正向模式自动微分 （Beta）

创建时间： 2021年12月07日 |上次更新时间：2023 年 4 月 18 日 |上次验证： Nov 05， 2024

本教程演示如何使用前向模式 AD 进行计算 方向导数（或等效的 Jacobian 向量积）。

下面的教程使用了一些仅在版本 >= 1.11 中可用的 API （或夜间构建）。

另请注意，前向模式 AD 目前处于测试阶段。API 是 可能会发生变化，运维覆盖范围仍不完整。

基本用法

与反向模式 AD 不同，正向模式 AD 急切地计算梯度 沿着前传。我们可以使用前向模式 AD 来计算 Directional 导数，如前所述执行 forward pass， 除了我们首先将我们的输入与另一个表示 方向导数的方向（或等效地，在 Jacobian 向量积中）。当一个输入（我们称为 “primal”）是 与我们称为 “tangent” 的 “direction” 张量相关联， 生成的 New Tensor 对象在其连接中称为 “dual Tensor” 设置为双数 [0]。v

在执行前向传递时，如果任何输入张量是双张量，则 执行额外的计算来传播 功能。

import torch
import torch.autograd.forward_ad as fwAD

primal = torch.randn(10, 10)
tangent = torch.randn(10, 10)

def fn(x, y):
    return x ** 2 + y ** 2

# All forward AD computation must be performed in the context of
# a ``dual_level`` context. All dual tensors created in such a context
# will have their tangents destroyed upon exit. This is to ensure that
# if the output or intermediate results of this computation are reused
# in a future forward AD computation, their tangents (which are associated
# with this computation) won't be confused with tangents from the later
# computation.
with fwAD.dual_level():
    # To create a dual tensor we associate a tensor, which we call the
    # primal with another tensor of the same size, which we call the tangent.
    # If the layout of the tangent is different from that of the primal,
    # The values of the tangent are copied into a new tensor with the same
    # metadata as the primal. Otherwise, the tangent itself is used as-is.
    #
    # It is also important to note that the dual tensor created by
    # ``make_dual`` is a view of the primal.
    dual_input = fwAD.make_dual(primal, tangent)
    assert fwAD.unpack_dual(dual_input).tangent is tangent

    # To demonstrate the case where the copy of the tangent happens,
    # we pass in a tangent with a layout different from that of the primal
    dual_input_alt = fwAD.make_dual(primal, tangent.T)
    assert fwAD.unpack_dual(dual_input_alt).tangent is not tangent

    # Tensors that do not have an associated tangent are automatically
    # considered to have a zero-filled tangent of the same shape.
    plain_tensor = torch.randn(10, 10)
    dual_output = fn(dual_input, plain_tensor)

    # Unpacking the dual returns a ``namedtuple`` with ``primal`` and ``tangent``
    # as attributes
    jvp = fwAD.unpack_dual(dual_output).tangent

assert fwAD.unpack_dual(dual_output).tangent is None

与模块一起使用

要与转发 AD 一起使用，请将 模型。在 写入时，无法创建双张量 'nn。参数的。解决方法是，必须注册 dual Tensor 作为模块的非参数属性。nn.Module

import torch.nn as nn

model = nn.Linear(5, 5)
input = torch.randn(16, 5)

params = {name: p for name, p in model.named_parameters()}
tangents = {name: torch.rand_like(p) for name, p in params.items()}

with fwAD.dual_level():
    for name, p in params.items():
        delattr(model, name)
        setattr(model, name, fwAD.make_dual(p, tangents[name]))

    out = model(input)
    jvp = fwAD.unpack_dual(out).tangent

使用功能模块 API（测试版）

与正向 AD 一起使用的另一种方法是使用 功能性模块 API（也称为无状态模块 API）。nn.Module

from torch.func import functional_call

# We need a fresh module because the functional call requires the
# the model to have parameters registered.
model = nn.Linear(5, 5)

dual_params = {}
with fwAD.dual_level():
    for name, p in params.items():
        # Using the same ``tangents`` from the above section
        dual_params[name] = fwAD.make_dual(p, tangents[name])
    out = functional_call(model, dual_params, input)
    jvp2 = fwAD.unpack_dual(out).tangent

# Check our results
assert torch.allclose(jvp, jvp2)

自定义 autograd 函数

自定义函数还支持正向模式 AD。创建自定义函数 支持正向模式 AD，注册 static 方法。是的 可能，但对于自定义函数来说，支持两者 forward 并非强制性的 和向后 AD。有关更多信息，请参阅文档。jvp()

class Fn(torch.autograd.Function):
    @staticmethod
    def forward(ctx, foo):
        result = torch.exp(foo)
        # Tensors stored in ``ctx`` can be used in the subsequent forward grad
        # computation.
        ctx.result = result
        return result

    @staticmethod
    def jvp(ctx, gI):
        gO = gI * ctx.result
        # If the tensor stored in`` ctx`` will not also be used in the backward pass,
        # one can manually free it using ``del``
        del ctx.result
        return gO

fn = Fn.apply

primal = torch.randn(10, 10, dtype=torch.double, requires_grad=True)
tangent = torch.randn(10, 10)

with fwAD.dual_level():
    dual_input = fwAD.make_dual(primal, tangent)
    dual_output = fn(dual_input)
    jvp = fwAD.unpack_dual(dual_output).tangent

# It is important to use ``autograd.gradcheck`` to verify that your
# custom autograd Function computes the gradients correctly. By default,
# ``gradcheck`` only checks the backward-mode (reverse-mode) AD gradients. Specify
# ``check_forward_ad=True`` to also check forward grads. If you did not
# implement the backward formula for your function, you can also tell ``gradcheck``
# to skip the tests that require backward-mode AD by specifying
# ``check_backward_ad=False``, ``check_undefined_grad=False``, and
# ``check_batched_grad=False``.
torch.autograd.gradcheck(Fn.apply, (primal,), check_forward_ad=True,
                         check_backward_ad=False, check_undefined_grad=False,
                         check_batched_grad=False)

True

功能 API（测试版）

我们还在 functorch 中提供了更高级别的函数式 API 用于计算您可能觉得使用更简单的雅可比向量积 具体取决于您的用例。

函数式 API 的好处是无需理解 或者使用较低级别的双张量 API，并且您可以使用 其他 functorch 转换（如 vmap）; 缺点是它为您提供的控制较少。

请注意，本教程的其余部分将需要 functorch （https://github.com/pytorch/functorch） 运行。请查找安装 说明。

import functorch as ft

primal0 = torch.randn(10, 10)
tangent0 = torch.randn(10, 10)
primal1 = torch.randn(10, 10)
tangent1 = torch.randn(10, 10)

def fn(x, y):
    return x ** 2 + y ** 2

# Here is a basic example to compute the JVP of the above function.
# The ``jvp(func, primals, tangents)`` returns ``func(*primals)`` as well as the
# computed Jacobian-vector product (JVP). Each primal must be associated with a tangent of the same shape.
primal_out, tangent_out = ft.jvp(fn, (primal0, primal1), (tangent0, tangent1))

# ``functorch.jvp`` requires every primal to be associated with a tangent.
# If we only want to associate certain inputs to `fn` with tangents,
# then we'll need to create a new function that captures inputs without tangents:
primal = torch.randn(10, 10)
tangent = torch.randn(10, 10)
y = torch.randn(10, 10)

import functools
new_fn = functools.partial(fn, y=y)
primal_out, tangent_out = ft.jvp(new_fn, (primal,), (tangent,))

/var/lib/workspace/intermediate_source/forward_ad_usage.py:203: FutureWarning:

We've integrated functorch into PyTorch. As the final step of the integration, `functorch.jvp` is deprecated as of PyTorch 2.0 and will be deleted in a future version of PyTorch >= 2.3. Please use `torch.func.jvp` instead; see the PyTorch 2.0 release notes and/or the `torch.func` migration guide for more details https://pytorch.org/docs/main/func.migrating.html

/var/lib/workspace/intermediate_source/forward_ad_usage.py:214: FutureWarning:

We've integrated functorch into PyTorch. As the final step of the integration, `functorch.jvp` is deprecated as of PyTorch 2.0 and will be deleted in a future version of PyTorch >= 2.3. Please use `torch.func.jvp` instead; see the PyTorch 2.0 release notes and/or the `torch.func` migration guide for more details https://pytorch.org/docs/main/func.migrating.html

将函数式 API 与模块一起使用

用于计算雅可比向量积 对于模型参数，我们需要将 AS 重新表述为同时接受模型参数和输入的函数 添加到模块中。nn.Modulefunctorch.jvpnn.Module

model = nn.Linear(5, 5)
input = torch.randn(16, 5)
tangents = tuple([torch.rand_like(p) for p in model.parameters()])

# Given a ``torch.nn.Module``, ``ft.make_functional_with_buffers`` extracts the state
# (``params`` and buffers) and returns a functional version of the model that
# can be invoked like a function.
# That is, the returned ``func`` can be invoked like
# ``func(params, buffers, input)``.
# ``ft.make_functional_with_buffers`` is analogous to the ``nn.Modules`` stateless API
# that you saw previously and we're working on consolidating the two.
func, params, buffers = ft.make_functional_with_buffers(model)

# Because ``jvp`` requires every input to be associated with a tangent, we need to
# create a new function that, when given the parameters, produces the output
def func_params_only(params):
    return func(params, buffers, input)

model_output, jvp_out = ft.jvp(func_params_only, (params,), (tangents,))

/var/lib/workspace/intermediate_source/forward_ad_usage.py:235: FutureWarning:

We've integrated functorch into PyTorch. As the final step of the integration, `functorch.make_functional_with_buffers` is deprecated as of PyTorch 2.0 and will be deleted in a future version of PyTorch >= 2.3. Please use `torch.func.functional_call` instead; see the PyTorch 2.0 release notes and/or the `torch.func` migration guide for more details https://pytorch.org/docs/main/func.migrating.html

/var/lib/workspace/intermediate_source/forward_ad_usage.py:242: FutureWarning:

We've integrated functorch into PyTorch. As the final step of the integration, `functorch.jvp` is deprecated as of PyTorch 2.0 and will be deleted in a future version of PyTorch >= 2.3. Please use `torch.func.jvp` instead; see the PyTorch 2.0 release notes and/or the `torch.func` migration guide for more details https://pytorch.org/docs/main/func.migrating.html

[0] https://en.wikipedia.org/wiki/Dual_number