[Quant] Serve 32-wide-K ue8m0 block-FP8 linears through the FlashInfer MXFP8 GEMMs (#40039)

python/sglang/srt/layers/quantization/fp8.py

@@ -56,15 +56,19 @@ from sglang.srt.layers.quantization.base_config import (
 from sglang.srt.layers.quantization.fp8_utils import (
     _use_aiter_bpreshuffle_gfx95,
     apply_fp8_linear,
+    block_fp8_scale_to_mxfp8_e8m0,
     can_auto_enable_marlin_fp8,
+    can_serve_block_fp8_as_mxfp8,
     cutlass_fp8_supported,
     deepgemm_w8a8_block_fp8_linear_with_fallback,
+    dispatch_block_fp8_mxfp8_linear,
     dispatch_w8a8_block_fp8_linear,
     dispatch_w8a8_mxfp8_linear,
     input_to_float8,
     mxfp8_group_quantize,
     normalize_e4m3fn_to_e4m3fnuz,
     requant_block_scale_ue8m0_for_deepgemm,
+    resolve_block_fp8_mxfp8_backend,
     resolve_mxfp8_dense_gemm_backend,
     torch_w8a8_block_fp8_linear,
     unshuffle_aiter_fp8_weight,
@@ -72,6 +76,7 @@ from sglang.srt.layers.quantization.fp8_utils import (
 )
 from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
 from sglang.srt.layers.quantization.marlin_utils_fp8 import prepare_fp8_layer_for_marlin
+from sglang.srt.layers.quantization.mxfp8_input import Mxfp8SwizzledInput
 from sglang.srt.layers.quantization.unquant import (
     UnquantizedFusedMoEMethod,
     UnquantizedLinearMethod,
@@ -282,6 +287,7 @@ class Fp8Config(QuantizationConfig):
         self.packed_modules_mapping = packed_modules_mapping or {}
         self.use_mxfp8 = use_mxfp8
         self.kv_cache_quant_algo = kv_cache_quant_algo
+        # "ue8m0" checkpoints quantize activations with power-of-two scales.
         self.scale_fmt = scale_fmt
         if weight_block_size is not None:
             if not is_checkpoint_fp8_serialized:
@@ -514,7 +520,26 @@ class Fp8LinearMethod(LinearMethodBase):
             self.mxfp8_dense_backend = resolve_mxfp8_dense_gemm_backend()
             self.w8a8_mxfp8_linear = dispatch_w8a8_mxfp8_linear()
         else:
-            self.w8a8_block_fp8_linear = dispatch_w8a8_block_fp8_linear()
+            # Dispatch on the block size the weight will have after loading: an
+            # MXFP8 checkpoint converted to block-fp8 ends up as [128, 128].
+            effective_block_size = (
+                [128, 128] if self.convert_mxfp8_to_block else self.weight_block_size
+            )
+            self.w8a8_block_fp8_linear = dispatch_w8a8_block_fp8_linear(
+                weight_block_size=effective_block_size,
+                act_scale_ue8m0=isinstance(self.quant_config, Fp8Config)
+                and self.quant_config.scale_fmt == "ue8m0",
+            )
+        # Method-wide gate; a layer that cannot take the MXFP8 view stays on the
+        # block kernel (see _prepare_block_fp8_as_mxfp8).
+        self.block_fp8_as_mxfp8 = not self.use_mxfp8 and can_serve_block_fp8_as_mxfp8(
+            self.weight_block_size, getattr(self.quant_config, "scale_fmt", None)
+        )
+        if self.block_fp8_as_mxfp8:
+            self.mxfp8_dense_backend = resolve_block_fp8_mxfp8_backend()
+            self.w8a8_mxfp8_linear = dispatch_block_fp8_mxfp8_linear(
+                self.mxfp8_dense_backend
+            )
         self.is_checkpoint_fp8_serialized = (
             self.quant_config.is_checkpoint_fp8_serialized
         )
@@ -768,17 +793,19 @@ class Fp8LinearMethod(LinearMethodBase):
 
         layer.weight.data = weight.data
         layer.weight_scale_inv.data = weight_scale.data
+        if self.block_fp8_as_mxfp8:
+            self._prepare_block_fp8_as_mxfp8(layer)
 
         # The preshuffle rewrites the weight into a layout only
         # aiter_w8a8_block_fp8_linear can read, so it is correct exactly when
         # this quant method is what consumes the weight. A layer whose weight is
         # read directly by the model (DeepSeek-V4 wo_a, whose absorb GEMM takes
         # .weight/.weight_scale_inv and runs its own batched kernel) sets
-        # skip_aiter_bpreshuffle and keeps the plain row-major layout.
+        # keep_plain_weight_layout and keeps the plain row-major layout.
         if (
             _use_aiter_bpreshuffle_gfx95
             and self.w8a8_block_fp8_linear is aiter_w8a8_block_fp8_linear
-            and not getattr(layer, "skip_aiter_bpreshuffle", False)
+            and not getattr(layer, "keep_plain_weight_layout", False)
         ):
             n, k = layer.weight.shape
             if not use_aiter_triton_gemm_w8a8_tuned_gfx950(n, k):
@@ -817,8 +844,30 @@ class Fp8LinearMethod(LinearMethodBase):
                 with torch.no_grad():
                     layer.weight_scale_inv.set_(scale_reordered)
 
-    def _process_mxfp8_linear_weight_scale(self, layer: Module) -> None:
-        if not self.use_mxfp8:
+    def _prepare_block_fp8_as_mxfp8(self, layer: Module) -> None:
+        layer.block_fp8_mxfp8_ready = False
+        if getattr(layer, "keep_plain_weight_layout", False):
+            # The model reads .weight / .weight_scale_inv directly.
+            return
+        n, k = layer.weight.shape
+        if k % 32 != 0:
+            return
+        try:
+            scale_u8 = block_fp8_scale_to_mxfp8_e8m0(
+                layer.weight_scale_inv.data, (n, k), self.weight_block_size
+            )
+        except ValueError as e:
+            logger.warning("Block-fp8 layer stays on the Triton kernel: %s", e)
+            return
+        # weight_scale_inv stays in place for the Triton fallback and raw readers;
+        # the swizzled copy is stored separately.
+        self._process_mxfp8_linear_weight_scale(layer, scale_u8=scale_u8)
+        layer.block_fp8_mxfp8_ready = True
+
+    def _process_mxfp8_linear_weight_scale(
+        self, layer: Module, scale_u8: Optional[torch.Tensor] = None
+    ) -> None:
+        if not (self.use_mxfp8 or scale_u8 is not None):
             return
 
         backend = self.mxfp8_dense_backend
@@ -826,7 +875,8 @@ class Fp8LinearMethod(LinearMethodBase):
             from flashinfer import shuffle_matrix_a, shuffle_matrix_sf_a
 
             weight = layer.weight.data
-            scale_u8 = layer.weight_scale_inv.data
+            if scale_u8 is None:
+                scale_u8 = layer.weight_scale_inv.data
             n, k = weight.shape
             epilogue_tile_m = 128
             sf_cols = k // 32
@@ -866,7 +916,8 @@ class Fp8LinearMethod(LinearMethodBase):
         elif backend.is_flashinfer_cutlass() or backend.is_flashinfer_cutedsl():
             from flashinfer import block_scale_interleave
 
-            scale_u8 = layer.weight_scale_inv.data
+            if scale_u8 is None:
+                scale_u8 = layer.weight_scale_inv.data
             # block_scale_interleave may pad and/or reshape scales,
             # so store swizzled scales separately to keep weight update working
             copy_or_rebind_param(
@@ -880,7 +931,8 @@ class Fp8LinearMethod(LinearMethodBase):
             )
 
             n, k = layer.weight.shape
-            scale_u8 = layer.weight_scale_inv.data
+            if scale_u8 is None:
+                scale_u8 = layer.weight_scale_inv.data
             layer.weight_scale_inv_swizzled = None
             if n % 64 != 0 or k % 128 != 0:
                 if not (get_platform().is_blackwell and is_flashinfer_available()):
@@ -1084,7 +1136,22 @@ class Fp8LinearMethod(LinearMethodBase):
                 bias=bias,
             )
 
-        if self.use_mxfp8:
+        mxfp8_view = self.use_mxfp8 or (
+            self.block_fp8_as_mxfp8 and layer.block_fp8_mxfp8_ready
+        )
+        if isinstance(x, Mxfp8SwizzledInput):
+            if not mxfp8_view or not (
+                self.mxfp8_dense_backend.is_flashinfer_cutlass()
+                or self.mxfp8_dense_backend.is_flashinfer_cutedsl()
+            ):
+                raise ValueError(
+                    "Mxfp8SwizzledInput needs a layer with an MXFP8 view on a "
+                    "FlashInfer CUTLASS / CuTe-DSL backend"
+                )
+        elif self.block_fp8_as_mxfp8 and isinstance(x, tuple):
+            # A legacy (q, scale) block-fp8 pair keeps the block kernel.
+            mxfp8_view = False
+        if mxfp8_view:
             backend = self.mxfp8_dense_backend
             extra_kwargs = {}
             if backend.is_flashinfer_cutlass() or backend.is_flashinfer_cutedsl():

python/sglang/srt/layers/quantization/fp8_utils.py

@@ -574,7 +574,10 @@ if get_platform().is_sm90 and is_flashinfer_available():
     from flashinfer.gemm import fp8_blockscale_gemm_sm90
 
 
-def dispatch_w8a8_block_fp8_linear() -> Callable:
+def dispatch_w8a8_block_fp8_linear(
+    weight_block_size: Optional[List[int]] = None,
+    act_scale_ue8m0: bool = False,
+) -> Callable:
     """
     Dispatch to the appropriate FP8 block linear implementation.
 
@@ -582,6 +585,11 @@ def dispatch_w8a8_block_fp8_linear() -> Callable:
     1. The --fp8-gemm-backend server argument (preferred)
     2. Auto-detection based on hardware capabilities
     """
+    # Only Triton reads the block size at launch; DeepGEMM, the FlashInfer
+    # groupwise kernels and CUTLASS take 128-wide K blocks only.
+    if weight_block_size is not None and weight_block_size[1] != 128:
+        return partial(triton_w8a8_block_fp8_linear, act_scale_ue8m0=act_scale_ue8m0)
+
     backend = get_fp8_gemm_runner_backend()
 
     # Handle explicit backend selection via --fp8-gemm-backend
@@ -710,6 +718,68 @@ def _unsupported_mxfp8_linear(*args, **kwargs) -> torch.Tensor:
     )
 
 
+def resolve_block_fp8_mxfp8_backend() -> Mxfp8DenseGemmBackend:
+    """The FlashInfer MXFP8 backend a 32-wide-K ue8m0 block-fp8 weight can run on."""
+    backend = get_fp8_gemm_runner_backend()
+    # Explicit CUTLASS / CuTe-DSL only: they leave the weight untouched and store
+    # the swizzled scale separately, so the block layout stays readable by Triton.
+    if not (backend.is_flashinfer_cutedsl() or backend.is_flashinfer_cutlass()):
+        return Mxfp8DenseGemmBackend.UNSUPPORTED
+    if not (_is_cuda and get_platform().is_blackwell and is_flashinfer_available()):
+        return Mxfp8DenseGemmBackend.UNSUPPORTED
+    resolved = resolve_mxfp8_dense_gemm_backend()
+    return resolved if resolved.is_flashinfer() else Mxfp8DenseGemmBackend.UNSUPPORTED
+
+
+def can_serve_block_fp8_as_mxfp8(
+    weight_block_size: Optional[List[int]], scale_fmt: Optional[str]
+) -> bool:
+    """Whether a block-fp8 linear can run on the MXFP8 dense GEMMs instead of Triton:
+    a 32-wide-K ue8m0 block weight is an MXFP8 operand (block_fp8_scale_to_mxfp8_e8m0)."""
+    if weight_block_size is None or len(weight_block_size) != 2:
+        return False
+    if weight_block_size[1] != 32 or scale_fmt != "ue8m0":
+        return False
+    return not resolve_block_fp8_mxfp8_backend().is_unsupported()
+
+
+def dispatch_block_fp8_mxfp8_linear(backend: Mxfp8DenseGemmBackend) -> Callable:
+    """The MXFP8 linear for a block-fp8 weight served as MXFP8."""
+    if backend.is_flashinfer_cutlass():
+        return partial(flashinfer_mxfp8_blockscaled_linear, backend="cutlass")
+    if backend.is_flashinfer_cutedsl():
+        return partial(flashinfer_mxfp8_blockscaled_linear, backend="cute-dsl")
+    return _unsupported_mxfp8_linear
+
+
+def block_fp8_scale_to_mxfp8_e8m0(
+    weight_scale: torch.Tensor,
+    weight_shape: Tuple[int, int],
+    weight_block_size: List[int],
+) -> torch.Tensor:
+    """Expand fp32 power-of-two block scales [ceil(N / bn), K // 32] into the MXFP8
+    per-row e8m0 layout [N, K // 32] (uint8 exponent bytes), bit-exact."""
+    n, k = weight_shape
+    block_n, block_k = weight_block_size
+    if block_k != 32 or k % 32 != 0:
+        raise ValueError(
+            f"MXFP8 needs a 32-wide K block and K % 32 == 0, got {block_k=} {k=}"
+        )
+    scale = weight_scale.detach().float().contiguous()
+    if tuple(scale.shape) != (ceil_div(n, block_n), k // 32):
+        raise ValueError(
+            f"unexpected block scale shape {tuple(scale.shape)} for weight {weight_shape}"
+        )
+    bits = scale.view(torch.int32)
+    # A positive normal power of two has a zero mantissa; its exponent field is the e8m0 code.
+    if not bool(torch.all((bits & 0x7FFFFF) == 0)) or not bool(torch.all(scale > 0)):
+        raise ValueError(
+            "block scales are not positive powers of two; cannot encode as e8m0"
+        )
+    e8m0 = (bits >> 23).to(torch.uint8)
+    return e8m0.repeat_interleave(block_n, dim=0)[:n].contiguous()
+
+
 def dispatch_w8a8_mxfp8_linear() -> Callable:
     backend = resolve_mxfp8_dense_gemm_backend()
     if backend.is_deep_gemm():
@@ -1334,6 +1404,7 @@ def triton_w8a8_block_fp8_linear(
     weight_scale: torch.Tensor,
     input_scale: Optional[torch.Tensor] = None,
     bias: Optional[torch.Tensor] = None,
+    act_scale_ue8m0: bool = False,
 ) -> torch.Tensor:
     if input_scale is not None:
         # Pre-quantized input: ``input`` is already fp8 and ``input_scale`` is
@@ -1348,9 +1419,15 @@ def triton_w8a8_block_fp8_linear(
         input_2d = input.view(-1, input.shape[-1])
         output_dtype = input_2d.dtype
         output_shape = [*input.shape[:-1], weight.shape[0]]
-        q_input, x_scale = per_token_group_quant_fp8(
-            input_2d, block_size[1], column_major_scales=False
-        )
+        if act_scale_ue8m0:
+            # Power-of-two scales in fp32 storage, as ue8m0 checkpoints quantize.
+            q_input, x_scale = sglang_per_token_group_quant_fp8(
+                input_2d, block_size[1], scale_ue8m0=True
+            )
+        else:
+            q_input, x_scale = per_token_group_quant_fp8(
+                input_2d, block_size[1], column_major_scales=False
+            )
 
     output = w8a8_block_fp8_matmul_triton(
         q_input, weight, x_scale, weight_scale, block_size, output_dtype=output_dtype

python/sglang/srt/layers/quantization/mxfp8_input.py

@@ -0,0 +1,16 @@
+"""Explicit input layout for a linear consuming prequantized MXFP8 activations."""
+
+from typing import NamedTuple
+
+import torch
+
+
+class Mxfp8SwizzledInput(NamedTuple):
+    """E4M3 activations and UE8M0 scales in FlashInfer's 128x4 layout.
+
+    A plain FP8 tuple may contain block-FP8 scales with a different layout.
+    This marker lets a converted block-FP8 linear distinguish the two.
+    """
+
+    data: torch.Tensor
+    scales: torch.Tensor

python/sglang/srt/models/deepseek_v4.py

@@ -777,7 +777,7 @@ class MqaAttentionBase(nn.Module):
             # that is aiter's B-preshuffle, which silently permutes the weight
             # in place (same shape, dtype and strides) and makes this GEMM
             # return noise.
-            self.wo_a.skip_aiter_bpreshuffle = True
+            self.wo_a.keep_plain_weight_layout = True
         self.wo_b = RowParallelLinear(
             self.n_groups * self.o_lora_rank,
             self.hidden_size,
@@ -3559,7 +3559,7 @@ class DeepseekV4ForCausalLM(nn.Module):
                 # ROCm: aiter's mxscale GEMM reads uint8 e8m0 block scales, and
                 # requantizes the weight when the checkpoint's scales are not
                 # already powers of two. It also needs the weight row-major, so
-                # check the linear method honoured skip_aiter_bpreshuffle: a
+                # check the linear method honoured keep_plain_weight_layout: a
                 # preshuffled weight has the same shape, dtype and strides and
                 # would only show up as garbage output.
                 assert not getattr(attn.wo_a, "aiter_bpreshuffled", False), (

test/registered/quant/test_fp8_utils.py

@@ -1,9 +1,10 @@
 import unittest
 from types import SimpleNamespace
-from unittest.mock import patch
+from unittest.mock import MagicMock, patch
 
 import torch
 
+from sglang.srt.layers.quantization import fp8_utils
 from sglang.srt.layers.quantization.fp8 import (
     Fp8MoEMethod,
     _is_cuda,
@@ -11,8 +12,13 @@ from sglang.srt.layers.quantization.fp8 import (
     _is_hip,
 )
 from sglang.srt.layers.quantization.fp8_utils import (
+    Fp8GemmRunnerBackend,
+    Mxfp8DenseGemmBackend,
+    block_fp8_scale_to_mxfp8_e8m0,
+    can_serve_block_fp8_as_mxfp8,
     inverse_transform_scale_ue8m0,
     quant_weight_ue8m0,
+    resolve_block_fp8_mxfp8_backend,
     transform_scale_ue8m0,
 )
 from sglang.srt.runtime_context import get_platform
@@ -104,6 +110,68 @@ class TestInverseTransformScaleUe8m0(CustomTestCase):
             )
 
 
+class TestBlockFp8AsMxfp8(CustomTestCase):
+    def test_block_scale_to_e8m0_matches_reference(self):
+        # Sibling classes leave torch's default device on cuda; stay on cpu.
+        gen = torch.Generator().manual_seed(0)
+        n, k, block_n = 100, 256, 32  # 4 scale rows, the last one partial
+        exps = torch.randint(-20, 21, (4, k // 32), generator=gen, device="cpu")
+        got = block_fp8_scale_to_mxfp8_e8m0(
+            torch.exp2(exps.float()), (n, k), [block_n, 32]
+        )
+        ref = (exps + 127).to(torch.uint8).repeat_interleave(block_n, dim=0)[:n]
+        self.assertTrue(torch.equal(got, ref))
+        with self.assertRaises(ValueError):  # 128-wide K block is not MXFP8
+            block_fp8_scale_to_mxfp8_e8m0(
+                torch.ones(2, 8, device="cpu"), (64, 1024), [32, 128]
+            )
+        with self.assertRaises(ValueError):  # not a power of two
+            block_fp8_scale_to_mxfp8_e8m0(
+                torch.full((2, 8), 1.5, device="cpu"), (64, 256), [32, 32]
+            )
+
+    def test_serve_gate(self):
+        platform = MagicMock()
+        platform.is_blackwell = True
+        cutedsl = next(b for b in Mxfp8DenseGemmBackend if b.is_flashinfer_cutedsl())
+        with (
+            patch.object(fp8_utils, "_is_cuda", True),
+            patch.object(fp8_utils, "get_platform", return_value=platform),
+            patch.object(fp8_utils, "is_flashinfer_available", return_value=True),
+            patch.object(
+                fp8_utils, "resolve_mxfp8_dense_gemm_backend", return_value=cutedsl
+            ),
+        ):
+            for name, expected in (
+                ("flashinfer_cutedsl", True),
+                ("flashinfer_cutlass", True),
+                ("flashinfer_trtllm", False),
+                ("triton", False),
+                ("auto", False),
+            ):
+                with (
+                    self.subTest(backend=name),
+                    patch.object(
+                        fp8_utils, "FP8_GEMM_RUNNER_BACKEND", Fp8GemmRunnerBackend(name)
+                    ),
+                ):
+                    self.assertEqual(
+                        can_serve_block_fp8_as_mxfp8([32, 32], "ue8m0"), expected
+                    )
+                    self.assertEqual(
+                        resolve_block_fp8_mxfp8_backend().is_unsupported(), not expected
+                    )
+            with patch.object(
+                fp8_utils,
+                "FP8_GEMM_RUNNER_BACKEND",
+                Fp8GemmRunnerBackend.FLASHINFER_CUTEDSL,
+            ):
+                self.assertFalse(can_serve_block_fp8_as_mxfp8([128, 128], "ue8m0"))
+                self.assertFalse(can_serve_block_fp8_as_mxfp8([32, 32], None))
+                platform.is_blackwell = False
+                self.assertFalse(can_serve_block_fp8_as_mxfp8([32, 32], "ue8m0"))
+
+
 class TestApplyFp8LinearScaleDispatch(CustomTestCase):
     @classmethod
     def setUpClass(cls):
@@ -268,6 +336,7 @@ class TestApplyFp8LinearScaleDispatch(CustomTestCase):
         native_method = native_fp8.Fp8LinearMethod.__new__(native_fp8.Fp8LinearMethod)
         native_method.use_marlin = False
         native_method.use_mxfp8 = False
+        native_method.block_fp8_as_mxfp8 = False
         native_method.block_quant = False
         native_method.cutlass_fp8_supported = True
         native_method.use_per_token_if_dynamic = False

test/registered/unit/layers/quantization/test_flashinfer_trtllm_fp8_fallback.py

@@ -22,12 +22,20 @@ from sglang.test.ci.ci_register import register_cpu_ci
 
 register_cpu_ci(est_time=10, suite="base-a-test-cpu")
 
+import functools
 import unittest
 from unittest.mock import MagicMock, patch
 
 import torch
 
+import sglang.srt.layers.quantization.fp8 as fp8
 import sglang.srt.layers.quantization.fp8_utils as fp8_utils
+from sglang.srt.layers.quantization.fp8 import Fp8Config, Fp8LinearMethod
+from sglang.srt.layers.quantization.fp8_utils import (
+    Fp8GemmRunnerBackend,
+    dispatch_w8a8_block_fp8_linear,
+    triton_w8a8_block_fp8_linear,
+)
 from sglang.test.test_utils import CustomTestCase
 
 BLOCK_SIZE = [128, 128]
@@ -100,3 +108,45 @@ class TestFlashinferTrtllmFp8Fallback(CustomTestCase):
 
 if __name__ == "__main__":
     unittest.main(verbosity=3)
+
+
+class TestBlockSizeDispatch(CustomTestCase):
+    """Non-128-wide K blocks dispatch to Triton regardless of --fp8-gemm-backend;
+    128-wide blocks keep the backend choice."""
+
+    def test_128_wide_k_blocks_keep_the_backend_choice(self):
+        for name in ("triton", "deep_gemm"):
+            with (
+                self.subTest(backend=name),
+                patch.object(
+                    fp8_utils, "FP8_GEMM_RUNNER_BACKEND", Fp8GemmRunnerBackend(name)
+                ),
+            ):
+                default = dispatch_w8a8_block_fp8_linear()
+                self.assertIs(dispatch_w8a8_block_fp8_linear([128, 128]), default)
+                self.assertIs(dispatch_w8a8_block_fp8_linear([1, 128]), default)
+                fn = dispatch_w8a8_block_fp8_linear([32, 32], act_scale_ue8m0=True)
+                self.assertIsInstance(fn, functools.partial)
+                self.assertIs(fn.func, triton_w8a8_block_fp8_linear)
+                self.assertEqual(fn.keywords, {"act_scale_ue8m0": True})
+
+    def test_method_dispatches_on_the_effective_block_size(self):
+        # An MXFP8 checkpoint converted to block-fp8 at load time is a [128, 128]
+        # weight; dispatching on the pre-conversion [1, 32] would pick Triton.
+        with (
+            patch.object(
+                fp8_utils, "FP8_GEMM_RUNNER_BACKEND", Fp8GemmRunnerBackend.TRITON
+            ),
+            patch.object(fp8, "_mxfp8_to_block_fp8_required", True),
+        ):
+            method = Fp8LinearMethod(
+                Fp8Config(
+                    is_checkpoint_fp8_serialized=True,
+                    use_mxfp8=True,
+                    weight_block_size=[1, 32],
+                    scale_fmt="ue8m0",
+                )
+            )
+            self.assertTrue(method.convert_mxfp8_to_block)
+            self.assertIs(method.w8a8_block_fp8_linear, triton_w8a8_block_fp8_linear)
+            self.assertFalse(method.block_fp8_as_mxfp8)

test/registered/unit/layers/quantization/test_fp8_blockwise_linear_backends.py

@@ -1,8 +1,9 @@
 """Numerics for the FP8 dense-linear GEMM backends (--fp8-gemm-backend).
 
-Real layer path vs a dequantized-reference matmul, in three formats: FP8
-blockwise, MXFP8, and per-tensor FP8 (auto dispatch). Backend sets adapt to
-the device SM, so one file covers SM90 / SM100 / SM120.
+Real layer path vs a dequantized-reference matmul, in four formats: FP8
+blockwise, MXFP8, 32-wide-K ue8m0 block FP8 served as MXFP8, and per-tensor
+FP8 (auto dispatch). Backend sets adapt to the device SM, so one file covers
+SM90 / SM100 / SM120.
 """
 
 import unittest
@@ -14,6 +15,7 @@ from sglang.srt.layers.quantization import fp8_utils
 from sglang.srt.layers.quantization.fp8 import Fp8Config
 from sglang.srt.layers.quantization.fp8_utils import Fp8GemmRunnerBackend
 from sglang.srt.layers.quantization.modelopt_quant import ModelOptFp8Config
+from sglang.srt.layers.quantization.mxfp8_input import Mxfp8SwizzledInput
 from sglang.srt.utils import get_device_sm
 from sglang.test.ci.ci_register import register_cuda_ci
 from sglang.test.layer_ut_utils import (
@@ -43,6 +45,12 @@ MXFP8_SHAPES = [
     (5, 384, 768),
 ]
 
+# (M, N, K); N % 64 == 0 and K % 128 == 0 for the FlashInfer MXFP8 scale swizzle.
+BLOCK32_SHAPES = [
+    (64, 512, 512),
+    (5, 384, 768),
+]
+
 # (M, N, K); per-tensor has no block-alignment constraints.
 PER_TENSOR_SHAPES = [
     (64, 512, 512),
@@ -88,6 +96,27 @@ def _quantize_fp8_blockwise(w: torch.Tensor, block: int = 128):
     return w_fp8.reshape(n, k), scale, w_dequant
 
 
+def _block32_backends():
+    # The block-fp8-as-MXFP8 route takes the FlashInfer CUTLASS / CuTe-DSL MXFP8
+    # kernels only, on SM100/103.
+    if get_device_sm() in (100, 103):
+        return ["flashinfer_cutlass", "flashinfer_cutedsl"]
+    return []
+
+
+def _quantize_fp8_block32_ue8m0(w: torch.Tensor, block: int = 32):
+    """Per (block, block) tile fp8 quantization with power-of-two scales; returns
+    checkpoint-format (w_fp8 [N, K], scale e8m0 [N/block, K/block]) and the
+    dequant reference."""
+    n, k = w.shape
+    tiles = w.float().reshape(n // block, block, k // block, block)
+    amax = tiles.abs().amax(dim=(1, 3)).clamp(min=1e-30)
+    scale = torch.exp2(torch.ceil(torch.log2(amax / FP8_MAX)))
+    w_fp8 = (tiles / scale[:, None, :, None]).to(torch.float8_e4m3fn)
+    w_dequant = (w_fp8.float() * scale[:, None, :, None]).reshape(n, k)
+    return w_fp8.reshape(n, k), scale.to(torch.float8_e8m0fnu), w_dequant
+
+
 def _quantize_mxfp8(w: torch.Tensor, block: int = 32):
     """Per (1, block) group e8m0 quantization; returns checkpoint-format
     (w_fp8 [N, K], scale uint8 [N, K/block]) and the dequant reference."""
@@ -238,6 +267,78 @@ class TestMxfp8LinearBackends(_LinearBackendCheck):
             is_backend_supported.assert_called_once_with("cute-dsl", 107)
 
 
+class TestBlockFp8AsMxfp8Linear(_LinearBackendCheck):
+    """A 32-wide-K ue8m0 block-fp8 weight served through the MXFP8 GEMMs."""
+
+    @staticmethod
+    def _build_layer(n: int, k: int, keep_plain_weight_layout: bool = False):
+        quant_config = Fp8Config(
+            is_checkpoint_fp8_serialized=True,
+            activation_scheme="dynamic",
+            weight_block_size=[32, 32],
+            scale_fmt="ue8m0",
+        )
+        layer = _make_linear(quant_config, n, k)
+        if keep_plain_weight_layout:
+            layer.keep_plain_weight_layout = True
+        w = torch.randn((n, k), device="cuda", dtype=torch.bfloat16) / 10
+        w_fp8, scale_e8m0, w_dequant = _quantize_fp8_block32_ue8m0(w)
+        load_linear_weights(layer, weight=w_fp8, weight_scale_inv=scale_e8m0)
+        return layer, w_dequant
+
+    def _run(self, backend: str):
+        self._check_backend(
+            backend, _block32_backends(), BLOCK32_SHAPES, self._build_layer
+        )
+
+    def test_flashinfer_cutlass(self):
+        self._run("flashinfer_cutlass")
+
+    def test_flashinfer_cutedsl(self):
+        self._run("flashinfer_cutedsl")
+
+    def test_mxfp8_view_and_swizzled_input(self):
+        if "flashinfer_cutedsl" not in _block32_backends():
+            self.skipTest(f"cutedsl not in SM{get_device_sm()} backend set")
+        from sglang.kernels.ops.attention.dsv4.wo_a_bf16 import (
+            _quantize_partial,
+            _wo_a_reduce,
+        )
+
+        torch.manual_seed(7)
+        with mock.patch.object(
+            fp8_utils,
+            "FP8_GEMM_RUNNER_BACKEND",
+            Fp8GemmRunnerBackend.FLASHINFER_CUTEDSL,
+        ):
+            n, k = 512, 2048
+            layer, _ = self._build_layer(n, k)
+            layer.quant_method.process_weights_after_loading(layer)
+            self.assertTrue(layer.quant_method.block_fp8_as_mxfp8)
+            self.assertTrue(layer.block_fp8_mxfp8_ready)
+            # Block scales stay in place for the Triton fallback and raw readers.
+            self.assertEqual(tuple(layer.weight_scale_inv.shape), (n // 32, k // 32))
+            self.assertIsNotNone(layer.weight_scale_inv_swizzled)
+
+            # A prequantized 128x4-swizzled MXFP8 activation must give the same
+            # output as the bf16 input the layer quantizes itself.
+            rows = 6
+            partial = torch.randn(8, rows, 2, k // 2, device="cuda")
+            bf16 = torch.empty(rows, k, dtype=torch.bfloat16, device="cuda")
+            _wo_a_reduce[(rows * 8,)](partial, bf16, rows * k, num_warps=4)
+            q, s = _quantize_partial(partial)
+            swizzled = layer.quant_method.apply(layer, Mxfp8SwizzledInput(q, s))
+            plain = layer.quant_method.apply(layer, bf16)
+            torch.testing.assert_close(swizzled, plain, rtol=0, atol=0)
+
+            # A layer that keeps the plain weight layout has no MXFP8 view.
+            plain_layer, _ = self._build_layer(n, k, keep_plain_weight_layout=True)
+            plain_layer.quant_method.process_weights_after_loading(plain_layer)
+            self.assertFalse(plain_layer.block_fp8_mxfp8_ready)
+            with self.assertRaises(ValueError):
+                plain_layer.quant_method.apply(plain_layer, Mxfp8SwizzledInput(q, s))
+
+
 @unittest.skipIf(get_device_sm() < 90, "FP8 GEMM backends require SM90+")
 class TestModeloptFp8PerTensorLinear(_LinearBackendCheck):
     """Per-tensor FP8 (ModelOptFp8LinearMethod, static scales) on the auto