I saw that jit supports the differential ability of some operators (about 80), but the number of ATen’s operators (about 700) is much larger than the current support.
When does the plan support the differential ability of all operators?

  
      github.com
  
  
    pytorch/pytorch/blob/master/torch/csrc/jit/autodiff.cpp#L30-L146 1

bool isDifferentiable(Node* n) {
// TODO: scalar-tensor ops should be canonicalized
static OperatorSet differentiable_ops = {
    "aten::add(Tensor self, Tensor other, *, Scalar alpha) -> Tensor",
    "aten::add(Tensor self, Scalar other, Scalar alpha) -> Tensor",
    "aten::sub(Tensor self, Tensor other, *, Scalar alpha) -> Tensor",
    "aten::sub(Tensor self, Scalar other, Scalar alpha) -> Tensor",
    "aten::mul(Tensor self, Tensor other) -> Tensor",
    "aten::mul(Tensor self, Scalar other) -> Tensor",
    "aten::div(Tensor self, Tensor other) -> Tensor",
    "aten::div(Tensor self, Scalar other) -> Tensor",
    "aten::max(Tensor self, Tensor other) -> Tensor",
    "aten::min(Tensor self, Tensor other) -> Tensor",
    "aten::sigmoid(Tensor self) -> Tensor",
    "aten::tanh(Tensor self) -> Tensor",
    "aten::relu(Tensor self) -> Tensor",
    "aten::threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor",
    "aten::erf(Tensor self) -> Tensor",
    "aten::erfc(Tensor self) -> Tensor",
    "aten::exp(Tensor self) -> Tensor",


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