#!/usr/bin/env python3
# pyre-strict
from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Type, Union
import torch
from captum._utils.common import (
_extract_device,
_format_additional_forward_args,
_format_output,
_format_tensor_into_tuples,
_run_forward,
)
from captum._utils.gradient import _forward_layer_eval
from captum._utils.typing import BaselineType, TargetType
from captum.attr._core.feature_ablation import FeatureAblation
from captum.attr._utils.attribution import LayerAttribution, PerturbationAttribution
from captum.log import log_usage
from torch import Tensor
from torch.nn import Module
from torch.nn.parallel.scatter_gather import scatter
[docs]
class LayerFeatureAblation(LayerAttribution, PerturbationAttribution):
r"""
A perturbation based approach to computing layer attribution, involving
replacing values in the input / output of a layer with a given baseline /
reference, and computing the difference in output. By default, each
neuron (scalar input / output value) within the layer is replaced
independently.
Passing a layer mask allows grouping neurons to be
ablated together.
Each neuron in the group will be given the same attribution value
equal to the change in target as a result of ablating the entire neuron
group.
"""
def __init__(
self,
forward_func: Callable[..., Tensor],
layer: Module,
device_ids: Union[None, List[int]] = None,
) -> None:
r"""
Args:
forward_func (Callable): The forward function of the model or any
modification of it
layer (torch.nn.Module): Layer for which attributions are computed.
Output size of attribute matches this layer's input or
output dimensions, depending on whether we attribute to
the inputs or outputs of the layer, corresponding to
attribution of each neuron in the input or output of
this layer.
device_ids (list[int]): Device ID list, necessary only if forward_func
applies a DataParallel model. This allows reconstruction of
intermediate outputs from batched results across devices.
If forward_func is given as the DataParallel model itself
(or otherwise has a device_ids attribute with the device
ID list), then it is not necessary to provide this
argument.
"""
LayerAttribution.__init__(self, forward_func, layer, device_ids)
PerturbationAttribution.__init__(self, forward_func)
[docs]
@log_usage()
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
layer_baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Optional[object] = None,
layer_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
attribute_to_layer_input: bool = False,
perturbations_per_eval: int = 1,
) -> Union[Tensor, Tuple[Tensor, ...]]:
r"""
Args:
inputs (Tensor or tuple[Tensor, ...]): Input for which layer
attributions are computed. If forward_func takes a single
tensor as input, a single input tensor should be provided.
If forward_func takes multiple tensors as input, a tuple
of the input tensors should be provided. It is assumed
that for all given input tensors, dimension 0 corresponds
to the number of examples, and if multiple input tensors
are provided, the examples must be aligned appropriately.
layer_baselines (scalar, Tensor, tuple of scalar, or Tensor, optional):
Layer baselines define reference values which replace each
layer input / output value when ablated.
Layer baselines should be a single tensor with dimensions
matching the input / output of the target layer (or
broadcastable to match it), based
on whether we are attributing to the input or output
of the target layer.
In the cases when `baselines` is not provided, we internally
use zero as the baseline for each neuron.
Default: None
target (int, tuple, Tensor, or list, optional): Output indices for
which gradients are computed (for classification cases,
this is usually the target class).
If the network returns a scalar value per example,
no target index is necessary.
For general 2D outputs, targets can be either:
- a single integer or a tensor containing a single
integer, which is applied to all input examples
- a list of integers or a 1D tensor, with length matching
the number of examples in inputs (dim 0). Each integer
is applied as the target for the corresponding example.
For outputs with > 2 dimensions, targets can be either:
- A single tuple, which contains #output_dims - 1
elements. This target index is applied to all examples.
- A list of tuples with length equal to the number of
examples in inputs (dim 0), and each tuple containing
#output_dims - 1 elements. Each tuple is applied as the
target for the corresponding example.
Default: None
additional_forward_args (Any, optional): If the forward function
requires additional arguments other than the inputs for
which attributions should not be computed, this argument
can be provided. It must be either a single additional
argument of a Tensor or arbitrary (non-tuple) type or a
tuple containing multiple additional arguments including
tensors or any arbitrary python types. These arguments
are provided to forward_func in order following the
arguments in inputs.
Note that attributions are not computed with respect
to these arguments.
Default: None
layer_mask (Tensor or tuple[Tensor, ...], optional):
layer_mask defines a mask for the layer, grouping
elements of the layer input / output which should be
ablated together.
layer_mask should be a single tensor with dimensions
matching the input / output of the target layer (or
broadcastable to match it), based
on whether we are attributing to the input or output
of the target layer. layer_mask
should contain integers in the range 0 to num_groups
- 1, and all elements with the same value are
considered to be in the same group.
If None, then a layer mask is constructed which assigns
each neuron within the layer as a separate group, which
is ablated independently.
Default: None
attribute_to_layer_input (bool, optional): Indicates whether to
compute the attributions with respect to the layer input
or output. If `attribute_to_layer_input` is set to True
then the attributions will be computed with respect to
layer's inputs, otherwise it will be computed with respect
to layer's outputs.
Note that currently it is assumed that either the input
or the output of the layer, depending on whether we
attribute to the input or output, is a single tensor.
Support for multiple tensors will be added later.
Default: False
perturbations_per_eval (int, optional): Allows ablation of multiple
neuron (groups) to be processed simultaneously in one
call to forward_fn.
Each forward pass will contain a maximum of
perturbations_per_eval * #examples samples.
For DataParallel models, each batch is split among the
available devices, so evaluations on each available
device contain at most
(perturbations_per_eval * #examples) / num_devices
samples.
Default: 1
Returns:
*Tensor* or *tuple[Tensor, ...]* of **attributions**:
- **attributions** (*Tensor* or *tuple[Tensor, ...]*):
Attribution of each neuron in given layer input or
output. Attributions will always be the same size as
the input or output of the given layer, depending on
whether we attribute to the inputs or outputs
of the layer which is decided by the input flag
`attribute_to_layer_input`
Attributions are returned in a tuple if
the layer inputs / outputs contain multiple tensors,
otherwise a single tensor is returned.
Examples::
>>> # SimpleClassifier takes a single input tensor of size Nx4x4,
>>> # and returns an Nx3 tensor of class probabilities.
>>> # It contains an attribute conv1, which is an instance of nn.conv2d,
>>> # and the output of this layer has dimensions Nx12x3x3.
>>> net = SimpleClassifier()
>>> # Generating random input with size 2 x 4 x 4
>>> input = torch.randn(2, 4, 4)
>>> # Defining LayerFeatureAblation interpreter
>>> ablator = LayerFeatureAblation(net, net.conv1)
>>> # Computes ablation attribution, ablating each of the 108
>>> # neurons independently.
>>> attr = ablator.attribute(input, target=1)
>>> # Alternatively, we may want to ablate neurons in groups, e.g.
>>> # grouping all the layer outputs in the same row.
>>> # This can be done by creating a layer mask as follows, which
>>> # defines the groups of layer inputs / outouts, e.g.:
>>> # +---+---+---+
>>> # | 0 | 0 | 0 |
>>> # +---+---+---+
>>> # | 1 | 1 | 1 |
>>> # +---+---+---+
>>> # | 2 | 2 | 2 |
>>> # +---+---+---+
>>> # With this mask, all the 36 neurons in a row / channel are ablated
>>> # simultaneously, and the attribution for each neuron in the same
>>> # group (0 - 2) per example are the same.
>>> # The attributions can be calculated as follows:
>>> # layer mask has dimensions 1 x 3 x 3
>>> layer_mask = torch.tensor([[[0,0,0],[1,1,1],
>>> [2,2,2]]])
>>> attr = ablator.attribute(input, target=1,
>>> layer_mask=layer_mask)
"""
def layer_forward_func(*args: Any) -> Union[Tensor]:
r"""
Args:
args (Any): Tensors comprising the layer input and the original
inputs, and an int representing the length of the layer input
"""
layer_length: int = args[-1]
layer_input: Tuple[Tensor, ...] = args[:layer_length]
original_inputs: Tuple[Tensor, ...] = args[layer_length:-1]
device_ids = self.device_ids
if device_ids is None:
device_ids = getattr(self.forward_func, "device_ids", None)
all_layer_inputs: Dict[torch.device, Tuple[Tensor, ...]] = {}
if device_ids is not None:
scattered_layer_input = scatter(layer_input, target_gpus=device_ids)
for device_tensors in scattered_layer_input:
all_layer_inputs[device_tensors[0].device] = device_tensors
else:
all_layer_inputs[layer_input[0].device] = layer_input
def forward_hook(
module: Module,
inp: Union[None, Tensor, Tuple[Tensor, ...]],
out: Union[None, Tensor, Tuple[Tensor, ...]] = None,
) -> Union[Tensor, Tuple[Tensor, ...]]:
device = _extract_device(module, inp, out)
is_layer_tuple = (
isinstance(out, tuple)
if out is not None
else isinstance(inp, tuple)
)
if device not in all_layer_inputs:
raise AssertionError(
"Layer input not placed on appropriate "
"device. If using a DataParallel model, either provide the "
"DataParallel model as forward_func or provide device ids"
" to the constructor."
)
if not is_layer_tuple:
return all_layer_inputs[device][0]
return all_layer_inputs[device]
hook = None
try:
if attribute_to_layer_input:
hook = self.layer.register_forward_pre_hook(forward_hook)
else:
hook = self.layer.register_forward_hook(forward_hook)
eval = _run_forward(self.forward_func, original_inputs, target=target)
finally:
if hook is not None:
hook.remove()
# _run_forward may return future of Tensor,
# but we don't support it here now
# And it will fail before here.
return cast(Tensor, eval)
with torch.no_grad():
inputs = _format_tensor_into_tuples(inputs)
additional_forward_args = _format_additional_forward_args(
additional_forward_args
)
layer_eval = _forward_layer_eval(
self.forward_func,
inputs,
self.layer,
additional_forward_args,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
layer_eval_len = (len(layer_eval),)
all_inputs = (
(inputs + additional_forward_args + layer_eval_len)
if additional_forward_args is not None
else inputs + layer_eval_len
)
ablator = self.attributor(layer_forward_func)
layer_attribs = ablator.attribute.__wrapped__(
ablator, # self
layer_eval,
baselines=layer_baselines,
additional_forward_args=all_inputs,
feature_mask=layer_mask,
perturbations_per_eval=perturbations_per_eval,
)
_attr = _format_output(len(layer_attribs) > 1, layer_attribs)
return _attr
@property
def attributor(self) -> Type[FeatureAblation]:
return FeatureAblation
