diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index 24be75c3d62..90461005fac 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -424,6 +424,7 @@ cc_library(
":xla_data_proto_cc",
"//tensorflow/core:lib",
"@com_google_absl//absl/base",
+ "@com_google_absl//absl/flags:flag",
"@com_google_absl//absl/memory",
"@com_google_absl//absl/strings",
"@com_google_absl//absl/strings:str_format",
diff --git a/tensorflow/compiler/xla/literal.cc b/tensorflow/compiler/xla/literal.cc
index bbea6081975..3a219673304 100644
--- a/tensorflow/compiler/xla/literal.cc
+++ b/tensorflow/compiler/xla/literal.cc
@@ -72,6 +72,16 @@ T GetRawValue(T val) {
}
uint16 GetRawValue(Eigen::half val) { return val.x; }
+bool LiteralProtoHasValues(const LiteralProto& proto) {
+ return proto.preds_size() || !proto.s8s().empty() || !proto.u8s().empty() ||
+ proto.s32s_size() || proto.s64s_size() || proto.u32s_size() ||
+ proto.u64s_size() || proto.f32s_size() || proto.f64s_size() ||
+ proto.c64s_size() || proto.c128s_size() ||
+ proto.tuple_literals_size() || !proto.f16s().empty() ||
+ !proto.bf16s().empty() || !proto.u16s().empty() ||
+ !proto.s16s().empty() || proto.sparse_indices_size();
+}
+
} // namespace
LiteralBase::~LiteralBase() {}
@@ -288,7 +298,7 @@ Status MutableLiteralBase::CopyElementFrom(const LiteralSlice& src_literal,
}
/* static */ StatusOr<Literal> MutableLiteralBase::CreateFromProto(
- const LiteralProto& proto) {
+ const LiteralProto& proto, bool prohibit_empty_literal) {
if (!proto.has_shape()) {
return InvalidArgument("LiteralProto has no shape");
}
@@ -328,7 +338,13 @@ Status MutableLiteralBase::CopyElementFrom(const LiteralSlice& src_literal,
}
CHECK(piece->subshape().IsArray());
- TF_RETURN_IF_ERROR(piece->CopyFromProto(*proto_element));
+
+ // When prohibit_empty_literal is false (allowing literal with no
+ // values), only copy from proto if the literal proto has values. This
+ // mode is used for a learned cost model.
+ if (prohibit_empty_literal || LiteralProtoHasValues(*proto_element)) {
+ TF_RETURN_IF_ERROR(piece->CopyFromProto(*proto_element));
+ }
return Status::OK();
}));
diff --git a/tensorflow/compiler/xla/literal.h b/tensorflow/compiler/xla/literal.h
index af15cab4a94..227717188ab 100644
--- a/tensorflow/compiler/xla/literal.h
+++ b/tensorflow/compiler/xla/literal.h
@@ -732,7 +732,8 @@ class MutableLiteralBase : public LiteralBase {
static Literal MoveIntoTuple(absl::Span<Literal> elements);
// Serialize from a proto.
- static StatusOr<Literal> CreateFromProto(const LiteralProto& proto);
+ static StatusOr<Literal> CreateFromProto(const LiteralProto& proto,
+ bool prohibit_empty_literal = true);
protected:
// Returns the piece at the given ShapeIndex.
diff --git a/tensorflow/compiler/xla/literal_test.cc b/tensorflow/compiler/xla/literal_test.cc
index 43863db5b3f..d1dd6b8fd77 100644
--- a/tensorflow/compiler/xla/literal_test.cc
+++ b/tensorflow/compiler/xla/literal_test.cc
@@ -1847,6 +1847,30 @@ TEST_F(LiteralUtilTest, InvalidProtoNoValues) {
HasSubstr("Expected 3 elements in LiteralProto"));
}
+TEST_F(LiteralUtilTest, ValidProtoNoValues) {
+ // Proto contains a shape, but no values.
+ LiteralProto proto;
+ *proto.mutable_shape() = ShapeUtil::MakeShape(F32, {3}).ToProto();
+ Status status =
+ Literal::CreateFromProto(proto, /*prohibit_empty_literal=*/false)
+ .status();
+ EXPECT_TRUE(status.ok());
+}
+
+TEST_F(LiteralUtilTest, ValidProtoWithClearedValues) {
+ auto literal = LiteralUtil::CreateR1<bool>({true, false, true});
+ LiteralProto proto = literal.ToProto();
+ EXPECT_EQ(proto.preds_size(), 3);
+
+ // Clear values.
+ proto.clear_preds();
+ EXPECT_EQ(proto.preds_size(), 0);
+ Status status =
+ Literal::CreateFromProto(proto, /*prohibit_empty_literal=*/false)
+ .status();
+ EXPECT_TRUE(status.ok());
+}
+
TEST_F(LiteralUtilTest, InvalidProtoNoShape) {
// Proto contains values, but no shape.
LiteralProto proto;
diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc
index 2e316b0f2d3..8f9ac5e2db7 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation.cc
@@ -640,16 +640,17 @@ HloComputationProto HloComputation::ToProto() const {
/* static */ StatusOr<std::unique_ptr<HloComputation>>
HloComputation::CreateFromProto(
const HloComputationProto& proto,
- const absl::flat_hash_map<int64, HloComputation*>& computation_map) {
+ const absl::flat_hash_map<int64, HloComputation*>& computation_map,
+ bool prohibit_empty_literal) {
absl::flat_hash_map<int64, HloInstruction*> instruction_map;
absl::flat_hash_map<HloInstruction*, int64> to_proto_id;
std::vector<std::unique_ptr<HloInstruction>> instructions;
int64 parameter_count = 0;
for (const HloInstructionProto& instruction_proto : proto.instructions()) {
- TF_ASSIGN_OR_RETURN(
- std::unique_ptr<HloInstruction> instruction,
- HloInstruction::CreateFromProto(instruction_proto, instruction_map,
- computation_map));
+ TF_ASSIGN_OR_RETURN(std::unique_ptr<HloInstruction> instruction,
+ HloInstruction::CreateFromProto(
+ instruction_proto, instruction_map, computation_map,
+ prohibit_empty_literal));
if (instruction->opcode() == HloOpcode::kParameter) {
parameter_count++;
}
diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h
index d66e0b63d64..f96c9046c19 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.h
+++ b/tensorflow/compiler/xla/service/hlo_computation.h
@@ -235,7 +235,8 @@ class HloComputation {
// calls.
static StatusOr<std::unique_ptr<HloComputation>> CreateFromProto(
const HloComputationProto& proto,
- const absl::flat_hash_map<int64, HloComputation*>& computation_map);
+ const absl::flat_hash_map<int64, HloComputation*>& computation_map,
+ bool prohibit_empty_literal = true);
using InstructionSequence = tensorflow::gtl::iterator_range<
UnwrappingIterator<std::list<std::unique_ptr<HloInstruction>>::iterator>>;
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index e66707eb7fd..368a3876f8c 100755
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -62,7 +62,8 @@ using absl::StrJoin;
StatusOr<std::unique_ptr<HloInstruction>> HloInstruction::CreateFromProto(
const HloInstructionProto& proto,
const absl::flat_hash_map<int64, HloInstruction*>& instruction_map,
- const absl::flat_hash_map<int64, HloComputation*>& computation_map) {
+ const absl::flat_hash_map<int64, HloComputation*>& computation_map,
+ bool prohibit_empty_literal) {
TF_RET_CHECK(!proto.opcode().empty());
HloOpcode opcode;
auto opcode_or = StringToHloOpcode(proto.opcode());
@@ -300,8 +301,9 @@ StatusOr<std::unique_ptr<HloInstruction>> HloInstruction::CreateFromProto(
case HloOpcode::kConstant: {
// TODO(b/110214922): Revert this to CHECK(proto.has_literal()).
if (proto.has_literal()) {
- TF_ASSIGN_OR_RETURN(auto literal,
- Literal::CreateFromProto(proto.literal()));
+ TF_ASSIGN_OR_RETURN(
+ auto literal,
+ Literal::CreateFromProto(proto.literal(), prohibit_empty_literal));
instruction = CreateConstant(std::move(literal));
// Literal's shape may have no/different tiling info.
TF_RET_CHECK(Shape::Equal().MinorToMajorOnlyInLayout()(
@@ -314,8 +316,9 @@ StatusOr<std::unique_ptr<HloInstruction>> HloInstruction::CreateFromProto(
}
case HloOpcode::kTrace: {
TF_RET_CHECK(proto.has_literal());
- TF_ASSIGN_OR_RETURN(auto literal,
- Literal::CreateFromProto(proto.literal()));
+ TF_ASSIGN_OR_RETURN(
+ auto literal,
+ Literal::CreateFromProto(proto.literal(), prohibit_empty_literal));
instruction = CreateTrace(literal.GetR1U8AsString(), operands(0));
break;
}
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index b35d9d07dcf..5e2e53ea6db 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -475,7 +475,8 @@ class HloInstruction {
static StatusOr<std::unique_ptr<HloInstruction>> CreateFromProto(
const HloInstructionProto& proto,
const absl::flat_hash_map<int64, HloInstruction*>& instruction_map,
- const absl::flat_hash_map<int64, HloComputation*>& computation_map);
+ const absl::flat_hash_map<int64, HloComputation*>& computation_map,
+ bool prohibit_empty_literal = true);
// Creates a parameter-retrieving instruction.
static std::unique_ptr<HloInstruction> CreateParameter(int64 parameter_number,
diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc
index 27b79049688..74ef9a1ec4c 100644
--- a/tensorflow/compiler/xla/service/hlo_module.cc
+++ b/tensorflow/compiler/xla/service/hlo_module.cc
@@ -294,7 +294,8 @@ Status HloModule::CheckUniqueNamesAndIdsForComputationsAndInstructions() const {
/* static */
StatusOr<std::unique_ptr<HloModule>> HloModule::CreateFromProto(
- const HloModuleProto& proto, const HloModuleConfig& module_config) {
+ const HloModuleProto& proto, const HloModuleConfig& module_config,
+ bool prohibit_empty_literal) {
VLOG(2) << "CreateFromProto()";
XLA_VLOG_LINES(3, proto.DebugString());
@@ -332,7 +333,8 @@ StatusOr<std::unique_ptr<HloModule>> HloModule::CreateFromProto(
for (const HloComputationProto& computation_proto : proto.computations()) {
TF_ASSIGN_OR_RETURN(
std::unique_ptr<HloComputation> computation,
- HloComputation::CreateFromProto(computation_proto, computation_map));
+ HloComputation::CreateFromProto(computation_proto, computation_map,
+ prohibit_empty_literal));
CHECK_NE(computation.get(), nullptr);
int64 computation_id = computation_proto.id();
TF_RET_CHECK(computation_id != -1);
diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h
index 40f3c972f63..9a96b787bb8 100644
--- a/tensorflow/compiler/xla/service/hlo_module.h
+++ b/tensorflow/compiler/xla/service/hlo_module.h
@@ -219,7 +219,8 @@ class HloModule {
// Convert an HloModule to or from a proto.
HloModuleProto ToProto() const;
static StatusOr<std::unique_ptr<HloModule>> CreateFromProto(
- const HloModuleProto& proto, const HloModuleConfig& module_config);
+ const HloModuleProto& proto, const HloModuleConfig& module_config,
+ bool prohibit_empty_literal = true);
// Creates and returns an HloModuleConfig with an appropriate program shape
// for the HLO module in the given proto.
diff --git a/tensorflow/tools/compatibility/README.md b/tensorflow/tools/compatibility/README.md
index 3611e85d28d..12d3a0faf44 100644
--- a/tensorflow/tools/compatibility/README.md
+++ b/tensorflow/tools/compatibility/README.md
@@ -30,10 +30,6 @@ tf_upgrade_v2 --intree coolcode --outtree coolcode-upgraded --copyotherfiles Fal
*Note: `tf_upgrade_v2` is installed automatically as a script by the pip install
after TensorFlow 1.12.
-You may want to retain revision history, especially when preparing a CL:
-```
-g4 integrate --retroactive coolcode/... coolcode-upgraded/...
-```
## Report