# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from typing import Callable, Union, Tuple, List, Any
import torch
import inspect
from functools import partial, wraps
import contextlib
from torch.utils._pytree import tree_flatten, tree_unflatten, tree_map
from .pytree_hacks import tree_map_, treespec_pprint
import torch.autograd.forward_ad as fwAD
from .vmap import vmap
from .decompositions import decomposition_table, decomposition_table_for_jvp
from functorch._C import (
_wrap_for_grad,
_unwrap_for_grad,
_grad_increment_nesting,
_grad_decrement_nesting,
_jvp_increment_nesting,
_jvp_decrement_nesting,
set_fwd_grad_enabled,
get_fwd_grad_enabled,
_wrap_functional_tensor,
_unwrap_functional_tensor,
_func_decrement_nesting,
_func_increment_nesting,
_assert_wrapped_functional,
_propagate_functional_input_mutation,
set_inplace_requires_grad_allowed,
get_inplace_requires_grad_allowed,
)
argnums_t = Union[int, Tuple[int, ...]]
@contextlib.contextmanager
def enable_inplace_requires_grad(enabled=True):
prev_state = get_inplace_requires_grad_allowed()
set_inplace_requires_grad_allowed(enabled)
try:
yield
finally:
set_inplace_requires_grad_allowed(prev_state)
def _create_differentiable(inps, level=None):
def create_differentiable(x):
if isinstance(x, torch.Tensor):
with enable_inplace_requires_grad():
return x.requires_grad_()
raise ValueError(f'Thing passed to transform API must be Tensor, '
f'got {type(x)}')
return tree_map(create_differentiable, inps)
def _undo_create_differentiable(inps, level=None):
def unwrap_tensors(x):
if isinstance(x, torch.Tensor):
return _unwrap_for_grad(x, level)
# TODO: Remove the following hack for namedtuples
if isinstance(x, tuple):
return tree_map(unwrap_tensors, tuple(x))
raise RuntimeError(f"Expected tensors, got unsupported type {type(x)}")
return tree_map(unwrap_tensors, inps)
def _is_differentiable(maybe_tensor):
if not isinstance(maybe_tensor, torch.Tensor):
return False
return maybe_tensor.requires_grad
def _any_differentiable(tensor_or_tuple_of_tensors):
flat_args, _ = tree_unflatten(tensor_or_tuple_of_tensors)
return any(tuple(map(_is_differentiable, flat_args)))
def _wrap_tensor_for_grad(maybe_tensor, level):
if not isinstance(maybe_tensor, torch.Tensor):
return maybe_tensor
return _wrap_for_grad(maybe_tensor, level)
def _wrap_all_tensors(tensor_pytree, level):
return tree_map(partial(_wrap_tensor_for_grad, level=level), tensor_pytree)
def _as_tuple(val):
if isinstance(val, tuple):
return val
return (val,)
# Version of autograd.grad that handles outputs that don't depend on inputs
def _autograd_grad(outputs, inputs, grad_outputs=None, retain_graph=False, create_graph=True):
if grad_outputs is None:
diff_outputs = tuple(out for out in outputs if out.requires_grad)
else:
result = tuple((out, go) for out, go in zip(outputs, grad_outputs) if out.requires_grad)
if len(result) == 0:
diff_outputs, grad_outputs = (), ()
else:
diff_outputs, grad_outputs = zip(*result)
if len(diff_outputs) == 0:
return tuple(torch.zeros_like(inp) for inp in inputs)
grad_inputs = torch.autograd.grad(diff_outputs, inputs, grad_outputs,
retain_graph=retain_graph,
create_graph=create_graph,
allow_unused=True)
grad_inputs = tuple(torch.zeros_like(inp) if gi is None else gi
for gi, inp in zip(grad_inputs, inputs))
return grad_inputs
# NOTE [grad and vjp interaction with no_grad]
#
# def f(x):
# with torch.no_grad():
# c = x ** 2
# return x - c
#
# The thing to consider is if enable_grad is on/off before grad gets called.
#
# Case 1: enable_grad is on.
# grad(f)(x)
# In this case, `grad` should respect the inner torch.no_grad.
#
# Case 2: enable_grad is off
# with torch.no_grad():
# grad(f)(x)
# In this case, `grad` should respect the inner torch.no_grad, but not the
# outer one. This is because `grad` is a "function transform": its result
# should not depend on the result of a context manager outside of `f`.
#
# This gives us the following desired behavior:
# - (nested) grad transforms must obey torch.no_grad inside them
# - (nested) grad transforms should not obey torch.no_grad outside them
#
# To achieve this behavior, upon entering grad/vjp:
# - we save the current ("previous") is_grad_enabled (*)
# - we unconditionally enable grad.
#
# Inside DynamicLayerBackFallback, when we're temporarily popping `grad` layer
# off the stack:
# - if grad_mode is disabled, then we do nothing. (there is a torch.no_grad
# active, all subsequent grad transforms must obey it).
# - if grad_mode is enabled, and the previous is_grad_enabled (*) is False,
# then we temporarily restore the previous `is_grad_enabled`. This is
# because we're crossing the boundary from a `grad` outside the
# no_grad to a `grad` inside the no_grad.
#
# NB: vjp has some interesting behavior because the vjp's callable can be called
# under a different grad_mode than the forward computation...
#
# NB: forward-mode AD: forward-mode AD doesn't respect torch.no_grad, but
# it respects c10::AutoFwGradMode. We've implemented the same logic for
# our jvp transform (it will have special handling if FwGradMode is disabled).
# How do we increment and decrement the nesting? I don't think we can.
[docs]def vjp(func: Callable, *primals, has_aux: bool = False):
"""
Standing for the vector-Jacobian product, returns a tuple containing the
results of :attr:`func` applied to :attr:`primals` and a function that, when
given ``cotangents``, computes the reverse-mode Jacobian of :attr:`func` with
respect to :attr:`primals` times ``cotangents``.
Args:
func (Callable): A Python function that takes one or more arguments. Must
return one or more Tensors.
primals (Tensors): Positional arguments to :attr:`func` that must all be
Tensors. The returned function will also be computing the
derivative with respect to these arguments
has_aux (bool): Flag indicating that :attr:`func` returns a
``(output, aux)`` tuple where the first element is the output of
the function to be differentiated and the second element is
other auxiliary objects that will not be differentiated.
Default: False.
Returns:
Returns a ``(output, vjp_fn)`` tuple containing the output of :attr:`func`
applied to :attr:`primals` and a function that computes the vjp of
:attr:`func` with respect to all :attr:`primals` using the cotangents passed
to the returned function. If ``has_aux is True``, then instead returns a
``(output, vjp_fn, aux)`` tuple.
The returned ``vjp_fn`` function will return a tuple of each VJP.
When used in simple cases, :func:`vjp` behaves the same as :func:`grad`
>>> x = torch.randn([5])
>>> f = lambda x: x.sin().sum()
>>> (_, vjpfunc) = functorch.vjp(f, x)
>>> grad = vjpfunc(torch.tensor(1.))[0]
>>> assert torch.allclose(grad, functorch.grad(f)(x))
However, :func:`vjp` can support functions with multiple outputs by
passing in the cotangents for each of the outputs
>>> x = torch.randn([5])
>>> f = lambda x: (x.sin(), x.cos())
>>> (_, vjpfunc) = functorch.vjp(f, x)
>>> vjps = vjpfunc((torch.ones([5]), torch.ones([5])))
>>> assert torch.allclose(vjps[0], x.cos() + -x.sin())
:func:`vjp` can even support outputs being Python structs
>>> x = torch.randn([5])
>>> f = lambda x: {'first': x.sin(), 'second': x.cos()}
>>> (_, vjpfunc) = functorch.vjp(f, x)
>>> cotangents = {'first': torch.ones([5]), 'second': torch.ones([5])}
>>> vjps = vjpfunc(cotangents)
>>> assert torch.allclose(vjps[0], x.cos() + -x.sin())
The function returned by :func:`vjp` will compute the partials with
respect to each of the :attr:`primals`
>>> x, y = torch.randn([5, 4]), torch.randn([4, 5])
>>> (_, vjpfunc) = functorch.vjp(torch.matmul, x, y)
>>> cotangents = torch.randn([5, 5])
>>> vjps = vjpfunc(cotangents)
>>> assert len(vjps) == 2
>>> assert torch.allclose(vjps[0], torch.matmul(cotangents, y.transpose(0, 1)))
>>> assert torch.allclose(vjps[1], torch.matmul(x.transpose(0, 1), cotangents))
:attr:`primals` are the positional arguments for :attr:`f`. All kwargs use their
default value
>>> x = torch.randn([5])
>>> def f(x, scale=4.):
>>> return x * 4.
>>>
>>> (_, vjpfunc) = functorch.vjp(f, x)
>>> vjps = vjpfunc(torch.ones_like(x))
>>> assert torch.allclose(vjps[0], torch.full(x.shape, 4.))
.. note::
Using PyTorch ``torch.no_grad`` together with ``vjp``.
Case 1: Using ``torch.no_grad`` inside a function:
>>> def f(x):
>>> with torch.no_grad():
>>> c = x ** 2
>>> return x - c
In this case, ``vjp(f)(x)`` will respect the inner ``torch.no_grad``.
Case 2: Using ``vjp`` inside ``torch.no_grad`` context manager:
>>> with torch.no_grad():
>>> vjp(f)(x)
In this case, ``vjp`` will respect the inner ``torch.no_grad``, but not the
outer one. This is because ``vjp`` is a "function transform": its result
should not depend on the result of a context manager outside of ``f``.
"""
level = _grad_increment_nesting()
try:
# See NOTE [grad and vjp interaction with no_grad]
with torch.enable_grad():
primals = _wrap_all_tensors(primals, level)
diff_primals = _create_differentiable(primals, level)
primals_out = func(*diff_primals)
if has_aux:
if not (isinstance(primals_out, tuple) and len(primals_out) == 2):
raise RuntimeError(
"vjp(f, *primals): output of function f should be a tuple: (output, aux) "
"if has_aux is True"
)
primals_out, aux = primals_out
aux = _undo_create_differentiable(aux, level)
flat_primals_out, primals_out_spec = tree_flatten(primals_out)
assert_non_empty_tensor_output(flat_primals_out, 'vjp(f, *primals)')
flat_diff_primals, primals_spec = tree_flatten(diff_primals)
results = _undo_create_differentiable(primals_out, level)
for primal_out in flat_primals_out:
assert isinstance(primal_out, torch.Tensor)
if primal_out.is_floating_point() or primal_out.is_complex():
continue
raise RuntimeError("vjp(f, ...): All outputs of f must be "
"floating-point or complex Tensors, got Tensor "
f"with dtype {primal_out.dtype}")
def wrapper(cotangents, retain_graph=True, create_graph=None):
if create_graph is None:
create_graph = torch.is_grad_enabled()
flat_cotangents, cotangents_spec = tree_flatten(cotangents)
if primals_out_spec != cotangents_spec:
raise RuntimeError(
f'Expected pytree structure of cotangents to be the same '
f'as pytree structure of outputs to the function. '
f'cotangents: {treespec_pprint(cotangents_spec)}, '
f'primal output: {treespec_pprint(primals_out_spec)}')
result = _autograd_grad(flat_primals_out, flat_diff_primals, flat_cotangents,
retain_graph=retain_graph, create_graph=create_graph)
return tree_unflatten(result, primals_spec)
finally:
_grad_decrement_nesting()
if has_aux:
return results, wrapper, aux
else:
return results, wrapper
def _safe_zero_index(x):
assert len(x) == 1
return x[0]
[docs]def jacrev(func: Callable, argnums: Union[int, Tuple[int]] = 0, *, has_aux=False):
"""
Computes the Jacobian of :attr:`func` with respect to the arg(s) at index
:attr:`argnum` using reverse mode autodiff
Args:
func (function): A Python function that takes one or more arguments,
one of which must be a Tensor, and returns one or more Tensors
argnums (int or Tuple[int]): Optional, integer or tuple of integers,
saying which arguments to get the Jacobian with respect to.
Default: 0.
has_aux (bool): Flag indicating that :attr:`func` returns a
``(output, aux)`` tuple where the first element is the output of
the function to be differentiated and the second element is
auxiliary objects that will not be differentiated.
Default: False.
Returns:
Returns a function that takes in the same inputs as :attr:`func` and
returns the Jacobian of :attr:`func` with respect to the arg(s) at
:attr:`argnums`. If ``has_aux is True``, then the returned function
instead returns a ``(jacobian, aux)`` tuple where ``jacobian``
is the Jacobian and ``aux`` is auxiliary objects returned by :attr:`func`.
A basic usage with a pointwise, unary operation will give a diagonal array
as the Jacobian
>>> from functorch import jacrev
>>> x = torch.randn(5)
>>> jacobian = jacrev(torch.sin)(x)
>>> expected = torch.diag(torch.cos(x))
>>> assert torch.allclose(jacobian, expected)
If you would like to compute the output of the function as well as the
jacobian of the function, use the ``has_aux`` flag to return the output
as an auxiliary object:
>>> from functorch import jacrev
>>> x = torch.randn(5)
>>>
>>> def f(x):
>>> return x.sin()
>>>
>>> def g(x):
>>> result = f(x)
>>> return result, result
>>>
>>> jacobian_f, f_x = jacrev(g, has_aux=True)(x)
>>> assert torch.allclose(f_x, f(x))
:func:`jacrev` can be composed with vmap to produce batched
Jacobians:
>>> from functorch import jacrev, vmap
>>> x = torch.randn(64, 5)
>>> jacobian = vmap(jacrev(torch.sin))(x)
>>> assert jacobian.shape == (64, 5, 5)
Additionally, :func:`jacrev` can be composed with itself to produce
Hessians
>>> from functorch import jacrev
>>> def f(x):
>>> return x.sin().sum()
>>>
>>> x = torch.randn(5)
>>> hessian = jacrev(jacrev(f))(x)
>>> assert torch.allclose(hessian, torch.diag(-x.sin()))
By default, :func:`jacrev` computes the Jacobian with respect to the first
input. However, it can compute the Jacboian with respect to a different
argument by using :attr:`argnums`:
>>> from functorch import jacrev
>>> def f(x, y):
>>> return x + y ** 2
>>>
>>> x, y = torch.randn(5), torch.randn(5)
>>> jacobian = jacrev(f, argnums=1)(x, y)
>>> expected = torch.diag(2 * y)
>>> assert torch.allclose(jacobian, expected)
Additionally, passing a tuple to :attr:`argnums` will compute the Jacobian
with respect to multiple arguments
>>> from functorch import jacrev
>>> def f(x, y):
>>> return x + y ** 2
>>>
>>> x, y = torch.randn(5), torch.randn(5)
>>> jacobian = jacrev(f, argnums=(0, 1))(x, y)
>>> expectedX = torch.diag(torch.ones_like(x))
>>> expectedY = torch.diag(2 * y)
>>> assert torch.allclose(jacobian[0], expectedX)
>>> assert torch.allclose(jacobian[1], expectedY)
.. note::
Using PyTorch ``torch.no_grad`` together with ``jacrev``.
Case 1: Using ``torch.no_grad`` inside a function:
>>> def f(x):
>>> with torch.no_grad():
>>> c = x ** 2
>>> return x - c
In this case, ``jacrev(f)(x)`` will respect the inner ``torch.no_grad``.
Case 2: Using ``jacrev`` inside ``torch.no_grad`` context manager:
>>> with torch.no_grad():
>>> jacrev(f)(x)
In this case, ``jacrev`` will respect the inner ``torch.no_grad``, but not the
outer one. This is because ``jacrev`` is a "function transform": its result
should not depend on the result of a context manager outside of ``f``.
"""
@wraps(func)
def wrapper_fn(*args):
f_wrapper, primals = _argnums_partial(func, args, argnums)
vjp_out = vjp(f_wrapper, *primals, has_aux=has_aux)
if has_aux:
output, vjp_fn, aux = vjp_out
else:
output, vjp_fn = vjp_out
# See NOTE: [Computing jacobian with vmap and vjp for multiple outputs]
flat_output, output_spec = tree_flatten(output)
# NB: vjp already checks that all outputs are tensors
# Step 1: Construct grad_outputs by splitting the standard basis
flat_output_numels = tuple(out.numel() for out in flat_output)
flat_basis = _construct_standard_basis_for(flat_output, flat_output_numels)
basis = tree_unflatten(flat_basis, output_spec)
results = vmap(vjp_fn)(basis)
flat_primals, primals_spec = tree_flatten(primals)
flat_results, results_spec = tree_flatten(results)
# Step 2: The returned jacobian is one big tensor per input. In this step,
# we split each Tensor by output.
flat_results = [result.split(flat_output_numels, dim=0) for result in flat_results]
flat_input_flat_output = [
tuple(split.view(out.shape + primal.shape)
for split, out in zip(splits, flat_output))
for splits, primal in zip(flat_results, flat_primals)
]
# Step 3: Right now, `jacobian` is a List[List[Tensor]].
# The outer List corresponds to the number of primals,
# the inner List corresponds to the number of outputs.
# We need to:
# a. Exchange the order of the outer List and inner List
# b. tree_unflatten the inner Lists (which correspond to the primals)
# c. handle the argnums=int case
# d. tree_unflatten the outer List (which corresponds to the outputs)
flat_output_flat_input = tuple(zip(*flat_input_flat_output))
flat_output_input = tuple(tree_unflatten(flat_input, primals_spec)
for flat_input in flat_output_flat_input)
if isinstance(argnums, int):
flat_output_input = tuple(_safe_zero_index(flat_input)
for flat_input in flat_output_input)
output_input = tree_unflatten(flat_output_input, output_spec)
if has_aux:
return output_input, aux
return output_input
return wrapper_fn
# NOTE: [Computing jacobian with vmap and vjp for multiple outputs]
#
# Let's consider f(x) = (x**2, x.sum()) and let x = torch.randn(3).
# It turns out we can compute the jacobian of this function with a single
# call to autograd.grad by using vmap over the correct grad_outputs.
#
# Firstly, one way to compute the jacobian is to stack x**2 and x.sum()
# into a 4D vector. E.g., use g(x) = torch.stack([x**2, x.sum()])
#
# To get the first row of the jacobian, we call
# >>> autograd.grad(g(x), x, grad_outputs=torch.tensor([1, 0, 0, 0]))
# To get the 2nd row of the jacobian, we call
# >>> autograd.grad(g(x), x, grad_outputs=torch.tensor([0, 1, 0, 0]))
# and so on.
#
# Using vmap, we can vectorize all 4 of these computations into one by
# passing the standard basis for R^4 as the grad_output.
# vmap(partial(autograd.grad, g(x), x))(torch.eye(4)).
#
# Now, how do we compute the jacobian *without stacking the output*?
# We can just split the standard basis across the outputs. So to
# compute the jacobian of f(x), we'd use
# >>> autograd.grad(f(x), x, grad_outputs=_construct_standard_basis_for(...))
# The grad_outputs looks like the following:
# ( torch.tensor([[1, 0, 0],
# [0, 1, 0],
# [0, 0, 1],
# [0, 0, 0]]),
# torch.tensor([[0],
# [0],
# [0],
# [1]]) )
#
# But we're not done yet!
# >>> vmap(partial(autograd.grad(f(x), x, grad_outputs=...)))
# returns a Tensor of shape [4, 3]. We have to remember to split the
# jacobian of shape [4, 3] into two:
# - one of shape [3, 3] for the first output
# - one of shape [ 3] for the second output
def _construct_standard_basis_for(tensors, tensor_numels):
# This function:
# - constructs a N=sum(tensor_numels) standard basis. i.e. an NxN identity matrix.
# - Splits the identity matrix into chunks with each chunk size determined by `tensor_numels`.
# - Each chunk corresponds to one tensor. The chunk has the same dtype and
# device as the tensor
#
# For example, with tensor_numels = [1, 2, 1], this function returns:
# ( tensor([[1], tensor([[0, 0], tensor([[0],
# [0], [1, 0], [0],
# [0], [0, 1], [0],
# [0]]) , [0, 0]]) , [1]]) )
#
# Precondition: tensor_numels == tuple(tensor.numel() for tensor in tensors)
# Precondition: tensors always has at least one element.
#
# See NOTE: [Computing jacobian with vmap and grad for multiple tensors]
# for context behind this function.
assert len(tensors) == len(tensor_numels)
assert len(tensors) > 0
total_numel = sum(tensor_numels)
diag_start_indices = (0, *torch.tensor(tensor_numels).cumsum(dim=0)[:-1].neg().unbind())
chunks = tuple(tensor.new_zeros(total_numel, tensor_numel)
for tensor, tensor_numel in zip(tensors, tensor_numels))
for chunk, diag_start_idx in zip(chunks, diag_start_indices):
chunk.diagonal(diag_start_idx).fill_(1)
chunks = tuple(chunk.view(total_numel, *tensor.shape)
for chunk, tensor in zip(chunks, tensors))
return chunks
def _validate_and_wrap_argnum(argnum, num_args):
if not isinstance(argnum, int):
raise RuntimeError(f'argnum must be int, got: {type(argnum)}')
if argnum >= 0 and argnum < num_args:
return argnum
if argnum < 0 and argnum >= -num_args:
return argnum + num_args
raise RuntimeError(f'Got argnum={argnum}, but only {num_args} positional inputs')
def _check_unique_non_empty(argnums):
if isinstance(argnums, tuple):
if len(argnums) == 0:
raise RuntimeError("argnums must be non-empty")
if len(set(argnums)) != len(argnums):
raise RuntimeError(f"argnums elements must be unique, got {argnums}")
def _replace_args(old_args, new_args, argnums):
if isinstance(argnums, int):
if len(new_args) != 1:
raise RuntimeError(f'new_args should be of size 1, was of size {len(new_args)}')
return tuple(new_args[0] if i == argnums else old_args[i] for i in range(len(old_args)))
if isinstance(argnums, tuple):
if len(new_args) != len(argnums):
raise RuntimeError(
"new_args should have the same size as argnums. "
f"Argnums size {len(argnums)}, new_args size {len(new_args)}")
def get_right_elem(i):
return new_args[argnums.index(i)] if i in argnums else old_args[i]
return tuple(get_right_elem(i) for i in range(len(old_args)))
raise RuntimeError(f'argnums must be int or Tuple[int, ...], got: {type(argnums)}')
def _validate_and_wrap_argnums(argnums, num_args):
if isinstance(argnums, int):
return _validate_and_wrap_argnum(argnums, num_args)
if isinstance(argnums, tuple):
return tuple(_validate_and_wrap_argnum(argnum, num_args) for argnum in argnums)
raise AssertionError("Should never get here")
def _slice_argnums(args, argnums, as_tuple=True):
if not isinstance(argnums, int) and not isinstance(argnums, tuple):
raise RuntimeError(f'argnums must be int or Tuple[int, ...], got: {type(argnums)}')
argnums = _validate_and_wrap_argnums(argnums, len(args))
_check_unique_non_empty(argnums)
if isinstance(argnums, int):
if as_tuple:
return (args[argnums],)
else:
return args[argnums]
return tuple(args[i] for i in argnums)
def _argnums_partial(f, args, argnums):
def f_wrapper(*wrapper_args):
replaced_args = _replace_args(args, wrapper_args, argnums)
return f(*replaced_args)
wrapper_args = _slice_argnums(args, argnums)
wrapper_args = wrapper_args if isinstance(wrapper_args, tuple) else (wrapper_args, )
return (f_wrapper, wrapper_args)
JVP_NESTING = 0
@contextlib.contextmanager
def noop():
yield
@contextlib.contextmanager
def enable_fwd_grad(enabled=True):
prev_state = get_fwd_grad_enabled()
set_fwd_grad_enabled(enabled)
try:
yield
finally:
set_fwd_grad_enabled(prev_state)
def assert_flat_tuple_of_tensors(elts: Any, api: str, argname: str) -> None:
if not isinstance(elts, tuple):
raise RuntimeError(
f'{api}: Expected {argname} to be a tuple of Tensors, got {type(elts)}')
for elt in elts:
if isinstance(elt, torch.Tensor):
continue
raise RuntimeError(
f'{api}: Expected {argname} to be a tuple of Tensors, got '
f'a tuple with an element of type {type(elt)}')
if len(elts) == 0:
raise RuntimeError(
f'{api}: Expected {argname} to be a non-empty tuple of Tensors.')
def assert_non_empty_tensor_output(output: List[Any], api: str) -> None:
if output == [None] or len(output) < 1:
raise RuntimeError(
f'{api}: Expected f to be a function that has non-empty output (got output = {output})'
)
for o in output:
if not isinstance(o, torch.Tensor):
raise RuntimeError(
f'{api}: expected f(*primals) to return only tensors'
f', got unsupported type {type(o)}'
)
def assert_output_is_tensor_or_tensors(output: Any, api: str) -> None:
if isinstance(output, torch.Tensor):
return
if not isinstance(output, tuple):
raise RuntimeError(
f'{api}: Expected output of f to be a Tensor or Tensors, got '
f'{type(output)}')
if len(output) == 0:
raise RuntimeError(
f'{api}: Expected output of f to be a non-empty tuple of Tensors.')
for out in output:
if isinstance(out, torch.Tensor):
continue
raise RuntimeError(
f'{api}: Expected output of f to be a Tensor or Tensors, got '
f'{type(out)} as an output')
def assert_non_empty_list_of_tensors(output: List[torch.Tensor], api: str, argname: str) -> None:
if len(output) == 0:
raise RuntimeError(
f'{api}: Expected {argname} to contain at least one Tensor.')
for out in output:
if isinstance(out, torch.Tensor):
continue
raise RuntimeError(
f'{api}: Expected {argname} to only contain Tensors, got '
f'{type(out)}')
jvp_str = 'jvp(f, primals, tangents)'
def safe_unpack_dual(dual, strict):
if not isinstance(dual, torch.Tensor):
raise RuntimeError(
f'{jvp_str}: expected f(*args) to return only tensors'
f', got unsupported type {type(dual)}'
)
primal, tangent = fwAD.unpack_dual(dual)
if tangent is None:
if strict:
raise RuntimeError(
'jvp(f, primals, tangents, strict=True): '
'The output of f is independent of '
'the inputs. This is not allowed with strict=True.')
tangent = torch.zeros_like(primal)
return primal, tangent
[docs]def jvp(func: Callable, primals: Any, tangents: Any, *, strict: bool = False, has_aux: bool = False):
"""
Standing for the Jacobian-vector product, returns a tuple containing
the output of `func(*primals)` and the "Jacobian of ``func`` evaluated at
``primals``" times ``tangents``. This is also known as forward-mode autodiff.
Args:
func (function): A Python function that takes one or more arguments,
one of which must be a Tensor, and returns one or more Tensors
primals (Tensors): Positional arguments to :attr:`func` that must all be
Tensors. The returned function will also be computing the
derivative with respect to these arguments
tangents (Tensors): The "vector" for which Jacobian-vector-product is
computed. Must be the same structure and sizes as the inputs to
``func``.
has_aux (bool): Flag indicating that :attr:`func` returns a
``(output, aux)`` tuple where the first element is the output of
the function to be differentiated and the second element is
other auxiliary objects that will not be differentiated.
Default: False.
Returns:
Returns a ``(output, jvp_out)`` tuple containing the output of ``func``
evaluated at ``primals`` and the Jacobian-vector product.
If ``has_aux is True``, then instead returns a ``(output, jvp_out, aux)`` tuple.
.. warning::
PyTorch's forward-mode AD coverage on operators is not very good at the
moment. You may see this API error out with "forward-mode AD not
implemented for operator X". If so, please file us a bug report and we
will prioritize it.
jvp is useful when you wish to compute gradients of a function R^1 -> R^N
>>> from functorch import jvp
>>> x = torch.randn([])
>>> f = lambda x: x * torch.tensor([1., 2., 3])
>>> value, grad = jvp(f, (x,), (torch.tensor(1.),))
>>> assert torch.allclose(value, f(x))
>>> assert torch.allclose(grad, torch.tensor([1., 2, 3]))
:func:`jvp` can support functions with multiple inputs by passing in the
tangents for each of the inputs
>>> from functorch import jvp
>>> x = torch.randn(5)
>>> y = torch.randn(5)
>>> f = lambda x, y: (x * y)
>>> _, output = jvp(f, (x, y), (torch.ones(5), torch.ones(5)))
>>> assert torch.allclose(output, x + y)
"""
if not isinstance(primals, tuple):
raise RuntimeError(
f'{jvp_str}: Expected primals to be a tuple. '
f'E.g. it should be valid to call f(*primals).')
flat_primals, primals_spec = tree_flatten(primals)
flat_tangents, tangents_spec = tree_flatten(tangents)
if primals_spec != tangents_spec:
raise RuntimeError(
f'{jvp_str}: Expected primals and tangents to have the same python '
f'structure. For example, if primals is a tuple of 3 tensors, '
f'tangents also must be. Got primals with structure {primals_spec} '
f'and tangents with structure {tangents_spec}')
assert_non_empty_list_of_tensors(flat_primals, jvp_str, 'primals')
assert_non_empty_list_of_tensors(flat_tangents, jvp_str, 'tangents')
level = _jvp_increment_nesting()
try:
global JVP_NESTING
JVP_NESTING += 1
with enable_fwd_grad():
ctx = fwAD.dual_level if JVP_NESTING == 1 else noop
with ctx():
flat_duals = tuple(fwAD.make_dual(p, t)
for p, t in zip(flat_primals, flat_tangents))
duals = tree_unflatten(flat_duals, primals_spec)
result_duals = func(*duals)
if has_aux:
if not (isinstance(result_duals, tuple) and len(result_duals) == 2):
raise RuntimeError(
f"{jvp_str}: output of function f should be a tuple: (output, aux) "
"if has_aux is True"
)
result_duals, aux = result_duals
aux = _undo_create_differentiable(aux, level)
result_duals, spec = tree_flatten(result_duals)
assert_non_empty_tensor_output(result_duals, jvp_str)
primals_out, tangents_out = \
zip(*[safe_unpack_dual(dual, strict) for dual in result_duals])
primals_out = tree_map(
partial(_undo_create_differentiable, level=level), primals_out)
tangents_out = tree_map(
partial(_undo_create_differentiable, level=level), tangents_out)
primals_out_unflatten = tree_unflatten(primals_out, spec)
tangents_out_unflatten = tree_unflatten(tangents_out, spec)
if has_aux:
return primals_out_unflatten, tangents_out_unflatten, aux
return primals_out_unflatten, tangents_out_unflatten
finally:
_jvp_decrement_nesting()
JVP_NESTING -= 1
def safe_unflatten(tensor, dim, shape):
if len(shape) == 0:
assert tensor.shape[dim] == 1
return tensor.squeeze(dim)
return tensor.unflatten(dim, shape)
[docs]def jacfwd(func: Callable, argnums: argnums_t = 0, has_aux: bool = False):
"""
Computes the Jacobian of :attr:`func` with respect to the arg(s) at index
:attr:`argnum` using forward-mode autodiff
Args:
func (function): A Python function that takes one or more arguments,
one of which must be a Tensor, and returns one or more Tensors
argnums (int or Tuple[int]): Optional, integer or tuple of integers,
saying which arguments to get the Jacobian with respect to.
Default: 0.
has_aux (bool): Flag indicating that :attr:`func` returns a
``(output, aux)`` tuple where the first element is the output of
the function to be differentiated and the second element is
auxiliary objects that will not be differentiated.
Default: False.
Returns:
Returns a function that takes in the same inputs as :attr:`func` and
returns the Jacobian of :attr:`func` with respect to the arg(s) at
:attr:`argnums`. If ``has_aux is True``, then the returned function
instead returns a ``(jacobian, aux)`` tuple where ``jacobian``
is the Jacobian and ``aux`` is auxiliary objects returned by :attr:`func`.
.. warning::
PyTorch's forward-mode AD coverage on operators is not very good at the
moment. You may see this API error out with "forward-mode AD not
implemented for operator X". If so, please file us a bug report and we
will prioritize it.
A basic usage with a pointwise, unary operation will give a diagonal array
as the Jacobian
>>> from functorch import jacfwd
>>> x = torch.randn(5)
>>> jacobian = jacfwd(torch.sin)(x)
>>> expected = torch.diag(torch.cos(x))
>>> assert torch.allclose(jacobian, expected)
:func:`jacfwd` can be composed with vmap to produce batched
Jacobians:
>>> from functorch import jacfwd, vmap
>>> x = torch.randn(64, 5)
>>> jacobian = vmap(jacfwd(torch.sin))(x)
>>> assert jacobian.shape == (64, 5, 5)
If you would like to compute the output of the function as well as the
jacobian of the function, use the ``has_aux`` flag to return the output
as an auxiliary object:
>>> from functorch import jacfwd
>>> x = torch.randn(5)
>>>
>>> def f(x):
>>> return x.sin()
>>>
>>> def g(x):
>>> result = f(x)
>>> return result, result
>>>
>>> jacobian_f, f_x = jacfwd(g, has_aux=True)(x)
>>> assert torch.allclose(f_x, f(x))
Additionally, :func:`jacrev` can be composed with itself or :func:`jacrev`
to produce Hessians
>>> from functorch import jacfwd, jacrev
>>> def f(x):
>>> return x.sin().sum()
>>>
>>> x = torch.randn(5)
>>> hessian = jacfwd(jacrev(f))(x)
>>> assert torch.allclose(hessian, torch.diag(-x.sin()))
By default, :func:`jacfwd` computes the Jacobian with respect to the first
input. However, it can compute the Jacboian with respect to a different
argument by using :attr:`argnums`:
>>> from functorch import jacfwd
>>> def f(x, y):
>>> return x + y ** 2
>>>
>>> x, y = torch.randn(5), torch.randn(5)
>>> jacobian = jacfwd(f, argnums=1)(x, y)
>>> expected = torch.diag(2 * y)
>>> assert torch.allclose(jacobian, expected)
Additionally, passing a tuple to :attr:`argnums` will compute the Jacobian
with respect to multiple arguments
>>> from functorch import jacfwd
>>> def f(x, y):
>>> return x + y ** 2
>>>
>>> x, y = torch.randn(5), torch.randn(5)
>>> jacobian = jacfwd(f, argnums=(0, 1))(x, y)
>>> expectedX = torch.diag(torch.ones_like(x))
>>> expectedY = torch.diag(2 * y)
>>> assert torch.allclose(jacobian[0], expectedX)
>>> assert torch.allclose(jacobian[1], expectedY)
"""
@wraps(func)
def wrapper_fn(*args):
f_wrapper, primals = _argnums_partial(func, args, argnums)
flat_primals, primals_spec = tree_flatten(primals)
flat_primals_numels = tuple(p.numel() for p in flat_primals)
flat_basis = _construct_standard_basis_for(flat_primals, flat_primals_numels)
basis = tree_unflatten(flat_basis, primals_spec)
def push_jvp(basis):
output = jvp(f_wrapper, primals, basis, has_aux=has_aux)
if has_aux:
_, jvp_out, aux = output
return jvp_out, aux
_, jvp_out = output
return jvp_out
results = vmap(push_jvp)(basis)
if has_aux:
results, aux = results
# aux is in the standard basis format, e.g. NxN matrix
# We need to fetch the first element as original `func` output
flat_aux, aux_spec = tree_flatten(aux)
flat_aux = [value[0] for value in flat_aux]
aux = tree_unflatten(flat_aux, aux_spec)
jac_outs, spec = tree_flatten(results)
# Most probably below output check can never raise an error
# as jvp should test the output before
# assert_non_empty_output(jac_outs, 'jacfwd(f, ...)(*args)')
jac_outs_ins = tuple(
tuple(
safe_unflatten(jac_out_in, -1, primal.shape)
for primal, jac_out_in in
zip(flat_primals, jac_out.movedim(0, -1).split(flat_primals_numels, dim=-1))
)
for jac_out in jac_outs
)
jac_outs_ins = tuple(tree_unflatten(jac_ins, primals_spec) for jac_ins in jac_outs_ins)
if isinstance(argnums, int):
jac_outs_ins = tuple(jac_ins[0] for jac_ins in jac_outs_ins)
if has_aux:
return tree_unflatten(jac_outs_ins, spec), aux
return tree_unflatten(jac_outs_ins, spec)
return wrapper_fn
[docs]def hessian(func, argnums=0):
"""
Computes the Hessian of :attr:`func` with respect to the arg(s) at index
:attr:`argnum` via a forward-over-reverse strategy.
The forward-over-reverse strategy (composing ``jacfwd(jacrev(func))``) is
a good default for good performance. It is possible to compute Hessians
through other compositions of :func:`jacfwd` and :func:`jacrev` like
``jacfwd(jacfwd(func))`` or ``jacrev(jacrev(func))``.
Args:
func (function): A Python function that takes one or more arguments,
one of which must be a Tensor, and returns one or more Tensors
argnums (int or Tuple[int]): Optional, integer or tuple of integers,
saying which arguments to get the Hessian with respect to.
Default: 0.
Returns:
Returns a function that takes in the same inputs as :attr:`func` and
returns the Hessian of :attr:`func` with respect to the arg(s) at
:attr:`argnums`.
.. warning::
PyTorch's forward-mode AD coverage on operators is not very good at the
moment. You may see this API error out with "forward-mode AD not
implemented for operator X". If so, please file us a bug report and we
will prioritize it.
A basic usage with a R^N -> R^1 function gives a N x N Hessian:
>>> from functorch import hessian
>>> def f(x):
>>> return x.sin().sum()
>>>
>>> x = torch.randn(5)
>>> hess = jacfwd(jacrev(f))(x)
>>> assert torch.allclose(hess, torch.diag(-x.sin()))
"""
return jacfwd(jacrev(func, argnums), argnums)
[docs]def grad_and_value(func: Callable, argnums: argnums_t = 0, has_aux: bool = False) -> Callable:
"""
Returns a function to compute a tuple of the gradient and primal, or
forward, computation.
Args:
func (Callable): A Python function that takes one or more arguments.
Must return a single-element Tensor. If specified :attr:`has_aux`
equals ``True``, function can return a tuple of single-element
Tensor and other auxiliary objects: ``(output, aux)``.
argnums (int or Tuple[int]): Specifies arguments to compute gradients
with respect to. :attr:`argnums` can be single integer or tuple of
integers. Default: 0.
has_aux (bool): Flag indicating that :attr:`func` returns a tensor and
other auxiliary objects: ``(output, aux)``. Default: False.
Returns:
Function to compute a tuple of gradients with respect to its inputs
and the forward computation. By default, the output of the function is
a tuple of the gradient tensor(s) with respect to the first argument
and the primal computation. If specified :attr:`has_aux` equals
``True``, tuple of gradients and tuple of the forward computation with
output auxiliary objects is returned. If :attr:`argnums` is a tuple of
integers, a tuple of a tuple of the output gradients with respect to
each :attr:`argnums` value and the forward computation is returned.
See :func:`grad` for examples
"""
@wraps(func)
def wrapper(*args, **kwargs):
level = _grad_increment_nesting()
try:
output, aux, grad_input = None, None, None
# See NOTE [grad and vjp interaction with no_grad]
with torch.enable_grad():
args = _wrap_all_tensors(args, level)
kwargs = _wrap_all_tensors(kwargs, level)
diff_args = _slice_argnums(args, argnums, as_tuple=False)
tree_map_(partial(_create_differentiable, level=level), diff_args)
output = func(*args, **kwargs)
if has_aux:
if not (isinstance(output, tuple) and len(output) == 2):
raise RuntimeError(
"grad_and_value(f)(*args): output of function f should be a tuple: (output, aux) "
"if has_aux is True"
)
output, aux = output
if not isinstance(output, torch.Tensor):
raise RuntimeError('grad_and_value(f)(*args): Expected f(*args) '
f'to return a Tensor, got {type(output)}')
if output.dim() != 0:
raise RuntimeError('grad_and_value(f)(*args): Expected f(*args) '
'to return a scalar Tensor, got tensor with '
f'{output.dim()} dims. Maybe you wanted to '
'use the vjp or jacrev APIs instead?')
flat_diff_args, spec = tree_flatten(diff_args)
# NB: need create_graph so that backward pass isn't run in no_grad mode
flat_outputs = _as_tuple(output)
flat_grad_input = _autograd_grad(flat_outputs, flat_diff_args, create_graph=True)
grad_input = tree_unflatten(flat_grad_input, spec)
grad_input = _undo_create_differentiable(grad_input, level)
output = _undo_create_differentiable(output, level)
if aux is not None:
aux = _undo_create_differentiable(aux, level)
if has_aux:
return grad_input, (output, aux)
return grad_input, output
finally:
_grad_decrement_nesting()
return wrapper
[docs]def grad(func: Callable, argnums: argnums_t = 0, has_aux: bool = False) -> Callable:
"""``grad`` operator helps computing gradients of :attr:`func` with respect to the
input(s) specified by :attr:`argnums`. This operator can be nested to
compute higher-order gradients.
Args:
func (Callable): A Python function that takes one or more arguments.
Must return a single-element Tensor. If specified :attr:`has_aux` equals ``True``,
function can return a tuple of single-element Tensor and other auxiliary objects:
``(output, aux)``.
argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to.
:attr:`argnums` can be single integer or tuple of integers. Default: 0.
has_aux (bool): Flag indicating that :attr:`func` returns a tensor and other
auxiliary objects: ``(output, aux)``. Default: False.
Returns:
Function to compute gradients with respect to its inputs. By default, the output of
the function is the gradient tensor(s) with respect to the first argument.
If specified :attr:`has_aux` equals ``True``, tuple of gradients and output auxiliary objects
is returned. If :attr:`argnums` is a tuple of integers, a tuple of output gradients with
respect to each :attr:`argnums` value is returned.
Example of using ``grad``:
>>> from functorch import grad
>>> x = torch.randn([])
>>> cos_x = grad(lambda x: torch.sin(x))(x)
>>> assert torch.allclose(cos_x, x.cos())
>>>
>>> # Second-order gradients
>>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x)
>>> assert torch.allclose(neg_sin_x, -x.sin())
When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients:
>>> from functorch import grad
>>> from functorch import vmap
>>> batch_size, feature_size = 3, 5
>>>
>>> def model(weights, feature_vec):
>>> # Very simple linear model with activation
>>> assert feature_vec.dim() == 1
>>> return feature_vec.dot(weights).relu()
>>>
>>> def compute_loss(weights, example, target):
>>> y = model(weights, example)
>>> return ((y - target) ** 2).mean() # MSELoss
>>>
>>> weights = torch.randn(feature_size, requires_grad=True)
>>> examples = torch.randn(batch_size, feature_size)
>>> targets = torch.randn(batch_size)
>>> inputs = (weights, examples, targets)
>>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))(*inputs)
Example of using ``grad`` with :attr:`has_aux` and :attr:`argnums`:
>>> from functorch import grad
>>> def my_loss_func(y, y_pred):
>>> loss_per_sample = (0.5 * y_pred - y) ** 2
>>> loss = loss_per_sample.mean()
>>> return loss, (y_pred, loss_per_sample)
>>>
>>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True)
>>> y_true = torch.rand(4)
>>> y_preds = torch.rand(4, requires_grad=True)
>>> out = fn(y_true, y_preds)
>>> > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample))
.. note::
Using PyTorch ``torch.no_grad`` together with ``grad``.
Case 1: Using ``torch.no_grad`` inside a function:
>>> def f(x):
>>> with torch.no_grad():
>>> c = x ** 2
>>> return x - c
In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``.
Case 2: Using ``grad`` inside ``torch.no_grad`` context manager:
>>> with torch.no_grad():
>>> grad(f)(x)
In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the
outer one. This is because ``grad`` is a "function transform": its result
should not depend on the result of a context manager outside of ``f``.
"""
@wraps(func)
def wrapper(*args, **kwargs):
results = grad_and_value(func, argnums, has_aux=has_aux)(*args, **kwargs)
if has_aux:
grad, (_, aux) = results
return grad, aux
grad, _ = results
return grad
return wrapper
def _maybe_wrap_functional_tensor(maybe_tensor, level):
if not isinstance(maybe_tensor, torch.Tensor):
return maybe_tensor
wrapped = _wrap_functional_tensor(maybe_tensor, level)
_assert_wrapped_functional(maybe_tensor, wrapped)
return wrapped
def _wrap_all_tensors_to_functional(tensor_pytree, level):
return tree_map(partial(_maybe_wrap_functional_tensor, level=level), tensor_pytree)
def _maybe_unwrap_functional_tensor(maybe_tensor, *, reapply_views: bool):
if not isinstance(maybe_tensor, torch.Tensor):
return maybe_tensor
if not torch._is_functional_tensor(maybe_tensor):
# If it's not a functional tensor, just return it.
# This can happen if we functionalize a fn that returns a global,
# which was never wrapped properly.
return maybe_tensor
return _unwrap_functional_tensor(maybe_tensor, reapply_views)
def _unwrap_all_tensors_from_functional(tensor_pytree, *, reapply_views: bool):
return tree_map(lambda t: _maybe_unwrap_functional_tensor(t, reapply_views=reapply_views), tensor_pytree)
[docs]def functionalize(func: Callable, *, remove: str = 'mutations') -> Callable:
"""
functionalize is a transform that can be used to remove (intermediate)
mutations and aliasing from a function, while preserving the function's
semantics.
``functionalize(func)`` returns a new function with the same semantics
as ``func``, but with all intermediate mutations removed.
Every inplace operation performed on an intermediate tensor:
``intermediate.foo_()``
gets replaced by its out-of-place equivalent:
``intermediate_updated = intermediate.foo()``.
functionalize is useful for shipping a pytorch program off to
backends or compilers that aren't able to easily represent
mutations or aliasing operators.
Args:
func (Callable): A Python function that takes one or more arguments.
remove (str): An optional string argument, that takes on either
the value 'mutations' or 'mutations_and_views'.
If 'mutations' is passed in then all mutating operators
will be replaced with their non-mutating equivalents.
If 'mutations_and_views' is passed in, then additionally, all aliasing
operators will be replaced with their non-aliasing equivalents.
Default: 'mutations'.
Returns:
Returns a new "functionalized" function. It takes the same inputs as
:attr:`func`, and has the same behavior, but any mutations
(and optionally aliasing) performed on intermeidate tensors
in the function will be removed.
functionalize will also remove mutations (and views) that were performed on function inputs.
However to preserve semantics, functionalize will "fix up" the mutations after
the transform has finished running, by detecting if any tensor inputs "should have"
been mutated, and copying the new data back to the inputs if necessary.
Example::
>>> import torch
>>> from functorch import make_fx
>>> from functorch.experimental import functionalize
>>>
>>> A function that uses mutations and views, but only on intermediate tensors.
>>> def f(a):
... b = a + 1
... c = b.view(-1)
... c.add_(1)
... return b
...
>>> inpt = torch.randn(2)
>>>
>>> out1 = f(inpt)
>>> out2 = functionalize(f)(inpt)
>>>
>>> # semantics are the same (outputs are equivalent)
>>> print(torch.allclose(out1, out2))
True
>>>
>>> f_traced = make_fx(f)(inpt)
>>> f_no_mutations_traced = make_fx(functionalize(f))(inpt)
>>> f_no_mutations_and_views_traced = make_fx(functionalize(f, remove='mutations_and_views'))(inpt)
>>>
>>> print(f_traced.code)
def forward(self, a_1):
add = torch.ops.aten.add(a_1, 1); a_1 = None
view = torch.ops.aten.view(add, [-1])
add_ = torch.ops.aten.add_(view, 1); view = None
return add
>>> print(f_no_mutations_traced.code)
def forward(self, a_1):
add = torch.ops.aten.add(a_1, 1); a_1 = None
view = torch.ops.aten.view(add, [-1]); add = None
add_1 = torch.ops.aten.add(view, 1); view = None
view_1 = torch.ops.aten.view(add_1, [2]); add_1 = None
return view_1
>>> print(f_no_mutations_and_views_traced.code)
def forward(self, a_1):
add = torch.ops.aten.add(a_1, 1); a_1 = None
view_copy = torch.ops.aten.view_copy(add, [-1]); add = None
add_1 = torch.ops.aten.add(view_copy, 1); view_copy = None
view_copy_1 = torch.ops.aten.view_copy(add_1, [2]); add_1 = None
return view_copy_1
>>> A function that mutates its input tensor
>>> def f(a):
... b = a.view(-1)
... b.add_(1)
... return a
...
>>> f_no_mutations_and_views_traced = make_fx(functionalize(f, remove='mutations_and_views'))(inpt)
>>>
>>> All mutations and views have been removed,
>>> but there is an extra copy_ in the graph to correctly apply the mutation to the input
>>> after the function has completed.
>>> print(f_no_mutations_and_views_traced.code)
def forward(self, a_1):
view_copy = torch.ops.aten.view_copy(a_1, [-1])
add = torch.ops.aten.add(view_copy, 1); view_copy = None
view_copy_1 = torch.ops.aten.view_copy(add, [2]); add = None
copy_ = torch.ops.aten.copy_(a_1, view_copy_1); a_1 = None
return view_copy_1
There are a few "failure modes" for functionalize that are worth calling out:
(1) Like other functorch transforms, `functionalize()` doesn't work with functions
that directly use `.backward()`. The same is true for torch.autograd.grad.
If you want to use autograd, you can compute gradients directly
with `functionalize(grad(f))`.
(2) Like other functorch transforms, `functionalize()` doesn't work with global state.
If you call `functionalize(f)` on a function that takes views / mutations of
non-local state, functionalization will simply no-op and pass the view/mutation
calls directly to the backend.
One way to work around this is is to ensure that any non-local state creation
is wrapped into a larger function, which you then call functionalize on.
(3) `resize_()` has some limitations: functionalize will only work on programs
that use resize_()` as long as the tensor being resized is not a view.
(4) `as_strided()` has some limitations: functionalize will not work on
`as_strided()` calls that result in tensors with overlapping memory.
Finally, a helpful mental model for understanding functionalization is that
most user pytorch programs are writting with the public torch API.
When executed, torch operators are generally decomposed into
our internal C++ "ATen" API.
The logic for functionalization happens entirely at the level of ATen.
Functionalization knows how to take every aliasing operator in ATen,
and map it to its non-aliasing equivalent
(e.g. ``tensor.view({-1})`` -> ``at::view_copy(tensor, {-1})``),
and how to take every mutating operator in ATen,
and map it to its non-mutating equivalent
(e.g. ``tensor.add_(1)`` -> ``at::add(tensor, -1)``),
while tracking aliases and mutations out-of-line to know when to fix things up.
Information about which ATen operators are aliasing or mutating all comes from
https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml.
"""
if remove == 'mutations':
reapply_views = True
elif remove == 'mutations_and_views':
reapply_views = False
else:
raise RuntimeError(
f"functionalize(f, remove='mutations'): received invalid argument for remove={remove}."
" Valid options are:\n"
" remove='mutations': all inplace and out= operators will be removed from the program, and replaced"
" with their out-of-place equivalents.\n"
" remove='mutations_and_views': In addition to the above, all aliasing operators {view} will be"
" replaced with their non-aliasing counterparts, {view}_copy.\n"
)
@wraps(func)
def wrapped(*args, **kwargs):
try:
func_level = _func_increment_nesting(reapply_views)
func_args = _wrap_all_tensors_to_functional(args, func_level)
func_kwargs = _wrap_all_tensors_to_functional(kwargs, func_level)
flattened_unwrapped_args, _ = tree_flatten(args)
flattened_wrapped_args, _ = tree_flatten(func_args)
flattened_unwrapped_kwargs, _ = tree_flatten(kwargs)
flattened_wrapped_kwargs, _ = tree_flatten(func_kwargs)
func_outputs = func(*func_args, **func_kwargs)
outputs = _unwrap_all_tensors_from_functional(func_outputs, reapply_views=reapply_views)
flat_outputs, func_out_spec = tree_flatten(outputs)
for a in flattened_wrapped_args + flattened_wrapped_kwargs:
if isinstance(a, torch.Tensor):
# Call sync_() on the inputs, to ensure that any pending mutations have been applied.
torch._sync(a)
# And if any mutations were applied to the inputs, we need to propagate them back to the user.
for unwrapped, wrapped in zip(flattened_unwrapped_args, flattened_wrapped_args):
if isinstance(unwrapped, torch.Tensor) and isinstance(wrapped, torch.Tensor):
_propagate_functional_input_mutation(unwrapped, wrapped)
for unwrapped, wrapped in zip(flattened_unwrapped_kwargs, flattened_wrapped_kwargs):
if isinstance(unwrapped, torch.Tensor) and isinstance(wrapped, torch.Tensor):
_propagate_functional_input_mutation(unwrapped, wrapped)
return outputs
finally:
_func_decrement_nesting()
return wrapped
def _register_jit_decomposition(decomp, use_python=False):
if decomp in decomposition_table_for_jvp:
decomposition_table_used = decomposition_table_for_jvp
elif decomp in decomposition_table:
decomposition_table_used = decomposition_table
else:
raise RuntimeError(f"could not find decomposition for {decomp}")
decomp_fn = decomposition_table_used[decomp]
if use_python:
decomp_fn = torch.jit.ignore(decomp_fn)
sig = inspect.signature(decomp_fn)
# Create a string wrapping the function from the signature
# example output:
# def wrapped_decomp(x: torch.Tensor, y: int, z: int):
# return decomp_fn(x, y, z)
# Thanks copilot!
def get_function_def(sig):
param_def = [f"{param_str}" for param_str in sig.parameters.values()]
param_use = [f"{param_str}" for param_str in sig.parameters.keys()]
return f"def wrapped_decomp({', '.join(param_def)}):\n return decomp_fn({', '.join(param_use)})\n"
f_str = get_function_def(sig)
graph = torch.jit.CompilationUnit(f_str).wrapped_decomp.graph
else:
graph = torch.jit.script(decomp_fn).graph
torch.jit._register_decomposition(decomp, graph)
_register_jit_decomposition(torch.ops.aten.trace.default)
_register_jit_decomposition(torch.ops.aten.nll_loss_backward.default)
_register_jit_decomposition(torch.ops.aten.nll_loss2d_backward.default)
_register_jit_decomposition(torch.ops.aten.mse_loss_backward.default)
_register_jit_decomposition(torch.ops.aten.l1_loss_backward.default)
_register_jit_decomposition(torch.ops.aten._log_softmax_backward_data.default)
_register_jit_decomposition(torch.ops.aten._softmax_backward_data.default)
_register_jit_decomposition(torch.ops.aten.log_sigmoid_forward.default)
_register_jit_decomposition(torch.ops.aten.binary_cross_entropy_backward.default)
_register_jit_decomposition(torch.ops.aten.binary_cross_entropy.default)
_register_jit_decomposition(torch.ops.aten.native_layer_norm_backward.default)