Add compatibility notes regarding unpacking quantized weights. (#56)

Co-authored-by: Bryce Arden <arden.bryce@gmail.com>

frontends/pytorch/docs/compatibility.md

@@ -0,0 +1,20 @@
+# Compatibility notes
+
+This document contains known compatibility issues with the PyTorch integration.
+Some items may be permanent limitations and other may just be capturing
+plans for future work. In general, this only applies to the default
+configuration, not the "type dispatch" (PyTorch 1.4) integration.
+
+
+## Pending Work Needed
+
+### Unpacking quantized weights
+
+Some of the torch::jit::Operator 's (especially the quantized::  ones) have already gone through some lowering steps. Specifically, the quantized::conv operators are pre-packed and stored as ConvPackedParam attributes on the Module instead of just passing the weight/bias tensors as SSA arguments to the Operator[0] [1]. How they are packed depends on whether the fbgemm or the XNNPACK backends are used...
+
+I think this comes back to the ability to pass a "CustomClass" as an SSA Value into an Operator, which may be difficult for us to lower to TCF...
+Glow (and others) get around this by adding custom passes to convert the PackedParams to a traditional glow::unpacked_quantized_conv operation [2], but that adds some layers of lowering in TorchScript land before we would want to call off to get_registered_ops on the python side (may not be avoidable?)
+
+[0]: https://github.com/pytorch/pytorch/blob/dc67b47bc9d53dbeb898a4d920b0225ac73629ec/aten/src/ATen/native/quantized/library.cpp#L63-L69
+[1]: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/quantized/cpu/conv_packed_params.h
+[2]: https://github.com/pytorch/glow/blob/b62ec449c43b77722c119b53b3ea5aec9be3edb9/torch_glow/src/TorchGlowBackend.cpp#L98-L116