Replaced calls to tensorflow::StringPiece::ToString with std::string conversions.

That is, instances of sp.ToString() are replaced with std::string(sp).

This will allow tensorflow::StringPiece::ToString to be removed, which is necessary before it can be replaced with absl::string_view.

PiperOrigin-RevId: 195689392

tensorflow/compiler/xla/service/hlo_creation_utils.cc

@@ -269,7 +269,7 @@ StatusOr<HloInstruction*> BroadcastZeros(
 StatusOr<std::unique_ptr<HloComputation>> CreateComputationWithSignature(
     ArraySlice<const Shape*> domain, const Shape& range,
     tensorflow::StringPiece name) {
-  HloComputation::Builder b(name.ToString());
+  HloComputation::Builder b{std::string(name)};
   int64 param_idx = 0;
   for (const Shape* param_shape : domain) {
     b.AddInstruction(HloInstruction::CreateParameter(

tensorflow/compiler/xla/service/hlo_graph_dumper.cc

@@ -325,7 +325,7 @@ class HloDotDumper {
                bool show_backend_config, const HloExecutionProfile* profile,
                NodeFilter filter)
       : computation_(computation),
-        label_(label.ToString()),
+        label_(std::string(label)),
         debug_options_(debug_options),
         show_metadata_(show_metadata),
         show_backend_config_(show_backend_config),

tensorflow/compiler/xla/service/hlo_instruction.cc

@@ -438,7 +438,7 @@ HloInstruction::CreateCrossReplicaSum(
       << "Outfeed shape " << shape << " must be compatible with operand shape "
       << operand->shape();
   instruction->AppendOperand(operand);
-  instruction->outfeed_config_ = outfeed_config.ToString();
+  instruction->outfeed_config_ = std::string(outfeed_config);
   instruction->outfeed_shape_ = shape;
   return instruction;
 }
@@ -1168,7 +1168,7 @@ bool HloInstruction::HasSideEffect() const {
   for (auto operand : operands) {
     instruction->AppendOperand(operand);
   }
-  instruction->custom_call_target_ = custom_call_target.ToString();
+  instruction->custom_call_target_ = std::string(custom_call_target);
   return instruction;
 }
 
@@ -1180,7 +1180,7 @@ bool HloInstruction::HasSideEffect() const {
   for (auto operand : operands) {
     instruction->AppendOperand(operand);
   }
-  instruction->channel_name_ = channel_name.ToString();
+  instruction->channel_name_ = std::string(channel_name);
   instruction->cost_estimate_ns_ = cost_estimate_ns;
   return instruction;
 }

tensorflow/compiler/xla/service/hlo_instruction.h

@@ -1264,7 +1264,7 @@ class HloInstruction {
 
   // Gets/sets the string identifier for this instruction.
   const string& name() const { return name_; }
-  void set_name(tensorflow::StringPiece name) { name_ = name.ToString(); }
+  void set_name(tensorflow::StringPiece name) { name_ = std::string(name); }
 
   // Use the given NameUniquer to select a unique name for the instruction based
   // on the instruction's existing name.

tensorflow/compiler/xla/service/hlo_pass_pipeline.cc

@@ -90,7 +90,7 @@ StatusOr<bool> HloPassPipeline::Run(HloModule* module) {
     return Status::OK();
   };
 
-  string prefix = name().ToString() + ": pipeline start";
+  string prefix = std::string(name()) + ": pipeline start";
   bool changed = false;
   string message;
   TF_RETURN_IF_ERROR(
@@ -98,12 +98,12 @@ StatusOr<bool> HloPassPipeline::Run(HloModule* module) {
   const string xla_dump_per_pass_hlo_proto_to =
       module->config().debug_options().xla_dump_per_pass_hlo_proto_to();
   if (!xla_dump_per_pass_hlo_proto_to.empty()) {
-    DumpModuleProto(*module, xla_dump_per_pass_hlo_proto_to, name().ToString(),
+    DumpModuleProto(*module, xla_dump_per_pass_hlo_proto_to,
-                    "pipeline_start");
+                    std::string(name()), "pipeline_start");
   }
 
   for (auto& pass : passes_) {
-    if (disabled_passes.count(pass->name().ToString()) > 0) {
+    if (disabled_passes.count(std::string(pass->name())) > 0) {
       VLOG(1) << "  Skipping HLO pass " << pass->name()
               << ", disabled by --xla_disable_hlo_passes";
       continue;
@@ -121,7 +121,7 @@ StatusOr<bool> HloPassPipeline::Run(HloModule* module) {
         run_invariant_checkers(StrCat("after running pass: ", pass->name())));
     if (!xla_dump_per_pass_hlo_proto_to.empty()) {
       DumpModuleProto(*module, xla_dump_per_pass_hlo_proto_to,
-                      name().ToString(), pass->name().ToString());
+                      std::string(name()), std::string(pass->name()));
     }
 
     changed |= changed_this_pass;

tensorflow/compiler/xla/service/human_readable_profile_builder.h

@@ -32,7 +32,7 @@ class HumanReadableProfileBuilder {
   explicit HumanReadableProfileBuilder(tensorflow::StringPiece computation_name,
                                        int64 total_cycles,
                                        double clock_rate_ghz)
-      : computation_name_(computation_name.ToString()),
+      : computation_name_(std::string(computation_name)),
         total_cycles_(total_cycles),
         clock_rate_ghz_(clock_rate_ghz) {
     CHECK_GE(clock_rate_ghz, 1e-9);
@@ -47,9 +47,10 @@ class HumanReadableProfileBuilder {
              tensorflow::StringPiece category, int64 cycles, int64 flop_count,
              int64 transcendental_count, int64 bytes_accessed,
              float optimal_seconds) {
-    op_infos_.push_back(
+    op_infos_.push_back({std::string(op_name), std::string(short_name),
-        {op_name.ToString(), short_name.ToString(), category.ToString(), cycles,
+                         std::string(category), cycles, flop_count,
-         flop_count, transcendental_count, bytes_accessed, optimal_seconds});
+                         transcendental_count, bytes_accessed,
+                         optimal_seconds});
   }
 
   // Gets the human-readable profile.

tensorflow/compiler/xla/service/name_uniquer.cc

@@ -53,7 +53,7 @@ NameUniquer::NameUniquer(const string& separator) {
 }
 
 string NameUniquer::GetUniqueName(tensorflow::StringPiece prefix) {
-  string root = GetSanitizedName(prefix.empty() ? "name" : prefix.ToString());
+  string root = GetSanitizedName(prefix.empty() ? "name" : std::string(prefix));
 
   // Strip away numeric suffix (if any). Only recognize separator if it is in
   // the middle of the name.

tensorflow/compiler/xla/service/shape_inference.cc

@@ -172,11 +172,11 @@ tensorflow::Status ExpectNotTupleOrOpaque(const Shape& shape,
                                           tensorflow::StringPiece op_type) {
   if (ShapeUtil::IsTuple(shape)) {
     return InvalidArgument("Expected non-tuple argument for %s, but got %s.",
-                           op_type.ToString().c_str(),
+                           std::string(op_type).c_str(),
                            ShapeUtil::HumanString(shape).c_str());
   } else if (ShapeUtil::IsOpaque(shape)) {
     return InvalidArgument("Expected non-opaque argument for %s, but got %s.",
-                           op_type.ToString().c_str(),
+                           std::string(op_type).c_str(),
                            ShapeUtil::HumanString(shape).c_str());
   } else {
     return tensorflow::Status::OK();

tensorflow/core/framework/function.cc

@@ -504,7 +504,7 @@ string Print(const NodeDef& n) {
   std::vector<string> dep;
   for (StringPiece s : n.input()) {
     if (str_util::ConsumePrefix(&s, "^")) {
-      dep.push_back(s.ToString());
+      dep.push_back(std::string(s));
     } else {
       dat.push_back(s);
     }

tensorflow/core/framework/node_def_builder.cc

@@ -24,22 +24,23 @@ limitations under the License.
 namespace tensorflow {
 
 NodeDefBuilder::NodeOut::NodeOut(StringPiece n, int i, DataType dt)
-    : node(n.ToString()), index(i), data_type(dt) {}
+    : node(std::string(n)), index(i), data_type(dt) {}
 
 NodeDefBuilder::NodeOut::NodeOut() {
   // uninitialized, call Reset() before use.
 }
 
 void NodeDefBuilder::NodeOut::Reset(StringPiece n, int i, DataType dt) {
-  node = n.ToString();
+  node = std::string(n);
   index = i;
   data_type = dt;
 }
 
 NodeDefBuilder::NodeDefBuilder(StringPiece name, StringPiece op_name,
                                const OpRegistryInterface* op_registry) {
-  node_def_.set_name(name.ToString());
+  node_def_.set_name(std::string(name));
-  const Status status = op_registry->LookUpOpDef(op_name.ToString(), &op_def_);
+  const Status status =
+      op_registry->LookUpOpDef(std::string(op_name), &op_def_);
   if (status.ok()) {
     Initialize();
   } else {
@@ -50,7 +51,7 @@ NodeDefBuilder::NodeDefBuilder(StringPiece name, StringPiece op_name,
 
 NodeDefBuilder::NodeDefBuilder(StringPiece name, const OpDef* op_def)
     : op_def_(op_def) {
-  node_def_.set_name(name.ToString());
+  node_def_.set_name(std::string(name));
   Initialize();
 }
 
@@ -170,7 +171,7 @@ void NodeDefBuilder::AddInput(StringPiece src_node, int src_index) {
   } else if (src_index > 0) {
     node_def_.add_input(strings::StrCat(src_node, ":", src_index));
   } else {
-    node_def_.add_input(src_node.ToString());
+    node_def_.add_input(std::string(src_node));
   }
 }
 
@@ -193,12 +194,12 @@ void NodeDefBuilder::VerifyInputRef(const OpDef::ArgDef* input_arg,
 }
 
 NodeDefBuilder& NodeDefBuilder::ControlInput(StringPiece src_node) {
-  control_inputs_.push_back(src_node.ToString());
+  control_inputs_.push_back(std::string(src_node));
   return *this;
 }
 
 NodeDefBuilder& NodeDefBuilder::Device(StringPiece device_spec) {
-  node_def_.set_device(device_spec.ToString());
+  node_def_.set_device(std::string(device_spec));
   return *this;
 }
 

tensorflow/core/framework/node_def_util.cc

@@ -245,7 +245,7 @@ DEFINE_GET_ATTR(NameAttrList, func, "func", emplace_back, v, ;);
 #undef DEFINE_GET_ATTR
 
 bool HasNodeAttr(const NodeDef& node_def, StringPiece attr_name) {
-  return node_def.attr().find(attr_name.ToString()) != node_def.attr().end();
+  return node_def.attr().find(std::string(attr_name)) != node_def.attr().end();
 }
 
 static const string& kEmptyString = *new string();
@@ -639,7 +639,7 @@ Status AttachDef(const Status& status, const Node& node) {
 
 void AddNodeAttr(StringPiece name, const AttrValue& value, NodeDef* node_def) {
   node_def->mutable_attr()->insert(
-      AttrValueMap::value_type(name.ToString(), value));
+      AttrValueMap::value_type(std::string(name), value));
 }
 
 #define ADD_NODE_ATTR(T)                                           \
@@ -677,7 +677,7 @@ ADD_NODE_ATTR(gtl::ArraySlice<NameAttrList>)
 #undef ADD_NODE_ATTR
 
 void AddAttr(StringPiece name, const AttrValue& value, AttrValueMap* map) {
-  map->insert(AttrValueMap::value_type(name.ToString(), value));
+  map->insert(AttrValueMap::value_type(std::string(name), value));
 }
 
 #define ADD_ATTR(T)                                            \

tensorflow/core/framework/op_def_builder.cc

@@ -527,7 +527,7 @@ void FinalizeDoc(const string& text, OpDef* op_def,
 }  // namespace
 
 OpDefBuilder::OpDefBuilder(StringPiece op_name) {
-  op_def()->set_name(op_name.ToString());  // NOLINT
+  op_def()->set_name(std::string(op_name));  // NOLINT
 }
 
 OpDefBuilder& OpDefBuilder::Attr(StringPiece spec) {
@@ -584,7 +584,7 @@ OpDefBuilder& OpDefBuilder::Deprecated(int version, StringPiece explanation) {
   } else {
     OpDeprecation* deprecation = op_def()->mutable_deprecation();
     deprecation->set_version(version);
-    deprecation->set_explanation(explanation.ToString());
+    deprecation->set_explanation(std::string(explanation));
   }
   return *this;
 }

tensorflow/core/framework/op_gen_lib.cc

@@ -185,7 +185,7 @@ static bool FindMultiline(StringPiece line, size_t colon, string* end) {
   while (str_util::ConsumePrefix(&line, " ")) {
   }
   if (str_util::ConsumePrefix(&line, "<<")) {
-    *end = line.ToString();
+    *end = std::string(line);
     return true;
   }
   return false;

tensorflow/core/framework/op_kernel.cc

@@ -923,7 +923,7 @@ void OpKernelContext::clear_recorded_memory() {
 struct KernelRegistration {
   KernelRegistration(const KernelDef& d, StringPiece c,
                      kernel_factory::OpKernelRegistrar::Factory f)
-      : def(d), kernel_class_name(c.ToString()), factory(f) {}
+      : def(d), kernel_class_name(std::string(c)), factory(f) {}
   const KernelDef def;
   const string kernel_class_name;
   const kernel_factory::OpKernelRegistrar::Factory factory;

tensorflow/core/framework/shape_inference_testutil.h

@@ -32,7 +32,7 @@ class Tensor;
 
 struct ShapeInferenceTestOp {
   typedef std::pair<string, DataType> ShapeAndType;
-  explicit ShapeInferenceTestOp(StringPiece name) : name(name.ToString()) {}
+  explicit ShapeInferenceTestOp(StringPiece name) : name(std::string(name)) {}
   string name;
   NodeDef node_def;
   std::vector<const Tensor*> input_tensors;

tensorflow/core/graph/graph.cc

@@ -695,7 +695,7 @@ Status Graph::AddWhileContext(StringPiece frame_name,
                               std::vector<OutputTensor> body_outputs,
                               WhileContext** result) {
   auto pair = while_ctxs_.insert(std::pair<string, WhileContext>(
-      frame_name.ToString(),
+      std::string(frame_name),
       WhileContext(frame_name, std::move(enter_nodes), std::move(exit_nodes),
                    cond_output, std::move(body_inputs),
                    std::move(body_outputs))));

tensorflow/core/graph/graph_constructor.cc

@@ -489,7 +489,7 @@ Status GraphConstructor::InitFromEdges() {
           num_control_edges++;
         } else {
           TensorId id(ParseTensorName(input_name));
-          if (next_iteration_nodes_.find(id.first.ToString()) !=
+          if (next_iteration_nodes_.find(std::string(id.first)) !=
               next_iteration_nodes_.end()) {
             has_loop_back_edge = true;
           }
@@ -811,7 +811,7 @@ void GraphConstructor::UniquifyNames(
     // We require that UniquifyNames() is called on all NodeDefs in topological
     // order. This guarantees that node_def's inputs will already be uniquified
     // if necessary.
-    auto iter = uniquified_names_.find(id.first.ToString());
+    auto iter = uniquified_names_.find(std::string(id.first));
     if (iter == uniquified_names_.end()) continue;
     id.first = iter->second;
     node_def->set_input(i, id.ToString());
@@ -830,7 +830,7 @@ void GraphConstructor::UpdateUniquifiedColocationNames() {
     for (int i = 0; i < coloc_values.size(); ++i) {
       StringPiece val(coloc_values[i]);
       if (str_util::ConsumePrefix(&val, kColocationGroupPrefix)) {
-        const auto& name_pair = uniquified_names_.find(val.ToString());
+        const auto& name_pair = uniquified_names_.find(std::string(val));
         if (name_pair == uniquified_names_.end()) continue;
         updated = true;
         coloc_values[i] =
@@ -856,7 +856,7 @@ bool GraphConstructor::NameExistsInGraphDef(StringPiece name) {
 }
 
 string GraphConstructor::FindUniqueName(StringPiece original_name) {
-  string name = original_name.ToString();
+  string name = std::string(original_name);
   int count = 0;
   // Check that any generated names don't collide with imported NodeDefs (as
   // well as nodes in g_).
@@ -989,7 +989,7 @@ Status GraphConstructor::Convert() {
             src_node->num_outputs(), " outputs");
       }
 
-      inputs.emplace_back(id.first.ToString(), src_node, src_index);
+      inputs.emplace_back(std::string(id.first), src_node, src_index);
     }
 
     if (has_data_back_edge && !IsMerge(*node_def)) {

tensorflow/core/graph/graph_constructor_test.cc

@@ -157,7 +157,7 @@ class GraphConstructorTest : public ::testing::Test {
     }
     StringPiece loc(value[0]);
     return str_util::ConsumePrefix(&loc, kColocationGroupPrefix)
-               ? loc.ToString()
+               ? std::string(loc)
                : "";
   }
 

tensorflow/core/graph/graph_def_builder.cc

@@ -44,12 +44,12 @@ GraphDefBuilder::Options GraphDefBuilder::Options::WithControlInputs(
 }
 GraphDefBuilder::Options GraphDefBuilder::Options::WithNameImpl(
     StringPiece name) {
-  name_ = name.ToString();
+  name_ = std::string(name);
   return *this;
 }
 GraphDefBuilder::Options GraphDefBuilder::Options::WithDeviceImpl(
     StringPiece device) {
-  device_ = device.ToString();
+  device_ = std::string(device);
   return *this;
 }
 GraphDefBuilder::Options GraphDefBuilder::Options::WithControlInputImpl(

tensorflow/core/graph/graph_def_builder.h

@@ -128,7 +128,7 @@ class GraphDefBuilder {
     Options WithControlInputsImpl(gtl::ArraySlice<Node*> control_inputs);
     template <class T>
     Options WithAttrImpl(StringPiece name, T&& value) {
-      attrs_.emplace_back(name.ToString(), AttrValue());
+      attrs_.emplace_back(std::string(name), AttrValue());
       SetAttrValue(std::forward<T>(value), &attrs_.back().second);
       return *this;
     }

tensorflow/core/graph/graph_partition.cc

@@ -785,7 +785,7 @@ Status TopologicalSortNodesWithTimePriority(
   for (int n = 0; n < gdef->node_size(); ++n) {
     const NodeDef* ndef = &gdef->node(n);
     for (int i = 0; i < ndef->input_size(); ++i) {
-      node_to_output_nodes[ParseTensorName(ndef->input(i)).first.ToString()]
+      node_to_output_nodes[std::string(ParseTensorName(ndef->input(i)).first)]
           .push_back(ndef);
     }
     int64 start_time;

tensorflow/core/graph/node_builder.cc

@@ -30,7 +30,7 @@ NodeBuilder::NodeOut::NodeOut(Node* n, int32 i)  // NOLINT(runtime/explicit)
       dt(SafeGetOutput(node, i, &error)) {}
 
 NodeBuilder::NodeOut::NodeOut(StringPiece n, int32 i, DataType t)
-    : node(nullptr), error(false), name(n.ToString()), index(i), dt(t) {}
+    : node(nullptr), error(false), name(std::string(n)), index(i), dt(t) {}
 
 NodeBuilder::NodeOut::NodeOut()
     : node(nullptr), error(true), index(0), dt(DT_FLOAT) {}

tensorflow/core/graph/while_context.cc

@@ -23,7 +23,7 @@ WhileContext::WhileContext(StringPiece frame_name,
                            OutputTensor cond_output,
                            std::vector<OutputTensor> body_inputs,
                            std::vector<OutputTensor> body_outputs)
-    : frame_name_(frame_name.ToString()),
+    : frame_name_(std::string(frame_name)),
       enter_nodes_(std::move(enter_nodes)),
       exit_nodes_(std::move(exit_nodes)),
       cond_output_(cond_output),