[CI] Add FusedMoE example (#555)

* [CI] Add FusedMoE example

* Lint

* Fix import bug

* Fix comment bug

* Update example_fusedmoe_torch.py

---------
Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>

examples/fusedmoe/example_fusedmoe_tilelang.py

+import math
+import torch
+import torch.nn as nn
+from typing import Dict, Tuple, Optional
+import tilelang
+import tilelang.language as T
+from tilelang.autotuner import *
+from example_fusedmoe_torch import *
+
+# tilelang.disable_cache()
+
+
+@tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True})
+def moe_forward_tilelang_shared(d_hidden,
+                                d_expert,
+                                n_shared_experts,
+                                dtype,
+                                num_tokens,
+                                block_token=128,
+                                block_dhidden=128,
+                                block_dexpert=128,
+                                threads=256,
+                                num_stages=1):
+
+    scale = 1.44269504  # log2(e)
+
+    # Parameters
+    dhidden = d_hidden
+    dexpert = d_expert * n_shared_experts
+
+    # Tensors: Note that input shape is reshape to (num_tokens, dhidden)
+    input_shape = (num_tokens, dhidden)
+    shared_W_gate_shape = (dexpert, dhidden)
+    shared_W_up_shape = (dexpert, dhidden)
+    shared_W_down_shape = (dhidden, dexpert)
+
+    accum_type = "float32"
+
+    @T.prim_func
+    def kernel_shared(
+            input: T.Tensor(input_shape, dtype),  # type: ignore
+            shared_W_gate: T.Tensor(shared_W_gate_shape, dtype),  # type: ignore
+            shared_W_up: T.Tensor(shared_W_up_shape, dtype),  # type: ignore
+            shared_W_down: T.Tensor(shared_W_down_shape, dtype),  # type: ignore
+            up_logits: T.Tensor((num_tokens, dexpert), dtype),  # type: ignore
+            output: T.Tensor(input_shape, dtype),  # type: ignore
+    ):
+        # Step 1: Compute gate and up logits
+        with T.Kernel(
+                T.ceildiv(num_tokens, block_token), T.ceildiv(dexpert, block_dexpert),
+                threads=threads) as (bx, by):
+            # Split the block to shared experts and routed experts
+            input_shared = T.alloc_fragment((block_token, block_dhidden), dtype=dtype)
+            W_gate_shared = T.alloc_shared((block_dexpert, block_dhidden), dtype=dtype)
+            W_up_shared = T.alloc_shared((block_dexpert, block_dhidden), dtype=dtype)
+            # Shared experts: no need to check expert_indices
+
+            gate_logits_local = T.alloc_fragment((block_token, block_dexpert), dtype=accum_type)
+            up_logits_local = T.alloc_fragment((block_token, block_dexpert), dtype=accum_type)
+
+            T.use_swizzle(10)
+            T.clear(gate_logits_local)
+            T.clear(up_logits_local)
+
+            # Parallel for gate and up matmul
+            for k in T.Pipelined(T.ceildiv(dhidden, block_dhidden), num_stages=num_stages):
+                T.copy(input[bx * block_token, k * block_dhidden], input_shared)
+                T.copy(shared_W_gate[by * block_dexpert, k * block_dhidden], W_gate_shared)
+                T.copy(shared_W_up[by * block_dexpert, k * block_dhidden], W_up_shared)
+                T.gemm(input_shared, W_gate_shared, gate_logits_local, transpose_B=True)
+                T.gemm(input_shared, W_up_shared, up_logits_local, transpose_B=True)
+
+            # Fuse with SiLU and element-wise product
+            for i, j in T.Parallel(block_token, block_dexpert):
+                gate_logits_local[i, j] = gate_logits_local[i, j] * (
+                    1.0 / (1.0 + T.exp2(-gate_logits_local[i, j] * scale)))
+                up_logits_local[i, j] = up_logits_local[i, j] * gate_logits_local[i, j]
+
+            T.copy(up_logits_local, up_logits[bx * block_token, by * block_dexpert])
+
+        # Step 2: Compute down logits
+        with T.Kernel(
+                T.ceildiv(num_tokens, block_token), T.ceildiv(dhidden, block_dhidden),
+                threads=threads) as (bx, by):
+            up_logits_shared = T.alloc_fragment((block_token, block_dexpert), dtype=dtype)
+            W_down_shared = T.alloc_shared((block_dhidden, block_dexpert), dtype=dtype)
+            output_local = T.alloc_fragment((block_token, block_dhidden), dtype=accum_type)
+
+            T.use_swizzle(10)
+            T.clear(output_local)
+
+            for k in T.Pipelined(T.ceildiv(dexpert, block_dexpert), num_stages=num_stages):
+                T.copy(up_logits[bx * block_token, k * block_dexpert], up_logits_shared)
+                T.copy(shared_W_down[by * block_dhidden, k * block_dexpert], W_down_shared)
+                T.gemm(up_logits_shared, W_down_shared, output_local, transpose_B=True)
+
+            T.copy(output_local, output[bx * block_token, by * block_dhidden])
+
+    return kernel_shared
+
+
+@tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True})
+def moe_forward_tilelang_routed(d_hidden,
+                                d_expert,
+                                n_routed_experts,
+                                dtype,
+                                group_sum,
+                                group_count,
+                                block_token=128,
+                                block_dhidden=128,
+                                block_dexpert=128,
+                                threads=256,
+                                num_stages=1,
+                                k_pack=1,
+                                coalesced_width=None):
+
+    scale = 1.44269504  # log2(e)
+
+    # Parameters
+    dhidden = d_hidden
+    dexpert = d_expert
+    n_routed_experts = n_routed_experts
+
+    # Group info
+    # group_sum = sum(group_sizes_list)
+    # group_count = len(group_sizes_list)
+    # M = sum([(group_size + block_token - 1) // block_token for group_size in group_sizes_list])
+    M = math.ceil(group_sum / block_token) + group_count
+    accum_dtype = "float32"
+
+    # Tensors: Note that input shape is reshape to (bs * seq_len * n_experts_per_token, dhidden) for grouped gemm
+    input_shape = (group_sum, dhidden)
+    intermediate_shape = (group_sum, dexpert)
+    routed_expert_gate_shape = (n_routed_experts, dexpert, dhidden)
+    routed_expert_up_shape = (n_routed_experts, dexpert, dhidden)
+    routed_expert_down_shape = (n_routed_experts, dhidden, dexpert)
+    routed_expert_weights_shape = (group_sum)
+    group_sizes_shape = (n_routed_experts)
+
+    @T.prim_func
+    def kernel(
+            input: T.Tensor(input_shape, dtype),  # type: ignore
+            routed_expert_gate: T.Tensor(routed_expert_gate_shape, dtype),  # type: ignore
+            routed_expert_up: T.Tensor(routed_expert_up_shape, dtype),  # type: ignore
+            routed_expert_down: T.Tensor(routed_expert_down_shape, dtype),  # type: ignore
+            routed_expert_weights: T.Tensor(routed_expert_weights_shape, dtype),  # type: ignore
+            group_sizes: T.Tensor(group_sizes_shape, "int32"),  # type: ignore
+            group_offsets: T.Tensor(group_sizes_shape, "int32"),  # type: ignore
+            group_padded_offsets: T.Tensor(group_sizes_shape, "int32"),  # type: ignore
+            group_idx_for_bx: T.Tensor((M,), "int32"),  # type: ignore
+            up_logits: T.Tensor(intermediate_shape, dtype),  # type: ignore
+            output: T.Tensor(input_shape, dtype),  # type: ignore
+    ):
+        # Step 1: Compute gate and up logits
+        with T.Kernel(M, T.ceildiv(dexpert, block_dexpert), threads=threads) as (bx, by):
+            input_shared = T.alloc_fragment((block_token, block_dhidden), dtype=dtype)
+            routed_expert_gate_shared = T.alloc_shared((block_dexpert, block_dhidden), dtype=dtype)
+            routed_expert_up_shared = T.alloc_shared((block_dexpert, block_dhidden), dtype=dtype)
+
+            gate_logits_local = T.alloc_fragment((block_token, block_dexpert), dtype=accum_dtype)
+            up_logits_local = T.alloc_fragment((block_token, block_dexpert), dtype=accum_dtype)
+
+            cur_group_idx = T.alloc_local([1], "int32")
+            cur_group_size = T.alloc_local([1], "int32")
+
+            T.use_swizzle(10, enable=True)
+
+            m_start_padded = bx * block_token
+
+            cur_group_idx[0] = group_idx_for_bx[bx]
+
+            cur_group_size[0] = group_sizes[cur_group_idx[0]]
+            m_start = m_start_padded - group_padded_offsets[cur_group_idx[0]] + group_offsets[
+                cur_group_idx[0]]
+            actual_rows = T.max(
+                0,
+                T.min(block_token, cur_group_size[0] -
+                      (m_start_padded - group_padded_offsets[cur_group_idx[0]])))
+
+            T.clear(gate_logits_local)
+            T.clear(up_logits_local)
+
+            for k in T.Pipelined(T.ceildiv(dhidden, block_dhidden), num_stages=num_stages):
+                T.copy(
+                    input[m_start:m_start + block_token, k * block_dhidden:(k + 1) * block_dhidden],
+                    input_shared,
+                    coalesced_width=coalesced_width)
+                T.copy(
+                    routed_expert_gate[cur_group_idx[0],
+                                       by * block_dexpert:(by + 1) * block_dexpert,
+                                       k * block_dhidden:(k + 1) * block_dhidden],
+                    routed_expert_gate_shared,
+                    coalesced_width=coalesced_width)
+                T.gemm(
+                    input_shared,
+                    routed_expert_gate_shared,
+                    gate_logits_local,
+                    k_pack=k_pack,
+                    transpose_B=True)
+                T.copy(
+                    routed_expert_up[cur_group_idx[0], by * block_dexpert:(by + 1) * block_dexpert,
+                                     k * block_dhidden:(k + 1) * block_dhidden],
+                    routed_expert_up_shared,
+                    coalesced_width=coalesced_width)
+                T.gemm(
+                    input_shared,
+                    routed_expert_up_shared,
+                    up_logits_local,
+                    k_pack=k_pack,
+                    transpose_B=True)
+
+            for i, j in T.Parallel(block_token, block_dexpert):
+                gate_logits_local[i, j] = gate_logits_local[i, j] * (
+                    1.0 / (1.0 + T.exp2(-gate_logits_local[i, j] * scale)))
+                up_logits_local[i, j] = up_logits_local[i, j] * gate_logits_local[i, j]
+
+            for i, j in T.Parallel(block_token, block_dexpert):
+                with T.If(i < actual_rows), T.Then():
+                    up_logits[m_start + i, by * block_dexpert + j] = up_logits_local[i, j]
+
+        # Step 2: Compute down logits
+        with T.Kernel(M, T.ceildiv(dhidden, block_dhidden), threads=threads) as (bx, by):
+            up_logits_shared = T.alloc_fragment((block_token, block_dexpert), dtype=dtype)
+            routed_expert_down_shared = T.alloc_shared((block_dhidden, block_dexpert), dtype=dtype)
+            output_local = T.alloc_fragment((block_token, block_dhidden), dtype=accum_dtype)
+
+            cur_group_idx = T.alloc_local([1], "int32")
+            cur_group_size = T.alloc_local([1], "int32")
+
+            T.use_swizzle(10, enable=True)
+
+            m_start_padded = bx * block_token
+
+            cur_group_idx[0] = group_idx_for_bx[bx]
+
+            cur_group_size[0] = group_sizes[cur_group_idx[0]]
+            m_start = m_start_padded - group_padded_offsets[cur_group_idx[0]] + group_offsets[
+                cur_group_idx[0]]
+            actual_rows = T.max(
+                0,
+                T.min(block_token, cur_group_size[0] -
+                      (m_start_padded - group_padded_offsets[cur_group_idx[0]])))
+
+            T.clear(output_local)
+
+            for k in T.Pipelined(T.ceildiv(dexpert, block_dexpert), num_stages=num_stages):
+                T.copy(
+                    up_logits[m_start:m_start + block_token,
+                              k * block_dexpert:(k + 1) * block_dexpert],
+                    up_logits_shared,
+                    coalesced_width=coalesced_width)
+                T.copy(
+                    routed_expert_down[cur_group_idx[0],
+                                       by * block_dhidden:(by + 1) * block_dhidden,
+                                       k * block_dexpert:(k + 1) * block_dexpert],
+                    routed_expert_down_shared,
+                    coalesced_width=coalesced_width)
+                T.gemm(
+                    up_logits_shared,
+                    routed_expert_down_shared,
+                    output_local,
+                    k_pack=k_pack,
+                    transpose_B=True)
+
+            for i, j in T.Parallel(block_token, block_dhidden):
+                with T.If(i < actual_rows), T.Then():
+                    output[m_start + i, by * block_dhidden +
+                           j] = output_local[i, j] * routed_expert_weights[m_start + i]
+
+    return kernel
+
+
+class Expert(nn.Module):
+
+    def __init__(self,
+                 config: Dict,
+                 gate: torch.Tensor,
+                 up: torch.Tensor,
+                 down: torch.Tensor,
+                 d_expert: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.act_fn = nn.SiLU()
+        self.d_hidden: int = config["d_hidden"]
+        self.d_expert: int = config["d_expert"] if d_expert is None else d_expert
+        self.device = torch.device("cuda")
+
+        self.W_gate_weight = gate.t().contiguous().to(self.device)
+        self.W_up_weight = up.t().contiguous().to(self.device)
+        self.W_down_weight = down.t().contiguous().to(self.device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate = self.act_fn(x @ self.W_gate_weight)
+        out = (gate * (x @ self.W_up_weight)) @ self.W_down_weight
+        return out
+
+
+class MoEGate(nn.Module):
+
+    def __init__(self, config: Dict, weights: Dict):
+        super().__init__()
+        self.top_k: int = config["n_experts_per_token"]
+        self.num_experts: int = config["n_routed_experts"]
+        self.d_hidden: int = config["d_hidden"]
+
+        self.W_g_weight = weights['router.weight'].t()
+
+    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        logits = x @ self.W_g_weight
+        scores = logits.softmax(dim=-1)
+        topk_scores, topk_indices = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+        return topk_indices, topk_scores
+
+
+class MoE(nn.Module):
+
+    def __init__(self,
+                 config: Dict,
+                 shared_kernel: tilelang.JITKernel,
+                 routed_kernel: tilelang.JITKernel,
+                 weights: Dict,
+                 padding_M: int = 128):
+        super().__init__()
+        self.config = config
+        self.shared_kernel = shared_kernel
+        self.routed_kernel = routed_kernel
+        self.padding_M = padding_M
+        self.experts = nn.ModuleList([
+            Expert(
+                config,
+                gate=weights[f'experts.{i}.0.weight'],
+                up=weights[f'experts.{i}.1.weight'],
+                down=weights[f'experts.{i}.2.weight']) for i in range(config["n_routed_experts"])
+        ])
+        self.device = torch.device("cuda")
+        self.gating_network = MoEGate(config, weights).to(self.device)
+        shared_expert_dim = config["d_expert"] * config["n_shared_experts"]
+        self.shared_expert = Expert(
+            config=config,
+            gate=weights['shared_experts.0.weight'],
+            up=weights['shared_experts.1.weight'],
+            down=weights['shared_experts.2.weight'],
+            d_expert=shared_expert_dim).to(self.device)
+        self.expert_cache = torch.zeros(
+            (config["batch_size"] * config["seq_len"], config["d_hidden"]),
+            dtype=torch.float16,
+            device=self.device)
+        self.stacked_expert_w_gate = torch.stack([expert.W_gate_weight for expert in self.experts],
+                                                 dim=0)
+        self.stacked_expert_w_up = torch.stack([expert.W_up_weight for expert in self.experts],
+                                               dim=0)
+        self.stacked_expert_w_down = torch.stack([expert.W_down_weight for expert in self.experts],
+                                                 dim=0)
+        self.stacked_expert_tokens = torch.empty(
+            (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"],
+             self.config["d_hidden"]),
+            dtype=torch.float16,
+            device=self.device)
+        self.stacked_expert_weights = torch.empty(
+            (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"], 1),
+            dtype=torch.float16,
+            device=self.device)
+        self.stacked_expert_tokens_idxs = torch.empty(
+            (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"], 1),
+            dtype=torch.int64,
+            device=self.device)
+
+        self.up_logits_shared = torch.empty(
+            (config["batch_size"] * config["seq_len"], self.config["d_expert"]),
+            dtype=torch.float16,
+            device=self.device)
+        self.expert_output_shared = torch.empty(
+            (config["batch_size"] * config["seq_len"], self.config["d_hidden"]),
+            dtype=torch.float16,
+            device=self.device)
+        self.up_logits_routed = torch.empty(
+            (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"],
+             self.config["d_expert"]),
+            dtype=torch.float16,
+            device=self.device)
+        self.expert_output_routed = torch.empty(
+            (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"],
+             self.config["d_hidden"]),
+            dtype=torch.float16,
+            device=self.device)
+
+    @torch.no_grad()
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        orig_shape = x.shape
+        batch_size, seq_len, hidden_dim = x.shape
+        expert_indices, expert_scores = self.gating_network(x)
+        flat_expert_indices = expert_indices.view(-1)
+        flat_expert_weights = expert_scores.view(-1, 1)
+        x_flat = x.view(-1, hidden_dim)
+
+        # Prepare for grouped GEMM
+        idxs = flat_expert_indices.argsort()
+        counts = flat_expert_indices.bincount().cpu().numpy()
+        # counts = flat_expert_indices.bincount()
+        tokens_per_expert = counts.cumsum()
+        # tokens_per_expert = torch.cumsum(counts, dim=0)
+        num_per_tok = self.config["n_experts_per_token"]
+        token_idxs = idxs // num_per_tok
+
+        # Get stacked expert tokens and expert weights
+
+        for expert_id, end_idx in enumerate(tokens_per_expert):
+            start_idx = 0 if expert_id == 0 else tokens_per_expert[expert_id - 1]
+            if start_idx == end_idx:
+                continue
+
+            exp_token_idxs = token_idxs[start_idx:end_idx]
+            expert_tokens = x_flat[exp_token_idxs]
+
+            self.stacked_expert_tokens[start_idx:end_idx] = expert_tokens
+            self.stacked_expert_tokens_idxs[start_idx:end_idx, 0] = exp_token_idxs
+            self.stacked_expert_weights[start_idx:end_idx] = flat_expert_weights[
+                idxs[start_idx:end_idx]]
+
+        group_sizes = torch.tensor(counts, dtype=torch.int32, device=self.device)
+        group_offset = torch.tensor(
+            tokens_per_expert - counts, dtype=torch.int32, device=self.device)
+
+        group_padded_offsets = [0 for _ in range(len(group_sizes))]
+        for i in range(1, len(group_sizes)):
+            group_padded_offsets[i] = group_padded_offsets[i - 1] + math.ceil(
+                (counts[i - 1] + 1) / self.padding_M) * self.padding_M
+
+        block_token = 128
+        M = math.ceil(
+            self.config["batch_size"] * self.config["seq_len"] *
+            self.config["n_experts_per_token"] / block_token) + self.config["n_routed_experts"]
+        group_idx_for_bx = [0 for _ in range(M)]
+
+        for bx in range(M):
+            m_start_padded = bx * block_token
+            for i in range(self.config["n_routed_experts"]):
+                if m_start_padded >= group_padded_offsets[i]:
+                    group_idx_for_bx[bx] = i
+
+        group_padded_offsets = torch.tensor(
+            group_padded_offsets, dtype=torch.int32, device=self.device)
+        group_idx_for_bx = torch.tensor(group_idx_for_bx, dtype=torch.int32, device=self.device)
+
+        # Multi-stream execution
+        shared_stream = torch.cuda.Stream()
+        routed_stream = torch.cuda.default_stream()
+        torch.cuda.synchronize()
+
+        with torch.cuda.stream(routed_stream):
+            # Tilelang version: Grouped GEMM
+            self.routed_kernel(self.stacked_expert_tokens, self.stacked_expert_w_gate,
+                               self.stacked_expert_w_up, self.stacked_expert_w_down,
+                               self.stacked_expert_weights, group_sizes, group_offset,
+                               group_padded_offsets, group_idx_for_bx, self.up_logits_routed,
+                               self.expert_output_routed)
+
+            # Scatter reduce
+            self.expert_cache = torch.scatter_reduce(
+                self.expert_cache,
+                0,
+                self.stacked_expert_tokens_idxs.view(-1, 1).repeat(1, x_flat.shape[-1]),
+                self.expert_output_routed,
+                reduce='sum')
+            routed_output = self.expert_cache.view(*orig_shape)
+
+        with torch.cuda.stream(shared_stream):
+
+            self.shared_kernel(x_flat, self.shared_expert.W_gate_weight,
+                               self.shared_expert.W_up_weight, self.shared_expert.W_down_weight,
+                               self.up_logits_shared, self.expert_output_shared)
+            shared_output = self.expert_output_shared.view(*orig_shape)
+
+        torch.cuda.synchronize()
+
+        return shared_output + routed_output
+
+
+def custom_kernel(data: Tuple[torch.Tensor, Dict, Dict]) -> torch.Tensor:
+    """
+    DeepSeek-style Mixture of Experts using Tilelang.
+    
+    Args:
+        data: Tuple of (input: torch.Tensor, weights: Dict[str, torch.Tensor], config: Dict)
+            - input: Input tensor of shape [batch_size, seq_len, hidden_size]
+            - weights: Dictionary containing model weights
+            - config: Dictionary containing model configuration parameters
+            
+    Returns:
+        Tuple containing:
+            - output: Processed tensor [batch_size, seq_len, d_model]
+    """
+    input_tensor, weights, config = data
+
+    dtype_str = "float16"
+
+    shared_kernel = moe_forward_tilelang_shared(
+        config["d_hidden"],
+        config["d_expert"],
+        config["n_shared_experts"],
+        dtype=dtype_str,
+        num_tokens=config["batch_size"] * config["seq_len"])
+    routed_kernel = moe_forward_tilelang_routed(
+        config["d_hidden"],
+        config["d_expert"],
+        config["n_routed_experts"],
+        dtype=dtype_str,
+        group_sum=config["batch_size"] * config["seq_len"] * config["n_experts_per_token"],
+        group_count=config["n_routed_experts"],
+        block_token=128,
+        block_dhidden=128,
+        block_dexpert=128,
+        threads=256,
+        num_stages=1,
+        k_pack=1,
+        coalesced_width=2)
+
+    moe = MoE(config, shared_kernel, routed_kernel, weights, padding_M=128)
+
+    output = moe(input_tensor)
+
+    return output
+
+
+def main():
+    config = {
+        "dhidden": 7168,
+        "dexpert": 2048,
+        "nroutedexperts": 8,
+        "nsharedexperts": 1,
+        "nexpertspertoken": 4,
+        "bs": 1,
+        "seqlen": 8192,
+        "seed": 81394
+    }
+
+    data = generate_input(**config)
+
+    torch.cuda.synchronize()
+    ref_output = ref_kernel(clone_data(data)).to(torch.float32)
+    torch.cuda.synchronize()
+    tilelang_output = custom_kernel(clone_data(data)).to(torch.float32)
+    torch.cuda.synchronize()
+
+    torch.testing.assert_close(ref_output, tilelang_output, atol=1e-2, rtol=1e-2)
+    print("✅ Tilelang and Torch match")
+
+
+if __name__ == "__main__":
+    main()

examples/fusedmoe/example_fusedmoe_torch.py

+import math
+import torch
+import torch.nn as nn
+from typing import Dict, Tuple, Optional
+
+
+# Reference code in PyTorch
+class ExpertTorch(nn.Module):
+
+    def __init__(self, config: Dict, d_expert: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.act_fn = nn.SiLU()
+        self.d_hidden: int = config["d_hidden"]
+        self.d_expert: int = config["d_expert"] if d_expert is None else d_expert
+
+        self.W_gate = nn.Linear(self.d_hidden, self.d_expert, bias=False)
+        self.W_up = nn.Linear(self.d_hidden, self.d_expert, bias=False)
+        self.W_down = nn.Linear(self.d_expert, self.d_hidden, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate = self.act_fn(self.W_gate(x))
+        out = self.W_down(gate * self.W_up(x))
+        return out
+
+
+class MoEGateTorch(nn.Module):
+
+    def __init__(self, config: Dict):
+        super().__init__()
+        self.top_k: int = config["n_experts_per_token"]
+        self.num_experts: int = config["n_routed_experts"]
+        self.d_hidden: int = config["d_hidden"]
+
+        self.W_g = nn.Linear(self.d_hidden, self.num_experts, bias=False)
+
+    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        logits = self.W_g(x)
+        scores = logits.softmax(dim=-1)
+        topk_scores, topk_indices = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+        return topk_indices, topk_scores
+
+
+class MoETorch(nn.Module):
+
+    def __init__(self, config: Dict):
+        super().__init__()
+        self.config = config
+        self.experts = nn.ModuleList(
+            [ExpertTorch(config) for _ in range(config["n_routed_experts"])])
+        self.gating_network = MoEGateTorch(config)
+        shared_expert_dim = config["d_expert"] * config["n_shared_experts"]
+        self.shared_expert = ExpertTorch(config=config, d_expert=shared_expert_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shared_output = self.shared_expert(x)
+        expert_indices, expert_scores = self.gating_network(x)
+        batch_size, seq_len, hidden_dim = x.shape
+        orig_shape = x.shape
+        x_flat = x.view(-1, hidden_dim)
+        flat_expert_indices = expert_indices.view(-1)
+        flat_expert_weights = expert_scores.view(-1, 1)
+        routed_output_flat = self.moe_infer(x_flat, flat_expert_indices, flat_expert_weights)
+
+        routed_output = routed_output_flat.view(*orig_shape)
+        return routed_output + shared_output
+
+    @torch.no_grad()
+    def moe_infer(self, x: torch.Tensor, flat_expert_indices: torch.Tensor,
+                  flat_expert_weights: torch.Tensor) -> torch.Tensor:
+        expert_cache = torch.zeros_like(x)
+        # test_expert_cache = torch.zeros((x.shape[0] * self.config["n_experts_per_token"], self.config["d_hidden"]))
+        # test_expert_tokens = torch.zeros((x.shape[0] * self.config["n_experts_per_token"], self.config["d_hidden"]))
+        # test_expert_ups = torch.zeros((self.config["n_routed_experts"], self.config["d_hidden"], self.config["d_expert"]))
+        # test_expert_tokens_num = torch.zeros((self.config["n_routed_experts"]))
+
+        idxs = flat_expert_indices.argsort()
+        counts = flat_expert_indices.bincount().cpu().numpy()
+        tokens_per_expert = counts.cumsum()
+        num_per_tok = self.config["n_experts_per_token"]
+        token_idxs = idxs // num_per_tok
+        for expert_id, end_idx in enumerate(tokens_per_expert):
+            start_idx = 0 if expert_id == 0 else tokens_per_expert[expert_id - 1]
+            if start_idx == end_idx:
+                continue
+
+            expert = self.experts[expert_id]
+            exp_token_idxs = token_idxs[start_idx:end_idx]
+            expert_tokens = x[exp_token_idxs]
+            expert_out = expert(expert_tokens)
+
+            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
+            expert_cache.scatter_reduce_(
+                0, exp_token_idxs.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce='sum')
+
+        return expert_cache
+
+
+def ref_kernel(data: Tuple[torch.Tensor, Dict, Dict]) -> torch.Tensor:
+    """
+    Reference implementation of DeepSeek-style Mixture of Experts using PyTorch.
+    
+    Args:
+        data: Tuple of (input: torch.Tensor, weights: Dict[str, torch.Tensor], config: Dict)
+            - input: Input tensor of shape [batch_size, seq_len, hidden_dim]
+            - weights: Dictionary containing model weights
+            - config: Dictionary containing model configuration parameters
+            
+    Returns:
+        Tuple containing:
+            - output: Processed tensor [batch_size, seq_len, d_model]
+    """
+    input_tensor, weights, config = data
+    num_experts = config["n_routed_experts"]
+    moe = MoETorch(config)
+
+    # Fill in the given weights of the model
+    moe.gating_network.W_g.weight = nn.Parameter(weights['router.weight'])
+
+    for i in range(num_experts):
+        gate_proj_weight = weights[f'experts.{i}.0.weight']
+        up_proj_weight = weights[f'experts.{i}.1.weight']
+        down_proj_weight = weights[f'experts.{i}.2.weight']
+
+        # Transpose weights to match expected shape for nn.Linear
+        moe.experts[i].W_gate.weight = nn.Parameter(gate_proj_weight.t())
+        moe.experts[i].W_up.weight = nn.Parameter(up_proj_weight.t())
+        moe.experts[i].W_down.weight = nn.Parameter(down_proj_weight.t())
+
+    moe.shared_expert.W_gate.weight = nn.Parameter(weights['shared_experts.0.weight'].t())
+    moe.shared_expert.W_up.weight = nn.Parameter(weights['shared_experts.1.weight'].t())
+    moe.shared_expert.W_down.weight = nn.Parameter(weights['shared_experts.2.weight'].t())
+
+    output = moe(input_tensor)
+
+    return output
+
+
+# Input generation for the reference code
+
+
+def generate_input(dhidden: int, dexpert: int, nroutedexperts: int, nsharedexperts: int,
+                   nexpertspertoken: int, bs: int, seqlen: int,
+                   seed: int) -> Tuple[torch.Tensor, Dict, Dict]:
+
+    # Really dumb but for now _ isn't parsing correctly.
+    d_hidden = dhidden
+    d_expert = dexpert
+    n_routed_experts = nroutedexperts
+    n_shared_experts = nsharedexperts
+    n_experts_per_token = nexpertspertoken
+    batch_size = bs
+    seq_len = seqlen
+
+    config = {
+        "d_hidden": d_hidden,
+        "d_expert": d_expert,
+        "n_routed_experts": n_routed_experts,
+        "n_shared_experts": n_shared_experts,
+        "n_experts_per_token": n_experts_per_token,
+        "batch_size": batch_size,
+        "seq_len": seq_len,
+    }
+
+    gen = torch.Generator(device='cuda')
+    gen.manual_seed(seed)
+
+    num_experts = n_routed_experts
+    expert_dim = d_expert
+    weights = {}
+
+    input_tensor = torch.randn((batch_size, seq_len, d_hidden),
+                               device='cuda',
+                               dtype=torch.float16,
+                               generator=gen).contiguous()
+
+    # Initialize router weights
+    weights['router.weight'] = torch.randn(
+        (num_experts, d_hidden), device="cuda", dtype=torch.float16,
+        generator=gen) / math.sqrt(d_hidden)
+
+    for i in range(num_experts):
+        weights[f'experts.{i}.0.weight'] = torch.randn(
+            (d_hidden, expert_dim), device='cuda', dtype=torch.float16,
+            generator=gen) / math.sqrt(expert_dim)
+
+        weights[f'experts.{i}.1.weight'] = torch.randn(
+            (d_hidden, expert_dim), device='cuda', dtype=torch.float16,
+            generator=gen) / math.sqrt(expert_dim)
+
+        weights[f'experts.{i}.2.weight'] = torch.randn(
+            (expert_dim, d_hidden), device='cuda', dtype=torch.float16,
+            generator=gen) / math.sqrt(d_hidden)
+
+    weights['shared_experts.0.weight'] = torch.randn(
+        (d_hidden, expert_dim * n_shared_experts),
+        device='cuda',
+        dtype=torch.float16,
+        generator=gen) / math.sqrt(expert_dim * n_shared_experts)
+    weights['shared_experts.1.weight'] = torch.randn(
+        (d_hidden, expert_dim * n_shared_experts),
+        device='cuda',
+        dtype=torch.float16,
+        generator=gen) / math.sqrt(expert_dim * n_shared_experts)
+    weights['shared_experts.2.weight'] = torch.randn((expert_dim * n_shared_experts, d_hidden),
+                                                     device='cuda',
+                                                     dtype=torch.float16,
+                                                     generator=gen) / math.sqrt(d_hidden)
+
+    return (input_tensor, weights, config)
+
+
+def clone_data(data):
+    """
+    Recursively goes through data and clones all tensors.
+    """
+    if isinstance(data, tuple):
+        return tuple(clone_data(x) for x in data)
+    elif isinstance(data, list):
+        return [clone_data(x) for x in data]
+    elif isinstance(data, dict):
+        return {k: clone_data(v) for k, v in data.items()}
+    elif isinstance(data, torch.Tensor):
+        return data.clone()
+    else:
+        return data

examples/fusedmoe/test_example_fusedmoe.py

+import tilelang.testing
+import example_fusedmoe_tilelang
+
+
+def test_example_fusedmoe_tilelang():
+    example_fusedmoe_tilelang.main()
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()