Hi,
after autotuning for opencl on android device, I get the following error:
Compile...
WARNING:autotvm:Cannot find config for target=opencl -device=mali, workload=('dense', (1, 512, 'float32'), (1000, 512, 'float32'), 0, 'float32'). A fallback configuration is used, which may bring great performance regression.
WARNING:autotvm:Cannot find config for target=opencl -device=mali, workload=('conv2d', (1, 3, 224, 224, 'float32'), (64, 3, 7, 7, 'float32'), (2, 2), (3, 3), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
[15:29:21] /home/SERILOCAL/n.perto/Documents/tvm/src/pass/vectorize_loop.cc:365: Detect vector condition in Vectorized Loop, scalarizing...
[15:29:21] /home/SERILOCAL/n.perto/Documents/tvm/src/pass/vectorize_loop.cc:365: Detect vector condition in Vectorized Loop, scalarizing...
Upload...
Evaluate inference time cost...
Traceback (most recent call last):
File "tutorials/autotvm/tune_relay_mobile_gpu.py", line 358, in <module>
tune_and_evaluate(tuning_option)
File "tutorials/autotvm/tune_relay_mobile_gpu.py", line 351, in tune_and_evaluate
prof_res = np.array(ftimer().results) * 1000 # convert to millisecond
File "/home/SERILOCAL/n.perto/Documents/tvm/python/tvm/module.py", line 194, in evaluator
blob = feval(*args)
File "/home/SERILOCAL/n.perto/Documents/tvm/python/tvm/_ffi/_ctypes/function.py", line 209, in __call__
raise get_last_ffi_error()
tvm._ffi.base.TVMError: Traceback (most recent call last):
[bt] (3) /home/SERILOCAL/n.perto/Documents/tvm/build/libtvm.so(TVMFuncCall+0x61) [0x7f039832c511]
[bt] (2) /home/SERILOCAL/n.perto/Documents/tvm/build/libtvm.so(+0x9f25ab) [0x7f039836f5ab]
[bt] (1) /home/SERILOCAL/n.perto/Documents/tvm/build/libtvm.so(+0x9e7af7) [0x7f0398364af7]
[bt] (0) /home/SERILOCAL/n.perto/Documents/tvm/build/libtvm.so(+0x173c82) [0x7f0397af0c82]
File "/home/SERILOCAL/n.perto/Documents/tvm/src/runtime/rpc/rpc_session.cc", line 962
TVMError: Check failed: code == RPCCode: :kReturn: code=4
I just ran the tutorial example with lower n_trial
.
Thanks in advance for your help.