Fix axis Bug in MKLDNN Softmax

src/operator/nn/mkldnn/mkldnn_softmax.cc

@@ -26,6 +26,7 @@
 #include "../softmax-inl.h"
 #include "./mkldnn_ops-inl.h"
 #include "./mkldnn_base-inl.h"
+#include "../../tensor/broadcast_reduce_op.h"
 
 #if MXNET_USE_MKLDNN == 1
 namespace mxnet {
@@ -38,11 +39,13 @@ void MKLDNNSoftmaxForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
   auto input_mem = in_data.GetMKLDNNData();
   mkldnn::memory::primitive_desc data_mpd = input_mem->get_primitive_desc();
   mkldnn::memory::desc data_md = data_mpd.desc();
+  int axis = CheckAxis(param.axis, in_data.shape().ndim());
+
   auto cpu_engine = data_mpd.get_engine();
   auto prop = ctx.is_train
     ? mkldnn::prop_kind::forward_training : mkldnn::prop_kind::forward_scoring;
   mkldnn::softmax_forward::desc desc = mkldnn::softmax_forward::desc(prop,
-      data_md, param.axis);
+      data_md, axis);
   mkldnn::softmax_forward::primitive_desc pdesc(desc, cpu_engine);
 
   auto output_memory = out_data.GetMKLDNNData();

src/operator/nn/softmax.cc

@@ -38,10 +38,8 @@ static void SoftmaxComputeExCPU(const nnvm::NodeAttrs& attrs,
                                 const std::vector<NDArray>& inputs,
                                 const std::vector<OpReqType>& req,
                                 const std::vector<NDArray>& outputs) {
-  const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
   // It seems MKLDNN softmax doesn't support training.
-  // and it only supports non-negative axis.
-  if (SupportMKLDNN(inputs[0]) && !ctx.is_train && param.axis >= 0) {
+  if (SupportMKLDNN(inputs[0]) && !ctx.is_train) {
     MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
     MKLDNNSoftmaxForward(attrs, ctx, inputs[0], req[0], outputs[0]);
     auto fn = SoftmaxCompute<cpu, mxnet_op::softmax_fwd>;

tests/python/unittest/test_operator.py

@@ -4098,7 +4098,7 @@ def test_new_softmax():
     for ndim in range(1, 5):
         for _ in range(5):
             shape = np.random.randint(1, 5, size=ndim)
-            axis = np.random.randint(0, ndim)
+            axis = np.random.randint(-ndim, ndim)
             data = np.random.uniform(-2, 2, size=shape)
             sym = mx.sym.softmax(axis=axis)
             check_symbolic_forward(sym, [data], [np_softmax(data, axis=axis)])