From 8bf051cd51adff79e1bc94e910adaea543aaa2e1 Mon Sep 17 00:00:00 2001
From: Garrett Goon <44747910+garrett361@users.noreply.github.com>
Date: Mon, 21 Sep 2026 18:38:38 -0400
Subject: [PATCH] [Bugfix][Kernel] Skip the fused silu-mul block-quant fast
 path when a swiglu clamp is set (#57984)

Signed-off-by: Garrett Goon <garrett@primeintellect.ai>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 tests/kernels/moe/test_block_fp8.py           | 55 +++++++++++++++++++
 .../layers/fused_moe/experts/triton_moe.py    |  3 +
 2 files changed, 58 insertions(+)

diff --git a/tests/kernels/moe/test_block_fp8.py b/tests/kernels/moe/test_block_fp8.py
index 1cbbbb45fb..63f64c735a 100644
--- a/tests/kernels/moe/test_block_fp8.py
+++ b/tests/kernels/moe/test_block_fp8.py
@@ -25,6 +25,7 @@ from vllm.model_executor.layers.fused_moe.all2all_utils import (
 from vllm.model_executor.layers.fused_moe.config import (
     fp8_w8a8_moe_quant_config,
 )
+from vllm.model_executor.layers.fused_moe.experts import triton_moe
 from vllm.model_executor.layers.fused_moe.experts.deep_gemm_moe import (
     _valid_deep_gemm_shape,
 )
@@ -246,6 +247,60 @@ def test_w8a8_block_fp8_fused_moe(
     torch.testing.assert_close(m_out, ref_out_m, atol=tol, rtol=tol)
 
 
+@pytest.mark.parametrize("clamp_limit", [None, 10.0])
+@torch.inference_mode()
+def test_w8a8_block_fp8_clamp_skips_fused_silu_quant(
+    clamp_limit, monkeypatch, workspace_init, disable_deepgemm_ue8m0
+):
+    """A SwiGLU clamp must bypass the fused SiLU+quant kernel, which cannot clamp."""
+    torch.manual_seed(0)
+    M, N, K, E, topk = 16, 256, 512, 8, 2
+    block_size = [128, 128]
+
+    a = torch.randn((M, K), dtype=torch.bfloat16) / 10
+    score = torch.randn((M, E), dtype=torch.bfloat16)
+    (_, w1, w1_s, _), (_, w2, w2_s, _) = make_test_weights(
+        E, N, K, torch.bfloat16, torch.float8_e4m3fn, block_shape=block_size
+    )
+    quant_config = fp8_w8a8_moe_quant_config(
+        w1_scale=w1_s,
+        w2_scale=w2_s,
+        block_shape=block_size,
+        gemm1_clamp_limit=clamp_limit,
+    )
+    m_fused_moe = modular_triton_fused_moe(make_dummy_moe_config(), quant_config)
+    topk_weights, topk_ids, _ = fused_topk(a, score.float(), topk, False)
+
+    fused_calls = 0
+    fused_op = triton_moe.ops.silu_and_mul_per_block_quant
+
+    def counting_fused_op(*args, **kwargs):
+        nonlocal fused_calls
+        fused_calls += 1
+        return fused_op(*args, **kwargs)
+
+    monkeypatch.setattr(
+        triton_moe.ops, "silu_and_mul_per_block_quant", counting_fused_op
+    )
+
+    with set_current_vllm_config(vllm_config):
+        m_fused_moe.apply(
+            a,
+            w1,
+            w2,
+            topk_weights,
+            topk_ids,
+            activation=MoEActivation.SILU,
+            apply_router_weight_on_input=False,
+            expert_map=None,
+            global_num_experts=E,
+        )
+
+    assert (fused_calls > 0) == (clamp_limit is None), (
+        f"fused fast path ran {fused_calls} times with clamp_limit={clamp_limit}"
+    )
+
+
 @pytest.mark.parametrize(("M", "N", "K"), MNK_FACTORS_DG)
 @pytest.mark.parametrize("E", E)
 @pytest.mark.parametrize("topk", TOP_KS)
diff --git a/vllm/model_executor/layers/fused_moe/experts/triton_moe.py b/vllm/model_executor/layers/fused_moe/experts/triton_moe.py
index 700d113921..1e21a4fe2c 100644
--- a/vllm/model_executor/layers/fused_moe/experts/triton_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/triton_moe.py
@@ -479,8 +479,11 @@ class TritonExperts(LoRAExpertsMixin, mk.FusedMoEExpertsModular):
         # Fuse SiLU+Mul + FP8 block quantize into a single kernel
         # when conditions permit (gated SiLU, fp8 block quant with
         # group_size=128, no LoRA requiring the BF16 intermediate).
+        # The fused kernel has no clamp parameter, so a configured
+        # SwiGLU clamp limit falls through to the unfused path.
         if (
             activation == MoEActivation.SILU
+            and self.activation_config.clamp_limit is None
             and self.quant_config.use_fp8_w8a8
             and self.block_shape == [128, 128]
             and lora_context is None