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[XLA] Clean up clang tidy readability warnings in compiler/xla

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* lambda capture 'builder' is not used
* using decl 'Printf' is unused
* lambda capture 'this' is not used (17 times)
* lambda capture 'buffer_liveness' is not used
* lambda capture 'computation' is not used
* lambda capture 'operand_to_generator' is not used
* lambda capture 'M' is not used
* using decl 'InvalidParameterArgument' is unused
* lambda capture 'sum' is not used
* lambda capture 's' is not used
* lambda capture 'epsilon' is not used

PiperOrigin-RevId: 207542895
This commit is contained in:
Benjamin Kramer 2018-08-06 07:39:01 -07:00 committed by TensorFlower Gardener
parent e70f94ee08
commit 2b8df9f406
21 changed files with 37 additions and 42 deletions
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2
tensorflow/compiler/xla/client/lib/prng.cc
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@ -56,7 +56,7 @@ ThreeFry2x32State ThreeFry2x32(ThreeFry2x32State input, ThreeFry2x32State key) {
// Performs a single round of the Threefry2x32 algorithm, with a rotation
// amount 'rotation'.
auto round = [builder](ThreeFry2x32State v, int rotation) {
auto round = [](ThreeFry2x32State v, int rotation) {
v[0] = v[0] + v[1];
v[1] = RotateLeftS32(v[1], rotation);
v[1] = v[0] ^ v[1];

1
tensorflow/compiler/xla/literal_util.cc
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@ -36,7 +36,6 @@ limitations under the License.
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/types.h"
using tensorflow::strings::Printf;
using tensorflow::strings::StrCat;
namespace xla {

2
tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
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@ -2006,7 +2006,7 @@ TEST_F(AlgebraicSimplifierTest, ConvertConvToMatmul) {
// Builds a convolution from <options> and runs algebraic simplification on
// the computation. Returns a string description of the result of
// simplification.
auto build_and_simplify = [&options, this]() -> string {
auto build_and_simplify = [&options]() -> string {
HloComputation::Builder b(TestName());
Window window;

10
tensorflow/compiler/xla/service/allocation_tracker.cc
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@ -109,11 +109,11 @@ Status AllocationTracker::Unregister(const GlobalDataHandle& data) {
ResolveInternal(data));
for (const auto& shaped_buffer : replicated_buffers) {
std::vector<ShapeIndex> shape_indices;
ShapeUtil::ForEachSubshape(shaped_buffer->on_device_shape(),
[this, &shape_indices](const Shape& /*subshape*/,
const ShapeIndex& index) {
shape_indices.push_back(index);
});
ShapeUtil::ForEachSubshape(
shaped_buffer->on_device_shape(),
[&shape_indices](const Shape& /*subshape*/, const ShapeIndex& index) {
shape_indices.push_back(index);
});
for (const ShapeIndex& index : shape_indices) {
TF_RETURN_IF_ERROR(DecrementRefCount(shaped_buffer->buffer(index),
shaped_buffer->device_ordinal()));

10
tensorflow/compiler/xla/service/buffer_assignment.cc
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@ -877,8 +877,8 @@ Status BufferAssigner::AssignBuffersForComputation(
// important reuse case where an elementwise instruction reuses one of its
// operand's buffer. This improves locality.
std::sort(sorted_buffers.begin(), sorted_buffers.end(),
[this, has_sequential_order, &liveness, &post_order_position,
assignment](const LogicalBuffer* a, const LogicalBuffer* b) {
[has_sequential_order, &liveness, &post_order_position, assignment](
const LogicalBuffer* a, const LogicalBuffer* b) {
// Primary sort is by decreasing buffer size.
const int64 a_size = assignment->buffer_size_(*a);
const int64 b_size = assignment->buffer_size_(*b);
@ -1441,9 +1441,9 @@ void BufferAssigner::BuildColocatedBufferSets(
const HloInstruction* while_hlo = instruction;
ShapeUtil::ForEachSubshape(
while_hlo->shape(),
[this, while_hlo, &points_to_analysis, &buffer_liveness,
buffer_size, computation, colocated_buffer_sets](
const Shape& /*subshape*/, const ShapeIndex& index) {
[this, while_hlo, &points_to_analysis, buffer_size,
colocated_buffer_sets](const Shape& /*subshape*/,
const ShapeIndex& index) {
std::vector<const LogicalBuffer*> colocated_set;
// Add while.init.
AddBufferToColocatedSet(while_hlo->operand(0), index,

2
tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc
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@ -220,7 +220,7 @@ TEST_F(InfeedTest, DISABLED_TwoInfeedsInTotalOrder) {
// The body adds the reduced value of the Infeed data (first tuple element)
// to the previous accumulator, and returns the accumulator and the continue
// flag (second tuple element) as a tuple.
const auto build_body = [this, &result_shape](const Shape& infeed_shape) {
const auto build_body = [&result_shape](const Shape& infeed_shape) {
XlaComputation body;
XlaBuilder builder("body");
auto prev = Parameter(&builder, 0, result_shape, "prev");

2
tensorflow/compiler/xla/service/elemental_ir_emitter.cc
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@ -2134,7 +2134,7 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator(
return EmitElementalDot(hlo, operand_to_generator, dot_result_index);
};
default:
return [this, hlo, &operand_to_generator](const IrArray::Index& index) {
return [hlo](const IrArray::Index& index) {
return Unimplemented("Unhandled opcode for elemental IR emission: %s",
HloOpcodeString(hlo->opcode()).c_str());
};

2
tensorflow/compiler/xla/service/gpu/gpu_executable.cc
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@ -293,7 +293,7 @@ StatusOr<ScopedShapedBuffer> GpuExecutable::ExecuteOnStream(
// the respective location in ShapedBuffer.
std::set<se::DeviceMemoryBase> buffers_in_result;
TF_RETURN_IF_ERROR(shaped_buffer.buffers().ForEachMutableElementWithStatus(
[&buffer_allocations, &buffers_in_result, &shaped_buffer, this](
[&buffer_allocations, &buffers_in_result, this](
const ShapeIndex& index, se::DeviceMemoryBase* device_memory) {
const auto& sources = this->GetRootPointsToSet().element(index);
// The points-to set is unambiguous so the set should be a

2
tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc
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@ -328,7 +328,7 @@ Status LinkLibdeviceIfNecessary(llvm::Module* module,
if (linker.linkInModule(
std::move(libdevice_module), llvm::Linker::Flags::LinkOnlyNeeded,
[](Module& M, const StringSet<>& GVS) {
internalizeModule(M, [&M, &GVS](const GlobalValue& GV) {
internalizeModule(M, [&GVS](const GlobalValue& GV) {
return !GV.hasName() || (GVS.count(GV.getName()) == 0);
});
})) {

12
tensorflow/compiler/xla/service/hlo_cost_analysis.cc
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@ -533,12 +533,12 @@ Status HloCostAnalysis::HandleCrossReplicaSum(const HloInstruction* crs) {
// TODO(b/33004697): Compute correct cost here, taking the actual number of
// replicas into account.
double flops = 0.0;
ShapeUtil::ForEachSubshape(
crs->shape(), [&, this](const Shape& subshape, const ShapeIndex&) {
if (ShapeUtil::IsArray(subshape)) {
flops += ShapeUtil::ElementsIn(subshape);
}
});
ShapeUtil::ForEachSubshape(crs->shape(),
[&](const Shape& subshape, const ShapeIndex&) {
if (ShapeUtil::IsArray(subshape)) {
flops += ShapeUtil::ElementsIn(subshape);
}
});
current_properties_[kFlopsKey] = flops;
return Status::OK();
}

4
tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
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@ -2365,7 +2365,7 @@ TEST_F(CanShareOperandBufferWithUserTest, FusionCanShareBufferCustomized) {
TEST_F(CanShareOperandBufferWithUserTest, WhileCanShare) {
Shape data_shape = ShapeUtil::MakeShape(F32, {8});
auto make_cond = [this, &data_shape]() {
auto make_cond = [&data_shape]() {
auto builder = HloComputation::Builder(TestName() + ".Cond");
auto data = builder.AddInstruction(
HloInstruction::CreateParameter(0, data_shape, "data"));
@ -2374,7 +2374,7 @@ TEST_F(CanShareOperandBufferWithUserTest, WhileCanShare) {
return builder.Build();
};
auto make_body = [this, &data_shape]() {
auto make_body = [&data_shape]() {
auto builder = HloComputation::Builder(TestName() + ".Body");
auto data = builder.AddInstruction(
HloInstruction::CreateParameter(0, data_shape, "data"));

2
tensorflow/compiler/xla/service/hlo_scheduling_test.cc
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@ -282,7 +282,7 @@ TEST_F(HloSchedulingTest, TuplesAreAccountedCorrectly) {
TF_ASSERT_OK_AND_ASSIGN(
SequentialHloOrdering::HloModuleSequence sequence,
ScheduleComputationsInModule(*module,
[&TUPLE_SIZE](const BufferValue& buffer) {
[](const BufferValue& buffer) {
return ShapeUtil::ByteSizeOf(
buffer.shape(), TUPLE_SIZE);
},

3
tensorflow/compiler/xla/service/hlo_value.cc
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@ -283,8 +283,7 @@ std::ostream& operator<<(std::ostream& out,
string InstructionValueSet::ToString() const {
string out =
StrCat("InstructionValueSet(", ShapeUtil::HumanString(shape()), ")\n");
ForEachElement([this, &out](const ShapeIndex& index,
const HloValueSet& value_set) {
ForEachElement([&out](const ShapeIndex& index, const HloValueSet& value_set) {
StrAppend(&out, " ", index.ToString(), " : ", value_set.ToString(), "\n");
});
return out;

2
tensorflow/compiler/xla/service/layout_assignment.cc
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@ -1228,7 +1228,7 @@ Status LayoutAssignment::PropagateUseConstraintToDefs(
const PointsToSet& points_to_set =
constraints->points_to_analysis().GetPointsToSet(instruction);
return points_to_set.ForEachElementWithStatus(
[this, &shape_layout, constraints](
[&shape_layout, constraints](
const ShapeIndex& index,
const PointsToSet::BufferList& buffers) -> Status {
if (ShapeUtil::IsLeafIndex(shape_layout.shape(), index)) {

1
tensorflow/compiler/xla/service/service.cc
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@ -56,7 +56,6 @@ limitations under the License.
using ::tensorflow::strings::Printf;
using ::tensorflow::strings::StrCat;
using ::xla::source_map_util::InvalidParameterArgument;
namespace xla {

9
tensorflow/compiler/xla/service/tuple_points_to_analysis.cc
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@ -232,8 +232,7 @@ Status TuplePointsToAnalysis::HandleGetTupleElement(
// Copy the points-to set (and tuple sources) at index {element_index} of the
// operand to the points-to set for this GetTupleElement instruction.
points_to_set.ForEachMutableElement(
[&, this](const ShapeIndex& target_index,
PointsToSet::BufferList* points_to) {
[&](const ShapeIndex& target_index, PointsToSet::BufferList* points_to) {
// Construct an index into the operand by prepending element_index to
// the index for the GetTupleElement instruction's points-to set.
ShapeIndex src_index;
@ -308,7 +307,7 @@ Status TuplePointsToAnalysis::HandleRecvDone(HloInstruction* recv_done) {
// Recursively copy the points to set of the operand tuple {0} to the output
// element {0}.
points_to_set.ForEachMutableElement(
[this, &points_to_set, &operand_points_to_set](
[&points_to_set, &operand_points_to_set](
const ShapeIndex& index, PointsToSet::BufferList* buffers) {
if (index.empty() || index[0] != 0) {
return;
@ -517,7 +516,7 @@ Status TuplePointsToAnalysis::GatherBuffersDefinedByInstruction(
const HloInstruction* instruction,
TuplePointsToAnalysis::BufferDefinitionVector* buffers) {
GetPointsToSet(instruction)
.ForEachElement([this, buffers, instruction](
.ForEachElement([buffers, instruction](
const ShapeIndex& index,
const PointsToSet::BufferList& source_buffers) {
// Add buffers which 'instruction' is the source of.
@ -547,7 +546,7 @@ PointsToSet& TuplePointsToAnalysis::CreateCopiedPointsToSet(
PointsToSet& dst_points_to_set = CreateEmptyPointsToSet(instruction);
const PointsToSet& src_points_to_set = GetPointsToSet(src);
dst_points_to_set.ForEachMutableElement(
[this, &dst_points_to_set, &src_points_to_set](
[&dst_points_to_set, &src_points_to_set](
const ShapeIndex& index, PointsToSet::BufferList* buffers) {
*buffers = src_points_to_set.element(index);
for (auto& tuple_source : src_points_to_set.tuple_sources(index)) {

4
tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
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@ -1118,7 +1118,7 @@ TEST_F(CanShareOperandBufferWithUserTest, OutputFusionCantAliasOperandBuffer) {
TEST_F(CanShareOperandBufferWithUserTest, WhileCanShare) {
Shape data_shape = ShapeUtil::MakeShape(F32, {8});
auto make_cond = [this, &data_shape]() {
auto make_cond = [&data_shape]() {
auto builder = HloComputation::Builder(TestName() + ".Cond");
auto data = builder.AddInstruction(
HloInstruction::CreateParameter(0, data_shape, "data"));
@ -1127,7 +1127,7 @@ TEST_F(CanShareOperandBufferWithUserTest, WhileCanShare) {
return builder.Build();
};
auto make_body = [this, &data_shape]() {
auto make_body = [&data_shape]() {
auto builder = HloComputation::Builder(TestName() + ".Body");
auto data = builder.AddInstruction(
HloInstruction::CreateParameter(0, data_shape, "data"));

2
tensorflow/compiler/xla/shape_tree_test.cc
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@ -172,7 +172,7 @@ TEST_F(ShapeTreeTest, TupleShape) {
// Write zero to all data elements.
shape_tree.ForEachMutableElement(
[&sum](const ShapeIndex& /*index*/, int* data) { *data = 0; });
[](const ShapeIndex& /*index*/, int* data) { *data = 0; });
EXPECT_EQ(0, shape_tree.element({}));
EXPECT_EQ(0, shape_tree.element({0}));
EXPECT_EQ(0, shape_tree.element({1}));

3
tensorflow/compiler/xla/shape_util.cc
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@ -596,8 +596,7 @@ StatusOr<Shape> ParseShapeStringInternal(tensorflow::StringPiece* s) {
};
auto comma_list_to_int64s =
[&s,
string_to_int64](const string& input) -> StatusOr<std::vector<int64>> {
[string_to_int64](const string& input) -> StatusOr<std::vector<int64>> {
std::vector<int64> results;
for (const string& piece : tensorflow::str_util::Split(input, ',')) {
TF_ASSIGN_OR_RETURN(int64 element, string_to_int64(piece));

2
tensorflow/compiler/xla/tests/batch_normalization_test.cc
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@ -733,7 +733,7 @@ XLA_TEST_P(BatchNormTestManySizes, RandomizedGradTests) {
var4D, [epsilon](float a) { return a + epsilon; });
auto rsqrt_var_add_epsilon = *ReferenceUtil::MapArray4D(
var_add_epsilon, [epsilon](float a) { return 1 / std::sqrt(a); });
var_add_epsilon, [](float a) { return 1 / std::sqrt(a); });
auto grad_output_times_var =
*ReferenceUtil::MapArray4D(grad_output_array, var_add_epsilon,

2
tensorflow/compiler/xla/tests/prng_test.cc
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@ -182,7 +182,7 @@ XLA_TEST_F(PrngTest, Uniformity256) {
XLA_TEST_F(PrngTest, MapUsingRng) {
// Build a x -> (x + U[0,1)) computation.
auto build_sum_rng = [this](XlaBuilder& builder) {
auto build_sum_rng = [](XlaBuilder& builder) {
auto b = builder.CreateSubBuilder("sum_with_rng");
auto x = Parameter(b.get(), 0, ShapeUtil::MakeShape(F32, {}), "input");
Add(x,