#!/usr/bin/env python3
from typing import Any, Callable, List, Tuple, 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 TargetType, TensorOrTupleOfTensorsGeneric
from captum.attr._core.feature_permutation import FeaturePermutation
from captum.attr._utils.attribution import LayerAttribution
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 LayerFeaturePermutation(LayerAttribution, FeaturePermutation):
r"""
A perturbation based approach to computing layer attribution similar to
LayerFeatureAblation, but using FeaturePermutation under the hood instead
of FeatureAblation.
"""
def __init__(
self,
forward_func: Callable,
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)
FeaturePermutation.__init__(self, forward_func)
[docs]
@log_usage()
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
target: TargetType = None,
additional_forward_args: Any = None,
layer_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
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.
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
perturbations_per_eval (int, optional): Allows permutation 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.
"""
def layer_forward_func(*args) -> Tensor:
layer_length = args[-1]
layer_input = args[:layer_length]
original_inputs = 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 = {}
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, inp, out=None):
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:
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()
return 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,
)
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
)
permutator = self.attributor(forward_func=layer_forward_func)
layer_attribs = permutator.attribute.__wrapped__(
permutator,
inputs=layer_eval,
target=target,
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):
return FeaturePermutation
