In ```tvm.transform.Passcontext```, one of parameters is ```opt_level```, the
explanation is " The optimization level of this pass.". Are there any more
detail explanation of it? what is the difference between ```opt_level = 1```
and ```opt_level = 2```? what will happen while ```opt_level = 1
Should add tvm/python to the paths of interpreter !
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I wrote a script to generate the corresponding header file，and it works(without
GetFunction.).
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I've updated my branch, so an environment variable is no longer used to set the
metrics. Here is an example of how to do it now:
https://github.com/tkonolige/incubator-tvm/blob/profiler_papi/tests/python/unittest/test_runtime_profiling.py#L86-L104
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Hello, I have a machine with 40 cpu cores, and a process that runs a TVM
application on 10 threads. I wish that each TVM thread will use 4 cpu cores so
I set TVM_NUM_THREADS=4.
Then what happens is without cpu affinity (TVM_BIND_THREADS=0), the 10 threads
will try to occupy cpus randomly, sl
Thanks for the reply.
I also find `relay.nn.batch_matmul` has a similar problem (no transpose for
`y`).
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Emm ... This seems to be a flaky problem.
The op representation of `dense` in relay support multi-dim(exp. doc string,
shape functions), while the current computation(exp. `topi.nn.dense`) does not.
I guess that dense op is desinged to support multi-dim, but guys only added
simpler computatio
The
[doc](https://tvm.apache.org/docs/api/python/relay/nn.html#tvm.relay.nn.dense)
says `relay.nn.dense` can have input tensors with shape `(d_1, d_2, …, d_n,
units_in)`. However when I use a 3d tensor as the input of dense, I get this
error:
```
File
"/root/anaconda3/envs/tvm0.8-dev/lib/
