experiments/STT-tensorflow
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Fix uses of private, mangled names for proto enumerators.

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PiperOrigin-RevId: 256476549
This commit is contained in:
A. Unique TensorFlower 2019-07-03 19:11:14 -07:00 committed by TensorFlower Gardener
parent 604988b5d4
commit eece01c80f
6 changed files with 277 additions and 280 deletions
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10
tensorflow/compiler/xla/client/sharding_builder.cc
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@ -20,13 +20,13 @@ namespace sharding_builder {
OpSharding Replicate() {
OpSharding result;
result.set_type(OpSharding::Type::OpSharding_Type_REPLICATED);
result.set_type(OpSharding::REPLICATED);
return result;
}
OpSharding AssignDevice(int device) {
OpSharding result;
result.set_type(OpSharding::Type::OpSharding_Type_MAXIMAL);
result.set_type(OpSharding::MAXIMAL);
result.add_tile_assignment_dimensions(1);
result.add_tile_assignment_devices(device);
return result;
@ -35,7 +35,7 @@ OpSharding AssignDevice(int device) {
OpSharding Tile(const Shape& tile_shape,
const TileAssignment& tile_assignment) {
OpSharding result;
result.set_type(OpSharding::Type::OpSharding_Type_OTHER);
result.set_type(OpSharding::OTHER);
*result.mutable_tile_shape() = tile_shape.ToProto();
for (int64 dim : tile_assignment.dimensions()) {
result.add_tile_assignment_dimensions(dim);
@ -48,7 +48,7 @@ OpSharding Tile(const Shape& tile_shape,
OpSharding Tile1D(const Shape& tile_shape, int64 num_tiles) {
OpSharding result;
result.set_type(OpSharding::Type::OpSharding_Type_OTHER);
result.set_type(OpSharding::OTHER);
CHECK_EQ(tile_shape.rank(), 1);
std::vector<int64> dimensions(1, num_tiles);
@ -65,7 +65,7 @@ OpSharding Tile1D(const Shape& tile_shape, int64 num_tiles) {
OpSharding Tuple(const ShapeTree<OpSharding>& shardings) {
OpSharding result;
result.set_type(OpSharding::Type::OpSharding_Type_TUPLE);
result.set_type(OpSharding::TUPLE);
for (const auto& index_to_sharding : shardings.leaves()) {
*result.add_tuple_shardings() = index_to_sharding.second;
}

13
tensorflow/compiler/xla/client/xla_builder.cc
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@ -1309,14 +1309,13 @@ XlaOp XlaBuilder::Infeed(const Shape& shape, const string& config) {
instr.set_infeed_config(config);
if (shape.IsArray() && sharding() &&
sharding()->type() == OpSharding::Type::OpSharding_Type_OTHER) {
sharding()->type() == OpSharding::OTHER) {
// TODO(b/110793772): Support tiled array-shaped infeeds.
return InvalidArgument(
"Tiled sharding is not yet supported for array-shaped infeeds");
}
if (sharding() &&
sharding()->type() == OpSharding::Type::OpSharding_Type_REPLICATED) {
if (sharding() && sharding()->type() == OpSharding::REPLICATED) {
return InvalidArgument(
"Replicated sharding is not yet supported for infeeds");
}
@ -1343,8 +1342,7 @@ XlaOp XlaBuilder::Infeed(const Shape& shape, const string& config) {
// data and a token. For tuple sharding type, the sharding must be changed
// to accommodate the token.
XlaOp infeed;
if (sharding() &&
sharding()->type() == OpSharding::Type::OpSharding_Type_TUPLE) {
if (sharding() && sharding()->type() == OpSharding::TUPLE) {
// TODO(b/80000000): Remove this when clients have been updated to handle
// tokens.
OpSharding infeed_instruction_sharding = *sharding();
@ -1385,14 +1383,13 @@ XlaOp XlaBuilder::InfeedWithToken(const XlaOp& token, const Shape& shape,
instr.set_infeed_config(config);
if (shape.IsArray() && sharding() &&
sharding()->type() == OpSharding::Type::OpSharding_Type_OTHER) {
sharding()->type() == OpSharding::OTHER) {
// TODO(b/110793772): Support tiled array-shaped infeeds.
return InvalidArgument(
"Tiled sharding is not yet supported for array-shaped infeeds");
}
if (sharding() &&
sharding()->type() == OpSharding::Type::OpSharding_Type_REPLICATED) {
if (sharding() && sharding()->type() == OpSharding::REPLICATED) {
return InvalidArgument(
"Replicated sharding is not yet supported for infeeds");
}

8
tensorflow/compiler/xla/service/hlo_parser.cc
View File

@ -1833,7 +1833,7 @@ bool HloParser::ParseSharding(OpSharding* sharding) {
}
} while (EatIfPresent(TokKind::kComma));
}
sharding->set_type(OpSharding::Type::OpSharding_Type_TUPLE);
sharding->set_type(OpSharding::TUPLE);
return ParseToken(TokKind::kRbrace, "expected '}' to end sharding attribute");
}
@ -1915,13 +1915,13 @@ bool HloParser::ParseSingleSharding(OpSharding* sharding,
return Error(loc,
"replicated shardings should not have any devices assigned");
}
sharding->set_type(OpSharding::Type::OpSharding_Type_REPLICATED);
sharding->set_type(OpSharding::REPLICATED);
} else if (maximal) {
if (devices.size() != 1) {
return Error(loc,
"maximal shardings should have exactly one device assigned");
}
sharding->set_type(OpSharding::Type::OpSharding_Type_MAXIMAL);
sharding->set_type(OpSharding::MAXIMAL);
sharding->add_tile_assignment_devices(devices[0]);
} else {
if (devices.size() <= 1) {
@ -1934,7 +1934,7 @@ bool HloParser::ParseSingleSharding(OpSharding* sharding,
"non-maximal shardings must have a tile assignment list including "
"dimensions");
}
sharding->set_type(OpSharding::Type::OpSharding_Type_OTHER);
sharding->set_type(OpSharding::OTHER);
for (int64 dim : tile_assignment_dimensions) {
sharding->add_tile_assignment_dimensions(dim);
}

14
tensorflow/compiler/xla/service/hlo_sharding.cc
View File

@ -359,7 +359,7 @@ Status HloSharding::ValidateNonTuple(const Shape& shape,
/*static*/ StatusOr<HloSharding> HloSharding::FromProto(
const OpSharding& proto) {
if (proto.type() == OpSharding::Type::OpSharding_Type_TUPLE) {
if (proto.type() == OpSharding::TUPLE) {
std::vector<HloSharding> tuple_shardings;
tuple_shardings.reserve(proto.tuple_shardings().size());
for (const OpSharding& tuple_sharding_proto : proto.tuple_shardings()) {
@ -368,13 +368,13 @@ Status HloSharding::ValidateNonTuple(const Shape& shape,
tuple_shardings.push_back(sharding);
}
return HloSharding(tuple_shardings);
} else if (proto.type() == OpSharding::Type::OpSharding_Type_REPLICATED) {
} else if (proto.type() == OpSharding::REPLICATED) {
return Replicate();
} else if (proto.tile_assignment_devices().size() == 1) {
return HloSharding(proto.tile_assignment_devices(0));
}
TF_RET_CHECK(proto.type() != OpSharding::Type::OpSharding_Type_MAXIMAL)
TF_RET_CHECK(proto.type() != OpSharding::MAXIMAL)
<< "Maximal sharding is expected to have single device assignment, but "
<< proto.tile_assignment_devices().size() << " has provided.";
@ -411,7 +411,7 @@ OpSharding HloSharding::ToProto() const {
for (const HloSharding& element : tuple_elements_) {
*result.add_tuple_shardings() = element.ToProto();
}
result.set_type(OpSharding::Type::OpSharding_Type_TUPLE);
result.set_type(OpSharding::TUPLE);
return result;
}
@ -422,11 +422,11 @@ OpSharding HloSharding::ToProto() const {
result.add_tile_assignment_devices(device);
}
if (IsReplicated()) {
result.set_type(OpSharding::Type::OpSharding_Type_REPLICATED);
result.set_type(OpSharding::REPLICATED);
} else if (IsTileMaximal()) {
result.set_type(OpSharding::Type::OpSharding_Type_MAXIMAL);
result.set_type(OpSharding::MAXIMAL);
} else {
result.set_type(OpSharding::Type::OpSharding_Type_OTHER);
result.set_type(OpSharding::OTHER);
}
return result;
}

2
tensorflow/compiler/xla/service/hlo_sharding_test.cc
View File

@ -132,7 +132,7 @@ TEST_F(HloShardingTest, NestedTuple) {
HloSharding tiled_sharding = HloSharding::Tile(Array<int64>({{0, 1}}));
OpSharding proto;
proto.set_type(OpSharding::Type::OpSharding_Type_TUPLE);
proto.set_type(OpSharding::TUPLE);
*proto.add_tuple_shardings() = HloSharding::Replicate().ToProto();
*proto.add_tuple_shardings() = HloSharding::AssignDevice(0).ToProto();
*proto.add_tuple_shardings() = tiled_sharding.ToProto();

510
tensorflow/opensource_only.files
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@ -1,282 +1,282 @@
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