Got exception in tune_relay_arm.py

Questions
#1

I am trying to autotune the mxnet nn by following code in sample of tune_relay_arm.py. The sample is good ,then I changed to my own net.
The tuning is good ,buf failed at compiling . I debug into the code ,and found exception has been existed in the stage of task creation as following code .

if try_winograd:
print(‘got total tasks %i’ %(len(tasks)))
for i in range(len(tasks)):
try: # try winograd template
tsk = autotvm.task.create(tasks[i].name, tasks[i].args,
tasks[i].target, tasks[i].target_host, ‘winograd’)
input_channel = tsk.workload[1][1]
if input_channel >= 64:
tasks[i] = tsk
except Exception:
print(’--------------------got exception No.%i’ %(i))
print(tasks[i])
pass

--------------------got exception No.0
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 1024, 13, 13), ‘float32’), (‘TENSOR’, (75, 1024, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 1024, 13, 13, ‘float32’), (75, 1024, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
--------------------got exception No.2
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 1024, 13, 13), ‘float32’), (‘TENSOR’, (512, 1024, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 1024, 13, 13, ‘float32’), (512, 1024, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))

so the No.1 task creation is good while no 0,2 has failed. Then I found the exception is caused by topi.nn.conv2d in topi_integration.py:

@register(“topi_nn_conv2d”)
def _topi_nn_conv2d(*args, **kwargs):
assert not kwargs, “Do not support kwargs in template function call”
args = deserialize_args(args)
A, W = args[:2]
layout = args[-2]
assert layout == ‘NCHW’, “only support NCHW currently”
C = topi.nn.conv2d(*args, **kwargs)
s = topi.generic.schedule_conv2d_nchw([C])
return s, [A, W, C]

  The line: 
         C = topi.nn.conv2d(*args, **kwargs)
  just create an exception and output no other information? 
I checked  the args and found it is good too .

What should I do to make forward? Thank you .

#2

Can you post the complete error message?

#3

code for task creation.

if try_winograd:
print(‘got total tasks %i’ %(len(tasks)))
for i in range(len(tasks)):
try: # try winograd template
tsk = autotvm.task.create(tasks[i].name, tasks[i].args,
tasks[i].target, tasks[i].target_host, ‘winograd’)
input_channel = tsk.workload[1][1]
if input_channel >= 64:
tasks[i] = tsk

print(’--------------------got normal No.%i’ % (i))
print(tasks[i])
except Exception:
print(’#####################got exception No.%i’ %(i))
print(tasks[i])
pass

# if we want to use spatial pack for depthwise convolution
print(’-----------------topi_nn_depthwise_conv2d_nchw---------------’)
if try_spatial_pack_depthwise:
tuner = ‘xgb_knob’
for i in range(len(tasks)):
if tasks[i].name == ‘topi_nn_depthwise_conv2d_nchw’:
try:
tsk = autotvm.task.create(tasks[i].name, tasks[i].args,
tasks[i].target, tasks[i].target_host,
‘contrib_spatial_pack’)
tasks[i] = tsk

# print(’--------------------got depthwise normal No.%i’ % (i))
# print(tasks[i])

except Exception:
print(’--------------------got depthwise exception No.%i’ % (i))
print(tasks[i])
pass

The output info is:
--------------------got normal No.0
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 128, 128), ‘float32’), (‘TENSOR’, (6, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 128, 128, ‘float32’), (6, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.1
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 56, 128, 128), ‘float32’), (‘TENSOR’, (32, 56, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 56, 128, 128, ‘float32’), (32, 56, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.2
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 192, 64, 64), ‘float32’), (‘TENSOR’, (32, 192, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 192, 64, 64, ‘float32’), (32, 192, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.3
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 144, 64, 64), ‘float32’), (‘TENSOR’, (32, 144, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 144, 64, 64, ‘float32’), (32, 144, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.4
Task(func_name=topi_nn_depthwise_conv2d_nchw, args=((‘TENSOR’, (1, 144, 128, 128), ‘float32’), (‘TENSOR’, (144, 1, 3, 3), ‘float32’), (2, 2), (1, 1), (1, 1), ‘float32’), kwargs={}, workload=(‘depthwise_conv2d_nchw’, (1, 144, 128, 128, ‘float32’), (144, 1, 3, 3, ‘float32’), (2, 2), (1, 1), (1, 1), ‘float32’))
#####################got exception No.5
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 24, 128, 128), ‘float32’), (‘TENSOR’, (144, 24, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 24, 128, 128, ‘float32’), (144, 24, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.6
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 144, 128, 128), ‘float32’), (‘TENSOR’, (24, 144, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 144, 128, 128, ‘float32’), (24, 144, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.7
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 96, 128, 128), ‘float32’), (‘TENSOR’, (24, 96, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 96, 128, 128, ‘float32’), (24, 96, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.8
Task(func_name=topi_nn_depthwise_conv2d_nchw, args=((‘TENSOR’, (1, 96, 256, 256), ‘float32’), (‘TENSOR’, (96, 1, 3, 3), ‘float32’), (2, 2), (1, 1), (1, 1), ‘float32’), kwargs={}, workload=(‘depthwise_conv2d_nchw’, (1, 96, 256, 256, ‘float32’), (96, 1, 3, 3, ‘float32’), (2, 2), (1, 1), (1, 1), ‘float32’))
#####################got exception No.9
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 16, 256, 256), ‘float32’), (‘TENSOR’, (96, 16, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 16, 256, 256, ‘float32’), (96, 16, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.10
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 256, 256), ‘float32’), (‘TENSOR’, (16, 32, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 256, 256, ‘float32’), (16, 32, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.11
Task(func_name=topi_nn_depthwise_conv2d_nchw, args=((‘TENSOR’, (1, 32, 256, 256), ‘float32’), (‘TENSOR’, (32, 1, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘float32’), kwargs={}, workload=(‘depthwise_conv2d_nchw’, (1, 32, 256, 256, ‘float32’), (32, 1, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘float32’))
#####################got exception No.12
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 256, 256), ‘float32’), (‘TENSOR’, (32, 32, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 256, 256, ‘float32’), (32, 32, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.13
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 3, 512, 512), ‘float32’), (‘TENSOR’, (32, 3, 3, 3), ‘float32’), (2, 2), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 3, 512, 512, ‘float32’), (32, 3, 3, 3, ‘float32’), (2, 2), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.14
Task(func_name=topi_nn_depthwise_conv2d_nchw, args=((‘TENSOR’, (1, 144, 128, 128), ‘float32’), (‘TENSOR’, (144, 1, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘float32’), kwargs={}, workload=(‘depthwise_conv2d_nchw’, (1, 144, 128, 128, ‘float32’), (144, 1, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘float32’))
#####################got exception No.15
Task(func_name=topi_nn_depthwise_conv2d_nchw, args=((‘TENSOR’, (1, 192, 64, 64), ‘float32’), (‘TENSOR’, (192, 1, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘float32’), kwargs={}, workload=(‘depthwise_conv2d_nchw’, (1, 192, 64, 64, ‘float32’), (192, 1, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘float32’))
#####################got exception No.16
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 64, 64), ‘float32’), (‘TENSOR’, (192, 32, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 64, 64, ‘float32’), (192, 32, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.17
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 24, 128, 128), ‘float32’), (‘TENSOR’, (24, 24, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 24, 128, 128, ‘float32’), (24, 24, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.18
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 64, 64), ‘float32’), (‘TENSOR’, (6, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 64, 64, ‘float32’), (6, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.19
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 64, 64, 64), ‘float32’), (‘TENSOR’, (32, 64, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 64, 64, 64, ‘float32’), (32, 64, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.20
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 576, 32, 32), ‘float32’), (‘TENSOR’, (96, 576, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 576, 32, 32, ‘float32’), (96, 576, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.21
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 384, 32, 32), ‘float32’), (‘TENSOR’, (96, 384, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 384, 32, 32, ‘float32’), (96, 384, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.22
Task(func_name=topi_nn_depthwise_conv2d_nchw, args=((‘TENSOR’, (1, 384, 32, 32), ‘float32’), (‘TENSOR’, (384, 1, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘float32’), kwargs={}, workload=(‘depthwise_conv2d_nchw’, (1, 384, 32, 32, ‘float32’), (384, 1, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘float32’))
#####################got exception No.23
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 64, 32, 32), ‘float32’), (‘TENSOR’, (384, 64, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 64, 32, 32, ‘float32’), (384, 64, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.24
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 384, 32, 32), ‘float32’), (‘TENSOR’, (64, 384, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 384, 32, 32, ‘float32’), (64, 384, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.25
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 192, 32, 32), ‘float32’), (‘TENSOR’, (64, 192, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 192, 32, 32, ‘float32’), (64, 192, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.26
Task(func_name=topi_nn_depthwise_conv2d_nchw, args=((‘TENSOR’, (1, 192, 64, 64), ‘float32’), (‘TENSOR’, (192, 1, 3, 3), ‘float32’), (2, 2), (1, 1), (1, 1), ‘float32’), kwargs={}, workload=(‘depthwise_conv2d_nchw’, (1, 192, 64, 64, ‘float32’), (192, 1, 3, 3, ‘float32’), (2, 2), (1, 1), (1, 1), ‘float32’))
#####################got exception No.27
Task(func_name=topi_nn_depthwise_conv2d_nchw, args=((‘TENSOR’, (1, 576, 32, 32), ‘float32’), (‘TENSOR’, (576, 1, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘float32’), kwargs={}, workload=(‘depthwise_conv2d_nchw’, (1, 576, 32, 32, ‘float32’), (576, 1, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘float32’))
#####################got exception No.28
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 96, 32, 32), ‘float32’), (‘TENSOR’, (576, 96, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 96, 32, 32, ‘float32’), (576, 96, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.29
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 64, 64), ‘float32’), (‘TENSOR’, (32, 32, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 64, 64, ‘float32’), (32, 32, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.30
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 32, 32), ‘float32’), (‘TENSOR’, (6, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 32, 32, ‘float32’), (6, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.31
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 128, 32, 32), ‘float32’), (‘TENSOR’, (32, 128, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 128, 32, 32, ‘float32’), (32, 128, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.32
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 960, 16, 16), ‘float32’), (‘TENSOR’, (160, 960, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 960, 16, 16, ‘float32’), (160, 960, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.33
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 576, 16, 16), ‘float32’), (‘TENSOR’, (160, 576, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 576, 16, 16, ‘float32’), (160, 576, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.34
Task(func_name=topi_nn_depthwise_conv2d_nchw, args=((‘TENSOR’, (1, 576, 32, 32), ‘float32’), (‘TENSOR’, (576, 1, 3, 3), ‘float32’), (2, 2), (1, 1), (1, 1), ‘float32’), kwargs={}, workload=(‘depthwise_conv2d_nchw’, (1, 576, 32, 32, ‘float32’), (576, 1, 3, 3, ‘float32’), (2, 2), (1, 1), (1, 1), ‘float32’))
#####################got exception No.35
Task(func_name=topi_nn_depthwise_conv2d_nchw, args=((‘TENSOR’, (1, 960, 16, 16), ‘float32’), (‘TENSOR’, (960, 1, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘float32’), kwargs={}, workload=(‘depthwise_conv2d_nchw’, (1, 960, 16, 16, ‘float32’), (960, 1, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘float32’))
#####################got exception No.36
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 160, 16, 16), ‘float32’), (‘TENSOR’, (960, 160, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 160, 16, 16, ‘float32’), (960, 160, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.37
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 96, 32, 32), ‘float32’), (‘TENSOR’, (96, 96, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 96, 32, 32, ‘float32’), (96, 96, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.38
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 16, 16), ‘float32’), (‘TENSOR’, (6, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 16, 16, ‘float32’), (6, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.39
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 192, 16, 16), ‘float32’), (‘TENSOR’, (32, 192, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 192, 16, 16, ‘float32’), (32, 192, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.40
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 128, 16, 16), ‘float32’), (‘TENSOR’, (256, 128, 3, 3), ‘float32’), (2, 2), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 128, 16, 16, ‘float32’), (256, 128, 3, 3, ‘float32’), (2, 2), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.41
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 160, 16, 16), ‘float32’), (‘TENSOR’, (128, 160, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 160, 16, 16, ‘float32’), (128, 160, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.42
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 160, 16, 16), ‘float32’), (‘TENSOR’, (160, 160, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 160, 16, 16, ‘float32’), (160, 160, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.43
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 8, 8), ‘float32’), (‘TENSOR’, (6, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 8, 8, ‘float32’), (6, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.44
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 288, 8, 8), ‘float32’), (‘TENSOR’, (32, 288, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 288, 8, 8, ‘float32’), (32, 288, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.45
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 128, 8, 8), ‘float32’), (‘TENSOR’, (256, 128, 3, 3), ‘float32’), (2, 2), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 128, 8, 8, ‘float32’), (256, 128, 3, 3, ‘float32’), (2, 2), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.46
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 256, 8, 8), ‘float32’), (‘TENSOR’, (128, 256, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 256, 8, 8, ‘float32’), (128, 256, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.47
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 256, 8, 8), ‘float32’), (‘TENSOR’, (256, 256, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 256, 8, 8, ‘float32’), (256, 256, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.48
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 4, 4), ‘float32’), (‘TENSOR’, (6, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 4, 4, ‘float32’), (6, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.49
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 256, 4, 4), ‘float32’), (‘TENSOR’, (32, 256, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 256, 4, 4, ‘float32’), (32, 256, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
#####################got exception No.50
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 256, 4, 4), ‘float32’), (‘TENSOR’, (256, 256, 1, 1), ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 256, 4, 4, ‘float32’), (256, 256, 1, 1, ‘float32’), (1, 1), (0, 0), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.51
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 128, 128), ‘float32’), (‘TENSOR’, (24, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 128, 128, ‘float32’), (24, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.52
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 64, 64), ‘float32’), (‘TENSOR’, (24, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 64, 64, ‘float32’), (24, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.53
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 32, 32), ‘float32’), (‘TENSOR’, (24, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 32, 32, ‘float32’), (24, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.54
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 16, 16), ‘float32’), (‘TENSOR’, (24, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 16, 16, ‘float32’), (24, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.55
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 8, 8), ‘float32’), (‘TENSOR’, (24, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 8, 8, ‘float32’), (24, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.56
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 4, 4), ‘float32’), (‘TENSOR’, (24, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 4, 4, ‘float32’), (24, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.57
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 128, 128), ‘float32’), (‘TENSOR’, (12, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 128, 128, ‘float32’), (12, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.58
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 64, 64), ‘float32’), (‘TENSOR’, (12, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 64, 64, ‘float32’), (12, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.59
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 32, 32), ‘float32’), (‘TENSOR’, (12, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 32, 32, ‘float32’), (12, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.60
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 16, 16), ‘float32’), (‘TENSOR’, (12, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 16, 16, ‘float32’), (12, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.61
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 8, 8), ‘float32’), (‘TENSOR’, (12, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 8, 8, ‘float32’), (12, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))
--------------------got normal No.62
Task(func_name=topi_nn_conv2d, args=((‘TENSOR’, (1, 32, 4, 4), ‘float32’), (‘TENSOR’, (12, 32, 3, 3), ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’), kwargs={}, workload=(‘conv2d’, (1, 32, 4, 4, ‘float32’), (12, 32, 3, 3, ‘float32’), (1, 1), (1, 1), (1, 1), ‘NCHW’, ‘float32’))

After tuning, the compiling error is:

WARNING:autotvm:Cannot find config for target=llvm -device=arm_cpu -target=aarch64-linux-gnu, workload=(‘conv2d_transpose_nchw’, (1, 32, 64, 64, ‘float32’), (32, 32, 4, 4, ‘float32’), (2, 2), (1, 1), ‘float32’). A fallback configuration is used, which may bring great performance regression.
Traceback (most recent call last):
File “/home/deep/workssd/arm/tvm_app/tune_relay_arm_cpu_rk3399.py”, line 436, in
tune_and_evaluate(tuning_option)
File “/home/deep/workssd/arm/tvm_app/tune_relay_arm_cpu_rk3399.py”, line 400, in tune_and_evaluate
net, target=target, params=params)
File “/home/deep/workssd/arm/tvm/python/tvm/relay/build_module.py”, line 196, in build
params)
File “/home/deep/workssd/arm/tvm/python/tvm/relay/build_module.py”, line 107, in build
self._build(func, target, target_host)
File "/home/deep/workssd/arm/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] (8) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::backend::GraphRuntimeCodegen::VisitExpr(tvm::relay::CallNode const*)+0xb28) [0x7f4ec1395818]
[bt] (7) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::backend::GraphRuntimeCodegen::VisitExpr(tvm::relay::Expr const&)+0x566) [0x7f4ec138ee46]
[bt] (6) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::backend::GraphRuntimeCodegen::VisitExpr(tvm::relay::CallNode const*)+0xb28) [0x7f4ec1395818]
[bt] (5) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::backend::GraphRuntimeCodegen::VisitExpr(tvm::relay::Expr const&)+0x566) [0x7f4ec138ee46]
[bt] (4) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::backend::GraphRuntimeCodegen::VisitExpr(tvm::relay::CallNode const*)+0x6b5) [0x7f4ec13953a5]
[bt] (3) /home/deep/workssd/arm/tvm/build/libtvm.so(+0x76eb4c) [0x7f4ec136ab4c]
[bt] (2) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::CompileEngineImpl::LowerInternal(tvm::relay::CCacheKey const&)+0x469) [0x7f4ec1372949]
[bt] (1) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::ScheduleGetter::Create(tvm::relay::Function const&)+0xd77) [0x7f4ec1371de7]
[bt] (0) /home/deep/workssd/arm/tvm/build/libtvm.so(+0xc2cebb) [0x7f4ec1828ebb]
File “/home/deep/workssd/arm/tvm/python/tvm/_ffi/_ctypes/function.py”, line 71, in cfun
rv = local_pyfunc(*pyargs)
File “/home/deep/workssd/arm/tvm/python/tvm/relay/op/nn/_nn.py”, line 159, in schedule_conv2d
assert op is not None
TVMError: AssertionError
Error in sys.excepthook:
Traceback (most recent call last):
File “/usr/lib/python3/dist-packages/apport_python_hook.py”, line 63, in apport_excepthook
from apport.fileutils import likely_packaged, get_recent_crashes
File “/usr/lib/python3/dist-packages/apport/init.py”, line 5, in
from apport.report import Report
File “/usr/lib/python3/dist-packages/apport/report.py”, line 30, in
import apport.fileutils
File “/usr/lib/python3/dist-packages/apport/fileutils.py”, line 23, in
from apport.packaging_impl import impl as packaging
File “/usr/lib/python3/dist-packages/apport/packaging_impl.py”, line 23, in
import apt
File “/usr/lib/python3/dist-packages/apt/init.py”, line 23, in
import apt_pkg
ModuleNotFoundError: No module named ‘apt_pkg’

Original exception was:
Traceback (most recent call last):
File “/home/deep/workssd/arm/tvm_app/tune_relay_arm_cpu_rk3399.py”, line 436, in
tune_and_evaluate(tuning_option)
File “/home/deep/workssd/arm/tvm_app/tune_relay_arm_cpu_rk3399.py”, line 400, in tune_and_evaluate
net, target=target, params=params)
File “/home/deep/workssd/arm/tvm/python/tvm/relay/build_module.py”, line 196, in build
params)
File “/home/deep/workssd/arm/tvm/python/tvm/relay/build_module.py”, line 107, in build
self._build(func, target, target_host)
File "/home/deep/workssd/arm/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] (8) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::backend::GraphRuntimeCodegen::VisitExpr(tvm::relay::CallNode const*)+0xb28) [0x7f4ec1395818]
[bt] (7) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::backend::GraphRuntimeCodegen::VisitExpr(tvm::relay::Expr const&)+0x566) [0x7f4ec138ee46]
[bt] (6) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::backend::GraphRuntimeCodegen::VisitExpr(tvm::relay::CallNode const*)+0xb28) [0x7f4ec1395818]
[bt] (5) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::backend::GraphRuntimeCodegen::VisitExpr(tvm::relay::Expr const&)+0x566) [0x7f4ec138ee46]
[bt] (4) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::backend::GraphRuntimeCodegen::VisitExpr(tvm::relay::CallNode const*)+0x6b5) [0x7f4ec13953a5]
[bt] (3) /home/deep/workssd/arm/tvm/build/libtvm.so(+0x76eb4c) [0x7f4ec136ab4c]
[bt] (2) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::CompileEngineImpl::LowerInternal(tvm::relay::CCacheKey const&)+0x469) [0x7f4ec1372949]
[bt] (1) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::ScheduleGetter::Create(tvm::relay::Function const&)+0xd77) [0x7f4ec1371de7]
[bt] (0) /home/deep/workssd/arm/tvm/build/libtvm.so(+0xc2cebb) [0x7f4ec1828ebb]
File “/home/deep/workssd/arm/tvm/python/tvm/_ffi/_ctypes/function.py”, line 71, in cfun
rv = local_pyfunc(*pyargs)
File “/home/deep/workssd/arm/tvm/python/tvm/relay/op/nn/_nn.py”, line 159, in schedule_conv2d
assert op is not None
TVMError: AssertionError

#4

The op.nn.conv2d will cause exception at line 644 in arm_cpu/conv2d.py
assert KH == 3 and KW == 3 and HPAD == 1 and WPAD == 1 and HSTR == 1 and WSTR == 1
This line means only kernel(3,3) will be allowed to tune .So the exception is a normal operation?

Now the problem is why compiling fails?

@kevinthesun

#5

It looks like this is the error during compilation. Can you try autotvm.FallbackContext() to compile?

#6

Thank you . I use the following code to compile:

compile kernels with history best records
#with autotvm.apply_history_best(log_file):
with autotvm.FallbackContext():
print(“Compile…”)
with relay.build_config(opt_level=3):
graph, lib, params = relay.build_module.build(
net, target=target, params=params)

It seems the error:

Traceback (most recent call last):
File “/home/deep/workssd/arm/tvm_app/deploy_mb3_yolov3_on_rk3399.py”, line 231, in
graph, lib, params = relay.build(func, target, params=params,target_host=target_host)
File “/home/deep/workssd/arm/tvm/python/tvm/relay/build_module.py”, line 196, in build
params)
File “/home/deep/workssd/arm/tvm/python/tvm/relay/build_module.py”, line 107, in build
self._build(func, target, target_host)
File "/home/deep/workssd/arm/tvm/python/tvm/_ffi/ctypes/function.py", line 209, in call
raise get_last_ffi_error()
ValueError: Traceback (most recent call last):
[bt] (8) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::ForwardRewriter::VisitExpr(tvm::relay::CallNode const*)+0x55a) [0x7f35db5901ca]
[bt] (7) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::ExprMutator::VisitExpr(tvm::relay::Expr const&)+0x9e) [0x7f35db3c227e]
[bt] (6) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>::VisitExpr(tvm::relay::Expr const&)+0xca) [0x7f35db38134a]
[bt] (5) /home/deep/workssd/arm/tvm/build/libtvm.so(std::_Function_handler<tvm::relay::Expr (tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>), tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>::InitVTable()::{lambda(tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>)#6}>::_M_invoke(std::Any_data const&, tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>*&&)+0x34) [0x7f35db37c804]
[bt] (4) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::ForwardRewriter::VisitExpr(tvm::relay::CallNode const*)+0x6dc) [0x7f35db59034c]
[bt] (3) /home/deep/workssd/arm/tvm/build/libtvm.so(std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), void tvm::runtime::TypedPackedFunc<tvm::relay::Expr (tvm::relay::Call const&, tvm::Array<tvm::relay::Expr, void> const&, tvm::NodeRef const&)>::AssignTypedLambda<tvm::relay::Expr ()(tvm::relay::Call const&, tvm::Array<tvm::relay::Expr, void> const&, tvm::NodeRef const&)>(tvm::relay::Expr ()(tvm::relay::Call const&, tvm::Array<tvm::relay::Expr, void> const&, tvm::NodeRef const&))::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)+0xad) [0x7f35db557bad]
[bt] (2) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::alter_op_layout::AlterOpLayoutRewrite(tvm::relay::Call const&, tvm::Array<tvm::relay::Expr, void> const&, tvm::NodeRef const&)+0x13cd) [0x7f35db55350d]
[bt] (1) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::alter_op_layout::CallAlter(tvm::relay::Call const&, std::vector<tvm::relay::Expr, std::allocatortvm::relay::Expr > const&)+0x6ad) [0x7f35db550e2d]
[bt] (0) /home/deep/workssd/arm/tvm/build/libtvm.so(+0xc2cebb) [0x7f35db7dfebb]
File “/home/deep/workssd/arm/tvm/python/tvm/_ffi/_ctypes/function.py”, line 71, in cfun
rv = local_pyfunc(*pyargs)
File “/home/deep/workssd/arm/tvm/python/tvm/relay/op/nn/_nn.py”, line 178, in alter_op_layout_conv2d
return topi.nn.conv2d_alter_layout(attrs, inputs, tinfos, op)
File “</usr/local/lib/python3.6/dist-packages/decorator.py:decorator-gen-21>”, line 2, in conv2d_alter_layout
File “/home/deep/workssd/arm/tvm/python/tvm/target.py”, line 372, in dispatch_func
return dispatch_dict[k](*args, **kwargs)
File “/home/deep/workssd/arm/tvm/topi/python/topi/arm_cpu/conv2d.py”, line 709, in _alter_conv2d_layout_arm
new_attrs[“channels”] = inputs[1].checked_type.shape[attrs[‘kernel_layout’].index(‘O’)]
File “/home/deep/workssd/arm/tvm/python/tvm/relay/expr.py”, line 47, in checked_type
raise ValueError(“The type checker has not populated”
ValueError: The type checker has not populated the checked_type for this node
Error in sys.excepthook:
Traceback (most recent call last):
File “/usr/lib/python3/dist-packages/apport_python_hook.py”, line 63, in apport_excepthook
from apport.fileutils import likely_packaged, get_recent_crashes
File “/usr/lib/python3/dist-packages/apport/init.py”, line 5, in
from apport.report import Report
File “/usr/lib/python3/dist-packages/apport/report.py”, line 30, in
import apport.fileutils
File “/usr/lib/python3/dist-packages/apport/fileutils.py”, line 23, in
from apport.packaging_impl import impl as packaging
File “/usr/lib/python3/dist-packages/apport/packaging_impl.py”, line 23, in
import apt
File “/usr/lib/python3/dist-packages/apt/init.py”, line 23, in
import apt_pkg
ModuleNotFoundError: No module named ‘apt_pkg’

Original exception was:
Traceback (most recent call last):
File “/home/deep/workssd/arm/tvm_app/deploy_mb3_yolov3_on_rk3399.py”, line 231, in
graph, lib, params = relay.build(func, target, params=params,target_host=target_host)
File “/home/deep/workssd/arm/tvm/python/tvm/relay/build_module.py”, line 196, in build
params)
File “/home/deep/workssd/arm/tvm/python/tvm/relay/build_module.py”, line 107, in build
self._build(func, target, target_host)
File "/home/deep/workssd/arm/tvm/python/tvm/_ffi/ctypes/function.py", line 209, in call
raise get_last_ffi_error()
ValueError: Traceback (most recent call last):
[bt] (8) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::ForwardRewriter::VisitExpr(tvm::relay::CallNode const*)+0x55a) [0x7f35db5901ca]
[bt] (7) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::ExprMutator::VisitExpr(tvm::relay::Expr const&)+0x9e) [0x7f35db3c227e]
[bt] (6) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>::VisitExpr(tvm::relay::Expr const&)+0xca) [0x7f35db38134a]
[bt] (5) /home/deep/workssd/arm/tvm/build/libtvm.so(std::_Function_handler<tvm::relay::Expr (tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>), tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>::InitVTable()::{lambda(tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>)#6}>::_M_invoke(std::Any_data const&, tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>*&&)+0x34) [0x7f35db37c804]
[bt] (4) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::ForwardRewriter::VisitExpr(tvm::relay::CallNode const*)+0x6dc) [0x7f35db59034c]
[bt] (3) /home/deep/workssd/arm/tvm/build/libtvm.so(std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), void tvm::runtime::TypedPackedFunc<tvm::relay::Expr (tvm::relay::Call const&, tvm::Array<tvm::relay::Expr, void> const&, tvm::NodeRef const&)>::AssignTypedLambda<tvm::relay::Expr ()(tvm::relay::Call const&, tvm::Array<tvm::relay::Expr, void> const&, tvm::NodeRef const&)>(tvm::relay::Expr ()(tvm::relay::Call const&, tvm::Array<tvm::relay::Expr, void> const&, tvm::NodeRef const&))::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)+0xad) [0x7f35db557bad]
[bt] (2) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::alter_op_layout::AlterOpLayoutRewrite(tvm::relay::Call const&, tvm::Array<tvm::relay::Expr, void> const&, tvm::NodeRef const&)+0x13cd) [0x7f35db55350d]
[bt] (1) /home/deep/workssd/arm/tvm/build/libtvm.so(tvm::relay::alter_op_layout::CallAlter(tvm::relay::Call const&, std::vector<tvm::relay::Expr, std::allocatortvm::relay::Expr > const&)+0x6ad) [0x7f35db550e2d]
[bt] (0) /home/deep/workssd/arm/tvm/build/libtvm.so(+0xc2cebb) [0x7f35db7dfebb]
File “/home/deep/workssd/arm/tvm/python/tvm/_ffi/_ctypes/function.py”, line 71, in cfun
rv = local_pyfunc(*pyargs)
File “/home/deep/workssd/arm/tvm/python/tvm/relay/op/nn/_nn.py”, line 178, in alter_op_layout_conv2d
return topi.nn.conv2d_alter_layout(attrs, inputs, tinfos, op)
File “</usr/local/lib/python3.6/dist-packages/decorator.py:decorator-gen-21>”, line 2, in conv2d_alter_layout
File “/home/deep/workssd/arm/tvm/python/tvm/target.py”, line 372, in dispatch_func
return dispatch_dict[k](*args, **kwargs)
File “/home/deep/workssd/arm/tvm/topi/python/topi/arm_cpu/conv2d.py”, line 709, in _alter_conv2d_layout_arm
new_attrs[“channels”] = inputs[1].checked_type.shape[attrs[‘kernel_layout’].index(‘O’)]
File “/home/deep/workssd/arm/tvm/python/tvm/relay/expr.py”, line 47, in checked_type
raise ValueError(“The type checker has not populated”
ValueError: The type checker has not populated the checked_type for this node

#7

The error is caused by this line:
@conv2d_alter_layout.register([“arm_cpu”])
def _alter_conv2d_layout_arm(attrs, inputs, tinfos, F):

copy_inputs = [s for s in inputs]

new_attrs = {k: attrs[k] for k in attrs.keys()}

if F.__name__ == 'tvm.relay.op':
    # Derive channels for frontends (e.g ONNX) that miss "channel" field.
    print(inputs[1])
    #print(inputs[1].checked_type.shape[attrs['kernel_layout']])
    #if(inputs[1].checked_type )
    #print(inputs[1].checked_type)
    new_attrs["channels"] = inputs[1].checked_type.shape[attrs['kernel_layout'].index('O')]

The inputs[1].checked_type is None . But the previous is good.

The last output of line ‘’‘print(inputs[1])’’’ seems to be:

v0.0.1
free_var %data: Tensor[(1, 3, 416, 416), float32]
%0 = nn.conv2d(%data, meta[relay.Constant][1] /* ty=Tensor[(16, 3, 3, 3), float32] / / ty=Tensor[(16, 3, 3, 3), float32] /, strides=[2, 2], padding=[1, 1], channels=16, kernel_size=[3, 3])
%1 = add(%0, meta[relay.Constant][2] / ty=Tensor[(16, 1, 1), float32] / / ty=Tensor[(16, 1, 1), float32] /)
%2 = add(%1, 3f / ty=float32 /)
%3 = maximum(%2, 0f / ty=float32 /)
%4 = minimum(%3, 6f / ty=float32 /)
%5 = multiply(%1, %4)
%6 = divide(%5, 6f / ty=float32 /)
%7 = nn.conv2d(%6, meta[relay.Constant][3] / ty=Tensor[(16, 16, 1, 1), float32] / / ty=Tensor[(16, 16, 1, 1), float32] /, channels=16, kernel_size=[1, 1])
%8 = add(%7, meta[relay.Constant][4] / ty=Tensor[(16, 1, 1), float32] / / ty=Tensor[(16, 1, 1), float32] /)
%9 = nn.relu(%8)
%10 = nn.conv2d(%9, meta[relay.Constant][5] / ty=Tensor[(16, 1, 3, 3), float32] / / ty=Tensor[(16, 1, 3, 3), float32] /, padding=[1, 1], groups=16, channels=16, kernel_size=[3, 3])
%11 = add(%10, meta[relay.Constant][6] / ty=Tensor[(16, 1, 1), float32] / / ty=Tensor[(16, 1, 1), float32] /)
%12 = nn.relu(%11)
%13 = nn.conv2d(%12, meta[relay.Constant][7] / ty=Tensor[(16, 16, 1, 1), float32] / / ty=Tensor[(16, 16, 1, 1), float32] /, channels=16, kernel_size=[1, 1])
%14 = add(%13, meta[relay.Constant][8] / ty=Tensor[(16, 1, 1), float32] / / ty=Tensor[(16, 1, 1), float32] /)
%15 = add(%14, %6)
%16 = nn.conv2d(%15, meta[relay.Constant][9] / ty=Tensor[(64, 16, 1, 1), float32] / / ty=Tensor[(64, 16, 1, 1), float32] /, channels=64, kernel_size=[1, 1])
%17 = add(%16, meta[relay.Constant][10] / ty=Tensor[(64, 1, 1), float32] / / ty=Tensor[(64, 1, 1), float32] /)
%18 = nn.relu(%17)
%19 = nn.conv2d(%18, meta[relay.Constant][11] / ty=Tensor[(64, 1, 3, 3), float32] / / ty=Tensor[(64, 1, 3, 3), float32] /, strides=[2, 2], padding=[1, 1], groups=64, channels=64, kernel_size=[3, 3])
%20 = add(%19, meta[relay.Constant][12] / ty=Tensor[(64, 1, 1), float32] / / ty=Tensor[(64, 1, 1), float32] /)
%21 = nn.relu(%20)
%22 = nn.conv2d(%21, meta[relay.Constant][13] / ty=Tensor[(24, 64, 1, 1), float32] / / ty=Tensor[(24, 64, 1, 1), float32] /, channels=24, kernel_size=[1, 1])
%23 = add(%22, meta[relay.Constant][14] / ty=Tensor[(24, 1, 1), float32] / / ty=Tensor[(24, 1, 1), float32] /)
%24 = nn.conv2d(%23, meta[relay.Constant][15] / ty=Tensor[(72, 24, 1, 1), float32] / / ty=Tensor[(72, 24, 1, 1), float32] /, channels=72, kernel_size=[1, 1])
%25 = add(%24, meta[relay.Constant][16] / ty=Tensor[(72, 1, 1), float32] / / ty=Tensor[(72, 1, 1), float32] /)
%26 = nn.relu(%25)
%27 = nn.conv2d(%26, meta[relay.Constant][17] / ty=Tensor[(72, 1, 3, 3), float32] / / ty=Tensor[(72, 1, 3, 3), float32] /, padding=[1, 1], groups=72, channels=72, kernel_size=[3, 3])
%28 = add(%27, meta[relay.Constant][18] / ty=Tensor[(72, 1, 1), float32] / / ty=Tensor[(72, 1, 1), float32] /)
%29 = nn.relu(%28)
%30 = nn.conv2d(%29, meta[relay.Constant][19] / ty=Tensor[(24, 72, 1, 1), float32] / / ty=Tensor[(24, 72, 1, 1), float32] /, channels=24, kernel_size=[1, 1])
%31 = add(%30, meta[relay.Constant][20] / ty=Tensor[(24, 1, 1), float32] / / ty=Tensor[(24, 1, 1), float32] /)
%32 = add(%31, %23)
%33 = nn.conv2d(%32, meta[relay.Constant][21] / ty=Tensor[(72, 24, 1, 1), float32] / / ty=Tensor[(72, 24, 1, 1), float32] /, channels=72, kernel_size=[1, 1])
%34 = add(%33, meta[relay.Constant][22] / ty=Tensor[(72, 1, 1), float32] / / ty=Tensor[(72, 1, 1), float32] /)
%35 = nn.relu(%34)
%36 = nn.conv2d(%35, meta[relay.Constant][23] / ty=Tensor[(72, 1, 5, 5), float32] / / ty=Tensor[(72, 1, 5, 5), float32] /, strides=[2, 2], padding=[2, 2], groups=72, channels=72, kernel_size=[5, 5])
%37 = add(%36, meta[relay.Constant][24] / ty=Tensor[(72, 1, 1), float32] / / ty=Tensor[(72, 1, 1), float32] /)
%38 = nn.relu(%37)
%39 = contrib.adaptive_avg_pool2d(%38, output_size=[1, 1])
%40 = nn.conv2d(%39, meta[relay.Constant][25] / ty=Tensor[(18, 72, 1, 1), float32] / / ty=Tensor[(18, 72, 1, 1), float32] /, channels=18, kernel_size=[1, 1])
%41 = expand_dims(meta[relay.Constant][26] / ty=Tensor[(18,), float32] / / ty=Tensor[(18,), float32] /, axis=1, num_newaxis=2) / ty=Tensor[(18, 1, 1), float32] /
%42 = add(%40, %41)
%43 = nn.relu(%42)
%44 = nn.conv2d(%43, meta[relay.Constant][27] / ty=Tensor[(72, 18, 1, 1), float32] / / ty=Tensor[(72, 18, 1, 1), float32] /, channels=72, kernel_size=[1, 1])
%45 = expand_dims(meta[relay.Constant][28] / ty=Tensor[(72,), float32] / / ty=Tensor[(72,), float32] /, axis=1, num_newaxis=2) / ty=Tensor[(72, 1, 1), float32] /
%46 = add(%44, %45)
%47 = add(%46, 3f / ty=float32 /)
%48 = maximum(%47, 0f / ty=float32 /)
%49 = minimum(%48, 6f / ty=float32 /)
%50 = divide(%49, 6f / ty=float32 /)
%51 = squeeze(%50, axis=[0, 2, 3])
%52 = expand_dims(%51, axis=1, num_newaxis=2)
multiply(meta[relay.Constant][0] / ty=Tensor[(40, 72, 1, 1), float32] / / ty=Tensor[(40, 72, 1, 1), float32] */, %52)
// meta data omitted. you can use show_meta_data=True to include meta data

#8

I have commented the line

if F.name == ‘tvm.relay.op’:
# Derive channels for frontends (e.g ONNX) that miss “channel” field.
print(inputs[1])
#print(inputs[1].checked_type.shape[attrs[‘kernel_layout’]])
# if(inputs[1].checked_type is None):
# print(‘get checked_type none’)
# else:
# new_attrs[“channels”] = inputs[1].checked_type.shape[attrs[‘kernel_layout’].index(‘O’)]

with autotvm.FallbackContext() compile successfully.

#9

I have tried to make the compiling pass.There’re 2 bug in origin compiling code:

  1. arm_cpu/conv2d.py

if F.name == ‘tvm.relay.op’:
# Derive channels for frontends (e.g ONNX) that miss “channel” field.
if(“channels” not in new_attrs.keys()):
new_attrs[“channels”] = inputs[1].checked_type.shape[attrs[‘kernel_layout’].index(‘O’)]

inputs[1].checked_type sometime is None.

  1. tvm/relay/op/nn/_nn.py

def find_conv2d_op(op):
“”“Find the op with conv2d in its tag by traversing.”""
#if ‘conv2d’ in op.tag:
if ‘conv’ in op.tag:
return op
#print(‘input------’)
for tensor in op.input_tensors:
#print(tensor)
op = find_conv2d_op(tensor.op)
if op is not None:
return op_
return None

some op name is op.name=depthwise_conv ,there’s no conv2d but just “conv”.
So the problem is caused by assiginning wrong op name in building process .

looking forward to got fixed.

@kevinthesun

#10

Have you tried to change the op tag in https://github.com/dmlc/tvm/blob/master/topi/python/topi/arm_cpu/depthwise_conv2d.py#L311 and https://github.com/dmlc/tvm/blob/master/topi/python/topi/arm_cpu/depthwise_conv2d.py#L137?

#11

I haven’t tried because I just start to learn tvm. I think that will fix the bug 2 too.
By the way, there are a lot stride(1,1) convolution in my network,why are they not to be tuned?

#12

Have this problem got coped ? I met same problem with tune_rely_cuda.py