preserve qwen shared expert execution order

defuser/modeling/unfused_moe/qwen2_moe.py

@@ -34,6 +34,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         """Route tokens exactly like HF Qwen2 MoE, then run explicit expert modules."""
         batch_size, sequence_length, hidden_dim = hidden_states.shape
         hidden_states = hidden_states.view(-1, hidden_dim)
+        shared_expert_output = self.shared_expert(hidden_states)
         _, routing_weights, selected_experts = self.gate(hidden_states)
         routing_weights = routing_weights.to(hidden_states.dtype)
         final_hidden_states = run_routed_experts(
@@ -44,7 +45,6 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             self.num_experts,
         )
 
-        shared_expert_output = self.shared_expert(hidden_states)
         shared_expert_output = F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output
 
         final_hidden_states = final_hidden_states + shared_expert_output

defuser/modeling/unfused_moe/qwen3_next.py

@@ -33,6 +33,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         """Route tokens exactly like HF Qwen3-Next MoE, then run explicit experts."""
         batch_size, sequence_length, hidden_dim = hidden_states.shape
         hidden_states = hidden_states.view(-1, hidden_dim)
+        shared_expert_output = self.shared_expert(hidden_states)
         _, routing_weights, selected_experts = self.gate(hidden_states)
         routing_weights = routing_weights.to(hidden_states.dtype)
         final_hidden_states = run_routed_experts(
@@ -43,7 +44,6 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             self.num_experts,
         )
 
-        shared_expert_output = self.shared_expert(hidden_states)
         shared_expert_output = F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output
 
         final_hidden_states = final_hidden_states + shared_expert_output

pyproject.toml

@@ -9,7 +9,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "Defuser"
-version = "0.0.20"
+version = "0.0.21"
 description = "Model defuser helper for HF Transformers."
 readme = "README.md"
 requires-python = ">=3.9"

tests/test_convert_model.py

@@ -402,6 +402,76 @@ def _assert_sparse_moe_defused_matches_fused_math(
     torch.testing.assert_close(actual, expected, **assert_close_kwargs)
 
 
+def _force_route_all_experts(block: nn.Module) -> None:
+    """Set MoE routers to select all experts so execution-order hooks always fire."""
+
+    router = getattr(block, "gate", None)
+    num_experts = getattr(block, "num_experts", None)
+    if router is None or num_experts is None:
+        return
+
+    for name in ("top_k", "num_experts_per_tok"):
+        if hasattr(router, name):
+            setattr(router, name, num_experts)
+            return
+
+
+def _semantic_sparse_moe_execution_order(block: nn.Module, hidden_states: torch.Tensor) -> list[str]:
+    """Record semantic MoE execution order for shared expert, router, routed experts, and shared gate."""
+
+    _force_route_all_experts(block)
+    raw_events: list[str] = []
+    handles = []
+
+    def _record(event_name: str):
+        def _hook(_module, _inputs):
+            raw_events.append(event_name)
+        return _hook
+
+    if hasattr(block, "shared_expert"):
+        handles.append(block.shared_expert.register_forward_pre_hook(_record("shared_expert")))
+    if hasattr(block, "gate"):
+        handles.append(block.gate.register_forward_pre_hook(_record("gate")))
+    if hasattr(block, "shared_expert_gate"):
+        handles.append(block.shared_expert_gate.register_forward_pre_hook(_record("shared_expert_gate")))
+
+    experts = getattr(block, "experts", None)
+    if isinstance(experts, nn.ModuleList):
+        for idx, expert in enumerate(experts):
+            handles.append(expert.register_forward_pre_hook(_record(f"expert_{idx}")))
+    elif isinstance(experts, nn.Module):
+        handles.append(experts.register_forward_pre_hook(_record("experts")))
+
+    try:
+        with torch.inference_mode():
+            block.eval()(hidden_states)
+    finally:
+        for handle in handles:
+            handle.remove()
+
+    semantic_events: list[str] = []
+    for event in raw_events:
+        normalized = "routed_experts" if event.startswith("expert_") or event == "experts" else event
+        if not semantic_events or semantic_events[-1] != normalized:
+            semantic_events.append(normalized)
+    return semantic_events
+
+
+def _assert_sparse_moe_defused_matches_fused_execution_order(
+    original_block: nn.Module,
+    defused_block: nn.Module,
+    hidden_states: torch.Tensor,
+) -> None:
+    """Defused blocks must preserve the same semantic execution order as fused HF blocks."""
+
+    _seed_floating_tensors(original_block)
+    _copy_sparse_moe_weights(original_block, defused_block)
+
+    expected = _semantic_sparse_moe_execution_order(original_block, hidden_states)
+    actual = _semantic_sparse_moe_execution_order(defused_block, hidden_states)
+    assert actual == expected
+
+
 def test_qwen2_moe():
     model_type = "qwen2_moe"
     replace_fused_blocks(model_type)
@@ -858,6 +928,17 @@ def test_qwen2_moe_defused_forward_matches_fused_math():
     )
 
 
+def test_qwen2_moe_defused_forward_matches_fused_execution_order():
+    config = _tiny_moe_config(Qwen2MoeConfig)
+    hidden_states = torch.randn(2, 3, config.hidden_size, dtype=torch.float32)
+
+    _assert_sparse_moe_defused_matches_fused_execution_order(
+        Qwen2MoeSparseMoeBlock(config),
+        LinearQwen2MoeSparseMoeBlock(config),
+        hidden_states,
+    )
+
+
 def test_qwen3_moe_defused_forward_matches_fused_math():
     config = _tiny_moe_config(Qwen3MoeConfig)
     hidden_states = torch.randn(2, 3, config.hidden_size, dtype=torch.float32)
@@ -880,6 +961,17 @@ def test_qwen3_next_defused_forward_matches_fused_math():
     )
 
 
+def test_qwen3_next_defused_forward_matches_fused_execution_order():
+    config = _tiny_moe_config(Qwen3NextConfig)
+    hidden_states = torch.randn(2, 3, config.hidden_size, dtype=torch.float32)
+
+    _assert_sparse_moe_defused_matches_fused_execution_order(
+        Qwen3NextSparseMoeBlock(config),
+        LinearQwen3NextSparseMoeBlock(config),
+        hidden_states,
+    )
+
+
 def test_qwen3_omni_defused_forward_matches_fused_math():
     config = _tiny_qwen3_omni_config().thinker_config.text_config
     hidden_states = torch.randn(2, 3, config.hidden_size, dtype=torch.float32)