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[v1.8.x][BUGFIX] Fix MKLDNN BatchNorm with even number of channels (#19150) #19299 #19425 #19428

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Oct 29, 2020
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16 changes: 6 additions & 10 deletions src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
Original file line number Diff line number Diff line change
Expand Up @@ -145,13 +145,6 @@ static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
return it->second;
}

template<typename DType>
static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
const OpContext &ctx, const NDArray &in_data,
mkldnn::normalization_flags flags) {
return GetBNForward<DType>(param, ctx, in_data.GetMKLDNNData(), flags);
}

template <typename DType>
void MKLDNNBatchNormForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
const std::vector<NDArray> &inputs, const std::vector<OpReqType> &req,
Expand Down Expand Up @@ -182,8 +175,11 @@ void MKLDNNBatchNormForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
aux_states,
ctx.is_train && !param.use_global_stats,
fuse_relu);
const NDArray &data = in_data[batchnorm::kData];
auto &fwd = GetBNForward<DType>(param, ctx, data, flags);
NDArray &data = in_data[batchnorm::kData];
if (data.IsMKLDNNData() && data.IsView())
data = data.Reorder2Default();
auto data_mem = data.GetMKLDNNData();
auto &fwd = GetBNForward<DType>(param, ctx, data_mem, flags);

// for output memory
auto out_mem = const_cast<NDArray &>(out).CreateMKLDNNData(fwd.GetPd().dst_desc());
Expand Down Expand Up @@ -221,7 +217,7 @@ void MKLDNNBatchNormForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
}

mkldnn_args_map_t net_args;
net_args[MKLDNN_ARG_SRC] = *data.GetMKLDNNData();
net_args[MKLDNN_ARG_SRC] = *data_mem;
net_args[MKLDNN_ARG_SCALE_SHIFT] = weight_mem;
net_args[MKLDNN_ARG_DST] = *out_mem;
if (fuse_relu) {
Expand Down
2 changes: 1 addition & 1 deletion tests/python/mkl/test_mkldnn.py
Original file line number Diff line number Diff line change
Expand Up @@ -294,7 +294,7 @@ def test_mkldnn_sum_inplace_with_cpu_layout():
@with_seed()
def test_batchnorm():
def check_batchnorm_training(stype):
for shape in [(2, 3), (2, 3, 2, 2)]:
for shape in [(2, 3), (2, 4), (2, 3, 2, 2), (2, 4, 2, 2)]:
data_tmp = np.random.normal(-0.1, 0.1, size=shape)
s = shape[1],
gamma = np.ones(s)
Expand Down
35 changes: 35 additions & 0 deletions tests/python/unittest/test_gluon.py
Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@

import mxnet as mx
from mxnet import gluon
from mxnet import init
from mxnet.gluon import nn
from mxnet.base import py_str, MXNetError
from mxnet.test_utils import assert_almost_equal, default_context
Expand Down Expand Up @@ -2179,6 +2180,40 @@ def hybrid_forward(self, F, x):
check_layer_forward_withinput(net, x)


@with_seed()
def test_batchnorm_chnls():
chn_list = [1024, 512, 256, 128, 64, 45, 32, 16, 3]
class Net(gluon.HybridBlock):
def __init__(self,
chn_num,
norm_kwargs=None,
in_channels=3,
**kwargs):
super(Net, self).__init__(**kwargs)
self.in_channels = in_channels
self.conv1 = gluon.nn.Conv3D(
in_channels=self.in_channels,
channels=chn_num,
kernel_size=(1, 7, 7),
strides=(1, 2, 2),
padding=(0, 3, 3),
use_bias=False,
)
self.bn1 = gluon.nn.BatchNorm(in_channels=chn_num, **({} if norm_kwargs is None else norm_kwargs))

def hybrid_forward(self, F, x):
"""Hybrid forward of R2+1D net"""
conv = self.conv1(x)
out = self.bn1(conv)
return out

for i in range(len(chn_list)):
net = Net(chn_list[i])
net.initialize(init=init.Constant(1))
x = mx.nd.zeros((1, 3, 8, 160, 160))
net(x).asnumpy()


@with_seed()
def test_concat():
chn_list = [16, 64]
Expand Down