renaming

src/nnsight/intervention/base.py

@@ -59,10 +59,11 @@ def _run(self, *inputs, **kwargs):
     def __init__(
         self,
         model: torch.nn.Module,
+        rename: Optional[Dict[str,str]] = None
     ) -> None:
 
         self._model: torch.nn.Module = model
-        self._envoy: Envoy[InterventionProxy, InterventionNode] = Envoy(self._model)
+        self._envoy: Envoy[InterventionProxy, InterventionNode] = Envoy(self._model, rename=rename)
 
         self._session: Optional[Session] = None
         self._default_graph: Optional[InterventionGraph] = None

src/nnsight/intervention/envoy.py

@@ -1,18 +1,20 @@
 from __future__ import annotations
 
 import inspect
+import re
 import warnings
 from contextlib import AbstractContextManager
-from typing import (TYPE_CHECKING, Any, Callable, Dict, Generic, Iterator, List,
-                    Optional, Tuple, Union)
+from typing import (TYPE_CHECKING, Any, Callable, Dict, Generic, Iterator,
+                    List, Optional, Tuple, Union)
 
 import torch
 from typing_extensions import Self
 
 from . import protocols
-from .contexts import InterventionTracer
-from .graph import InterventionProxyType, InterventionNodeType
 from .backends import EditingBackend
+from .contexts import InterventionTracer
+from .graph import InterventionNodeType, InterventionProxyType
+
 
 class Envoy(Generic[InterventionProxyType, InterventionNodeType]):
     """Envoy objects act as proxies for torch modules themselves within a model's module tree in order to add nnsight functionality.
@@ -28,15 +30,20 @@ class Envoy(Generic[InterventionProxyType, InterventionNodeType]):
         _children (List[Envoy]): Immediate Envoy children of this Envoy.
         _fake_outputs (List[torch.Tensor]): List of 'meta' tensors built from the outputs most recent _scan. Is list as there can be multiple shapes for a module called more than once.
         _fake_inputs (List[torch.Tensor]): List of 'meta' tensors built from the inputs most recent _scan. Is list as there can be multiple shapes for a module called more than once.
-
+        _rename (Optional[Dict[str,str]]): Optional mapping of (old name -> new name).
+            For example to rename all gpt 'attn' modules to 'attention' you would: rename={r"attn": "attention"}
+            Not this does not actually change the underlying module names, just how you access its envoy. Renaming will replace Envoy.path but Envoy._path represents the pre-renamed true attribute path.
         _tracer (nnsight.context.Tracer.Tracer): Object which adds this Envoy's module's output and input proxies to an intervention graph. Must be set on Envoys objects manually by the Tracer.
     """
 
-    def __init__(self, module: torch.nn.Module, module_path: str = ""):
+    def __init__(self, module: torch.nn.Module, module_path: str = "", alias_path:Optional[str] = None, rename: Optional[Dict[str,str]] = None):
 
-        self.path = module_path
+        self.path = alias_path or module_path
+        self._path = module_path
 
         self._module = module
+
+        self._rename = rename
 
         self._iteration_stack = [0]
 
@@ -111,7 +118,7 @@ def output(self) -> InterventionProxyType:
                 else:
                     fake_output = self._fake_outputs[iteration]
 
-                module_path = f"{self.path}.output"
+                module_path = f"{self._path}.output"
 
                 output = protocols.InterventionProtocol.add(
                     self._tracer.graph,
@@ -169,7 +176,7 @@ def inputs(self) -> InterventionProxyType:
                 else:
                     fake_input = self._fake_inputs[iteration]
 
-                module_path = f"{self.path}.input"
+                module_path = f"{self._path}.input"
 
                 input = protocols.InterventionProtocol.add(
                     self._tracer.graph,
@@ -330,8 +337,24 @@ def _add_envoy(self, module: torch.nn.Module, name: str) -> None:
             module (torch.nn.Module): Module to create Envoy for.
             name (str): name of envoy/attribute.
         """
-
-        envoy = Envoy(module, module_path=f"{self.path}.{name}")
+
+        alias_path = None
+
+        module_path = f"{self.path}.{name}"
+
+        if self._rename is not None:
+
+            for key, value in self._rename.items():
+
+                if name == key:
+
+                    name = value
+
+                    alias_path = f"{self.path}.{name}"
+
+                    break
+
+        envoy = Envoy(module, module_path=module_path, alias_path=alias_path, rename=self._rename)
 
         self._children.append(envoy)
 

src/nnsight/modeling/mixins/loadable.py

@@ -1,11 +1,13 @@
+from typing import Dict, Optional
+
 import torch
 
 from ...intervention import NNsight
 
 
 class LoadableMixin(NNsight):
 
-    def __init__(self, *args, **kwargs) -> None:
+    def __init__(self, *args, rename: Optional[Dict[str,str]] = None, **kwargs) -> None:
 
         if not isinstance(args[0], torch.nn.Module):
 
@@ -15,7 +17,7 @@ def __init__(self, *args, **kwargs) -> None:
 
             model = args[0]
 
-        super().__init__(model)
+        super().__init__(model, rename=rename)
 
     def _load(self, *args, **kwargs) -> torch.nn.Module:
 

src/nnsight/modeling/mixins/meta.py

@@ -1,3 +1,5 @@
+from typing import Dict, Optional
+
 import torch
 from accelerate import init_empty_weights
 
@@ -8,7 +10,7 @@
 class MetaMixin(LoadableMixin):
 
     def __init__(
-        self, *args, dispatch: bool = False, meta_buffers: bool = True, **kwargs
+        self, *args, dispatch: bool = False, meta_buffers: bool = True, rename: Optional[Dict[str,str]] = None, **kwargs
     ) -> None:
 
         self.dispatched = dispatch
@@ -23,7 +25,7 @@ def __init__(
 
                 model = self._load_meta(*args, **kwargs)
 
-            NNsight.__init__(self, model)
+            NNsight.__init__(self, model, rename=rename)
 
         self.args = args
         self.kwargs = kwargs