[Refactor] Replace activation: str with MoEActivation enum

benchmarks/kernels/benchmark_cutlass_moe_fp8.py

@@ -11,6 +11,7 @@
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from tests.kernels.moe.utils import make_dummy_moe_config
 from vllm import _custom_ops as ops
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
 from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
@@ -161,7 +162,7 @@ def bench_run(
                 w2_fp8q_cutlass,
                 topk_weights,
                 topk_ids,
-                activation="silu",
+                activation=MoEActivation.SILU,
                 global_num_experts=num_experts,
             )
     torch.cuda.synchronize()

benchmarks/kernels/benchmark_moe.py

@@ -16,6 +16,7 @@
 from ray.experimental.tqdm_ray import tqdm
 
 from vllm.model_executor.layers.fused_moe import fused_topk
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEParallelConfig,
@@ -211,7 +212,8 @@ def run():
                         hidden_dim=hidden_size,
                         intermediate_size_per_partition=shard_intermediate_size,
                         num_local_experts=num_experts,
-                        activation="silu",
+                        num_logical_experts=num_experts,
+                        activation=MoEActivation.SILU,
                         moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
                         in_dtype=init_dtype,
                         routing_method=RoutingMethodType.TopK,

tests/kernels/moe/modular_kernel_tools/common.py

@@ -22,6 +22,7 @@
 )
 from vllm.forward_context import set_forward_context
 from vllm.model_executor.layers.fused_moe import fused_topk
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.all2all_utils import (
     maybe_make_prepare_finalize,
 )
@@ -599,7 +600,7 @@ def next_power_of_2(x):
         moe_parallel_config=moe_parallel_config,
         in_dtype=config.dtype,
         max_num_tokens=next_power_of_2(config.M),
-        activation="silu",
+        activation=MoEActivation.SILU,
         device=vllm_config.device_config.device,
         routing_method=RoutingMethodType.DeepSeekV3,
     )

tests/kernels/moe/test_cpu_fused_moe.py

@@ -6,6 +6,7 @@
 
 from tests.kernels.allclose_default import get_default_atol, get_default_rtol
 from vllm._custom_ops import cpu_fused_moe, cpu_prepack_moe_weight
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.cpu_fused_moe import _CPU_MOE_ACT_FN
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import set_random_seed
@@ -19,7 +20,7 @@
 HIDDEN_DIM = [128, 2880]
 INTERMEDIATE_DIM = [128, 2880]
 BATCH_SIZE = [1, 64, 256]
-ACT = ["silu", "swigluoai"]
+ACT = [MoEActivation.SILU, MoEActivation.SWIGLUOAI]
 USE_BIAS = [True, False]
 ISA = ["amx", "vec"] if torch._C._cpu._is_amx_tile_supported() else ["vec"]
 DTYPE = [torch.bfloat16]
@@ -33,7 +34,7 @@ def ref_fused_moe(
     w2_bias: torch.Tensor | None,
     topk_weights: torch.Tensor,
     topk_ids: torch.Tensor,
-    activation: str,
+    activation: MoEActivation,
 ) -> torch.Tensor:
     len_experts = w13.size(0)
 
@@ -103,7 +104,7 @@ def test_cpu_fused_moe(
     intermediate_size: int,
     use_bias: bool,
     dtype: torch.dtype,
-    act: str,
+    act: MoEActivation,
     isa: str,
 ):
     set_random_seed(0)
@@ -153,7 +154,7 @@ def test_cpu_fused_moe(
         w2_bias,
         topk_weight,
         topk_ids,
-        act,
+        act.value,
         isa,
     )
 

tests/kernels/moe/test_cutlass_moe.py

@@ -12,6 +12,7 @@
 from vllm import _custom_ops as ops
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.fused_moe import fused_experts, fused_topk
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
     FUSED_MOE_UNQUANTIZED_CONFIG,
     FusedMoEQuantConfig,
@@ -531,7 +532,7 @@ def test_run_cutlass_moe_fp8(
         c_strides1 = torch.full((e,), 2 * n, device="cuda", dtype=torch.int64)
         c_strides2 = torch.full((e,), k, device="cuda", dtype=torch.int64)
 
-        activation = "silu"
+        activation = MoEActivation.SILU
         a1q, a1q_scale = moe_kernel_quantize_input(
             mt.a, mt.a_scale, torch.float8_e4m3fn, per_act_token
         )

tests/kernels/moe/test_deepep_deepgemm_moe.py

@@ -16,6 +16,7 @@
 
 from vllm.config import VllmConfig, set_current_vllm_config
 from vllm.forward_context import set_forward_context
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEQuantConfig,
     fp8_w8a8_moe_quant_config,
@@ -324,7 +325,7 @@ def build_expert_map():
             w2=w2,
             topk_weights=test_tensors.topk_weights,
             topk_ids=test_tensors.topk,
-            activation="silu",
+            activation=MoEActivation.SILU,
             global_num_experts=num_experts,
             expert_map=build_expert_map(),
             apply_router_weight_on_input=False,

tests/kernels/moe/test_deepep_moe.py

@@ -15,6 +15,7 @@
 from vllm.config import VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import TritonExperts
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEQuantConfig,
 )
@@ -260,7 +261,7 @@ def process_chunk(chunk_start, chunk_end, skip_result_store=False):
             w2=w2,
             topk_weights=topk_weights_chunk,
             topk_ids=topk_chunk,
-            activation="silu",
+            activation=MoEActivation.SILU,
             global_num_experts=num_experts,
             expert_map=build_expert_map(),
             apply_router_weight_on_input=False,

tests/kernels/moe/test_flashinfer.py

@@ -7,6 +7,7 @@
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEParallelConfig,
@@ -93,9 +94,14 @@ class TestData:
 
     @staticmethod
     def make_moe_tensors_8bit(
-        m: int, k: int, n: int, e: int, is_trtllm: bool, activation: str = "silu"
+        m: int,
+        k: int,
+        n: int,
+        e: int,
+        is_trtllm: bool,
+        activation: MoEActivation = MoEActivation.SILU,
     ) -> "TestData":
-        is_gated = activation != "relu2_no_mul"
+        is_gated = activation.is_gated
 
         hidden_states = torch.randn((m, k), device="cuda", dtype=torch.bfloat16) / 10
         w13 = torch.randn(
@@ -194,7 +200,7 @@ def test_flashinfer_per_tensor_moe_fp8_no_graph(
             topk_weights=topk_weights,
             topk_ids=topk_ids,
             inplace=False,
-            activation="silu",
+            activation=MoEActivation.SILU,
             global_num_experts=e,
             expert_map=None,
             apply_router_weight_on_input=True,
@@ -219,21 +225,19 @@ def test_flashinfer_per_tensor_moe_fp8_no_graph(
 @pytest.mark.parametrize("m,n,k", MNK_FACTORS)
 @pytest.mark.parametrize("e", NUM_EXPERTS)
 @pytest.mark.parametrize("topk", TOP_KS)
-@pytest.mark.parametrize("activation", ["silu", "relu2_no_mul"])
+@pytest.mark.parametrize("activation", [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL])
 def test_flashinfer_cutlass_moe_fp8_no_graph(
     m: int,
     n: int,
     k: int,
     e: int,
     topk: int,
-    activation: str,
+    activation: MoEActivation,
     monkeypatch,
     workspace_init,
 ):
     set_random_seed(7)
     monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "8192")
-    assert activation in ["silu", "relu2_no_mul"]
-    is_act_and_mul = activation == "silu_and_mul"
     with set_current_vllm_config(vllm_config):
         td = TestData.make_moe_tensors_8bit(
             m, k, n, e, is_trtllm=False, activation=activation
@@ -292,7 +296,7 @@ def get_fused_moe_quant_config(n: torch.nn.Module) -> FusedMoEQuantConfig:
             device="cuda",
             moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
             in_dtype=torch.bfloat16,
-            is_act_and_mul=is_act_and_mul,
+            is_act_and_mul=activation.is_gated,
             routing_method=RoutingMethodType.TopK,
         )
 

tests/kernels/moe/test_flashinfer_moe.py

@@ -13,6 +13,7 @@
 from vllm import _custom_ops as ops
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.fused_moe import fused_topk
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEParallelConfig,
@@ -54,7 +55,7 @@
 @pytest.mark.parametrize("e", [40, 64, 256])
 @pytest.mark.parametrize("topk", [1, 6, 8])
 @pytest.mark.parametrize("dtype", [torch.bfloat16])
-@pytest.mark.parametrize("activation", ["silu_and_mul", "relu2"])
+@pytest.mark.parametrize("activation", [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL])
 @torch.inference_mode()
 def test_flashinfer_fp4_moe_no_graph(
     m: int,
@@ -63,7 +64,7 @@ def test_flashinfer_fp4_moe_no_graph(
     e: int,
     topk: int,
     dtype: torch.dtype,
-    activation: str,
+    activation: MoEActivation,
     workspace_init,
 ):
     set_random_seed(7)
@@ -73,7 +74,7 @@ def test_flashinfer_fp4_moe_no_graph(
         a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
 
         quant_blocksize = 16
-        is_gated_act = activation == "silu_and_mul"
+        is_gated_act = activation.is_gated
 
         w1_q, w2_q, quant_config = make_test_quant_config(
             e,
@@ -112,15 +113,13 @@ def test_flashinfer_fp4_moe_no_graph(
             inplace=False,
         )
 
-        fi_activation = {"silu_and_mul": "silu", "relu2": "relu2_no_mul"}[activation]
-
         flashinfer_output = flashinfer_experts(
             hidden_states=a,
             w1=w1_q,
             w2=w2_q,
             topk_weights=topk_weights,
             topk_ids=topk_ids,
-            activation=fi_activation,
+            activation=activation,
         )
 
         # Reference check:

tests/kernels/moe/test_modular_oai_triton_moe.py

@@ -7,6 +7,7 @@
 import pytest
 import torch
 
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.utils.import_utils import has_triton_kernels
 
 if not has_triton_kernels():
@@ -192,7 +193,7 @@ def oai_triton_moe_impl(
         w2=w2,
         topk_weights=topk_weights,
         topk_ids=topk_ids,
-        activation="swigluoai",
+        activation=MoEActivation.SWIGLUOAI,
         global_num_experts=num_experts,
         expert_map=None,
         apply_router_weight_on_input=False,

tests/kernels/moe/test_moe.py

@@ -29,6 +29,7 @@
 from vllm.distributed.parallel_state import init_distributed_environment
 from vllm.forward_context import get_forward_context, set_forward_context
 from vllm.model_executor.layers.fused_moe import (
+    MoEActivation,
     fused_topk,
 )
 from vllm.model_executor.layers.fused_moe.config import (
@@ -1155,7 +1156,10 @@ def test_fused_marlin_moe_with_bias(m):
 @pytest.mark.parametrize("m", [1, 64, 256])
 @pytest.mark.parametrize("n,k", [(1024, 1024), (2048, 2048)])
 @pytest.mark.parametrize("e,topk", [(8, 2), (64, 4)])
-def test_fused_marlin_moe_non_gated(m: int, n: int, k: int, e: int, topk: int):
+@pytest.mark.parametrize("activation", [MoEActivation.RELU2_NO_MUL])
+def test_fused_marlin_moe_non_gated(
+    m: int, n: int, k: int, e: int, topk: int, activation: MoEActivation
+):
     """Test Marlin MoE with non-gated activation (relu2_no_mul).
 
     Non-gated activations like relu2 don't have the gate-up projection pattern,
@@ -1198,7 +1202,7 @@ def test_fused_marlin_moe_non_gated(m: int, n: int, k: int, e: int, topk: int):
             w2_data.w_ref,
             score,
             topk,
-            activation="relu2",
+            activation=activation,
         )
 
     marlin_output = fused_marlin_moe(
@@ -1223,7 +1227,7 @@ def test_fused_marlin_moe_non_gated(m: int, n: int, k: int, e: int, topk: int):
         w2_zeros=w2_data.zeros,
         quant_type_id=quant_type.id,
         is_k_full=is_k_full,
-        activation="relu2_no_mul",
+        activation=activation,
     )
 
     torch.testing.assert_close(marlin_output, torch_output, atol=1e-1, rtol=0)
@@ -1330,9 +1334,18 @@ def test_moe_sum(m: int, topk: int, k: int, dtype: torch.dtype):
 @pytest.mark.parametrize("topk", [2])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
 @pytest.mark.parametrize("with_bias", [False, True])
-@pytest.mark.parametrize("activation", ["silu"])
+@pytest.mark.parametrize("activation", [MoEActivation.SILU])
 @pytest.mark.skipif(not current_platform.is_cpu(), reason="CPU only test")
-def test_cpu_fused_moe_basic(m, n, k, e, topk, dtype, with_bias, activation):
+def test_cpu_fused_moe_basic(
+    m: int,
+    n: int,
+    k: int,
+    e: int,
+    topk: int,
+    dtype: torch.dtype,
+    with_bias: bool,
+    activation: MoEActivation,
+):
     from vllm.model_executor.layers.fused_moe.cpu_fused_moe import CPUFusedMOE
 
     device = "cpu"
@@ -1608,6 +1621,7 @@ def test_unquantized_bf16_flashinfer_trtllm_backend(
         hidden_dim=k,
         intermediate_size_per_partition=n,
         num_local_experts=e,
+        num_logical_experts=e,
         activation="silu",
         device="cuda",
         moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),

tests/kernels/moe/test_pplx_cutlass_moe.py

@@ -9,6 +9,7 @@
 from vllm import _custom_ops as ops
 from vllm.config import VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.fused_moe import fused_topk
+from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEParallelConfig,
@@ -149,7 +150,7 @@ def make_moe_config() -> FusedMoEConfig:
             num_local_experts=num_local_experts,
             num_logical_experts=num_experts,
             moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
-            activation="silu",
+            activation=MoEActivation.SILU,
             in_dtype=torch.bfloat16,
             device="cuda",
             routing_method=RoutingMethodType.Llama4,