feat: port NaiveBatchedExperts from ds_vllm — view transpose + cublas transB

Source: upstream_ref/ds_vllm/vllm/model_executor/layers/fused_moe/experts/fused_batched_moe.py
        upstream_ref/ds_vllm/vllm/model_executor/layers/fused_moe/activation.py

New files (ported from ds_vllm, adapted for BI-V100):
  ex_engine/moe/__init__.py
  ex_engine/moe/activation.py
    - MoEActivation enum + apply_moe_activation
    - torch.ops._C.silu_and_mul replaced with F.silu(gate)*up fallback
  ex_engine/moe/naive_batched_experts.py
    - naive_batched_moe_forward()
    - Decode: per-expert loop, w13[eid].transpose(0,1) is VIEW (zero copy)
    - @ operator → cublas passes transB=CUBLAS_OP_T internally
    - Prefill: group tokens by expert, batch @ per expert

Modified:
  qwen3_6_scripts/qwen3_5.py
    - Import naive_batched_moe_forward
    - Tier 0.5: after ix_fused_moe, before corex point-optimized loop
    - Uses existing topk routing (xllm/corex/pytorch)

Key difference from previous approach:
  - NO physical transpose (was 22ms overhead)
  - NO weight gather into contiguous buffer
  - View transpose is O(0), cublas handles transB

ex_engine/moe/naive_batched_experts.py

@@ -0,0 +1,134 @@
+"""
+naive_batched_experts.py — MoE expert computation for BI-V100
+
+Ported from:
+  upstream_ref/ds_vllm/vllm/model_executor/layers/fused_moe/experts/fused_batched_moe.py
+  class NaiveBatchedExperts.apply()
+
+Key design from upstream:
+  - w1[expert].transpose(0, 1) is a VIEW (zero copy)
+  - @ operator lets cublas pass transB=CUBLAS_OP_T internally
+  - No physical transpose, no gather of full weight matrices
+  - Per-expert loop with early exit on num_tokens == 0
+
+Adaptations for BI-V100:
+  - Removed modular_kernel / FusedMoEExpertsModular base class
+  - Removed triton kernels (BatchedTritonExperts)
+  - Removed quantization (FP8, INT8, INT4)
+  - Removed workspace_shapes / MoEActivation enum dependency
+  - activation uses F.silu directly (torch.ops._C.silu_and_mul not available)
+  - Standalone function, not a class — called from qwen3_5.py
+"""
+
+import torch
+import torch.nn.functional as F
+from typing import Optional
+
+
+def _resize_cache(x: torch.Tensor, v: tuple) -> torch.Tensor:
+    """Shrink tensor and reshape. From ds_vllm utils.py."""
+    from math import prod
+    assert prod(v) <= x.numel(), f"{v} ({prod(v)}) <= {x.shape} ({x.numel()})"
+    return x.flatten()[:prod(v)].view(*v)
+
+
+def naive_batched_moe_forward(
+    hidden_states: torch.Tensor,     # (T, H) or (1, H) for decode
+    w13: torch.Tensor,               # (E, 2*I, H) — gate+up fused weights
+    w2: torch.Tensor,                # (E, H, I)   — down weights
+    topk_ids: torch.Tensor,          # (T, top_k) — selected expert ids
+    topk_weights: torch.Tensor,      # (T, top_k) — routing weights
+    act_fn: Optional[object] = None, # SiluAndMul instance or None
+) -> torch.Tensor:
+    """
+    MoE expert forward — ported from NaiveBatchedExperts.apply().
+
+    For each selected expert:
+      1. FC1: input @ w1[expert].transpose(0, 1)  — view transpose, cublas transB
+      2. Activation: silu_and_mul (gated)
+      3. FC2: act @ w2[expert].transpose(0, 1)
+
+    Source: upstream_ref/ds_vllm/.../experts/fused_batched_moe.py lines 611-647
+    """
+    T = hidden_states.shape[0]
+    H = hidden_states.shape[1]
+    I = w2.shape[2]          # intermediate size (per partition)
+    top_k = topk_ids.shape[1]
+
+    # Output accumulator
+    out = torch.zeros(T, H, dtype=hidden_states.dtype, device=hidden_states.device)
+
+    if T == 1:
+        # === Decode path (single token) ===
+        # From NaiveBatchedExperts.apply():
+        #   input = hidden_states[expert, :num, :] @ w1[expert].transpose(0, 1)
+        #
+        # For decode, each expert sees exactly 1 token.
+        # expert ids are in topk_ids[0] (shape: top_k,)
+        eids = topk_ids[0]        # (top_k,)
+        ws = topk_weights[0]      # (top_k,)
+
+        for i in range(top_k):
+            eid = eids[i].item()
+
+            # FC1: (1, H) @ (H, 2*I) → (1, 2*I)
+            # w13[eid] is (2*I, H), .transpose(0, 1) is (H, 2*I) — VIEW, zero copy
+            # @ lets cublas use transB=CUBLAS_OP_T
+            gate_up = hidden_states @ w13[eid].transpose(0, 1)  # (1, 2*I)
+
+            # Activation: silu_and_mul
+            # From upstream apply_moe_activation():
+            #   gate = input[..., :d], up = input[..., d:]
+            #   output = F.silu(gate) * up
+            if act_fn is not None:
+                act = act_fn(gate_up)     # SiluAndMul: (1, 2*I) → (1, I)
+            else:
+                gate = gate_up[..., :I]
+                up = gate_up[..., I:]
+                act = F.silu(gate) * up   # (1, I)
+
+            # FC2: (1, I) @ (I, H) → (1, H)
+            # w2[eid] is (H, I), .transpose(0, 1) is (I, H) — VIEW, zero copy
+            expert_out = act @ w2[eid].transpose(0, 1)  # (1, H)
+
+            # Weighted accumulate
+            out += ws[i] * expert_out
+
+    else:
+        # === Prefill path (multiple tokens) ===
+        # Group tokens by expert, then batch-process each expert.
+        # From NaiveBatchedExperts.apply() — the for-expert loop.
+        flat_eids = topk_ids.reshape(-1)                 # (T * top_k,)
+        flat_weights = topk_weights.reshape(-1)           # (T * top_k,)
+        flat_token_ids = torch.arange(
+            T, device=hidden_states.device
+        ).repeat_interleave(top_k)                        # (T * top_k,)
+
+        num_experts = w13.shape[0]
+        for expert in range(num_experts):
+            mask = (flat_eids == expert)
+            if not mask.any():
+                continue
+
+            token_ids = flat_token_ids[mask]               # tokens assigned to this expert
+            weights = flat_weights[mask]                   # their routing weights
+            expert_input = hidden_states[token_ids]        # (num, H)
+
+            # FC1: (num, H) @ (H, 2*I) → (num, 2*I)
+            gate_up = expert_input @ w13[expert].transpose(0, 1)
+
+            # Activation
+            if act_fn is not None:
+                act = act_fn(gate_up)
+            else:
+                gate = gate_up[..., :I]
+                up = gate_up[..., I:]
+                act = F.silu(gate) * up
+
+            # FC2: (num, I) @ (I, H) → (num, H)
+            expert_out = act @ w2[expert].transpose(0, 1)
+
+            # Weighted scatter-add back
+            out.index_add_(0, token_ids, expert_out * weights.unsqueeze(1))
+
+    return out