Contextualized bias mitigation (#5176)

* added linear and hard debiasers * worked on documentation * committing changes before branch switch * committing changes before switching branch * finished bias direction, linear and hard debiasers, need to write tests * finished bias direction test * Commiting changes before switching branch * finished hard and linear debiasers * finished OSCaR * bias mitigators tests and bias metrics remaining * added bias mitigator tests * added bias mitigator tests * finished tests for bias mitigation methods * fixed gpu issues * fixed gpu issues * fixed gpu issues * resolve issue with count_nonzero not being differentiable * added more references * fairness during finetuning * finished bias mitigator wrapper * added reference * updated CHANGELOG and fixed minor docs issues * move id tensors to embedding device * fixed to use predetermined bias direction * fixed minor doc errors * snli reader registration issue * fixed _pretrained from params issue * fixed device issues * evaluate bias mitigation initial commit * finished evaluate bias mitigation * handles multiline prediction files * fixed minor bugs * fixed minor bugs * improved prediction diff JSON format * forgot to resolve a conflict * Refactored evaluate bias mitigation to use NLI metric * Added SNLIPredictionsDiff class * ensured dataloader is same for bias mitigated and baseline models * finished evaluate bias mitigation * Update CHANGELOG.md * Replaced local data files with github raw content links * Update allennlp/fairness/bias_mitigator_applicator.py Co-authored-by: Pete <[email protected]> * deleted evaluate_bias_mitigation from git tracking * removed evaluate-bias-mitigation instances from rest of repo * addressed Akshita's comments * moved bias mitigator applicator test to allennlp-models * removed unnecessary files Co-authored-by: Arjun Subramonian <[email protected]> Co-authored-by: Arjun Subramonian <[email protected]> Co-authored-by: Arjun Subramonian <[email protected]> Co-authored-by: Arjun Subramonian <[email protected]> Co-authored-by: Akshita Bhagia <[email protected]> Co-authored-by: Pete <[email protected]>
allenai · Jun 2, 2021 · b92fd9a · b92fd9a
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -24,6 +24,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 
 - Added `TaskSuite` base class and command line functionality for running [`checklist`](https://github.com/marcotcr/checklist) test suites, along with implementations for `SentimentAnalysisSuite`, `QuestionAnsweringSuite`, and `TextualEntailmentSuite`. These can be found in the `allennlp.confidence_checks.task_checklists` module.
+- Added `BiasMitigatorApplicator`, which wraps any Model and mitigates biases by finetuning
+on a downstream task.
 - Added `allennlp diff` command to compute a diff on model checkpoints, analogous to what `git diff` does on two files.
 - Meta data defined by the class `allennlp.common.meta.Meta` is now saved in the serialization directory and archive file
   when training models from the command line. This is also now part of the `Archive` named tuple that's returned from `load_archive()`.
@@ -54,7 +56,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed `wandb` callback to work in distributed training.
 - Fixed `tqdm` logging into multiple files with `allennlp-optuna`.
 
-
 ## [v2.4.0](https://github.com/allenai/allennlp/releases/tag/v2.4.0) - 2021-04-22
 
 ### Added
@@ -80,8 +81,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Add new dimension to the `interpret` module: influence functions via the `InfluenceInterpreter` base class, along with a concrete implementation: `SimpleInfluence`.
 - Added a `quiet` parameter to the `MultiProcessDataLoading` that disables `Tqdm` progress bars.
 - The test for distributed metrics now takes a parameter specifying how often you want to run it.
-- Created the fairness module and added four fairness metrics: `Independence`, `Separation`, and `Sufficiency`.
-- Added three bias metrics to the fairness module: `WordEmbeddingAssociationTest`, `EmbeddingCoherenceTest`, `NaturalLanguageInference`, and `AssociationWithoutGroundTruth`.
+- Created the fairness module and added three fairness metrics: `Independence`, `Separation`, and `Sufficiency`.
+- Added four bias metrics to the fairness module: `WordEmbeddingAssociationTest`, `EmbeddingCoherenceTest`, `NaturalLanguageInference`, and `AssociationWithoutGroundTruth`.
 - Added four bias direction methods (`PCABiasDirection`, `PairedPCABiasDirection`, `TwoMeansBiasDirection`, `ClassificationNormalBiasDirection`) and four bias mitigation methods (`LinearBiasMitigator`, `HardBiasMitigator`, `INLPBiasMitigator`, `OSCaRBiasMitigator`).
 
 ### Changed

diff --git a/allennlp/fairness/__init__.py b/allennlp/fairness/__init__.py
@@ -3,7 +3,8 @@
 
 1. measure the fairness of models according to multiple definitions of fairness
 2. measure bias amplification
-3. debias embeddings during training time and post-processing
+3. mitigate bias in static and contextualized embeddings during training time and
+post-processing
 """
 
 from allennlp.fairness.fairness_metrics import Independence, Separation, Sufficiency
@@ -25,3 +26,17 @@
     INLPBiasMitigator,
     OSCaRBiasMitigator,
 )
+from allennlp.fairness.bias_utils import load_words, load_word_pairs
+from allennlp.fairness.bias_mitigator_applicator import BiasMitigatorApplicator
+from allennlp.fairness.bias_mitigator_wrappers import (
+    HardBiasMitigatorWrapper,
+    LinearBiasMitigatorWrapper,
+    INLPBiasMitigatorWrapper,
+    OSCaRBiasMitigatorWrapper,
+)
+from allennlp.fairness.bias_direction_wrappers import (
+    PCABiasDirectionWrapper,
+    PairedPCABiasDirectionWrapper,
+    TwoMeansBiasDirectionWrapper,
+    ClassificationNormalBiasDirectionWrapper,
+)
diff --git a/allennlp/fairness/bias_direction_wrappers.py b/allennlp/fairness/bias_direction_wrappers.py
@@ -0,0 +1,269 @@
+import torch
+from typing import Union, Optional
+from os import PathLike
+
+from allennlp.fairness.bias_direction import (
+    BiasDirection,
+    PCABiasDirection,
+    PairedPCABiasDirection,
+    TwoMeansBiasDirection,
+    ClassificationNormalBiasDirection,
+)
+from allennlp.fairness.bias_utils import load_word_pairs, load_words
+
+from allennlp.common import Registrable
+from allennlp.data.tokenizers.tokenizer import Tokenizer
+from allennlp.data import Vocabulary
+
+
+class BiasDirectionWrapper(Registrable):
+    """
+    Parent class for bias direction wrappers.
+    """
+
+    def __init__(self):
+        self.direction: BiasDirection = None
+        self.noise: float = None
+
+    def __call__(self, module):
+        raise NotImplementedError
+
+    def train(self, mode: bool = True):
+        """
+
+        # Parameters
+
+        mode : `bool`, optional (default=`True`)
+            Sets `requires_grad` to value of `mode` for bias direction.
+        """
+        self.direction.requires_grad = mode
+
+    def add_noise(self, t: torch.Tensor):
+        """
+
+        # Parameters
+
+        t : `torch.Tensor`
+            Tensor to which to add small amount of Gaussian noise.
+        """
+        return t + self.noise * torch.randn(t.size(), device=t.device)
+
+
+@BiasDirectionWrapper.register("pca")
+class PCABiasDirectionWrapper(BiasDirectionWrapper):
+    """
+
+    # Parameters
+
+    seed_words_file : `Union[PathLike, str]`
+        Path of file containing seed words.
+    tokenizer : `Tokenizer`
+        Tokenizer used to tokenize seed words.
+    direction_vocab : `Vocabulary`, optional (default=`None`)
+        Vocabulary of tokenizer. If `None`, assumes tokenizer is of
+        type `PreTrainedTokenizer` and uses tokenizer's `vocab` attribute.
+    namespace : `str`, optional (default=`"tokens"`)
+        Namespace of direction_vocab to use when tokenizing.
+        Disregarded when direction_vocab is `None`.
+    requires_grad : `bool`, optional (default=`False`)
+        Option to enable gradient calculation for bias direction.
+    noise : `float`, optional (default=`1e-10`)
+        To avoid numerical instability if embeddings are initialized uniformly.
+    """
+
+    def __init__(
+        self,
+        seed_words_file: Union[PathLike, str],
+        tokenizer: Tokenizer,
+        direction_vocab: Optional[Vocabulary] = None,
+        namespace: str = "tokens",
+        requires_grad: bool = False,
+        noise: float = 1e-10,
+    ):
+        self.ids = load_words(seed_words_file, tokenizer, direction_vocab, namespace)
+        self.direction = PCABiasDirection(requires_grad=requires_grad)
+        self.noise = noise
+
+    def __call__(self, module):
+        # embed subword token IDs and mean pool to get
+        # embedding of original word
+        ids_embeddings = []
+        for i in self.ids:
+            i = i.to(module.weight.device)
+            ids_embeddings.append(torch.mean(module.forward(i), dim=0, keepdim=True))
+        ids_embeddings = torch.cat(ids_embeddings)
+
+        # adding trivial amount of noise
+        # to eliminate linear dependence amongst all embeddings
+        # when training first starts
+        ids_embeddings = self.add_noise(ids_embeddings)
+
+        return self.direction(ids_embeddings)
+
+
+@BiasDirectionWrapper.register("paired_pca")
+class PairedPCABiasDirectionWrapper(BiasDirectionWrapper):
+    """
+
+    # Parameters
+
+    seed_word_pairs_file : `Union[PathLike, str]`
+        Path of file containing seed word pairs.
+    tokenizer : `Tokenizer`
+        Tokenizer used to tokenize seed words.
+    direction_vocab : `Vocabulary`, optional (default=`None`)
+        Vocabulary of tokenizer. If `None`, assumes tokenizer is of
+        type `PreTrainedTokenizer` and uses tokenizer's `vocab` attribute.
+    namespace : `str`, optional (default=`"tokens"`)
+        Namespace of direction_vocab to use when tokenizing.
+        Disregarded when direction_vocab is `None`.
+    requires_grad : `bool`, optional (default=`False`)
+        Option to enable gradient calculation for bias direction.
+    noise : `float`, optional (default=`1e-10`)
+        To avoid numerical instability if embeddings are initialized uniformly.
+    """
+
+    def __init__(
+        self,
+        seed_word_pairs_file: Union[PathLike, str],
+        tokenizer: Tokenizer,
+        direction_vocab: Optional[Vocabulary] = None,
+        namespace: str = "tokens",
+        requires_grad: bool = False,
+        noise: float = 1e-10,
+    ):
+        self.ids1, self.ids2 = load_word_pairs(
+            seed_word_pairs_file, tokenizer, direction_vocab, namespace
+        )
+        self.direction = PairedPCABiasDirection(requires_grad=requires_grad)
+        self.noise = noise
+
+    def __call__(self, module):
+        # embed subword token IDs and mean pool to get
+        # embedding of original word
+        ids1_embeddings = []
+        for i in self.ids1:
+            i = i.to(module.weight.device)
+            ids1_embeddings.append(torch.mean(module.forward(i), dim=0, keepdim=True))
+        ids2_embeddings = []
+        for i in self.ids2:
+            i = i.to(module.weight.device)
+            ids2_embeddings.append(torch.mean(module.forward(i), dim=0, keepdim=True))
+        ids1_embeddings = torch.cat(ids1_embeddings)
+        ids2_embeddings = torch.cat(ids2_embeddings)
+
+        ids1_embeddings = self.add_noise(ids1_embeddings)
+        ids2_embeddings = self.add_noise(ids2_embeddings)
+
+        return self.direction(ids1_embeddings, ids2_embeddings)
+
+
+@BiasDirectionWrapper.register("two_means")
+class TwoMeansBiasDirectionWrapper(BiasDirectionWrapper):
+    """
+
+    # Parameters
+
+    seed_word_pairs_file : `Union[PathLike, str]`
+        Path of file containing seed word pairs.
+    tokenizer : `Tokenizer`
+        Tokenizer used to tokenize seed words.
+    direction_vocab : `Vocabulary`, optional (default=`None`)
+        Vocabulary of tokenizer. If `None`, assumes tokenizer is of
+        type `PreTrainedTokenizer` and uses tokenizer's `vocab` attribute.
+    namespace : `str`, optional (default=`"tokens"`)
+        Namespace of direction_vocab to use when tokenizing.
+        Disregarded when direction_vocab is `None`.
+    requires_grad : `bool`, optional (default=`False`)
+        Option to enable gradient calculation for bias direction.
+    noise : `float`, optional (default=`1e-10`)
+        To avoid numerical instability if embeddings are initialized uniformly.
+    """
+
+    def __init__(
+        self,
+        seed_word_pairs_file: Union[PathLike, str],
+        tokenizer: Tokenizer,
+        direction_vocab: Optional[Vocabulary] = None,
+        namespace: str = "tokens",
+        requires_grad: bool = False,
+        noise: float = 1e-10,
+    ):
+        self.ids1, self.ids2 = load_word_pairs(
+            seed_word_pairs_file, tokenizer, direction_vocab, namespace
+        )
+        self.direction = TwoMeansBiasDirection(requires_grad=requires_grad)
+        self.noise = noise
+
+    def __call__(self, module):
+        # embed subword token IDs and mean pool to get
+        # embedding of original word
+        ids1_embeddings = []
+        for i in self.ids1:
+            i = i.to(module.weight.device)
+            ids1_embeddings.append(torch.mean(module.forward(i), dim=0, keepdim=True))
+        ids2_embeddings = []
+        for i in self.ids2:
+            i = i.to(module.weight.device)
+            ids2_embeddings.append(torch.mean(module.forward(i), dim=0, keepdim=True))
+        ids1_embeddings = torch.cat(ids1_embeddings)
+        ids2_embeddings = torch.cat(ids2_embeddings)
+
+        ids1_embeddings = self.add_noise(ids1_embeddings)
+        ids2_embeddings = self.add_noise(ids2_embeddings)
+
+        return self.direction(ids1_embeddings, ids2_embeddings)
+
+
+@BiasDirectionWrapper.register("classification_normal")
+class ClassificationNormalBiasDirectionWrapper(BiasDirectionWrapper):
+    """
+
+    # Parameters
+
+    seed_word_pairs_file : `Union[PathLike, str]`
+        Path of file containing seed word pairs.
+    tokenizer : `Tokenizer`
+        Tokenizer used to tokenize seed words.
+    direction_vocab : `Vocabulary`, optional (default=`None`)
+        Vocabulary of tokenizer. If `None`, assumes tokenizer is of
+        type `PreTrainedTokenizer` and uses tokenizer's `vocab` attribute.
+    namespace : `str`, optional (default=`"tokens"`)
+        Namespace of direction_vocab to use when tokenizing.
+        Disregarded when direction_vocab is `None`.
+    noise : `float`, optional (default=`1e-10`)
+        To avoid numerical instability if embeddings are initialized uniformly.
+    """
+
+    def __init__(
+        self,
+        seed_word_pairs_file: Union[PathLike, str],
+        tokenizer: Tokenizer,
+        direction_vocab: Optional[Vocabulary] = None,
+        namespace: str = "tokens",
+        noise: float = 1e-10,
+    ):
+        self.ids1, self.ids2 = load_word_pairs(
+            seed_word_pairs_file, tokenizer, direction_vocab, namespace
+        )
+        self.direction = ClassificationNormalBiasDirection()
+        self.noise = noise
+
+    def __call__(self, module):
+        # embed subword token IDs and mean pool to get
+        # embedding of original word
+        ids1_embeddings = []
+        for i in self.ids1:
+            i = i.to(module.weight.device)
+            ids1_embeddings.append(torch.mean(module.forward(i), dim=0, keepdim=True))
+        ids2_embeddings = []
+        for i in self.ids2:
+            i = i.to(module.weight.device)
+            ids2_embeddings.append(torch.mean(module.forward(i), dim=0, keepdim=True))
+        ids1_embeddings = torch.cat(ids1_embeddings)
+        ids2_embeddings = torch.cat(ids2_embeddings)
+
+        ids1_embeddings = self.add_noise(ids1_embeddings)
+        ids2_embeddings = self.add_noise(ids2_embeddings)
+
+        return self.direction(ids1_embeddings, ids2_embeddings)
diff --git a/allennlp/fairness/bias_metrics.py b/allennlp/fairness/bias_metrics.py
@@ -258,6 +258,8 @@ class NaturalLanguageInference(Metric):
     3. Threshold:tau (T:tau): A parameterized measure that reports the fraction
     of examples whose probability of neutral is above tau.
 
+    # Parameters
+
     neutral_label : `int`, optional (default=`2`)
         The discrete integer label corresponding to a neutral entailment prediction.
     taus : `List[float]`, optional (default=`[0.5, 0.7]`)