feat(moe): Add is_act_and_mul=False support for Triton MoE kernels

tests/kernels/moe/test_triton_moe_no_act_mul.py

@@ -0,0 +1,129 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Test for is_act_and_mul=False MoE using Triton.
+
+This tests the code path used by models like Nemotron-H that use
+non-fused activations (e.g., relu2_no_mul) instead of SwiGLU-style
+fused activations.
+
+This feature is supported on both CUDA and ROCm (with AITER disabled).
+"""
+
+import pytest
+import torch
+
+from vllm.platforms import current_platform
+
+pytestmark = pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="Tests for is_act_and_mul=False MoE require CUDA or ROCm",
+)
+
+
+@pytest.fixture
+def disable_aiter_on_rocm(monkeypatch):
+    """Fixture to disable AITER on ROCm to use Triton path."""
+    if current_platform.is_rocm():
+        from vllm._aiter_ops import rocm_aiter_ops
+
+        monkeypatch.setenv("VLLM_ROCM_USE_AITER", "0")
+        monkeypatch.setenv("VLLM_ROCM_USE_AITER_MOE", "0")
+        rocm_aiter_ops.refresh_env_variables()
+
+        yield
+
+        rocm_aiter_ops.refresh_env_variables()
+    else:
+        # On CUDA, no special setup needed
+        yield
+
+
+@pytest.fixture
+def init_workspace():
+    """Initialize workspace manager for MoE tests."""
+    from vllm.v1.worker.workspace import (
+        init_workspace_manager,
+        reset_workspace_manager,
+    )
+
+    torch.manual_seed(42)
+    init_workspace_manager(torch.cuda.current_device())
+
+    yield
+
+    reset_workspace_manager()
+
+
+@pytest.mark.parametrize("m", [1, 33, 64, 222])
+@pytest.mark.parametrize("n", [128, 256, 1024])
+@pytest.mark.parametrize("k", [128, 512])
+@pytest.mark.parametrize("e", [4, 8])
+@pytest.mark.parametrize("topk", [2])
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
+@pytest.mark.parametrize("activation", ["relu2_no_mul", "silu_no_mul", "gelu_no_mul"])
+@torch.inference_mode()
+def test_moe_no_act_mul(
+    disable_aiter_on_rocm,
+    init_workspace,
+    m: int,
+    n: int,
+    k: int,
+    e: int,
+    topk: int,
+    dtype: torch.dtype,
+    activation: str,
+):
+    """Test MoE with is_act_and_mul=False using Triton."""
+    from vllm.model_executor.layers.fused_moe import TritonExperts, fused_topk
+    from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+    from vllm.model_executor.layers.fused_moe.modular_kernel import (
+        FusedMoEModularKernel,
+    )
+    from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+        MoEPrepareAndFinalizeNoEP,
+    )
+
+    a = torch.randn((m, k), device="cuda", dtype=dtype)
+    w1 = torch.randn((e, n, k), device="cuda", dtype=dtype) / 10
+    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
+
+    quant_config = FusedMoEQuantConfig.make(is_act_and_mul=False)
+
+    score = torch.randn((m, e), device="cuda", dtype=dtype)
+    topk_weights, topk_ids, _ = fused_topk(a, score, topk, renormalize=True)
+
+    fused_experts = FusedMoEModularKernel(
+        MoEPrepareAndFinalizeNoEP(),
+        TritonExperts(quant_config),
+    )
+
+    output = fused_experts(
+        hidden_states=a,
+        w1=w1,
+        w2=w2,
+        topk_weights=topk_weights,
+        topk_ids=topk_ids,
+        activation=activation,
+    )
+
+    assert output.shape == (m, k), f"Expected shape {(m, k)}, got {output.shape}"
+    assert not torch.isnan(output).any(), "Output contains NaN"
+    assert not torch.isinf(output).any(), "Output contains Inf"
+    assert output.abs().sum() > 0, "Output is all zeros"
+
+
+@torch.inference_mode()
+def test_moe_workspace_shapes_no_act_mul(disable_aiter_on_rocm):
+    """Test workspace_shapes returns correct sizes for is_act_and_mul=False."""
+    from vllm.model_executor.layers.fused_moe import TritonExperts
+    from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+
+    M, N, K, topk = 64, 256, 128, 2
+
+    quant_config = FusedMoEQuantConfig.make(is_act_and_mul=False)
+    experts = TritonExperts(quant_config)
+    ws1, ws2, out = experts.workspace_shapes(M, N, K, topk, 8, 8, None)
+
+    assert ws1[2] == max(N, K)
+    assert out == (M, K)

vllm/model_executor/layers/fused_moe/config.py

@@ -201,6 +201,11 @@ class FusedMoEQuantConfig:
     _w1: FusedMoEQuantDesc
     _w2: FusedMoEQuantDesc
 
+    # Whether activation is fused with gate multiplication (SwiGLU-style).
+    # When True: intermediate_size = N // 2 (gate and up are combined)
+    # When False: intermediate_size = N (no gate multiplication)
+    is_act_and_mul: bool = True
+
     def __post_init__(self):
         assert not self.per_act_token_quant or self.block_shape is None, (
             "illegal quantization"
@@ -435,6 +440,7 @@ def make(
         w1_zp: torch.Tensor | None = None,
         w2_zp: torch.Tensor | None = None,
         weight_dtype: torch.dtype | str | None = None,
+        is_act_and_mul: bool = True,
     ) -> "FusedMoEQuantConfig":
         """
         General builder function for a FusedMoEQuantConfig.
@@ -494,6 +500,7 @@ def make(
             _w2=FusedMoEQuantDesc(
                 weight_dtype, w_shape, w2_scale, g2_alphas, w2_zp, w2_bias
             ),
+            is_act_and_mul=is_act_and_mul,
         )
         assert quant_config.per_act_token_quant == per_act_token_quant
         assert quant_config.per_out_ch_quant == per_out_ch_quant
@@ -806,6 +813,7 @@ def awq_marlin_moe_quant_config(
 def biased_moe_quant_config(
     w1_bias: torch.Tensor | None,
     w2_bias: torch.Tensor | None,
+    is_act_and_mul: bool = True,
 ) -> FusedMoEQuantConfig:
     """
     Construct a quant config for unquantized activations with biases.
@@ -815,6 +823,7 @@ def biased_moe_quant_config(
         _a2=FusedMoEQuantDesc(),
         _w1=FusedMoEQuantDesc(bias=w1_bias),
         _w2=FusedMoEQuantDesc(bias=w2_bias),
+        is_act_and_mul=is_act_and_mul,
     )
 
 

vllm/model_executor/layers/fused_moe/fused_batched_moe.py

@@ -871,8 +871,11 @@ def workspace_shapes(
         num_dp = self.num_dispatchers
         num_experts = local_num_experts
         max_num_tokens = self.max_num_tokens
+        # For fused activations (SwiGLU): N = 2 * intermediate, after act = N/2
+        # For non-fused activations: N = intermediate, after act = N
+        intermediate_size = N // 2 if self.quant_config.is_act_and_mul else N
         workspace13 = (num_experts, max_num_tokens * num_dp, max(K, N))
-        workspace2 = (num_experts, max_num_tokens * num_dp, (N // 2))
+        workspace2 = (num_experts, max_num_tokens * num_dp, intermediate_size)
         output = (num_experts, max_num_tokens * num_dp, K)
         return (workspace13, workspace2, output)
 
@@ -947,7 +950,11 @@ def apply(
         # We can reuse the memory between these because by the time we need
         # cache3, we're done with cache1
         intermediate_cache1 = _resize_cache(workspace13, (E, max_num_tokens, N))
-        intermediate_cache2 = _resize_cache(workspace2, (E, max_num_tokens, N // 2))
+        # For fused activations (SwiGLU): output is N/2, for non-fused: output is N
+        intermediate_size = N // 2 if self.quant_config.is_act_and_mul else N
+        intermediate_cache2 = _resize_cache(
+            workspace2, (E, max_num_tokens, intermediate_size)
+        )
 
         # TODO(bnell): should this be done for any quantized type?
         if self.quant_config.use_fp8_w8a8:
@@ -978,7 +985,7 @@ def apply(
         # TODO (bnell): use triton utility from batched deep gemm.
         self.activation(
             activation,
-            intermediate_cache2.view(-1, N // 2),
+            intermediate_cache2.view(-1, intermediate_size),
             intermediate_cache1.view(-1, N),
         )
 

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -2292,7 +2292,10 @@ def workspace_shapes(
         local_num_experts: int,
         expert_tokens_meta: mk.ExpertTokensMetadata | None,
     ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
-        workspace1 = (M, topk, max(N // 2, K))
+        # For fused activations (SwiGLU): N = 2 * intermediate, after act = N/2
+        # For non-fused activations: N = intermediate, after act = N
+        intermediate_size = N // 2 if self.quant_config.is_act_and_mul else N
+        workspace1 = (M, topk, max(intermediate_size, K))
         workspace2 = (M, topk, max(N, K))
         output = (M, K)
         return (workspace1, workspace2, output)
@@ -2367,8 +2370,10 @@ def apply(
 
         # Note that the output tensor might be in workspace1
         intermediate_cache1 = _resize_cache(workspace2, (num_tokens, top_k_num, N))
+        # For fused activations (SwiGLU): output is N/2, for non-fused: output is N
+        intermediate_size = N // 2 if self.quant_config.is_act_and_mul else N
         intermediate_cache2 = _resize_cache(
-            workspace13, (num_tokens * top_k_num, N // 2)
+            workspace13, (num_tokens * top_k_num, intermediate_size)
         )
         intermediate_cache3 = _resize_cache(workspace2, (num_tokens, top_k_num, K))
 

vllm/model_executor/layers/fused_moe/layer.py

@@ -603,9 +603,15 @@ def _get_quant_method() -> FusedMoEMethodBase:
                     "is_act_and_mul=False is supported only for unquantized "
                     ", ModelOpt FP8, and ModelOpt NvFp4 checkpoints"
                 )
-            if not current_platform.is_cuda():
+            # ROCm without AITER MoE uses Triton which supports
+            # is_act_and_mul=False via standard PyTorch ops (F.silu, F.gelu)
+            rocm_without_aiter_moe = (
+                current_platform.is_rocm() and not rocm_aiter_ops.is_fused_moe_enabled()
+            )
+            if not current_platform.is_cuda() and not rocm_without_aiter_moe:
                 raise NotImplementedError(
-                    "is_act_and_mul=False is supported only for CUDA for now"
+                    "is_act_and_mul=False is supported only for CUDA, or ROCm "
+                    "(when AITER MoE is disabled) for now"
                 )
 
         if self.enable_eplb and not self.quant_method.supports_eplb:

vllm/model_executor/layers/fused_moe/modular_kernel.py

@@ -4,7 +4,7 @@
 from collections.abc import Callable
 from dataclasses import dataclass
 from enum import Enum
-from math import prod
+from math import prod, sqrt
 from typing import final
 
 import torch
@@ -575,14 +575,35 @@ def workspace_shapes(
     def activation(
         self, activation: str, output: torch.Tensor, input: torch.Tensor
     ) -> None:
-        assert output.size(-1) * 2 == input.size(-1)
+        # Fused activations (SwiGLU-style): output is half the size of input
         if activation == "silu":
+            assert output.size(-1) * 2 == input.size(-1)
             torch.ops._C.silu_and_mul(output, input)
         elif activation == "gelu":
+            assert output.size(-1) * 2 == input.size(-1)
             torch.ops._C.gelu_and_mul(output, input)
         elif activation == "swigluoai":
             # alpha = 1.702, limit = 7.0
+            assert output.size(-1) * 2 == input.size(-1)
             torch.ops._C.swigluoai_and_mul(output, input)
+        # Non-fused activations (is_act_and_mul=False): output same size as input
+        elif activation == "silu_no_mul":
+            assert output.size(-1) == input.size(-1)
+            # Use out= argument to avoid intermediate tensor
+            torch.sigmoid(input, out=output)
+            output.mul_(input)
+        elif activation == "gelu_no_mul":
+            assert output.size(-1) == input.size(-1)
+            # GELU(x) = 0.5 * x * (1 + erf(x / sqrt(2)))
+            # Use out= and in-place ops to avoid intermediate tensors
+            output.copy_(input).div_(sqrt(2))
+            torch.erf(output, out=output)
+            output.add_(1).mul_(input).mul_(0.5)
+        elif activation == "relu2_no_mul":
+            assert output.size(-1) == input.size(-1)
+            # ReLU²: clamp has out=, then in-place square
+            torch.clamp(input, min=0, out=output)
+            output.square_()
         else:
             raise ValueError(f"Unsupported FusedMoe activation: {activation}")
 

vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py

@@ -299,7 +299,12 @@ def get_fused_moe_quant_config(self, layer: torch.nn.Module) -> FusedMoEQuantCon
             return biased_moe_quant_config(
                 layer.w13_bias,
                 layer.w2_bias,
+                is_act_and_mul=self.moe.is_act_and_mul,
             )
+        elif not self.moe.is_act_and_mul:
+            # Create a config with is_act_and_mul=False since
+            # FUSED_MOE_UNQUANTIZED_CONFIG has is_act_and_mul=True
+            return FusedMoEQuantConfig.make(is_act_and_mul=False)
         else:
             return FUSED_MOE_UNQUANTIZED_CONFIG