[Diffusion][minimax-h3] Add SM120 support for SubBlock sparse attention (#37332)

Co-authored-by: 全力 <liquanli.lql@antgroup.com>

python/sglang/multimodal_gen/runtime/layers/attention/backends/subblock_sparse/README.md

@@ -1,7 +1,8 @@
 # SubBlock sparse attention — training-free block sparsity for the MiniMax-H3 DiT
 
 Routes the same 64-token SubBlock plan to SGLang's CuTe-DSL block-sparse
-FlashAttention kernel on SM90 or FlashInfer's `bsa_attn_blk64_fwd` on SM100.
+FlashAttention kernel on SM90 or FlashInfer's architecture-specific blk64
+kernels on SM100 and SM120.
 Nothing is trained and no weights change: a cheap estimator runs before
 attention and hands the selected kernel a `q2k_block_index`.
 
@@ -18,13 +19,17 @@ sglang serve --model-path MiniMaxAI/MiniMax-H3 --model-variant fl2va \
                                "min_seq_len": 4096}'
 ```
 
-**`text_encoder=fa` is not optional.** `--attention-backend` applies to every
+**The text-encoder override is not optional.** `--attention-backend` applies to
-component, and the Qwen3-VL text encoder admits only `fa` / `torch_sdpa` /
+every component, and the Qwen3-VL text encoder admits only `fa`, `torch_sdpa`,
-`sage_attn_3`; without the override it raises and the server never starts. Put
+or `sage_attn_3`; without the override it raises and the server never starts.
-the override on the *encoder*, not the DiT — `transformer=subblock_sparse_attn`
+Put the override on the *encoder*, not the DiT —
-appears to work and silently does nothing, because H3 resolves the DiT backend
+`transformer=subblock_sparse_attn` appears to work and silently does nothing,
-lazily on the first forward, outside the component-loading context that the
+because H3 resolves the DiT backend lazily on the first forward, outside the
-override applies to.
+component-loading context that the override applies to.
+
+On SM120, use `text_encoder=torch_sdpa` instead. The CUDA platform selects
+Torch SDPA for dense attention on SM12.x, and component-specific backend
+requests are validated strictly.
 
 `--attention-backend-config` is optional and overrides only the keys it names,
 so `'{"sparsity": 0.85}'` alone trades quality for another 6%. Inline JSON gets
@@ -38,7 +43,7 @@ are listed below.
 
 | | |
 | --- | --- |
-| GPU | **compute capability 9.0 or 10.0** — H100 / H200 use SGLang's CuTe-DSL SM90 block-sparse FlashAttention kernel; B200 / GB200 use FlashInfer's architecture-specific `sm_100a` kernel. Other capabilities, including 10.3 (B300 / GB300) and 12.x (RTX PRO 6000, RTX 50xx), are rejected. |
+| GPU | **compute capability 9.0, 10.0, or 12.0** — H100 / H200 use SGLang's CuTe-DSL SM90 block-sparse FlashAttention kernel; B200 / GB200 use FlashInfer's architecture-specific `sm_100a` kernel; SM120 devices use FlashInfer's `bsa_attn_sm120_blk64_fwd` CuTe-DSL kernel. Other capabilities, including 10.3 (B300 / GB300), are rejected. |
 | dtype | bfloat16 |
 | head_dim | 128 |
 | attention | non-causal, one contiguous sequence per call |
@@ -48,10 +53,11 @@ refiner, sequences under `min_seq_len`, non-bf16 activations, head_dim != 128
 falls back to dense for that call, so no layer has to be excluded by hand.
 
 **On an unsupported GPU it is not a fallback, it is an error at startup.** The
-resolver accepts exactly compute capability 9.0 or 10.0 before loading either
+resolver accepts exactly compute capability 9.0, 10.0, or 12.0 before loading
-kernel, so a B300 or an SM12x GPU fails at launch rather than after ten dense
+the selected kernel, so a B300 or another unsupported capability fails at
-denoise steps. The exact 10.0 check is required because FlashInfer's kernel is
+launch rather than after ten dense denoise steps. The exact 10.0 check is
-built for `sm_100a` and has no forward-compatible 10.3 cubin.
+required because FlashInfer's kernel is built for `sm_100a` and has no
+forward-compatible 10.3 cubin.
 
 ## How the score works
 

python/sglang/multimodal_gen/runtime/layers/attention/backends/subblock_sparse/__init__.py

@@ -6,11 +6,19 @@ Originally vendored from the standalone SubBlock repository; ``router.py`` and
 
 ``router.py`` scores every (query block, key block) pair from sub-block-pooled
 Q/K and turns the scores into a ``q2k_block_index`` consumed by SGLang's SM90
-CuTe-DSL block-sparse FlashAttention or FlashInfer's SM100
+CuTe-DSL block-sparse FlashAttention or FlashInfer's architecture-specific
-``bsa_attn_blk64_fwd`` (bf16, head_dim 128). The estimator and the measurements
+SM100/SM120 blk64 kernels (bf16, head_dim 128). The estimator and the
-behind its defaults are documented there.
+measurements behind its defaults are documented there.
 """
 
-from .router import SubBlockRouter, load_bsa_attn_blk64_fwd
+from .router import (
+    SubBlockRouter,
+    load_bsa_attn_blk64_fwd,
+    load_bsa_attn_sm120_blk64_fwd,
+)
 
-__all__ = ["SubBlockRouter", "load_bsa_attn_blk64_fwd"]
+__all__ = [
+    "SubBlockRouter",
+    "load_bsa_attn_blk64_fwd",
+    "load_bsa_attn_sm120_blk64_fwd",
+]

python/sglang/multimodal_gen/runtime/layers/attention/backends/subblock_sparse/router.py

@@ -128,6 +128,22 @@ def load_bsa_attn_blk64_fwd():
     return mod.bsa_attn_blk64_fwd
 
 
+@functools.lru_cache(maxsize=1)
+def load_bsa_attn_sm120_blk64_fwd():
+    """FlashInfer's CuTe-DSL 64-block sparse attention entry point for SM120."""
+    try:
+        from flashinfer.cute_dsl.sparse.bsa_attn_sm120 import (
+            bsa_attn_sm120_blk64_fwd,
+        )
+    except Exception as exc:
+        raise ImportError(
+            "SM120 SubBlock sparse attention requires FlashInfer's "
+            "flashinfer.cute_dsl.sparse.bsa_attn_sm120 module"
+        ) from exc
+
+    return bsa_attn_sm120_blk64_fwd
+
+
 LOG2E = 1.4426950408889634
 BLOCK = 64  # the kernel's block granularity (kSparseBlockSize=64)
 BUDGET_GRANULARITY = 8  # blocks per query row the kernel bills in, padding to fit

python/sglang/multimodal_gen/runtime/layers/attention/backends/subblock_sparse_attn.py

@@ -2,8 +2,9 @@
 """SubBlock block-sparse attention backend.
 
 Routes the same 64-token SubBlock plan to SGLang's CuTe-DSL block-sparse
-FlashAttention kernel on SM90 or FlashInfer's kernel on SM100. A log-sum-exp
+FlashAttention kernel on SM90 or FlashInfer's architecture-specific kernels on
-over query/key sub-block pairs selects the blocks (see ``backends/subblock_sparse/``).
+SM100 and SM120. A log-sum-exp over query/key sub-block pairs selects the blocks
+(see ``backends/subblock_sparse/``).
 Everything is training-free: the router runs before attention and produces
 the ``q2k_block_index`` the selected kernel consumes.
 
@@ -18,15 +19,16 @@ individual keys of the defaults below::
     --attention-backend-config '{"sparsity": 0.85}'
 
 Requirements inherited from the kernels: compute capability 9.0 (Hopper) or
-10.0 (B200 / GB200), bf16, head_dim 128. Hopper uses SGLang's CuTe-DSL SM90
+10.0/12.0 (Blackwell), bf16, head_dim 128. Hopper uses SGLang's CuTe-DSL SM90
-block-sparse FlashAttention kernel; B200 uses FlashInfer's ``sm_100a`` blk64
+block-sparse FlashAttention kernel; B200 and SM120 devices use FlashInfer's
-kernel. Inside the DiT, any call the kernels cannot serve -- cross/refiner
+architecture-specific blk64 kernels. Inside the DiT, any call the kernels cannot
-attention, short sequences, non-bf16 -- runs dense instead. On any other GPU
+serve -- cross/refiner attention, short sequences, non-bf16 -- runs dense instead.
-the resolver refuses the backend at startup rather than falling back.
+On any other GPU the resolver refuses the backend at startup rather than falling back.
 
 ``--attention-backend`` reaches every component, and the text encoder admits
-only fa / torch_sdpa / sage_attn_3, so pair it with
+only fa / torch_sdpa / sage_attn_3. Pair it with
-``--component-attention-backends text_encoder=fa``; see the README.
+``--component-attention-backends text_encoder=fa`` on SM90/SM100, or
+``text_encoder=torch_sdpa`` on SM120; see the README.
 """
 
 from __future__ import annotations
@@ -48,6 +50,7 @@ from sglang.multimodal_gen.runtime.layers.attention.backends.attention_backend i
 from sglang.multimodal_gen.runtime.layers.attention.backends.subblock_sparse import (
     SubBlockRouter,
     load_bsa_attn_blk64_fwd,
+    load_bsa_attn_sm120_blk64_fwd,
 )
 from sglang.multimodal_gen.runtime.managers.forward_context import get_forward_context
 from sglang.multimodal_gen.runtime.platforms import AttentionBackendEnum
@@ -198,6 +201,30 @@ def _sm100_sparse_attention(
     return out[0] if isinstance(out, tuple) else out
 
 
+def _sm120_sparse_attention(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    q2k_block_index: torch.Tensor,
+    topk: int,
+    softmax_scale: float,
+    block_counts: torch.Tensor | None = None,
+) -> torch.Tensor:
+    """Run a SubBlock routing plan through FlashInfer's SM120 kernel."""
+    logger.info_once("SubBlock sparse attention kernel active: FlashInfer SM120 blk64")
+    out = load_bsa_attn_sm120_blk64_fwd()(
+        q,
+        k,
+        v,
+        q2k_block_index,
+        topk,
+        block_sizes=_cached_block_sizes(k.shape[1], k.device),
+        q2k_block_nums=block_counts,
+        softmax_scale=softmax_scale,
+    )
+    return out[0] if isinstance(out, tuple) else out
+
+
 @functools.lru_cache(maxsize=None)
 def _get_subblock_sparse_attention_runner(device: torch.device):
     """Resolve the architecture-specific kernel once per CUDA device."""
@@ -206,8 +233,10 @@ def _get_subblock_sparse_attention_runner(device: torch.device):
         return _sm90_sparse_attention
     if capability == (10, 0):
         return _sm100_sparse_attention
+    if capability == (12, 0):
+        return _sm120_sparse_attention
     raise RuntimeError(
-        "SubBlock sparse attention supports compute capability 9.0 or 10.0; "
+        "SubBlock sparse attention supports compute capability 9.0, 10.0, or 12.0; "
         f"this tensor is on a {capability[0]}.{capability[1]} device."
     )
 
@@ -224,8 +253,9 @@ def _run_subblock_sparse_attention(
     """Dispatch a prepared 64x64 routing plan to Hopper or Blackwell.
 
     SM90 requires every active index prefix to be sorted in ascending order;
-    SM100 accepts the router's original order. Heterogeneous callers must sort
+    SM100 and SM120 accept the router's original order. Heterogeneous callers
-    compact sparse prefixes before expanding them to full-width dense rows.
+    must sort compact sparse prefixes before expanding them to full-width dense
+    rows.
     """
     runner = _get_subblock_sparse_attention_runner(q.device)
     return runner(

python/sglang/multimodal_gen/runtime/platforms/cuda.py

@@ -381,10 +381,10 @@ class _VMOBAAttentionBackendResolver(_CudaAttentionBackendResolver):
 class _SubBlockSparseAttentionBackendResolver(_CudaAttentionBackendResolver):
     backend = AttentionBackendEnum.SUBBLOCK_SPARSE_ATTN
 
-    # Hopper uses SGLang's SM90 CuTe-DSL block-sparse kernel. Blackwell uses the
+    # Hopper uses SGLang's SM90 CuTe-DSL block-sparse kernel. SM100 uses
-    # FlashInfer blk64 kernel built specifically for sm_100a; 10.3 and 12.x do
+    # FlashInfer's architecture-specific sm_100a kernel; SM120 uses FlashInfer's
-    # not have a compatible cubin and must still fail closed.
+    # CuTe-DSL SM120 blk64 kernel. Other capabilities still fail closed.
-    supported_capabilities = {(9, 0), (10, 0)}
+    supported_capabilities = {(9, 0), (10, 0), (12, 0)}
 
     @classmethod
     def resolve(cls, platform) -> str:
@@ -395,8 +395,8 @@ class _SubBlockSparseAttentionBackendResolver(_CudaAttentionBackendResolver):
         if capability_tuple not in cls.supported_capabilities:
             found = capability.as_version_str() if capability else "unknown"
             raise ValueError(
-                "SubBlock sparse attention needs compute capability 9.0 "
+                "SubBlock sparse attention needs compute capability 9.0, 10.0, "
-                f"(Hopper) or 10.0 (B200 / GB200); this device reports {found}."
+                f"or 12.0; this device reports {found}."
             )
         try:
             from sglang.multimodal_gen.runtime.layers.attention.backends.subblock_sparse_attn import (  # noqa: F401
@@ -412,20 +412,31 @@ class _SubBlockSparseAttentionBackendResolver(_CudaAttentionBackendResolver):
                 from sglang.kernels.ops.attention.flash_attn.cute.interface import (  # noqa: F401
                     flash_attn_func,
                 )
-            else:
+            elif capability_tuple == (10, 0):
                 from sglang.multimodal_gen.runtime.layers.attention.backends.subblock_sparse import (  # noqa: F401
                     load_bsa_attn_blk64_fwd,
                 )
 
                 load_bsa_attn_blk64_fwd()
+            else:
+                from sglang.multimodal_gen.runtime.layers.attention.backends.subblock_sparse import (
+                    load_bsa_attn_sm120_blk64_fwd,
+                )
+
+                load_bsa_attn_sm120_blk64_fwd()
             return "sglang.multimodal_gen.runtime.layers.attention.backends.subblock_sparse_attn.SubBlockSparseAttentionBackend"
         except Exception as e:
             logger.error("Failed to import SubBlock sparse attention: %s", str(e))
             dependency = (
                 "SGLang's SM90 CuTe-DSL FlashAttention dependencies"
                 if capability_tuple == (9, 0)
-                else "FlashInfer with the blk64 block-sparse kernel "
+                else (
+                    "FlashInfer with the SM100 blk64 block-sparse kernel "
                     "(flashinfer.cute_dsl.sparse.bsa_attn_blk64_fwd)"
+                    if capability_tuple == (10, 0)
+                    else "FlashInfer with the SM120 blk64 block-sparse kernel "
+                    "(flashinfer.cute_dsl.sparse.bsa_attn_sm120)"
+                )
             )
             raise ImportError(f"SubBlock sparse attention needs {dependency}.") from e
 

python/sglang/multimodal_gen/test/unit/test_subblock_sparse_attention.py

@@ -2,8 +2,9 @@
 """SubBlock block-sparse attention backend.
 
 The schedule and adapter tests are pure CPU. The numerical tests need either
-an SM90 GPU with SGLang's CuTe-DSL dependencies or an SM100 GPU with
+an SM90 GPU with SGLang's CuTe-DSL dependencies, an SM100 GPU with
-FlashInfer's ``bsa_attn_blk64_fwd`` and are skipped otherwise.
+FlashInfer's ``bsa_attn_blk64_fwd``, or an SM120 GPU with FlashInfer's
+``bsa_attn_sm120_blk64_fwd`` and are skipped otherwise.
 
 The trick that makes the sparse kernel checkable against dense attention: at
 ``sparsity`` just above 0 every block is inside the budget, so the block-sparse
@@ -52,6 +53,12 @@ def _subblock_kernel_available() -> bool:
             )
 
             load_bsa_attn_blk64_fwd()
+        elif capability == (12, 0):
+            from sglang.multimodal_gen.runtime.layers.attention.backends.subblock_sparse import (
+                load_bsa_attn_sm120_blk64_fwd,
+            )
+
+            load_bsa_attn_sm120_blk64_fwd()
         else:
             return False
     except Exception:
@@ -60,7 +67,8 @@ def _subblock_kernel_available() -> bool:
 
 
 requires_subblock_kernel = unittest.skipUnless(
-    _subblock_kernel_available(), "needs an SM90 or SM100 SubBlock attention kernel"
+    _subblock_kernel_available(),
+    "needs an SM90, SM100, or SM120 SubBlock attention kernel",
 )
 
 
@@ -491,6 +499,11 @@ class TestSubBlockNumerics(unittest.TestCase):
             self.skipTest("requires the SM100 SubBlock kernel")
         self._assert_kernel_backed_mixed_query_mask()
 
+    def test_sm120_kernel_backed_mixed_query_mask(self):
+        if torch.cuda.get_device_capability() != (12, 0):
+            self.skipTest("requires the SM120 SubBlock kernel")
+        self._assert_kernel_backed_mixed_query_mask()
+
     def test_skipped_step_is_bitwise_dense(self):
         device = torch.device("cuda")
         q, k, v = _structured_qkv(self.seq_len, device)

test/registered/cpu/test_subblock_sparse_attention.py

@@ -14,6 +14,7 @@ from sglang.multimodal_gen.runtime.layers.attention.backends.subblock_sparse_att
     _get_subblock_sparse_attention_runner,
     _sm90_sparse_attention,
     _sm100_sparse_attention,
+    _sm120_sparse_attention,
 )
 from sglang.multimodal_gen.runtime.models.dits.minimax_h3 import (
     _minimax_h3_attention_core_impl,
@@ -36,6 +37,9 @@ from sglang.multimodal_gen.runtime.platforms import (
     AttentionBackendEnum,
     current_platform,
 )
+from sglang.multimodal_gen.runtime.platforms.cuda import (
+    _SubBlockSparseAttentionBackendResolver,
+)
 from sglang.test.ci.ci_register import register_cpu_ci
 from sglang.test.test_utils import CustomTestCase
 
@@ -66,12 +70,76 @@ class TestSubBlockSparseAttentionDispatch(CustomTestCase):
 
         self.assertIs(runner, _sm100_sparse_attention)
 
+    def test_dispatches_sm120(self):
+        device = torch.device("cuda:0")
+        with patch("torch.cuda.get_device_capability", return_value=(12, 0)):
+            runner = _get_subblock_sparse_attention_runner(device)
+
+        self.assertIs(runner, _sm120_sparse_attention)
+
+    def test_platform_resolver_loads_sm120_dependency(self):
+        capability = Mock(major=12, minor=0)
+        capability.as_version_str.return_value = "12.0"
+        platform = Mock()
+        platform.get_device_capability.return_value = capability
+
+        with patch(
+            "sglang.multimodal_gen.runtime.layers.attention.backends."
+            "subblock_sparse.load_bsa_attn_sm120_blk64_fwd"
+        ) as load_sm120:
+            resolved = _SubBlockSparseAttentionBackendResolver.resolve(platform)
+
+        self.assertEqual(
+            resolved,
+            "sglang.multimodal_gen.runtime.layers.attention.backends."
+            "subblock_sparse_attn.SubBlockSparseAttentionBackend",
+        )
+        load_sm120.assert_called_once_with()
+
+    def test_sm120_adapter_forwards_subblock_plan(self):
+        q = torch.empty((1, 64, 2, 128), dtype=torch.bfloat16)
+        k = torch.empty((1, 65, 2, 128), dtype=torch.bfloat16)
+        v = torch.empty_like(k)
+        q2k_block_index = torch.zeros((1, 2, 1, 2), dtype=torch.int32)
+        block_counts = torch.tensor([[[2], [1]]], dtype=torch.int32)
+        expected = torch.empty_like(q)
+        kernel = Mock(return_value=(expected, None))
+
+        with patch(
+            "sglang.multimodal_gen.runtime.layers.attention.backends."
+            "subblock_sparse_attn.load_bsa_attn_sm120_blk64_fwd",
+            return_value=kernel,
+        ):
+            result = _sm120_sparse_attention(
+                q,
+                k,
+                v,
+                q2k_block_index,
+                topk=2,
+                softmax_scale=0.125,
+                block_counts=block_counts,
+            )
+
+        self.assertIs(result, expected)
+        kernel.assert_called_once()
+        args, kwargs = kernel.call_args
+        self.assertIs(args[0], q)
+        self.assertIs(args[1], k)
+        self.assertIs(args[2], v)
+        self.assertIs(args[3], q2k_block_index)
+        self.assertEqual(args[4], 2)
+        torch.testing.assert_close(
+            kwargs["block_sizes"], torch.tensor([64, 1], dtype=torch.int32)
+        )
+        self.assertIs(kwargs["q2k_block_nums"], block_counts)
+        self.assertEqual(kwargs["softmax_scale"], 0.125)
+
     def test_rejects_unsupported_compute_capability(self):
         device = torch.device("cuda:0")
         with patch("torch.cuda.get_device_capability", return_value=(10, 3)):
             with self.assertRaisesRegex(
                 RuntimeError,
-                "supports compute capability 9.0 or 10.0;.*10.3 device",
+                "supports compute capability 9.0, 10.0, or 12.0;.*10.3 device",
             ):
                 _get_subblock_sparse_attention_runner(device)
 
@@ -352,6 +420,7 @@ class TestSubBlockSparseAttentionModalities(CustomTestCase):
         for runner, sparse_rows in (
             (_sm90_sparse_attention, ([1, 4, 7], [0, 3, 5])),
             (_sm100_sparse_attention, ([7, 1, 4], [5, 0, 3])),
+            (_sm120_sparse_attention, ([7, 1, 4], [5, 0, 3])),
         ):
             with (
                 self.subTest(runner=runner.__name__),