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Subject [GitHub] kpot opened a new issue #8337: mx.autograd.grad's works or fails depending on use of slices
Date Thu, 01 Jan 1970 00:00:00 GMT
kpot opened a new issue #8337: mx.autograd.grad's works or fails depending on use of slices
   Hello guys,
   Testing latest master branch 349ebc36ff3693376287098d280b11069dafbb55, I found that the
same code using `autograd.grad` works fine or fails depending on whether I use full slice
of a variable or not.
   Working example:
   import mxnet as mx
   from mxnet import nd, autograd
   ctx = mx.cpu()
   a = mx.nd.array([1, 2, 3, 4], ctx=ctx)
   with autograd.record():
       b = nd.sum(2 * (a ** 2))   # works without slicing
   grads = autograd.grad(b, [a], create_graph=True, retain_graph=True)
   da_sym = autograd.get_symbol(grads[0])
   executor = da_sym.bind(ctx=ctx, args=[mx.nd.ones_like(b), a])
   but if I change the line `b = nd.sum(2 * (a ** 2))` with a technically identical expression
`b = nd.sum(2 * (a[0:4] ** 2))` with variable `a` being sliced, the code begins to fail on
`da_sym.bind(...)` with message
   Traceback (most recent call last):
     File "", line 14, in <module>
       executor = da_sym.bind(ctx=ctx, args=[mx.nd.ones_like(b), a])
     File "/home/kpot/pyves/mxnet/lib/python3.6/site-packages/mxnet-0.12.0-py3.6.egg/mxnet/symbol/",
line 1666, in bind
     File "/home/kpot/pyves/mxnet/lib/python3.6/site-packages/mxnet-0.12.0-py3.6.egg/mxnet/",
line 146, in check_call
       raise MXNetError(py_str(_LIB.MXGetLastError()))
   mxnet.base.MXNetError: [19:32:23] src/executor/ InferShape pass cannot
decide shapes for the following arguments (0s means unknown dimensions). Please consider providing
them as inputs:
   <just blank lines here>
   Stack trace returned 10 entries:
   [bt] (0) /home/kpot/pyves/mxnet/lib/python3.6/site-packages/mxnet-0.12.0-py3.6.egg/mxnet/
   Environment: Ubuntu 14.04, latest master branch of mxnet (rev 349ebc36ff3693376287098d280b11069dafbb55)
built from sources following [official instructions](
with `make USE_OPENCV=1 USE_BLAS=openblas`
   My initial goal was to try to build a computational graph for back-propagation so I could
later use it with nnvm and thus launch it on OpenCL devices. I would appreciate any help,
if my approach is wrong and I should try do this differently.
   Full diagnostics:
   ----------Python Info----------
   Version      : 3.6.3
   Compiler     : GCC 4.8.4
   Build        : ('default', 'Oct 13 2017 11:22:10')
   Arch         : ('64bit', 'ELF')
   ------------Pip Info-----------
   Version      : 9.0.1
   Directory    : /home/kpot/pyves/mxnet/lib/python3.6/site-packages/pip
   ----------MXNet Info-----------
   Version      : 0.12.0
   Directory    : /home/kpot/pyves/mxnet/lib/python3.6/site-packages/mxnet-0.12.0-py3.6.egg/mxnet
   Traceback (most recent call last):
     File "", line 108, in check_mxnet
       with open(commit_hash, 'r') as f:
   FileNotFoundError: [Errno 2] No such file or directory: '/home/kpot/pyves/mxnet/lib/python3.6/site-packages/mxnet-0.12.0-py3.6.egg/mxnet/COMMIT_HASH'
   ----------System Info----------
   Platform     : Linux-3.13.0-133-generic-x86_64-with-debian-jessie-sid
   system       : Linux
   node         : eagle
   release      : 3.13.0-133-generic
   version      : #182-Ubuntu SMP Tue Sep 19 15:49:21 UTC 2017
   ----------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):                4
   On-line CPU(s) list:   0-3
   Thread(s) per core:    2
   Core(s) per socket:    2
   Socket(s):             1
   NUMA node(s):          1
   Vendor ID:             AuthenticAMD
   CPU family:            21
   Model:                 1
   Stepping:              2
   CPU MHz:               3800.000
   BogoMIPS:              7634.99
   Virtualization:        AMD-V
   L1d cache:             16K
   L1i cache:             64K
   L2 cache:              2048K
   L3 cache:              4096K
   NUMA node0 CPU(s):     0-3
   ----------Network Test----------
   Setting timeout: 10
   Timing for MXNet:, DNS: 0.1282 sec, LOAD: 2.0946
   Timing for Gluon Tutorial(en):, DNS: 0.3062 sec, LOAD: 0.6475 sec.
   Timing for Gluon Tutorial(cn):, DNS: 0.3328 sec, LOAD: 1.3280 sec.
   Timing for FashionMNIST:,
DNS: 0.1119 sec, LOAD: 1.1860 sec.
   Timing for PYPI:, DNS: 0.0681 sec, LOAD: 0.4717 sec.
   Timing for Conda:, DNS: 0.1242 sec, LOAD: 0.3178 sec.

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