Add additional gradient-based attribution methods to LLM Attribution (#…

…1337)

Summary:
Add `LayerGradientXActivation` and `LayerGradientShap` to the supported gradient-based LLM attribution methods.

Pull Request resolved: #1337

Test Plan: `pytest tests/attr -k TestLLMGradAttr` with new test cases via parameterized library

Reviewed By: cyrjano

Differential Revision: D62221000

Pulled By: craymichael

fbshipit-source-id: fb5f170e13a62355357d46d3ef7a2464e8eb80ab

captum/attr/_core/deep_lift.py

@@ -110,7 +110,7 @@ def __init__(
                         Default: 1e-10
         """
         GradientAttribution.__init__(self, model)
-        self.model = model
+        self.model: nn.Module = model
         self.eps = eps
         self.forward_handles: List[RemovableHandle] = []
         self.backward_handles: List[RemovableHandle] = []
@@ -324,7 +324,8 @@ def attribute(  # type: ignore
         warnings.warn(
             """Setting forward, backward hooks and attributes on non-linear
                activations. The hooks and attributes will be removed
-            after the attribution is finished"""
+            after the attribution is finished""",
+            stacklevel=2,
         )
         # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
         #  `TensorOrTupleOfTensorsGeneric`.

captum/attr/_core/layer/layer_deep_lift.py

@@ -351,9 +351,9 @@ def chunk_output_fn(out: TensorOrTupleOfTensorsGeneric) -> Sequence:
                 grad_kwargs=grad_kwargs,
             )
 
-            attr_inputs = tuple(map(lambda attr: attr[0], attrs))
-            attr_baselines = tuple(map(lambda attr: attr[1], attrs))
-            gradients = tuple(map(lambda grad: grad[0], gradients))
+            attr_inputs = tuple(attr[0] for attr in attrs)
+            attr_baselines = tuple(attr[1] for attr in attrs)
+            gradients = tuple(grad[0] for grad in gradients)
 
             if custom_attribution_func is None:
                 if self.multiplies_by_inputs:

captum/attr/_core/llm_attr.py

@@ -10,6 +10,8 @@
 from captum._utils.typing import TokenizerLike
 from captum.attr._core.feature_ablation import FeatureAblation
 from captum.attr._core.kernel_shap import KernelShap
+from captum.attr._core.layer.layer_gradient_shap import LayerGradientShap
+from captum.attr._core.layer.layer_gradient_x_activation import LayerGradientXActivation
 from captum.attr._core.layer.layer_integrated_gradients import LayerIntegratedGradients
 from captum.attr._core.lime import Lime
 from captum.attr._core.shapley_value import ShapleyValues, ShapleyValueSampling
@@ -452,7 +454,11 @@ class LLMGradientAttribution(Attribution):
     and returns LLMAttributionResult
     """
 
-    SUPPORTED_METHODS = (LayerIntegratedGradients,)
+    SUPPORTED_METHODS = (
+        LayerGradientShap,
+        LayerGradientXActivation,
+        LayerIntegratedGradients,
+    )
     SUPPORTED_INPUTS = (TextTokenInput,)
 
     def __init__(
@@ -473,53 +479,21 @@ class created with the llm model that follows huggingface style
 
         super().__init__(attr_method.forward_func)
 
-        # shallow copy is enough to avoid modifying original instance
-        self.attr_method: GradientAttribution = copy(attr_method)
-        self.attr_method.forward_func = self._forward_func
-
         # alias, we really need a model and don't support wrapper functions
         # coz we need call model.forward, model.generate, etc.
         self.model: nn.Module = cast(nn.Module, self.forward_func)
 
+        # shallow copy is enough to avoid modifying original instance
+        self.attr_method: GradientAttribution = copy(attr_method)
+        self.attr_method.forward_func = GradientForwardFunc(self)
+
         self.tokenizer: TokenizerLike = tokenizer
         self.device: torch.device = (
             cast(torch.device, self.model.device)
             if hasattr(self.model, "device")
             else next(self.model.parameters()).device
         )
 
-    def _forward_func(
-        self,
-        perturbed_tensor: Tensor,
-        inp: InterpretableInput,
-        target_tokens: Tensor,  # 1D tensor of target token ids
-        cur_target_idx: int,  # current target index
-    ) -> Tensor:
-        perturbed_input = self._format_model_input(inp.to_model_input(perturbed_tensor))
-
-        if cur_target_idx:
-            # the input batch size can be expanded by attr method
-            output_token_tensor = (
-                target_tokens[:cur_target_idx]
-                .unsqueeze(0)
-                .expand(perturbed_input.size(0), -1)
-                .to(self.device)
-            )
-            new_input_tensor = torch.cat([perturbed_input, output_token_tensor], dim=1)
-        else:
-            new_input_tensor = perturbed_input
-
-        output_logits = self.model(new_input_tensor)
-
-        new_token_logits = output_logits.logits[:, -1]
-        log_probs = torch.nn.functional.log_softmax(new_token_logits, dim=1)
-
-        target_token = target_tokens[cur_target_idx]
-        token_log_probs = log_probs[..., target_token]
-
-        # the attribution target is limited to the log probability
-        return token_log_probs
-
     def _format_model_input(self, model_input: Tensor) -> Tensor:
         """
         Convert str to tokenized tensor
@@ -643,3 +617,48 @@ def attribute_future(self) -> Callable:
         raise NotImplementedError(
             "attribute_future is not implemented for LLMGradientAttribution"
         )
+
+
+class GradientForwardFunc(nn.Module):
+    """
+    A wrapper class for the forward function of a model in LLMGradientAttribution
+    """
+
+    def __init__(self, attr: LLMGradientAttribution) -> None:
+        super().__init__()
+        self.attr = attr
+        self.model: nn.Module = attr.model
+
+    def forward(
+        self,
+        perturbed_tensor: Tensor,
+        inp: InterpretableInput,
+        target_tokens: Tensor,  # 1D tensor of target token ids
+        cur_target_idx: int,  # current target index
+    ) -> Tensor:
+        perturbed_input = self.attr._format_model_input(
+            inp.to_model_input(perturbed_tensor)
+        )
+
+        if cur_target_idx:
+            # the input batch size can be expanded by attr method
+            output_token_tensor = (
+                target_tokens[:cur_target_idx]
+                .unsqueeze(0)
+                .expand(perturbed_input.size(0), -1)
+                .to(self.attr.device)
+            )
+            new_input_tensor = torch.cat([perturbed_input, output_token_tensor], dim=1)
+        else:
+            new_input_tensor = perturbed_input
+
+        output_logits = self.model(new_input_tensor)
+
+        new_token_logits = output_logits.logits[:, -1]
+        log_probs = torch.nn.functional.log_softmax(new_token_logits, dim=1)
+
+        target_token = target_tokens[cur_target_idx]
+        token_log_probs = log_probs[..., target_token]
+
+        # the attribution target is limited to the log probability
+        return token_log_probs

tests/attr/test_llm_attr.py

@@ -3,19 +3,19 @@
 # pyre-strict
 
 import copy
-from typing import Any, cast, Dict, List, NamedTuple, Optional, Type, Union
+from typing import Any, cast, Dict, List, NamedTuple, Optional, Tuple, Type, Union
 
 import torch
-from captum._utils.models.linear_model import (  # @manual=//pytorch/captum/captum/_utils/models/linear_model:linear_model  # noqa: E501
-    SkLearnLasso,
-)
+from captum._utils.models.linear_model import SkLearnLasso
 from captum.attr._core.feature_ablation import FeatureAblation
 from captum.attr._core.kernel_shap import KernelShap
+from captum.attr._core.layer.layer_gradient_shap import LayerGradientShap
+from captum.attr._core.layer.layer_gradient_x_activation import LayerGradientXActivation
 from captum.attr._core.layer.layer_integrated_gradients import LayerIntegratedGradients
 from captum.attr._core.lime import Lime
 from captum.attr._core.llm_attr import LLMAttribution, LLMGradientAttribution
 from captum.attr._core.shapley_value import ShapleyValues, ShapleyValueSampling
-from captum.attr._utils.attribution import PerturbationAttribution
+from captum.attr._utils.attribution import GradientAttribution, PerturbationAttribution
 from captum.attr._utils.interpretable_input import TextTemplateInput, TextTokenInput
 from parameterized import parameterized, parameterized_class
 from tests.helpers import BaseTest
@@ -379,15 +379,30 @@ def test_futures_not_implemented(self) -> None:
 class TestLLMGradAttr(BaseTest):
     device: str
 
-    def test_llm_attr(self) -> None:
+    @parameterized.expand(
+        [
+            (LayerIntegratedGradients, None),
+            (LayerGradientXActivation, None),
+            (LayerGradientShap, (torch.tensor([[1, 0, 1, 0]]),)),
+        ]
+    )
+    def test_llm_attr(
+        self, AttrClass: Type[GradientAttribution], baselines: Optional[Tuple[Tensor]]
+    ) -> None:
         llm = DummyLLM()
         llm.to(self.device)
         tokenizer = DummyTokenizer()
-        attr = LayerIntegratedGradients(llm, llm.emb)
+        attr = AttrClass(llm, llm.emb)  # type: ignore[call-arg]
         llm_attr = LLMGradientAttribution(attr, tokenizer)
 
+        attr_kws: Dict[str, Any] = {}
+        if baselines is not None:
+            attr_kws["baselines"] = tuple(
+                baseline.to(self.device) for baseline in baselines
+            )
+
         inp = TextTokenInput("a b c", tokenizer)
-        res = llm_attr.attribute(inp, "m n o p q")
+        res = llm_attr.attribute(inp, "m n o p q", **attr_kws)
 
         # 5 output tokens, 4 input tokens including sos
         self.assertEqual(res.seq_attr.shape, (4,))
@@ -402,15 +417,30 @@ def test_llm_attr(self) -> None:
         assert res.token_attr is not None  # make pyre/mypy happy
         self.assertEqual(token_attr.device.type, self.device)  # type: ignore
 
-    def test_llm_attr_without_target(self) -> None:
+    @parameterized.expand(
+        [
+            (LayerIntegratedGradients, None),
+            (LayerGradientXActivation, None),
+            (LayerGradientShap, (torch.tensor([[1, 0, 1, 0]]),)),
+        ]
+    )
+    def test_llm_attr_without_target(
+        self, AttrClass: Type[GradientAttribution], baselines: Optional[Tuple[Tensor]]
+    ) -> None:
         llm = DummyLLM()
         llm.to(self.device)
         tokenizer = DummyTokenizer()
-        attr = LayerIntegratedGradients(llm, llm.emb)
+        attr = AttrClass(llm, llm.emb)  # type: ignore[call-arg]
         llm_attr = LLMGradientAttribution(attr, tokenizer)
 
+        attr_kws: Dict[str, Any] = {}
+        if baselines is not None:
+            attr_kws["baselines"] = tuple(
+                baseline.to(self.device) for baseline in baselines
+            )
+
         inp = TextTokenInput("a b c", tokenizer)
-        res = llm_attr.attribute(inp, gen_args={"mock_response": "x y z"})
+        res = llm_attr.attribute(inp, gen_args={"mock_response": "x y z"}, **attr_kws)
 
         self.assertEqual(res.seq_attr.shape, (4,))
         assert res.token_attr is not None  # make pyre/mypy happy
@@ -424,15 +454,30 @@ def test_llm_attr_without_target(self) -> None:
         assert res.token_attr is not None  # make pyre/mypy happy
         self.assertEqual(token_attr.device.type, self.device)  # type: ignore
 
-    def test_llm_attr_with_skip_tokens(self) -> None:
+    @parameterized.expand(
+        [
+            (LayerIntegratedGradients, None),
+            (LayerGradientXActivation, None),
+            (LayerGradientShap, (torch.tensor([[1, 0, 1]]),)),
+        ]
+    )
+    def test_llm_attr_with_skip_tokens(
+        self, AttrClass: Type[GradientAttribution], baselines: Optional[Tuple[Tensor]]
+    ) -> None:
         llm = DummyLLM()
         llm.to(self.device)
         tokenizer = DummyTokenizer()
-        attr = LayerIntegratedGradients(llm, llm.emb)
+        attr = AttrClass(llm, llm.emb)  # type: ignore[call-arg]
         llm_attr = LLMGradientAttribution(attr, tokenizer)
 
+        attr_kws: Dict[str, Any] = {}
+        if baselines is not None:
+            attr_kws["baselines"] = tuple(
+                baseline.to(self.device) for baseline in baselines
+            )
+
         inp = TextTokenInput("a b c", tokenizer, skip_tokens=[0])
-        res = llm_attr.attribute(inp, "m n o p q")
+        res = llm_attr.attribute(inp, "m n o p q", **attr_kws)
 
         # 5 output tokens, 4 input tokens including sos
         self.assertEqual(res.seq_attr.shape, (3,))