diff --git a/include/tvm/relay/attrs/transform.h b/include/tvm/relay/attrs/transform.h
index 2a421182d5f03..c4cb10aed3a47 100644
--- a/include/tvm/relay/attrs/transform.h
+++ b/include/tvm/relay/attrs/transform.h
@@ -146,11 +146,11 @@ struct GatherAttrs : public tvm::AttrsNode<GatherAttrs> {
 
 struct GatherNDAttrs : public tvm::AttrsNode<GatherNDAttrs> {
   Integer batch_dims;
-  Integer gather_dim;
+  Integer num_indices_per_tuple;
 
   TVM_DECLARE_ATTRS(GatherAttrs, "relay.attrs.GatherNDAttrs") {
     TVM_ATTR_FIELD(batch_dims).set_default(Integer(0)).describe("The number of batch dimensions.");
-    TVM_ATTR_FIELD(gather_dim)
+    TVM_ATTR_FIELD(num_indices_per_tuple)
         .set_default(Integer(-1))
         .describe(
             "The size of an indexing tuple, which is a fixed value. Only needed when the number of "
diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
index e67ce52898dba..6feed09269d5b 100644
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -1418,8 +1418,8 @@ def _impl_common(cls, data, indices, batch_dims=0):
         indices_dims = len(infer_shape(indices))
         indices_shape = infer_shape(indices)
         indices = _op.transpose(indices, axes=[-1] + list(range(indices_dims - 1)))
-        gather_dim = indices_shape[-1]
-        return _op.gather_nd(data, indices, batch_dims, gather_dim)
+        num_indices_per_tuple = indices_shape[-1]
+        return _op.gather_nd(data, indices, batch_dims, num_indices_per_tuple)
 
     @classmethod
     def _impl_v1(cls, inputs, attr, params):
diff --git a/python/tvm/relay/op/_transform.py b/python/tvm/relay/op/_transform.py
index a3caaf6344990..60d642d829254 100644
--- a/python/tvm/relay/op/_transform.py
+++ b/python/tvm/relay/op/_transform.py
@@ -1077,14 +1077,14 @@ def unique_shape_func(attrs, inputs, _):
 
 
 @script
-def _gather_nd_shape(data_shape, indices_shape, batch_dims, gather_dim):
+def _gather_nd_shape(data_shape, indices_shape, batch_dims, num_indices_per_tuple):
     ndim = data_shape.shape[0]
     # using mdim = indices_shape[0] wouldn't work because a rank cannot
     # depend on a runtime shape dimension of indices tensor, even if the
     # dimension is always a known, fixed value. As a workaround, we assume that
     # the fixed gather dimension (the size of an indexing tuple) is recorded
     # in `gather_nd` op attribute.
-    mdim = gather_dim
+    mdim = num_indices_per_tuple
     kdim = indices_shape.shape[0] - 1
     out_shape = output_tensor((kdim + ndim - (mdim + batch_dims),), "int64")
     for i in range(1, kdim + 1):
@@ -1100,6 +1100,6 @@ def gather_nd_shape_func(attrs, inputs, _):
     Shape func for ghater_nd operator.
     """
     batch_dims = get_const_int(attrs.batch_dims)
-    gather_dim = get_const_int(attrs.gather_dim)
-    assert gather_dim > 0, "gather_dim needs to be specified for dynamic gather_nd"
-    return [_gather_nd_shape(inputs[0], inputs[1], convert(batch_dims), convert(gather_dim))]
+    num_indices_per_tuple = get_const_int(attrs.num_indices_per_tuple)
+    assert num_indices_per_tuple > 0, "num_indices_per_tuple needs to be specified for dynamic gather_nd"
+    return [_gather_nd_shape(inputs[0], inputs[1], convert(batch_dims), convert(num_indices_per_tuple))]
diff --git a/python/tvm/relay/op/transform.py b/python/tvm/relay/op/transform.py
index 8c2f0e9bb3305..fdd86b316353a 100644
--- a/python/tvm/relay/op/transform.py
+++ b/python/tvm/relay/op/transform.py
@@ -1072,7 +1072,7 @@ def gather(data, axis, indices):
     return _make.gather(data, axis, indices)
 
 
-def gather_nd(data, indices, batch_dims=0, gather_dim=-1):
+def gather_nd(data, indices, batch_dims=0, num_indices_per_tuple=-1):
     """Gather elements or slices from data and store to a tensor whose shape is
     defined by indices.
 
@@ -1087,7 +1087,7 @@ def gather_nd(data, indices, batch_dims=0, gather_dim=-1):
     batch_dims : int
         The number of batch dimensions.
 
-    gather_dim : int
+    num_indices_per_tuple : int
         The size of an indexing tuple, which is a fixed value and the same as indices.shape[0]
         Only needed when other dimensions of indices are dynamic.
 
@@ -1112,7 +1112,7 @@ def gather_nd(data, indices, batch_dims=0, gather_dim=-1):
         indices = [[1, 0]]
         relay.gather_nd(data, indices, batch_dims=1) = [[2,3],[4,5]]
     """
-    return _make.gather_nd(data, indices, batch_dims, gather_dim)
+    return _make.gather_nd(data, indices, batch_dims, num_indices_per_tuple)
 
 
 def sequence_mask(data, valid_length, mask_value=0, axis=0):
diff --git a/src/relay/op/tensor/transform.cc b/src/relay/op/tensor/transform.cc
index c6efb9ee64bb2..2128685d18f35 100644
--- a/src/relay/op/tensor/transform.cc
+++ b/src/relay/op/tensor/transform.cc
@@ -3373,11 +3373,11 @@ Array<te::Tensor> GatherNDCompute(const Attrs& attrs, const Array<te::Tensor>& i
   return {topi::gather_nd(inputs[0], inputs[1], param->batch_dims)};
 }
 
-Expr MakeGatherND(Expr data, Expr indices, int batch_dims = 0, int gather_dim = -1) {
+Expr MakeGatherND(Expr data, Expr indices, int batch_dims = 0, int num_indices_per_tuple = -1) {
   static const Op& op = Op::Get("gather_nd");
   auto attrs = make_object<GatherNDAttrs>();
   attrs->batch_dims = batch_dims;
-  attrs->gather_dim = gather_dim;
+  attrs->num_indices_per_tuple = num_indices_per_tuple;
   return Call(op, {data, indices}, Attrs(attrs));
 }