# Source code for captum.attr._core.deep_lift

#!/usr/bin/env python3
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F

import numpy as np

from .._utils.common import (
_format_input,
_format_baseline,
_format_callable_baseline,
_format_tensor_into_tuples,
_run_forward,
_validate_input,
_expand_target,
_tensorize_baseline,
_compute_conv_delta_and_format_attrs,
ExpansionTypes,
)

# Check if module backward hook can safely be used for the module that produced
# this inputs / outputs mapping
"""
Checks if the output has been cloned. This happens especially in case of
layer deeplift.
"""
return (
output_fn[0].next_functions is not None
)

first_next = curr_fn.next_functions[0]
try:
# if inputs in the input to the network then the grad_fn is None and
# for that input backward_hook isn't computed. That's the reason why we
# need to check on inputs_grad_fns[first_next[1]] being None.
return (
)
except IndexError:
return False

def __init__(self, model):
r"""
Args:

model (nn.Module):  The reference to PyTorch model instance.
"""
self.model = model
self.forward_handles = []
self.backward_handles = []

[docs]    def attribute(
self,
inputs,
baselines=None,
target=None,
return_convergence_delta=False,
):
r""""
Implements DeepLIFT algorithm based on the following paper:
Learning Important Features Through Propagating Activation Differences,
Avanti Shrikumar, et. al.
https://arxiv.org/abs/1704.02685

and the gradient formulation proposed in:
deep neural networks,  Marco Ancona, et.al.
https://openreview.net/pdf?id=Sy21R9JAW

This implementation supports only Rescale rule. RevealCancel rule will
be supported in later releases.
In addition to that, in order to keep the implementation cleaner, DeepLIFT
for internal neurons and layers extends current implementation and is
implemented separately in LayerDeepLift and NeuronDeepLift.
Although DeepLIFT's(Rescale Rule) attribution quality is comparable with
Integrated Gradients, it runs significantly faster than Integrated
Gradients and is preferred for large datasets.

Currently we only support a limited number of non-linear activations
but the plan is to expand the list in the future.

Note: As we know, currently we cannot access the building blocks,
of PyTorch's built-in LSTM, RNNs and GRUs such as Tanh and Sigmoid.
Nonetheless, it is possible to build custom LSTMs, RNNS and GRUs
with performance similar to built-in ones using TorchScript.
More details on how to build custom RNNs can be found here:
https://pytorch.org/blog/optimizing-cuda-rnn-with-torchscript/

Args:

inputs (tensor or tuple of tensors):  Input for which
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 (aka batch size), and if
multiple input tensors are provided, the examples must
be aligned appropriately.
baselines (scalar, tensor, tuple of scalars or tensors, optional):
Baselines define reference samples that are compared with
the inputs. In order to assign attribution scores DeepLift
computes the differences between the inputs/outputs and
corresponding references.
Baselines can be provided as:

- a single tensor, if inputs is a single tensor, with
exactly the same dimensions as inputs or the first
dimension is one and the remaining dimensions match
with inputs.

- a single scalar, if inputs is a single tensor, which will
be broadcasted for each input value in input tensor.

- a tuple of tensors or scalars, the baseline corresponding
to each tensor in the inputs' tuple can be:
- either a tensor with matching dimensions to
corresponding tensor in the inputs' tuple
or the first dimension is one and the remaining
dimensions match with the corresponding
input tensor.
- or a scalar, corresponding to a tensor in the
inputs' tuple. This scalar value is broadcasted
for corresponding input tensor.

In the cases when baselines is not provided, we internally
use zero scalar corresponding to each input tensor.

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
return_convergence_delta (bool, optional): Indicates whether to return
convergence delta or not. If return_convergence_delta
is set to True convergence delta will be returned in
Default: False
custom_attribution_func (callable, optional): A custom function for
computing final attribution scores. This function can take
at least one and at most three arguments with the
following signature:
In case this function is not provided, we use the default
logic defined as: multipliers * (inputs - baselines)
It is assumed that all input arguments, multipliers,
inputs and baselines are provided in tuples of same
length. custom_attribution_func returns a tuple of
attribution tensors that have the same length as the
inputs.

Default: None

Returns:
- **attributions** (*tensor* or tuple of *tensors*):
Attribution score computed based on DeepLift rescale rule with respect
to each input feature. Attributions will always be
the same size as the provided inputs, with each value
providing the attribution of the corresponding input index.
If a single tensor is provided as inputs, a single tensor is
returned. If a tuple is provided for inputs, a tuple of
corresponding sized tensors is returned.
- **delta** (*tensor*, returned if return_convergence_delta=True):
This is computed using the property that
the total sum of forward_func(inputs) - forward_func(baselines)
must equal the total sum of the attributions computed
based on DeepLift's rescale rule.
Delta is calculated per example, meaning that the number of
elements in returned delta tensor is equal to the number of
of examples in input.
Note that the logic described for deltas is guaranteed when the
default logic for attribution computations is used, meaning that the
custom_attribution_func=None, otherwise it is not guaranteed and
depends on the specifics of the custom_attribution_func.

Examples::

>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> net = ImageClassifier()
>>> dl = DeepLift(net)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Computes deeplift attribution scores for class 3.
"""

# Keeps track whether original input is a tuple or not before
# converting it into a tuple.
is_inputs_tuple = isinstance(inputs, tuple)

inputs = _format_input(inputs)
baselines = _format_baseline(baselines, inputs)

_validate_input(inputs, baselines)

# set hooks for baselines
warnings.warn(
"""Setting forward, backward hooks and attributes on non-linear
activations. The hooks and attributes will be removed
)

baselines = _tensorize_baseline(inputs, baselines)
main_model_pre_hook = self._pre_hook_main_model()

self.model.apply(self._register_hooks)

)
)
expanded_target = _expand_target(
target, 2, expansion_type=ExpansionTypes.repeat
)

wrapped_forward_func = self._construct_forward_func(
)
)
else:
)
# remove hooks from all activations
main_model_pre_hook.remove()
self._remove_hooks()

return _compute_conv_delta_and_format_attrs(
self,
return_convergence_delta,
baselines,
inputs,
target,
is_inputs_tuple,
)

def _construct_forward_func(
):
def forward_fn():

if hasattr(forward_func, "device_ids"):
forward_fn.device_ids = forward_func.device_ids
return forward_fn

def _is_non_linear(self, module):
return type(module) in SUPPORTED_NON_LINEAR.keys()

def _forward_pre_hook_ref(self, module, inputs):
inputs = _format_tensor_into_tuples(inputs)
module.input_ref = tuple(input.clone().detach() for input in inputs)

def _forward_pre_hook(self, module, inputs):
"""
For the modules that perform in-place operations such as ReLUs, we cannot
use inputs from forward hooks. This is because in that case inputs
and outputs are the same. We need access the inputs in pre-hooks and
set necessary hooks on inputs there.
"""
inputs = _format_tensor_into_tuples(inputs)
module.input = inputs[0].clone().detach()

raise RuntimeError(
"""Module {} was detected as not supporting correctly module
backward hook. You should modify your hook to ignore the given
grad_inputs (recompute them by hand if needed) and save the
as an example.""".format(
module
)
)

# the hook is set by default but it will be used only for
# failure cases and will be removed otherwise
handle = inputs[0].register_hook(tensor_backward_hook)
module.input_hook = handle

def _forward_hook(self, module, inputs, outputs):
r"""
we need forward hook to access and detach the inputs and
outputs of a neuron
"""
outputs = _format_tensor_into_tuples(outputs)
module.output = outputs[0].clone().detach()
warnings.warn(
"""An invalid module {} is detected. Saved gradients will
be used as the gradients of the module's input tensor.
See MaxPool1d as an example.""".format(
module
)
)
module.is_invalid = True
self.forward_handles.append(module.input_hook)
else:
module.is_invalid = False
# removing the hook if there is no failure case
module.input_hook.remove()
del module.input_hook

r"""
grad_input is the gradient of the neuron with respect to its input
grad_output is the gradient of the neuron with respect to its output
we can override grad_input according to chain rule with.
grad_output * delta_out / delta_in.

"""
# before accessing the attributes from the module we want
# to ensure that the properties exist, if not, then it is
# likely that the module is being reused.
attr_criteria = self.satisfies_attribute_criteria(module)
if not attr_criteria:
raise RuntimeError(
"A Module {} was detected that does not contain some of "
"the input/output attributes that are required for DeepLift "
"computations. This can occur, for example, if "
"your module is being used more than once in the network."
"Please, ensure that module is being used only once in the "
"network.".format(module)
)
multipliers = tuple(
SUPPORTED_NON_LINEAR[type(module)](
)
)
# remove all the properies that we set for the inputs and output
del module.input
del module.output

return multipliers

def satisfies_attribute_criteria(self, module):
return hasattr(module, "input") and hasattr(module, "output")

def _can_register_hook(self, module):
# TODO find a better way of checking if a module is a container or not
module_fullname = str(type(module))
return not (
"nn.modules.container" in module_fullname
or not self._is_non_linear(module)
)

def _register_hooks(self, module):
if not self._can_register_hook(module):
return
# adds forward hook to leaf nodes that are non-linear
forward_handle = module.register_forward_hook(self._forward_hook)
pre_forward_handle = module.register_forward_pre_hook(self._forward_pre_hook)
backward_handle = module.register_backward_hook(self._backward_hook)
self.forward_handles.append(forward_handle)
self.forward_handles.append(pre_forward_handle)
self.backward_handles.append(backward_handle)

def _remove_hooks(self):
for forward_handle in self.forward_handles:
forward_handle.remove()
for backward_handle in self.backward_handles:
backward_handle.remove()

def _pre_hook_main_model(self):

baseline_input_tsr = tuple(
torch.cat([input, baseline])
for input, baseline in zip(inputs, baselines)
)
return baseline_input_tsr

if isinstance(self.model, nn.DataParallel):
return self.model.module.register_forward_pre_hook(pre_hook)
else:
return self.model.register_forward_pre_hook(pre_hook)

[docs]    def has_convergence_delta(self):
return True

[docs]class DeepLiftShap(DeepLift):
def __init__(self, model):
r"""
Args:

model (nn.Module):  The reference to PyTorch model instance.
"""
DeepLift.__init__(self, model)

[docs]    def attribute(
self,
inputs,
baselines,
target=None,
return_convergence_delta=False,
):
r"""
Extends DeepLift algorithm and approximates SHAP values using Deeplift.
For each input sample it computes DeepLift attribution with respect to
each baseline and averages resulting attributions.
More details about the algorithm can be found here:

http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf

Note that the explanation model:
1. Assumes that input features are independent of one another
2. Is linear, meaning that the explanations are modeled through
the additive composition of feature effects.
Although, it assumes a linear model for each explanation, the overall
model across multiple explanations can be complex and non-linear.

Args:

inputs (tensor or tuple of tensors):  Input for which
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 (aka batch size), and if
multiple input tensors are provided, the examples must
be aligned appropriately.
baselines (tensor, tuple of tensors, callable):
Baselines define reference samples that are compared with
the inputs. In order to assign attribution scores DeepLift
computes the differences between the inputs/outputs and
corresponding references. Baselines can be provided as:

- a single tensor, if inputs is a single tensor, with
the first dimension equal to the number of examples
in the baselines' distribution. The remaining dimensions
must match with input tensor's dimension starting from
the second dimension.

- a tuple of tensors, if inputs is a tuple of tensors,
with the first dimension of any tensor inside the tuple
equal to the number of examples in the baseline's
distribution. The remaining dimensions must match
the dimensions of the corresponding input tensor
starting from the second dimension.

- callable function, optionally takes inputs as an
argument and either returns a single tensor
or a tuple of those.

It is recommended that the number of samples in the baselines'
tensors is larger than one.
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
return_convergence_delta (bool, optional): Indicates whether to return
convergence delta or not. If return_convergence_delta
is set to True convergence delta will be returned in
Default: False
custom_attribution_func (callable, optional): A custom function for
computing final attribution scores. This function can take
at least one and at most three arguments with the
following signature:
In case this function is not provided we use the default
logic defined as: multipliers * (inputs - baselines)
It is assumed that all input arguments, multipliers,
inputs and baselines are provided in tuples of same
length. custom_attribution_func returns a tuple of
attribution tensors that have the same length as the
inputs.
Default: None

Returns:
- **attributions** (*tensor* or tuple of *tensors*):
Attribution score computed based on DeepLift rescale rule with
respect to each input feature. Attributions will always be
the same size as the provided inputs, with each value
providing the attribution of the corresponding input index.
If a single tensor is provided as inputs, a single tensor is
returned. If a tuple is provided for inputs, a tuple of
corresponding sized tensors is returned.
- **delta** (*tensor*, returned if return_convergence_delta=True):
This is computed using the property that the
total sum of forward_func(inputs) - forward_func(baselines)
must be very close to the total sum of attributions
computed based on approximated SHAP values using
Deeplift's rescale rule.
Delta is calculated for each example input and baseline pair,
meaning that the number of elements in returned delta tensor
is equal to the
number of examples in input * number of examples
in baseline. The deltas are ordered in the first place by
input example, followed by the baseline.
Note that the logic described for deltas is guaranteed
when the default logic for attribution computations is used,
meaning that the custom_attribution_func=None, otherwise
it is not guaranteed and depends on the specifics of the
custom_attribution_func.

Examples::

>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> net = ImageClassifier()
>>> dl = DeepLiftShap(net)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Computes shap values using deeplift for class 3.
"""
baselines = _format_callable_baseline(baselines, inputs)

assert isinstance(baselines[0], torch.Tensor) and baselines[0].shape[0] > 1, (
"Baselines distribution has to be provided in form of a torch.Tensor"
" with more than one example but found: {}."
" If baselines are provided in shape of scalars or with a single"
" baseline example, DeepLift"
" approach can be used instead.".format(baselines[0])
)

# Keeps track whether original input is a tuple or not before
# converting it into a tuple.
is_inputs_tuple = isinstance(inputs, tuple)

inputs = _format_input(inputs)

# batch sizes
inp_bsz = inputs[0].shape[0]
base_bsz = baselines[0].shape[0]

(
exp_inp,
exp_base,
exp_tgt,
) = self._expand_inputs_baselines_targets(
)
exp_inp,
exp_base,
target=exp_tgt,
return_convergence_delta=return_convergence_delta,
)
if return_convergence_delta:

)

if return_convergence_delta:
else:

def _expand_inputs_baselines_targets(
):
inp_bsz = inputs[0].shape[0]
base_bsz = baselines[0].shape[0]

expanded_inputs = tuple(
[
for input in inputs
]
)
expanded_baselines = tuple(
[
baseline.repeat(
(inp_bsz,) + tuple([1] * (len(baseline.shape) - 1))
for baseline in baselines
]
)
expanded_target = _expand_target(
target, base_bsz, expansion_type=ExpansionTypes.repeat_interleave
)
else None
)
return (
expanded_inputs,
expanded_baselines,
expanded_target,
)

# Average for multiple references
attr_shape = (inp_bsz, base_bsz)

r"""
grad_input: (dLoss / dprev_layer_out, dLoss / wij, dLoss / bij)
https://github.com/pytorch/pytorch/issues/12331
"""
delta_in, delta_out = _compute_diffs(inputs, outputs)

# supported non-linear modules take only single tensor as input hence accessing
# only the first element in grad_input and grad_output
)

# If the module is invalid, save the newly computed gradients
# The original_grad_input will be overridden later in the Tensor hook
if module.is_invalid:

delta_in, delta_out = _compute_diffs(inputs, outputs)

)
# normalizing

# updating only the first half

return maxpool(
module,
F.max_pool1d,
F.max_unpool1d,
inputs,
outputs,
eps=eps,
)

return maxpool(
module,
F.max_pool2d,
F.max_unpool2d,
inputs,
outputs,
eps=eps,
)

return maxpool(
module,
F.max_pool3d,
F.max_unpool3d,
inputs,
outputs,
eps=eps,
)

def maxpool(
):
input, input_ref = inputs.chunk(2)
output, output_ref = outputs.chunk(2)

delta_in = input - input_ref
delta_in = torch.cat(2 * [delta_in])
# Extracts cross maximum between the outputs of maxpool for the
# actual inputs and its corresponding references. In case the delta outputs
# for the references are larger the method relies on the references and
# corresponding gradients to compute the multiplies and contributions.
delta_out_xmax = torch.max(output, output_ref)
delta_out = torch.cat([delta_out_xmax - output_ref, output - delta_out_xmax])

_, indices = pool_func(
module.input,
module.kernel_size,
module.stride,
module.dilation,
module.ceil_mode,
True,
)
unpool_func(
indices,
module.kernel_size,
module.stride,
list(module.input.shape),
),
2,
)

# If the module is invalid, we need to recompute the grad_input
if module.is_invalid:
unpool_func(
indices,
module.kernel_size,
module.stride,
list(module.input.shape),
),
)

)
# If the module is invalid, save the newly computed gradients
# The original_grad_input will be overridden later in the Tensor hook
if module.is_invalid:
else:

def _compute_diffs(inputs, outputs):
input, input_ref = inputs.chunk(2)
# if the model is a single non-linear module and we apply Rescale rule on it
# we might not be able to perform chunk-ing because the output of the module is
# usually being replaced by model output.
output, output_ref = outputs.chunk(2)
delta_in = input - input_ref
delta_out = output - output_ref

SUPPORTED_NON_LINEAR = {
nn.ReLU: nonlinear,
nn.ELU: nonlinear,
nn.LeakyReLU: nonlinear,
nn.Sigmoid: nonlinear,
nn.Tanh: nonlinear,
nn.Softplus: nonlinear,
nn.MaxPool1d: maxpool1d,
nn.MaxPool2d: maxpool2d,
nn.MaxPool3d: maxpool3d,
nn.Softmax: softmax,
}