I think it makes sense to be stricter. :+1:
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I think fp32 makes sense, the problem is that NumPy defaults are arch. specific
and on 32-bit platforms the default sizing for both integers ("int") and
floating point ("float") are different. A problem that has plagued us
repeatedly. The behavior of `const` iirc is trying to normalize away th
Sorry for the lack of updates been super oversubscribed lately (in the process
of finishing my thesis) plus lots of stuff at OctoML. @mbrookhart is a hero
and has a production quality version he has been working on in C++. I think he
is getting really close to shipping a first version of the
Besides mutually recursive globals deferring type checking provides no
benefits, code that doesn't type check is not a valid Relay program and can not
be used to do anything, analysis, optimization, or code generation.
We can defer type checking to the first pass, but I don't see it providing
Haven't had the chance to read the full post yet, but wanted to just say this
looks great, and the kind of things we need more of! We've been chatting about
outlining a new structure for the docs and I think this kind of technical
documentation would be good to put into a dev's guide to workin
This is one of the errors from the code produced by compiling TVM's LLVM
output. It looks like you are most likely corrupting the DLTensor pointer in
some way.
`int32(arg0.shape[0]) == 33554432`, this part is complaining that the first
dimension of the first argument's shape is wrong.
What a
It should be disabled by default, it is set at optimization level 2, so I'm not
sure why it is executing.
Can you try:
```
with relay.build_module.build_config(opt_level=2):
graph_json, lib, params = relay.build_module.build(...)
```
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This looks like someone introduced a bug or regression into the alter layout
pass, could you open an issue against dmlc master so we can CC the appropriate
people to work on this. You can try to turn
off the alter-layout optimization if you want to make progress.
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