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gradient.py
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gradient.py
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#!/usr/bin/env python3
import threading
import typing
import warnings
from collections import defaultdict
from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
import torch
from captum._utils.common import (
_reduce_list,
_run_forward,
_sort_key_list,
_verify_select_neuron,
)
from captum._utils.sample_gradient import SampleGradientWrapper
from captum._utils.typing import (
Literal,
ModuleOrModuleList,
TargetType,
TensorOrTupleOfTensorsGeneric,
)
from torch import device, Tensor
from torch.nn import Module
def apply_gradient_requirements(
inputs: Tuple[Tensor, ...], warn: bool = True
) -> List[bool]:
"""
Iterates through tuple on input tensors and sets requires_grad to be true on
each Tensor, and ensures all grads are set to zero. To ensure that the input
is returned to its initial state, a list of flags representing whether or not
a tensor originally required grad is returned.
"""
assert isinstance(
inputs, tuple
), "Inputs should be wrapped in a tuple prior to preparing for gradients"
grad_required = []
for index, input in enumerate(inputs):
assert isinstance(input, torch.Tensor), "Given input is not a torch.Tensor"
grad_required.append(input.requires_grad)
inputs_dtype = input.dtype
# Note: torch 1.2 doesn't support is_complex for dtype that's why we check
# on the existance of is_complex method.
if not inputs_dtype.is_floating_point and not (
hasattr(inputs_dtype, "is_complex") and inputs_dtype.is_complex
):
if warn:
warnings.warn(
"""Input Tensor %d has a dtype of %s.
Gradients cannot be activated
for these data types."""
% (index, str(inputs_dtype))
)
elif not input.requires_grad:
if warn:
warnings.warn(
"Input Tensor %d did not already require gradients, "
"required_grads has been set automatically." % index
)
input.requires_grad_()
return grad_required
def undo_gradient_requirements(
inputs: Tuple[Tensor, ...], grad_required: List[bool]
) -> None:
"""
Iterates through list of tensors, zeros each gradient, and sets required
grad to false if the corresponding index in grad_required is False.
This method is used to undo the effects of prepare_gradient_inputs, making
grads not required for any input tensor that did not initially require
gradients.
"""
assert isinstance(
inputs, tuple
), "Inputs should be wrapped in a tuple prior to preparing for gradients."
assert len(inputs) == len(
grad_required
), "Input tuple length should match gradient mask."
for index, input in enumerate(inputs):
assert isinstance(input, torch.Tensor), "Given input is not a torch.Tensor"
if not grad_required[index]:
input.requires_grad_(False)
def compute_gradients(
forward_fn: Callable,
inputs: Union[Tensor, Tuple[Tensor, ...]],
target_ind: TargetType = None,
additional_forward_args: Any = None,
) -> Tuple[Tensor, ...]:
r"""
Computes gradients of the output with respect to inputs for an
arbitrary forward function.
Args:
forward_fn: forward function. This can be for example model's
forward function.
input: Input at which gradients are evaluated,
will be passed to forward_fn.
target_ind: Index of the target class for which gradients
must be computed (classification only).
additional_forward_args: Additional input arguments that forward
function requires. It takes an empty tuple (no additional
arguments) if no additional arguments are required
"""
with torch.autograd.set_grad_enabled(True):
# runs forward pass
outputs = _run_forward(forward_fn, inputs, target_ind, additional_forward_args)
assert outputs[0].numel() == 1, (
"Target not provided when necessary, cannot"
" take gradient with respect to multiple outputs."
)
# torch.unbind(forward_out) is a list of scalar tensor tuples and
# contains batch_size * #steps elements
grads = torch.autograd.grad(torch.unbind(outputs), inputs)
return grads
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
@typing.overload
def _forward_layer_eval(
forward_fn: Callable,
inputs: Union[Tensor, Tuple[Tensor, ...]],
layer: Module,
additional_forward_args: Any = None,
device_ids: Union[None, List[int]] = None,
attribute_to_layer_input: bool = False,
grad_enabled: bool = False,
) -> Tuple[Tensor, ...]:
...
@typing.overload
def _forward_layer_eval(
forward_fn: Callable,
inputs: Union[Tensor, Tuple[Tensor, ...]],
layer: List[Module],
additional_forward_args: Any = None,
device_ids: Union[None, List[int]] = None,
attribute_to_layer_input: bool = False,
grad_enabled: bool = False,
) -> List[Tuple[Tensor, ...]]:
...
def _forward_layer_eval(
forward_fn: Callable,
inputs: Union[Tensor, Tuple[Tensor, ...]],
layer: ModuleOrModuleList,
additional_forward_args: Any = None,
device_ids: Union[None, List[int]] = None,
attribute_to_layer_input: bool = False,
grad_enabled: bool = False,
) -> Union[Tuple[Tensor, ...], List[Tuple[Tensor, ...]]]:
return _forward_layer_eval_with_neuron_grads(
forward_fn,
inputs,
layer,
additional_forward_args=additional_forward_args,
gradient_neuron_selector=None,
grad_enabled=grad_enabled,
device_ids=device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
@typing.overload
def _forward_layer_distributed_eval(
forward_fn: Callable,
inputs: Any,
layer: ModuleOrModuleList,
target_ind: TargetType = None,
additional_forward_args: Any = None,
attribute_to_layer_input: bool = False,
forward_hook_with_return: Literal[False] = False,
require_layer_grads: bool = False,
) -> Dict[Module, Dict[device, Tuple[Tensor, ...]]]:
...
@typing.overload
def _forward_layer_distributed_eval(
forward_fn: Callable,
inputs: Any,
layer: ModuleOrModuleList,
target_ind: TargetType = None,
additional_forward_args: Any = None,
attribute_to_layer_input: bool = False,
*,
forward_hook_with_return: Literal[True],
require_layer_grads: bool = False,
) -> Tuple[Dict[Module, Dict[device, Tuple[Tensor, ...]]], Tensor]:
...
def _forward_layer_distributed_eval(
forward_fn: Callable,
inputs: Any,
layer: ModuleOrModuleList,
target_ind: TargetType = None,
additional_forward_args: Any = None,
attribute_to_layer_input: bool = False,
forward_hook_with_return: bool = False,
require_layer_grads: bool = False,
) -> Union[
Tuple[Dict[Module, Dict[device, Tuple[Tensor, ...]]], Tensor],
Dict[Module, Dict[device, Tuple[Tensor, ...]]],
]:
r"""
A helper function that allows to set a hook on model's `layer`, run the forward
pass and returns intermediate layer results, stored in a dictionary,
and optionally also the output of the forward function. The keys in the
dictionary are the device ids and the values are corresponding intermediate layer
results, either the inputs or the outputs of the layer depending on whether we set
`attribute_to_layer_input` to True or False.
This is especially useful when we execute forward pass in a distributed setting,
using `DataParallel`s for example.
"""
saved_layer: Dict[Module, Dict[device, Tuple[Tensor, ...]]] = defaultdict(dict)
lock = threading.Lock()
all_layers: List[Module] = [layer] if isinstance(layer, Module) else layer
# Set a forward hook on specified module and run forward pass to
# get layer output tensor(s).
# For DataParallel models, each partition adds entry to dictionary
# with key as device and value as corresponding Tensor.
def hook_wrapper(original_module):
def forward_hook(module, inp, out=None):
eval_tsrs = inp if attribute_to_layer_input else out
is_eval_tuple = isinstance(eval_tsrs, tuple)
if not is_eval_tuple:
eval_tsrs = (eval_tsrs,)
if require_layer_grads:
apply_gradient_requirements(eval_tsrs, warn=False)
with lock:
nonlocal saved_layer
# Note that cloning behaviour of `eval_tsr` is different
# when `forward_hook_with_return` is set to True. This is because
# otherwise `backward()` on the last output layer won't execute.
if forward_hook_with_return:
saved_layer[original_module][eval_tsrs[0].device] = eval_tsrs
eval_tsrs_to_return = tuple(
eval_tsr.clone() for eval_tsr in eval_tsrs
)
if not is_eval_tuple:
eval_tsrs_to_return = eval_tsrs_to_return[0]
return eval_tsrs_to_return
else:
saved_layer[original_module][eval_tsrs[0].device] = tuple(
eval_tsr.clone() for eval_tsr in eval_tsrs
)
return forward_hook
all_hooks = []
try:
for single_layer in all_layers:
if attribute_to_layer_input:
all_hooks.append(
single_layer.register_forward_pre_hook(hook_wrapper(single_layer))
)
else:
all_hooks.append(
single_layer.register_forward_hook(hook_wrapper(single_layer))
)
output = _run_forward(
forward_fn,
inputs,
target=target_ind,
additional_forward_args=additional_forward_args,
)
finally:
for hook in all_hooks:
hook.remove()
if len(saved_layer) == 0:
raise AssertionError("Forward hook did not obtain any outputs for given layer")
if forward_hook_with_return:
return saved_layer, output
return saved_layer
def _gather_distributed_tensors(
saved_layer: Dict[device, Tuple[Tensor, ...]],
device_ids: Union[None, List[int]] = None,
key_list: Union[None, List[device]] = None,
) -> Tuple[Tensor, ...]:
r"""
A helper function to concatenate intermediate layer results stored on
different devices in `saved_layer`. `saved_layer` is a dictionary that
contains `device_id` as a key and intermediate layer results (either
the input or the output of the layer) stored on the device corresponding to
the key.
`key_list` is a list of devices in appropriate ordering for concatenation
and if not provided, keys are sorted based on device ids.
If only one key exists (standard model), key list simply has one element.
"""
if key_list is None:
key_list = _sort_key_list(list(saved_layer.keys()), device_ids)
return _reduce_list([saved_layer[device_id] for device_id in key_list])
def _extract_device_ids(
forward_fn: Callable,
saved_layer: Dict[Module, Dict[device, Tuple[Tensor, ...]]],
device_ids: Union[None, List[int]],
) -> Union[None, List[int]]:
r"""
A helper function to extract device_ids from `forward_function` in case it is
provided as part of a `DataParallel` model or if is accessible from
`forward_fn`.
In case input device_ids is not None, this function returns that value.
"""
# Multiple devices / keys implies a DataParallel model, so we look for
# device IDs if given or available from forward function
# (DataParallel model object).
if (
max(len(saved_layer[single_layer]) for single_layer in saved_layer) > 1
and device_ids is None
):
if (
hasattr(forward_fn, "device_ids")
and cast(Any, forward_fn).device_ids is not None
):
device_ids = cast(Any, forward_fn).device_ids
else:
raise AssertionError(
"Layer tensors are saved on multiple devices, however unable to access"
" device ID list from the `forward_fn`. Device ID list must be"
" accessible from `forward_fn`. For example, they can be retrieved"
" if `forward_fn` is a model of type `DataParallel`. It is used"
" for identifying device batch ordering."
)
return device_ids
@typing.overload
def _forward_layer_eval_with_neuron_grads(
forward_fn: Callable,
inputs: Union[Tensor, Tuple[Tensor, ...]],
layer: Module,
additional_forward_args: Any = None,
*,
gradient_neuron_selector: Union[int, Tuple[Union[int, slice], ...], Callable],
grad_enabled: bool = False,
device_ids: Union[None, List[int]] = None,
attribute_to_layer_input: bool = False,
) -> Tuple[Tuple[Tensor, ...], Tuple[Tensor, ...]]:
...
@typing.overload
def _forward_layer_eval_with_neuron_grads(
forward_fn: Callable,
inputs: Union[Tensor, Tuple[Tensor, ...]],
layer: Module,
additional_forward_args: Any = None,
gradient_neuron_selector: None = None,
grad_enabled: bool = False,
device_ids: Union[None, List[int]] = None,
attribute_to_layer_input: bool = False,
) -> Tuple[Tensor, ...]:
...
@typing.overload
def _forward_layer_eval_with_neuron_grads(
forward_fn: Callable,
inputs: Union[Tensor, Tuple[Tensor, ...]],
layer: List[Module],
additional_forward_args: Any = None,
gradient_neuron_selector: None = None,
grad_enabled: bool = False,
device_ids: Union[None, List[int]] = None,
attribute_to_layer_input: bool = False,
) -> List[Tuple[Tensor, ...]]:
...
def _forward_layer_eval_with_neuron_grads(
forward_fn: Callable,
inputs: Union[Tensor, Tuple[Tensor, ...]],
layer: ModuleOrModuleList,
additional_forward_args: Any = None,
gradient_neuron_selector: Union[
None, int, Tuple[Union[int, slice], ...], Callable
] = None,
grad_enabled: bool = False,
device_ids: Union[None, List[int]] = None,
attribute_to_layer_input: bool = False,
) -> Union[
Tuple[Tuple[Tensor, ...], Tuple[Tensor, ...]],
Tuple[Tensor, ...],
List[Tuple[Tensor, ...]],
]:
"""
This method computes forward evaluation for a particular layer using a
forward hook. If a gradient_neuron_selector is provided, then gradients with
respect to that neuron in the layer output are also returned.
These functionalities are combined due to the behavior of DataParallel models
with hooks, in which hooks are executed once per device. We need to internally
combine the separated tensors from devices by concatenating based on device_ids.
Any necessary gradients must be taken with respect to each independent batched
tensor, so the gradients are computed and combined appropriately.
More information regarding the behavior of forward hooks with DataParallel models
can be found in the PyTorch data parallel documentation. We maintain the separate
evals in a dictionary protected by a lock, analogous to the gather implementation
for the core PyTorch DataParallel implementation.
"""
grad_enabled = True if gradient_neuron_selector is not None else grad_enabled
with torch.autograd.set_grad_enabled(grad_enabled):
saved_layer = _forward_layer_distributed_eval(
forward_fn,
inputs,
layer,
additional_forward_args=additional_forward_args,
attribute_to_layer_input=attribute_to_layer_input,
)
device_ids = _extract_device_ids(forward_fn, saved_layer, device_ids)
# Identifies correct device ordering based on device ids.
# key_list is a list of devices in appropriate ordering for concatenation.
# If only one key exists (standard model), key list simply has one element.
key_list = _sort_key_list(list(next(iter(saved_layer.values())).keys()), device_ids)
if gradient_neuron_selector is not None:
assert isinstance(
layer, Module
), "Cannot compute neuron gradients for multiple layers simultaneously!"
inp_grads = _neuron_gradients(
inputs, saved_layer[layer], key_list, gradient_neuron_selector
)
return (
_gather_distributed_tensors(saved_layer[layer], key_list=key_list),
inp_grads,
)
else:
if isinstance(layer, Module):
return _gather_distributed_tensors(saved_layer[layer], key_list=key_list)
else:
return [
_gather_distributed_tensors(saved_layer[curr_layer], key_list=key_list)
for curr_layer in layer
]
@typing.overload
def compute_layer_gradients_and_eval(
forward_fn: Callable,
layer: Module,
inputs: Union[Tensor, Tuple[Tensor, ...]],
target_ind: TargetType = None,
additional_forward_args: Any = None,
*,
gradient_neuron_selector: Union[int, Tuple[Union[int, slice], ...], Callable],
device_ids: Union[None, List[int]] = None,
attribute_to_layer_input: bool = False,
output_fn: Union[None, Callable] = None,
) -> Tuple[Tuple[Tensor, ...], Tuple[Tensor, ...], Tuple[Tensor, ...]]:
...
@typing.overload
def compute_layer_gradients_and_eval(
forward_fn: Callable,
layer: List[Module],
inputs: Union[Tensor, Tuple[Tensor, ...]],
target_ind: TargetType = None,
additional_forward_args: Any = None,
gradient_neuron_selector: None = None,
device_ids: Union[None, List[int]] = None,
attribute_to_layer_input: bool = False,
output_fn: Union[None, Callable] = None,
) -> Tuple[List[Tuple[Tensor, ...]], List[Tuple[Tensor, ...]]]:
...
@typing.overload
def compute_layer_gradients_and_eval(
forward_fn: Callable,
layer: Module,
inputs: Union[Tensor, Tuple[Tensor, ...]],
target_ind: TargetType = None,
additional_forward_args: Any = None,
gradient_neuron_selector: None = None,
device_ids: Union[None, List[int]] = None,
attribute_to_layer_input: bool = False,
output_fn: Union[None, Callable] = None,
) -> Tuple[Tuple[Tensor, ...], Tuple[Tensor, ...]]:
...
def compute_layer_gradients_and_eval(
forward_fn: Callable,
layer: ModuleOrModuleList,
inputs: Union[Tensor, Tuple[Tensor, ...]],
target_ind: TargetType = None,
additional_forward_args: Any = None,
gradient_neuron_selector: Union[
None, int, Tuple[Union[int, slice], ...], Callable
] = None,
device_ids: Union[None, List[int]] = None,
attribute_to_layer_input: bool = False,
output_fn: Union[None, Callable] = None,
) -> Union[
Tuple[Tuple[Tensor, ...], Tuple[Tensor, ...]],
Tuple[Tuple[Tensor, ...], Tuple[Tensor, ...], Tuple[Tensor, ...]],
Tuple[List[Tuple[Tensor, ...]], List[Tuple[Tensor, ...]]],
]:
r"""
Computes gradients of the output with respect to a given layer as well
as the output evaluation of the layer for an arbitrary forward function
and given input.
For data parallel models, hooks are executed once per device ,so we
need to internally combine the separated tensors from devices by
concatenating based on device_ids. Any necessary gradients must be taken
with respect to each independent batched tensor, so the gradients are
computed and combined appropriately.
More information regarding the behavior of forward hooks with DataParallel
models can be found in the PyTorch data parallel documentation. We maintain
the separate inputs in a dictionary protected by a lock, analogous to the
gather implementation for the core PyTorch DataParallel implementation.
NOTE: To properly handle inplace operations, a clone of the layer output
is stored. This structure inhibits execution of a backward hook on the last
module for the layer output when computing the gradient with respect to
the input, since we store an intermediate clone, as
opposed to the true module output. If backward module hooks are necessary
for the final module when computing input gradients, utilize
_forward_layer_eval_with_neuron_grads instead.
Args:
forward_fn: forward function. This can be for example model's
forward function.
layer: Layer for which gradients / output will be evaluated.
inputs: Input at which gradients are evaluated,
will be passed to forward_fn.
target_ind: Index of the target class for which gradients
must be computed (classification only).
output_fn: An optional function that is applied to the layer inputs or
outputs depending whether the `attribute_to_layer_input` is
set to `True` or `False`
args: Additional input arguments that forward function requires.
It takes an empty tuple (no additional arguments) if no
additional arguments are required
Returns:
2-element tuple of **gradients**, **evals**:
- **gradients**:
Gradients of output with respect to target layer output.
- **evals**:
Target layer output for given input.
"""
with torch.autograd.set_grad_enabled(True):
# saved_layer is a dictionary mapping device to a tuple of
# layer evaluations on that device.
saved_layer, output = _forward_layer_distributed_eval(
forward_fn,
inputs,
layer,
target_ind=target_ind,
additional_forward_args=additional_forward_args,
attribute_to_layer_input=attribute_to_layer_input,
forward_hook_with_return=True,
require_layer_grads=True,
)
assert output[0].numel() == 1, (
"Target not provided when necessary, cannot"
" take gradient with respect to multiple outputs."
)
device_ids = _extract_device_ids(forward_fn, saved_layer, device_ids)
# Identifies correct device ordering based on device ids.
# key_list is a list of devices in appropriate ordering for concatenation.
# If only one key exists (standard model), key list simply has one element.
key_list = _sort_key_list(
list(next(iter(saved_layer.values())).keys()), device_ids
)
all_outputs: Union[Tuple[Tensor, ...], List[Tuple[Tensor, ...]]]
if isinstance(layer, Module):
all_outputs = _reduce_list(
[
saved_layer[layer][device_id]
if output_fn is None
else output_fn(saved_layer[layer][device_id])
for device_id in key_list
]
)
else:
all_outputs = [
_reduce_list(
[
saved_layer[single_layer][device_id]
if output_fn is None
else output_fn(saved_layer[single_layer][device_id])
for device_id in key_list
]
)
for single_layer in layer
]
all_layers: List[Module] = [layer] if isinstance(layer, Module) else layer
grad_inputs = tuple(
layer_tensor
for single_layer in all_layers
for device_id in key_list
for layer_tensor in saved_layer[single_layer][device_id]
)
saved_grads = torch.autograd.grad(torch.unbind(output), grad_inputs)
offset = 0
all_grads: List[Tuple[Tensor, ...]] = []
for single_layer in all_layers:
num_tensors = len(next(iter(saved_layer[single_layer].values())))
curr_saved_grads = [
saved_grads[i : i + num_tensors]
for i in range(
offset, offset + len(key_list) * num_tensors, num_tensors
)
]
offset += len(key_list) * num_tensors
if output_fn is not None:
curr_saved_grads = [
output_fn(curr_saved_grad) for curr_saved_grad in curr_saved_grads
]
all_grads.append(_reduce_list(curr_saved_grads))
layer_grads: Union[Tuple[Tensor, ...], List[Tuple[Tensor, ...]]]
layer_grads = all_grads
if isinstance(layer, Module):
layer_grads = all_grads[0]
if gradient_neuron_selector is not None:
assert isinstance(
layer, Module
), "Cannot compute neuron gradients for multiple layers simultaneously!"
inp_grads = _neuron_gradients(
inputs, saved_layer[layer], key_list, gradient_neuron_selector
)
return (
cast(Tuple[Tensor, ...], layer_grads),
cast(Tuple[Tensor, ...], all_outputs),
inp_grads,
)
return layer_grads, all_outputs # type: ignore
def construct_neuron_grad_fn(
layer: Module,
neuron_selector: Union[int, Tuple[Union[int, slice], ...], Callable],
device_ids: Union[None, List[int]] = None,
attribute_to_neuron_input: bool = False,
) -> Callable:
def grad_fn(
forward_fn: Callable,
inputs: TensorOrTupleOfTensorsGeneric,
target_ind: TargetType = None,
additional_forward_args: Any = None,
) -> Tuple[Tensor, ...]:
_, grads = _forward_layer_eval_with_neuron_grads(
forward_fn,
inputs,
layer,
additional_forward_args,
gradient_neuron_selector=neuron_selector,
device_ids=device_ids,
attribute_to_layer_input=attribute_to_neuron_input,
)
return grads
return grad_fn
def _compute_jacobian_wrt_params(
model: Module,
inputs: Tuple[Any, ...],
labels: Optional[Tensor] = None,
loss_fn: Optional[Union[Module, Callable]] = None,
) -> Tuple[Tensor, ...]:
r"""
Computes the Jacobian of a batch of test examples given a model, and optional
loss function and target labels. This method is equivalent to calculating the
gradient for every individual example in the minibatch.
Args:
model (torch.nn.Module): The trainable model providing the forward pass
inputs (tuple of Any): The minibatch for which the forward pass is computed.
It is unpacked before passing to `model`, so it must be a tuple. The
individual elements of `inputs` can be anything.
labels (Tensor or None): Labels for input if computing a loss function.
loss_fn (torch.nn.Module or Callable or None): The loss function. If a library
defined loss function is provided, it would be expected to be a
torch.nn.Module. If a custom loss is provided, it can be either type,
but must behave as a library loss function would if `reduction='none'`.
Returns:
grads (tuple of Tensor): Returns the Jacobian for the minibatch as a
tuple of gradients corresponding to the tuple of trainable parameters
returned by `model.parameters()`. Each object grads[i] references to the
gradients for the parameters in the i-th trainable layer of the model.
Each grads[i] object is a tensor with the gradients for the `inputs`
batch. For example, grads[i][j] would reference the gradients for the
parameters of the i-th layer, for the j-th member of the minibatch.
"""
with torch.autograd.set_grad_enabled(True):
out = model(*inputs)
assert out.dim() != 0, "Please ensure model output has at least one dimension."
if labels is not None and loss_fn is not None:
loss = loss_fn(out, labels)
if hasattr(loss_fn, "reduction"):
msg0 = "Please ensure loss_fn.reduction is set to `none`"
assert loss_fn.reduction == "none", msg0 # type: ignore
else:
msg1 = (
"Loss function is applying a reduction. Please ensure "
f"Output shape: {out.shape} and Loss shape: {loss.shape} "
"are matching."
)
assert loss.dim() != 0, msg1
assert out.shape[0] == loss.shape[0], msg1
out = loss
grads_list = [
torch.autograd.grad(
outputs=out[i],
inputs=model.parameters(), # type: ignore
grad_outputs=torch.ones_like(out[i]),
retain_graph=True,
)
for i in range(out.shape[0])
]
grads = tuple([torch.stack(x) for x in zip(*grads_list)])
return tuple(grads)
def _compute_jacobian_wrt_params_with_sample_wise_trick(
model: Module,
inputs: Tuple[Any, ...],
labels: Optional[Tensor] = None,
loss_fn: Optional[Union[Module, Callable]] = None,
reduction_type: Optional[str] = "sum",
) -> Tuple[Any, ...]:
r"""
Computes the Jacobian of a batch of test examples given a model, and optional
loss function and target labels. This method uses sample-wise gradients per
batch trick to fully vectorize the Jacobian calculation. Currently, only
linear and conv2d layers are supported.
User must `add_hooks(model)` before calling this function.
Args:
model (torch.nn.Module): The trainable model providing the forward pass
inputs (tuple of Any): The minibatch for which the forward pass is computed.
It is unpacked before passing to `model`, so it must be a tuple. The
individual elements of `inputs` can be anything.
labels (Tensor or None): Labels for input if computing a loss function.
loss_fn (torch.nn.Module or Callable or None): The loss function. If a library
defined loss function is provided, it would be expected to be a
torch.nn.Module. If a custom loss is provided, it can be either type,
but must behave as a library loss function would if `reduction='sum'` or
`reduction='mean'`.
reduction_type (str): The type of reduction applied. If a loss_fn is passed,
this should match `loss_fn.reduction`. Else if gradients are being
computed on direct model outputs (scores), then 'sum' should be used.
Defaults to 'sum'.
Returns:
grads (tuple of Tensor): Returns the Jacobian for the minibatch as a
tuple of gradients corresponding to the tuple of trainable parameters
returned by `model.parameters()`. Each object grads[i] references to the
gradients for the parameters in the i-th trainable layer of the model.
Each grads[i] object is a tensor with the gradients for the `inputs`
batch. For example, grads[i][j] would reference the gradients for the
parameters of the i-th layer, for the j-th member of the minibatch.
"""
with torch.autograd.set_grad_enabled(True):
sample_grad_wrapper = SampleGradientWrapper(model)
try:
sample_grad_wrapper.add_hooks()
out = model(*inputs)
assert (
out.dim() != 0
), "Please ensure model output has at least one dimension."
if labels is not None and loss_fn is not None:
loss = loss_fn(out, labels)
# TODO: allow loss_fn to be Callable
if isinstance(loss_fn, Module) and hasattr(loss_fn, "reduction"):
msg0 = (
"Please ensure that loss_fn.reduction is set to `sum` or `mean`"
)
assert loss_fn.reduction != "none", msg0
msg1 = (
f"loss_fn.reduction ({loss_fn.reduction}) does not match"
f"reduction type ({reduction_type}). Please ensure they are"
" matching."
)
assert loss_fn.reduction == reduction_type, msg1
msg2 = (
"Please ensure custom loss function is applying either a "
"sum or mean reduction."
)
assert out.shape != loss.shape, msg2
if reduction_type != "sum" and reduction_type != "mean":
raise ValueError(
f"{reduction_type} is not a valid value for reduction_type. "
"Must be either 'sum' or 'mean'."
)
out = loss
sample_grad_wrapper.compute_param_sample_gradients(
out, loss_mode=reduction_type
)
grads = tuple(
param.sample_grad # type: ignore
for param in model.parameters()
if hasattr(param, "sample_grad")
)
finally:
sample_grad_wrapper.remove_hooks()
return grads