Conversion improved test coverage

tests/unit/weight_conversion/conversion_utils/conversion_steps/test_arithmatic_weight_conversion.py

@@ -28,33 +28,33 @@
 )
 def test_arithmetic_operations(operation, value, input_tensor, expected_output):
     conversion = ArithmeticWeightConversion(operation, value)
-    output = conversion.handle_conversion(input_tensor)
+    output = conversion.convert(input_tensor)
     assert torch.allclose(output, expected_output), f"Expected {expected_output}, but got {output}"
 
 
 def test_scalar_operations():
     conversion = ArithmeticWeightConversion(OperationTypes.MULTIPLICATION, 10)
-    assert conversion.handle_conversion(5) == 50
+    assert conversion.convert(5) == 50
 
 
 def test_tensor_operations():
     input_tensor = torch.tensor([1.0, 2.0, 3.0])
     conversion = ArithmeticWeightConversion(OperationTypes.ADDITION, torch.tensor([1.0, 1.0, 1.0]))
     expected_output = torch.tensor([2.0, 3.0, 4.0])
-    assert torch.allclose(conversion.handle_conversion(input_tensor), expected_output)
+    assert torch.allclose(conversion.convert(input_tensor), expected_output)
 
 
 def test_input_filter():
     def input_filter(x):
         return x * 2  # Double the input before applying the operation
 
     conversion = ArithmeticWeightConversion(OperationTypes.ADDITION, 3, input_filter=input_filter)
-    assert conversion.handle_conversion(torch.tensor(2.0)) == 7.0  # (2 * 2) + 3 = 7
+    assert conversion.convert(torch.tensor(2.0)) == 7.0  # (2 * 2) + 3 = 7
 
 
 def test_output_filter():
     def output_filter(x):
         return x / 2  # Halve the result after applying the operation
 
     conversion = ArithmeticWeightConversion(OperationTypes.ADDITION, 3, output_filter=output_filter)
-    assert conversion.handle_conversion(torch.tensor(2.0)) == 2.5  # (2 + 3) / 2 = 2.5
+    assert conversion.convert(torch.tensor(2.0)) == 2.5  # (2 + 3) / 2 = 2.5

tests/unit/weight_conversion/conversion_utils/conversion_steps/test_base_weight_conversion.py

@@ -11,37 +11,44 @@ def handle_conversion(self, weight):
         return weight + 5
 
 
-def test_process_weight_conversion_applies_conversion():
-    weight_conversion = MockWeightConversion()
-    weight = torch.zeros(2, 2)
-
-    converted_weight = weight_conversion.process_weight_conversion(weight)
-
-    expected = torch.tensor((2, 2)) + 5
-
-    assert torch.all(converted_weight == expected)
-
-
-def test_process_weight_conversion_applies_filters():
-    def mock_input_filter(weight):
-        return weight * 2
-
-    def mock_output_filter(weight):
-        return weight - 1
-
-    weight_conversion = MockWeightConversion(
-        input_filter=mock_input_filter, output_filter=mock_output_filter
-    )
-
-    weight = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
-
-    converted_weight = weight_conversion.process_weight_conversion(weight)
-
-    expected_weight = (weight * 2) + 5 - 1
-    assert torch.allclose(converted_weight, expected_weight)
-
 
 def test_base_weight_conversion_convert_throws_error():
     weight_conversion = BaseWeightConversion()
     with pytest.raises(NotImplementedError):
         weight_conversion.convert(torch.zeros(1, 4))
+
+def test_mock_weight_conversion_adds_five():
+    """
+    Verify that the mock subclass adds 5 to every element of the tensor.
+    """
+    weight_conversion = MockWeightConversion()
+    input_tensor = torch.zeros((1, 4), dtype=torch.float32)
+    output_tensor = weight_conversion.convert(input_tensor)
+    expected_tensor = torch.full((1, 4), 5.0, dtype=torch.float32)
+
+    # Option 1: simple equality check
+    assert torch.equal(output_tensor, expected_tensor)
+
+    # Option 2: more robust approximate check
+    # torch.testing.assert_close(output_tensor, expected_tensor)
+
+@pytest.mark.parametrize("shape", [(1, 4), (2, 2), (3,)])
+def test_mock_weight_conversion_various_shapes(shape):
+    """
+    Test multiple shapes to ensure .convert() works for different dims.
+    """
+    weight_conversion = MockWeightConversion()
+    input_tensor = torch.zeros(shape)
+    output_tensor = weight_conversion.convert(input_tensor)
+    expected_tensor = torch.full(shape, 5.0)
+    assert torch.equal(output_tensor, expected_tensor)
+
+def test_mock_weight_conversion_empty_tensor():
+    """
+    Ensure code doesn't crash on an empty tensor.
+    """
+    weight_conversion = MockWeightConversion()
+    input_tensor = torch.zeros((0, 4))
+    output_tensor = weight_conversion.convert(input_tensor)
+    assert output_tensor.shape == (0, 4)
+    # Since shape is (0,4), we can just check shape correctness

tests/unit/weight_conversion/conversion_utils/test_model_search.py → tests/unit/weight_conversion/conversion_utils/test_conversion_helpers.py

@@ -2,7 +2,7 @@
 
 import torch
 
-from transformer_lens.weight_conversion.conversion_utils.model_search import (
+from transformer_lens.weight_conversion.conversion_utils.conversion_helpers import (
     find_property,
 )
 

transformer_lens/weight_conversion/conversion_utils/architecture_conversion.py

@@ -1,6 +1,6 @@
 from torch import nn
 
-from .conversion_steps.base_weight_conversion import FIELD_SET
+from .conversion_steps.types import FIELD_SET
 from .conversion_steps.weight_conversion_set import WeightConversionSet
 
 

transformer_lens/weight_conversion/conversion_utils/model_search.py → transformer_lens/weight_conversion/conversion_utils/conversion_helpers.py

@@ -1,3 +1,5 @@
+
+
 def find_property(needle: str, haystack):
     needle_levels = needle.split(".")
     first_key = needle_levels.pop(0)
@@ -8,3 +10,4 @@ def find_property(needle: str, haystack):
         return find_property(".".join(needle_levels), current_level)
 
     return current_level
+

transformer_lens/weight_conversion/conversion_utils/conversion_steps/__init__.py

@@ -1,8 +1,8 @@
-from .base_weight_conversion import BaseWeightConversion, FIELD_SET
+from .types import CONVERSION_ACTION, CONVERSION, FIELD_SET
+from .base_weight_conversion import BaseWeightConversion
 from .callable_weight_conversion import CallableWeightConversion
 from .arithmetic_weight_conversion import ArithmeticWeightConversion, OperationTypes
 from .rearrange_weight_conversion import RearrangeWeightConversion
 from .repeat_weight_conversion import RepeatWeightConversion
 from .weight_conversion_set import WeightConversionSet
 from .ternary_weight_conversion import TernaryWeightConversion
-from .zeros_like_conversion import ZerosLikeConversion

transformer_lens/weight_conversion/conversion_utils/conversion_steps/arithmetic_weight_conversion.py

@@ -4,7 +4,7 @@
 
 import torch
 
-from .base_weight_conversion import BaseWeightConversion
+from transformer_lens.weight_conversion.conversion_utils.conversion_steps.base_weight_conversion import BaseWeightConversion
 
 
 class OperationTypes(Enum):

transformer_lens/weight_conversion/conversion_utils/conversion_steps/base_weight_conversion.py

@@ -1,16 +1,6 @@
 from collections.abc import Callable
 from typing import Optional
 
-import torch
-
-from transformer_lens.weight_conversion.conversion_utils.model_search import (
-    find_property,
-)
-
-CONVERSION = tuple[str, "BaseWeightConversion"]
-CONVERSION_ACTION = torch.Tensor | str | CONVERSION
-FIELD_SET = dict[CONVERSION_ACTION]
-
 
 class BaseWeightConversion:
     def __init__(
@@ -26,22 +16,6 @@ def convert(self, input_value):
         output = self.handle_conversion(input_value)
         return self.output_filter(output) if self.output_filter is not None else output
 
-    def process_weight_conversion(self, input_value, conversion_details: CONVERSION_ACTION):
-        if isinstance(conversion_details, torch.Tensor):
-            return conversion_details
-        elif isinstance(conversion_details, str):
-            return find_property(conversion_details, input_value)
-        else:
-            (remote_field, conversion) = conversion_details
-            weight = find_property(remote_field, input_value)
-            if isinstance(conversion, "WeightConversionSet"):
-                result = []
-                for layer in weight:
-                    result.append(conversion.convert(layer))
-                return result
-
-            else:
-                return conversion.convert(weight)
 
     def handle_conversion(self, input_value):
         raise NotImplementedError(

transformer_lens/weight_conversion/conversion_utils/conversion_steps/ternary_weight_conversion.py

@@ -3,7 +3,8 @@
 
 import torch
 
-from .base_weight_conversion import CONVERSION_ACTION, BaseWeightConversion
+from .types import CONVERSION_ACTION
+from .base_weight_conversion import BaseWeightConversion
 
 PRIMARY_CONVERSION = torch.Tensor | BaseWeightConversion | None
 

transformer_lens/weight_conversion/conversion_utils/conversion_steps/types.py

@@ -0,0 +1,20 @@
+import torch
+
+from .base_weight_conversion import BaseWeightConversion 
+
+# This type is used to indicate the position of a field in the remote model
+REMOTE_FIELD = str
+# This is the typing for a weight conversion when operations are needed on the REMOTE_FIELD. 
+# The BaseWeightConversion will be the instructions on the operations needed to bring the field
+# into TransformerLens
+CONVERSION = tuple[REMOTE_FIELD, BaseWeightConversion]
+# This is the full range of actions that can be taken to bring a field into TransformerLens
+# These can be configured as a predefined tensor, or a direction copy of the REMOTE_FIELD into
+# TransformerLens, or a more in depth CONVERSION
+CONVERSION_ACTION = torch.Tensor | REMOTE_FIELD | CONVERSION
+# This type is for a full set of conversions from a remote model into TransformerLens. Each key in
+# this dictionary will correspond to a field within a TransformerLens module, and each
+# CONVERSION_ACTION will instruction TransformerLens on how to bring the field into a
+# TransformerLens model. This type is repeated in both the root level of a model, as well as any
+# layers within the model
+FIELD_SET = dict[str, CONVERSION_ACTION]

transformer_lens/weight_conversion/conversion_utils/conversion_steps/weight_conversion_set.py

@@ -1,29 +1,49 @@
-from collections.abc import Callable
-from typing import Optional
+import torch
 
-from .base_weight_conversion import FIELD_SET, BaseWeightConversion
+from .types import FIELD_SET, CONVERSION_ACTION, CONVERSION
+from transformer_lens.weight_conversion.conversion_utils.conversion_helpers import find_property
+from .base_weight_conversion import BaseWeightConversion
+from transformer_lens.weight_conversion.conversion_utils.weight_conversion_utils import WeightConversionUtils
 
 
 class WeightConversionSet(BaseWeightConversion):
     def __init__(
         self,
         weights: FIELD_SET,
-        input_filter: Optional[Callable] = None,
-        output_filter: Optional[Callable] = None,
     ):
-        super().__init__(input_filter=input_filter, output_filter=output_filter)
+        super().__init__()
         self.weights = weights
 
     def handle_conversion(self, input_value):
         result = {}
         for weight_name in self.weights:
-            result[weight_name] = super().process_weight_conversion(
+            result[weight_name] = self.process_conversion_action(
                 input_value,
                 conversion_details=self.weights[weight_name],
             )
 
         return result
 
+    def process_conversion_action(self, input_value, conversion_details: CONVERSION_ACTION):
+        if isinstance(conversion_details, torch.Tensor):
+            return conversion_details
+        elif isinstance(conversion_details, str):
+            return find_property(conversion_details, input_value)
+        else:
+            (remote_field, conversion) = conversion_details
+            return self.process_conversion(input_value, remote_field, conversion)
+
+    def process_conversion(self, input_value, remote_field: str, conversion: CONVERSION):
+        field = find_property(remote_field, input_value)
+        if isinstance(field, WeightConversionSet):
+            result = []
+            for layer in field:
+                result.append(conversion.convert(layer))
+            return result
+
+        else:
+            return conversion.convert(field)
+
     def __repr__(self):
         conversion_string = (
             "Is composed of a set of nested conversions with the following details {\n\t"