Zha0q1 opened a new issue #18953:
URL: https://github.com/apache/incubator-mxnet/issues/18953
```
import mxnet
from mxnet import np, npx
A = np.ones((10))
B = np.broadcast_to(A, (2, 10))
print(B.shape)
# errors out
#C = npx.broadcast_like(A, B)
# works
C = npx.broadcast_like(A.reshape(1, 10), B)
print(C.shape)
```
np.broadcast_to can broadcast (x, ) to (2, x) but npx.braodcast_like cannot.
```
ubuntu@ip-172-31-10-124:~/incubator-mxnet$ python broadcast_comparision.py
[22:53:51] ../src/storage/storage.cc:198: Using Pooled (Naive)
StorageManager for CPU
(2, 10)
Traceback (most recent call last):
File "broadcast_comparision.py", line 8, in <module>
C = npx.broadcast_like(A, B)
File "<string>", line 61, in broadcast_like
File "/home/ubuntu/incubator-mxnet/python/mxnet/_ctypes/ndarray.py", line
91, in _imperative_invoke
ctypes.byref(out_stypes)))
File "/home/ubuntu/incubator-mxnet/python/mxnet/base.py", line 246, in
check_call
raise get_last_ffi_error()
mxnet.base.MXNetError: Traceback (most recent call last):
File "../src/operator/tensor/./broadcast_reduce_op.h", line 446
MXNetError: Check failed: lhs_shape.ndim() == rhs_shape.ndim() (1 vs. 2) :
Operand of shape [10] cannot be broadcasted to [2,10]
```
Is there any reason to make them different? Otherwise should we make them
consistent?
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