When I Used VMExecutor to run a CNN model, it threw an error
```
RuntimeError: Check failed: VerifyMemory(func): Direct host side access to
device memory is detected. Did you forget to bind?
PrimFunc([placeholder, transform_weight]) attrs={"global_symbol":
"fused_nn_contrib_conv2d_winograd_wei
Unfortunately I'm not able to reproduce in a docker right now. I'll update here
if I find a way to reproduce it
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When `USE_THRUST=ON`, unknown CUDA error happened:
```
File "/home/ubuntu/tvm/src/runtime/cuda/cuda_device_api.cc", line 108
CUDA: Check failed: e == cudaSuccess || e == cudaErrorCudartUnloading: unknown
error
```
It can be reproduced with the following script
```
import numpy as np
import tvm
It is a missing feature. Rules should be added to
https://github.com/apache/incubator-tvm/blob/master/python/tvm/relay/quantize/_annotate.py
and
https://github.com/apache/incubator-tvm/blob/master/src/relay/quantize/calibrate.cc
For performance part, you might also need to take a look of `conv
You need call traverse_inline in your schedule function, which should be
similar to `schedule_conv2d_nchw`
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Okay I will send a patch
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solved in latest master
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The above example after annotation:
```
data
||
sim_quantize(QINPUT) sim_quantize(QINPUT)
||
add(bn_bias)
|
... /
|
add
```
data is usually output of previous conv2d. There are duplicated
simula
This issue is introduced by https://github.com/dmlc/tvm/pull/2605
It is useful to print relay ir after passes for debug. Adding
`print(ir_pass.pass_debug_print(func, show_meta_data=False))` after
`ir_pass.fuse_ops` in relay.build_module, sub-functions are printted in reverse
order which are d
