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src/operator/nn/dnnl/dnnl_where-inl.h

@@ -28,8 +28,8 @@
 #include <memory>
 #include <unordered_map>
 #include <vector>
-#include "./dnnl_base-inl.h"
-#include "./dnnl_ops-inl.h"
+#include "dnnl_base-inl.h"
+#include "dnnl_ops-inl.h"
 
 namespace mxnet {
 namespace op {

src/operator/nn/dnnl/dnnl_where.cc

@@ -88,12 +88,8 @@ static mxnet::TShape GetBroadcastableShape(const mxnet::TShape& in_shape,
 
   mxnet::TShape broadcastable_in_shape(out_shape.ndim(), -1);
   const int lack_dims = out_shape.ndim() - in_shape.ndim();
-  for (int i = 0; i < out_shape.ndim(); ++i) {
-    int y = 1;
-    if (i >= lack_dims) {
-      y = in_shape[i - lack_dims];
-    }
-    broadcastable_in_shape[i] = y;
+  for (int i = lack_dims; i < out_shape.ndim(); ++i) {
+    broadcastable_in_shape[i] = in_shape[i - lack_dims];
   }
   return broadcastable_in_shape;
 }

src/operator/numpy/np_where_forward_op.cc

@@ -148,9 +148,8 @@ NNVM_REGISTER_OP(_npi_where)
 #endif
     .set_attr<nnvm::FGradient>(
         "FGradient",
-        // Use the following lambda function instead of ElemwiseGradUseIn
-        // for best efficiency. grad[condition] = 0; to calculate grad[x] and
-        // grad[y] we need only condition from input.
+        // Use the following lambda function instead of ElemwiseGradUseIn for best efficiency.
+        // grad[condition] = 0; to calculate grad[x] and grad[y] we need only condition from input.
         [](const nnvm::ObjectPtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
           std::vector<nnvm::NodeEntry> ret;
           // make zero grad node for grad[condition]