Delete mock_submission

mock_submission/4_answer_MCQA_Gemma2ForCausalLM_answer_pointer/DAS_Gemma2ForCausalLM_answer_pointer/4_answer_MCQA_Gemma2ForCausalLM_submission_answer_pointer__results.json

@@ -1,100 +0,0 @@
-{
-  "method_name": "submission",
-  "model_name": "Gemma2ForCausalLM",
-  "task_name": "4_answer_MCQA",
-  "dataset": {
-    "answerPosition_testprivate": {
-      "model_unit": {
-        "[[AtomicModelUnit(id='ResidualStream(Layer-0,Token-correct_symbol)')]]": {
-          "metadata": {
-            "layer": 0,
-            "position": "correct_symbol"
-          },
-          "answer_pointer": {
-            "average_score": 0.68
-          }
-        },
-        "[[AtomicModelUnit(id='ResidualStream(Layer-0,Token-correct_symbol_period)')]]": {
-          "metadata": {
-            "layer": 0,
-            "position": "correct_symbol_period"
-          },
-          "answer_pointer": {
-            "average_score": 0.0
-          }
-        },
-        "[[AtomicModelUnit(id='ResidualStream(Layer-0,Token-last_token)')]]": {
-          "metadata": {
-            "layer": 0,
-            "position": "last_token"
-          },
-          "answer_pointer": {
-            "average_score": 0.0
-          }
-        }
-      }
-    },
-    "randomLetter_testprivate": {
-      "model_unit": {
-        "[[AtomicModelUnit(id='ResidualStream(Layer-0,Token-correct_symbol)')]]": {
-          "metadata": {
-            "layer": 0,
-            "position": "correct_symbol"
-          },
-          "answer_pointer": {
-            "average_score": 0.8235294117647058
-          }
-        },
-        "[[AtomicModelUnit(id='ResidualStream(Layer-0,Token-correct_symbol_period)')]]": {
-          "metadata": {
-            "layer": 0,
-            "position": "correct_symbol_period"
-          },
-          "answer_pointer": {
-            "average_score": 1.0
-          }
-        },
-        "[[AtomicModelUnit(id='ResidualStream(Layer-0,Token-last_token)')]]": {
-          "metadata": {
-            "layer": 0,
-            "position": "last_token"
-          },
-          "answer_pointer": {
-            "average_score": 1.0
-          }
-        }
-      }
-    },
-    "answerPosition_randomLetter_testprivate": {
-      "model_unit": {
-        "[[AtomicModelUnit(id='ResidualStream(Layer-0,Token-correct_symbol)')]]": {
-          "metadata": {
-            "layer": 0,
-            "position": "correct_symbol"
-          },
-          "answer_pointer": {
-            "average_score": 0.0625
-          }
-        },
-        "[[AtomicModelUnit(id='ResidualStream(Layer-0,Token-correct_symbol_period)')]]": {
-          "metadata": {
-            "layer": 0,
-            "position": "correct_symbol_period"
-          },
-          "answer_pointer": {
-            "average_score": 0.0
-          }
-        },
-        "[[AtomicModelUnit(id='ResidualStream(Layer-0,Token-last_token)')]]": {
-          "metadata": {
-            "layer": 0,
-            "position": "last_token"
-          },
-          "answer_pointer": {
-            "average_score": 0.0
-          }
-        }
-      }
-    }
-  }
-}

mock_submission/4_answer_MCQA_Gemma2ForCausalLM_answer_pointer/DAS_Gemma2ForCausalLM_answer_pointer/ResidualStream(Layer-0,Token-correct_symbol)_featurizer

@@ -1,3 +0,0 @@
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mock_submission/4_answer_MCQA_Gemma2ForCausalLM_answer_pointer/DAS_Gemma2ForCausalLM_answer_pointer/ResidualStream(Layer-0,Token-correct_symbol)_indices

@@ -1 +0,0 @@
-null

mock_submission/4_answer_MCQA_Gemma2ForCausalLM_answer_pointer/DAS_Gemma2ForCausalLM_answer_pointer/ResidualStream(Layer-0,Token-correct_symbol)_inverse_featurizer

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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mock_submission/4_answer_MCQA_Gemma2ForCausalLM_answer_pointer/DAS_Gemma2ForCausalLM_answer_pointer/ResidualStream(Layer-0,Token-correct_symbol_period)_featurizer

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-version https://git-lfs.github.com/spec/v1
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mock_submission/4_answer_MCQA_Gemma2ForCausalLM_answer_pointer/DAS_Gemma2ForCausalLM_answer_pointer/ResidualStream(Layer-0,Token-correct_symbol_period)_indices

@@ -1 +0,0 @@
-null

mock_submission/4_answer_MCQA_Gemma2ForCausalLM_answer_pointer/DAS_Gemma2ForCausalLM_answer_pointer/ResidualStream(Layer-0,Token-correct_symbol_period)_inverse_featurizer

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mock_submission/4_answer_MCQA_Gemma2ForCausalLM_answer_pointer/DAS_Gemma2ForCausalLM_answer_pointer/ResidualStream(Layer-0,Token-last_token)_featurizer

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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mock_submission/4_answer_MCQA_Gemma2ForCausalLM_answer_pointer/DAS_Gemma2ForCausalLM_answer_pointer/ResidualStream(Layer-0,Token-last_token)_indices

@@ -1 +0,0 @@
-null

mock_submission/4_answer_MCQA_Gemma2ForCausalLM_answer_pointer/DAS_Gemma2ForCausalLM_answer_pointer/ResidualStream(Layer-0,Token-last_token)_inverse_featurizer

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mock_submission/featurizer.py

@@ -1,52 +0,0 @@
-"""
-Copy of the existing SubspaceFeaturizer implementation for submission.
-This file provides the same SubspaceFeaturizer functionality in a self-contained format.
-"""
-
-import torch
-import torch.nn as nn
-import pyvene as pv
-from CausalAbstraction.neural.featurizers import Featurizer
-
-
-class SubspaceFeaturizerModuleCopy(torch.nn.Module):
-    def __init__(self, rotate_layer):
-        super().__init__()
-        self.rotate = rotate_layer
-        
-    def forward(self, x):
-        r = self.rotate.weight.T
-        f = x.to(r.dtype) @ r.T
-        error = x - (f @ r).to(x.dtype)
-        return f, error
-
-
-class SubspaceInverseFeaturizerModuleCopy(torch.nn.Module):
-    def __init__(self, rotate_layer):
-        super().__init__()
-        self.rotate = rotate_layer
-        
-    def forward(self, f, error):
-        r = self.rotate.weight.T
-        return (f.to(r.dtype) @ r).to(f.dtype) + error.to(f.dtype)
-
-
-class SubspaceFeaturizerCopy(Featurizer):
-    def __init__(self, shape=None, rotation_subspace=None, trainable=True, id="subspace"):
-        assert shape is not None or rotation_subspace is not None, "Either shape or rotation_subspace must be provided."
-        if shape is not None:
-            self.rotate = pv.models.layers.LowRankRotateLayer(*shape, init_orth=True)
-        elif rotation_subspace is not None:
-            shape = rotation_subspace.shape
-            self.rotate = pv.models.layers.LowRankRotateLayer(*shape, init_orth=False)
-            self.rotate.weight.data.copy_(rotation_subspace)
-        self.rotate = torch.nn.utils.parametrizations.orthogonal(self.rotate)
-
-        if not trainable:
-            self.rotate.requires_grad_(False)
-
-        # Create module-based featurizer and inverse_featurizer
-        featurizer = SubspaceFeaturizerModuleCopy(self.rotate)
-        inverse_featurizer = SubspaceInverseFeaturizerModuleCopy(self.rotate)
-            
-        super().__init__(featurizer, inverse_featurizer, n_features=self.rotate.weight.shape[1], id=id)

mock_submission/token_position.py

@@ -1,65 +0,0 @@
-"""
-Token position definitions for MCQA task submission.
-This file provides token position functions that identify key tokens in MCQA prompts.
-"""
-
-import re
-from CausalAbstraction.neural.LM_units import TokenPosition, get_last_token_index
-
-
-def get_token_positions(pipeline, causal_model):
-    """
-    Get token positions for the simple MCQA task.
-    
-    Args:
-        pipeline: The language model pipeline with tokenizer
-        causal_model: The causal model for the task
-        
-    Returns:
-        list[TokenPosition]: List of TokenPosition objects for intervention experiments
-    """
-    def get_correct_symbol_index(input, pipeline, causal_model):
-        """
-        Find the index of the correct answer symbol in the prompt.
-        
-        Args:
-            input (Dict): The input dictionary to a causal model
-            pipeline: The tokenizer pipeline
-            causal_model: The causal model
-            
-        Returns:
-            list[int]: List containing the index of the correct answer symbol token
-        """
-        # Run the model to get the answer position
-        output = causal_model.run_forward(input)
-        pointer = output["answer_pointer"]
-        correct_symbol = output[f"symbol{pointer}"]
-        prompt = input["raw_input"]
-        
-        # Find all single uppercase letters in the prompt
-        matches = list(re.finditer(r"\b[A-Z]\b", prompt))
-        
-        # Find the match corresponding to our correct symbol
-        symbol_match = None
-        for match in matches:
-            if prompt[match.start():match.end()] == correct_symbol:
-                symbol_match = match
-                break
-                
-        if not symbol_match:
-            raise ValueError(f"Could not find correct symbol {correct_symbol} in prompt: {prompt}")
-        
-        # Get the substring up to the symbol match end
-        substring = prompt[:symbol_match.end()]
-        tokenized_substring = list(pipeline.load(substring)["input_ids"][0])
-        
-        # The symbol token will be at the end of the substring
-        return [len(tokenized_substring) - 1]
-
-    # Create TokenPosition objects
-    token_positions = [
-        TokenPosition(lambda x: get_correct_symbol_index(x, pipeline, causal_model), pipeline, id="correct_symbol"),
-        TokenPosition(lambda x: [get_correct_symbol_index(x, pipeline, causal_model)[0]+1], pipeline, id="correct_symbol_period"),
-        TokenPosition(lambda x: get_last_token_index(x, pipeline), pipeline, id="last_token")
-    ]
-    return token_positions