Run relay_quick_start.py Wrong

[CallmeZhangChenchen]

zzjhtest@zzjhtest:~/ZCC/testtvm$ python3 relay_quick_start.py
v0.0.4
def @main(%data: Tensor[(1, 3, 224, 224), float32], %bn_data_gamma: Tensor[(3), float32], %bn_data_beta: Tensor[(3), float32], %bn_data_moving_mean: Tensor[(3), float32], %bn_data_moving_var: Tensor[(3), float32], %conv0_weight: Tensor[(64, 3, 7, 7), float32], %bn0_gamma: Tensor[(64), float32], %bn0_beta: Tensor[(64), float32], %bn0_moving_mean: Tensor[(64), float32], %bn0_moving_var: Tensor[(64), float32], %stage1_unit1_bn1_gamma: Tensor[(64), float32], %stage1_unit1_bn1_beta: Tensor[(64), float32], %stage1_unit1_bn1_moving_mean: Tensor[(64), float32], %stage1_unit1_bn1_moving_var: Tensor[(64), float32], %stage1_unit1_conv1_weight: Tensor[(64, 64, 3, 3), float32], %stage1_unit1_bn2_gamma: Tensor[(64), float32], %stage1_unit1_bn2_beta: Tensor[(64), float32], %stage1_unit1_bn2_moving_mean: Tensor[(64), float32], %stage1_unit1_bn2_moving_var: Tensor[(64), float32], %stage1_unit1_conv2_weight: Tensor[(64, 64, 3, 3), float32], %stage1_unit1_sc_weight: Tensor[(64, 64, 1, 1), float32], %stage1_unit2_bn1_gamma: Tensor[(64), float32], %stage1_unit2_bn1_beta: Tensor[(64), float32], %stage1_unit2_bn1_moving_mean: Tensor[(64), float32], %stage1_unit2_bn1_moving_var: Tensor[(64), float32], %stage1_unit2_conv1_weight: Tensor[(64, 64, 3, 3), float32], %stage1_unit2_bn2_gamma: Tensor[(64), float32], %stage1_unit2_bn2_beta: Tensor[(64), float32], %stage1_unit2_bn2_moving_mean: Tensor[(64), float32], %stage1_unit2_bn2_moving_var: Tensor[(64), float32], %stage1_unit2_conv2_weight: Tensor[(64, 64, 3, 3), float32], %stage2_unit1_bn1_gamma: Tensor[(64), float32], %stage2_unit1_bn1_beta: Tensor[(64), float32], %stage2_unit1_bn1_moving_mean: Tensor[(64), float32], %stage2_unit1_bn1_moving_var: Tensor[(64), float32], %stage2_unit1_conv1_weight: Tensor[(128, 64, 3, 3), float32], %stage2_unit1_bn2_gamma: Tensor[(128), float32], %stage2_unit1_bn2_beta: Tensor[(128), float32], %stage2_unit1_bn2_moving_mean: Tensor[(128), float32], %stage2_unit1_bn2_moving_var: Tensor[(128), float32], %stage2_unit1_conv2_weight: Tensor[(128, 128, 3, 3), float32], %stage2_unit1_sc_weight: Tensor[(128, 64, 1, 1), float32], %stage2_unit2_bn1_gamma: Tensor[(128), float32], %stage2_unit2_bn1_beta: Tensor[(128), float32], %stage2_unit2_bn1_moving_mean: Tensor[(128), float32], %stage2_unit2_bn1_moving_var: Tensor[(128), float32], %stage2_unit2_conv1_weight: Tensor[(128, 128, 3, 3), float32], %stage2_unit2_bn2_gamma: Tensor[(128), float32], %stage2_unit2_bn2_beta: Tensor[(128), float32], %stage2_unit2_bn2_moving_mean: Tensor[(128), float32], %stage2_unit2_bn2_moving_var: Tensor[(128), float32], %stage2_unit2_conv2_weight: Tensor[(128, 128, 3, 3), float32], %stage3_unit1_bn1_gamma: Tensor[(128), float32], %stage3_unit1_bn1_beta: Tensor[(128), float32], %stage3_unit1_bn1_moving_mean: Tensor[(128), float32], %stage3_unit1_bn1_moving_var: Tensor[(128), float32], %stage3_unit1_conv1_weight: Tensor[(256, 128, 3, 3), float32], %stage3_unit1_bn2_gamma: Tensor[(256), float32], %stage3_unit1_bn2_beta: Tensor[(256), float32], %stage3_unit1_bn2_moving_mean: Tensor[(256), float32], %stage3_unit1_bn2_moving_var: Tensor[(256), float32], %stage3_unit1_conv2_weight: Tensor[(256, 256, 3, 3), float32], %stage3_unit1_sc_weight: Tensor[(256, 128, 1, 1), float32], %stage3_unit2_bn1_gamma: Tensor[(256), float32], %stage3_unit2_bn1_beta: Tensor[(256), float32], %stage3_unit2_bn1_moving_mean: Tensor[(256), float32], %stage3_unit2_bn1_moving_var: Tensor[(256), float32], %stage3_unit2_conv1_weight: Tensor[(256, 256, 3, 3), float32], %stage3_unit2_bn2_gamma: Tensor[(256), float32], %stage3_unit2_bn2_beta: Tensor[(256), float32], %stage3_unit2_bn2_moving_mean: Tensor[(256), float32], %stage3_unit2_bn2_moving_var: Tensor[(256), float32], %stage3_unit2_conv2_weight: Tensor[(256, 256, 3, 3), float32], %stage4_unit1_bn1_gamma: Tensor[(256), float32], %stage4_unit1_bn1_beta: Tensor[(256), float32], %stage4_unit1_bn1_moving_mean: Tensor[(256), float32], %stage4_unit1_bn1_moving_var: Tensor[(256), float32], %stage4_unit1_conv1_weight: Tensor[(512, 256, 3, 3), float32], %stage4_unit1_bn2_gamma: Tensor[(512), float32], %stage4_unit1_bn2_beta: Tensor[(512), float32], %stage4_unit1_bn2_moving_mean: Tensor[(512), float32], %stage4_unit1_bn2_moving_var: Tensor[(512), float32], %stage4_unit1_conv2_weight: Tensor[(512, 512, 3, 3), float32], %stage4_unit1_sc_weight: Tensor[(512, 256, 1, 1), float32], %stage4_unit2_bn1_gamma: Tensor[(512), float32], %stage4_unit2_bn1_beta: Tensor[(512), float32], %stage4_unit2_bn1_moving_mean: Tensor[(512), float32], %stage4_unit2_bn1_moving_var: Tensor[(512), float32], %stage4_unit2_conv1_weight: Tensor[(512, 512, 3, 3), float32], %stage4_unit2_bn2_gamma: Tensor[(512), float32], %stage4_unit2_bn2_beta: Tensor[(512), float32], %stage4_unit2_bn2_moving_mean: Tensor[(512), float32], %stage4_unit2_bn2_moving_var: Tensor[(512), float32], %stage4_unit2_conv2_weight: Tensor[(512, 512, 3, 3), float32], %bn1_gamma: Tensor[(512), float32], %bn1_beta: Tensor[(512), float32], %bn1_moving_mean: Tensor[(512), float32], %bn1_moving_var: Tensor[(512), float32], %fc1_weight: Tensor[(1000, 512), float32], %fc1_bias: Tensor[(1000), float32]) -> Tensor[(1, 1000), float32] {
%0 = nn.batch_norm(%data, %bn_data_gamma, %bn_data_beta, %bn_data_moving_mean, %bn_data_moving_var, epsilon=2e-05f, scale=False) /* ty=(Tensor[(1, 3, 224, 224), float32], Tensor[(3), float32], Tensor[(3), float32]) /;
%1 = %0.0;
%2 = nn.conv2d(%1, %conv0_weight, strides=[2, 2], padding=[3, 3, 3, 3], channels=64, kernel_size=[7, 7]) / ty=Tensor[(1, 64, 112, 112), float32] /;
%3 = nn.batch_norm(%2, %bn0_gamma, %bn0_beta, %bn0_moving_mean, %bn0_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 64, 112, 112), float32], Tensor[(64), float32], Tensor[(64), float32]) /;
%4 = %3.0;
%5 = nn.relu(%4) / ty=Tensor[(1, 64, 112, 112), float32] /;
%6 = nn.max_pool2d(%5, pool_size=[3, 3], strides=[2, 2], padding=[1, 1]) / ty=Tensor[(1, 64, 56, 56), float32] /;
%7 = nn.batch_norm(%6, %stage1_unit1_bn1_gamma, %stage1_unit1_bn1_beta, %stage1_unit1_bn1_moving_mean, %stage1_unit1_bn1_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64), float32], Tensor[(64), float32]) /;
%8 = %7.0;
%9 = nn.relu(%8) / ty=Tensor[(1, 64, 56, 56), float32] /;
%10 = nn.conv2d(%9, %stage1_unit1_conv1_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) / ty=Tensor[(1, 64, 56, 56), float32] /;
%11 = nn.batch_norm(%10, %stage1_unit1_bn2_gamma, %stage1_unit1_bn2_beta, %stage1_unit1_bn2_moving_mean, %stage1_unit1_bn2_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64), float32], Tensor[(64), float32]) /;
%12 = %11.0;
%13 = nn.relu(%12) / ty=Tensor[(1, 64, 56, 56), float32] /;
%14 = nn.conv2d(%13, %stage1_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) / ty=Tensor[(1, 64, 56, 56), float32] /;
%15 = nn.conv2d(%9, %stage1_unit1_sc_weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) / ty=Tensor[(1, 64, 56, 56), float32] /;
%16 = add(%14, %15) / ty=Tensor[(1, 64, 56, 56), float32] /;
%17 = nn.batch_norm(%16, %stage1_unit2_bn1_gamma, %stage1_unit2_bn1_beta, %stage1_unit2_bn1_moving_mean, %stage1_unit2_bn1_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64), float32], Tensor[(64), float32]) /;
%18 = %17.0;
%19 = nn.relu(%18) / ty=Tensor[(1, 64, 56, 56), float32] /;
%20 = nn.conv2d(%19, %stage1_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) / ty=Tensor[(1, 64, 56, 56), float32] /;
%21 = nn.batch_norm(%20, %stage1_unit2_bn2_gamma, %stage1_unit2_bn2_beta, %stage1_unit2_bn2_moving_mean, %stage1_unit2_bn2_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64), float32], Tensor[(64), float32]) /;
%22 = %21.0;
%23 = nn.relu(%22) / ty=Tensor[(1, 64, 56, 56), float32] /;
%24 = nn.conv2d(%23, %stage1_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) / ty=Tensor[(1, 64, 56, 56), float32] /;
%25 = add(%24, %16) / ty=Tensor[(1, 64, 56, 56), float32] /;
%26 = nn.batch_norm(%25, %stage2_unit1_bn1_gamma, %stage2_unit1_bn1_beta, %stage2_unit1_bn1_moving_mean, %stage2_unit1_bn1_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64), float32], Tensor[(64), float32]) /;
%27 = %26.0;
%28 = nn.relu(%27) / ty=Tensor[(1, 64, 56, 56), float32] /;
%29 = nn.conv2d(%28, %stage2_unit1_conv1_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) / ty=Tensor[(1, 128, 28, 28), float32] /;
%30 = nn.batch_norm(%29, %stage2_unit1_bn2_gamma, %stage2_unit1_bn2_beta, %stage2_unit1_bn2_moving_mean, %stage2_unit1_bn2_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 128, 28, 28), float32], Tensor[(128), float32], Tensor[(128), float32]) /;
%31 = %30.0;
%32 = nn.relu(%31) / ty=Tensor[(1, 128, 28, 28), float32] /;
%33 = nn.conv2d(%32, %stage2_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) / ty=Tensor[(1, 128, 28, 28), float32] /;
%34 = nn.conv2d(%28, %stage2_unit1_sc_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) / ty=Tensor[(1, 128, 28, 28), float32] /;
%35 = add(%33, %34) / ty=Tensor[(1, 128, 28, 28), float32] /;
%36 = nn.batch_norm(%35, %stage2_unit2_bn1_gamma, %stage2_unit2_bn1_beta, %stage2_unit2_bn1_moving_mean, %stage2_unit2_bn1_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 128, 28, 28), float32], Tensor[(128), float32], Tensor[(128), float32]) /;
%37 = %36.0;
%38 = nn.relu(%37) / ty=Tensor[(1, 128, 28, 28), float32] /;
%39 = nn.conv2d(%38, %stage2_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) / ty=Tensor[(1, 128, 28, 28), float32] /;
%40 = nn.batch_norm(%39, %stage2_unit2_bn2_gamma, %stage2_unit2_bn2_beta, %stage2_unit2_bn2_moving_mean, %stage2_unit2_bn2_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 128, 28, 28), float32], Tensor[(128), float32], Tensor[(128), float32]) /;
%41 = %40.0;
%42 = nn.relu(%41) / ty=Tensor[(1, 128, 28, 28), float32] /;
%43 = nn.conv2d(%42, %stage2_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) / ty=Tensor[(1, 128, 28, 28), float32] /;
%44 = add(%43, %35) / ty=Tensor[(1, 128, 28, 28), float32] /;
%45 = nn.batch_norm(%44, %stage3_unit1_bn1_gamma, %stage3_unit1_bn1_beta, %stage3_unit1_bn1_moving_mean, %stage3_unit1_bn1_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 128, 28, 28), float32], Tensor[(128), float32], Tensor[(128), float32]) /;
%46 = %45.0;
%47 = nn.relu(%46) / ty=Tensor[(1, 128, 28, 28), float32] /;
%48 = nn.conv2d(%47, %stage3_unit1_conv1_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) / ty=Tensor[(1, 256, 14, 14), float32] /;
%49 = nn.batch_norm(%48, %stage3_unit1_bn2_gamma, %stage3_unit1_bn2_beta, %stage3_unit1_bn2_moving_mean, %stage3_unit1_bn2_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 256, 14, 14), float32], Tensor[(256), float32], Tensor[(256), float32]) /;
%50 = %49.0;
%51 = nn.relu(%50) / ty=Tensor[(1, 256, 14, 14), float32] /;
%52 = nn.conv2d(%51, %stage3_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) / ty=Tensor[(1, 256, 14, 14), float32] /;
%53 = nn.conv2d(%47, %stage3_unit1_sc_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) / ty=Tensor[(1, 256, 14, 14), float32] /;
%54 = add(%52, %53) / ty=Tensor[(1, 256, 14, 14), float32] /;
%55 = nn.batch_norm(%54, %stage3_unit2_bn1_gamma, %stage3_unit2_bn1_beta, %stage3_unit2_bn1_moving_mean, %stage3_unit2_bn1_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 256, 14, 14), float32], Tensor[(256), float32], Tensor[(256), float32]) /;
%56 = %55.0;
%57 = nn.relu(%56) / ty=Tensor[(1, 256, 14, 14), float32] /;
%58 = nn.conv2d(%57, %stage3_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) / ty=Tensor[(1, 256, 14, 14), float32] /;
%59 = nn.batch_norm(%58, %stage3_unit2_bn2_gamma, %stage3_unit2_bn2_beta, %stage3_unit2_bn2_moving_mean, %stage3_unit2_bn2_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 256, 14, 14), float32], Tensor[(256), float32], Tensor[(256), float32]) /;
%60 = %59.0;
%61 = nn.relu(%60) / ty=Tensor[(1, 256, 14, 14), float32] /;
%62 = nn.conv2d(%61, %stage3_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) / ty=Tensor[(1, 256, 14, 14), float32] /;
%63 = add(%62, %54) / ty=Tensor[(1, 256, 14, 14), float32] /;
%64 = nn.batch_norm(%63, %stage4_unit1_bn1_gamma, %stage4_unit1_bn1_beta, %stage4_unit1_bn1_moving_mean, %stage4_unit1_bn1_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 256, 14, 14), float32], Tensor[(256), float32], Tensor[(256), float32]) /;
%65 = %64.0;
%66 = nn.relu(%65) / ty=Tensor[(1, 256, 14, 14), float32] /;
%67 = nn.conv2d(%66, %stage4_unit1_conv1_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) / ty=Tensor[(1, 512, 7, 7), float32] /;
%68 = nn.batch_norm(%67, %stage4_unit1_bn2_gamma, %stage4_unit1_bn2_beta, %stage4_unit1_bn2_moving_mean, %stage4_unit1_bn2_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 512, 7, 7), float32], Tensor[(512), float32], Tensor[(512), float32]) /;
%69 = %68.0;
%70 = nn.relu(%69) / ty=Tensor[(1, 512, 7, 7), float32] /;
%71 = nn.conv2d(%70, %stage4_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) / ty=Tensor[(1, 512, 7, 7), float32] /;
%72 = nn.conv2d(%66, %stage4_unit1_sc_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) / ty=Tensor[(1, 512, 7, 7), float32] /;
%73 = add(%71, %72) / ty=Tensor[(1, 512, 7, 7), float32] /;
%74 = nn.batch_norm(%73, %stage4_unit2_bn1_gamma, %stage4_unit2_bn1_beta, %stage4_unit2_bn1_moving_mean, %stage4_unit2_bn1_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 512, 7, 7), float32], Tensor[(512), float32], Tensor[(512), float32]) /;
%75 = %74.0;
%76 = nn.relu(%75) / ty=Tensor[(1, 512, 7, 7), float32] /;
%77 = nn.conv2d(%76, %stage4_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) / ty=Tensor[(1, 512, 7, 7), float32] /;
%78 = nn.batch_norm(%77, %stage4_unit2_bn2_gamma, %stage4_unit2_bn2_beta, %stage4_unit2_bn2_moving_mean, %stage4_unit2_bn2_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 512, 7, 7), float32], Tensor[(512), float32], Tensor[(512), float32]) /;
%79 = %78.0;
%80 = nn.relu(%79) / ty=Tensor[(1, 512, 7, 7), float32] /;
%81 = nn.conv2d(%80, %stage4_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) / ty=Tensor[(1, 512, 7, 7), float32] /;
%82 = add(%81, %73) / ty=Tensor[(1, 512, 7, 7), float32] /;
%83 = nn.batch_norm(%82, %bn1_gamma, %bn1_beta, %bn1_moving_mean, %bn1_moving_var, epsilon=2e-05f) / ty=(Tensor[(1, 512, 7, 7), float32], Tensor[(512), float32], Tensor[(512), float32]) /;
%84 = %83.0;
%85 = nn.relu(%84) / ty=Tensor[(1, 512, 7, 7), float32] /;
%86 = nn.global_avg_pool2d(%85) / ty=Tensor[(1, 512, 1, 1), float32] /;
%87 = nn.batch_flatten(%86) / ty=Tensor[(1, 512), float32] /;
%88 = nn.dense(%87, %fc1_weight, units=1000) / ty=Tensor[(1, 1000), float32] /;
%89 = nn.bias_add(%88, %fc1_bias, axis=-1) / ty=Tensor[(1, 1000), float32] /;
nn.softmax(%89) / ty=Tensor[(1, 1000), float32] */
}

download failed due to URLError(ConnectionRefusedError(111, 'Connection refused'),), retrying, 2 attempts left
download failed due to URLError(ConnectionRefusedError(111, 'Connection refused'),), retrying, 1 attempt left
WARNING:root:Failed to download tophub package for cuda: <urlopen error [Errno 111] Connection refused>
Traceback (most recent call last):

File "relay_quick_start.py", line 100, in
graph, lib, params = relay.build(mod, target, params=params)

File "/home/zzjhtest/.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/zzjhtest/.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 "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/_ffi/_ctypes/packed_func.py", line 213, in call
raise get_last_ffi_error()

tvm.ffi.base.TVMError: Traceback (most recent call last):
[bt] (8) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xb09669) [0x7f2d6a7c0669]
[bt] (7) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xb095cc) [0x7f2d6a7c05cc]
[bt] (6) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ExprFunctor<tvm::runtime::ObjectRef (tvm::RelayExpr const&)>::VisitExpr(tvm::RelayExpr const&)+0x91) [0x7f2d6a7ca881]
[bt] (5) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ExprFunctor<tvm::runtime::ObjectRef (tvm::RelayExpr const&)>::InitVTable()::{lambda(tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::runtime::ObjectRef (tvm::RelayExpr const&)>)#6}::_FUN(tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::runtime::ObjectRef (tvm::RelayExpr const&)>)+0x27) [0x7f2d6a7c0da7]
[bt] (4) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::Interpreter::VisitExpr(tvm::relay::CallNode const*)+0x554) [0x7f2d6a7cda74]
[bt] (3) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::Interpreter::Invoke(tvm::relay::InterpreterClosure const&, tvm::Array<tvm::runtime::ObjectRef, void> const&, tvm::relay::Var const&)+0xd38) [0x7f2d6a7c9ce8]
[bt] (2) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::Interpreter::InvokePrimitiveOp(tvm::relay::Function const&, tvm::Array<tvm::runtime::ObjectRef, void> const&)+0x541) [0x7f2d6a7c7751]
[bt] (1) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::CompileEngineImpl::JIT(tvm::relay::CCacheKey const&)+0x14e) [0x7f2d6a79b4ce]
[bt] (0) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xbd75db) [0x7f2d6a88e5db]
File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun
rv = local_pyfunc(pyargs)
File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/relay/backend/_backend.py", line 83, in build
return tvm.driver.build(mod, target=target, target_host=target_host)
File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/driver/build_module.py", line 410, in build
rt_mod_host = codegen.build_module(mod_host_all, target_host)
File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/target/codegen.py", line 40, in build_module
return _ffi_api.Build(mod, target)
File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/_ffi/_ctypes/packed_func.py", line 213, in call
raise get_last_ffi_error()
[bt] (2) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(TVMFuncCall+0x65) [0x7f2d6a893175]
[bt] (1) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue), tvm::runtime::TypedPackedFunc<tvm::runtime::Module (tvm::IRModule, tvm::Target const&)>::AssignTypedLambda<tvm::runtime::Module ()(tvm::IRModule, tvm::Target const&)>(tvm::runtime::Module ()(tvm::IRModule, tvm::Target const&))::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)+0x84) [0x7f2d6a402d94]
[bt] (0) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::codegen::Build(tvm::IRModule, tvm::Target const&)+0x91f) [0x7f2d6a3fdddf]
File "/home/zzjhtest/ZCC/tvm/src/target/codegen.cc", line 53
TVMError: Check failed: bf != nullptr: target.build.llvm is not enabled

[CallmeZhangChenchen]

i think tvm is no question .

[CallmeZhangChenchen]

and llvm no question

[tqchen]

Please open a new thread on https://discuss.tvm.ai/