Hecmay edited a comment on issue #6634:
URL: https://github.com/apache/incubator-tvm/issues/6634#issuecomment-704469969
Hi @comaniac Cody, thanks for your reply.
I just used the default parameters of 10 trails. All of 10 trails returned
the same error (as shown below) when running the evaluation. The finally
returned schedule (from `Autoschedule(policy, options)`) is the unoptimized
schedule (with no thread or block binding so I was not able run it on GPU).
```shell
] (0) /home/sx/dlcb/build/tvm/build/libtvm.so(+0x1791844) [0x7f6429edf844]
File "/home/sx/dlcb/build/tvm/src/runtime/cuda/cuda_device_api.cc", line
115
CUDA: Check failed: e == cudaSuccess || e == cudaErrorCudartUnloading:
initialization error
, all_cost:0.55, Tstamp:1602008338.18)
```
I tried to look into the problematic function (AllocateDataSpace:108) in
`cuda_device_api.cc`. It seems that `cudaSetDevice()` does not able to find the
device, and program builder just aborted from there.
```c++
if (ctx.device_type == kDLCPUPinned) {
CUDA_CALL(cudaMallocHost(&ret, nbytes));
} else {
CUDA_CALL(cudaSetDevice(ctx.device_id));
CUDA_CALL(cudaMalloc(&ret, nbytes));
}
```
This is quite wired. Everything can run smoothly using the python API. But
when using C++ APIs, the runtime cannot detect the device or context, and thus
cannot find usable schedules.
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