import torch
from typing import Tuple, List

# Utility functions

def _as_tuple(inp, arg_name, fn_name):
# Ensures that inp is a tuple of Tensors
# Returns whether or not the original inp was a tuple and the tupled version of the input
is_inp_tuple = True
if not isinstance(inp, tuple):
inp = (inp,)
is_inp_tuple = False

for i, el in enumerate(inp):
if not isinstance(el, torch.Tensor):
if is_inp_tuple:
raise TypeError("The {} given to {} must be either a Tensor or a tuple of Tensors but the"
" value at index {} has type {}.".format(arg_name, fn_name, i, type(el)))
else:
raise TypeError("The {} given to {} must be either a Tensor or a tuple of Tensors but the"
" given {} has type {}.".format(arg_name, fn_name, arg_name, type(el)))

return is_inp_tuple, inp

def _tuple_postprocess(res, to_unpack):
# Unpacks a potentially nested tuple of Tensors
# to_unpack should be a single boolean or a tuple of two booleans.
# It is used to:
# - invert _as_tuple when res should match the inp given to _as_tuple
# - optionally remove nesting of two tuples created by multiple calls to _as_tuple
if isinstance(to_unpack, tuple):
assert len(to_unpack) == 2
if not to_unpack[1]:
res = tuple(el[0] for el in res)
if not to_unpack[0]:
res = res[0]
else:
if not to_unpack:
res = res[0]
return res

# Preprocess the inputs to make sure they require gradient
# inputs is a tuple of Tensors to preprocess
# create_graph specifies if the user wants gradients to flow back to the Tensors in inputs
# need_graph specifies if we internally want gradients to flow back to the Tensors in res
# Note that we *always* create a new Tensor object to be able to see the difference between
# inputs given as arguments and the same Tensors automatically captured by the user function.
# Check this issue for more details on how that can happen: https://github.com/pytorch/pytorch/issues/32576
res = []
for inp in inputs:
# Create at least a new Tensor object in a differentiable way
if not inp.is_sparse:
# Use .view_as() to get a shallow copy
res.append(inp.view_as(inp))
else:
# We cannot use view for sparse Tensors so we clone
res.append(inp.clone())
else:
return tuple(res)

# Postprocess the generated Tensors to avoid returning Tensors with history when the user did not
# request it.
if isinstance(inputs[0], torch.Tensor):
if not create_graph:
return tuple(inp.detach() for inp in inputs)
else:
return inputs
else:
return tuple(_grad_postprocess(inp, create_graph) for inp in inputs)

def _validate_v(v, other, is_other_tuple):
# This assumes that other is the correct shape, and v should match
# Both are assumed to be tuples of Tensors
if len(other) != len(v):
if is_other_tuple:
raise RuntimeError("v is a tuple of invalid length: should be {} but got {}.".format(len(other), len(v)))
else:
raise RuntimeError("The given v should contain a single Tensor.")

for idx, (el_v, el_other) in enumerate(zip(v, other)):
if el_v.size() != el_other.size():
prepend = ""
if is_other_tuple:
prepend = "Entry {} in ".format(idx)
raise RuntimeError("{}v has invalid size: should be {} but got {}.".format(
prepend, el_other.size(), el_v.size()))

# Used to make all the necessary checks to raise nice errors in strict mode.
if not strict:
return

if input_type not in ["outputs", "grad_inputs", "jacobian", "hessian"]:
for i, inp in enumerate(inputs):
if inp is None:
# This can only be reached for grad_inputs.
raise RuntimeError("The output of the user-provided function is independent of input {}."
" This is not allowed in strict mode.".format(i))
if input_type == "hessian":
raise RuntimeError("The hessian of the user-provided function with respect to input {}"
" is independent of the input. This is not allowed in strict mode."
" You should ensure that your function is thrice differentiable and that"
" the hessian depends on the inputs.".format(i))
elif input_type == "jacobian":
raise RuntimeError("While computing the hessian, found that the jacobian of the user-provided"
" function with respect to input {} is independent of the input. This is not"
" allowed in strict mode. You should ensure that your function is twice"
" differentiable and that the jacobian depends on the inputs (this would be"
" violated by a linear function for example).".format(i))
raise RuntimeError("The gradient with respect to input {} is independent of the inputs of the"
" user-provided function. This is not allowed in strict mode.".format(i))
else:
raise RuntimeError("Output {} of the user-provided function does not require gradients."
" The outputs must be computed in a differentiable manner from the input"
" when running in strict mode.".format(i))

# Version of autograd.grad that accepts None in outputs and do not compute gradients for them.
# This has the extra constraint that inputs has to be a tuple
assert isinstance(outputs, tuple)

new_outputs: Tuple[torch.Tensor, ...] = tuple()
if out is not None and out.requires_grad:
new_outputs += (out,)

if len(new_outputs) == 0:
# No differentiable output, we don't need to call the autograd engine
return (None,) * len(inputs)
else:
create_graph=create_graph, retain_graph=retain_graph)

def _fill_in_zeros(grads, refs, strict, create_graph, stage):
# Used to detect None in the grads and depending on the flags, either replace them
# with Tensors full of 0s of the appropriate size based on the refs or raise an error.
# strict and create graph allow us to detect when it is appropriate to raise an error
# stage gives us information of which backward call we consider to give good error message
if stage not in ["back", "back_trick", "double_back", "double_back_trick"]:
raise RuntimeError("Invalid stage argument '{}' to _fill_in_zeros".format(stage))

res: Tuple[torch.Tensor, ...] = tuple()
if strict:
if stage == "back":
raise RuntimeError("The output of the user-provided function is independent of "
"input {}. This is not allowed in strict mode.".format(i))
elif stage == "back_trick":
raise RuntimeError("The gradient with respect to the input is independent of entry {}"
" in the grad_outputs when using the double backward trick to compute"
" forward mode gradients. This is not allowed in strict mode.".format(i))
elif stage == "double_back":
raise RuntimeError("The jacobian of the user-provided function is independent of "
"input {}. This is not allowed in strict mode.".format(i))
else:
raise RuntimeError("The hessian of the user-provided function is independent of "
"entry {} in the grad_jacobian. This is not allowed in strict "
"mode as it prevents from using the double backward trick to "

else:
if "double" not in stage:
raise RuntimeError("The jacobian of the user-provided function is independent of "
"input {}. This is not allowed in strict mode when create_graph=True.".format(i))
else:
raise RuntimeError("The hessian of the user-provided function is independent of "
"input {}. This is not allowed in strict mode when create_graph=True.".format(i))

[docs]def vjp(func, inputs, v=None, create_graph=False, strict=False): r"""Function that computes the dot product between a vector v and the Jacobian of the given function at the point given by the inputs. Args: func (function): a Python function that takes Tensor inputs and returns a tuple of Tensors or a Tensor. inputs (tuple of Tensors or Tensor): inputs to the function func. v (tuple of Tensors or Tensor): The vector for which the vector Jacobian product is computed. Must be the same size as the output of func. This argument is optional when the output of func contains a single element and (if it is not provided) will be set as a Tensor containing a single 1. create_graph (bool, optional): If True, both the output and result will be computed in a differentiable way. Note that when strict is False, the result can not require gradients or be disconnected from the inputs. Defaults to False. strict (bool, optional): If True, an error will be raised when we detect that there exists an input such that all the outputs are independent of it. If False, we return a Tensor of zeros as the vjp for said inputs, which is the expected mathematical value. Defaults to False. Returns: vjp (tuple of Tensors or Tensor): result of the dot product with the same shape as the inputs. Example: >>> def exp_reducer(x): ... return x.exp().sum(dim=1) >>> inputs = torch.rand(4, 4) >>> v = torch.ones(4) >>> vjp(exp_reducer, inputs, v) (tensor([5.7817, 7.2458, 5.7830, 6.7782]), tensor([[1.4458, 1.3962, 1.3042, 1.6354], [2.1288, 1.0652, 1.5483, 2.5035], [2.2046, 1.1292, 1.1432, 1.3059], [1.3225, 1.6652, 1.7753, 2.0152]])) >>> vjp(exp_reducer, inputs, v, create_graph=True) (tensor([5.7817, 7.2458, 5.7830, 6.7782], grad_fn=<SumBackward1>), tensor([[1.4458, 1.3962, 1.3042, 1.6354], [2.1288, 1.0652, 1.5483, 2.5035], [2.2046, 1.1292, 1.1432, 1.3059], [1.3225, 1.6652, 1.7753, 2.0152]], grad_fn=<MulBackward0>)) >>> def adder(x, y): ... return 2 * x + 3 * y >>> inputs = (torch.rand(2), torch.rand(2)) >>> v = torch.ones(2) >>> vjp(adder, inputs, v) (tensor([2.4225, 2.3340]), (tensor([2., 2.]), tensor([3., 3.]))) """ is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "vjp") inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True) outputs = func(*inputs) is_outputs_tuple, outputs = _as_tuple(outputs, "outputs of the user-provided function", "vjp") _check_requires_grad(outputs, "outputs", strict=strict) if v is not None: _, v = _as_tuple(v, "v", "vjp") v = _grad_preprocess(v, create_graph=create_graph, need_graph=False) _validate_v(v, outputs, is_outputs_tuple) else: if len(outputs) != 1 or outputs[0].nelement() != 1: raise RuntimeError("The vector v can only be None if the " "user-provided function returns " "a single Tensor with a single element.") grad_res = _autograd_grad(outputs, inputs, v, create_graph=create_graph) vjp = _fill_in_zeros(grad_res, inputs, strict, create_graph, "back") # Cleanup objects and return them to the user outputs = _grad_postprocess(outputs, create_graph) vjp = _grad_postprocess(vjp, create_graph) return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(vjp, is_inputs_tuple)
[docs]def jvp(func, inputs, v=None, create_graph=False, strict=False): r"""Function that computes the dot product between the Jacobian of the given function at the point given by the inputs and a vector v. Args: func (function): a Python function that takes Tensor inputs and returns a tuple of Tensors or a Tensor. inputs (tuple of Tensors or Tensor): inputs to the function func. v (tuple of Tensors or Tensor): The vector for which the Jacobian vector product is computed. Must be the same size as the input of func. This argument is optional when the input to func contains a single element and (if it is not provided) will be set as a Tensor containing a single 1. create_graph (bool, optional): If True, both the output and result will be computed in a differentiable way. Note that when strict is False, the result can not require gradients or be disconnected from the inputs. Defaults to False. strict (bool, optional): If True, an error will be raised when we detect that there exists an input such that all the outputs are independent of it. If False, we return a Tensor of zeros as the jvp for said inputs, which is the expected mathematical value. Defaults to False. Returns: jvp (tuple of Tensors or Tensor): result of the dot product with the same shape as the output. Example: >>> def exp_reducer(x): ... return x.exp().sum(dim=1) >>> inputs = torch.rand(4, 4) >>> v = torch.ones(4, 4) >>> jvp(exp_reducer, inputs, v) (tensor([6.3090, 4.6742, 7.9114, 8.2106]), tensor([6.3090, 4.6742, 7.9114, 8.2106])) >>> jvp(exp_reducer, inputs, v, create_graph=True) (tensor([6.3090, 4.6742, 7.9114, 8.2106], grad_fn=<SumBackward1>), tensor([6.3090, 4.6742, 7.9114, 8.2106], grad_fn=<SqueezeBackward1>)) >>> def adder(x, y): ... return 2 * x + 3 * y >>> inputs = (torch.rand(2), torch.rand(2)) >>> v = (torch.ones(2), torch.ones(2)) >>> jvp(adder, inputs, v) (tensor([2.2399, 2.5005]), tensor([5., 5.])) Note: The jvp is currently computed by using the backward of the backward (sometimes called the double backwards trick) as we don't have support for forward mode AD in PyTorch at the moment. """ is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "jvp") inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True) if v is not None: _, v = _as_tuple(v, "v", "jvp") v = _grad_preprocess(v, create_graph=create_graph, need_graph=False) _validate_v(v, inputs, is_inputs_tuple) else: if len(inputs) != 1 or inputs[0].nelement() != 1: raise RuntimeError("The vector v can only be None if the input to " "the user-provided function is a single Tensor " "with a single element.") outputs = func(*inputs) is_outputs_tuple, outputs = _as_tuple(outputs, "outputs of the user-provided function", "jvp") _check_requires_grad(outputs, "outputs", strict=strict) # The backward is linear so the value of grad_outputs is not important as # it won't appear in the double backward graph. We only need to ensure that # it does not contain inf or nan. grad_outputs = tuple(torch.zeros_like(out, requires_grad=True) for out in outputs) grad_inputs = _autograd_grad(outputs, inputs, grad_outputs, create_graph=True) _check_requires_grad(grad_inputs, "grad_inputs", strict=strict) grad_res = _autograd_grad(grad_inputs, grad_outputs, v, create_graph=create_graph) jvp = _fill_in_zeros(grad_res, outputs, strict, create_graph, "back_trick") # Cleanup objects and return them to the user outputs = _grad_postprocess(outputs, create_graph) jvp = _grad_postprocess(jvp, create_graph) return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(jvp, is_outputs_tuple)
[docs]def jacobian(func, inputs, create_graph=False, strict=False): r"""Function that computes the Jacobian of a given function. Args: func (function): a Python function that takes Tensor inputs and returns a tuple of Tensors or a Tensor. inputs (tuple of Tensors or Tensor): inputs to the function func. create_graph (bool, optional): If True, the Jacobian will be computed in a differentiable manner. Note that when strict is False, the result can not require gradients or be disconnected from the inputs. Defaults to False. strict (bool, optional): If True, an error will be raised when we detect that there exists an input such that all the outputs are independent of it. If False, we return a Tensor of zeros as the jacobian for said inputs, which is the expected mathematical value. Defaults to False. Returns: Jacobian (Tensor or nested tuple of Tensors): if there is a single input and output, this will be a single Tensor containing the Jacobian for the linearized inputs and output. If one of the two is a tuple, then the Jacobian will be a tuple of Tensors. If both of them are tuples, then the Jacobian will be a tuple of tuple of Tensors where Jacobian[i][j] will contain the Jacobian of the i\th output and j\th input and will have as size the concatenation of the sizes of the corresponding output and the corresponding input. Example: >>> def exp_reducer(x): ... return x.exp().sum(dim=1) >>> inputs = torch.rand(2, 2) >>> jacobian(exp_reducer, inputs) tensor([[[1.4917, 2.4352], [0.0000, 0.0000]], [[0.0000, 0.0000], [2.4369, 2.3799]]]) >>> jacobian(exp_reducer, inputs, create_graph=True) tensor([[[1.4917, 2.4352], [0.0000, 0.0000]], [[0.0000, 0.0000], [2.4369, 2.3799]]], grad_fn=<ViewBackward>) >>> def exp_adder(x, y): ... return 2 * x.exp() + 3 * y >>> inputs = (torch.rand(2), torch.rand(2)) >>> jacobian(exp_adder, inputs) (tensor([[2.8052, 0.0000], [0.0000, 3.3963]]), tensor([[3., 0.], [0., 3.]])) """ is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "jacobian") inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True) outputs = func(*inputs) is_outputs_tuple, outputs = _as_tuple(outputs, "outputs of the user-provided function", "jacobian") _check_requires_grad(outputs, "outputs", strict=strict) jacobian: Tuple[torch.Tensor, ...] = tuple() for i, out in enumerate(outputs): # mypy complains that expression and variable have different types due to the empty list jac_i: Tuple[List[torch.Tensor]] = tuple([] for _ in range(len(inputs))) # type: ignore for j in range(out.nelement()): vj = _autograd_grad((out.reshape(-1)[j],), inputs, retain_graph=True, create_graph=create_graph) for el_idx, (jac_i_el, vj_el, inp_el) in enumerate(zip(jac_i, vj, inputs)): if vj_el is not None: if strict and create_graph and not vj_el.requires_grad: msg = ("The jacobian of the user-provided function is " "independent of input {}. This is not allowed in " "strict mode when create_graph=True.".format(i)) raise RuntimeError(msg) jac_i_el.append(vj_el) else: if strict: msg = ("Output {} of the user-provided function is " "independent of input {}. This is not allowed in " "strict mode.".format(i, el_idx)) raise RuntimeError(msg) jac_i_el.append(torch.zeros_like(inp_el)) jacobian += (tuple(torch.stack(jac_i_el, dim=0).view(out.size() + inputs[el_idx].size()) for (el_idx, jac_i_el) in enumerate(jac_i)), ) jacobian = _grad_postprocess(jacobian, create_graph) return _tuple_postprocess(jacobian, (is_outputs_tuple, is_inputs_tuple))
[docs]def hessian(func, inputs, create_graph=False, strict=False): r"""Function that computes the Hessian of a given scalar function. Args: func (function): a Python function that takes Tensor inputs and returns a Tensor with a single element. inputs (tuple of Tensors or Tensor): inputs to the function func. create_graph (bool, optional): If True, the Hessian will be computed in a differentiable manner. Note that when strict is False, the result can not require gradients or be disconnected from the inputs. Defaults to False. strict (bool, optional): If True, an error will be raised when we detect that there exists an input such that all the outputs are independent of it. If False, we return a Tensor of zeros as the hessian for said inputs, which is the expected mathematical value. Defaults to False. Returns: Hessian (Tensor or a tuple of tuple of Tensors): if there is a single input, this will be a single Tensor containing the Hessian for the input. If it is a tuple, then the Hessian will be a tuple of tuples where Hessian[i][j] will contain the Hessian of the i\th input and j\th input with size the sum of the size of the i\th input plus the size of the j\th input. Example: >>> def pow_reducer(x): ... return x.pow(3).sum() >>> inputs = torch.rand(2, 2) >>> hessian(pow_reducer, inputs) tensor([[[[5.2265, 0.0000], [0.0000, 0.0000]], [[0.0000, 4.8221], [0.0000, 0.0000]]], [[[0.0000, 0.0000], [1.9456, 0.0000]], [[0.0000, 0.0000], [0.0000, 3.2550]]]]) >>> hessian(pow_reducer, inputs, create_graph=True) tensor([[[[5.2265, 0.0000], [0.0000, 0.0000]], [[0.0000, 4.8221], [0.0000, 0.0000]]], [[[0.0000, 0.0000], [1.9456, 0.0000]], [[0.0000, 0.0000], [0.0000, 3.2550]]]], grad_fn=<ViewBackward>) >>> def pow_adder_reducer(x, y): ... return (2 * x.pow(2) + 3 * y.pow(2)).sum() >>> inputs = (torch.rand(2), torch.rand(2)) >>> hessian(pow_adder_reducer, inputs) ((tensor([[4., 0.], [0., 4.]]), tensor([[0., 0.], [0., 0.]])), (tensor([[0., 0.], [0., 0.]]), tensor([[6., 0.], [0., 6.]]))) """ is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "hessian") def ensure_single_output_function(*inp): out = func(*inp) is_out_tuple, t_out = _as_tuple(out, "outputs of the user-provided function", "hessian") _check_requires_grad(t_out, "outputs", strict=strict) if is_out_tuple or not isinstance(out, torch.Tensor): raise RuntimeError("The function given to hessian should return a single Tensor") if out.nelement() != 1: raise RuntimeError("The Tensor returned by the function given to hessian should contain a single element") return out.squeeze() def jac_func(*inp): jac = jacobian(ensure_single_output_function, inp, create_graph=True) _check_requires_grad(jac, "jacobian", strict=strict) return jac res = jacobian(jac_func, inputs, create_graph=create_graph, strict=strict) return _tuple_postprocess(res, (is_inputs_tuple, is_inputs_tuple))
[docs]def vhp(func, inputs, v=None, create_graph=False, strict=False): r"""Function that computes the dot product between a vector v and the Hessian of a given scalar function at the point given by the inputs. Args: func (function): a Python function that takes Tensor inputs and returns a Tensor with a single element. inputs (tuple of Tensors or Tensor): inputs to the function func. v (tuple of Tensors or Tensor): The vector for which the vector Hessian product is computed. Must be the same size as the input of func. This argument is optional when func's input contains a single element and (if it is not provided) will be set as a Tensor containing a single 1. create_graph (bool, optional): If True, both the output and result will be computed in a differentiable way. Note that when strict is False, the result can not require gradients or be disconnected from the inputs. Defaults to False. strict (bool, optional): If True, an error will be raised when we detect that there exists an input such that all the outputs are independent of it. If False, we return a Tensor of zeros as the vhp for said inputs, which is the expected mathematical value. Defaults to False. Returns: output (tuple): tuple with: func_output (tuple of Tensors or Tensor): output of func(inputs) vhp (tuple of Tensors or Tensor): result of the dot product with the same shape as the inputs. Example: >>> def pow_reducer(x): ... return x.pow(3).sum() >>> inputs = torch.rand(2, 2) >>> v = torch.ones(2, 2) >>> vhp(pow_reducer, inputs, v) (tensor(0.5591), tensor([[1.0689, 1.2431], [3.0989, 4.4456]])) >>> vhp(pow_reducer, inputs, v, create_graph=True) (tensor(0.5591, grad_fn=<SumBackward0>), tensor([[1.0689, 1.2431], [3.0989, 4.4456]], grad_fn=<MulBackward0>)) >>> def pow_adder_reducer(x, y): ... return (2 * x.pow(2) + 3 * y.pow(2)).sum() >>> inputs = (torch.rand(2), torch.rand(2)) >>> v = (torch.zeros(2), torch.ones(2)) >>> vhp(pow_adder_reducer, inputs, v) (tensor(4.8053), (tensor([0., 0.]), tensor([6., 6.]))) """ is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "vhp") inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True) if v is not None: _, v = _as_tuple(v, "v", "vhp") v = _grad_preprocess(v, create_graph=create_graph, need_graph=False) _validate_v(v, inputs, is_inputs_tuple) else: if len(inputs) != 1 or inputs[0].nelement() != 1: raise RuntimeError("The vector v can only be None if the input to the user-provided function " "is a single Tensor with a single element.") outputs = func(*inputs) is_outputs_tuple, outputs = _as_tuple(outputs, "outputs of the user-provided function", "vhp") _check_requires_grad(outputs, "outputs", strict=strict) if is_outputs_tuple or not isinstance(outputs[0], torch.Tensor): raise RuntimeError("The function given to vhp should return a single Tensor") if outputs[0].nelement() != 1: raise RuntimeError("The Tensor returned by the function given to vhp should contain a single element") jac = _autograd_grad(outputs, inputs, create_graph=True) _check_requires_grad(jac, "jacobian", strict=strict) grad_res = _autograd_grad(jac, inputs, v, create_graph=create_graph) vhp = _fill_in_zeros(grad_res, inputs, strict, create_graph, "double_back") outputs = _grad_postprocess(outputs, create_graph) vhp = _grad_postprocess(vhp, create_graph) return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(vhp, is_inputs_tuple)
[docs]def hvp(func, inputs, v=None, create_graph=False, strict=False): r"""Function that computes the dot product between the Hessian of a given scalar function and a vector v at the point given by the inputs. Args: func (function): a Python function that takes Tensor inputs and returns a Tensor with a single element. inputs (tuple of Tensors or Tensor): inputs to the function func. v (tuple of Tensors or Tensor): The vector for which the Hessian vector product is computed. Must be the same size as the input of func. This argument is optional when func's input contains a single element and (if it is not provided) will be set as a Tensor containing a single 1. create_graph (bool, optional): If True, both the output and result will be computed in a differentiable way. Note that when strict is False, the result can not require gradients or be disconnected from the inputs. Defaults to False. strict (bool, optional): If True, an error will be raised when we detect that there exists an input such that all the outputs are independent of it. If False, we return a Tensor of zeros as the hvp for said inputs, which is the expected mathematical value. Defaults to False. Returns: output (tuple): tuple with: func_output (tuple of Tensors or Tensor): output of func(inputs) hvp (tuple of Tensors or Tensor): result of the dot product with the same shape as the inputs. Example: >>> def pow_reducer(x): ... return x.pow(3).sum() >>> inputs = torch.rand(2, 2) >>> v = torch.ones(2, 2) >>> hvp(pow_reducer, inputs, v) (tensor(0.1448), tensor([[2.0239, 1.6456], [2.4988, 1.4310]])) >>> hvp(pow_reducer, inputs, v, create_graph=True) (tensor(0.1448, grad_fn=<SumBackward0>), tensor([[2.0239, 1.6456], [2.4988, 1.4310]], grad_fn=<MulBackward0>)) >>> def pow_adder_reducer(x, y): ... return (2 * x.pow(2) + 3 * y.pow(2)).sum() >>> inputs = (torch.rand(2), torch.rand(2)) >>> v = (torch.zeros(2), torch.ones(2)) >>> hvp(pow_adder_reducer, inputs, v) (tensor(2.3030), (tensor([0., 0.]), tensor([6., 6.]))) Note: This function is significantly slower than vhp due to backward mode AD constraints. If your functions is twice continuously differentiable, then hvp = vhp.t(). So if you know that your function satisfies this condition, you should use vhp instead that is much faster with the current implementation. """ is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "hvp") inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True) if v is not None: _, v = _as_tuple(v, "v", "hvp") v = _grad_preprocess(v, create_graph=create_graph, need_graph=False) _validate_v(v, inputs, is_inputs_tuple) else: if len(inputs) != 1 or inputs[0].nelement() != 1: raise RuntimeError("The vector v can only be None if the input to the user-provided function " "is a single Tensor with a single element.") outputs = func(*inputs) is_outputs_tuple, outputs = _as_tuple(outputs, "outputs of the user-provided function", "hvp") _check_requires_grad(outputs, "outputs", strict=strict) if is_outputs_tuple or not isinstance(outputs[0], torch.Tensor): raise RuntimeError("The function given to hvp should return a single Tensor") if outputs[0].nelement() != 1: raise RuntimeError("The Tensor returned by the function given to hvp should contain a single element") jac = _autograd_grad(outputs, inputs, create_graph=True) _check_requires_grad(jac, "jacobian", strict=strict) grad_jac = tuple(torch.zeros_like(inp, requires_grad=True) for inp in inputs) double_back = _autograd_grad(jac, inputs, grad_jac, create_graph=True) _check_requires_grad(jac, "hessian", strict=strict) grad_res = _autograd_grad(double_back, grad_jac, v, create_graph=create_graph) hvp = _fill_in_zeros(grad_res, inputs, strict, create_graph, "double_back_trick") outputs = _grad_postprocess(outputs, create_graph) hvp = _grad_postprocess(hvp, create_graph) return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(hvp, is_inputs_tuple)