Hi electriclilies, I'm glad to hear that! It will be gorgeous if the new
quantization framework is easy to add new methods. Noteworthily, the quantize
method depends on the graph structure.
Thanks a lot.
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Like a pattern that exists across multiple functions? Yes, we'll need a
FunctionPattern.
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@mbrookhart So I tried the way you suggested and I'm able to rewrite the
pattern inside a function. I wonder if it's also possible to partition and
rewrite a pattern across multiple functions? I suspect if this would need the
support of the potential `FunctionPattern`.
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What @matt-arm pointed out is correct. In addition, your figure is not exactly
correct. relay.build actually goes the same flow as AutoTVM/TE schedule. When
calling relay.build, it lowers each operator to TE according to Relay op
strategy. The op strategy will select a TE compute/schedule for
I am working on the new quantization framework right now -- it is currently in
progress.
Our rough timeline is an RFC in three weeks to a month from now, and then
upstreaming the finalized quantization framework in the two weeks after that.
It would be great to get your feedback on the final
I'm no expert with this, but if you take a look in build_module.py, you can see
what TIR passes are run after the schedules have been lowered. I'll paste them
here for convenience:
```
tvm.tir.transform.InjectPrefetch(),
tvm.tir.transform.StorageFlatten(64, instrument_bound_checkers),
tvm.tir.t
Hi developers：
How can I add the new quantization method for TVM? Or is there a tutorial ?
The community experts could help to clarify the question? I would highly
appreciate your response.
Thanks a lot!
Best Regards, Fred
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Hi guys, I'm new to TVM and I was trying to test the performance of a single
operator on NVGPU.
So far I found the doc about how to test model
benchmark(https://github.com/apache/incubator-tvm/blob/main/apps/benchmark/README.md).
And the doc about [Tuning High Performance Convolution on NVIDIA
I am a newer to TVM, from my point, compute + schedule + auto tuning convert
the relay IR to Tensor IR.
this process includes many hardware special optimizations related with schedule
primitives. After the auto tuning, Tensor IR was generated.
Q1: Besides the AutoTVM(include schedules), what
