[GitHub] [incubator-mxnet] Neutron3529 opened a new issue #17870: batch transform with dataloader

[GitBox <gi...@apache.org>]

Neutron3529 opened a new issue #17870: batch transform with dataloader
URL: https://github.com/apache/incubator-mxnet/issues/17870
 
 
   ## Description
   I found a [performance regression](https://github.com/apache/incubator-mxnet/issues/15655) last year, which is due to the current strategy of executing what meet `transform_first`'s setting.
   
   The current choice is perform what meets `transform_first`'s setting on the data **before** collecting data into a batch, if batch_size=500, we have to execute what meets `transform_first`'s setting for 500 times within a batch, which is extremely ineffective.
   
   if we collecting the data first, and send the full data batch to what meet `transform_first`'s settings, the processing may be much faster.
   
   Here is the test result, 2 nets are provided to simulate when to perform what meets `transform_first`'s setting:
   ```
   Python 3.7.7 (tags/v3.7.7:d7c567b08f, Mar 10 2020, 10:41:24) [MSC v.1900 64 bit (AMD64)] on win32
   Type "help", "copyright", "credits" or "license" for more information.
   >>> import mxnet as mx
   >>> from mxnet.gluon.nn import Dense
   >>> import time
   >>> net=mx.gluon.nn.HybridSequential()#calculate before batchify
   >>> with net.name_scope():
   ...  net.add(Dense(10))
   ...  net.add(Dense(35))
   ...  net.add(Dense(10))
   ...
   >>> n2=mx.gluon.nn.HybridSequential()#calculate after batchify
   >>> with n2.name_scope():
   ...  n2.add(Dense(10))
   ...  n2.add(Dense(35))
   ...  n2.add(Dense(10))
   ...
   >>> a=[mx.nd.random.uniform(shape=(60,)) for i in range(500)]#data is mx.nd.random.uniform(shape=(60,)) with  batch_size=500
   >>> ctx=mx.cpu(0)
   >>> n2.initialize(mx.init.Uniform(), ctx=ctx,force_reinit=True)
   >>> net.initialize(mx.init.Uniform(), ctx=ctx,force_reinit=True)
   >>> for i in range(10):
   ...  ii=time.time()
   ...  _=mx.nd.stack(*(net(a) for a in a))#calculate before batchify, which is the default order of calculation which MXNet applies.
   ...  jj=time.time()-ii
   ...  ii=time.time()
   ...  b=mx.nd.stack(*a)#batchify
   ...  _=mx.nd.stack(n2(b))#calculate, which will be faster.
   ...  kk=time.time()-ii
   ...  print((jj,kk))
   ...
   (0.44376635551452637, 0.008976459503173828)
   (0.34108686447143555, 0.0029942989349365234)
   (0.48074865341186523, 0.002991914749145508)
   (0.3600330352783203, 0.0029861927032470703)
   (0.35801076889038086, 0.003988027572631836)
   (0.3530876636505127, 0.0029916763305664062)
   (0.4198474884033203, 0.0019931793212890625)
   (0.41489434242248535, 0.0019941329956054688)
   (0.3879544734954834, 0.003995418548583984)
   (0.419874906539917, 0.0029935836791992188)
   >>>#which means, batchify after transform is a totally waste of time.
   ```
   I tried to force a transform after batchify, but I cannot find any convinent apporaches since most of the built-in transform function is not designed to work after batchify (e.g., ), if I want to batchify before transform, I have to write an equivlent transform function myself.
   
   Actually I've submitted [what I found](https://github.com/apache/incubator-mxnet/issues/15655#issuecomment-515744051), but the problem still exists. It tooks extremely long time training CIFAR-10 for me.
   (with num_worker=3, it may took a while, but with default num_workers=8, an OOM happened and crush the training step since each python.exe eats 2GB of my physical memory(loading libmxnet.dll tooks a lot of time and ~2G memory))

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[GitHub] [incubator-mxnet] Neutron3529 closed issue #17870: batch transform with dataloader

[GitBox <gi...@apache.org>]

Neutron3529 closed issue #17870:
URL: https://github.com/apache/incubator-mxnet/issues/17870


   


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