Support Qwen3 MoE

src/heretic/model.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: AGPL-3.0-or-later
 # Copyright (C) 2025  Philipp Emanuel Weidmann <pew@worldwidemann.com>
 
+from contextlib import suppress
 from typing import Any
 
 import torch
@@ -57,14 +58,12 @@ class Model:
         if self.model is None:
             raise Exception("Failed to load model with all configured dtypes.")
 
-        layers = self.model.model.layers
-        print(f"* Transformer model with [bold]{len(layers)}[/] layers")
-
-        assert layers[0].self_attn.o_proj is not None
-        print("* [bold]self_attn.o_proj[/] found")
-
-        assert layers[0].mlp.down_proj is not None
-        print("* [bold]mlp.down_proj[/] found")
+        print(
+            f"* Transformer model with [bold]{len(self.model.model.layers)}[/] layers"
+        )
+        print(
+            f"* [bold]{len(self.get_layer_matrices(0))}[/] abliterable matrices per layer"
+        )
 
     def reload_model(self):
         dtype = self.model.dtype
@@ -79,6 +78,35 @@ class Model:
             device_map=self.settings.device_map,
         )
 
+    def get_layer_matrices(self, layer_index: int) -> list[torch.Tensor]:
+        layer = self.model.model.layers[layer_index]
+
+        matrices = []
+
+        def try_add(matrix: Any):
+            assert torch.is_tensor(matrix)
+            matrices.append(matrix)
+
+        # Most dense models.
+        if not matrices:
+            with suppress(Exception):
+                try_add(layer.mlp.down_proj.weight)
+
+        # Some MoE models (e.g. Qwen3).
+        if not matrices:
+            with suppress(Exception):
+                for expert in layer.mlp.experts:
+                    try_add(expert.down_proj.weight)
+
+        # We need at least one MLP down-projection.
+        assert matrices
+
+        # Exceptions aren't suppressed here, because there is currently
+        # no alternative location for the attention out-projection.
+        try_add(layer.self_attn.o_proj.weight)
+
+        return matrices
+
     def abliterate(
         self,
         refusal_directions: torch.Tensor,
@@ -89,8 +117,8 @@ class Model:
     ):
         # Note that some implementations of abliteration also orthogonalize
         # the embedding matrix, but it's unclear if that has any benefits.
-        for i, layer in enumerate(self.model.model.layers):
-            distance = abs(i - max_weight_position)
+        for layer_index in range(len(self.model.model.layers)):
+            distance = abs(layer_index - max_weight_position)
 
             # Don't orthogonalize layers that are more than
             # min_weight_distance away from max_weight_position.
@@ -105,13 +133,13 @@ class Model:
 
             # The index must be shifted by 1 because the first element
             # of refusal_directions is the direction for the embeddings.
-            refusal_direction = refusal_directions[i + 1]
+            refusal_direction = refusal_directions[layer_index + 1]
 
             # Projects any right-multiplied vector(s) onto the subspace
             # spanned by the refusal direction.
             projector = torch.outer(refusal_direction, refusal_direction)
 
-            for matrix in [layer.self_attn.o_proj.weight, layer.mlp.down_proj.weight]:
+            for matrix in self.get_layer_matrices(layer_index):
                 # In-place subtraction is safe as we're not using Autograd.
                 matrix.sub_(weight * (projector @ matrix))