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Add MultiNodeBatchNormalization #106

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merged 20 commits into from
Aug 24, 2017
Merged

Add MultiNodeBatchNormalization #106

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1b40b9e
Add MultiNodeBatchNormalizationFunction
iwiwi Aug 10, 2017
c6d7983
Add MultiNodeBatchNormalization
iwiwi Aug 10, 2017
71afd30
Add experimental flag
iwiwi Aug 10, 2017
24713de
Add __init__.py
iwiwi Aug 10, 2017
79b8b8f
Support CPU execution
iwiwi Aug 10, 2017
56621c2
Add tests for MultiNodeBatchNormalization
iwiwi Aug 10, 2017
12d8475
Fix PEP8
iwiwi Aug 10, 2017
9df2831
Fix PEP8
iwiwi Aug 15, 2017
74a3701
Add tests to see that this test is valid
iwiwi Aug 15, 2017
3a19dfa
Fix the test class name
iwiwi Aug 15, 2017
568c117
Add MultiNodeBatchNormalization to document
iwiwi Aug 15, 2017
3db296a
Raise RuntimeError when Chainer is version 1
iwiwi Aug 15, 2017
947563a
Add version checks to tests
iwiwi Aug 15, 2017
71dd347
Add checks to see that no exception raising
iwiwi Aug 16, 2017
bc0c2dc
Add a note about chainer version
iwiwi Aug 16, 2017
71b25cb
Add explanation of tests
iwiwi Aug 16, 2017
73efe61
Fix import error for Chainer v1
iwiwi Aug 17, 2017
629643e
Add comments to highlight the differences from original Chainer imple…
iwiwi Aug 17, 2017
680ce24
Fix PEP8
iwiwi Aug 17, 2017
37a5ab8
Fix the test by ignoring configurations with a single node
iwiwi Aug 23, 2017
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Fix import error for Chainer v1
  • Loading branch information
@iwiwi
iwiwi committed Aug 17, 2017
commit 73efe61903b6151ad6618651c98e86235429deec
9 changes: 4 additions & 5 deletions chainermn/functions/batch_normalization.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
import chainer
from chainer import configuration
from chainer import cuda
from chainer import function
import chainer.utils
Expand Down Expand Up @@ -84,7 +83,7 @@ def check_type_forward(self, in_types):
def forward(self, inputs):
xp = cuda.get_array_module(*inputs)
x, gamma, beta = inputs[:3]
if configuration.config.train:
if chainer.configuration.config.train:
if self.running_mean is None:
self.running_mean = xp.zeros_like(gamma)
self.running_var = xp.zeros_like(gamma)
Expand Down Expand Up @@ -113,7 +112,7 @@ def forward(self, inputs):

cudnn_updated_running_stats = False

if configuration.config.train:
if chainer.configuration.config.train:
axis = (0,) + tuple(range(head_ndim, x.ndim))

mpi_comm = self.comm.mpi_comm
Expand Down Expand Up @@ -150,7 +149,7 @@ def forward(self, inputs):
'bn_fwd')(x, mean[expander], self.std[expander], gamma,
beta)

if configuration.config.train and (not cudnn_updated_running_stats):
if chainer.configuration.config.train and (not cudnn_updated_running_stats):
# Note: If in training mode, the cuDNN forward training function
# will do this for us, so
# only run following code if cuDNN was not used.
Expand Down Expand Up @@ -193,7 +192,7 @@ def backward(self, inputs, grad_outputs):
return gx, ggamma, gbeta, gmean, gvar

# Note: If length of inputs is not 5, we must be in train mode.
assert configuration.config.train
assert chainer.configuration.config.train

# It is wrong to multiply m by mpi_comm.size
# (instead of multiplying 1/size to gbeta, ggamma)
Expand Down
3 changes: 1 addition & 2 deletions chainermn/links/batch_normalization.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
import chainer
from chainer import configuration
from chainer import cuda
from chainer.functions.normalization import batch_normalization
from chainer import initializers
Expand Down Expand Up @@ -91,7 +90,7 @@ def __call__(self, x, finetune=False):
beta = variable.Variable(self.xp.zeros(
self.avg_mean.shape, dtype=x.dtype))

if configuration.config.train:
if chainer.configuration.config.train:
if finetune:
self.N += 1
decay = 1. - 1. / self.N
Expand Down