Pytorch中如何调用forward()函数

目录

• Pytorch调用forward()函数
• Pytorch函数调用的问题和源码解读
• 总结

Pytorch调用forward()函数

Module类是nn模块里提供的一个模型构造类，是所有神经网络模块的基类，我们可以继承它来定义我们想要的模型。

下面继承Module类构造本节开头提到的多层感知机。

这里定义的MLP类重载了Module类的__init__函数和forward函数。

它们分别用于创建模型参数和定义前向计算。

前向计算也即正向传播。

import torch
from torch import nn

class MLP(nn.Module):
  # 声明带有模型参数的层，这里声明了两个全连接层
  def __init__(self, **kwargs):
    # 调用MLP父类Module的构造函数来进行必要的初始化。这样在构造实例时还可以指定其他函数
    # 参数，如“模型参数的访问、初始化和共享”一节将介绍的模型参数params
    super(MLP, self).__init__(**kwargs)
    self.hidden = nn.Linear(784, 256) # 隐藏层
    self.act = nn.ReLU()
    self.output = nn.Linear(256, 10) # 输出层


  # 定义模型的前向www.devze.com计算，即如何根据输入x计算返回所需要的模型输出
  def forward(self, x):
    a = self.act(self.hidden(x))
    return self.output(a)

X = torch.rand(2, 784)
net = MLP()
print(net)
net(X)

输出：

MLP( (hidden): Linear(in_features=784, out_features=256, bias=True) (act): ReLU() (output): Linear(in_features=256, out_features=10, bias=True) ) tensor([[-0.1798, -0.2253, 0.0206, -0.1067, -0.0889, 0.1818, -0.1474, 0.1845, -0.1870, 0.1970], [-0.1843, -0.1562, -0.0090, 0.0351, -0.1538, 0.0992, -0.0883, 0.0911, -0.2293, 0.2360]], grad_fn=<ThAddmmBackward>)

为什么会调用forward()呢，是因为Module中定义了__call__()函数，该函数调用了forward()函数，当执行net(x)的时候，会自动调用__call__()函数

Pytorch函数调用的问题和源码解读

最近用到 softmax 函数，但是发现 softmax 的写法五花八门，记录如下

# torch._C._VariableFunctions
torch.softmax(x, dim=-1)

# class
softmax = torch.nn.Softmax(dim=-1)
x=softmax(x)

# function
x = tjsorch.nn.functional.softmax(x, dim=-1)

简单测试了一下，用 torch.nn.Softmax 类是最慢的，另外两个差不多

torch.nn.Softmax 源码如下，可以看到这是个类，而他这里的 return F.softmax(input, self.dim, _stacklevel=5) 调用的是 torch.nn.functional.softmax

class Softmax(Module):
  r"""Applies the Softmax function to an n-dimensional input Tensor
  rescaling them so that the elements of the n-dimensional output Tensor
  lie in the range [0,1] and sum to 1.

  Softmax is defined as:

  .. math::
    \text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}

  When the input Tensor is a sparse tensor then the unspecifed
  values are treated as ``-inf``.

  Shape:
    - Input: :math:`(*)` where `*` means, any number of additional
     dimensions
    - Output: :math:`(*)`, same shape as the input

  Returns:
    a Tensor of the same dimension and shape as the input with
    values in the range [0, 1]

  Args:
    dim (int): A dimension along which Softmax will be computed (so every slice
      along dim will sum to 1).

  .. note::
    This module doesn't work directly with NLLLoss,
    which expects the Log to be computed between the Softmax and itself.
    Use `LogSoftmax` instead (it's faster and has better numerical properties).

  Examples::

    >>> m = nn.Softmax(dim=1)
    >>> input = torch.randn(2, 3)
    >>> output = m(inputjs)

  """
  __constants__ = ['dim']
  dim: Optional[int]

  def __init__(self, dim: Optional[int] = None) -> None:
    super(Softmax, self).__init__()
    self.dim = dim

  def __setstate__(self, state):
    self.__dict__.update(state)
    if not hasattr(self, 'dim'):
      self.dim = None

  def forward(self, input: Tensor) -> Tensor:
    return F.softmax(input, self.dim, _stacklevel=5)

  def extra_repr(self) -> str:
    return 'dim={dim}'.format(dim=self.dim)

torch.nn.functional.softmax 函数源码如下，可以看到 ret = input.softmax(dim) 实际上调用了 torch._C._VariableFunctions 中的 softmax 函数

def softmax(input: Tensor, dim: Optional[int] = None, _stacklevel: int = 3, dtype: Optional[DType] = None) -> Tensor:
  r"""Applies a softmax function.

  Softmax is defined as:

  :math:`\text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}`

  It is applied to all slices along dim, and will re-scale them so that the elements
  lie in the range `[0, 1]` and sum to 1.

  See :class:`~torch.nn.Softmax` for more details.

  Args:
    input (Tensor): input
    dim (int): A dimension along which sBruijgroftmax will be computed.
    dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
     If specified, the input tensor is casted to :attr:`dtype` before the operation
     is performed. This is useful for preventphping data type overflows. Default: None.

  .. note::
    This function doesn't work directly with NLLLoss,
    which expects the Lo开发者_JS教程g to be computed between the Softmax and itself.
    Use log_softmax instead (it's faster and has better numerical properties).

  """
  if has_torch_function_unary(input):
    return handle_torch_function(softmax, (input,), input, dim=dim, _stacklevel=_stacklevel, dtype=dtype)
  if dim is None:
    dim = _get_softmax_dim("softmax", input.dim(), _stacklevel)
  if dtype is None:
    ret = input.softmax(dim)
  else:
    ret = input.softmax(dim, dtype=dtype)
  return ret

那么不如直接调用 built-in C 的函数？

但是有个博客 A selective excursion into the internals of PyTorch 里说

Note: That bilinear is exported as torch.bilinear is somewhat accidental. Do use the documented interfaces, here torch.nn.functional.bilinear whenever you can!

意思是说 built-in C 能被 torch.xxx 直接调用是意外的，强烈建议使用 torch.nn.functional.xxx 这样的接口

看到最新的 transformer 官方代码里也用的是 torch.nn.functional.softmax，还是和他们一致更好（虽然他们之前用的是类。。。）

总结

以上为个人经验，希望能给大家一个参考，也希望大家多多支持我们。