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_attributions,
    _format_tensor_into_tuples,
    _format_additional_forward_args,
    _run_forward,
    _validate_input,
    _expand_target,
    _expand_additional_forward_args,
    _tensorize_baseline,
    _call_custom_attribution_func,
    _compute_conv_delta_and_format_attrs,
    ExpansionTypes,
)
from .._utils.attribution import GradientAttribution
from .._utils.gradient import apply_gradient_requirements, undo_gradient_requirements


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

    curr_fn = outputs.grad_fn
    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 (
            inputs_grad_fn is None
            or first_next[0] == inputs_grad_fn
            or is_output_cloned(first_next, inputs_grad_fn)
        )
    except IndexError:
        return False


[docs]class DeepLift(GradientAttribution): def __init__(self, model): r""" Args: model (nn.Module): The reference to PyTorch model instance. """ GradientAttribution.__init__(self, model) self.model = model self.forward_handles = [] self.backward_handles = []
[docs] def attribute( self, inputs, baselines=None, target=None, additional_forward_args=None, return_convergence_delta=False, custom_attribution_func=None, ): 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: Towards better understanding of gradient-based attribution methods for 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 a tuple following attributions. 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: - custom_attribution_func(multipliers) - custom_attribution_func(multipliers, inputs) - custom_attribution_func(multipliers, inputs, baselines) 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** or 2-element tuple of **attributions**, **delta**: - **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. >>> attribution = dl.attribute(input, target=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) gradient_mask = apply_gradient_requirements(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 after the attribution is finished""" ) baselines = _tensorize_baseline(inputs, baselines) main_model_pre_hook = self._pre_hook_main_model() self.model.apply(self._register_hooks) additional_forward_args = _format_additional_forward_args( additional_forward_args ) input_base_additional_args = _expand_additional_forward_args( additional_forward_args, 2, ExpansionTypes.repeat ) expanded_target = _expand_target( target, 2, expansion_type=ExpansionTypes.repeat ) wrapped_forward_func = self._construct_forward_func( self.model, (inputs, baselines), expanded_target, input_base_additional_args ) gradients = self.gradient_func(wrapped_forward_func, inputs,) if custom_attribution_func is None: attributions = tuple( (input - baseline) * gradient for input, baseline, gradient in zip(inputs, baselines, gradients) ) else: attributions = _call_custom_attribution_func( custom_attribution_func, gradients, inputs, baselines ) # remove hooks from all activations main_model_pre_hook.remove() self._remove_hooks() undo_gradient_requirements(inputs, gradient_mask) return _compute_conv_delta_and_format_attrs( self, return_convergence_delta, attributions, baselines, inputs, additional_forward_args, target, is_inputs_tuple, )
def _construct_forward_func( self, forward_func, inputs, target=None, additional_forward_args=None ): def forward_fn(): return _run_forward(forward_func, inputs, target, additional_forward_args) 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() module.input_grad_fns = inputs[0].grad_fn def tensor_backward_hook(grad): if module.saved_grad is None: 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 newly computed grad_inputs in module.saved_grad. See MaxPool1d as an example.""".format( module ) ) return module.saved_grad # 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() if not _check_valid_module(module.input_grad_fns, outputs[0]): 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 module.saved_grad = None 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 del module.input_grad_fns def _backward_hook(self, module, grad_input, grad_output, eps=1e-10): 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)]( module, module.input, module.output, grad_input, grad_output, eps=eps ) ) # 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)) has_already_hooks = len(module._backward_hooks) > 0 return not ( "nn.modules.container" in module_fullname or has_already_hooks 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): def pre_hook(module, baseline_inputs_add_args): inputs = baseline_inputs_add_args[0] baselines = baseline_inputs_add_args[1] additional_args = None if len(baseline_inputs_add_args) > 2: additional_args = baseline_inputs_add_args[2:] baseline_input_tsr = tuple( torch.cat([input, baseline]) for input, baseline in zip(inputs, baselines) ) if additional_args is not None: return (*baseline_input_tsr, *additional_args) 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, additional_forward_args=None, return_convergence_delta=False, custom_attribution_func=None, ): 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 a tuple following attributions. 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: - custom_attribution_func(multipliers) - custom_attribution_func(multipliers, inputs) - custom_attribution_func(multipliers, inputs, baselines) 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** or 2-element tuple of **attributions**, **delta**: - **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. >>> attribution = dl.attribute(input, target=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, exp_addit_args, ) = self._expand_inputs_baselines_targets( baselines, inputs, target, additional_forward_args ) attributions = super().attribute( exp_inp, exp_base, target=exp_tgt, additional_forward_args=exp_addit_args, return_convergence_delta=return_convergence_delta, custom_attribution_func=custom_attribution_func, ) if return_convergence_delta: attributions, delta = attributions attributions = tuple( self._compute_mean_across_baselines(inp_bsz, base_bsz, attribution) for attribution in attributions ) if return_convergence_delta: return _format_attributions(is_inputs_tuple, attributions), delta else: return _format_attributions(is_inputs_tuple, attributions)
def _expand_inputs_baselines_targets( self, baselines, inputs, target, additional_forward_args ): inp_bsz = inputs[0].shape[0] base_bsz = baselines[0].shape[0] expanded_inputs = tuple( [ input.repeat_interleave(base_bsz, dim=0).requires_grad_() for input in inputs ] ) expanded_baselines = tuple( [ baseline.repeat( (inp_bsz,) + tuple([1] * (len(baseline.shape) - 1)) ).requires_grad_() for baseline in baselines ] ) expanded_target = _expand_target( target, base_bsz, expansion_type=ExpansionTypes.repeat_interleave ) input_additional_args = ( _expand_additional_forward_args(additional_forward_args, base_bsz) if additional_forward_args is not None else None ) return ( expanded_inputs, expanded_baselines, expanded_target, input_additional_args, ) def _compute_mean_across_baselines(self, inp_bsz, base_bsz, attribution): # Average for multiple references attr_shape = (inp_bsz, base_bsz) if len(attribution.shape) > 1: attr_shape += attribution.shape[1:] return torch.mean(attribution.view(attr_shape), axis=1, keepdim=False)
def nonlinear(module, inputs, outputs, grad_input, grad_output, eps=1e-10): r""" grad_input: (dLoss / dprev_layer_out, dLoss / wij, dLoss / bij) grad_output: (dLoss / dlayer_out) https://github.com/pytorch/pytorch/issues/12331 """ delta_in, delta_out = _compute_diffs(inputs, outputs) new_grad_inp = list(grad_input) # supported non-linear modules take only single tensor as input hence accessing # only the first element in `grad_input` and `grad_output` new_grad_inp[0] = torch.where( abs(delta_in) < eps, new_grad_inp[0], grad_output[0] * delta_out / delta_in, ) # 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: module.saved_grad = new_grad_inp[0] return new_grad_inp def softmax(module, inputs, outputs, grad_input, grad_output, eps=1e-10): delta_in, delta_out = _compute_diffs(inputs, outputs) new_grad_inp = list(grad_input) grad_input_unnorm = torch.where( abs(delta_in) < eps, new_grad_inp[0], grad_output[0] * delta_out / delta_in, ) # normalizing n = np.prod(grad_input[0].shape) # updating only the first half new_grad_inp[0] = grad_input_unnorm - grad_input_unnorm.sum() * 1 / n return new_grad_inp def maxpool1d(module, inputs, outputs, grad_input, grad_output, eps=1e-10): return maxpool( module, F.max_pool1d, F.max_unpool1d, inputs, outputs, grad_input, grad_output, eps=eps, ) def maxpool2d(module, inputs, outputs, grad_input, grad_output, eps=1e-10): return maxpool( module, F.max_pool2d, F.max_unpool2d, inputs, outputs, grad_input, grad_output, eps=eps, ) def maxpool3d(module, inputs, outputs, grad_input, grad_output, eps=1e-10): return maxpool( module, F.max_pool3d, F.max_unpool3d, inputs, outputs, grad_input, grad_output, eps=eps, ) def maxpool( module, pool_func, unpool_func, inputs, outputs, grad_input, grad_output, eps=1e-10, ): with torch.no_grad(): 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.padding, module.dilation, module.ceil_mode, True, ) grad_output_updated = grad_output[0] unpool_grad_out_delta, unpool_grad_out_ref_delta = torch.chunk( unpool_func( grad_output_updated * delta_out, indices, module.kernel_size, module.stride, module.padding, list(module.input.shape), ), 2, ) unpool_grad_out_delta = unpool_grad_out_delta + unpool_grad_out_ref_delta unpool_grad_out_delta = torch.cat(2 * [unpool_grad_out_delta]) # If the module is invalid, we need to recompute the grad_input if module.is_invalid: original_grad_input = grad_input grad_input = ( unpool_func( grad_output_updated, indices, module.kernel_size, module.stride, module.padding, list(module.input.shape), ), ) new_grad_inp = torch.where( abs(delta_in) < eps, grad_input[0], unpool_grad_out_delta / delta_in ) # 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: module.saved_grad = new_grad_inp return original_grad_input else: return (new_grad_inp,) 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 return torch.cat(2 * [delta_in]), torch.cat(2 * [delta_out]) 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, }