experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

[XLA:GPU][NFC] Prefer using * to acces absl::optional<> values.

Browse Source 
See https://abseil.io/tips/181

PiperOrigin-RevId: 355681618
Change-Id: I6a1ba85e049461de4555647ec123bc91145a4c36
This commit is contained in:
Rahul Joshi 2021-02-04 12:07:56 -08:00 committed by TensorFlower Gardener
parent 2f4acbfb7c
commit 9f9c094ef1
7 changed files with 12 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
tensorflow/compiler/mlir/xla/hlo_function_importer.cc
View File

@ -959,7 +959,7 @@ mlir::NamedAttribute HloFunctionImporter::ConvertReplicaGroups(
mlir::NamedAttribute HloFunctionImporter::ConvertChannelHandle(
absl::optional<tensorflow::int64> channel_id) {
xla::ChannelHandle channel_handle;
if (channel_id.has_value()) channel_handle.set_handle(channel_id.value());
if (channel_id) channel_handle.set_handle(*channel_id);
return ConvertChannelHandle(channel_handle);
}

9
tensorflow/compiler/mlir/xla/mlir_hlo_to_hlo.cc
View File

@ -1337,7 +1337,7 @@ LogicalResult ConvertToHloModule::Lower(
xla::OpSharding sharding;
sharding.set_type(xla::OpSharding::TUPLE);
for (auto& ret_sharding : ret_shardings)
*sharding.add_tuple_shardings() = ret_sharding.value();
*sharding.add_tuple_shardings() = *ret_sharding;
builder->SetSharding(sharding);
}
@ -1490,8 +1490,7 @@ LogicalResult ConvertToHloModule::SetEntryTupleShardings(
xla::OpSharding sharding;
sharding.set_type(xla::OpSharding::TUPLE);
for (auto arg_sharding : llvm::enumerate(arg_shardings)) {
auto hlo_sharding =
xla::HloSharding::FromProto(arg_sharding.value().value());
auto hlo_sharding = xla::HloSharding::FromProto(*arg_sharding.value());
if (!hlo_sharding.ok())
return block->getParentOp()->emitError()
<< hlo_sharding.status().error_message();
@ -1502,7 +1501,7 @@ LogicalResult ConvertToHloModule::SetEntryTupleShardings(
if (!status.ok())
return block->getParentOp()->emitError() << status.error_message();
*sharding.add_tuple_shardings() = arg_sharding.value().value();
*sharding.add_tuple_shardings() = *arg_sharding.value();
}
builder->SetSharding(sharding);
@ -1543,7 +1542,7 @@ LogicalResult ConvertToHloModule::LowerBasicBlockAsFunction(
!arg_shardings.empty() && AllOptionalShardingsAreSet(arg_shardings);
for (BlockArgument& arg : block->getArguments()) {
if (set_tuple_element_sharding)
builder->SetSharding(arg_shardings[arg.getArgNumber()].value());
builder->SetSharding(*arg_shardings[arg.getArgNumber()]);
lowering[arg] = xla::GetTupleElement(tuple, arg.getArgNumber());
}
builder->ClearSharding();

6
tensorflow/compiler/xla/service/gpu/alias_passthrough_params_test.cc
View File

@ -37,9 +37,9 @@ TEST_F(AliasPassthroughParamsTest, AliasPassThroughParams) {
.ValueOrDie();
EXPECT_TRUE(AliasPassthroughParams().Run(module.get()).ValueOrDie());
const auto& alias_config = module->input_output_alias_config();
EXPECT_EQ(0, alias_config.GetAliasedParameter({0}).value().parameter_number);
EXPECT_EQ(0, alias_config.GetAliasedParameter({0})->parameter_number);
EXPECT_FALSE(alias_config.OutputHasAlias({1}));
EXPECT_EQ(1, alias_config.GetAliasedParameter({2}).value().parameter_number);
EXPECT_EQ(1, alias_config.GetAliasedParameter({2})->parameter_number);
}
TEST_F(AliasPassthroughParamsTest, DoNotAliasPassThroughParamsMoreThanOnce) {
@ -53,7 +53,7 @@ TEST_F(AliasPassthroughParamsTest, DoNotAliasPassThroughParamsMoreThanOnce) {
.ValueOrDie();
EXPECT_TRUE(AliasPassthroughParams().Run(module.get()).ValueOrDie());
const auto& alias_config = module->input_output_alias_config();
EXPECT_EQ(0, alias_config.GetAliasedParameter({0}).value().parameter_number);
EXPECT_EQ(0, alias_config.GetAliasedParameter({0})->parameter_number);
EXPECT_FALSE(alias_config.OutputHasAlias({1}));
}

3
tensorflow/compiler/xla/service/gpu/gemm_algorithm_picker.cc
View File

@ -237,7 +237,6 @@ static StatusOr<absl::optional<se::blas::AlgorithmType>> DoGemmAutotune(
// Don't run autotuning concurrently on the same GPU.
tensorflow::mutex_lock gpu_lock = LockGpu(stream->parent());
GemmCacheKey key =
std::make_tuple(stream->parent(), lhs->shape(), rhs->shape(),
instr->shape(), gemm_config.SerializeAsString());
@ -253,7 +252,7 @@ static StatusOr<absl::optional<se::blas::AlgorithmType>> DoGemmAutotune(
if (it != autotune_cache.end()) {
cache_hits++;
VLOG(4) << "Autotuning cache hit, using algorithm: "
<< (it->second.has_value() ? absl::StrCat(it->second.value())
<< (it->second.has_value() ? absl::StrCat(*(it->second))
: "<generic>");
return it->second;
}

2
tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc
View File

@ -93,7 +93,7 @@ void HloExecutionProfiler::FinishHloComputation(
absl::optional<size_t> profile_index) {
if (do_profile_) {
profile_->SetCyclesTakenBy(
profile_index.value(),
*profile_index,
GetCyclesTaken(&timers_, sub_streams_, stream_, clock_rate_ghz_));
}
}

2
tensorflow/compiler/xla/service/gpu/nccl_all_reduce_thunk.cc
View File

@ -104,7 +104,7 @@ static NcclAllReduceConfig GetNcclAllReduceConfig(mlir::lmhlo::AllReduceOp op,
NcclAllReduceConfig config;
config.config = GetNcclCollectiveConfigForMlir(op, replica_count);
config.reduction_kind = reduction_kind.value();
config.reduction_kind = *reduction_kind;
return config;
}

2
tensorflow/compiler/xla/service/gpu/nccl_collective_thunk.cc
View File

@ -68,7 +68,7 @@ NcclCollectiveConfig GetNcclCollectiveConfig(const HloInstruction* hlo,
if (hlo->channel_id().has_value()) {
config.collective_op_kind = RendezvousKey::kCrossModule;
config.op_id = hlo->channel_id().value();
config.op_id = *hlo->channel_id();
} else {
config.collective_op_kind = RendezvousKey::kCrossReplica;
config.op_id = static_cast<int64>(hlo->GetModule()->unique_id());