[TOPI] Fix in interpretation of empty axis parameter in reduction fun…

…ctions

include/tvm/topi/nn.h

@@ -685,7 +685,6 @@ inline Tensor nll_loss(const Tensor& predictions, const Tensor& targets, const T
       return T;
     }
   }
-
   auto T = tvm::te::compute(
       targets->shape,
       [&](const tvm::Array<tvm::tir::Var>& target_indices) {
@@ -710,9 +709,10 @@ inline Tensor nll_loss(const Tensor& predictions, const Tensor& targets, const T
                                   tvm::tir::make_const(predictions->dtype, 0));
         },
         name, tag);
-    return topi::divide(topi::sum(T, {}), topi::sum(W, {}));
+    return topi::divide(topi::sum(T, tvm::Array<Integer>(nullptr)),
+                        topi::sum(W, tvm::Array<Integer>(nullptr)));
   } else if (reduction == "sum") {
-    return topi::sum(T, {});
+    return topi::sum(T, tvm::Array<Integer>(nullptr));
   } else {  // reduction == "none"
     return T;
   }

include/tvm/topi/reduction.h

@@ -57,14 +57,14 @@ using FCommReduce = std::function<Array<PrimExpr>(Array<PrimExpr> exprs, const A
  * \param ndim Number of dimensions in the target.
  * \param axis The axis parameter.
  *
- * \return A non-empty sorted array of valid dimension indices, with no duplicates.
- * If the input axis is empty, the result will be an axis including all dimensions.
+ * \return A sorted array of valid dimension indices, with no duplicates.
+ * If the input axis is None, the result will be an axis including all dimensions.
  * If any input element is negative, it will be treated as an offset from the
  * last dimension (same as python indexing rules).
  */
 inline std::vector<int> GetRealAxis(int ndim, const Array<Integer>& axis) {
   std::vector<int> real_axis;
-  if (!axis.defined() || axis.size() == 0) {
+  if (!axis.defined()) {
     for (int i = 0; i < ndim; ++i) {
       real_axis.push_back(i);
     }
@@ -75,7 +75,7 @@ inline std::vector<int> GetRealAxis(int ndim, const Array<Integer>& axis) {
       if (val < 0) {
         val += ndim;
       }
-      ICHECK_LE(val, ndim) << " exceeds the maximum dimension " << ndim;
+      ICHECK_LT(val, ndim) << " exceeds the maximum dimension " << ndim;
       ICHECK_GE(val, 0);
       real_axis.push_back(static_cast<int>(val));
     }

tests/python/topi/python/test_topi_reduce.py

@@ -46,6 +46,33 @@
     ((128, 24, 128, 24), 2, False, "any", "bool"),
     ((128, 24, 128, 24), 2, False, "sum", "bool"),
     ((128, 24, 128, 24), 0, True, "sum", "bool"),
+    ((3, 4, 5), None, False, "prod", "float32"),
+    ((3, 4, 5), (2,), False, "prod", "float32"),
+    ((3, 4, 5), (1, 2), True, "prod", "float32"),
+    ((3, 4, 5), (), False, "sum", "float32"),
+    ((3, 4, 5), (), True, "sum", "float32"),
+    ((3, 4, 5), (0, 1, 2), False, "sum", "float32"),
+    ((3, 4, 5), (0, 1, 2), True, "sum", "float32"),
+    ((3, 4, 5), (), False, "prod", "float32"),
+    ((3, 4, 5), (), True, "prod", "float32"),
+    ((3, 4, 5), (0, 1, 2), False, "prod", "float32"),
+    ((3, 4, 5), (0, 1, 2), True, "prod", "float32"),
+    ((3, 4, 5), (), False, "min", "float32"),
+    ((3, 4, 5), (), True, "min", "float32"),
+    ((3, 4, 5), (0, 1, 2), False, "min", "float32"),
+    ((3, 4, 5), (0, 1, 2), True, "min", "float32"),
+    ((3, 4, 5), (), False, "max", "float32"),
+    ((3, 4, 5), (), True, "max", "float32"),
+    ((3, 4, 5), (0, 1, 2), False, "max", "float32"),
+    ((3, 4, 5), (0, 1, 2), True, "max", "float32"),
+    ((3, 4, 5), (), False, "any", "bool"),
+    ((3, 4, 5), (), True, "any", "bool"),
+    ((3, 4, 5), (0, 1, 2), False, "any", "bool"),
+    ((3, 4, 5), (0, 1, 2), True, "any", "bool"),
+    ((3, 4, 5), (), False, "all", "bool"),
+    ((3, 4, 5), (), True, "all", "bool"),
+    ((3, 4, 5), (0, 1, 2), False, "all", "bool"),
+    ((3, 4, 5), (0, 1, 2), True, "all", "bool"),
 )
 
 
@@ -63,6 +90,8 @@ def ref_data(in_shape, axis, keepdims, reduce_type, dtype):
             out_npy = in_npy_map.sum(axis=axis, keepdims=keepdims, dtype="bool")
         else:
             out_npy = in_npy_map.sum(axis=axis, keepdims=keepdims)
+    elif reduce_type == "prod":
+        out_npy = in_npy_map.prod(axis=axis, keepdims=keepdims)
     elif reduce_type == "all" and dtype == "bool":
         out_npy = in_npy_map.all(axis=axis, keepdims=keepdims)
     elif reduce_type == "any" and dtype == "bool":
@@ -108,7 +137,7 @@ def _my_npy_argmin(arr, axis, keepdims):
 
 def test_reduce_map(target, dev, ref_data, in_shape, axis, keepdims, reduce_type, dtype):
     target = tvm.target.Target(target)
-    if target.kind.name == "vulkan" and reduce_type in ["sum", "any", "all"]:
+    if target.kind.name == "vulkan" and reduce_type in ["sum", "prod", "any", "all"]:
         pytest.xfail(f"Vulkan backend has known errors on {reduce_type}")
 
     in_npy, in_npy_map, out_npy = ref_data
@@ -122,6 +151,8 @@ def test_reduce_map(target, dev, ref_data, in_shape, axis, keepdims, reduce_type
             B = topi.sum(A, axis=axis, keepdims=keepdims)
         else:
             B = topi.sum(A1, axis=axis, keepdims=keepdims)
+    elif reduce_type == "prod":
+        B = topi.prod(A1, axis=axis, keepdims=keepdims)
     elif reduce_type == "all":
         B = topi.all(A, axis=axis, keepdims=keepdims)
     elif reduce_type == "any":