Simplify gradient exclusions data to speed up compilation w/ clang on windows.

PiperOrigin-RevId: 294998085
Change-Id: Ie56b8f2cf4ed1e5fd8e2a641947b2d69f316e86a

tensorflow/python/eager/BUILD

@@ -54,6 +54,7 @@ cc_library(
         "@com_google_absl//absl/container:flat_hash_map",
         "@com_google_absl//absl/hash",
         "@com_google_absl//absl/strings",
+        "@com_google_absl//absl/types:optional",
         "@com_google_absl//absl/types:variant",
     ],
 )

tensorflow/python/eager/gradient_input_output_exclusions.py

@@ -63,10 +63,33 @@ limitations under the License.
 _INCLUDES = """
 #include "tensorflow/python/eager/pywrap_gradient_exclusions.h"
 
+#include "absl/types/optional.h"
 #include "tensorflow/core/lib/gtl/flatmap.h"
 #include "tensorflow/core/lib/gtl/flatset.h"
 
 using tensorflow::string;
+
+namespace {
+// Keep static data in a format that's easy to init statically.
+struct OpIndexInfo {
+  const char *op_name;
+  int num_indices;
+  std::array<int, 4> unused_indices;
+};
+
+// Helper function to initialize FlatMap<string,FlatSet> from OpIndexInfo.
+template <typename T>
+auto OpGradientInfoInit(const T &a) {
+  auto *m = new tensorflow::gtl::FlatMap<string, tensorflow::gtl::FlatSet<int>>;
+  for (const auto &item : a) {
+    m->emplace(string(item.op_name),
+               tensorflow::gtl::FlatSet<int>(
+                   item.unused_indices.begin(),
+                   item.unused_indices.begin() + item.num_indices));
+  }
+  return m;
+}
+}  // namespace
 """
 
 _EXCLUDED_OPS = [
@@ -281,7 +304,6 @@ def get_entries(attr_name):
   """
   assert attr_name in ["inputs", "outputs"]
   entries = {}
-  spaces = "          "
   for op_type in ops._gradient_registry.list():  # pylint: disable=protected-access
     if op_type in _EXCLUDED_OPS:
       continue
@@ -291,72 +313,57 @@ def get_entries(attr_name):
     if gradient_fn is None:
       # NotDifferentiable
       if num_values != -1:
-        entries[op_type] = spaces + "{\"%s\", {true, {}}}," % op_type
+        entries[op_type] = "{\"%s\"}," % op_type
       continue
     used_tensors = _live_tensors(gradient_fn, attr_name=attr_name)
     if used_tensors is _ALL:
       continue
     elif not used_tensors:
-      entries[op_type] = spaces + "{\"%s\", {true, {}}}," % op_type
+      entries[op_type] = "{\"%s\"}," % op_type
     else:
       all_tensors = set(range(num_values))
       unused_tensors = all_tensors - used_tensors
       if unused_tensors:
-        entries[op_type] = spaces + "{\"%s\", {false, {%s}}}," % (
-            op_type, ", ".join(str(i) for i in sorted(list(unused_tensors))))
+        unused_tensor_list = sorted(list(unused_tensors))
+        entries[op_type] = "{\"%s\", %d, {%s}}," % (
+            op_type, len(unused_tensor_list), ", ".join(
+                str(i) for i in unused_tensor_list))
   return entries
 
 
-def get_contents():
-  """Returns contents for the generated file."""
-  contents = ""
-  contents += _GENERATED_FILE_HEADER + _INCLUDES
+def get_function(name, entries):
+  """Generates lookup function with given name and lookup table entries."""
+  contents = """
+absl::optional<tensorflow::gtl::FlatSet<int>> {name}(
+    const tensorflow::string &op_name) {{
+  static std::array<OpIndexInfo, {count}> a = {{{{
+""".format(
+    name=name, count=len(entries) + 1)
+  contents += "      "
+  contents += "\n      ".join(entries[op_type] for op_type in sorted(entries))
+  contents += "\n      {\"VarHandleOp\"},"
   contents += """
-bool OpGradientDoesntRequireInputIndices(
-    const string& op_name,
-    std::pair<bool, tensorflow::gtl::FlatSet<int>>** output) {
-  static tensorflow::gtl::FlatMap<
-      string, std::pair<bool, tensorflow::gtl::FlatSet<int>>>* m =
-      new tensorflow::gtl::FlatMap<
-          string, std::pair<bool, tensorflow::gtl::FlatSet<int>>>({
-"""
-
-  entries = get_entries("inputs")
-  contents += "\n".join(entries[op_type] for op_type in sorted(entries))
-  contents += "\n          {\"VarHandleOp\", {true, {}}},\n"
-  contents += """      });
-
-  auto it = m->find(op_name);
-
-  if (it == m->end()) return false;
-
-  *output = &it->second;
-  return true;
+  }};
+  static const auto &m = *OpGradientInfoInit(a);
+
+  auto it = m.find(op_name);
+  if (it != m.end()) {
+    return it->second;
+  }
+  return absl::nullopt;
 }
 """
-  contents += """
-bool OpGradientDoesntRequireOutputIndices(
-    const string& op_name,
-    std::pair<bool, tensorflow::gtl::FlatSet<int>>** output) {
-  static tensorflow::gtl::FlatMap<
-      string, std::pair<bool, tensorflow::gtl::FlatSet<int>>>* m =
-      new tensorflow::gtl::FlatMap<
-          string, std::pair<bool, tensorflow::gtl::FlatSet<int>>>({
-"""
-
-  entries = get_entries("outputs")
-  contents += "\n".join(entries[op_type] for op_type in sorted(entries))
-  contents += "\n          {\"VarHandleOp\", {true, {}}},\n"
-  contents += """      });
-
-  auto it = m->find(op_name);
+  return contents
 
-  if (it == m->end()) return false;
 
-  *output = &it->second;
-  return true;
-}
-"""
+def get_contents():
+  """Returns contents for the generated file."""
+  contents = ""
+  contents += _GENERATED_FILE_HEADER + _INCLUDES
+  contents += get_function("OpGradientUnusedInputIndices",
+                           get_entries("inputs"))
+  contents += get_function("OpGradientUnusedOutputIndices",
+                           get_entries("outputs"))
   return contents
 
 

tensorflow/python/eager/pywrap_gradient_exclusions.h

@@ -15,15 +15,24 @@ limitations under the License.
 #ifndef TENSORFLOW_PYTHON_EAGER_PYWRAP_GRADIENT_EXCLUSIONS_H_
 #define TENSORFLOW_PYTHON_EAGER_PYWRAP_GRADIENT_EXCLUSIONS_H_
 
+#include "absl/types/optional.h"
 #include "tensorflow/core/lib/gtl/flatmap.h"
 #include "tensorflow/core/lib/gtl/flatset.h"
 
-bool OpGradientDoesntRequireInputIndices(
-    const tensorflow::string& op_name,
-    std::pair<bool, tensorflow::gtl::FlatSet<int>>** output);
+// Lookup whether the Op with the given op_name has unused input indices.
+// Returns absl::nullopt if all inputs are used, set of unused indices
+// otherwise. Empty set indicates that all indices are unused. The latter is
+// necessary because sometimes it may not be possible to enumerate all indices
+// just using OpDef e.g. when there are `list(T)` or `N * T` type inputs.
+absl::optional<tensorflow::gtl::FlatSet<int>> OpGradientUnusedInputIndices(
+    const tensorflow::string& op_name);
 
-bool OpGradientDoesntRequireOutputIndices(
-    const tensorflow::string& op_name,
-    std::pair<bool, tensorflow::gtl::FlatSet<int>>** output);
+// Lookup whether the Op with the given op_name has unused output indices.
+// Returns absl::nullopt if all outputs are used, set of unused indices
+// otherwise. Empty set indicates that all indices are unused. The latter is
+// necessary because sometimes it may not be possible to enumerate all indices
+// just using OpDef e.g. when there are `list(T)` or `N * T` type outputs.
+absl::optional<tensorflow::gtl::FlatSet<int>> OpGradientUnusedOutputIndices(
+    const tensorflow::string& op_name);
 
 #endif  // TENSORFLOW_PYTHON_EAGER_PYWRAP_GRADIENT_EXCLUSIONS_H_

tensorflow/python/eager/pywrap_tfe_src.cc

@@ -2944,15 +2944,15 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs,
 
   PyObject* op_outputs;
   bool op_outputs_tuple_created = false;
-  std::pair<bool, tensorflow::gtl::FlatSet<int>>* outputs_not_required;
 
-  if (OpGradientDoesntRequireOutputIndices(c_op_name, &outputs_not_required)) {
-    if (outputs_not_required->first) {
+  if (const auto unused_output_indices =
+          OpGradientUnusedOutputIndices(c_op_name)) {
+    if (unused_output_indices->empty()) {
       op_outputs = Py_None;
     } else {
       op_outputs_tuple_created = true;
-      op_outputs = CopySequenceSettingIndicesToNull(
-          results, outputs_not_required->second);
+      op_outputs =
+          CopySequenceSettingIndicesToNull(results, *unused_output_indices);
     }
   } else {
     op_outputs = results;
@@ -2960,15 +2960,15 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs,
 
   PyObject* op_inputs;
   bool op_inputs_tuple_created = false;
-  std::pair<bool, tensorflow::gtl::FlatSet<int>>* inputs_not_required;
 
-  if (OpGradientDoesntRequireInputIndices(c_op_name, &inputs_not_required)) {
-    if (inputs_not_required->first) {
+  if (const auto unused_input_indices =
+          OpGradientUnusedInputIndices(c_op_name)) {
+    if (unused_input_indices->empty()) {
       op_inputs = Py_None;
     } else {
       op_inputs_tuple_created = true;
       op_inputs =
-          CopySequenceSettingIndicesToNull(inputs, inputs_not_required->second);
+          CopySequenceSettingIndicesToNull(inputs, *unused_input_indices);
     }
   } else {
     op_inputs = inputs;