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Unable to use Batchnormalization with multi-GPUs in MXNet backend #63

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sandeep-krishnamurthy opened this issue Jun 2, 2017 · 4 comments
Open

Unable to use Batchnormalization with multi-GPUs in MXNet backend #63

sandeep-krishnamurthy opened this issue Jun 2, 2017 · 4 comments

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@sandeep-krishnamurthy
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sandeep-krishnamurthy commented Jun 2, 2017 •
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Summary

Unable to use batchnormalization with MXNet backend when using multiple GPUs. After debugging the issue, I found that there is a mismatch in the shape of batchnorm param in KVStore. in mxnet/model.py -> KVStore is being initialized with a (64,) shape but is being tried to update with a (256,64,1,1) shape.

Stacktrace and Debug messages

Below is the stack trace and my debug messages from "initialize_kvstore" and "update_params_on_kvstore" functions. Observe that param shape at index 4, there is a mismatch.

In initialize kvstore

kvstore - <mxnet.kvstore.KVStore object at 0x7fbfdb6729d0>
len of param_arrays - 304
len of arg_params - 304
len of param_names - 304
update_on_kvstore - True
Index - 0
Param name - normal1
Arg params - <NDArray 3x7x7 @cpu(0)>
Index - 1
Param name - convolution2d_1_b
Arg params - <NDArray 1 @cpu(0)>
Index - 2
Param name - batchnormalization_1_running_mean
Arg params - <NDArray 1 @cpu(0)>
Index - 3
Param name - batchnormalization_1_running_std
Arg params - <NDArray 1 @cpu(0)>
Index - 4
Param name - batchnormalization_1_gamma
Arg params - <NDArray 1 @cpu(0)>
arg_params in idx 4 - <NDArray 64 @cpu(0)>
param name at idx 4 - batchnormalization_1_gamma

In update_params_on_kvstore
param_arrays - 304
grad_arrays - 304
kvstore - <mxnet.kvstore.KVStore object at 0x7fbfdb6729d0>
Index - 0
arg_list[0] <NDArray 64x3x7x7 @gpu(0)>
Current index - 0
Index - 1
arg_list[0] <NDArray 64 @gpu(0)>
Current index - 1
Index - 2
arg_list[0] <NDArray 64 @gpu(0)>
Current index - 2
Index - 3
arg_list[0] <NDArray 64 @gpu(0)>
Current index - 3
Index - 4
arg_list[0] <NDArray 256x64x1x1 @gpu(0)>
Current index - 4
Len of arg_list - 16
Len of grad_list - 16
Arg list[0] - <NDArray 256x64x1x1 @gpu(0)>
Grad list[0] - <NDArray 256x64x1x1 @gpu(0)>
[16:14:11] /home/ubuntu/mxnet/dmlc-core/include/dmlc/./logging.h:304: [16:14:11] src/ndarray/ndarray.cc:319: Check failed: from.shape() == to->shape() operands shape mismatchfrom.shape = (256,64,1,1) to.shape=(64,)

Stack trace returned 10 entries:
[bt] (0) /usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/libmxnet.so(_ZN4dmlc15LogMessageFatalD1Ev+0x3c) [0x7fc120b0a46c]
[bt] (1) /usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet10CopyFromToERKNS_7NDArrayEPS0_i+0x546) [0x7fc12154c056]
[bt] (2) /usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet7kvstore10CommDevice6ReduceEiRKSt6vectorINS_7NDArrayESaIS3_EEi+0x384) [0x7fc121925de4]
[bt] (3) /usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet7kvstore12KVStoreLocal4PushERKSt6vectorIiSaIiEERKS2_INS_7NDArrayESaIS7_EEi+0x175) [0x7fc121928015]
[bt] (4) /usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/libmxnet.so(MXKVStorePush+0x7b0) [0x7fc1218cbbc0]
[bt] (5) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call_unix64+0x4c) [0x7fc0b1ef8e40]
[bt] (6) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call+0x2eb) [0x7fc0b1ef88ab]
[bt] (7) /usr/lib/python2.7/lib-dynload/_ctypes.x86_64-linux-gnu.so(_ctypes_callproc+0x48f) [0x7fc0ba1083df]
[bt] (8) /usr/lib/python2.7/lib-dynload/_ctypes.x86_64-linux-gnu.so(+0x11d82) [0x7fc0ba10cd82]
[bt] (9) python(PyObject_Call+0x43) [0x4b0cb3]

Traceback (most recent call last):
File "/home/ubuntu/keras_benchmarks/test_cifar_resnet.py", line 131, in
run_time, memory_usage = profile(train_model)
File "/home/ubuntu/keras_benchmarks/profiler.py", line 84, in profile
func_to_profile()
File "/home/ubuntu/keras_benchmarks/test_cifar_resnet.py", line 125, in train_model
validation_data=(X_test, Y_test))
File "/usr/local/lib/python2.7/dist-packages/Keras-1.2.2-py2.7.egg/keras/engine/training.py", line 1559, in fit_generator
class_weight=class_weight)
File "/usr/local/lib/python2.7/dist-packages/Keras-1.2.2-py2.7.egg/keras/engine/training.py", line 1322, in train_on_batch
outputs = self.train_function(ins)
File "/usr/local/lib/python2.7/dist-packages/Keras-1.2.2-py2.7.egg/keras/engine/training.py", line 1959, in train_function
self._mod.update()
File "/usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/module/bucketing_module.py", line 408, in update
self._curr_module.update()
File "/usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/module/module.py", line 575, in update
self._kvstore)
File "/usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/model.py", line 132, in _update_params_on_kvstore
kvstore.push(index, grad_list, priority=-index)
File "/usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/kvstore.py", line 162, in push
ctypes.c_int(priority)))
File "/usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/base.py", line 85, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [16:14:11] src/ndarray/ndarray.cc:319: Check failed: from.shape() == to->shape() operands shape mismatchfrom.shape = (256,64,1,1) to.shape=(64,)

Note: I used Resnet50 architecture on CIFAR dataset with batchsize=32.

@mli @piiswrong @madjam @bhavinthaker
@mli
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mli commented Jun 2, 2017 via email

@sandeep-krishnamurthy
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Thanks Mu.

Most of the convolution and fully connected networks would require batchnormalization. Can we still go ahead with keras beta release with this as a known issue?

@mli
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mli commented Jun 5, 2017

another possible fix is that not pushing the batchnorm parameters into kvstore. They actually don't need to be synchronized.

@madjam
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madjam commented Jun 5, 2017

@mli Can you please clarify what is causing this problem?

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@mli @sandeep-krishnamurthy @madjam