mx.nd.topk does not work with ndarray of type float16

[hetong007]

Maybe there are some operators don't work well on some data types, but I just encountered the float16-topk() pair, didn't run a full scan of all possibly broken pairs.

The following is a few lines of code to reproduce it, including the error message:

Python 2.7.12 (default, Dec  4 2017, 14:50:18)
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import mxnet as mx
>>> a = mx.nd.array([1,2,3])
>>> a.topk()

[2.]
<NDArray 1 @cpu(0)>
>>> a.astype('float32').topk()

[2.]
<NDArray 1 @cpu(0)>
>>> a.astype('float16').max()

[3.]
<NDArray 1 @cpu(0)>
>>> a.astype('float16').topk()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/ubuntu/incubator-mxnet/python/mxnet/ndarray/ndarray.py", line 189, in __repr__
    return '\n%s\n<%s %s @%s>' % (str(self.asnumpy()),
  File "/home/ubuntu/incubator-mxnet/python/mxnet/ndarray/ndarray.py", line 1894, in asnumpy
    ctypes.c_size_t(data.size)))
  File "/home/ubuntu/incubator-mxnet/python/mxnet/base.py", line 210, in check_call
    raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [18:53:33] include/mxnet/././tensor_blob.h:203: Check failed: mshadow::DataType<DType>::kFlag == type_flag_ TBlob.get_with_shape: data type do not match specified type.Expected: 2 v.s. given 0

Stack trace returned 10 entries:
[bt] (0) /home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::StackTrace[abi:cxx11]()+0x5b) [0x7f96f88955fb]
[bt] (1) /home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x28) [0x7f96f8896168]
[bt] (2) /home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(float* mxnet::TBlob::dptr<float>() const+0xfa) [0x7f96f890d2ba]
[bt] (3) /home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(void mxnet::op::TopKImpl<mshadow::cpu>(mxnet::RunContext, mxnet::Resource, mxnet::TBlob const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, mxnet::op::TopKParam const&)+0x97b) [0x7f96f9d39e0b]
[bt] (4) /home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(void mxnet::op::TopK<mshadow::cpu>(nnvm::NodeAttrs const&, mxnet::OpContext const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&)+0x108) [0x7f96f9d3f5c8]
[bt] (5) /home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::imperative::PushFCompute(std::function<void (nnvm::NodeAttrs const&, mxnet::OpContext const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&)> const&, nnvm::Op const*, nnvm::NodeAttrs const&, mxnet::Context const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::Resource, std::allocator<mxnet::Resource> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<unsigned int, std::allocator<unsigned int> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&)::{lambda(mxnet::RunContext)#1}::operator()(mxnet::RunContext) const+0x294) [0x7f96faf83454]
[bt] (6) /home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(+0x372bdeb) [0x7f96fb3d0deb]
[bt] (7) /home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::engine::ThreadedEngine::ExecuteOprBlock(mxnet::RunContext, mxnet::engine::OprBlock*)+0x8e5) [0x7f96fb3cbc75]
[bt] (8) /home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler<void (std::shared_ptr<dmlc::ManualEvent>), mxnet::engine::ThreadedEnginePerDevice::PushToExecute(mxnet::engine::OprBlock*, bool)::{lambda()#1}::operator()() const::{lambda(std::shared_ptr<dmlc::ManualEvent>)#1}>::_M_invoke(std::_Any_data const&, std::shared_ptr<dmlc::ManualEvent>&&)+0xe2) [0x7f96fb3e24b2]
[bt] (9) /home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(std::thread::_Impl<std::_Bind_simple<std::function<void (std::shared_ptr<dmlc::ManualEvent>)> (std::shared_ptr<dmlc::ManualEvent>)> >::_M_run()+0x4a) [0x7f96fb3dc6ea]

Environment info (Required)

----------Python Info----------
('Version      :', '2.7.12')
('Compiler     :', 'GCC 5.4.0 20160609')
('Build        :', ('default', 'Dec  4 2017 14:50:18'))
('Arch         :', ('64bit', 'ELF'))
------------Pip Info-----------
('Version      :', '10.0.0')
('Directory    :', '/usr/local/lib/python2.7/dist-packages/pip')
----------MXNet Info-----------
('Version      :', '1.3.0')
('Directory    :', '/home/ubuntu/incubator-mxnet/python/mxnet')
Hashtag not found. Not installed from pre-built package.
----------System Info----------
('Platform     :', 'Linux-4.4.0-1060-aws-x86_64-with-Ubuntu-16.04-xenial')
('system       :', 'Linux')
('node         :', 'ip-172-31-3-103')
('release      :', '4.4.0-1060-aws')
('version      :', '#69-Ubuntu SMP Sun May 20 13:42:07 UTC 2018')
----------Hardware Info----------
('machine      :', 'x86_64')
('processor    :', 'x86_64')
Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                64
On-line CPU(s) list:   0-63
Thread(s) per core:    2
Core(s) per socket:    16
Socket(s):             2
NUMA node(s):          2
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 79
Model name:            Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
Stepping:              1
CPU MHz:               2699.984
CPU max MHz:           3000.0000
CPU min MHz:           1200.0000
BogoMIPS:              4600.12
Hypervisor vendor:     Xen
Virtualization type:   full
L1d cache:             32K
L1i cache:             32K
L2 cache:              256K
L3 cache:              46080K
NUMA node0 CPU(s):     0-15,32-47
NUMA node1 CPU(s):     16-31,48-63
Flags:                 fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq monitor est ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single kaiser fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt ida
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0020 sec, LOAD: 0.8709 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0024 sec, LOAD: 0.3203 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0077 sec, LOAD: 0.0831 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0832 sec, LOAD: 0.7508 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.1036 sec, LOAD: 0.0853 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.2463 sec, LOAD: 0.1423 sec.

[eric-haibin-lin]

@haojin2 @rahul003

[haojin2]

Taking a look at it now

[samskalicky]

Rerunning this now results in the following message:

>>> import mxnet as mx
>>> a = mx.nd.array([1,2,3])
>>> a.astype('float16').max()

[3.]
<NDArray 1 @cpu(0)>
>>> a.astype('float16').topk()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/ubuntu/topk_fp16/python/mxnet/ndarray/ndarray.py", line 189, in __repr__
    return '\n%s\n<%s %s @%s>' % (str(self.asnumpy()),
  File "/home/ubuntu/topk_fp16/python/mxnet/ndarray/ndarray.py", line 1972, in asnumpy
    ctypes.c_size_t(data.size)))
  File "/home/ubuntu/topk_fp16/python/mxnet/base.py", line 252, in check_call
    raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [22:58:33] /home/ubuntu/topk_fp16/src/operator/tensor/./ordering_op-inl.h:535: This operation does not support float16

This is due to change #12250 that improves the messaging that float16 is not supported.

We should change the tags on this issue to [Operator, Feature Request] and remove [Bug] now that its been handled as not currently supported.