# Source code for captum.influence._core.tracincp

```
#!/usr/bin/env python3
import glob
import warnings
from abc import abstractmethod
from os.path import join
from typing import (
Any,
Callable,
Iterator,
List,
NamedTuple,
Optional,
Tuple,
Type,
Union,
)
import torch
from captum._utils.av import AV
from captum._utils.common import _get_module_from_name, _parse_version
from captum._utils.gradient import (
_compute_jacobian_wrt_params,
_compute_jacobian_wrt_params_with_sample_wise_trick,
)
from captum._utils.progress import NullProgress, progress
from captum.influence._core.influence import DataInfluence
from captum.influence._utils.common import (
_check_loss_fn,
_format_inputs_dataset,
_get_k_most_influential_helper,
_gradient_dot_product,
_load_flexible_state_dict,
_self_influence_by_batches_helper,
)
from captum.log import log_usage
from torch import Tensor
from torch.nn import Module
from torch.utils.data import DataLoader, Dataset
r"""
Note: methods starting with "_" are protected, not private, and can be overridden in
child classes. They are not part of the API.
Implements abstract DataInfluence class and provides implementation details for
influence computation based on the logic provided in TracIn paper
(https://arxiv.org/abs/2002.08484).
The TracIn paper proposes an idealized notion of influence which can be represented by
the total amount a training example reduces loss for a test example via a training
process such as stochastic gradient descent. As this idealized notion of influence is
impractical to compute, the TracIn paper proposes instead to compute an influence
score, which uses a first-order approximation for the change in loss for a test example
by a training example, which is accumulated across saved model checkpoints. This
influence score is accumulated via a summed dot-product of gradient vectors for the
scores/loss of a test and training example.
"""
"""
TODO: Support for checkpoint type. Currently only supports model parameters as saved
checkpoints. Can use enum or string.
Potential implementation from design doc:
checkpoint_type (Enum = [Parameters | Loss_Grad]): For performance,
saved / loaded checkpoints can be either model parameters, or
gradient of the loss function on an input w.r.t parameters.
"""
class KMostInfluentialResults(NamedTuple):
"""
This namedtuple stores the results of using the `influence` method. This method
is implemented by all subclasses of `TracInCPBase` to calculate
proponents / opponents. The `indices` field stores the indices of the
proponents / opponents for each example in the test dataset. For example, if
finding opponents, `indices[i][j]` stores the index in the training data of the
example with the `j`-th highest influence score on the `i`-th example in the test
dataset. Similarly, the `influence_scores` field stores the actual influence scores,
so that `influence_scores[i][j]` is the influence score of example `indices[i][j]`
in the training data on example `i` of the test dataset. Please see
`TracInCPBase.influence` for more details.
"""
indices: Tensor
influence_scores: Tensor
[docs]class TracInCPBase(DataInfluence):
"""
To implement the `influence` method, classes inheriting from `TracInCPBase` will
separately implement the private `_self_influence`, `_get_k_most_influential`,
and `_influence` methods. The public `influence` method is a wrapper for these
private methods.
"""
def __init__(
self,
model: Module,
train_dataset: Union[Dataset, DataLoader],
checkpoints: Union[str, List[str], Iterator],
checkpoints_load_func: Callable = _load_flexible_state_dict,
loss_fn: Optional[Union[Module, Callable]] = None,
batch_size: Union[int, None] = 1,
test_loss_fn: Optional[Union[Module, Callable]] = None,
) -> None:
r"""
Args:
model (torch.nn.Module): An instance of pytorch model. This model should
define all of its layers as attributes of the model.
train_dataset (torch.utils.data.Dataset or torch.utils.data.DataLoader):
In the `influence` method, we compute the influence score of
training examples on examples in a test batch.
This argument represents the training dataset containing those
training examples. In order to compute those influence scores, we
will create a Pytorch DataLoader yielding batches of training
examples that is then used for processing. If this argument is
already a Pytorch Dataloader, that DataLoader can be directly
used for processing. If it is instead a Pytorch Dataset, we will
create a DataLoader using it, with batch size specified by
`batch_size`. For efficiency purposes, the batch size of the
DataLoader used for processing should be as large as possible, but
not too large, so that certain intermediate quantities created
from a batch still fit in memory. Therefore, if
`train_dataset` is a Dataset, `batch_size` should be large.
If `train_dataset` was already a DataLoader to begin with,
it should have been constructed to have a large batch size. It is
assumed that the Dataloader (regardless of whether it is created
from a Pytorch Dataset or not) yields tuples. For a `batch` that is
yielded, of length `L`, it is assumed that the forward function of
`model` accepts `L-1` arguments, and the last element of `batch` is
the label. In other words, `model(*batch[:-1])` gives the output of
`model`, and `batch[-1]` are the labels for the batch.
checkpoints (str, list[str], or Iterator): Either the directory of the
path to store and retrieve model checkpoints, a list of
filepaths with checkpoints from which to load, or an iterator which
returns objects from which to load checkpoints.
checkpoints_load_func (Callable, optional): The function to load a saved
checkpoint into a model to update its parameters, and get the
learning rate if it is saved. By default uses a utility to load a
model saved as a state dict.
Default: _load_flexible_state_dict
loss_fn (Callable, optional): The loss function applied to model.
Default: None
batch_size (int or None, optional): Batch size of the DataLoader created to
iterate through `train_dataset`, if it is a Dataset.
`batch_size` should be chosen as large as possible so that certain
intermediate quantities created from a batch still fit in memory.
Specific implementations of `TracInCPBase` will detail the size of
the intermediate quantities. `batch_size` must be an int if
`train_dataset` is a Dataset. If `train_dataset`
is a DataLoader, then `batch_size` is ignored as an argument.
Default: 1
test_loss_fn (Callable, optional): In some cases, one may want to use a
separate loss functions for training examples, i.e. those in
`train_dataset`, and for test examples, i.e. those
represented by the `inputs` and `targets` arguments to the
`influence` method. For example, if one wants to calculate the
influence score of a training example on a test example's
prediction for a fixed class, `test_loss_fn` could map from the
logits for all classes to the logits for a fixed class.
`test_loss_fn` needs to satisfy the same constraints as `loss_fn`.
If not provided, the loss function for test examples is assumed to
be the same as the loss function for training examples, i.e.
`loss_fn`.
Default: None
"""
self.model = model
if isinstance(checkpoints, str):
self.checkpoints = AV.sort_files(glob.glob(join(checkpoints, "*")))
elif isinstance(checkpoints, List) and isinstance(checkpoints[0], str):
self.checkpoints = AV.sort_files(checkpoints)
else:
self.checkpoints = list(checkpoints) # cast to avoid mypy error
if isinstance(self.checkpoints, List):
assert len(self.checkpoints) > 0, "No checkpoints saved!"
self.checkpoints_load_func = checkpoints_load_func
self.loss_fn = loss_fn
# If test_loss_fn not provided, it's assumed to be same as loss_fn
self.test_loss_fn = loss_fn if test_loss_fn is None else test_loss_fn
self.batch_size = batch_size
if not isinstance(train_dataset, DataLoader):
assert isinstance(batch_size, int), (
"since the `train_dataset` argument was a `Dataset`, "
"`batch_size` must be an int."
)
self.train_dataloader = DataLoader(train_dataset, batch_size, shuffle=False)
else:
self.train_dataloader = train_dataset
self.train_dataloader_len: Optional[int] = None
try:
# since we will calculate the number of batches in
# `self.train_dataloader` whenever we use progress bar, calculate
# it once in initialization, for re-use.
self.train_dataloader_len = len(self.train_dataloader)
except TypeError:
warnings.warn(
"Unable to determine the number of batches in training dataset "
"`train_dataset`. Therefore, if showing the progress of computations, "
"only the number of batches processed can be displayed, and not the "
"percentage completion of the computation, nor any time estimates."
)
[docs] @abstractmethod
def self_influence(
self,
inputs: Optional[Union[Tuple[Any, ...], DataLoader]] = None,
show_progress: bool = False,
) -> Tensor:
"""
If `inputs` is not specified calculates the self influence
scores for the training dataset `train_dataset`. Otherwise, computes
self influence scores for the examples in `inputs`,
which is either a single batch or a Pytorch `DataLoader` that yields
batches. Therefore, in this case, the computed self influence scores
are *not* for the examples in training dataset `train_dataset`.
Note that if `inputs` is a single batch, this
will call `model` on that single batch, and if `inputs` yields
batches, this will call `model` on each batch that is yielded. Therefore,
please ensure that for both cases, the batch(es) that `model` is called
with are not too large, so that there will not be an out-of-memory error.
Args:
inputs (tuple or DataLoader, optional): This specifies the
dataset for which self influence scores will be computed.
Either a single tuple of any, or a `DataLoader`, where each
batch yielded is a tuple of type any. In either case, the tuple
represents a single batch, where the last element is assumed to
be the labels for the batch. That is, `model(*batch[0:-1])`
produces the output for `model`, and `batch[-1]` are the labels,
if any. This is the same assumption made for each batch yielded
by training dataset `train_dataset`. Please see documentation for
the `train_dataset` argument to `TracInCP.__init__` for
more details on the assumed structure of a batch. If not provided
or `None`, self influence scores will be computed for training
dataset `train_dataset`, which yields batches satisfying the
above assumptions.
Default: None.
show_progress (bool, optional): Computation of self influence scores can
take a long time if `inputs` represents many examples. If
`show_progress` is true, the progress of this computation will be
displayed. In more detail, this computation will iterate over all
checkpoints (provided as the `checkpoints` initialization argument)
in an outer loop, and iterate over all batches that
`inputs` represents in an inner loop. Therefore, the
total number of (checkpoint, batch) combinations that need to be
iterated over is
(# of checkpoints x # of batches that `inputs` represents).
If `show_progress` is True, the total progress of both the outer
iteration over checkpoints and the inner iteration over batches is
displayed. It will try to use tqdm if available for advanced
features (e.g. time estimation). Otherwise, it will fallback to a
simple output of progress.
Default: False
Returns:
self_influence_scores (Tensor): This is a 1D tensor containing the self
influence scores of all examples in `inputs`, regardless of
whether it represents a single batch or a `DataLoader` that yields
batches.
"""
pass
@abstractmethod
def _get_k_most_influential(
self,
inputs: Tuple[Any, ...],
k: int = 5,
proponents: bool = True,
show_progress: bool = False,
) -> KMostInfluentialResults:
r"""
Args:
inputs (tuple): `inputs` is the test batch and is a tuple of
any, where the last element is assumed to be the labels for the
batch. That is, `model(*batch[0:-1])` produces the output for
`model`, and `batch[-1]` are the labels, if any. This is the same
assumption made for each batch yielded by training dataset
`train_dataset` - please see its documentation in `__init__` for
more details on the assumed structure of a batch.
k (int, optional): The number of proponents or opponents to return per test
example.
Default: 5
proponents (bool, optional): Whether seeking proponents (`proponents=True`)
or opponents (`proponents=False`)
Default: True
show_progress (bool, optional): To compute the proponents (or opponents)
for the batch of examples, we perform computation for each batch in
training dataset `train_dataset`, If `show_progress` is
true, the progress of this computation will be displayed. In
particular, the number of batches for which the computation has
been performed will be displayed. It will try to use tqdm if
available for advanced features (e.g. time estimation). Otherwise,
it will fallback to a simple output of progress.
Default: False
Returns:
(indices, influence_scores) (namedtuple): `indices` is a torch.long Tensor
that contains the indices of the proponents (or opponents) for each
test example. Its dimension is `(inputs_batch_size, k)`, where
`inputs_batch_size` is the number of examples in `inputs`. For
example, if `proponents==True`, `indices[i][j]` is the index of the
example in training dataset `train_dataset` with the
k-th highest influence score for the j-th example in `inputs`.
`indices` is a `torch.long` tensor so that it can directly be used
to index other tensors. Each row of `influence_scores` contains the
influence scores for a different test example, in sorted order. In
particular, `influence_scores[i][j]` is the influence score of
example `indices[i][j]` in training dataset `train_dataset`
on example `i` in the test batch represented by `inputs`.
"""
pass
@abstractmethod
def _influence(
self,
inputs: Tuple[Any, ...],
show_progress: bool = False,
) -> Tensor:
r"""
Args:
inputs (tuple): `inputs` is the test batch and is a tuple of
any, where the last element is assumed to be the labels for the
batch. That is, `model(*batch[0:-1])` produces the output for
`model`, and `batch[-1]` are the labels, if any. This is the same
assumption made for each batch yielded by training dataset
`train_dataset` - please see its documentation in `__init__` for
more details on the assumed structure of a batch.
show_progress (bool, optional): To compute the influence of examples in
training dataset `train_dataset`, we compute the influence
of each batch. If `show_progress` is true, the progress of this
computation will be displayed. In particular, the number of batches
for which influence has been computed will be displayed. It will
try to use tqdm if available for advanced features (e.g. time
estimation). Otherwise, it will fallback to a simple output of
progress.
Default: False
Returns:
influence_scores (Tensor): Influence scores over the entire
training dataset `train_dataset`. Dimensionality is
(inputs_batch_size, src_dataset_size). For example:
influence_scores[i][j] = the influence score for the j-th training
example to the i-th example in the test batch.
"""
pass
[docs] @abstractmethod
def influence( # type: ignore[override]
self,
inputs: Tuple[Any, ...],
k: Optional[int] = None,
proponents: bool = True,
unpack_inputs: bool = True,
show_progress: bool = False,
) -> Union[Tensor, KMostInfluentialResults]:
r"""
This is the key method of this class, and can be run in 2 different modes,
where the mode that is run depends on the arguments passed to this method:
- influence score mode: This mode is used if `k` is None. This mode computes
the influence score of every example in training dataset `train_dataset`
on every example in the test batch represented by `inputs`.
- k-most influential mode: This mode is used if `k` is not None, and an int.
This mode computes the proponents or opponents of every example in the
test batch represented by `inputs`. In particular, for each test example in
the test batch, this mode computes its proponents (resp. opponents),
which are the indices in the training dataset `train_dataset` of the
training examples with the `k` highest (resp. lowest) influence scores on the
test example. Proponents are computed if `proponents` is True. Otherwise,
opponents are computed. For each test example, this method also returns the
actual influence score of each proponent (resp. opponent) on the test
example.
Args:
inputs (tuple): `inputs` is the test batch and is a tuple of
any, where the last element is assumed to be the labels for the
batch. That is, `model(*batch[0:-1])` produces the output for
`model`, and `batch[-1]` are the labels, if any. This is the same
assumption made for each batch yielded by training dataset
`train_dataset` - please see its documentation in `__init__` for
more details on the assumed structure of a batch.
k (int, optional): If not provided or `None`, the influence score mode will
be run. Otherwise, the k-most influential mode will be run,
and `k` is the number of proponents / opponents to return per
example in the test batch.
Default: None
proponents (bool, optional): Whether seeking proponents (`proponents=True`)
or opponents (`proponents=False`), if running in k-most influential
mode.
Default: True
show_progress (bool, optional): For all modes, computation of results
requires "training dataset computations": computations for each
batch in the training dataset `train_dataset`, which may
take a long time. If `show_progress` is true, the progress of
"training dataset computations" will be displayed. In particular,
the number of batches for which computations have been performed
will be displayed. It will try to use tqdm if available for
advanced features (e.g. time estimation). Otherwise, it will
fallback to a simple output of progress.
Default: False
Returns:
The return value of this method depends on which mode is run.
- influence score mode: if this mode is run (`k` is None), returns a 2D
tensor `influence_scores` of shape `(input_size, train_dataset_size)`,
where `input_size` is the number of examples in the test dataset, and
`train_dataset_size` is the number of examples in training dataset
`train_dataset`. In other words, `influence_scores[i][j]` is the
influence score of the `j`-th example in `train_dataset` on the `i`-th
example in the test batch.
- k-most influential mode: if this mode is run (`k` is an int), returns
a namedtuple `(indices, influence_scores)`. `indices` is a 2D tensor of
shape `(input_size, k)`, where `input_size` is the number of examples in
the test batch. If computing proponents (resp. opponents),
`indices[i][j]` is the index in training dataset `train_dataset` of the
example with the `j`-th highest (resp. lowest) influence score (out of
the examples in `train_dataset`) on the `i`-th example in the test
dataset. `influence_scores` contains the corresponding influence scores.
In particular, `influence_scores[i][j]` is the influence score of example
`indices[i][j]` in `train_dataset` on example `i` in the test batch
represented by `inputs`.
"""
pass
[docs] @classmethod
def get_name(cls: Type["TracInCPBase"]) -> str:
r"""
Create readable class name. Due to the nature of the names of `TracInCPBase`
subclasses, simplies returns the class name. For example, for a class called
TracInCP, we return the string TracInCP.
Returns:
name (str): a readable class name
"""
return cls.__name__
def _influence_route_to_helpers(
influence_instance: TracInCPBase,
inputs: Tuple[Any, ...],
k: Optional[int] = None,
proponents: bool = True,
**kwargs,
) -> Union[Tensor, KMostInfluentialResults]:
"""
This is a helper function called by `TracInCP.influence` and
`TracInCPFast.influence`. Those methods share a common logic in that they assume
an instance of their respective classes implement 2 private methods
(``_influence`, `_get_k_most_influential`), and the logic of
which private method to call is common, as described in the documentation of the
`influence` method. The arguments and return values of this function are the exact
same as the `influence` method. Note that `influence_instance` refers to the
instance for which the `influence` method was called.
"""
if k is None:
return influence_instance._influence(inputs, **kwargs)
else:
return influence_instance._get_k_most_influential(
inputs,
k,
proponents,
**kwargs,
)
[docs]class TracInCP(TracInCPBase):
def __init__(
self,
model: Module,
train_dataset: Union[Dataset, DataLoader],
checkpoints: Union[str, List[str], Iterator],
checkpoints_load_func: Callable = _load_flexible_state_dict,
layers: Optional[List[str]] = None,
loss_fn: Optional[Union[Module, Callable]] = None,
batch_size: Union[int, None] = 1,
test_loss_fn: Optional[Union[Module, Callable]] = None,
sample_wise_grads_per_batch: bool = False,
) -> None:
r"""
Args:
model (torch.nn.Module): An instance of pytorch model. This model should
define all of its layers as attributes of the model.
train_dataset (torch.utils.data.Dataset or torch.utils.data.DataLoader):
In the `influence` method, we compute the influence score of
training examples on examples in a test batch.
This argument represents the training dataset containing those
training examples. In order to compute those influence scores, we
will create a Pytorch DataLoader yielding batches of training
examples that is then used for processing. If this argument is
already a Pytorch Dataloader, that DataLoader can be directly
used for processing. If it is instead a Pytorch Dataset, we will
create a DataLoader using it, with batch size specified by
`batch_size`. For efficiency purposes, the batch size of the
DataLoader used for processing should be as large as possible, but
not too large, so that certain intermediate quantities created
from a batch still fit in memory. Therefore, if
`train_dataset` is a Dataset, `batch_size` should be large.
If `train_dataset` was already a DataLoader to begin with,
it should have been constructed to have a large batch size. It is
assumed that the Dataloader (regardless of whether it is created
from a Pytorch Dataset or not) yields tuples. For a `batch` that is
yielded, of length `L`, it is assumed that the forward function of
`model` accepts `L-1` arguments, and the last element of `batch` is
the label. In other words, `model(*batch[:-1])` gives the output of
`model`, and `batch[-1]` are the labels for the batch.
checkpoints (str, list[str], or Iterator): Either the directory of the
path to store and retrieve model checkpoints, a list of
filepaths with checkpoints from which to load, or an iterator which
returns objects from which to load checkpoints.
checkpoints_load_func (Callable, optional): The function to load a saved
checkpoint into a model to update its parameters, and get the
learning rate if it is saved. By default uses a utility to load a
model saved as a state dict.
Default: _load_flexible_state_dict
layers (list[str] or None, optional): A list of layer names for which
gradients should be computed. If `layers` is None, gradients will
be computed for all layers. Otherwise, they will only be computed
for the layers specified in `layers`.
Default: None
loss_fn (Callable, optional): The loss function applied to model. There
are two options for the return type of `loss_fn`. First, `loss_fn`
can be a "per-example" loss function - returns a 1D Tensor of
losses for each example in a batch. `nn.BCELoss(reduction="none")`
would be an "per-example" loss function. Second, `loss_fn` can be
a "reduction" loss function that reduces the per-example losses,
in a batch, and returns a single scalar Tensor. For this option,
the reduction must be the *sum* or the *mean* of the per-example
losses. For instance, `nn.BCELoss(reduction="sum")` is acceptable.
Note for the first option, the `sample_wise_grads_per_batch`
argument must be False, and for the second option,
`sample_wise_grads_per_batch` must be True. Also note that for
the second option, if `loss_fn` has no "reduction" attribute,
the implementation assumes that the reduction is the *sum* of the
per-example losses. If this is not the case, i.e. the reduction
is the *mean*, please set the "reduction" attribute of `loss_fn`
to "mean", i.e. `loss_fn.reduction = "mean"`.
Default: None
batch_size (int or None, optional): Batch size of the DataLoader created to
iterate through `train_dataset`, if it is a Dataset.
`batch_size` should be chosen as large as possible so that certain
intermediate quantities created from a batch still fit in memory.
Specific implementations of `TracInCPBase` will detail the size of
the intermediate quantities. `batch_size` must be an int if
`train_dataset` is a Dataset. If `train_dataset`
is a DataLoader, then `batch_size` is ignored as an argument.
Default: 1
test_loss_fn (Callable, optional): In some cases, one may want to use a
separate loss functions for training examples, i.e. those in
`train_dataset`, and for test examples, i.e. those
represented by the `inputs` and `targets` arguments to the
`influence` method. For example, if one wants to calculate the
influence score of a training example on a test example's
prediction for a fixed class, `test_loss_fn` could map from the
logits for all classes to the logits for a fixed class.
`test_loss_fn` needs satisfy the same constraints as `loss_fn`.
Thus, the same checks that we apply to `loss_fn` are also applied
to `test_loss_fn`, if the latter is provided. Note that the
constraints on both `loss_fn` and `test_loss_fn` both depend on
`sample_wise_grads_per_batch`. This means `loss_fn` and
`test_loss_fn` must either both be "per-example" loss functions,
or both be "reduction" loss functions. If not provided, the loss
function for test examples is assumed to be the same as the loss
function for training examples, i.e. `loss_fn`.
Default: None
sample_wise_grads_per_batch (bool, optional): PyTorch's native gradient
computations w.r.t. model parameters aggregates the results for a
batch and does not allow to access sample-wise gradients w.r.t.
model parameters. This forces us to iterate over each sample in
the batch if we want sample-wise gradients which is computationally
inefficient. We offer an implementation of batch-wise gradient
computations w.r.t. to model parameters which is computationally
more efficient. This implementation can be enabled by setting the
`sample_wise_grad_per_batch` argument to `True`, and should be
enabled if and only if the `loss_fn` argument is a "reduction" loss
function. For example, `nn.BCELoss(reduction="sum")` would be a
valid `loss_fn` if this implementation is enabled (see
documentation for `loss_fn` for more details). Note that our
current implementation enables batch-wise gradient computations
only for a limited number of PyTorch nn.Modules: Conv2D and Linear.
This list will be expanded in the near future. Therefore, please
do not enable this implementation if gradients will be computed
for other kinds of layers.
Default: False
"""
TracInCPBase.__init__(
self,
model,
train_dataset,
checkpoints,
checkpoints_load_func,
loss_fn,
batch_size,
test_loss_fn,
)
self.sample_wise_grads_per_batch = sample_wise_grads_per_batch
# check `loss_fn`
self.reduction_type = _check_loss_fn(
self, loss_fn, "loss_fn", sample_wise_grads_per_batch
)
# check `test_loss_fn` if it was provided
self.test_reduction_type = (
self.reduction_type
if test_loss_fn is None
else _check_loss_fn(
self, test_loss_fn, "test_loss_fn", sample_wise_grads_per_batch
)
)
r"""
TODO: Either restore model state after done (would have to place functionality
within influence to restore after every influence call)? or make a copy so that
changes to grad_requires aren't persistent after using TracIn.
"""
self.layer_modules = None
if layers is not None:
assert isinstance(layers, List), "`layers` should be a list!"
assert len(layers) > 0, "`layers` cannot be empty!"
assert isinstance(
layers[0], str
), "`layers` should contain str layer names."
self.layer_modules = [
_get_module_from_name(self.model, layer) for layer in layers
]
for layer, layer_module in zip(layers, self.layer_modules):
for name, param in layer_module.named_parameters():
if not param.requires_grad:
warnings.warn(
"Setting required grads for layer: {}, name: {}".format(
".".join(layer), name
)
)
param.requires_grad = True
[docs] @log_usage()
def influence( # type: ignore[override]
self,
inputs: Tuple[Any, ...],
k: Optional[int] = None,
proponents: bool = True,
show_progress: bool = False,
) -> Union[Tensor, KMostInfluentialResults]:
r"""
This is the key method of this class, and can be run in 2 different modes,
where the mode that is run depends on the arguments passed to this method:
- influence score mode: This mode is used if `k` is None. This mode computes
the influence score of every example in training dataset `train_dataset`
on every example in the test batch represented by `inputs`.
- k-most influential mode: This mode is used if `k` is not None, and an int.
This mode computes the proponents or opponents of every example in the
test batch represented by `inputs`. In particular, for each test example in
the test batch, this mode computes its proponents (resp. opponents),
which are the indices in the training dataset `train_dataset` of the
training examples with the `k` highest (resp. lowest) influence scores on the
test example. Proponents are computed if `proponents` is True. Otherwise,
opponents are computed. For each test example, this method also returns the
actual influence score of each proponent (resp. opponent) on the test
example.
Args:
inputs (tuple): `inputs` is the test batch and is a tuple of
any, where the last element is assumed to be the labels for the
batch. That is, `model(*batch[0:-1])` produces the output for
`model`, and `batch[-1]` are the labels, if any. This is the same
assumption made for each batch yielded by training dataset
`train_dataset` - please see its documentation in `__init__` for
more details on the assumed structure of a batch.
k (int, optional): If not provided or `None`, the influence score mode will
be run. Otherwise, the k-most influential mode will be run,
and `k` is the number of proponents / opponents to return per
example in the test batch.
Default: None
proponents (bool, optional): Whether seeking proponents (`proponents=True`)
or opponents (`proponents=False`), if running in k-most influential
mode.
Default: True
show_progress (bool, optional): For all modes, computation of results
requires "training dataset computations": computations for each
batch in the training dataset `train_dataset`, which may
take a long time. If `show_progress` is true, the progress of
"training dataset computations" will be displayed. In particular,
the number of batches for which computations have been performed
will be displayed. It will try to use tqdm if available for
advanced features (e.g. time estimation). Otherwise, it will
fallback to a simple output of progress.
Default: False
Returns:
The return value of this method depends on which mode is run.
- influence score mode: if this mode is run (`k` is None), returns a 2D
tensor `influence_scores` of shape `(input_size, train_dataset_size)`,
where `input_size` is the number of examples in the test batch, and
`train_dataset_size` is the number of examples in training dataset
`train_dataset`. In other words, `influence_scores[i][j]` is the
influence score of the `j`-th example in `train_dataset` on the `i`-th
example in the test batch.
- k-most influential mode: if this mode is run (`k` is an int), returns
a namedtuple `(indices, influence_scores)`. `indices` is a 2D tensor of
shape `(input_size, k)`, where `input_size` is the number of examples in
the test batch. If computing proponents (resp. opponents),
`indices[i][j]` is the index in training dataset `train_dataset` of the
example with the `j`-th highest (resp. lowest) influence score (out of
the examples in `train_dataset`) on the `i`-th example in the test
batch. `influence_scores` contains the corresponding influence scores.
In particular, `influence_scores[i][j]` is the influence score of example
`indices[i][j]` in `train_dataset` on example `i` in the test batch
represented by `inputs`.
"""
assert inputs is not None, (
"`inputs` argument is required."
"If you wish to calculate self influence scores,"
" please use the `self_influence` method instead."
)
return _influence_route_to_helpers(
self,
inputs,
k,
proponents,
show_progress=show_progress,
)
def _sum_jacobians(
self,
inputs: DataLoader,
loss_fn: Optional[Union[Module, Callable]] = None,
reduction_type: Optional[str] = None,
):
"""
sums the jacobians of all examples in `inputs`. result is of the
same format as layer_jacobians, but the batch dimension has size 1
"""
inputs_iter = iter(inputs)
inputs_batch = next(inputs_iter)
def get_batch_contribution(inputs_batch):
_input_jacobians = self._basic_computation_tracincp(
inputs_batch[0:-1],
inputs_batch[-1],
loss_fn,
reduction_type,
)
return tuple(
torch.sum(jacobian, dim=0).unsqueeze(0) for jacobian in _input_jacobians
)
inputs_jacobians = get_batch_contribution(inputs_batch)
for inputs_batch in inputs_iter:
inputs_batch_jacobians = get_batch_contribution(inputs_batch)
inputs_jacobians = tuple(
[
inputs_jacobian + inputs_batch_jacobian
for (inputs_jacobian, inputs_batch_jacobian) in zip(
inputs_jacobians, inputs_batch_jacobians
)
]
)
return inputs_jacobians
def _concat_jacobians(
self,
inputs: DataLoader,
loss_fn: Optional[Union[Module, Callable]] = None,
reduction_type: Optional[str] = None,
):
all_inputs_batch_jacobians = [
self._basic_computation_tracincp(
inputs_batch[0:-1],
inputs_batch[-1],
loss_fn,
reduction_type,
)
for inputs_batch in inputs
]
return tuple(
torch.cat(all_inputs_batch_jacobian, dim=0)
for all_inputs_batch_jacobian in zip(*all_inputs_batch_jacobians)
)
[docs] @log_usage()
def compute_intermediate_quantities(
self,
inputs: Union[Tuple[Any, ...], DataLoader],
aggregate: bool = False,
) -> Tensor:
"""
Computes "embedding" vectors for all examples in a single batch, or a
`Dataloader` that yields batches. These embedding vectors are constructed so
that the influence score of a training example on a test example is simply the
dot-product of their corresponding vectors. Allowing a `DataLoader`
yielding batches to be passed in (as opposed to a single batch) gives the
potential to improve efficiency, because we load each checkpoint only once in
this method call. Thus if a `DataLoader` yielding batches is passed in, this
reduces the total number of times each checkpoint is loaded for a dataset,
compared to if a single batch is passed in. The reason we do not just increase
the batch size is that for large models, large batches do not fit in memory.
If `aggregate` is True, the *sum* of the vectors for all examples is returned,
instead of the vectors for each example. This can be useful for computing the
influence of a given training example on the total loss over a validation
dataset, because due to properties of the dot-product, this influence is the
dot-product of the training example's vector with the sum of the vectors in the
validation dataset. Also, by doing the sum aggregation within this method as
opposed to outside of it (by computing all vectors for the validation dataset,
then taking the sum) allows memory usage to be reduced.
Args:
inputs (Tuple, or DataLoader): Either a single tuple of any, or a
`DataLoader`, where each batch yielded is a tuple of any. In
either case, the tuple represents a single batch, where the last
element is assumed to be the labels for the batch. That is,
`model(*batch[0:-1])` produces the output for `model`, and
and `batch[-1]` are the labels, if any. Here, `model` is model
provided in initialization. This is the same assumption made for
each batch yielded by training dataset `train_dataset`.
aggregate (bool): Whether to return the sum of the vectors for all
examples, as opposed to vectors for each example.
Returns:
intermediate_quantities (Tensor): A tensor of dimension
(N, D * C). Here, N is the total number of examples in
`inputs` if `aggregate` is False, and 1, otherwise (so that
a 2D tensor is always returned). C is the number of checkpoints
passed as the `checkpoints` argument of `TracInCP.__init__`, and
each row represents the vector for an example. Regarding D: Let I
be the dimension of the output of the last fully-connected layer
times the dimension of the input of the last fully-connected layer.
If `self.projection_dim` is specified in initialization,
D = min(I * C, `self.projection_dim` * C). Otherwise, D = I * C.
In summary, if `self.projection_dim` is None, the dimension of each
vector will be determined by the size of the input and output of
the last fully-connected layer of `model`. Otherwise,
`self.projection_dim` must be an int, and random projection will be
performed to ensure that the vector is of dimension no more than
`self.projection_dim` * C. `self.projection_dim` corresponds to
the variable d in the top of page 15 of the TracIn paper:
https://arxiv.org/pdf/2002.08484.pdf.
"""
# If `inputs` is not a `DataLoader`, turn it into one.
inputs = _format_inputs_dataset(inputs)
def get_checkpoint_contribution(checkpoint):
assert (
checkpoint is not None
), "None returned from `checkpoints`, cannot load."
learning_rate = self.checkpoints_load_func(self.model, checkpoint)
# get jacobians as tuple of tensors
if aggregate:
inputs_jacobians = self._sum_jacobians(
inputs, self.loss_fn, self.reduction_type
)
else:
inputs_jacobians = self._concat_jacobians(
inputs, self.loss_fn, self.reduction_type
)
# flatten into single tensor
return learning_rate * torch.cat(
[
input_jacobian.flatten(start_dim=1)
for input_jacobian in inputs_jacobians
],
dim=1,
)
return torch.cat(
[
get_checkpoint_contribution(checkpoint)
for checkpoint in self.checkpoints
],
dim=1,
)
def _influence_batch_tracincp(
self,
test_batch: Tuple[Any, ...],
train_batch: Tuple[Any, ...],
):
"""
computes influence scores for a single training batch
"""
def get_checkpoint_contribution(checkpoint):
assert (
checkpoint is not None
), "None returned from `checkpoints`, cannot load."
learning_rate = self.checkpoints_load_func(self.model, checkpoint)
input_jacobians = self._basic_computation_tracincp(
test_batch[0:-1],
test_batch[-1],
self.test_loss_fn,
self.test_reduction_type,
)
return (
_gradient_dot_product(
input_jacobians,
self._basic_computation_tracincp(
train_batch[0:-1],
train_batch[-1],
self.loss_fn,
self.reduction_type,
),
)
* learning_rate
)
batch_tracin_scores = get_checkpoint_contribution(self.checkpoints[0])
for checkpoint in self.checkpoints[1:]:
batch_tracin_scores += get_checkpoint_contribution(checkpoint)
return batch_tracin_scores
def _influence(
self,
inputs: Tuple[Any, ...],
show_progress: bool = False,
) -> Tensor:
r"""
Computes the influence of examples in training dataset `train_dataset`
on the examples in the test batch represented by `inputs`.
This implementation does not require knowing the number of training examples
in advance. Instead, the number of training examples is inferred from the
output of `self._basic_computation_tracincp`.
Args:
inputs (tuple): `inputs` is the test batch and is a tuple of
any, where the last element is assumed to be the labels for the
batch. That is, `model(*batch[0:-1])` produces the output for
`model`, and `batch[-1]` are the labels, if any. This is the same
assumption made for each batch yielded by training dataset
`train_dataset` - please see its documentation in `__init__` for
more details on the assumed structure of a batch.
show_progress (bool, optional): To compute the influence of examples in
training dataset `train_dataset`, we compute the influence
of each batch. If `show_progress` is true, the progress of this
computation will be displayed. In particular, the number of batches
for which influence has been computed will be displayed. It will
try to use tqdm if available for advanced features (e.g. time
estimation). Otherwise, it will fallback to a simple output of
progress.
Default: False
Returns:
influence_scores (Tensor): Influence scores from the TracInCP method.
Its shape is `(input_size, train_dataset_size)`, where `input_size`
is the number of examples in the test batch, and
`train_dataset_size` is the number of examples in
training dataset `train_dataset`. For example:
`influence_scores[i][j]` is the influence score for the j-th training
example to the i-th example in the test batch.
"""
train_dataloader = self.train_dataloader
if show_progress:
train_dataloader = progress(
train_dataloader,
desc=(
f"Using {self.get_name()} to compute "
"influence for training batches"
),
total=self.train_dataloader_len,
)
return torch.cat(
[
self._influence_batch_tracincp(inputs, batch)
for batch in train_dataloader
],
dim=1,
)
def _get_k_most_influential(
self,
inputs: Tuple[Any, ...],
k: int = 5,
proponents: bool = True,
show_progress: bool = False,
) -> KMostInfluentialResults:
r"""
Args:
inputs (tuple): `inputs` is the test batch and is a tuple of
any, where the last element is assumed to be the labels for the
batch. That is, `model(*batch[0:-1])` produces the output for
`model`, and `batch[-1]` are the labels, if any. This is the same
assumption made for each batch yielded by training dataset
`train_dataset` - please see its documentation in `__init__` for
more details on the assumed structure of a batch.
k (int, optional): The number of proponents or opponents to return per test
example.
Default: 5
proponents (bool, optional): Whether seeking proponents (`proponents=True`)
or opponents (`proponents=False`)
Default: True
show_progress (bool, optional): To compute the proponents (or opponents)
for the batch of examples, we perform computation for each batch in
training dataset `train_dataset`, If `show_progress` is
true, the progress of this computation will be displayed. In
particular, the number of batches for which the computation has
been performed will be displayed. It will try to use tqdm if
available for advanced features (e.g. time estimation). Otherwise,
it will fallback to a simple output of progress.
Default: False
Returns:
(indices, influence_scores) (namedtuple): `indices` is a torch.long Tensor
that contains the indices of the proponents (or opponents) for each
test example. Its dimension is `(inputs_batch_size, k)`, where
`inputs_batch_size` is the number of examples in `inputs`. For
example, if `proponents==True`, `indices[i][j]` is the index of the
example in training dataset `train_dataset` with the
k-th highest influence score for the j-th example in `inputs`.
`indices` is a `torch.long` tensor so that it can directly be used
to index other tensors. Each row of `influence_scores` contains the
influence scores for a different test example, in sorted order. In
particular, `influence_scores[i][j]` is the influence score of
example `indices[i][j]` in training dataset `train_dataset`
on example `i` in the test batch represented by `inputs`.
"""
desc = (
None
if not show_progress
else (
(
f"Using {self.get_name()} to perform computation for "
f'getting {"proponents" if proponents else "opponents"}. '
"Processing training batches"
)
)
)
return KMostInfluentialResults(
*_get_k_most_influential_helper(
self.train_dataloader,
self._influence_batch_tracincp,
inputs,
k,
proponents,
show_progress,
desc,
)
)
def _self_influence_by_checkpoints(
self,
inputs: Union[Tuple[Any, ...], DataLoader],
show_progress: bool = False,
) -> Tensor:
"""
Computes self influence scores for the examples in `inputs`, which is
either a single batch or a Pytorch `DataLoader` that yields batches. Therefore,
the computed self influence scores are *not* for the examples in training
dataset `train_dataset` (unlike when computing self influence scores using the
`influence` method). Note that if `inputs` is a single batch, this
will call `model` on that single batch, and if `inputs` yields
batches, this will call `model` on each batch that is yielded. Therefore,
please ensure that for both cases, the batch(es) that `model` is called
with are not too large, so that there will not be an out-of-memory error. This
implementation performs an outer iteration over checkpoints, and an inner
iteration over all batches that `inputs` represents. The pros of this
implementation are that the checkpoints do not need to be loaded too many
times.
Args:
batches (tuple or DataLoader): Either a single tuple of any, or a
`DataLoader`, where each batch yielded is a tuple of any. In
either case, the tuple represents a single batch, where the last
element is assumed to be the labels for the batch. That is,
`model(*batch[0:-1])` produces the output for `model`,
and `batch[-1]` are the labels, if any. This is the same
assumption made for each batch yielded by training dataset
`train_dataset`. Please see documentation for the
`train_dataset` argument to `TracInCP.__init__` for
more details on the assumed structure of a batch.
show_progress (bool, optional): Computation of self influence scores can
take a long time if `inputs` represents many examples. If
`show_progress` is true, the progress of this computation will be
displayed. In more detail, this computation will iterate over all
checkpoints (provided as the `checkpoints` initialization argument)
in an outer loop, and iterate over all batches that
`inputs` represents in an inner loop. Thus if
`show_progress` is True, the progress of both the outer
iteration and the inner iterations will be displayed. To show
progress, it will try to use tqdm if available for advanced
features (e.g. time estimation). Otherwise, it will fallback to a
simple output of progress.
Default: False
Returns:
self_influence_scores (Tensor): This is a 1D tensor containing the self
influence scores of all examples in `inputs`, regardless of
whether it represents a single batch or a `DataLoader` that yields
batches.
"""
# If `inputs` is not a `DataLoader`, turn it into one.
inputs = _format_inputs_dataset(inputs)
# If `show_progress` is true, create an outer progress bar that keeps track of
# how many checkpoints have been processed
if show_progress:
# Try to determine length of inner progress bar if possible, with a default
# of `None`.
inputs_len = None
try:
inputs_len = len(inputs)
except TypeError:
warnings.warn(
"Unable to determine the number of batches in `inputs`. "
"Therefore, if showing the progress of the computation of self "
"influence scores, only the number of batches processed can be "
"displayed, and not the percentage completion of the computation, "
"nor any time estimates."
)
def calculate_via_vector_norm(layer_jacobian):
# Helper to efficiently calculate vector norm if pytorch version permits.
return (
torch.linalg.vector_norm(
layer_jacobian,
dim=list(range(1, len(layer_jacobian.shape))),
)
** 2
)
def calculate_via_flatten(layer_jacobian):
return torch.sum(layer_jacobian.flatten(start_dim=1) ** 2, dim=1)
def get_checkpoint_contribution(checkpoint):
# This function returns a 1D tensor representing the contribution to the
# self influence score for the given checkpoint, for all batches in
# `inputs`. The length of the 1D tensor is the total number of
# examples in `inputs`.
assert (
checkpoint is not None
), "None returned from `checkpoints`, cannot load."
learning_rate = self.checkpoints_load_func(self.model, checkpoint)
# This will store a list of the contribution of the self influence score
# from each batch. Each element is a 1D tensor of length batch_size - the
# batch size of each batch in `inputs` (they do not need to be all
# the same)
checkpoint_contribution = []
_inputs = inputs
# If `show_progress` is true, create an inner progress bar that keeps track
# of how many batches have been processed for the current checkpoint
if show_progress:
_inputs = progress(
inputs,
desc=(
f"Using {self.get_name()} to compute self "
"influence. Processing batch"
),
total=inputs_len,
)
for batch in _inputs:
layer_jacobians = self._basic_computation_tracincp(
batch[0:-1],
batch[-1],
self.loss_fn,
self.reduction_type,
)
# Note that all variables in this function are for an entire batch.
# Each `layer_jacobian` in `layer_jacobians` corresponds to a different
# layer. `layer_jacobian` is the jacobian w.r.t to a given layer's
# parameters. If the given layer's parameters are of shape *, then
# `layer_jacobian` is of shape (batch_size, *). For each layer, we need
# the squared jacobian for each example. So we square the jacobian and
# sum over all dimensions except the 0-th (the batch dimension). We then
# sum the contribution over all layers. For Pytorch > 1.10 we use the
# optimized torch.linalg.vector_norm as opposed to the explicit flatten.
calculate_fn = calculate_via_flatten
if _parse_version(torch.__version__) >= (1, 10, 0):
calculate_fn = calculate_via_vector_norm
checkpoint_contribution.append(
torch.sum(
torch.stack(
[
calculate_fn(layer_jacobian)
for layer_jacobian in layer_jacobians
],
dim=0,
),
dim=0,
)
* learning_rate
)
# We concatenate the contributions from each batch into a single 1D tensor,
# which represents the contributions for all batches in `inputs`
return torch.cat(checkpoint_contribution, dim=0)
if show_progress:
checkpoints_progress = progress(
desc=(
f"Using {self.get_name()} to compute self "
"influence. Processing checkpoint"
),
total=len(self.checkpoints),
mininterval=0.0,
)
else:
checkpoints_progress = NullProgress()
with checkpoints_progress:
batches_self_tracin_scores = get_checkpoint_contribution(
self.checkpoints[0]
)
checkpoints_progress.update()
# The self influence score for all examples is the sum of contributions from
# each checkpoint
for checkpoint in self.checkpoints[1:]:
batches_self_tracin_scores += get_checkpoint_contribution(checkpoint)
checkpoints_progress.update()
return batches_self_tracin_scores
[docs] @log_usage()
def self_influence(
self,
inputs: Optional[Union[Tuple[Any, ...], DataLoader]] = None,
show_progress: bool = False,
outer_loop_by_checkpoints: bool = False,
) -> Tensor:
"""
Computes self influence scores for the examples in `inputs`, which is
either a single batch or a Pytorch `DataLoader` that yields batches.
If `inputs` is not specified or `None` calculates self influence
score for the training dataset `train_dataset`. Note that if `inputs`
is a single batch, this will call `model` on that single batch, and if
`inputs` yields batches, this will call `model` on each batch that is
yielded. Therefore, please ensure that for both cases, the batch(es) that
`model` is called with are not too large, so that there will not be an
out-of-memory error.
Internally, this computation requires iterating both over the batches in
`inputs`, as well as different model checkpoints. There are two ways
this iteration can be done. If `outer_loop_by_checkpoints` is False, the outer
iteration will be over batches, and the inner iteration will be over
checkpoints. This has the pro that displaying the progress of the computation
is more intuitive, involving displaying the number of batches for which self
influence scores have been computed. If `outer_loop_by_checkpoints` is True,
the outer iteration will be over checkpoints, and the inner iteration will be
over batches. This has the pro that the checkpoints do not need to be loaded
for each batch. For large models, loading checkpoints can be time-intensive.
Args:
inputs (tuple or DataLoader, optional): This specifies the
dataset for which self influence scores will be computed.
Either a single tuple of any, or a `DataLoader`, where each
batch yielded is a tuple of type any. In either case, the tuple
represents a single batch, where the last element is assumed to
be the labels for the batch. That is, `model(*batch[0:-1])`
produces the output for `model`, and `batch[-1]` are the labels,
if any. This is the same assumption made for each batch yielded
by training dataset `train_dataset`. Please see documentation for
the `train_dataset` argument to `TracInCP.__init__` for
more details on the assumed structure of a batch. If not provided
or `None`, self influence scores will be computed for training
dataset `train_dataset`, which yields batches satisfying the
above assumptions.
Default: None.
show_progress (bool, optional): Computation of self influence scores can
take a long time if `inputs` represents many examples. If
`show_progress`is true, the progress of this computation will be
displayed. In more detail, if `outer_loop_by_checkpoints` is False,
this computation will iterate over all batches in an outer loop.
Thus if `show_progress` is True, the number of batches for which
self influence scores have been computed will be displayed. If
`outer_loop_by_checkpoints` is True, this computation will iterate
over all checkpoints (provided as the `checkpoints` initialization
argument) in an outer loop, and iterate over all batches that
`inputs` represents in an inner loop. Thus if
`show_progress` is True, the progress of both the outer
iteration and the inner iterations will be displayed. To show
progress, it will try to use tqdm if available for advanced
features (e.g. time estimation). Otherwise, it will fallback to a
simple output of progress.
Default: False
outer_loop_by_checkpoints (bool, optional): If performing an outer
iteration over checkpoints; see method description for more
details.
Default: False
"""
inputs = inputs if inputs is not None else self.train_dataloader
if outer_loop_by_checkpoints:
return self._self_influence_by_checkpoints(inputs, show_progress)
return _self_influence_by_batches_helper(
self._self_influence_by_checkpoints,
self.get_name(),
inputs,
show_progress,
)
def _basic_computation_tracincp(
self,
inputs: Tuple[Any, ...],
targets: Optional[Tensor] = None,
loss_fn: Optional[Union[Module, Callable]] = None,
reduction_type: Optional[str] = None,
) -> Tuple[Tensor, ...]:
"""
For instances of TracInCP, computation of influence scores or self influence
scores repeatedly calls this function for different checkpoints
and batches. In particular, this function computes the jacobian of a loss
function w.r.t. parameters in the `layers` initialization argument.
Args:
inputs (tuple[Any, ...]): A batch of examples, which could be a training
batch or test batch, depending which method is the caller. Does not
represent labels, which are passed as `targets`. The assumption is
that `model(*inputs)` produces the predictions for the batch.
targets (tensor or None): If computing influence scores on a loss function,
these are the labels corresponding to the batch `inputs`.
Default: none
loss_fn (Callable, optional): The loss function to use when computing the
jacobian.
reduction_type (str, optional): The reduction type of `loss_fn`. This
argument is only used if `sample_wise_grads_per_batch` was true in
initialization.
"""
if self.sample_wise_grads_per_batch:
return _compute_jacobian_wrt_params_with_sample_wise_trick(
self.model,
inputs,
targets,
loss_fn,
reduction_type,
self.layer_modules,
)
return _compute_jacobian_wrt_params(
self.model,
inputs,
targets,
loss_fn,
self.layer_modules,
)
```