pytroch中ctx和self的区别

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阅读某个pytorch模型源代码时碰见的ctx参数, 查阅了资料大概总结一下

1. ctx是context的缩写, 翻译成"上下文; 环境"
2. ctx专门用在静态方法中
3. self指的是实例对象; 而ctx用在静态方法中, 调用的时候不需要实例化对象, 直接通过类名就可以调用, 所以self在静态方法中没有意义
4. 自定义的forward()方法和backward()方法的第一个参数必须是ctx; ctx可以保存forward()中的变量,以便在backward()中继续使用, 下一条是具体的示例
5. ​​ctx.save_for_backward(a, b)​​​能够保存forward()静态方法中的张量, 从而可以在backward()静态方法中调用, 具体地, 下面地代码通过​​a, b = ctx.saved_tensors​​重新得到a和b
6. ​​ctx.needs_input_grad​​​是一个元组, 元素是True或者False, 表示forward()中对应的输入是否需要求导, 比如​​ctx.needs_input_grad[0]​​指的是下面forwad()代码中indices是否需要求导

class SpecialSpmmFunction(torch.autograd.Function):
    """
    Special function for only sparse region backpropataion layer.
    """
    # 自定义前向传播过程
    @staticmethod
    def forward(ctx, indices, values, shape, b):
        assert indices.requires_grad == False
        a = torch.sparse_coo_tensor(indices, values, shape)
        ctx.save_for_backward(a, b)
        ctx.N = shape[0]
        return torch.matmul(a, b)
    # 自定义反向传播过程
    @staticmethod
    def backward(ctx, grad_output):
        a, b = ctx.saved_tensors
        grad_values = grad_b = None
        if ctx.needs_input_grad[1]:
            grad_a_dense = grad_output.matmul(b.t())
            edge_idx = a._indices()[0, :] * ctx.N + a._indices()[1, :]
            grad_values = grad_a_dense.view(-1)[edge_idx]

        if ctx.needs_input_grad[3]:
            grad_b = a.t().matmul(grad_output)
        return None, grad_values, None, grad_b

ctx还能调用很多方法, pytorch1.3.1源码中竟然说"no doc", 没有相关的文档…

class _FunctionBase(object):
    # no doc
    @classmethod
    def apply(cls, *args, **kwargs): # real signature unknown
        pass

    def register_hook(self, *args, **kwargs): # real signature unknown
        pass

    def _do_backward(self, *args, **kwargs): # real signature unknown
        pass

    def _do_forward(self, *args, **kwargs): # real signature unknown
        pass

    def _register_hook_dict(self, *args, **kwargs): # real signature unknown
        pass

    def __init__(self, *args, **kwargs): # real signature unknown
        pass

    @staticmethod # known case of __new__
    def __new__(*args, **kwargs): # real signature unknown
        """ Create and return a new object.  See help(type) for accurate signature. """
        pass

    dirty_tensors = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default

    metadata = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default

    needs_input_grad = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default

    next_functions = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default

    non_differentiable = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default

    requires_grad = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default

    saved_tensors = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default

    saved_variables = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default

    to_save = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default