Running the tune_conv2d_cuda.py
script appeared to run, returning:
Finish loading 40 records
Time cost of this operator: 0.004278
However, jumping back in the output showed that there was a series of errors:
No: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(TVMFuncCall+0x70) [0x7faaa6bf00]\n [bt] (3) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(+0x5fea80) [0x7faa013a80]\n [bt] (2) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(tvm::tran
sform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x2b0) [0x7faa012970]\n [bt] (1) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(tvm::tir::tr
ansform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x9c4) [0x7faa26e214]\n [bt] (0) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(+0x1053f
b0) [0x7faaa68fb0]\n File "/home/user/tools/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 81, in cfun\n rv = local_pyfunc(*pyargs)\n File "/home/user/tools/tvm/python/tvm/autotvm/mea
sure/measure_methods.py", line 747, in verify_pass\n raise InstantiationError("Skipped because of invalid gpu kernel")\ntvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel',),), error_no=1, all_cost=0.1579885482788086, timestamp=1612291653.9391131) [('tile_f', [-1, 8, 2, 32]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc',
[-1, 8, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5915333
No: 2 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(TVMFuncCa
ll+0x70) [0x7faaa6bf00]\n [bt] (3) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(+0x5fea80) [0x7faa013a80]\n [bt] (2) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(tvm::tran
sform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x2b0) [0x7faa012970]\n [bt] (1) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(tvm::tir::tr
ansform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x9c4) [0x7faa26e214]\n [bt] (0) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(+0x1053f
b0) [0x7faaa68fb0]\n File "/home/user/tools/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 81, in cfun\n rv = local_pyfunc(*pyargs)\n File "/home/user/tools/tvm/python/tvm/autotvm/mea
sure/measure_methods.py", line 747, in verify_pass\n raise InstantiationError("Skipped because of invalid gpu kernel")\ntvm.autotvm.task.space.InstantiationError: Skipped because of invali
d gpu kernel',),), error_no=1, all_cost=0.1726396083831787, timestamp=1612291653.9654841) [('tile_f', [-1, 4, 2, 2]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [
-1, 8, 64]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,8497126
No: 3 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(TVMFuncCa
ll+0x70) [0x7faaa6bf00]\n [bt] (3) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(+0x5fea80) [0x7faa013a80]\n [bt] (2) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(tvm::tran
sform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x2b0) [0x7faa012970]\n [bt] (1) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(tvm::tir::tr
ansform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x9c4) [0x7faa26e214]\n [bt] (0) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(+0x1053f
b0) [0x7faaa68fb0]\n File "/home/user/tools/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 81, in cfun\n rv = local_pyfunc(*pyargs)\n File "/home/user/tools/tvm/python/tvm/autotvm/mea
sure/measure_methods.py", line 747, in verify_pass\n raise InstantiationError("Skipped because of invalid gpu kernel")\ntvm.autotvm.task.space.InstantiationError: Skipped because of invali
d gpu kernel',),), error_no=1, all_cost=0.1727280616760254, timestamp=1612291653.9658356) [('tile_f', [-1, 4, 8, 8]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [
-1, 1, 512]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10257876
No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(TVMFuncCa
ll+0x70) [0x7faaa6bf00]\n [bt] (3) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(+0x5fea80) [0x7faa013a80]\n [bt] (2) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(tvm::tran
sform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x2b0) [0x7faa012970]\n [bt] (1) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(tvm::tir::tr
ansform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x9c4) [0x7faa26e214]\n [bt] (0) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(+0x1053f
b0) [0x7faaa68fb0]\n File "/home/user/tools/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 81, in cfun\n rv = local_pyfunc(*pyargs)\n File "/home/user/tools/tvm/python/tvm/autotvm/mea
sure/measure_methods.py", line 747, in verify_pass\n raise InstantiationError("Skipped because of invalid gpu kernel")\ntvm.autotvm.task.space.InstantiationError: Skipped because of invali
d gpu kernel',),), error_no=1, all_cost=0.1717667579650879, timestamp=1612291653.9876938) [('tile_f', [-1, 2, 1, 2]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [
-1, 64, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7447936
No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(TVMFuncCa
ll+0x70) [0x7faaa6bf00]\n [bt] (3) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(+0x5fea80) [0x7faa013a80]\n [bt] (2) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(tvm::tran
sform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x2b0) [0x7faa012970]\n [bt] (1) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(tvm::tir::tr
ansform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x9c4) [0x7faa26e214]\n [bt] (0) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(+0x1053f
b0) [0x7faaa68fb0]\n File "/home/user/tools/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 81, in cfun\n rv = local_pyfunc(*pyargs)\n File "/home/user/tools/tvm/python/tvm/autotvm/mea
sure/measure_methods.py", line 747, in verify_pass\n raise InstantiationError("Skipped because of invalid gpu kernel")\ntvm.autotvm.task.space.InstantiationError: Skipped because of invali
d gpu kernel',),), error_no=1, all_cost=0.14327168464660645, timestamp=1612291654.3898673) [('tile_f', [-1, 64, 8, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc',
[-1, 256, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2161093
No: 6 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(TVMFuncCa
ll+0x70) [0x7faaa6bf00]\n [bt] (3) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(+0x5fea80) [0x7faa013a80]\n [bt] (2) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(tvm::tran
sform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x2b0) [0x7faa012970]\n [bt] (1) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(tvm::tir::tr
ansform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x9c4) [0x7faa26e214]\n [bt] (0) /home/user/tools/incubator-tvm-wheest/build/libtvm.so(+0x1053f
b0) [0x7faaa68fb0]\n File "/home/user/tools/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 81, in cfun\n rv = local_pyfunc(*pyargs)\n File "/home/user/tools/tvm/python/tvm/autotvm/mea
sure/measure_methods.py", line 747, in verify_pass\n raise InstantiationError("Skipped because of invalid gpu kernel")\ntvm.autotvm.task.space.InstantiationError: Skipped because of invali
d gpu kernel',),), error_no=1, all_cost=0.14082765579223633, timestamp=1612291654.3903658) [('tile_f', [-1, 64, 2, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc',
[-1, 4, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,462994
....
Best config:
[('tile_f', [-1, 4, 2, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 2]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5475910
Finish loading 40 records
Time cost of this operator: 0.004278
I am able to use this GPU with TensorRT, and didn’t have any issues building TVM with CUDA.
I get a Segmentation fault (core dumped)
with no other information for my full ONNX model.