Separate transpose options for fused expert weights (account for differences between Qwen3Moe and Qwen3_5Moe)

exllamav3/architecture/qwen3_5.py

@@ -381,7 +381,7 @@ class Qwen3_5BaseModel(Model):
                             key_down_split = "experts.down_proj",
                             key_routing_gate = "gate",
                             key_shared_gate = "shared_expert_gate",
-                            transposed_load = False,
+                            transpose_fused_weights = False,
                             qmap = "block.mlp",
                             interm_dtype = torch.half,
                             out_dtype = torch.float,

exllamav3/modules/block_sparse_mlp.py

@@ -188,6 +188,7 @@ class BlockSparseMLP(Module):
         routing_last: int | None = None,
         routing_device: int | None = None,
         transposed_load: bool = True,
+        transpose_fused_weights: bool = True,
     ):
         super().__init__(config, key, None)
 
@@ -285,6 +286,7 @@ class BlockSparseMLP(Module):
                     qmap = qmap + ".input",
                     out_dtype = self.interm_dtype,
                     transposed_load = transposed_load,
+                    transpose_fused_weights = transpose_fused_weights,
                 )
                 up = Linear(
                     config = config,
@@ -297,6 +299,7 @@ class BlockSparseMLP(Module):
                     qmap = qmap + ".input",
                     out_dtype = self.interm_dtype,
                     transposed_load = transposed_load,
+                    transpose_fused_weights = transpose_fused_weights,
                 )
                 down = Linear(
                     config = config,
@@ -309,6 +312,7 @@ class BlockSparseMLP(Module):
                     out_dtype = self.out_dtype,
                     allow_input_padding = True,
                     transposed_load = transposed_load,
+                    transpose_fused_weights = transpose_fused_weights,
                 )
 
                 self.ups.append(up)

exllamav3/modules/linear.py

@@ -38,6 +38,7 @@ class Linear(Module):
         allow_input_padding: bool = False,
         post_scale: float = 1.0,
         transposed_load: bool = True,
+        transpose_fused_weights: bool = True,
     ):
         super().__init__(config, key, qmap)
 
@@ -62,6 +63,7 @@ class Linear(Module):
         self.out_dtype = out_dtype
         self.post_scale = post_scale
         self.transposed_load = transposed_load
+        self.transpose_fused_weights = transpose_fused_weights
 
         assert self.in_features_unpadded == self.in_features or allow_input_padding, \
             f"Input padding is not allowed for {self.key}, in_dim: {self.in_features_unpadded}, pad_to: {pad_to}"
@@ -168,7 +170,7 @@ class Linear(Module):
             weight = self.config.stc.get_tensor(
                 self.fkey,
                 self.device,
-                transpose = self.transposed_load,
+                transpose = self.transpose_fused_weights,
                 no_defer = True,
                 fidx = self.fidx
             )