Specialize BroadcastIndexesRange for the case where there is only 1 contiguous input

kernels/portable/cpu/util/broadcast_indexes_range.h

@@ -236,6 +236,87 @@ class BroadcastIndexesIterator {
   // shape would contain 1s.
   std::array<ShapeType, kNumInputs> effective_input_broadcast_strides_;
 };
+
+// When there is only 1 input and no noncontiguous tensor support
+// required, there is no actual broadcasting to do.
+template <>
+class BroadcastIndexesIterator<1, false> {
+ public:
+  using difference_type = ssize_t;
+  using value_type = std::array<ssize_t, 2>;
+  using reference = value_type;
+  using pointer = const value_type*;
+  using iterator_category = std::forward_iterator_tag;
+
+  BroadcastIndexesIterator() = default;
+
+  explicit BroadcastIndexesIterator(
+      [[maybe_unused]] const Tensor& output,
+      [[maybe_unused]] const Tensor& input) {}
+
+  struct make_end_t {
+    explicit constexpr make_end_t() = default;
+  };
+
+  BroadcastIndexesIterator(
+      make_end_t,
+      const Tensor& output,
+      [[maybe_unused]] const Tensor& input)
+      : current_indexes_({output.numel(), output.numel()}) {}
+
+  bool operator==(const BroadcastIndexesIterator& rhs) const {
+    return current_index() == rhs.current_index();
+  }
+
+  bool operator!=(const BroadcastIndexesIterator& rhs) const {
+    return current_index() != rhs.current_index();
+  }
+
+  reference operator*() const {
+    return current_indexes_;
+  }
+
+  pointer operator->() const {
+    return &current_indexes_;
+  }
+
+  BroadcastIndexesIterator& operator++() {
+    add_to_current_index(1);
+    return *this;
+  }
+
+  BroadcastIndexesIterator operator++(int) {
+    auto it = *this;
+    operator++();
+    return it;
+  }
+
+  BroadcastIndexesIterator& operator+=(difference_type n) {
+    add_to_current_index(n);
+    return *this;
+  }
+
+  BroadcastIndexesIterator operator+(difference_type n) {
+    auto it = *this;
+    it += n;
+    return it;
+  }
+
+  difference_type operator-(const BroadcastIndexesIterator& rhs) const {
+    return difference_type(current_index() - rhs.current_index());
+  }
+
+ private:
+  ssize_t current_index() const {
+    return current_indexes_[0];
+  }
+
+  void add_to_current_index(ssize_t n) {
+    current_indexes_[0] += n;
+    current_indexes_[1] = current_indexes_[0];
+  }
+  value_type current_indexes_ = {{0, 0}};
+};
 } // namespace internal
 
 /**

kernels/portable/cpu/util/test/broadcast_indexes_range_test.cpp

@@ -52,24 +52,6 @@ TEST(BroadcastIndexesRangeTest, OneDNotBroadcasted) {
   }
 }
 
-// [1] -> [W]
-TEST(BroadcastIndexesRangeTest, ScalarBroadcastToOneD) {
-  TensorFactory<ScalarType::Int> tf;
-
-  Tensor out = tf.zeros({5});
-  Tensor in = tf.zeros({1});
-
-  auto actual = range_to_vec(BroadcastIndexesRange<1>(out, in));
-  decltype(actual) expected = {
-      {0, 0},
-      {1, 0},
-      {2, 0},
-      {3, 0},
-      {4, 0},
-  };
-  EXPECT_EQ(expected, actual);
-}
-
 template <typename Range>
 void test_operator_plus(const Range& range) {
   size_t idx = 0;