Method backward

tinygrad/tensor.py:852–871

Propagates the gradient of a tensor backwards through the computation graph. If the 'gradient' argument is not provided, the tensor must be a scalar, and the gradient is implicitly set to 1.0. ```python exec="true" source="above" session="tensor" result="python" t = Tensor([1.0, 2.0

(self, gradient:Tensor|None=None)

    return ret

  def backward(self, gradient:Tensor|None=None) -> Tensor:
    """
    Propagates the gradient of a tensor backwards through the computation graph.
    If the 'gradient' argument is not provided, the tensor must be a scalar, and the gradient is implicitly set to 1.0.
    ```python exec="true" source="above" session="tensor" result="python"
    t = Tensor([1.0, 2.0, 3.0, 4.0])
    t.sum().backward()
    print(t.grad.numpy())
    ```
    """
    all_uops = self.uop.toposort()
    # backward fills .grad for every in-scope non-CONST float tensor
    tensors_need_grad: list[Tensor] = [t for tref in all_tensors if (t:=tref()) is not None and \
                                       t.uop in all_uops and t.is_floating_point() and t.uop.op is not Ops.CONST]
    # clear contexts
    for t,g in zip(tensors_need_grad, self.gradient(*tensors_need_grad, gradient=gradient)):
      assert g.shape == t.shape, f"grad shape must match tensor shape, {g.shape!r} != {t.shape!r}"
      if t.grad is None: t.grad = g
      else: t.grad.assign(t.grad + g.to(t.grad.device))
    return self

  # ***** movement ops *****