ycdfwzy opened a new issue #7101:
URL: https://github.com/apache/tvm/issues/7101
Hello!
I installed TVM on win10. After running
`tutorials\get_started\relay_quick_start.py`, I got the following outputs, but
did't know how to solve it
```
#[version = "0.0.5"]
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_be
ta: 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), flo
at32], %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, 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] */
}
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connection was forcibly closed by the remote host', None, 10054, None)),
retrying, 2 attempts left
download failed due to URLError(ConnectionResetError(10054, 'An existing
connection was forcibly closed by the remote host', None, 10054, None)),
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WARNING:root:Failed to download tophub package for cuda: <urlopen error
[WinError 10054] An existing connection was forcibly closed by the remote host>
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.
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu
-max_num_threads=1024 -model=unknown -thread_warp_size=32,
workload=('dense_small_batch.cuda', ('TENSOR', (1, 512), 'float32'), ('TENSOR',
(1000, 512), 'float32'), None, 'float32'). A fallback configuration is used,
which may bring great performance regression.download failed due to
URLError(ConnectionResetError(10054, 'An existing connection was forcibly
closed by the remote host', None, 10054, None)), retrying, 2 attempts left
download failed due to URLError(ConnectionResetError(10054, 'An existing
connection was forcibly closed by the remote host', None, 10054, None)),
retrying, 1 attempt left
Traceback (most recent call last):
File "relay_quick_start.py", line 101, in <module>
lib = relay.build(mod, target, params=params)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\relay\build_module.py",
line 275, in build
graph_json, mod, params = bld_mod.build(mod, target, target_host, params)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\relay\build_module.py",
line 138, in build
self._build(mod, target, target_host)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\_ffi\_ctypes\packed_func.py",
line 237, in __call__
raise get_last_ffi_error()
tvm._ffi.base.TVMError: Traceback (most recent call last):
File "D:\softwares\anaconda3\envs\tvm-dev\lib\urllib\request.py", line
1350, in do_open
encode_chunked=req.has_header('Transfer-encoding'))
File "D:\softwares\anaconda3\envs\tvm-dev\lib\http\client.py", line 1277,
in request
self._send_request(method, url, body, headers, encode_chunked)
File "D:\softwares\anaconda3\envs\tvm-dev\lib\http\client.py", line 1323,
in _send_request
self.endheaders(body, encode_chunked=encode_chunked)
File "D:\softwares\anaconda3\envs\tvm-dev\lib\http\client.py", line 1272,
in endheaders
self._send_output(message_body, encode_chunked=encode_chunked)
File "D:\softwares\anaconda3\envs\tvm-dev\lib\http\client.py", line 1032,
in _send_output
self.send(msg)
File "D:\softwares\anaconda3\envs\tvm-dev\lib\http\client.py", line 972,
in send
self.connect()
File "D:\softwares\anaconda3\envs\tvm-dev\lib\http\client.py", line 1447,
in connect
server_hostname=server_hostname)
File "D:\softwares\anaconda3\envs\tvm-dev\lib\ssl.py", line 423, in
wrap_socket
session=session
File "D:\softwares\anaconda3\envs\tvm-dev\lib\ssl.py", line 870, in _create
self.do_handshake()
File "D:\softwares\anaconda3\envs\tvm-dev\lib\ssl.py", line 1139, in
do_handshake
self._sslobj.do_handshake()
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\_ffi\_ctypes\packed_func.py",
line 81, in cfun
rv = local_pyfunc(*pyargs)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\relay\op\strategy\generic.py",
line 47, in wrapper
return topi_schedule(outs)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\autotvm\task\topi_integration.py",
line 235, in wrapper
return topi_schedule(cfg, outs, *args, **kwargs)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\topi\cuda\conv2d.py",
line 47, in schedule_conv2d_nchw
traverse_inline(s, outs[0].op, _callback)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\topi\utils.py",
line 70, in traverse_inline
_traverse(final_op)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\topi\utils.py",
line 67, in _traverse
_traverse(tensor.op)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\topi\utils.py",
line 67, in _traverse
_traverse(tensor.op)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\topi\utils.py",
line 67, in _traverse
_traverse(tensor.op)
[Previous line repeated 1 more time]
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\topi\utils.py",
line 68, in _traverse
callback(op)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\topi\cuda\conv2d.py",
line 45, in _callback
schedule_direct_cuda(cfg, s, op.output(0))
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\topi\cuda\conv2d_direct.py",
line 48, in schedule_direct_cuda
target.kind.name, target.model, "conv2d_nchw.cuda"
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\autotvm\tophub.py",
line 224, in load_reference_log
download_package(tophub_location, package_name)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\autotvm\tophub.py",
line 188, in download_package
download(download_url, os.path.join(rootpath, package_name), True,
verbose=0)
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\contrib\download.py",
line 114, in download
raise err
File
"C:\Users\ycdfwzy\AppData\Roaming\Python\Python37\site-packages\tvm-0.8.dev338+g28647f2e7-py3.7-win-amd64.egg\tvm\contrib\download.py",
line 100, in download
urllib2.urlretrieve(url, tempfile, reporthook=_download_progress)
File "D:\softwares\anaconda3\envs\tvm-dev\lib\urllib\request.py", line
247, in urlretrieve
with contextlib.closing(urlopen(url, data)) as fp:
File "D:\softwares\anaconda3\envs\tvm-dev\lib\urllib\request.py", line
222, in urlopen
return opener.open(url, data, timeout)
File "D:\softwares\anaconda3\envs\tvm-dev\lib\urllib\request.py", line
525, in open
response = self._open(req, data)
File "D:\softwares\anaconda3\envs\tvm-dev\lib\urllib\request.py", line
543, in _open
'_open', req)
File "D:\softwares\anaconda3\envs\tvm-dev\lib\urllib\request.py", line
503, in _call_chain
result = func(*args)
File "D:\softwares\anaconda3\envs\tvm-dev\lib\urllib\request.py", line
1393, in https_open
context=self._context, check_hostname=self._check_hostname)
File "D:\softwares\anaconda3\envs\tvm-dev\lib\urllib\request.py", line
1352, in do_open
raise URLError(err)
ConnectionResetError: [WinError 10054] An existing connection was forcibly
closed by the remote host
During handling of the above exception, another exception occurred:
urllib.error.URLError: <urlopen error [WinError 10054] An existing
connection was forcibly closed by the remote host>
```
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