[https://nvbugs/5986434][fix] Fall back from TRTLLM MoE backend on pre-Blackwell GPUs

tensorrt_llm/_torch/modules/fused_moe/create_moe.py

@@ -158,6 +158,19 @@ def get_moe_cls(
             return CutlassFusedMoE
         return DenseGEMMFusedMoE
     elif moe_backend.upper() == "TRTLLM":
+        # TRTLLM-Gen MoE kernels only exist for Blackwell-family GPUs; on other
+        # architectures the backend fails at engine init (autotuner profile
+        # lookup or "No kernel found" during kernel selection). Fall back like
+        # the other arch-gated backends. CutlassFusedMoE matches what AUTO
+        # resolves to for these quantizations on non-SM100 GPUs.
+        from tensorrt_llm._utils import get_sm_version
+        sm_version = get_sm_version()
+        if sm_version not in TRTLLMGenFusedMoE._SUPPORTED_SM_VERSIONS:
+            logger.warning(
+                f"{layer_prefix}TRTLLMGenFusedMoE only supports SM "
+                f"{list(TRTLLMGenFusedMoE._SUPPORTED_SM_VERSIONS)} "
+                f"(got SM {sm_version}). Using CutlassFusedMoE instead.")
+            return CutlassFusedMoE
         has_quant = quant_config is not None and quant_config.quant_mode.has_any_quant(
             exclude_kv_cache=True)
         if has_quant and (quant_config.quant_mode.has_fp8_block_scales()

tensorrt_llm/_torch/modules/fused_moe/fused_moe_trtllm_gen.py

@@ -98,6 +98,10 @@ class TRTLLMGenFusedMoE(MoE):
         onesided_workspace_dtype=torch.bfloat16,
     )
 
+    # SM versions the TRTLLM-Gen MoE kernels are built for (Blackwell family).
+    # Read by create_moe.get_moe_cls to fall back on other architectures.
+    _SUPPORTED_SM_VERSIONS = (100, 103)
+
     # Supported quantization algorithms for TRTLLMGenFusedMoE
     _SUPPORTED_QUANT_ALGOS = {
         QuantAlgo.NVFP4,
@@ -161,7 +165,7 @@ def can_implement(
         sm_version = get_sm_version()
 
         # TRTLLMGenFusedMoE requires SM in {100, 103}
-        if sm_version not in {100, 103}:
+        if sm_version not in cls._SUPPORTED_SM_VERSIONS:
             return _warn_and_return(
                 f"TRTLLMGenFusedMoE requires SM100 or SM103, got SM{sm_version}"
             )

tests/integration/test_lists/test-db/l0_b200.yml

@@ -132,6 +132,8 @@ l0_b200:
   - unittest/_torch/modules/moe/test_moe_backend.py::test_trtllm_bf16_unquantized_moe
   - unittest/_torch/modules/moe/test_moe_backend.py::test_trtllm_fp8_block_scales_fused_shared_experts
   - unittest/_torch/modules/moe/test_moe_backend.py::test_trtllm_fp8_block_scales_fuse_shared_expert_layout
+  - unittest/_torch/modules/moe/test_moe_backend.py::test_get_moe_cls_trtllm_falls_back_to_cutlass_on_unsupported_sm
+  - unittest/_torch/modules/moe/test_moe_backend.py::test_get_moe_cls_trtllm_selects_trtllm_gen_on_blackwell
   # ------------- MoE: test_single_gpu (by backend) ---------------
   - unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_single_gpu -k "CUTLASS and not None"
   - unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_single_gpu -k "TRTLLM"

tests/integration/test_lists/test-db/l0_b300.yml

@@ -41,6 +41,8 @@ l0_b300:
   - unittest/_torch/modules/moe/test_moe_backend.py::test_trtllm_bf16_unquantized_moe
   - unittest/_torch/modules/moe/test_moe_backend.py::test_trtllm_fp8_block_scales_fused_shared_experts
   - unittest/_torch/modules/moe/test_moe_backend.py::test_trtllm_fp8_block_scales_fuse_shared_expert_layout
+  - unittest/_torch/modules/moe/test_moe_backend.py::test_get_moe_cls_trtllm_falls_back_to_cutlass_on_unsupported_sm
+  - unittest/_torch/modules/moe/test_moe_backend.py::test_get_moe_cls_trtllm_selects_trtllm_gen_on_blackwell
   # ------------- MoE: test_single_gpu (specific quant per backend) ---------------
   # CUTLASS backend: FP8, NVFP4, W4A8_MXFP4_MXFP8, W8A16
   - unittest/_torch/modules/moe/test_moe_module.py::test_configurable_moe_single_gpu[e60_k4_h2048_i1408-seq=1-dtype=torch.bfloat16-backend=CUTLASS-quant=FP8-routing=Renormalize]

tests/unittest/_torch/modules/moe/test_moe_backend.py

@@ -61,6 +61,7 @@
 from tensorrt_llm._torch.modules.fused_moe.create_moe import create_moe_backend, get_moe_cls
 from tensorrt_llm._torch.modules.fused_moe.fused_moe_cutlass import CutlassFusedMoE
 from tensorrt_llm._torch.modules.fused_moe.fused_moe_marlin import MarlinFusedMoE
+from tensorrt_llm._torch.modules.fused_moe.fused_moe_trtllm_gen import TRTLLMGenFusedMoE
 from tensorrt_llm._torch.modules.fused_moe.impl_contract import MoECommPlan, MoERunContext
 from tensorrt_llm._torch.modules.fused_moe.interface import (
     MoE,
@@ -407,6 +408,33 @@ def test_get_moe_cls_marlin_override_quant_config_per_layer():
     )
 
 
+def _trtllm_model_config(quant_algo=QuantAlgo.FP8_BLOCK_SCALES):
+    cfg = ModelConfig()
+    cfg.moe_backend = "TRTLLM"
+    cfg.quant_config = QuantConfig(quant_algo=quant_algo) if quant_algo else None
+    return cfg
+
+
+@pytest.mark.parametrize("sm", [90, 120])
+def test_get_moe_cls_trtllm_falls_back_to_cutlass_on_unsupported_sm(sm, monkeypatch):
+    """TRTLLM-Gen MoE kernels only exist for SM100/SM103. Requesting the TRTLLM
+    backend elsewhere (e.g. DeepSeek FP8_BLOCK_SCALES on Hopper) must fall back
+    to Cutlass instead of failing at engine init: on 1.3.0rc7 this crashed with
+    'list assignment index out of range' in AutoTuner._find_nearest_profile,
+    later with 'No kernel found' during trtllm-gen kernel selection.
+    The parametrized SMs are ones CutlassFusedMoE supports for FP8_BLOCK_SCALES,
+    so the fallback selection is also runnable."""
+    monkeypatch.setattr("tensorrt_llm._utils.get_sm_version", lambda: sm)
+    assert get_moe_cls(_trtllm_model_config()) is CutlassFusedMoE
+
+
+@pytest.mark.parametrize("sm", [100, 103])
+def test_get_moe_cls_trtllm_selects_trtllm_gen_on_blackwell(sm, monkeypatch):
+    """The SM gate must not change selection on Blackwell-family GPUs."""
+    monkeypatch.setattr("tensorrt_llm._utils.get_sm_version", lambda: sm)
+    assert get_moe_cls(_trtllm_model_config()) is TRTLLMGenFusedMoE
+
+
 def test_megamoe_cutedsl_post_load_weights_uses_staged_hooks():
     moe = MegaMoECuteDsl.__new__(MegaMoECuteDsl)
     torch.nn.Module.__init__(moe)