gzuchow commented on issue #19065:
URL:
https://github.com/apache/incubator-mxnet/issues/19065#issuecomment-709995786
Thanks @access2rohit for reporting this issue.
I have tried to run your script to reproduce issue, with flag
USE_INT64_TENSOR_SIZE.
With this changes you can run it:
```
import mxnet as mx
from mxnet import np, npx
Size0 = 2
Size1 = 1000000000
A = np.ones((Size0, Size1))
gamma = np.ones((Size1))
beta = np.zeros((Size1))
mov_mean = np.ones((Size1))
mov_var = np.ones((Size1))
A.attach_grad()
with mx.autograd.record():
B = npx.batch_norm(A, gamma, beta, mov_mean, mov_var, axis = 1)
print("output={}".format(B))
B.backward()
print("gradient={}".format(A.grad))
```
By this tweaks with a code there are no ```malloc_consolidate``` errors.
Additional memory is required to execute calculation by oneDNN to split the
calculation between threads. It is equal number of threads * size of input
tensor along "axis". In case above it is ```Size1``` * "number of threads". So
you can decrease decrease memory consumption by using less thread, by setting
```OMP_NUM_THREADS```.
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