fix patch

Author: xadupre

onnx_diagnostic/torch_export_patches/patches/patch_transformers.py

@@ -1484,92 +1484,105 @@ def forward(
         return attn_output
 
 
-class patched_Qwen3MoeSparseMoeBlock(torch.nn.Module):
-    _PATCHES_ = ["forward", "_forward_expert_loop"]
-    _PATCHED_CLASS_ = transformers.models.qwen3_moe.modeling_qwen3_moe.Qwen3MoeSparseMoeBlock
+try:
+    import transformers.models.qwen3_moe
 
-    def _forward_expert_loop(
-        self,
-        final_hidden_states,
-        expert_mask_idx,
-        hidden_states,
-        routing_weights,
-        expert_idx: int,
-    ):
-        # idx, top_x = torch.where(expert_mask_idx.squeeze(0))
-        idx, top_x = torch.nonzero(expert_mask_idx, as_tuple=True)
-        hidden_dim = hidden_states.shape[-1]
-        current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
-        expert_current_state = self.experts[expert_idx](current_state)
-        current_hidden_states = expert_current_state * routing_weights[top_x, idx, None]
-        return final_hidden_states.index_add(
-            0, top_x, current_hidden_states.to(hidden_states.dtype)
-        )
+    patch_qwen3 = True
+except ImportError:
+    patch_qwen3 = False
 
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        """ """
-        batch_size, sequence_length, hidden_dim = hidden_states.shape
-        hidden_states = hidden_states.view(-1, hidden_dim)
-        # router_logits: (batch * sequence_length, n_experts)
-        router_logits = self.gate(hidden_states)
-
-        routing_weights = torch.nn.functional.softmax(router_logits, dim=1, dtype=torch.float)
-        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
-        if self.norm_topk_prob:  # only diff with mixtral sparse moe block!
-            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-        # we cast back to the input dtype
-        routing_weights = routing_weights.to(hidden_states.dtype)
-
-        final_hidden_states = torch.zeros(
-            (batch_size * sequence_length, hidden_dim),
-            dtype=hidden_states.dtype,
-            device=hidden_states.device,
+if patch_qwen3:
+
+    class patched_Qwen3MoeSparseMoeBlock(torch.nn.Module):
+        _PATCHES_ = ["forward", "_forward_expert_loop"]
+        _PATCHED_CLASS_ = (
+            transformers.models.qwen3_moe.modeling_qwen3_moe.Qwen3MoeSparseMoeBlock
         )
 
-        # One hot encode the selected experts to create an expert mask
-        # this will be used to easily index which expert is going to be sollicitated
-        expert_mask = torch.nn.functional.one_hot(
-            selected_experts, num_classes=self.num_experts
-        ).permute(2, 1, 0)
-
-        # Loop over all available experts in the model
-        # and perform the computation on each expert
-        expert_sum = expert_mask.sum(dim=(-1, -2))
-        # expert_hit = torch.greater(expert_sum, 0).nonzero()
-        # for expert_idx in expert_hit:
-        for expert_idx in range(self.num_experts):
-            expert_mask_idx = expert_mask[expert_idx].squeeze(0)
-            final_hidden_states = torch.cond(
-                (expert_sum[expert_idx] > 0).item(),
-                lambda final_hidden_states, expert_mask, hidden_states, routing_weights, _i=expert_idx: self._forward_expert_loop(  # noqa: E501
-                    final_hidden_states,
-                    expert_mask,
-                    hidden_states,
-                    routing_weights,
-                    expert_idx=_i,
-                ),
-                lambda final_hidden_states, *args: final_hidden_states.clone(),
-                [final_hidden_states, expert_mask_idx, hidden_states, routing_weights],
+        def _forward_expert_loop(
+            self,
+            final_hidden_states,
+            expert_mask_idx,
+            hidden_states,
+            routing_weights,
+            expert_idx: int,
+        ):
+            # idx, top_x = torch.where(expert_mask_idx.squeeze(0))
+            idx, top_x = torch.nonzero(expert_mask_idx, as_tuple=True)
+            hidden_dim = hidden_states.shape[-1]
+            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+            expert_current_state = self.experts[expert_idx](current_state)
+            current_hidden_states = expert_current_state * routing_weights[top_x, idx, None]
+            return final_hidden_states.index_add(
+                0, top_x, current_hidden_states.to(hidden_states.dtype)
             )
 
-            # if expert_sum[expert_idx] > 0:
-            #    idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
-
-            # Index the correct hidden states and compute the expert hidden state for
-            # the current expert. We need to make sure to multiply the output hidden
-            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
-            #    current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
-            #    current_hidden_states = (
-            #        expert_layer(current_state) * routing_weights[top_x, idx, None]
-            #    )
-
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            #    final_hidden_states.index_add_(
-            #        0, top_x, current_hidden_states.to(hidden_states.dtype)
-            #    )
-
-        final_hidden_states = final_hidden_states.reshape(
-            batch_size, sequence_length, hidden_dim
-        )
-        return final_hidden_states, router_logits
+        def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+            """ """
+            batch_size, sequence_length, hidden_dim = hidden_states.shape
+            hidden_states = hidden_states.view(-1, hidden_dim)
+            # router_logits: (batch * sequence_length, n_experts)
+            router_logits = self.gate(hidden_states)
+
+            routing_weights = torch.nn.functional.softmax(
+                router_logits, dim=1, dtype=torch.float
+            )
+            routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+            if self.norm_topk_prob:  # only diff with mixtral sparse moe block!
+                routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+            # we cast back to the input dtype
+            routing_weights = routing_weights.to(hidden_states.dtype)
+
+            final_hidden_states = torch.zeros(
+                (batch_size * sequence_length, hidden_dim),
+                dtype=hidden_states.dtype,
+                device=hidden_states.device,
+            )
+
+            # One hot encode the selected experts to create an expert mask
+            # this will be used to easily index which expert is going to be sollicitated
+            expert_mask = torch.nn.functional.one_hot(
+                selected_experts, num_classes=self.num_experts
+            ).permute(2, 1, 0)
+
+            # Loop over all available experts in the model
+            # and perform the computation on each expert
+            expert_sum = expert_mask.sum(dim=(-1, -2))
+            # expert_hit = torch.greater(expert_sum, 0).nonzero()
+            # for expert_idx in expert_hit:
+            for expert_idx in range(self.num_experts):
+                expert_mask_idx = expert_mask[expert_idx].squeeze(0)
+                final_hidden_states = torch.cond(
+                    (expert_sum[expert_idx] > 0).item(),
+                    lambda final_hidden_states, expert_mask, hidden_states, routing_weights, _i=expert_idx: self._forward_expert_loop(  # noqa: E501
+                        final_hidden_states,
+                        expert_mask,
+                        hidden_states,
+                        routing_weights,
+                        expert_idx=_i,
+                    ),
+                    lambda final_hidden_states, *args: final_hidden_states.clone(),
+                    [final_hidden_states, expert_mask_idx, hidden_states, routing_weights],
+                )
+
+                # if expert_sum[expert_idx] > 0:
+                #    idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
+
+                # Index the correct hidden states and compute the expert hidden state for
+                # the current expert. We need to make sure to multiply the output hidden
+                # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+                #    current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+                #    current_hidden_states = (
+                #        expert_layer(current_state) * routing_weights[top_x, idx, None]
+                #    )
+
+                # However `index_add_` only support torch tensors for indexing so we'll use
+                # the `top_x` tensor here.
+                #    final_hidden_states.index_add_(
+                #        0, top_x, current_hidden_states.to(hidden_states.dtype)
+                #    )
+
+            final_hidden_states = final_hidden_states.reshape(
+                batch_size, sequence_length, hidden_dim
+            )
+            return final_hidden_states, router_logits