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mergekit/merge_methods/generalized_task_arithmetic.py

@@ -64,6 +64,13 @@ def tensor_parameters(self) -> List[ConfigParameterDef]:
             ConfigParameterDef(name="random_mask", required=False, default_value= 0.0),
             ConfigParameterDef(name="random_mask_seed", required=False, default_value= None),
         ]
+        if self.sparsification_method == SparsificationMethod.magnitude_outliers:
+            res.append(
+                ConfigParameterDef(
+                    name="gamma",
+                    default_value=0.01,
+                )
+            )
         return res
 
     def make_task(
@@ -83,15 +90,15 @@ def make_task(
             normalize=parameters["normalize"],
             rescale=parameters["rescale"],
             swapping=parameters["swapping"],
-            out_tensor_name=output_weight.name,
+            weight_info=output_weight,
         )
 
 
 class GTATask(Task[torch.Tensor]):
     method: GeneralizedTaskArithmeticMerge
     tensors: GatherTensors
     base_model: ModelReference
-    out_tensor_name: str
+    weight_info: WeightInfo
     tensor_parameters: ImmutableMap[ModelReference, Any]
     int8_mask: bool
     normalize: bool
@@ -111,7 +118,7 @@ def execute(
     ) -> torch.Tensor:
         # collect task vectors
         tvs, base = get_task_vectors(
-            self.out_tensor_name,
+            self.weight_info,
             self.base_model,
             tensors,
             tensor_parameters=self.tensor_parameters.data,
@@ -123,11 +130,15 @@ def execute(
         # sparsify
         if self.method.sparsification_method:
             for tv_info in tvs:
+                kwargs = {}
+                if "gamma" in tv_info:
+                    kwargs["gamma"] = tv_info["gamma"]
                 tv_info["delta"] = sparsify(
                     tv_info["delta"],
                     density=tv_info["density"],
                     method=self.method.sparsification_method,
                     rescale=self.rescale,
+                    **kwargs,
                 )
 
         deltas = torch.stack([tv["delta"] for tv in tvs], dim=0)
@@ -218,14 +229,15 @@ def rand_mask(base, x, percent, seed=None):
 
 
 def get_task_vectors(
-    parameter_name: str,
+    weight_info: WeightInfo,
     base_model: ModelReference,
     tensors: ImmutableMap[ModelReference, torch.Tensor],
     tensor_parameters: ImmutableMap[ModelReference, ImmutableMap[str, Any]],
     swapping: bool,
 ) -> Tuple[List[Dict[str, Any]], torch.Tensor]:
     keys = list(tensors.keys())
     base = tensors[base_model]
+    parameter_name = weight_info.name
 
     res = []
     for model in keys:
@@ -235,7 +247,7 @@ def get_task_vectors(
         x = tensors[model].to(base.dtype)
 
         if x.shape != base.shape:
-            if "lm_head" in parameter_name or "embed_tokens" in parameter_name:
+            if weight_info.is_embed:
                 x = x[: base.shape[0], : base.shape[1]]
                 logging.warning(f"Using submatrix of {model}:{parameter_name}")
             else: