Hello!
After debugging I have some new discoveries
In the
https://github.com/apache/incubator-tvm/blob/1014fefa54b5f0a359501b6d19ea3b5a52d6dca6/python/tvm/autotvm/measure/local_executor.py#L130
Parameter "do_fork" will affect the success of autotvm when the schedule
increases the acquisition
Hello!
After debugging I have some new discoveries
In the
https://github.com/apache/incubator-tvm/blob/1014fefa54b5f0a359501b6d19ea3b5a52d6dca6/python/tvm/autotvm/measure/local_executor.py#L130
Parameter "do_fork" will affect the success of autotvm when the schedule
increases the acquisition
Thank you for your reply!
I tried to build the model directly without running AutoTVM and it can run
normally. But when I used autotvm to tune conv2d, the program always reported
this error even if I set n_trial to 5000.
To reproduce the problem, I modified the code:
[incubator-tvm](https://
Hello!
I added a judgment about tensorcore
(“nvcc.have_tensorcore(tvm.gpu(0).compute_version)”) in conv2d schedule, like
this:
"""schedule optimized for NHWC direct conv2d"""
pad_data, kernel = s[Conv].op.input_tensors
s[pad_data].compute_inline()
test_tensorcore = nvcc.have_
Hello!
I wrote an op composed of four CUDA kernels, and now I want to optimize the op,
so I need to know the time ratio of the four kernels.
I tried nvprof but was unable to use it due to permission issues.
Is there a similar test function in TVM?
My current test code is as follows:
mod
