从 relu 的多种实现来看 torch.nn 与 torch.nn.functional 的区别与联系

relu多种实现之间的关系

relu 函数在 pytorch 中总共有 3 次出现：

1. torch.nn.ReLU()
2. torch.nn.functional.relu_() torch.nn.functional.relu_()
3. torch.relu() torch.relu_()

而这3种不同的实现其实是有固定的包装关系，由上至下是由表及里的过程。

其中最后一个实际上并不被 pytorch 的官方文档包含，同时也找不到对应的 python 代码，只是在 __init__.pyi 中存在，因为他们来自于通过C++编写的THNN库。

下面通过分析源码来进行具体分析：

1. torch.nn.ReLU()
   torch.nn 中的类代表的是神经网络层，这里我们看到作为类出现的 ReLU() 实际上只是调用了 torch.nn.functional 中的 relu relu_ 实现。

class ReLU(Module):    r"""Applies the rectified linear unit function element-wise:    :math:`\text{ReLU}(x)= \max(0, x)`    Args:        inplace: can optionally do the operation in-place. Default: ``False``    Shape:        - Input: :math:`(N, *)` where `*` means, any number of additional          dimensions        - Output: :math:`(N, *)`, same shape as the input    .. image:: scripts/activation_images/ReLU.png    Examples::        >>> m = nn.ReLU()        >>> input = torch.randn(2)        >>> output = m(input)      An implementation of CReLU - https://arxiv.org/abs/1603.05201        >>> m = nn.ReLU()        >>> input = torch.randn(2).unsqueeze(0)        >>> output = torch.cat((m(input),m(-input)))    """    __constants__ = ['inplace']    def __init__(self, inplace=False):        super(ReLU, self).__init__()        self.inplace = inplace    @weak_script_method    def forward(self, input):      # F 来自于 import nn.functional as F        return F.relu(input, inplace=self.inplace)    def extra_repr(self):        inplace_str = 'inplace' if self.inplace else ''        return inplace_str

1. torch.nn.functional.relu() torch.nn.functional.relu_()
   其实这两个函数也是调用了 torch.relu() and torch.relu_()

def relu(input, inplace=False):    # type: (Tensor, bool) -> Tensor    r"""relu(input, inplace=False) -> Tensor    Applies the rectified linear unit function element-wise. See    :class:`~torch.nn.ReLU` for more details.    """    if inplace:        result = torch.relu_(input)    else:        result = torch.relu(input)    return resultrelu_ = _add_docstr(torch.relu_, r"""relu_(input) -> TensorIn-place version of :func:`~relu`.""")

至此我们对 RELU 函数在 torch 中的出现有了一个深入的认识。实际上作为基础的两个包，torch.nn 与 torch.nn.functional 的关系是引用与包装的关系。

torch.nn 与 torch.nn.functional 的区别与联系

结合上述对 relu 的分析，我们能够更清晰的认识到两个库之间的联系。

通常来说 torch.nn.functional 调用了 THNN库，实现核心计算，但是不对 learnable_parameters 例如 weight bias ，进行管理，为模型的使用带来不便。而 torch.nn 中实现的模型则对 torch.nn.functional，本质上是官方给出的对 torch.nn.functional的使用范例，我们通过直接调用这些范例能够快速方便的使用 pytorch ，但是范例可能不能够照顾到所有人的使用需求，因此保留 torch.nn.functional 来为这些用户提供灵活性，他们可以自己组装需要的模型。因此 pytorch 能够在灵活性与易用性上取得平衡。

特别注意的是，torch.nn不全都是对torch.nn.functional的范例，有一些调用了来自其他库的函数，例如常用的RNN型神经网络族即没有在torch.nn.functional中出现。

我们带着这样的思考再来看下一个例子作为结束：

对于Linear请注意⚠️对比两个库下实现的不同：

1. 对learnable parameters的管理
2. 相互之间的调用关系
3. 初始化过程

class Linear(Module):    r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`    Args:        in_features: size of each input sample        out_features: size of each output sample        bias: If set to ``False``, the layer will not learn an additive bias.            Default: ``True``    Shape:        - Input: :math:`(N, *, H_{in})` where :math:`*` means any number of          additional dimensions and :math:`H_{in} = \text{in\_features}`        - Output: :math:`(N, *, H_{out})` where all but the last dimension          are the same shape as the input and :math:`H_{out} = \text{out\_features}`.    Attributes:        weight: the learnable weights of the module of shape            :math:`(\text{out\_features}, \text{in\_features})`. The values are            initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where            :math:`k = \frac{1}{\text{in\_features}}`        bias:   the learnable bias of the module of shape :math:`(\text{out\_features})`.                If :attr:`bias` is ``True``, the values are initialized from                :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where                :math:`k = \frac{1}{\text{in\_features}}`    Examples::        >>> m = nn.Linear(20, 30)        >>> input = torch.randn(128, 20)        >>> output = m(input)        >>> print(output.size())        torch.Size([128, 30])    """    __constants__ = ['bias']    def __init__(self, in_features, out_features, bias=True):        super(Linear, self).__init__()        self.in_features = in_features        self.out_features = out_features        self.weight = Parameter(torch.Tensor(out_features, in_features))        if bias:            self.bias = Parameter(torch.Tensor(out_features))        else:            self.register_parameter('bias', None)        self.reset_parameters()    def reset_parameters(self):        init.kaiming_uniform_(self.weight, a=math.sqrt(5))        if self.bias is not None:            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)            bound = 1 / math.sqrt(fan_in)            init.uniform_(self.bias, -bound, bound)    @weak_script_method    def forward(self, input):        return F.linear(input, self.weight, self.bias)    def extra_repr(self):        return 'in_features={}, out_features={}, bias={}'.format(            self.in_features, self.out_features, self.bias is not None        )

def linear(input, weight, bias=None):    # type: (Tensor, Tensor, Optional[Tensor]) -> Tensor    r"""    Applies a linear transformation to the incoming data: :math:`y = xA^T + b`.    Shape:        - Input: :math:`(N, *, in\_features)` where `*` means any number of          additional dimensions        - Weight: :math:`(out\_features, in\_features)`        - Bias: :math:`(out\_features)`        - Output: :math:`(N, *, out\_features)`    """    if input.dim() == 2 and bias is not None:        # fused op is marginally faster        ret = torch.addmm(bias, input, weight.t())    else:        output = input.matmul(weight.t())        if bias is not None:            output += bias        ret = output    return ret