[XLA:CPU] Re-use the same llvm::GlobalVariable for identical literals

This isn't necessary today, but it will be after an optimization change I'm
about to make.

LLVM has a constant merging pass too, but one of the motivations here is to
avoid the LLVM compile time overhead of having many large arrays in the IR.

PiperOrigin-RevId: 195032900

1 files changed, 22 insertions, 0 deletions

diff --git a/tensorflow/compiler/xla/layout_util.cc b/tensorflow/compiler/xla/layout_util.cc
index fdc4bbdd8b..c6f8f6766e 100644
--- a/tensorflow/compiler/xla/layout_util.cc
+++ b/tensorflow/compiler/xla/layout_util.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/hash/hash.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
@@ -465,4 +466,25 @@ std::ostream& operator<<(std::ostream& out, const Layout& layout) {
   return out;
 }
 
+/*static*/ size_t LayoutUtil::Hash(const Layout& layout) {
+  using tensorflow::hash;
+  using tensorflow::Hash64Combine;
+
+  size_t hash_value = hash<Format>()(layout.format());
+
+  for (int64 minor_to_major : layout.minor_to_major()) {
+    hash_value = Hash64Combine(hash_value, hash<int64>()(minor_to_major));
+  }
+
+  for (int64 padded_dim : layout.padded_dimensions()) {
+    hash_value = Hash64Combine(hash_value, hash<int64>()(padded_dim));
+  }
+
+  hash_value =
+      Hash64Combine(hash_value, hash<PaddingValue>()(layout.padding_value()));
+  hash_value = Hash64Combine(hash_value, layout.max_sparse_elements());
+
+  return hash_value;
+}
+
 }  // namespace xla