vafl opened a new issue #17914: `broadcast_add` and `+` give different results when hybridizing URL: https://github.com/apache/incubator-mxnet/issues/17914 ## Description In some cases `broadcast_add` and `+` give different results. This issue seems to be new in 1.6. ## To Reproduce The following network gives the wrong outputs when hybridizing and running on a GPU. ```python import mxnet as mx import numpy as np class MyModel(mx.gluon.HybridBlock): def __init__(self) -> None: super().__init__() with self.name_scope(): self.cs = self.params.get_constant("cs", np.arange(10)) def hybrid_forward(self, F, dummy, cs): u = F.broadcast_add(cs, dummy.zeros_like()) # r = F.broadcast_add( # F.slice_axis(u, axis=-1, begin=0, end=-1), # F.slice_axis(u, axis=-1, begin=1, end=None) # ) / 2.0 r = ( F.slice_axis(u, axis=-1, begin=0, end=-1) + F.slice_axis(u, axis=-1, begin=1, end=None) ) / 2.0 return r def main(): ctx = mx.Context('gpu') # ctx = mx.Context('cpu') model = MyModel() model.collect_params().initialize(mx.init.Xavier(), ctx=ctx) print("hybridizing"); model.hybridize() dummy = mx.nd.array( [ np.ones(10), np.ones(10), np.ones(10), ], ctx=ctx ) with mx.autograd.record(): out = model(dummy) print(out.asnumpy()) if __name__ == '__main__': main() ``` It returns: ``` [[0. 1. 2. 3. 4. 5. 6. 7. 8. ] [9. 0. 1. 2. 3. 4. 5. 6. 7. ] [8. 9. 0. 0.5 1. 1.5 2. 2.5 3. ]] ``` The correct result is: ``` [[0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5] [0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5] [0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5]] ``` Any of the following changes will give the correct result: - switch to cpu - don't hybridize - switch to `broadcast_add` instead of `+` (see code that is commented out) This did not happen on mxnet 1.5. I think it is related to the `get_constant` and broadcasting that happens before in the network. ## Environment ``` ----------Python Info---------- Version : 3.6.5 Compiler : GCC 7.2.0 Build : ('default', 'Apr 29 2018 16:14:56') Arch : ('64bit', '') ------------Pip Info----------- Version : 19.3.1 Directory : /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages/pip ----------MXNet Info----------- Version : 1.6.0 Directory : /home/ubuntu/anaconda3/envs/amazonei_mxnet_p36/lib/python3.6/site-packages/mxnet Num GPUs : 1 Commit Hash : 6eec9da55c5096079355d1f1a5fa58dcf35d6752 ----------System Info---------- Platform : Linux-4.4.0-1104-aws-x86_64-with-debian-stretch-sid system : Linux node : ip-172-31-28-101 release : 4.4.0-1104-aws version : #115-Ubuntu SMP Mon Mar 2 06:35:35 UTC 2020 ----------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): 8 On-line CPU(s) list: 0-7 Thread(s) per core: 2 Core(s) per socket: 4 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 79 Model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz Stepping: 1 CPU MHz: 2213.210 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): 0-7 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 rep_good nopl xtopology nonstop_tsc aperfmperf pni pclmulqdq 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 ----------Network Test---------- Setting timeout: 10 Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0022 sec, LOAD: 0.6087 sec. Timing for GluonNLP GitHub: https://github.com/dmlc/gluon-nlp, DNS: 0.0006 sec, LOAD: 0.5831 sec. Timing for GluonNLP: http://gluon-nlp.mxnet.io, DNS: 0.1805 sec, LOAD: 0.4238 sec. Timing for D2L: http://d2l.ai, DNS: 0.0274 sec, LOAD: 0.0487 sec. Timing for D2L (zh-cn): http://zh.d2l.ai, DNS: 0.0148 sec, LOAD: 0.1029 sec. Timing for FashionMNIST: https://repo.mxnet.io/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.1235 sec, LOAD: 0.1370 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0178 sec, LOAD: 0.3628 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0015 sec, LOAD: 0.0679 sec. ```
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