Hi, I upgraded the TVM source code to the latest version. When I running “relay_quick_start.py”, some error printed.
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WARNING:root:Failed to download tophub package for cuda: <urlopen error [Errno 111] Connection refused>
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=(‘conv2d_nchw.cuda’, (‘TENSOR’, (1, 3, 224, 224), ‘float32’), (‘TENSOR’, (64, 3, 7, 7), ‘float32’), (2, 2), (3, 3, 3, 3), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=(‘conv2d_nchw.cuda’, (‘TENSOR’, (1, 64, 56, 56), ‘float32’), (‘TENSOR’, (64, 64, 3, 3), ‘float32’), (1, 1), (1, 1, 1, 1), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=(‘conv2d_nchw.cuda’, (‘TENSOR’, (1, 64, 56, 56), ‘float32’), (‘TENSOR’, (64, 64, 1, 1), ‘float32’), (1, 1), (0, 0, 0, 0), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=(‘conv2d_nchw.cuda’, (‘TENSOR’, (1, 64, 56, 56), ‘float32’), (‘TENSOR’, (128, 64, 3, 3), ‘float32’), (2, 2), (1, 1, 1, 1), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=(‘conv2d_nchw.cuda’, (‘TENSOR’, (1, 128, 28, 28), ‘float32’), (‘TENSOR’, (128, 128, 3, 3), ‘float32’), (1, 1), (1, 1, 1, 1), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=(‘conv2d_nchw.cuda’, (‘TENSOR’, (1, 64, 56, 56), ‘float32’), (‘TENSOR’, (128, 64, 1, 1), ‘float32’), (2, 2), (0, 0, 0, 0), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=(‘conv2d_nchw.cuda’, (‘TENSOR’, (1, 128, 28, 28), ‘float32’), (‘TENSOR’, (256, 128, 3, 3), ‘float32’), (2, 2), (1, 1, 1, 1), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=(‘conv2d_nchw.cuda’, (‘TENSOR’, (1, 256, 14, 14), ‘float32’), (‘TENSOR’, (256, 256, 3, 3), ‘float32’), (1, 1), (1, 1, 1, 1), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=(‘conv2d_nchw.cuda’, (‘TENSOR’, (1, 128, 28, 28), ‘float32’), (‘TENSOR’, (256, 128, 1, 1), ‘float32’), (2, 2), (0, 0, 0, 0), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=(‘conv2d_nchw.cuda’, (‘TENSOR’, (1, 256, 14, 14), ‘float32’), (‘TENSOR’, (512, 256, 3, 3), ‘float32’), (2, 2), (1, 1, 1, 1), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=(‘conv2d_nchw.cuda’, (‘TENSOR’, (1, 512, 7, 7), ‘float32’), (‘TENSOR’, (512, 512, 3, 3), ‘float32’), (1, 1), (1, 1, 1, 1), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=(‘conv2d_nchw.cuda’, (‘TENSOR’, (1, 256, 14, 14), ‘float32’), (‘TENSOR’, (512, 256, 1, 1), ‘float32’), (2, 2), (0, 0, 0, 0), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
Traceback (most recent call last): File “/media/cvg/DATA/tvm/tutorials/relay_quick_start.py”, line 100, in graph, lib, params = relay.build(mod, target, params=params)
File “/home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/relay/build_module.py”, line 251, in build graph_json, mod, params = bld_mod.build(mod, target, target_host, params)
File “/home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/relay/build_module.py”, line 120, in build self._build(mod, target, target_host)
File “tvm/_ffi/_cython/./packed_func.pxi”, line 321, in core.PackedFuncBase.call
File “tvm/_ffi/_cython/./packed_func.pxi”, line 256, in core.FuncCall
File “tvm/_ffi/_cython/./packed_func.pxi”, line 245, in core.FuncCall3
File “tvm/_ffi/_cython/./base.pxi”, line 160, in core.CALL tvm._ffi.base.TVMError: Traceback (most recent call last):
[bt] (8) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::backend::MemoizedExprTranslator<tvm::runtime::Array<tvm::te::Tensor, void> >::VisitExpr(tvm::RelayExpr const&)+0xa9) [0x7f8df37db339]
[bt] (7) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void> (tvm::RelayExpr const&)>::VisitExpr(tvm::RelayExpr const&)+0x82) [0x7f8df37db102]
[bt] (6) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void> (tvm::RelayExpr const&)>::InitVTable()::{lambda(tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void> (tvm::RelayExpr const&)>)#6}::_FUN(tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void> (tvm::RelayExpr const&)>)+0x27) [0x7f8df37ce0b7]
[bt] (5) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ScheduleGetter::VisitExpr_(tvm::relay::CallNode const*)+0x14f) [0x7f8df37d373f]
[bt] (4) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::backend::MemoizedExprTranslator<tvm::runtime::Array<tvm::te::Tensor, void> >::VisitExpr(tvm::RelayExpr const&)+0xa9) [0x7f8df37db339]
[bt] (3) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void> (tvm::RelayExpr const&)>::VisitExpr(tvm::RelayExpr const&)+0x82) [0x7f8df37db102]
[bt] (2) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void> (tvm::RelayExpr const&)>::InitVTable()::{lambda(tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void> (tvm::RelayExpr const&)>)#6}::_FUN(tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void> (tvm::RelayExpr const&)>)+0x27) [0x7f8df37ce0b7]
[bt] (1) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ScheduleGetter::VisitExpr_(tvm::relay::CallNode const*)+0x694) [0x7f8df37d3c84]
[bt] (0) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0x160928b) [0x7f8df396228b]
File “tvm/_ffi/_cython/./packed_func.pxi”, line 55, in core.tvm_callback
File “/home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/relay/backend/compile_engine.py”, line 263, in lower_call op, call.attrs, inputs, ret_type, target)
File “/home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/relay/backend/compile_engine.py”, line 182, in select_implementation all_impls = get_valid_implementations(op, attrs, inputs, out_type, target)
File “/home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/relay/backend/compile_engine.py”, line 123, in get_valid_implementations strategy = fstrategy(attrs, inputs, out_type, target)
File “/home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/target/generic_func.py”, line 45, in call return _ffi_api.GenericFuncCallFunc(self, *args)
File “tvm/_ffi/_cython/./packed_func.pxi”, line 321, in core.PackedFuncBase.call
File “tvm/_ffi/_cython/./packed_func.pxi”, line 266, in core.FuncCall
File “tvm/_ffi/_cython/./base.pxi”, line 160, in core.CALL
[bt] (3) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(TVMFuncCall+0x61) [0x7f8df3965ba1]
[bt] (2) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0x102b047) [0x7f8df3384047]
[bt] (1) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::GenericFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const+0x1b8) [0x7f8df3383d98]
[bt] (0) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0x160928b) [0x7f8df396228b]
File “tvm/_ffi/_cython/./packed_func.pxi”, line 55, in core.tvm_callback
File “/home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/relay/op/strategy/cuda.py”, line 462, in dense_strategy_cuda if nvcc.have_tensorcore(tvm.gpu(0).compute_version):
File “/home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/_ffi/runtime_ctypes.py”, line 233, in compute_version self.device_type, self.device_id, 4)
File “/home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/_ffi/runtime_ctypes.py”, line 195, in _GetDeviceAttr device_type, device_id, attr_id)
File “tvm/_ffi/_cython/./packed_func.pxi”, line 321, in core.PackedFuncBase.call
File “tvm/_ffi/_cython/./packed_func.pxi”, line 256, in core.FuncCall
File “tvm/_ffi/_cython/./packed_func.pxi”, line 245, in core.FuncCall3
File “tvm/_ffi/_cython/./base.pxi”, line 160, in core.CALL
[bt] (4) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(TVMFuncCall+0x61) [0x7f8df3965ba1]
[bt] (3) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0x160af4d) [0x7f8df3963f4d]
[bt] (2) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::runtime::DeviceAPIManager::GetAPI(int, bool)+0x15c) [0x7f8df39680dc]
[bt] (1) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::runtime::DeviceAPIManager::GetAPI(std::__cxx11::basic_string<char, std::char_traits, std::allocator >, bool)+0x2e6) [0x7f8df3967e16]
[bt] (0) /home/cvg/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0x16093c2) [0x7f8df39623c2]
File “/media/cvg/DATA/tvm/src/runtime/c_runtime_api.cc”, line 131
TVMError: Check failed: allow_missing: Device API gpu is not enabled.
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I found that running relay.build automatically call autoTVM. If I want to disable autoTVM, what should I do?