pengzhao-intel commented on a change in pull request #13530: Integrate MKLDNN
Conv1d and support 3d layout
URL: https://github.com/apache/incubator-mxnet/pull/13530#discussion_r243874967
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File path: src/operator/nn/mkldnn/mkldnn_base-inl.h
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@@ -253,14 +254,23 @@ inline static mkldnn::memory::desc GetWeightDesc(const
NDArray &arr,
if (num_groups == 1) {
return GetMemDesc(arr);
} else {
- CHECK_EQ(arr.shape().ndim(), 4U);
- mkldnn::memory::dims tz = mkldnn::memory::dims{ num_groups,
- static_cast<int>(arr.shape()[0] / num_groups),
- static_cast<int>(arr.shape()[1]),
- static_cast<int>(arr.shape()[2]),
- static_cast<int>(arr.shape()[3])};
- return mkldnn::memory::desc{tz, get_mkldnn_type(arr.dtype()),
- mkldnn::memory::format::any};
+ CHECK((arr.shape().ndim() == 3) || (arr.shape().ndim() == 4))
+ << "MKL-DNN weight currectly supports 3d and 4d layout";
+ const int N = 0, H = 2, W = 3, C = 1;
+ if (arr.shape().ndim() == 3) {
+ mkldnn::memory::dims tz = mkldnn::memory::dims{
+ num_groups, static_cast<int>(arr.shape()[N] / num_groups),
+ static_cast<int>(arr.shape()[C]), static_cast<int>(arr.shape()[H])};
+ return mkldnn::memory::desc{tz, get_mkldnn_type(arr.dtype()),
+ mkldnn::memory::format::any};
+ } else {
+ mkldnn::memory::dims tz = mkldnn::memory::dims{
+ num_groups, static_cast<int>(arr.shape()[N] / num_groups),
+ static_cast<int>(arr.shape()[C]), static_cast<int>(arr.shape()[H]),
+ static_cast<int>(arr.shape()[W])};
+ return mkldnn::memory::desc{tz, get_mkldnn_type(arr.dtype()),
Review comment:
This line is the common part of dim3 and dim4, right?
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