[MoE Refactor] Move Test Impl into Test Dirs (#32129)

Signed-off-by: Robert Shaw <rshaw@neuralmagic.com>
Co-authored-by: Robert Shaw <rshaw@neuralmagic.com>

tests/kernels/moe/test_moe.py

@@ -37,9 +37,6 @@ from vllm.model_executor.layers.fused_moe.fused_moe import (
     fused_topk,
     modular_triton_fused_moe,
 )
-from vllm.model_executor.layers.fused_moe.moe_torch_iterative import (
-    fused_moe as iterative_moe,
-)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     marlin_permute_bias,
 )
@@ -61,6 +58,64 @@ from vllm.scalar_type import ScalarType, scalar_types
 from vllm.utils.torch_utils import set_random_seed
 from vllm.v1.worker.workspace import init_workspace_manager
 
+
+def iterative_moe(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    global_num_experts: int,
+    expert_map: torch.Tensor = None,
+    renormalize: bool = False,
+) -> torch.Tensor:
+    """
+    Baseline implementation of fused moe.
+
+    Args:
+        hidden_states: [*, hidden_size]
+        w1: [num_experts, intermediate_size * 2, hidden_size]
+        w2: [num_experts, hidden_size, intermediate_size]
+        gating_output: [*, num_experts]
+        expert_map: [num_experts]
+    """
+    orig_shape = hidden_states.shape
+    hidden_size = hidden_states.shape[-1]
+    num_tokens = hidden_states.shape[:-1].numel()
+    num_experts = w1.shape[0]
+    intermediate_size = w2.shape[-1]
+    dtype = hidden_states.dtype
+
+    hidden_states = hidden_states.view(num_tokens, hidden_size)
+    gating_output = gating_output.view(num_tokens, global_num_experts)
+    topk_weights = gating_output.softmax(dim=-1, dtype=torch.float)
+    topk_weights, selected_experts = topk_weights.topk(topk, dim=-1)
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+    topk_weights = topk_weights.to(dtype)
+
+    if expert_map is not None:
+        selected_experts = expert_map[selected_experts]
+
+    final_hidden_states = None
+    for expert_idx in range(num_experts):
+        expert_w1 = w1[expert_idx]
+        expert_w2 = w2[expert_idx]
+        expert_mask = selected_experts == expert_idx
+        expert_weights = (topk_weights * expert_mask).sum(dim=-1, keepdim=True)
+        x = F.linear(hidden_states, expert_w1)
+        gate = F.silu(x[:, :intermediate_size])
+        x = x[:, intermediate_size:] * gate
+        x = F.linear(x, expert_w2)
+        current_hidden_states = x * expert_weights
+        if final_hidden_states is None:
+            final_hidden_states = current_hidden_states
+        else:
+            final_hidden_states = final_hidden_states + current_hidden_states
+
+    return final_hidden_states.view(orig_shape)  # type: ignore
+
+
 NUM_EXPERTS = [8, 64, 192]
 NUM_EXPERTS_LARGE = [128, 256]
 EP_SIZE = [1, 4]