jwfromm opened a new pull request #7063:
URL: https://github.com/apache/tvm/pull/7063
The recent addition of tensorcore schedules has broken TVM's ability to
compile for cuda on a machine without a GPU. This is because the strategy
registration for tensorcores calls `tvm.gpu(0).compute_version`, which fails
when no gpu is present. I've changed the behavior of `nvcc.have_tensorcore` to
check `AutotvmGlobalScope.current.cuda_target_arch` when a GPU isn't present.
This allows a user to call something like
`tvm.autotvm.measure.measure_methods.set_cuda_target_arch("sm_62")` to specify
a cuda cross compilation target on a machine without a GPU and build correctly.
I'm not sure how to test this since it would require a CPU node that's built
with the cuda toolkit. Let me know if you have an opinion on tests to add to
prevent an error like this from sneaking in again.
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