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Posted to issues@mxnet.apache.org by GitBox <gi...@apache.org> on 2020/10/13 10:17:35 UTC
[GitHub] [incubator-mxnet] fhieber opened a new issue #19343: Still too strict type requirements for broadcast_like
fhieber opened a new issue #19343:
URL: https://github.com/apache/incubator-mxnet/issues/19343
## Description
`mx.nd.broadcast_like` has unnecessarily strict dtype requirements for its two data inputs. PR #17977 was aimed to relax them but in MXNet 1.7 I still get the following error from this minimum example:
```python
> a = mx.nd.ones((96, 1), dtype='float32')
> b = mx.nd.ones((96, 32, 32), dtype='float16')
> mx.nd.broadcast_like(a, b, lhs_axes=(1,), rhs_axes=(1,))
```
### Error Message
```python
---------------------------------------------------------------------------
MXNetError Traceback (most recent call last)
<ipython-input-17-3cde0a810695> in <module>
----> 1 mx.nd.broadcast_like(a, b, lhs_axes=(1,), rhs_axes=(1,))
~/miniconda3/lib/python3.7/site-packages/mxnet/ndarray/register.py in broadcast_like(lhs, rhs, lhs_axes, rhs_axes, out, name, **kwargs)
~/miniconda3/lib/python3.7/site-packages/mxnet/_ctypes/ndarray.py in _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op, output_is_list)
89 c_str_array(keys),
90 c_str_array([str(s) for s in vals]),
---> 91 ctypes.byref(out_stypes)))
92
93 create_ndarray_fn = _global_var._np_ndarray_cls if is_np_op else _global_var._ndarray_cls
~/miniconda3/lib/python3.7/site-packages/mxnet/base.py in check_call(ret)
244 """
245 if ret != 0:
--> 246 raise get_last_ffi_error()
247
248
MXNetError: Traceback (most recent call last):
File "/home/centos/mxnet/3rdparty/mshadow/../../src/operator/tensor/../elemwise_op_common.h", line 135
MXNetError: Check failed: assign(&dattr, vec.at(i)): Incompatible attr in node at 1-th input: expected float32, got float16
```
## To Reproduce
```python
a = mx.nd.ones((96, 1), dtype='float32')
b = mx.nd.ones((96, 32, 32), dtype='float16')
mx.nd.broadcast_like(a, b, lhs_axes=(1,), rhs_axes=(1,))
```
## What have you tried to solve it?
aligning dtypes by either casting a or b to the other dtype resolves the issue. But this is not a practical solution in my use case as both tensors are fairly large and I want to avoid a copy.
## Environment
```
mxnet-cu92 1.7.0
```
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[GitHub] [incubator-mxnet] ptrendx commented on issue #19343: Still too strict dtype requirements for broadcast_like
Posted by GitBox <gi...@apache.org>.
ptrendx commented on issue #19343:
URL: https://github.com/apache/incubator-mxnet/issues/19343#issuecomment-708063299
Seems that PR #17977 was merged to master after 1.x branch creation, so would need to be cherry-picked to 1.x branch.
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