[TF] [XLA] Fix a number of compiler warnings on Mac OS X.

* Mostly these are unused/dead code.
* Fix an ignored Status in shape_util.h

PiperOrigin-RevId: 222126279

tensorflow/compiler/xla/array2d.h

@@ -104,7 +104,7 @@ std::unique_ptr<Array2D<NativeT>> MakeLinspaceArray2D(double from, double to,
   int64 count = n1 * n2;
   NativeT step =
       static_cast<NativeT>((count > 1) ? (to - from) / (count - 1) : 0);
-  auto set = [&array, n1, n2](int64 index, NativeT value) {
+  auto set = [&array, n2](int64 index, NativeT value) {
     (*array)(index / n2, index % n2) = value;
   };
   for (int64 i = 0; i < count - 1; ++i) {

tensorflow/compiler/xla/service/buffer_assignment.cc

@@ -746,8 +746,7 @@ StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::Run(
     LogicalBuffer::AlignmentFunction color_alignment,
     bool allow_input_output_aliasing, bool allocate_buffers_for_constants,
     BufferLiveness::Colorer colorer, ReuseAllocationFunction reuse_checker) {
-  BufferAssigner assigner(allow_input_output_aliasing,
-                          allocate_buffers_for_constants, std::move(colorer),
+  BufferAssigner assigner(allocate_buffers_for_constants, std::move(colorer),
                           std::move(reuse_checker));
   return assigner.CreateAssignment(module, std::move(hlo_ordering),
                                    std::move(buffer_size),

tensorflow/compiler/xla/service/buffer_assignment.h

@@ -545,12 +545,10 @@ class BufferAssigner {
       ReuseAllocationFunction reuse_checker = nullptr);
 
  private:
-  BufferAssigner(bool allow_input_output_aliasing,
-                 bool allocate_buffers_for_constants,
+  BufferAssigner(bool allocate_buffers_for_constants,
                  BufferLiveness::Colorer colorer,
                  ReuseAllocationFunction reuse_checker)
-      : allow_input_output_aliasing_(allow_input_output_aliasing),
-        allocate_buffers_for_constants_(allocate_buffers_for_constants),
+      : allocate_buffers_for_constants_(allocate_buffers_for_constants),
         colorer_(colorer),
         reuse_checker_(reuse_checker) {}
   virtual ~BufferAssigner() = default;
@@ -640,10 +638,6 @@ class BufferAssigner {
                       LogicalBuffer::Color::Hasher>
   SplitBuffersByColor(const absl::flat_hash_set<const LogicalBuffer*>& buffers);
 
-  // If true, buffer assignments assumes that input parameter buffers and output
-  // buffers can be shared if their sizes match.
-  bool allow_input_output_aliasing_;
-
   // If true, allocate buffers for constant instructions.
   bool allocate_buffers_for_constants_;
 

tensorflow/compiler/xla/service/computation_placer.h

@@ -105,8 +105,6 @@ class ComputationPlacer {
   // Map from platform kind to computation placer singleton.
   static std::map<se::Platform::Id, State>* GetPlatformComputationPlacers();
 
-  se::Platform::Id platform_id_;
-
   TF_DISALLOW_COPY_AND_ASSIGN(ComputationPlacer);
 };
 

tensorflow/compiler/xla/service/copy_insertion.cc

@@ -442,7 +442,6 @@ class CopyRemover {
               const HloOrdering& ordering, HloModule* module)
       : module_(module),
         alias_analysis_(alias_analysis),
-        ordering_(ordering),
         buffer_value_tracker_(*module, alias_analysis, ordering) {}
 
   // Try to elide the given copy. The copy is elided if the instruction is not
@@ -1003,7 +1002,6 @@ class CopyRemover {
 
   HloModule* module_;
   const HloAliasAnalysis& alias_analysis_;
-  const HloOrdering& ordering_;
 
   // Object tracking the HLO values contained in each HLO buffer.
   BufferValueTracker buffer_value_tracker_;

tensorflow/compiler/xla/service/cpu/compiler_functor.cc

@@ -61,17 +61,15 @@ Disabling these as a starting point.
 // TODO(b/64227304) Creating a custom pass pipeline will replace this.
 
 namespace {
+
+// TODO(sanjoy): remove this class.
 class FilteredFunctionPassManager : public llvm::legacy::FunctionPassManager {
  public:
-  FilteredFunctionPassManager(llvm::Module* m, bool disable_expensive_passes)
-      : llvm::legacy::FunctionPassManager(m),
-        disable_expensive_passes_(disable_expensive_passes) {}
+  explicit FilteredFunctionPassManager(llvm::Module* m)
+      : llvm::legacy::FunctionPassManager(m) {}
   void add(llvm::Pass* p) override {
     llvm::legacy::FunctionPassManager::add(p);
   }
-
- private:
-  bool disable_expensive_passes_;
 };
 
 class FilteredPassManager : public llvm::legacy::PassManager {
@@ -96,8 +94,7 @@ class FilteredPassManager : public llvm::legacy::PassManager {
 std::unique_ptr<llvm::MemoryBuffer> CompilerFunctor::operator()(
     llvm::Module& module) const {
   FilteredPassManager module_passes(disable_expensive_passes_);
-  FilteredFunctionPassManager function_passes(&module,
-                                              disable_expensive_passes_);
+  FilteredFunctionPassManager function_passes(&module);
 
   VLOG(2) << "IR before optimizations";
   XLA_VLOG_LINES(2, llvm_ir::DumpModuleToString(module));

tensorflow/compiler/xla/service/cpu/cpu_options.cc

@@ -22,7 +22,6 @@ limitations under the License.
 namespace {
 
 const char* const kXlaOptimizeForSizeCpuOption = "xla_cpu_optimize_for_size";
-const char* const kXlaDisableVectorizedReduce = "xla_disable_vectorized_reduce";
 const char* const kLlvmIrDotTilingFactor = "xla_llvm_dot_tiling_factor";
 const char* const kXlaEnableExperimentalLlvmIrGemm =
     "xla_enable_experimental_llvm_ir_gemm";

tensorflow/compiler/xla/service/cpu/ir_emitter.cc

@@ -140,7 +140,7 @@ StatusOr<llvm::Function*> IrEmitter::EmitComputation(
   // readcyclecounter if it is unavailable.
   bool use_rdtscp = arch_type_ == llvm::Triple::ArchType::x86 ||
                     arch_type_ == llvm::Triple::ArchType::x86_64;
-  profiling_state_ = ProfilingState(use_rdtscp, GetProfileCountersArgument());
+  profiling_state_ = ProfilingState(use_rdtscp);
   if (instruction_order == nullptr) {
     TF_RETURN_IF_ERROR(computation->Accept(this));
   } else {
@@ -1379,33 +1379,6 @@ Status IrEmitter::HandleCrossReplicaSum(HloInstruction* crs) {
   return Status::OK();
 }
 
-// Fills up the free variables in 'index_with_free_var' with values from
-// 'filler_index'. The size of free variables must be the same as the
-// size of 'filler_index'.
-//
-// This is often used after dimension reduction, where
-// 'index_with_free_var' has one or more dimensions reduced, which serves as
-// free variables (represented as nullptr). For example, if we have a 4
-// dimensional input and index for the dimension being reduced is
-// 2 (third dimension), we will have an index like [i, j, NULL, k]
-// after reduced dimension.
-//
-// Here we fill up that free variable by 'filler_index', which contains
-// the value in the reduced dimension.
-static llvm_ir::IrArray::Index FillReducedDimensionIndex(
-    llvm_ir::IrArray::Index index_with_free_var,
-    llvm_ir::IrArray::Index filler_index) {
-  llvm_ir::IrArray::Index::const_iterator it = filler_index.begin();
-
-  for (size_t i = 0; i < index_with_free_var.size(); ++i) {
-    if (index_with_free_var[i] == nullptr) {
-      index_with_free_var[i] = *it++;
-    }
-  }
-  CHECK(filler_index.end() == it);
-  return index_with_free_var;
-}
-
 Status IrEmitter::HandleParameter(HloInstruction* parameter) {
   VLOG(2) << "HandleParameter: " << parameter->ToString();
   return EmitTargetAddressForOp(parameter);
@@ -2194,14 +2167,6 @@ Status IrEmitter::HandlePad(HloInstruction* pad) {
   return Status::OK();
 }
 
-// If `hlo` is a Transpose, returns its operand; otherwise returns `hlo` itself.
-static const HloInstruction* StripTranspose(const HloInstruction& hlo) {
-  if (hlo.IsRank2Transpose()) {
-    return hlo.operand(0);
-  }
-  return &hlo;
-}
-
 Status IrEmitter::HandleFusion(HloInstruction* fusion) {
   auto* root = fusion->fused_expression_root();
   if (llvm_ir::CanEmitFusedDynamicUpdateSliceInPlace(fusion, assignment_)) {

tensorflow/compiler/xla/service/cpu/ir_emitter.h

@@ -467,9 +467,8 @@ class IrEmitter : public DfsHloVisitorWithDefault,
   // profiling a computation.
   class ProfilingState {
    public:
-    ProfilingState() : use_rdtscp_(false), prof_counters_(nullptr) {}
-    ProfilingState(bool use_rdtscp, llvm::Value* prof_counters)
-        : use_rdtscp_(use_rdtscp), prof_counters_(prof_counters) {}
+    ProfilingState() : use_rdtscp_(false) {}
+    explicit ProfilingState(bool use_rdtscp) : use_rdtscp_(use_rdtscp) {}
 
     // Record the cycle counter before an HLO executes.
     void RecordCycleStart(llvm::IRBuilder<>* b, HloInstruction* hlo);
@@ -494,9 +493,6 @@ class IrEmitter : public DfsHloVisitorWithDefault,
     // intrinsic?
     bool use_rdtscp_;
 
-    // The argument which corresponds to the profile counter buffer.
-    llvm::Value* prof_counters_;
-
     // The first read cycle counter in the program.
     llvm::Value* first_read_cycle_start_ = nullptr;
 

tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h

@@ -593,7 +593,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault {
     return Status::OK();
   }
 
-  Status HandleDivide(HloInstruction* divide) {
+  Status HandleDivide(HloInstruction* divide) override {
     return HandleDivide<ElementwiseT>(divide);
   }
 

tensorflow/compiler/xla/service/hlo_instruction.cc

@@ -2628,36 +2628,6 @@ Status HloInstruction::AcceptWithOperandOrder(
   return Status::OK();
 }
 
-namespace {
-
-// Returns true if the given order is a topological sort of the instructions
-// it contains.
-bool OrderIsTopologicalSort(const std::vector<const HloInstruction*>& order) {
-  // Create a map from instruction to its position in 'order'.
-  std::unordered_map<const HloInstruction*, int> order_position;
-  for (int i = 0; i < order.size(); i++) {
-    if (!order_position.insert({order[i], i}).second) {
-      // Instruction order[i] is duplicated in the order.
-      return false;
-    }
-  }
-  // Verify that the operand of each instruction in the order is also in the
-  // order *and* the operand's position is earlier (defs are before uses for
-  // all ops).
-  for (auto* instruction : order) {
-    for (auto* operand : instruction->operands()) {
-      if (!ContainsKey(order_position, operand) ||
-          order_position.at(operand) >= order_position.at(instruction)) {
-        return false;
-      }
-    }
-  }
-
-  return true;
-}
-
-}  // namespace
-
 Status HloInstruction::Accept(
     const std::function<Status(HloInstruction*)>& visitor_func) {
   FunctionVisitor visitor(visitor_func);

tensorflow/compiler/xla/service/hlo_value.h

@@ -166,9 +166,6 @@ class HloValue : public BufferValue {
 
   // Whether this value is live out of the HLO module.
   bool live_out_of_module_ = false;
-
-  // Whether this value is live out of its computation.
-  bool live_out_of_computation_ = false;
 };
 
 std::ostream& operator<<(std::ostream& out, const HloValue& hlo_value);

tensorflow/compiler/xla/service/service.cc

@@ -957,21 +957,6 @@ Status Service::TransferToClient(const TransferToClientRequest* arg,
   return Status::OK();
 }
 
-namespace {
-
-// Creates a clone of the given shaped buffer with the given device ordinal. The
-// shape and DeviceMemoryBase values of the clone are identical to the original.
-std::unique_ptr<ShapedBuffer> CloneShapedBufferOnDevice(
-    const ShapedBuffer& shaped_buffer, int device_ordinal) {
-  auto clone = absl::make_unique<ShapedBuffer>(
-      shaped_buffer.on_host_shape(), shaped_buffer.on_device_shape(),
-      shaped_buffer.platform(), device_ordinal);
-  clone->buffers() = shaped_buffer.buffers();
-  return clone;
-}
-
-}  // namespace
-
 Status Service::TransferToServer(const TransferToServerRequest* arg,
                                  TransferToServerResponse* result) {
   TF_ASSIGN_OR_RETURN(Literal literal,

tensorflow/compiler/xla/shape_tree.h

@@ -667,12 +667,11 @@ void ShapeTree<T>::CopySubtreeFrom(const ShapeTree<T>& other,
 template <typename T>
 bool ShapeTree<T>::operator==(const ShapeTree<T>& other) const {
   bool equal = true;
-  ForEachElement(
-      [this, &other, &equal](const ShapeIndex& index, const T& data) {
-        if (data != other.element(index)) {
-          equal = false;
-        }
-      });
+  ForEachElement([&other, &equal](const ShapeIndex& index, const T& data) {
+    if (data != other.element(index)) {
+      equal = false;
+    }
+  });
   return equal;
 }
 

tensorflow/compiler/xla/shape_util.h

@@ -37,6 +37,7 @@ limitations under the License.
 #include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/types.h"
 
 namespace xla {
@@ -756,10 +757,18 @@ class ShapeUtil {
       pool.emplace(tensorflow::Env::Default(), "foreach", kNumThreads);
     }
 
+    tensorflow::mutex mu;
+    Status status;  // Guarded by mu
+
     while (n < rank) {
       if (pool != absl::nullopt) {
-        pool->Schedule(
-            [indexes, &visitor_function] { visitor_function(indexes); });
+        pool->Schedule([indexes, &visitor_function, &mu, &status] {
+          StatusOr<bool> result = visitor_function(indexes);
+          if (!result.ok()) {
+            tensorflow::mutex_lock lock(mu);
+            status = status.ok() ? result.status() : status;
+          }
+        });
       } else {
         TF_ASSIGN_OR_RETURN(bool should_continue, visitor_function(indexes));
         if (!should_continue) {
@@ -777,7 +786,9 @@ class ShapeUtil {
       }
     }
 
-    return Status::OK();
+    // Waits for the scheduled work to complete.
+    pool.reset();
+    return status;
   }
 
   TF_DISALLOW_COPY_AND_ASSIGN(ShapeUtil);

tensorflow/core/common_runtime/collective_param_resolver_local.cc

@@ -511,7 +511,7 @@ void CollectiveParamResolverLocal::FindInstanceRec(
         if (irec->is_init) {
           exit_outside_locks = true;
         } else {
-          irec->init_waiters.push_back([this, gr, cp, done](InstanceRec* irec) {
+          irec->init_waiters.push_back([this, done](InstanceRec* irec) {
             CallbackWithStatus(done, irec);
           });
           return;

tensorflow/core/common_runtime/collective_rma_local.cc

@@ -38,7 +38,7 @@ void CollectiveRemoteAccessLocal::RecvFromPeer(
     return;
   }
   buf_rendezvous_.ConsumeBuf(
-      key, [this, to_tensor, to_device_ctx, to_device, to_alloc_attr,
+      key, [to_tensor, to_device_ctx, to_device, to_alloc_attr,
             dev_to_dev_stream_index,
             done](const Status& s, BufRendezvous::Hook* hook) {
         if (!s.ok()) {

tensorflow/core/graph/graph.h

@@ -59,7 +59,7 @@ class EdgeSetTest;
 class Graph;
 class GraphDef;
 class Node;
-class OutputTensor;
+struct OutputTensor;
 class VersionDef;
 class WhileContext;