:
     override = os.environ.get("VLLM_OMNI_USE_QUACK_FP8")
     if override is not None:
         return override.lower() in _TRUTHY
-    return _is_blackwell()
+    return _is_quack_capable()
 
 
 def _set_persistent_cache_dir() -> None: