Merge branch 'master' into master

Author: vivekmig

captum/attr/_utils/lrp_rules.py

@@ -4,7 +4,7 @@
 
 import torch
 
-from ..._utils.common import _format_tensor_into_tuples
+from captum._utils.common import _format_tensor_into_tuples
 
 
 class PropagationRule(ABC):

captum/influence/_core/arnoldi_influence_function.py

@@ -6,6 +6,12 @@
 
 from captum._utils.gradient import _extract_parameters_from_layers
 
+from captum.influence._core.influence_function import (
+    _get_dataset_embeddings_intermediate_quantities_influence_function,
+    InfluenceFunctionBase,
+    IntermediateQuantitiesInfluenceFunction,
+)
+
 from captum.influence._utils.common import (
     _compute_batch_loss_influence_function_base,
     _compute_jacobian_sample_wise_grads_per_batch,
@@ -35,12 +41,6 @@
 from torch.utils.data import DataLoader, Dataset
 from tqdm import tqdm
 
-from .influence_function import (
-    _get_dataset_embeddings_intermediate_quantities_influence_function,
-    InfluenceFunctionBase,
-    IntermediateQuantitiesInfluenceFunction,
-)
-
 
 def _parameter_arnoldi(
     hvp: Callable,

captum/insights/attr_vis/app.py

@@ -228,7 +228,7 @@ def _update_config(self, settings):
 
     @log_usage()
     def render(self, debug=True):
-        from captum.insights.attr_vis.widget import CaptumInsights
+        from captum.insights.attr_vis.widget.widget import CaptumInsights
         from IPython.display import display
 
         widget = CaptumInsights(visualizer=self)

tests/attr/layer/test_layer_lrp.py

@@ -5,8 +5,8 @@
 from captum.attr import LayerLRP
 from captum.attr._utils.lrp_rules import Alpha1_Beta0_Rule, EpsilonRule, GammaRule
 
-from ...helpers.basic import assertTensorAlmostEqual, BaseTest
-from ...helpers.basic_models import BasicModel_ConvNet_One_Conv, SimpleLRPModel
+from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
+from tests.helpers.basic_models import BasicModel_ConvNet_One_Conv, SimpleLRPModel
 
 
 def _get_basic_config():

tests/attr/models/test_pytext.py

@@ -20,11 +20,13 @@
     from pytext.config.doc_classification import ModelInputConfig, TargetConfig
     from pytext.config.field_config import FeatureConfig, WordFeatConfig
     from pytext.data import CommonMetadata
-    from pytext.data.doc_classification_data_handler import DocClassificationDataHandler
+    from pytext.data.doc_classification_data_handler import (  # @manual=//pytext:main_lib
+        DocClassificationDataHandler,
+    )
     from pytext.data.featurizer import SimpleFeaturizer
     from pytext.fields import FieldMeta
     from pytext.models.decoders.mlp_decoder import MLPDecoder
-    from pytext.models.doc_model import DocModel_Deprecated
+    from pytext.models.doc_model import DocModel_Deprecated  # @manual=//pytext:main_lib
     from pytext.models.embeddings.word_embedding import WordEmbedding
     from pytext.models.representations.bilstm_doc_attention import BiLSTMDocAttention
 except ImportError:

tests/concept/test_tcav.py

@@ -1193,8 +1193,6 @@ def test_TCAV_x_1_0_1_w_flipped_class_id(self) -> None:
     # Testing TCAV with default classifier and experimental sets of varying lengths
     def test_exp_sets_with_diffent_lengths(self) -> None:
         try:
-            import sklearn
-            import sklearn.linear_model
             import sklearn.svm  # noqa: F401
         except ImportError:
             raise unittest.SkipTest("sklearn is not available.")

tests/influence/_core/test_tracin_show_progress.py

@@ -1,6 +1,5 @@
 import io
 import tempfile
-import unittest
 import unittest.mock
 from typing import Callable
 

tutorials/Llama2_LLM_Attribution.ipynb

@@ -85,6 +85,11 @@ class TutorialHome extends React.Component {
                 Using Captum and Integrated Gradients we interpret the output of several test questions and analyze the attribution scores
                 of the text and visual parts of the model. Find the tutorial <a href="Multimodal_VQA_Interpret">here</a>.
 
+                <h4>Understanding Llama2 with Captum LLM Attribution:</h4>
+                This tutorial demonstrates how to easily use the LLM attribution functionality to interpret the large langague models (LLM) in text generation.
+                It takes Llama2 as the example and shows the step-by-step improvements from the basic attribution setting to more advanced techniques.
+                Find the tutorial <a href="Llama2_LLM_Attribution">here</a>.
+
                 <h4>Interpreting question answering with BERT Part 1:</h4>
                 This tutorial demonstrates how to use Captum to interpret a BERT model for question answering.
                 We use a pre-trained model from Hugging Face fine-tuned on the SQUAD dataset and show how to use hooks to

website/pages/tutorials/index.js

@@ -46,6 +46,10 @@
       "id": "Image_and_Text_Classification_LIME",
       "title": "Interpreting vision and text models with LIME"
     },
+    {
+      "id": "Llama2_LLM_Attribution",
+      "title": "Understanding Llama2 with Captum LLM Attribution"
+    },
     {
       "title": "Interpreting BERT",
       "children": [