Merge pull request #397 from goldsborough/custom-op-faq

Change format of note about types in custom TorchScript ops

Author: soumith

advanced_source/torch_script_custom_ops.rst

@@ -85,6 +85,15 @@ in Python). The return type of our ``warp_perspective`` function will also be a
   allocate and initialize new tensor objects in C++ (not required for this
   operator).
 
+.. attention::
+
+  The TorchScript compiler understands a fixed number of types. Only these types
+  can be used as arguments to your custom operator. Currently these types are:
+  ``torch::Tensor``, ``torch::Scalar``, ``double``, ``int64_t`` and
+  ``std::vector``s of these types. Note that __only__ ``double`` and __not__
+  ``float``, and __only__ ``int64_t`` and __not__ other integral types such as
+  ``int``, ``short`` or ``long`` are supported.
+
 Inside of our function, the first thing we need to do is convert our PyTorch
 tensors to OpenCV matrices, as OpenCV's ``warpPerspective`` expects ``cv::Mat``
 objects as inputs. Fortunately, there is a way to do this **without copying
@@ -874,7 +883,9 @@ lines between Python, TorchScript and C++ to blend smoothly.
 
 As always, if you run into any problems or have questions, you can use our
 `forum <https://discuss.pytorch.org/>`_ or `GitHub issues
-<https://github.com/pytorch/pytorch/issues>`_ to get in touch.
+<https://github.com/pytorch/pytorch/issues>`_ to get in touch. Also, our
+`frequently asked questions (FAQ) page
+<https://pytorch.org/cppdocs/notes/faq.html>`_ may have helpful information.
 
 Appendix A: More Ways of Building Custom Operators
 --------------------------------------------------
@@ -1076,34 +1087,3 @@ visible to TorchScript:
   >>> torch.ops.load_library("warp_perspective.so")
   >>> print(torch.ops.custom.warp_perspective)
   <built-in method custom::warp_perspective of PyCapsule object at 0x7ff51c5b7bd0>
-
-
-FAQ
----------------------------------------
-
-
-Custom Ops Types
-*******************
-
-TorchScript custom Ops supports a limited number of types. The types supported by JIT custom ops are declared `here<https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/core/jit_type.h>`_.
-
-Currently, the types that are supported are:
-
-- torch::Tensor
-- at::Scalar
-
-This represents the python number
-
-- double
-
-Note that float is not currently supported.
-
-- int64_t
-
-Note that int is not currently supported.
-
-- bool
-- string
-- std::vector<torch::Tensor>
-- std::vector<double>
-- std::vector<int64_t>