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[TF] [NFC] Replace absl::optional w/ std::optional in SE and compiler/

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PiperOrigin-RevId: 452781234
This commit is contained in:
George Karpenkov
2022-06-03 09:27:30 -07:00
committed by TensorFlower Gardener
parent fd23e69082
commit 0a723ddac4
17 changed files with 23 additions and 23 deletions
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2
tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc
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@@ -51,7 +51,7 @@ class BatchMatMulOp : public XlaOpKernel {
private:
bool adj_x_;
bool adj_y_;
absl::optional<xla::PrimitiveType> preferred_element_type_;
std::optional<xla::PrimitiveType> preferred_element_type_;
};
REGISTER_XLA_OP(Name("BatchMatMul"), BatchMatMulOp);

2
tensorflow/compiler/tf2xla/kernels/gather_op.cc
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@@ -160,7 +160,7 @@ Status XlaGatherWithBatchDimsOpImpl(XlaOpKernelContext* context,
auto indices = context->Input(1);
auto indices_shape = context->InputShape(1);
absl::optional<int64_t> axis;
std::optional<int64_t> axis;
if (context->num_inputs() == 3) {
const TensorShape axis_shape = context->InputShape(2);
if (!TensorShapeUtils::IsScalar(axis_shape)) {

4
tensorflow/compiler/tf2xla/kernels/if_while_utils.cc
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@@ -42,9 +42,9 @@ absl::InlinedVector<int, 5> ConvertCompileTimeConstArgumentsToConst(
// If we can infer the constant values of an inner computation's argument,
// replace them with constants. If that fails, we fallback to infer the
// bounds of the argument.
StatusOr<absl::optional<Tensor>> maybe_constant =
StatusOr<std::optional<Tensor>> maybe_constant =
expression.ResolveConstant(ctx->compiler()->client());
StatusOr<absl::optional<Tensor>> bounds =
StatusOr<std::optional<Tensor>> bounds =
expression.ResolveConstant(ctx->compiler()->client(), false,
xla::ValueInferenceMode::kUpperBound);
if ((maybe_constant.ok() && maybe_constant->has_value()) ||

2
tensorflow/compiler/tf2xla/kernels/image_resize_ops.h
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@@ -62,7 +62,7 @@ class ResizeBilinearGradOp : public XlaOpKernel {
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
// Fallback light outside compilation kernel for the option combination we do
// not support.
absl::optional<CallTfKernelOp> fallback_tf_kernel_;
std::optional<CallTfKernelOp> fallback_tf_kernel_;
#endif
};

2
tensorflow/compiler/tf2xla/kernels/pooling_ops.cc
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@@ -190,7 +190,7 @@ class MaxPoolOp : public PoolingOp {
// For VECT_C max-pool ops, transpose to plain NCHW, do the max-pool, and
// transpose back. This isn't necessarily the most efficient algorithm, but
// it's ok for starters.
absl::optional<int64_t> vect_width;
std::optional<int64_t> vect_width;
if (data_format_ == FORMAT_NCHW_VECT_C) {
vect_width = input_shape->dimensions().back();
input = xla::Collapse(xla::Transpose(input, {0, 1, 4, 2, 3}), {1, 2});

2
tensorflow/compiler/tf2xla/kernels/sharding_op.cc
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@@ -42,7 +42,7 @@ class ShardingOp : public XlaOpKernel {
// The builder might create a broadcast from a constant, so we clear
// sharding for the input.
xla::XlaScopedShardingAssignment no_sharding(ctx->builder(),
absl::nullopt);
std::nullopt);
input = ctx->Input(0);
}
auto shape_or = ctx->builder()->GetShape(input);

4
tensorflow/compiler/tf2xla/kernels/unique_op.cc
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@@ -174,8 +174,8 @@ class UniqueOpBase : public XlaOpKernel {
sort_keys.push_back(iota);
sort_types.push_back(xla::S32);
std::vector<absl::optional<xla::XlaOp (*)(xla::XlaOp, xla::XlaOp,
absl::Span<const int64_t>)>>
std::vector<std::optional<xla::XlaOp (*)(xla::XlaOp, xla::XlaOp,
absl::Span<const int64_t>)>>
generators(sort_types.size(), xla::LtTotalOrder);
auto lt_chain = xla::CreateScalarComparisonComputation(
"UniqueV2Lt", sort_types, generators, ctx->builder());

4
tensorflow/compiler/tf2xla/kernels/xla_conv_op.cc
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@@ -39,7 +39,7 @@ class XlaConvOp : public XlaOpKernel {
OP_REQUIRES(context,
precision_config_.ParsePartialFromString(precision_config_attr),
errors::InvalidArgument("Error parsing precision config."));
preferred_element_type_ = absl::nullopt;
preferred_element_type_ = std::nullopt;
batch_group_count_ = 1;
}
@@ -85,7 +85,7 @@ class XlaConvOp : public XlaOpKernel {
}
protected:
absl::optional<xla::PrimitiveType> preferred_element_type_;
std::optional<xla::PrimitiveType> preferred_element_type_;
int64_t batch_group_count_;
private:

4
tensorflow/compiler/tf2xla/kernels/xla_dot_op.cc
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@@ -42,7 +42,7 @@ class XlaDotOp : public XlaOpKernel {
context,
precision_config_.ParsePartialFromString(precision_config_attr),
errors::InvalidArgument("Error parsing convolution dimension numbers"));
preferred_element_type_ = absl::nullopt;
preferred_element_type_ = std::nullopt;
}
void Compile(XlaOpKernelContext* context) override {
@@ -58,7 +58,7 @@ class XlaDotOp : public XlaOpKernel {
}
protected:
absl::optional<xla::PrimitiveType> preferred_element_type_;
std::optional<xla::PrimitiveType> preferred_element_type_;
private:
xla::DotDimensionNumbers dnums_;

2
tensorflow/stream_executor/tpu/noncopyable_buffer.h
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@@ -54,7 +54,7 @@ class NoncopyableBuffer {
// Allocates an owning buffer and initializes it with the specified data. Size
// is specified in number of uint32's.
NoncopyableBuffer(size_t size_in_u32s, absl::optional<uint32_t> value,
NoncopyableBuffer(size_t size_in_u32s, std::optional<uint32_t> value,
BufferAllocator allocator = DefaultAllocator)
: NoncopyableBuffer(size_in_u32s * sizeof(uint32_t), allocator) {
#ifndef MEMORY_SANITIZER

2
tensorflow/stream_executor/tpu/tpu_executable.h
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@@ -49,7 +49,7 @@ class TpuExecutable : public xla::TpuExecutableInterface {
const ServiceExecutableRunOptions& run_options,
absl::Span<const stream_executor::DeviceMemoryBase> arguments,
stream_executor::DeviceMemoryBase result,
absl::optional<stream_executor::DeviceMemoryBase>
std::optional<stream_executor::DeviceMemoryBase>
cross_program_prefetch_addr) override {
LOG(FATAL) << "LoadProgramAndEnqueueToStream unimplemented";
}

6
tensorflow/stream_executor/tpu/tpu_executable_interface.cc
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@@ -76,7 +76,7 @@ TpuExecutableInterface::AllocateOutputMemoryWithInputReuse(
TF_RETURN_IF_ERROR(alias_config.ForEachAliasWithStatus(
[&](const ShapeIndex& output_index,
absl::optional<HloInputOutputAliasConfig::Alias> alias) {
std::optional<HloInputOutputAliasConfig::Alias> alias) {
if (alias && alias->must_alias()) {
VLOG(1) << alias->ToString();
const MaybeOwningDeviceMemory& original_input =
@@ -121,7 +121,7 @@ TpuExecutableInterface::AllocateOutputMemoryWithInputReuse(
result_index.ToString());
}
absl::optional<HloInputOutputAliasConfig::Alias> alias =
std::optional<HloInputOutputAliasConfig::Alias> alias =
alias_config.GetAliasedParameter(result_index);
if (alias) {
TF_RET_CHECK(alias->parameter_number < arguments->size());
@@ -204,7 +204,7 @@ StatusOr<ExecutionOutput> TpuExecutableInterface::ExecuteAsyncOnStream(
run_options->run_options().host_to_device_stream()));
// Address of the buffer in TPU memory that is being speculated.
absl::optional<se::DeviceMemoryBase> cross_program_prefetch_addr;
std::optional<se::DeviceMemoryBase> cross_program_prefetch_addr;
if (hlo_module_) {
for (const auto& prefetch : hlo_module_->CrossProgramPrefetches()) {
const auto& parameter = prefetch.first;

2
tensorflow/stream_executor/tpu/tpu_executable_interface.h
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@@ -73,7 +73,7 @@ class TpuExecutableInterface : public Executable {
const ServiceExecutableRunOptions& run_options,
absl::Span<const stream_executor::DeviceMemoryBase> arguments,
stream_executor::DeviceMemoryBase result,
absl::optional<stream_executor::DeviceMemoryBase>
std::optional<stream_executor::DeviceMemoryBase>
cross_program_prefetch_addr) = 0;
virtual absl::string_view fingerprint() const = 0;

2
tensorflow/stream_executor/tpu/tpu_executor.cc
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@@ -220,7 +220,7 @@ bool TpuExecutor::DeviceMemoryUsage(int64_t* free, int64_t* total) const {
return false;
}
absl::optional<stream_executor::AllocatorStats>
std::optional<stream_executor::AllocatorStats>
TpuExecutor::GetAllocatorStats() {
SE_AllocatorStats c_stats;
if (tpu::ExecutorApiFn()->TpuExecutor_GetAllocatorStatsFn(executor_,

2
tensorflow/stream_executor/tpu/tpu_executor.h
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@@ -101,7 +101,7 @@ class TpuExecutor : public tensorflow::tpu::TpuExecutorInterface {
Status EnqueueInfeed(int32_t infeed_queue_index,
absl::Span<const uint8> bytes);
absl::optional<stream_executor::AllocatorStats> GetAllocatorStats() override;
std::optional<stream_executor::AllocatorStats> GetAllocatorStats() override;
tpu::TpuCoreLocationExternal GetCoreLocationExternal() const override;

2
tensorflow/stream_executor/tpu/tpu_op_executable.cc
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@@ -38,7 +38,7 @@ Status TpuOpExecutable::LoadProgramAndEnqueueToStream(
const xla::ServiceExecutableRunOptions& run_options,
absl::Span<const se::DeviceMemoryBase> arguments,
se::DeviceMemoryBase result,
absl::optional<se::DeviceMemoryBase> cross_program_prefetch_addr) {
std::optional<se::DeviceMemoryBase> cross_program_prefetch_addr) {
SE_DeviceMemoryBase* arguments_bases = nullptr;
if (!arguments.empty()) {
arguments_bases = new SE_DeviceMemoryBase[arguments.size()];

2
tensorflow/stream_executor/tpu/tpu_op_executable.h
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@@ -53,7 +53,7 @@ class TpuOpExecutable : public xla::TpuExecutableInterface {
const xla::ServiceExecutableRunOptions& run_options,
absl::Span<const stream_executor::DeviceMemoryBase> arguments,
stream_executor::DeviceMemoryBase result,
absl::optional<stream_executor::DeviceMemoryBase>
std::optional<stream_executor::DeviceMemoryBase>
cross_program_prefetch_addr) override;
xla::Shape HostShapeToDeviceShape(const xla::Shape& host_shape) override;