Support module-wise influence computations

logix/analysis/influence_function.py

@@ -74,7 +74,8 @@ def compute_influence(
         tgt_log: Tuple[str, Dict[str, Dict[str, torch.Tensor]]],
         mode: Optional[str] = "dot",
         precondition: Optional[bool] = True,
-        precondition_hessian: Optional[str] = "auto",
+        hessian: Optional[str] = "auto",
+        influence_groups: Optional[List[str]] = None,
         damping: Optional[float] = None,
     ):
         """
@@ -92,12 +93,18 @@ def compute_influence(
         result = {}
         if precondition:
             src_log = self.precondition(
-                src_log=src_log, damping=damping, hessian=precondition_hessian
+                src_log=src_log, damping=damping, hessian=hessian
             )
 
         src_ids, src = src_log
         tgt_ids, tgt = tgt_log
+
+        # Initialize influence
         total_influence = 0
+        if influence_groups is not None:
+            total_influence = {"total": 0}
+            for influence_group in influence_groups:
+                total_influence[influence_group] = 0
 
         # Compute influence scores. By default, we should compute the basic influence
         # scores, which is essentially the inner product between the source and target
@@ -113,27 +120,58 @@ def compute_influence(
         else:
             synchronize_device(src, tgt)
             for module_name in src.keys():
-                total_influence += cross_dot_product(
+                module_influence = cross_dot_product(
                     src[module_name]["grad"], tgt[module_name]["grad"]
                 )
+                if influence_groups is None:
+                    total_influence += module_influence
+                else:
+                    total_influence["total"] += module_influence
+                    in_groups = [ig for ig in influence_groups if ig in module_name]
+                    for group in in_groups:
+                        total_influence[group] += module_influence
 
         if mode == "cosine":
             tgt_norm = self.compute_self_influence(
-                tgt_log, precondition=True, damping=damping
+                tgt_log,
+                precondition=True,
+                hessian=hessian,
+                influence_groups=influence_groups,
+                damping=damping,
             )
-            total_influence /= torch.sqrt(tgt_norm.unsqueeze(0))
+            if influence_groups is None:
+                total_influence /= torch.sqrt(tgt_norm.unsqueeze(0))
+            else:
+                for key in total_influence.keys():
+                    total_influence[key] /= tgt_norm[key]
         elif mode == "l2":
             tgt_norm = self.compute_self_influence(
-                tgt_log, precondition=True, damping=damping
+                tgt_log,
+                precondition=True,
+                hessian=hessian,
+                influence_groups=influence_groups,
+                damping=damping,
             )
-            total_influence = 2 * total_influence - tgt_norm.unsqueeze(0)
+            if influence_groups is None:
+                total_influence -= 0.5 * tgt_norm.unsqueeze(0)
+            else:
+                for key in total_influence.keys():
+                    total_influence[key] -= 0.5 * tgt_norm[key].unsqueeze(0)
 
-        assert total_influence.shape[0] == len(src_ids)
-        assert total_influence.shape[1] == len(tgt_ids)
+        # Move influence scores to CPU to save memory
+        if influence_groups is None:
+            assert total_influence.shape[0] == len(src_ids)
+            assert total_influence.shape[1] == len(tgt_ids)
+            total_influence = total_influence.cpu()
+        else:
+            for key, value in total_influence.items():
+                assert value.shape[0] == len(src_ids)
+                assert value.shape[1] == len(tgt_ids)
+                total_influence[key] = value.cpu()
 
         result["src_ids"] = src_ids
         result["tgt_ids"] = tgt_ids
-        result["influence"] = total_influence.cpu()
+        result["influence"] = total_influence
 
         return result
 
@@ -142,6 +180,8 @@ def compute_self_influence(
         self,
         src_log: Tuple[str, Dict[str, Dict[str, torch.Tensor]]],
         precondition: Optional[bool] = True,
+        hessian: Optional[str] = "auto",
+        influence_groups: Optional[List[str]] = None,
         damping: Optional[float] = None,
     ):
         """
@@ -160,18 +200,44 @@ def compute_self_influence(
             src = unflatten_log(
                 log=src, path=self._state.get_state("model_module")["path"]
             )
-        tgt = self.precondition(src_log, damping)[1] if precondition else src
 
-        # Compute self-influence scores
+        tgt = src
+        if precondition:
+            tgt = self.precondition(src_log, hessian=hessian, damping=damping)[1]
+
+        # Initialize influence
         total_influence = 0
+        if influence_groups is not None:
+            total_influence = {"total": 0}
+            for influence_group in influence_groups:
+                total_influence[influence_group] = 0
+
+        # Compute self-influence scores
         for module_name in src.keys():
             src_module = src[module_name]["grad"]
             tgt_module = tgt[module_name]["grad"] if tgt is not None else src_module
-            module_influence = reduce(src_module * tgt_module, "n a b -> n", "sum")
-            total_influence += module_influence.reshape(-1)
+            module_influence = reduce(
+                src_module * tgt_module, "n a b -> n", "sum"
+            ).reshape(-1)
+            if influence_groups is None:
+                total_influence += module_influence
+            else:
+                total_influence["total"] += module_influence
+                in_groups = [ig for ig in influence_groups if ig in module_name]
+                for group in in_groups:
+                    total_influence[group] += module_influence
+
+        # Move influence scores to CPU to save memory
+        if influence_groups is not None:
+            assert len(total_influence) == len(src_ids)
+            total_influence = total_influence.cpu()
+        else:
+            for key, value in total_influence.items():
+                assert len(value) == len(src_ids)
+                total_influence[key] = value.cpu()
 
         result["src_ids"] = src_ids
-        result["influence"] = total_influence.cpu()
+        result["influence"] = total_influence
 
         return result
 
@@ -181,6 +247,8 @@ def compute_influence_all(
         loader: torch.utils.data.DataLoader,
         mode: Optional[str] = "dot",
         precondition: Optional[bool] = True,
+        hessian: Optional[str] = "auto",
+        influence_groups: Optional[List[str]] = None,
         damping: Optional[float] = None,
     ):
         """
@@ -195,21 +263,34 @@ def compute_influence_all(
             damping (Optional[float], optional): Damping parameter for preconditioning. Defaults to None.
         """
         if precondition:
-            src_log = self.precondition(src_log, damping)
+            src_log = self.precondition(src_log, hessian=hessian, damping=damping)
 
         result_all = None
         for tgt_log in tqdm(loader, desc="Compute IF"):
             result = self.compute_influence(
-                src_log, tgt_log, mode=mode, precondition=False, damping=damping
+                src_log,
+                tgt_log,
+                mode=mode,
+                precondition=False,
+                hessian=hessian,
+                influence_groups=influence_groups,
+                damping=damping,
             )
 
             # Merge results
             if result_all is None:
                 result_all = result
-            else:
-                result_all["tgt_ids"].extend(result["tgt_ids"])
+                continue
+            result_all["tgt_ids"].extend(result["tgt_ids"])
+            if influence_groups is None:
                 result_all["influence"] = torch.cat(
                     [result_all["influence"], result["influence"]], dim=1
                 )
+            else:
+                for key in result_all["influence"].keys():
+                    result_all["influence"][key] = torch.cat(
+                        [result_all["influence"][key], result["influence"][key]],
+                        dim=1,
+                    )
 
         return result_all