def neuron_forward_func(*args: Any):
with torch.no_grad():
layer_eval = _forward_layer_eval(
self.forward_func,
args,
self.layer,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_neuron_input,
)
return _verify_select_neuron(layer_eval, neuron_selector)
def _neuron_gradients(
inputs: Union[Tensor, Tuple[Tensor, ...]],
saved_layer: Dict[device, Tuple[Tensor, ...]],
key_list: List[device],
gradient_neuron_selector: Union[int, Tuple[Union[int, slice], ...],
Callable],
) -> Tuple[Tensor, ...]:
with torch.autograd.set_grad_enabled(True):
gradient_tensors = []
for key in key_list:
current_out_tensor = _verify_select_neuron(
saved_layer[key], gradient_neuron_selector)
gradient_tensors.append(
torch.autograd.grad(
torch.unbind(current_out_tensor)
if current_out_tensor.numel() > 1 else current_out_tensor,
inputs,
))
_total_gradients = _reduce_list(gradient_tensors, sum)
return _total_gradients
def _attribute(
self,
inputs: Tuple[Tensor, ...],
neuron_selector: Union[int, Tuple[int, ...], Callable],
baselines: Tuple[Union[Tensor, int, float], ...],
target: TargetType = None,
additional_forward_args: Any = None,
n_steps: int = 50,
method: str = "riemann_trapezoid",
attribute_to_neuron_input: bool = False,
step_sizes_and_alphas: Union[None, Tuple[List[float],
List[float]]] = None,
) -> Tuple[Tensor, ...]:
num_examples = inputs[0].shape[0]
total_batch = num_examples * n_steps
if step_sizes_and_alphas is None:
# retrieve step size and scaling factor for specified approximation method
step_sizes_func, alphas_func = approximation_parameters(method)
step_sizes, alphas = step_sizes_func(n_steps), alphas_func(n_steps)
else:
step_sizes, alphas = step_sizes_and_alphas
# Compute scaled inputs from baseline to final input.
scaled_features_tpl = tuple(
torch.cat(
[baseline + alpha * (input - baseline) for alpha in alphas],
dim=0).requires_grad_()
for input, baseline in zip(inputs, baselines))
additional_forward_args = _format_additional_forward_args(
additional_forward_args)
# apply number of steps to additional forward args
# currently, number of steps is applied only to additional forward arguments
# that are nd-tensors. It is assumed that the first dimension is
# the number of batches.
# dim -> (#examples * #steps x additional_forward_args[0].shape[1:], ...)
input_additional_args = (_expand_additional_forward_args(
additional_forward_args, n_steps) if additional_forward_args
is not None else None)
expanded_target = _expand_target(target, n_steps)
# Conductance Gradients - Returns gradient of output with respect to
# hidden layer and hidden layer evaluated at each input.
layer_gradients, layer_eval, input_grads = compute_layer_gradients_and_eval(
forward_fn=self.forward_func,
layer=self.layer,
inputs=scaled_features_tpl,
target_ind=expanded_target,
additional_forward_args=input_additional_args,
gradient_neuron_selector=neuron_selector,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_neuron_input,
)
mid_grads = _verify_select_neuron(layer_gradients, neuron_selector)
scaled_input_gradients = tuple(
input_grad * mid_grads.reshape((total_batch, ) + (1, ) *
(len(input_grad.shape) - 1))
for input_grad in input_grads)
# Mutliplies by appropriate step size.
scaled_grads = tuple(
scaled_input_gradient.contiguous().view(n_steps, -1) *
torch.tensor(step_sizes).view(n_steps, 1).to(
scaled_input_gradient.device)
for scaled_input_gradient in scaled_input_gradients)
# Aggregates across all steps for each tensor in the input tuple
total_grads = tuple(
_reshape_and_sum(scaled_grad, n_steps, num_examples,
input_grad.shape[1:])
for (scaled_grad, input_grad) in zip(scaled_grads, input_grads))
if self.multiplies_by_inputs:
# computes attribution for each tensor in input tuple
# attributions has the same dimensionality as inputs
attributions = tuple(total_grad * (input - baseline)
for total_grad, input, baseline in zip(
total_grads, inputs, baselines))
else:
attributions = total_grads
return attributions