Integrate triton moe kernel (#7689)

Co-authored-by: luoyuan.luo <luoyuan.luo@antgroup.com>

benchmark/kernels/fused_moe_triton/benchmark_sglang_fused_moe_triton.py

+# python3 benchmark/kernels/fused_moe_triton/sglang_fused_moe_triton.py --model /DeepSeek-V3/ --tp-size 8
+import argparse
+
+import torch
+import triton
+from transformers import AutoConfig
+
+from sglang.srt.distributed.parallel_state import (
+    destroy_distributed_environment,
+    destroy_model_parallel,
+    init_distributed_environment,
+    initialize_model_parallel,
+)
+from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
+    fused_moe as fused_moe_sglang,
+)
+from sglang.srt.layers.moe.fused_moe_triton.triton_kernels_moe import (
+    triton_kernel_moe_forward,
+)
+
+
+def get_model_config(model_name: str, tp_size: int):
+    """Get model configuration parameters"""
+    config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+
+    if config.architectures[0] == "Qwen2MoeForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] == "Qwen3MoeForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] in ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]:
+        E = (
+            config.n_routed_experts + 1
+            if config.architectures[0] in ["DeepseekV3ForCausalLM"]
+            else config.n_routed_experts
+        )
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    else:
+        # Default: Mixtral
+        E = config.num_local_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+
+    block_shape = None
+    if (
+        hasattr(config, "quantization_config")
+        and "weight_block_size" in config.quantization_config
+    ):
+        block_shape = config.quantization_config["weight_block_size"]
+        assert len(block_shape) == 2
+
+    shape_configs = {
+        "num_experts": E,
+        "topk": topk,
+        "hidden_size": config.hidden_size,
+        "shard_intermediate_size": shard_intermediate_size,
+        "dtype": config.torch_dtype,
+        "block_shape": block_shape,
+    }
+    print(f"{shape_configs=}")
+    return shape_configs
+
+
+def fused_moe_triton_api(
+    x,
+    w1,
+    w2,
+    input_gating,
+    topk,
+):
+    return triton_kernel_moe_forward(
+        x,
+        w1,
+        w2,
+        input_gating,
+        topk,
+        renormalize=False,
+    )
+
+
+def fused_moe_sglang_api(
+    x,
+    w1,
+    w2,
+    input_gating,
+    topk,
+    use_fp8_w8a8=False,
+    w1_scale=None,
+    w2_scale=None,
+    a1_scale=None,
+    a2_scale=None,
+    block_shape=None,
+):
+    return fused_moe_sglang(
+        x,
+        w1,
+        w2,
+        input_gating,
+        topk,
+        renormalize=False,
+        inplace=True,
+        use_fp8_w8a8=use_fp8_w8a8,
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
+        a1_scale=a1_scale,
+        a2_scale=a2_scale,
+        block_shape=block_shape,
+    )
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["batch_size"],
+        x_vals=list([128, 256, 512, 1024, 2048, 4096, 8192]),
+        line_arg="provider",
+        line_vals=[
+            "sglang_fused_moe_triton_v340",
+            "sglang_fused_moe_triton",
+        ],
+        line_names=[
+            "sglang_fused_moe_triton_v340",
+            "sglang_fused_moe_triton",
+        ],
+        styles=[
+            ("blue", "-"),
+            ("green", "-"),
+        ],
+        ylabel="Time (ms)",
+        plot_name="fused-moe-performance",
+        args={},
+    )
+)
+def benchmark(
+    batch_size,
+    provider,
+    model_config,
+    use_fp8_w8a8=False,
+    use_cuda_graph: bool = False,
+):
+    print(f"benchmark {provider} with batch_size={batch_size}")
+    torch.set_default_device("cuda")
+    torch.cuda.manual_seed_all(0)
+
+    num_tokens = batch_size
+    num_experts = model_config["num_experts"]
+    hidden_size = model_config["hidden_size"]
+    shard_intermediate_size = model_config["shard_intermediate_size"]
+    topk = model_config["topk"]
+    dtype = model_config["dtype"]
+    block_shape = model_config["block_shape"]
+
+    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
+
+    w1 = torch.randn(num_experts, shard_intermediate_size, hidden_size, dtype=dtype)
+    w2 = torch.randn(
+        num_experts, hidden_size, shard_intermediate_size // 2, dtype=dtype
+    )
+
+    w1_tri = w1.clone()
+    w2_tri = w2.clone()
+    w1_tri = w1_tri.transpose(-2, -1).contiguous()
+    w2_tri = w2_tri.transpose(-2, -1).contiguous()
+
+    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
+
+    if provider == "sglang_fused_moe_triton_v340":
+        api_func = fused_moe_triton_api
+        api_kwargs = {
+            "x": x,
+            "w1": w1_tri,
+            "w2": w2_tri,
+            "input_gating": input_gating,
+            "topk": topk,
+        }
+    else:
+        api_func = fused_moe_sglang_api
+        api_kwargs = {
+            "x": x,
+            "w1": w1,
+            "w2": w2,
+            "input_gating": input_gating,
+            "topk": topk,
+            "use_fp8_w8a8": use_fp8_w8a8,
+            "block_shape": block_shape,
+        }
+
+    # Warmup
+    for _ in range(10):
+        _ = api_func(**api_kwargs)
+    torch.cuda.synchronize()
+
+    if use_cuda_graph:
+        stream = torch.cuda.Stream()
+        graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(graph, stream=stream):
+            api_func(**api_kwargs)
+        torch.cuda.synchronize()
+
+        bench_lambda = lambda: graph.replay()
+    else:
+        bench_lambda = lambda: api_func(**api_kwargs)
+
+    quantiles = [0.5, 0.2, 0.8]
+    ms, min_ms, max_ms = triton.testing.do_bench(bench_lambda, quantiles=quantiles)
+    return ms, min_ms, max_ms
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1"
+    )
+    parser.add_argument("--tp-size", type=int, default=2)
+    parser.add_argument("--use-fp8-w8a8", action="store_true")
+    parser.add_argument(
+        "--use-cuda-graph", action="store_true", help="Enable CUDA Graph capture/replay"
+    )
+    parser.add_argument(
+        "--save-path",
+        type=str,
+        default="./configs/benchmark_ops/sglang_fused_moe/",
+    )
+    parser.add_argument("--trust-remote-code", action="store_true")
+    args = parser.parse_args()
+
+    try:
+        if not torch.distributed.is_initialized():
+            torch.distributed.init_process_group(
+                backend="nccl" if torch.cuda.is_available() else "gloo",
+                init_method="tcp://127.0.0.1:23456",
+                world_size=1,
+                rank=0,
+            )
+
+        init_distributed_environment(
+            world_size=1,
+            rank=0,
+            distributed_init_method="tcp://127.0.0.1:23456",
+            local_rank=0,
+            backend="nccl" if torch.cuda.is_available() else "gloo",
+        )
+
+        initialize_model_parallel(
+            tensor_model_parallel_size=1,
+            pipeline_model_parallel_size=1,
+        )
+
+        model_config = get_model_config(args.model, args.tp_size)
+        benchmark.run(
+            show_plots=True,
+            print_data=True,
+            save_path=args.save_path,
+            model_config=model_config,
+            use_fp8_w8a8=args.use_fp8_w8a8,
+            use_cuda_graph=args.use_cuda_graph,
+        )
+    finally:
+        destroy_model_parallel()
+        destroy_distributed_environment()
+
+
+if __name__ == "__main__":
+    main()

python/sglang/srt/layers/moe/fused_moe_triton/fused_moe.py

@@ -1737,6 +1737,7 @@ def fused_moe(
     renormalize: bool,
     inplace: bool = False,
     activation: str = "silu",
+    apply_router_weight_on_input: bool = False,
     use_grouped_topk: bool = False,
     num_expert_group: Optional[int] = None,
     num_fused_shared_experts: int = 0,
@@ -1822,6 +1823,7 @@ def fused_moe(
         topk_ids,
         inplace=inplace,
         activation=activation,
+        apply_router_weight_on_input=apply_router_weight_on_input,
         use_fp8_w8a8=use_fp8_w8a8,
         use_int8_w8a8=use_int8_w8a8,
         use_int8_w8a16=use_int8_w8a16,

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

 # Adapted from https://github.com/vllm-project/vllm/blob/a6221a144af772fd1a68fe7e627935dc53e81738/vllm/model_executor/layers/fused_moe/layer.py
 
+import importlib
 from abc import abstractmethod
 from enum import Enum
 from typing import Callable, List, Optional, Tuple
@@ -19,6 +20,7 @@ from sglang.srt.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
 )
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_loader.weight_utils import narrow_padded_param_and_loaded_weight
 from sglang.srt.utils import (
     cpu_has_amx_support,
@@ -29,8 +31,15 @@ from sglang.srt.utils import (
     use_intel_amx_backend,
 )
 
+has_triton_kernels = importlib.util.find_spec("triton_kernels") is not None
+
 if torch.cuda.is_available():
     from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
+
+    if has_triton_kernels:
+        from sglang.srt.layers.moe.fused_moe_triton.triton_kernels_moe import (
+            triton_kernel_moe_forward,
+        )
 else:
     fused_experts = None  # type: ignore
 
@@ -87,6 +96,10 @@ class FusedMoEMethodBase(QuantizeMethodBase):
 class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
     """MoE method without quantization."""
 
+    def __init__(self, use_triton_kernels: bool = False):
+        super().__init__()
+        self.use_triton_kernels = use_triton_kernels
+
     def create_weights(
         self,
         layer: torch.nn.Module,
@@ -97,20 +110,25 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         **extra_weight_attrs,
     ):
         # Fused gate_up_proj (column parallel)
+        w13_weight_n, w13_weight_k = 2 * intermediate_size, hidden_size
+        if self.use_triton_kernels:
+            w13_weight_n, w13_weight_k = w13_weight_k, w13_weight_n
         w13_weight = torch.nn.Parameter(
-            torch.empty(
-                num_experts, 2 * intermediate_size, hidden_size, dtype=params_dtype
-            ),
+            torch.empty(num_experts, w13_weight_n, w13_weight_k, dtype=params_dtype),
             requires_grad=False,
         )
         layer.register_parameter("w13_weight", w13_weight)
         set_weight_attrs(w13_weight, extra_weight_attrs)
 
         # down_proj (row parallel)
+        w2_weight_n, w2_weight_k = (
+            hidden_size,
+            intermediate_size,
+        )
+        if self.use_triton_kernels:
+            w2_weight_n, w2_weight_k = w2_weight_k, w2_weight_n
         w2_weight = torch.nn.Parameter(
-            torch.empty(
-                num_experts, hidden_size, intermediate_size, dtype=params_dtype
-            ),
+            torch.empty(num_experts, w2_weight_n, w2_weight_k, dtype=params_dtype),
             requires_grad=False,
         )
         layer.register_parameter("w2_weight", w2_weight)
@@ -192,6 +210,17 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         no_combine: bool = False,
         routed_scaling_factor: Optional[float] = None,
     ) -> torch.Tensor:
+
+        if self.use_triton_kernels:
+            return triton_kernel_moe_forward(
+                hidden_states=x,
+                w1=layer.w13_weight,
+                w2=layer.w2_weight,
+                gating_output=router_logits,
+                topk=top_k,
+                renormalize=renormalize,
+            )
+        else:
             topk_weights, topk_ids = select_experts(
                 hidden_states=x,
                 router_logits=router_logits,
@@ -228,7 +257,9 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
                     topk_weights,
                     topk_ids,
                     activation=(
-                    ActivationType.Silu if activation == "silu" else ActivationType.Gelu
+                        ActivationType.Silu
+                        if activation == "silu"
+                        else ActivationType.Gelu
                     ),
                 )
             else:
@@ -475,9 +506,13 @@ class FusedMoE(torch.nn.Module):
         self.inplace = inplace
         self.no_combine = no_combine
 
+        self.use_triton_kernels = (
+            not _is_cpu and global_server_args_dict["enable_triton_kernel_moe"]
+        )
+
         if quant_config is None:
-            self.quant_method: Optional[QuantizeMethodBase] = (
-                UnquantizedFusedMoEMethod()
+            self.quant_method: Optional[QuantizeMethodBase] = UnquantizedFusedMoEMethod(
+                self.use_triton_kernels
             )
         else:
             self.quant_method = quant_config.get_quant_method(self, prefix)
@@ -597,6 +632,8 @@ class FusedMoE(torch.nn.Module):
             )
         else:
             if not self.use_presharded_weights:
+                if self.use_triton_kernels:
+                    loaded_weight = loaded_weight.transpose(-2, -1)
                 loaded_weight = loaded_weight.narrow(
                     shard_dim, shard_size * tp_rank, shard_size
                 )
@@ -630,6 +667,8 @@ class FusedMoE(torch.nn.Module):
             )
         else:
             if not self.use_presharded_weights:
+                if self.use_triton_kernels:
+                    loaded_weight = loaded_weight.transpose(-2, -1)
                 loaded_weight = loaded_weight.narrow(
                     shard_dim, shard_size * tp_rank, shard_size
                 )
@@ -716,6 +755,8 @@ class FusedMoE(torch.nn.Module):
         # should be whatever dimension intermediate_size is
         is_transposed = getattr(param, "is_transposed", False)
         shard_dim = SHARD_ID_TO_SHARDED_DIM[shard_id]
+        if self.use_triton_kernels:
+            is_transposed = True
         if is_transposed:
             shard_dim = int(not shard_dim)
 

python/sglang/srt/layers/moe/fused_moe_triton/triton_kernels_moe.py

+# Adapted from https://github.com/vllm-project/vllm/pull/18595/files#diff-f426a6de78c82ffec568eff6811bfbf0043dab5f87f1a8c0cffdbdcb8a81e035
+from typing import Optional
+
+import torch
+from sgl_kernel import gelu_and_mul, silu_and_mul
+from triton_kernels.matmul_ogs import matmul_ogs
+from triton_kernels.routing import GatherIndx, RoutingData, ScatterIndx, routing
+
+from sglang.srt.utils import direct_register_custom_op
+
+
+def triton_kernel_moe_forward(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    inplace: bool = False,
+    activation: str = "silu",
+    apply_router_weight_on_input: bool = False,
+    use_fp8_w8a8: bool = False,
+    per_channel_quant: bool = False,
+    global_num_experts: int = -1,
+    expert_map: Optional[torch.Tensor] = None,
+    w1_scale: Optional[torch.Tensor] = None,
+    w2_scale: Optional[torch.Tensor] = None,
+    a1_scale: Optional[torch.Tensor] = None,
+    a2_scale: Optional[torch.Tensor] = None,
+    block_shape: Optional[list[int]] = None,
+) -> torch.Tensor:
+
+    if not renormalize:
+        gating_output = torch.softmax(gating_output, dim=-1)
+    routing_data, gather_idx, scatter_idx = routing(gating_output, topk, renormalize)
+
+    return triton_kernel_fused_experts(
+        hidden_states,
+        w1,
+        w2,
+        routing_data,
+        gather_idx,
+        scatter_idx,
+        inplace=inplace,
+        activation=activation,
+        apply_router_weight_on_input=apply_router_weight_on_input,
+        use_fp8_w8a8=use_fp8_w8a8,
+        per_channel_quant=per_channel_quant,
+        global_num_experts=global_num_experts,
+        expert_map=expert_map,
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
+        a1_scale=a1_scale,
+        a2_scale=a2_scale,
+        block_shape=block_shape,
+    )
+
+
+# This is a triton implementation of the fused_experts function
+def triton_kernel_fused_experts(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    routing_data: RoutingData,
+    gather_indx: GatherIndx,
+    scatter_indx: ScatterIndx,
+    inplace: bool = False,
+    activation: str = "silu",
+    apply_router_weight_on_input: bool = False,
+    use_fp8_w8a8: bool = False,
+    per_channel_quant: bool = False,
+    global_num_experts: int = -1,
+    expert_map: Optional[torch.Tensor] = None,
+    w1_scale: Optional[torch.Tensor] = None,
+    w2_scale: Optional[torch.Tensor] = None,
+    a1_scale: Optional[torch.Tensor] = None,
+    a2_scale: Optional[torch.Tensor] = None,
+    block_shape: Optional[list[int]] = None,
+) -> torch.Tensor:
+
+    assert use_fp8_w8a8 == False, "use_fp8_w8a8 is not supported"
+    assert per_channel_quant == False, "per_channel_quant is not supported"
+    assert expert_map == None, "expert_map is not supported"
+    assert w1_scale == None, "w1_scale is not supported"
+    assert w2_scale == None, "w2_scale is not supported"
+    assert a1_scale == None, "a1_scale is not supported"
+    assert a2_scale == None, "a2_scale is not supported"
+    assert block_shape == None, "block_shape is not supported"
+
+    # type check
+    assert hidden_states.dtype == torch.bfloat16, "hidden_states must be bfloat16"
+    assert w1.dtype == torch.bfloat16, "w1 must be bfloat16"
+    assert w2.dtype == torch.bfloat16, "w2 must be bfloat16"
+
+    # Shape check
+    assert hidden_states.ndim == 2, "hidden_states must be 2D"
+    assert (
+        hidden_states.shape[-1] == w1.shape[-2]
+    ), f"hidden_states shape[-1] {hidden_states.shape} must be equal to w1 shape[-2] {w1.shape}"
+    assert (
+        w2.shape[-1] == w1.shape[1]
+    ), f"w2 shape[-1] {w2.shape[-1]} must be equal to w1 shape[1] {w1.shape[1]}"
+
+    # feature check
+    assert inplace == False, "Inplace is not supported in new triton MoE kernel"
+
+    M, K = hidden_states.shape
+    E, _, N = w1.shape
+    n_expts_act = routing_data.n_expts_act
+    dtype = hidden_states.dtype
+
+    if global_num_experts == -1:
+        global_num_experts = E
+
+    # consistent with default implementation
+    intermediate_cache2 = torch.empty(
+        (M * n_expts_act, N // 2), device="cuda", dtype=dtype
+    )
+
+    intermediate_cache1 = matmul_ogs(
+        hidden_states,
+        w1,
+        None,
+        routing_data,
+        gather_indx=gather_indx,
+        gammas=routing_data.gate_scal if apply_router_weight_on_input else None,
+    )
+
+    if activation == "silu":
+        silu_and_mul(intermediate_cache1.view(-1, N), intermediate_cache2)
+    elif activation == "gelu":
+        gelu_and_mul(intermediate_cache1.view(-1, N), intermediate_cache2)
+    else:
+        raise ValueError(f"Unsupported FusedMoe activation: {activation}")
+
+    intermediate_cache3 = matmul_ogs(
+        intermediate_cache2,
+        w2,
+        None,
+        routing_data,
+        scatter_indx=scatter_indx,
+        gammas=None if apply_router_weight_on_input else routing_data.gate_scal,
+    )
+
+    return intermediate_cache3
+
+
+def triton_kernel_moe_forward_fake(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    inplace: bool = False,
+    activation: str = "silu",
+    apply_router_weight_on_input: bool = False,
+    use_fp8_w8a8: bool = False,
+    per_channel_quant: bool = False,
+    global_num_experts: int = -1,
+    expert_map: Optional[torch.Tensor] = None,
+    w1_scale: Optional[torch.Tensor] = None,
+    w2_scale: Optional[torch.Tensor] = None,
+    a1_scale: Optional[torch.Tensor] = None,
+    a2_scale: Optional[torch.Tensor] = None,
+    block_shape: Optional[list[int]] = None,
+) -> torch.Tensor:
+    return torch.empty_like(hidden_states)
+
+
+direct_register_custom_op(
+    op_name="forward_cuda_triton",
+    op_func=triton_kernel_moe_forward,
+    mutates_args=[],
+    fake_impl=triton_kernel_moe_forward_fake,
+)

python/sglang/srt/managers/schedule_batch.py

@@ -101,6 +101,7 @@ GLOBAL_SERVER_ARGS_KEYS = [
     "triton_attention_reduce_in_fp32",
     "num_reserved_decode_tokens",
     "weight_loader_disable_mmap",
+    "enable_triton_kernel_moe",
 ]
 
 # Put some global args for easy access

python/sglang/srt/server_args.py

@@ -222,6 +222,7 @@ class ServerArgs:
     disable_chunked_prefix_cache: bool = False
     disable_fast_image_processor: bool = False
     enable_return_hidden_states: bool = False
+    enable_triton_kernel_moe: bool = False
     warmups: Optional[str] = None
 
     # Debug tensor dumps
@@ -1554,6 +1555,11 @@ class ServerArgs:
             action="store_true",
             help="Enable returning hidden states with responses.",
         )
+        parser.add_argument(
+            "--enable-triton-kernel-moe",
+            action="store_true",
+            help="Use triton moe grouped gemm kernel.",
+        )
         parser.add_argument(
             "--warmups",
             type=str,

test/srt/test_triton_fused_moe.py

+import unittest
+
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.moe.fused_moe_triton.triton_kernels_moe import (
+    triton_kernel_moe_forward,
+)
+from sglang.test.test_utils import CustomTestCase
+
+
+class TestFusedMOE(CustomTestCase):
+    NUM_EXPERTS = [8, 64]
+    TOP_KS = [2, 4]
+
+    @staticmethod
+    def create_random_cuda_tensor(shape, dtype, mean=0, std=0.01):
+        """Create a random CUDA tensor
+
+        Args:
+            shape: Tensor shape
+            dtype: Data type
+            mean: Mean value
+            std: Standard deviation
+
+        Returns:
+            torch.Tensor: Randomly initialized CUDA tensor
+        """
+        return torch.empty(shape, dtype=dtype, device="cuda").normal_(mean, std)
+
+    def get_tolerance(self, dtype):
+        """Get tolerance values for different data types
+
+        Args:
+            dtype: Data type
+
+        Returns:
+            tuple: (relative tolerance, absolute tolerance)
+        """
+        if dtype == torch.float32:
+            return 1e-5, 1e-5
+        elif dtype in [torch.float16, torch.bfloat16]:
+            return 1e-5, 1e-5
+        else:
+            return 1e-2, 1e-2  # Default values for other types
+
+    def torch_naive_moe(
+        self,
+        a,
+        w1,
+        w2,
+        score,
+        topk,
+    ):
+        B, D = a.shape
+        a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
+        out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
+        score = torch.softmax(score, dim=-1, dtype=torch.float32)
+        topk_weight, topk_ids = torch.topk(score, topk)
+        topk_weight = topk_weight.view(-1)
+        topk_ids = topk_ids.view(-1)
+
+        if w1.dtype == torch.float8_e4m3fn:
+            w1_compute = w1.to(a.dtype)
+            w2_compute = w2.to(a.dtype)
+        else:
+            w1_compute = w1
+            w2_compute = w2
+
+        for i in range(w1_compute.shape[0]):
+            mask = topk_ids == i
+            if mask.sum():
+                out[mask] = SiluAndMul()(
+                    a[mask] @ w1_compute[i].transpose(0, 1)
+                ) @ w2_compute[i].transpose(0, 1)
+
+        return (
+            out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype)
+        ).sum(dim=1)
+
+    def _test_case(self, m, n, k, e, topk, dtype):
+        rtol, atol = self.get_tolerance(dtype)
+
+        a = self.create_random_cuda_tensor((m, k), dtype)
+        w1 = self.create_random_cuda_tensor((e, 2 * n, k), dtype)
+        w2 = self.create_random_cuda_tensor((e, k, n), dtype)
+        w1_tri = w1.clone()
+        w2_tri = w2.clone()
+        w1_tri = w1_tri.transpose(-2, -1).contiguous()
+        w2_tri = w2_tri.transpose(-2, -1).contiguous()
+        score = self.create_random_cuda_tensor((m, e), dtype)
+
+        triton_output = triton_kernel_moe_forward(
+            a, w1_tri, w2_tri, score, topk, renormalize=False
+        )
+        torch_output = self.torch_naive_moe(a, w1, w2, score, topk)
+        torch.testing.assert_close(triton_output, torch_output, rtol=rtol, atol=atol)
+
+    def test_various_configurations(self):
+        m_values = [1, 32, 64, 256]
+        n_values = [128, 1024]
+        k_values = [128, 512, 1024]
+        dtypes = [torch.bfloat16]
+
+        # Calculate total number of tests
+        total_tests = (
+            len(m_values)
+            * len(n_values)
+            * len(k_values)
+            * len(self.NUM_EXPERTS)
+            * len(self.TOP_KS)
+            * len(dtypes)
+        )
+
+        # Create progress bar
+        with tqdm(total=total_tests, desc="Running MoE tests") as pbar:
+            for m in m_values:
+                for n in n_values:
+                    for k in k_values:
+                        for e in self.NUM_EXPERTS:
+                            for topk in self.TOP_KS:
+                                for dtype in dtypes:
+                                    with self.subTest(
+                                        m=m,
+                                        n=n,
+                                        k=k,
+                                        e=e,
+                                        topk=topk,
+                                        dtype=dtype,
+                                    ):
+                                        self._test_case(
+                                            m,
+                                            n,
+                                            k,
+                                            e,
+                                            topk,
+                                            dtype,
+                                        )
+                                        torch.cuda.empty_cache()
+                                    pbar.update(1)
+
+
+if __name__ == "__main__":
+    unittest.main()