Neutron3529 commented on issue #19649:
URL:
https://github.com/apache/incubator-mxnet/issues/19649#issuecomment-758560138
> @Neutron3529 I think It has nothing to do with tf32. I've tested with
`NVIDIA_TF32_OVERRIDE=0` as you suggested, the problem is not solved.
my result (v1.x, compiled by myself):
```python
>>> import os
>>> os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '0'
>>> import mxnet as mx
>>> import numpy as np
>>> from mxnet.gluon.model_zoo.vision.resnet import resnet18_v1
>>> def testrestnet(ctx=mx.gpu(0)):
mx_model = resnet18_v1(pretrained=True,ctx=ctx)
mx_model.hybridize()
x_mx = mx.nd.ones(shape=(1,3,224,224), ctx=ctx)
y_mx = mx_model.features[0:6](x_mx)
res = y_mx.asnumpy().sum()
print(res)
...
>>> testrestnet(mx.cpu())
Downloading
/me/mxnet/models/resnet18_v1-a0666292.zipa165046a-afde-4d5a-a034-0163a93f6047
from
https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
13064.974
>>> testrestnet(mx.cpu())
13064.974
>>> testrestnet(mx.cpu())
13064.974
>>> testrestnet(mx.gpu())
13064.976
>>> testrestnet(mx.gpu())
13064.976
--- using mxnet without TF_OVERRIDE
>>> import os
>>> os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '0'
>>> import mxnet as mx
>>> import numpy as np
>>> from mxnet.gluon.model_zoo.vision.resnet import resnet18_v1
>>> def testrestnet(ctx=mx.gpu(0)):
mx_model = resnet18_v1(pretrained=True,ctx=ctx)
mx_model.hybridize()
x_mx = mx.nd.ones(shape=(1,3,224,224), ctx=ctx)
y_mx = mx_model.features[0:6](x_mx)
res = y_mx.asnumpy().sum()
print(res)
...
>>> testrestnet()
13065.814
>>> testrestnet(mx.cpu())
13064.974
```
it seems that `NVIDIA_TF32_OVERRIDE=0` works for me, and without it may
bring a huge bias.
what's more, my CPU generated the different result (`13064.974` vs
`13064.977`)compared to yours, maybe the error is normal and do not worth an
issue.
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