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4 changes: 4 additions & 0 deletions docs/static_site/src/pages/api/faq/env_var.md
Original file line number Diff line number Diff line change
Expand Up @@ -298,6 +298,10 @@ If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
- Values: Int ```(default=-1)```
- Flag to set num of elements that MKLDNN cache can hold. Default is -1 which means cache size is unbounded. Should only be set if your model has variable input shapes, as cache size may grow unbounded. The number represents the number of items in the cache and is proportional to the number of layers that use MKLDNN and different input shape.

* MXNET_MKLDNN_DISABLE_BRGEMM_FC
- Values: 0, 1 ```(default=1)```
- Flag which disables BRGEMM kernels in FullyConnected executed with MKLDNN support - Should only be set to 0 if your model has constant input shapes or FullyConnected is calculated with large tensors. Supported on machines with AVX512-VNNI.

* MXNET_ENFORCE_DETERMINISM
- Values: 0(false) or 1(true) ```(default=0)```
- If set to true, MXNet will only use deterministic algorithms in forward and backward computation.
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3 changes: 2 additions & 1 deletion src/operator/nn/mkldnn/mkldnn_base-inl.h
Original file line number Diff line number Diff line change
Expand Up @@ -312,7 +312,8 @@ inline static mkldnn::memory::desc GetFCWeightDesc(const NDArray &arr, int dtype
for (size_t i = 0; i < dims.size(); i++) dims[i] = arr.shape()[i];
auto format = mkldnn::memory::format_tag::any;
// for batch 256 alexnet benchmark test
if (dims.size() == 2) {
const bool brgemm_disabled = dmlc::GetEnv("MXNET_MKLDNN_DISABLE_BRGEMM_FC", true);
if (dims.size() == 2 && brgemm_disabled) {

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Could you please provide more benchmarking data with different m/n/k and formats?

BTW, actually brgemm_disabled looks misleading to me. According to the code change, i would rather call the flag force_plain_format or force_ab_format.

format = mkldnn::memory::format_tag::ab;
}

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