[XLA] Use absl::make_unique instead of xla::MakeUnique.

Same for WrapUnique.

PiperOrigin-RevId: 209531124

tensorflow/compiler/aot/BUILD

@@ -55,6 +55,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:protos_all_cc",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -193,6 +194,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
         "@llvm//:core",
         "@llvm//:support",
         "@llvm//:target",

tensorflow/compiler/aot/codegen.cc

@@ -19,6 +19,7 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/aot/embedded_protocol_buffers.h"
 #include "tensorflow/compiler/tf2xla/cpu_function_runtime.h"
 #include "tensorflow/compiler/tf2xla/str_util.h"
@@ -617,7 +618,7 @@ Status GenerateMetadata(const CodegenOpts& opts,
 
   if (opts.gen_program_shape) {
     program_shape =
-        tensorflow::MakeUnique<xla::ProgramShape>(compile_result.program_shape);
+        absl::make_unique<xla::ProgramShape>(compile_result.program_shape);
     // The parameter names are currently meaningless, and redundant with the
     // rest of our metadata, so clear them out to avoid confusion and save
     // space.

tensorflow/compiler/aot/embedded_protocol_buffers.cc

@@ -18,6 +18,7 @@ limitations under the License.
 #include <memory>
 #include <string>
 
+#include "absl/memory/memory.h"
 #include "llvm/ADT/Triple.h"
 #include "llvm/IR/GlobalVariable.h"
 #include "llvm/IR/LLVMContext.h"
@@ -27,7 +28,6 @@ limitations under the License.
 #include "llvm/Target/TargetMachine.h"
 #include "llvm/Target/TargetOptions.h"
 #include "tensorflow/compiler/tf2xla/str_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
 #include "tensorflow/compiler/xla/util.h"
 
@@ -105,7 +105,7 @@ GetTargetMachineFromTriple(StringPiece target_triple) {
                               error.c_str());
   }
 
-  return WrapUnique(target->createTargetMachine(
+  return absl::WrapUnique(target->createTargetMachine(
       normalized_triple, /*CPU=*/"",
       /*Features=*/"", llvm::TargetOptions(), llvm::None));
 }
@@ -118,7 +118,7 @@ StatusOr<EmbeddedProtocolBuffers> CreateEmbeddedProtocolBuffers(
 
   llvm::LLVMContext llvm_context;
   std::unique_ptr<llvm::Module> module_with_serialized_proto =
-      MakeUnique<llvm::Module>("embedded_data_module", llvm_context);
+      absl::make_unique<llvm::Module>("embedded_data_module", llvm_context);
 
   EmbeddedProtocolBuffers result;
 

tensorflow/compiler/jit/BUILD

@@ -128,11 +128,11 @@ cc_library(
         "//tensorflow/compiler/tf2xla:common",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/service:shaped_buffer",
-        "//tensorflow/core:core_cpu",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -191,6 +191,7 @@ cc_library(
         "//tensorflow/core/kernels/data:generator_dataset_op",
         "//tensorflow/core/kernels/data:iterator_ops",
         "//tensorflow/core/kernels/data:prefetch_dataset_op",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -235,6 +236,7 @@ cc_library(
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/kernels:variable_ops",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -283,6 +285,7 @@ cc_library(
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
+        "@com_google_absl//absl/memory",
     ],
     alwayslink = 1,
 )
@@ -303,6 +306,7 @@ tf_cc_test(
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
         "//tensorflow/core:testlib",
+        "@com_google_absl//absl/memory",
     ],
 )
 

tensorflow/compiler/jit/create_xla_launch_op.cc

@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 #include "tensorflow/compiler/jit/create_xla_launch_op.h"
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/jit/defs.h"
 #include "tensorflow/compiler/jit/kernels/xla_launch_op.h"
 #include "tensorflow/compiler/jit/mark_for_compilation_pass.h"
@@ -223,8 +224,8 @@ Status CreateXlaLaunchOp(FunctionLibraryRuntime* flr, const NodeDef& node_def,
       &fbody->fdef.signature(), flr, fbody->arg_types, input_memory_types,
       fbody->ret_types, output_memory_types, flr->graph_def_version(), &s);
 
-  *kernel = MakeUnique<XlaLocalLaunchBase>(&construction, constant_arg_indices,
+  *kernel = absl::make_unique<XlaLocalLaunchBase>(
-                                           resource_arg_indices, function);
+      &construction, constant_arg_indices, resource_arg_indices, function);
   return s;
 }
 

tensorflow/compiler/jit/create_xla_launch_op_test.cc

@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/jit/create_xla_launch_op.h"
 
+#include "absl/memory/memory.h"
 #include "tensorflow/core/common_runtime/device_factory.h"
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/framework/function_testlib.h"
@@ -65,11 +66,11 @@ class CreateXlaLaunchOpTest : public ::testing::Test {
     for (const auto& fdef : flib) {
       *(proto.add_function()) = fdef;
     }
-    lib_def_ =
+    lib_def_ = absl::make_unique<FunctionLibraryDefinition>(
-        MakeUnique<FunctionLibraryDefinition>(OpRegistry::Global(), proto);
+        OpRegistry::Global(), proto);
     OptimizerOptions opts;
-    device_mgr_ = MakeUnique<DeviceMgr>(devices_);
+    device_mgr_ = absl::make_unique<DeviceMgr>(devices_);
-    pflr_ = MakeUnique<ProcessFunctionLibraryRuntime>(
+    pflr_ = absl::make_unique<ProcessFunctionLibraryRuntime>(
         device_mgr_.get(), Env::Default(), TF_GRAPH_DEF_VERSION, lib_def_.get(),
         opts, /*default_thread_pool=*/nullptr, /*cluster_flr=*/nullptr);
     flr_ = pflr_->GetFLR("/job:localhost/replica:0/task:0/cpu:0");

tensorflow/compiler/jit/xla_device.cc

@@ -18,6 +18,7 @@ limitations under the License.
 #include <stdlib.h>
 #include <unordered_set>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/jit/defs.h"
 #include "tensorflow/compiler/jit/xla_compile_on_demand_op.h"
 #include "tensorflow/compiler/jit/xla_device_context.h"
@@ -101,7 +102,7 @@ XlaDeviceAllocator* XlaDeviceAllocatorState::GetOrCreateXlaDeviceAllocator(
   }
 
   std::unique_ptr<XlaDeviceAllocator> alloc =
-      xla::MakeUnique<XlaDeviceAllocator>();
+      absl::make_unique<XlaDeviceAllocator>();
   XlaDeviceAllocator* alloc_ptr = alloc.get();
   state.allocators_[{backend, device_ordinal}] = std::move(alloc);
   return alloc_ptr;
@@ -327,7 +328,7 @@ xla::StatusOr<XlaDeviceContext*> XlaDevice::GetDeviceContextLocked() {
   // to those methods; see the bug for details. Our only saving grace at the
   // moment is that this race doesn't seem to occur in practice.
   if (use_gpu_device_info_) {
-    auto gpu_device_info = MakeUnique<GpuDeviceInfo>();
+    auto gpu_device_info = absl::make_unique<GpuDeviceInfo>();
     gpu_device_info->stream = stream_.get();
     gpu_device_info->default_context = device_context_;
     set_tensorflow_gpu_device_info(gpu_device_info.get());

tensorflow/compiler/jit/xla_launch_util.cc

@@ -17,6 +17,7 @@ limitations under the License.
 
 #include <memory>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/jit/defs.h"
 #include "tensorflow/compiler/tf2xla/shape_util.h"
 #include "tensorflow/compiler/tf2xla/xla_compiler.h"
@@ -175,7 +176,7 @@ void XlaComputationLaunchContext::PopulateInputs(
           << " not the same as on-host shape "
           << xla::ShapeUtil::HumanStringWithLayout(shape);
       se::DeviceMemoryBase dmem = XlaTensor::DeviceMemoryFromTensor(*t);
-      arg_buffers_[i] = xla::MakeUnique<ShapedBuffer>(
+      arg_buffers_[i] = absl::make_unique<ShapedBuffer>(
           /*on_host_shape=*/shape, /*on_device_shape=*/shape,
           client_->platform(), client_->default_device_ordinal());
       arg_buffers_[i]->set_buffer(dmem, /*index=*/{});

tensorflow/compiler/jit/xla_tensor.h

@@ -18,6 +18,7 @@ limitations under the License.
 
 #include <memory>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/service/shaped_buffer.h"
 #include "tensorflow/core/framework/allocator.h"
@@ -70,7 +71,7 @@ class XlaTensor {
   // Mutates the XlaTensor to set the ShapedBuffer.
   void set_shaped_buffer(xla::ScopedShapedBuffer shaped_buffer) {
     shaped_buffer_ =
-        xla::MakeUnique<xla::ScopedShapedBuffer>(std::move(shaped_buffer));
+        absl::make_unique<xla::ScopedShapedBuffer>(std::move(shaped_buffer));
   }
 
   // Some tensors on the device may have known values on the host. We use these

tensorflow/compiler/tf2xla/BUILD

@@ -211,6 +211,7 @@ cc_library(
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
     alwayslink = 1,
 )
@@ -475,12 +476,12 @@ cc_library(
         "//tensorflow/compiler/tf2xla:dump_graph",
         "//tensorflow/compiler/tf2xla/ops:xla_ops",
         "//tensorflow/compiler/xla:status_macros",
-        "//tensorflow/compiler/xla:util",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:graph",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -501,12 +502,12 @@ cc_library(
         "//tensorflow/compiler/tf2xla:dump_graph",
         "//tensorflow/compiler/tf2xla/ops:xla_ops",
         "//tensorflow/compiler/xla:status_macros",
-        "//tensorflow/compiler/xla:util",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:graph",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -525,12 +526,12 @@ cc_library(
         "//tensorflow/compiler/tf2xla:dump_graph",
         "//tensorflow/compiler/tf2xla/ops:xla_ops",
         "//tensorflow/compiler/xla:status_macros",
-        "//tensorflow/compiler/xla:util",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:graph",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 

tensorflow/compiler/tf2xla/functionalize_cond.cc

@@ -21,11 +21,11 @@ limitations under the License.
 #include <unordered_set>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/jit/union_find.h"
 #include "tensorflow/compiler/tf2xla/dump_graph.h"
 #include "tensorflow/compiler/tf2xla/functionalize_control_flow_util.h"
 #include "tensorflow/compiler/tf2xla/tf2xla_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/framework/graph_to_functiondef.h"
 #include "tensorflow/core/framework/node_def_builder.h"
@@ -399,7 +399,8 @@ Status Conditional::BuildArgumentNodes() {
 Status Conditional::ExtractBodies(Graph* graph) {
   VLOG(2) << "Extracting bodies for " << name();
   for (auto b : {BranchType::kElseBranch, BranchType::kThenBranch}) {
-    bodies_[static_cast<int>(b)] = xla::MakeUnique<Graph>(graph->op_registry());
+    bodies_[static_cast<int>(b)] =
+        absl::make_unique<Graph>(graph->op_registry());
   }
 
   auto find_branch = [&](const Edge* e) {

tensorflow/compiler/tf2xla/functionalize_control_flow.cc

@@ -21,13 +21,13 @@ limitations under the License.
 #include <unordered_set>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/jit/union_find.h"
 #include "tensorflow/compiler/tf2xla/dump_graph.h"
 #include "tensorflow/compiler/tf2xla/functionalize_cond.h"
 #include "tensorflow/compiler/tf2xla/functionalize_control_flow_util.h"
 #include "tensorflow/compiler/tf2xla/functionalize_while.h"
 #include "tensorflow/compiler/tf2xla/tf2xla_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/framework/graph_to_functiondef.h"

tensorflow/compiler/tf2xla/functionalize_while.cc

@@ -21,11 +21,11 @@ limitations under the License.
 #include <unordered_set>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/jit/union_find.h"
 #include "tensorflow/compiler/tf2xla/dump_graph.h"
 #include "tensorflow/compiler/tf2xla/functionalize_control_flow_util.h"
 #include "tensorflow/compiler/tf2xla/tf2xla_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/framework/graph_to_functiondef.h"
@@ -143,7 +143,7 @@ StatusOr<Node*> BuildArgNode(Graph* graph, DataType type, int index) {
 Status BuildLoopCondition(const Graph& graph, Frame* frame,
                           std::unique_ptr<Graph>* cond_output) {
   VLOG(2) << "Building loop condition for " << frame->name;
-  *cond_output = xla::MakeUnique<Graph>(graph.op_registry());
+  *cond_output = absl::make_unique<Graph>(graph.op_registry());
   Graph* output = cond_output->get();
 
   // Map from nodes in the original graph to the condition graph.
@@ -180,7 +180,7 @@ Status BuildLoopBody(const Graph& graph, Frame* frame,
                      DataTypeVector* arg_types,
                      std::unique_ptr<Graph>* body_output) {
   VLOG(2) << "Building loop body for " << frame->name;
-  *body_output = xla::MakeUnique<Graph>(graph.op_registry());
+  *body_output = absl::make_unique<Graph>(graph.op_registry());
   Graph* output = body_output->get();
 
   // Map from nodes in the original graph to the condition graph.

tensorflow/compiler/tf2xla/xla_compiler.cc

@@ -18,6 +18,7 @@ limitations under the License.
 #include <numeric>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/tf2xla/dump_graph.h"
 #include "tensorflow/compiler/tf2xla/functionalize_control_flow.h"
 #include "tensorflow/compiler/tf2xla/graph_compiler.h"
@@ -310,7 +311,7 @@ Status ExecuteGraph(XlaContext* xla_context, std::unique_ptr<Graph> graph,
   // unique_ptr so we can capture the cleanup status in the end.
   xla_context->Ref();
   Status status;
-  auto step_container = xla::MakeUnique<ScopedStepContainer>(
+  auto step_container = absl::make_unique<ScopedStepContainer>(
       step_id, [&status, device](const string& name) {
         status = device->resource_manager()->Cleanup(name);
       });

tensorflow/compiler/xla/BUILD

@@ -161,7 +161,6 @@ cc_library(
         "iterator_util.h",
         "map_util.h",
         "overflow_util.h",
-        "ptr_util.h",
         "util.h",
     ],
     visibility = ["//visibility:public"],
@@ -172,8 +171,8 @@ cc_library(
         ":types",
         ":xla_data_proto",
         "//tensorflow/core:lib",
-        "//tensorflow/core:ptr_util",
         "@com_google_absl//absl/algorithm:container",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -211,6 +210,7 @@ tf_cc_test(
         ":test",
         ":util",
         "//tensorflow/core:test_main",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -298,6 +298,7 @@ cc_library(
         ":util",
         ":xla_data_proto",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -316,6 +317,7 @@ tf_cc_test(
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -336,6 +338,7 @@ cc_library(
         ":util",
         ":xla_data_proto",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -406,8 +409,8 @@ cc_library(
     deps = [
         ":array",
         ":types",
-        ":util",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -490,6 +493,7 @@ cc_library(
         ":util",
         ":xla_data_proto",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -522,6 +526,7 @@ cc_library(
         ":xla_data_proto",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -577,10 +582,10 @@ cc_library(
     deps = [
         ":shape_util",
         ":status_macros",
-        ":util",
         ":xla_data_proto",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -594,6 +599,7 @@ tf_cc_test(
         ":xla_data_proto",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -643,6 +649,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:shape_inference",
         "//tensorflow/compiler/xla/service/cpu:runtime_single_threaded_matmul",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -661,6 +668,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/client:padding",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test_main",
+        "@com_google_absl//absl/memory",
     ],
 )
 

tensorflow/compiler/xla/array2d.h

@@ -24,8 +24,8 @@ limitations under the License.
 #include <random>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/array.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/core/lib/core/bits.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -101,7 +101,7 @@ class Array2D : public Array<T> {
 template <typename NativeT = float>
 std::unique_ptr<Array2D<NativeT>> MakeLinspaceArray2D(double from, double to,
                                                       int64 n1, int64 n2) {
-  auto array = MakeUnique<Array2D<NativeT>>(n1, n2);
+  auto array = absl::make_unique<Array2D<NativeT>>(n1, n2);
   int64 count = n1 * n2;
   NativeT step =
       static_cast<NativeT>((count > 1) ? (to - from) / (count - 1) : 0);

tensorflow/compiler/xla/client/BUILD

@@ -71,12 +71,12 @@ cc_library(
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
-        "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla:xla_proto",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:hlo_proto",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -104,7 +104,6 @@ cc_library(
         "//tensorflow/compiler/xla:executable_run_options",
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
-        "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/service:backend",
         "//tensorflow/compiler/xla/service:compiler",
@@ -117,6 +116,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:stream_pool",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
         "@llvm//:support",
     ],
 )
@@ -130,11 +130,11 @@ cc_library(
         ":xla_computation",
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
-        "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/service:compile_only_service",
         "//tensorflow/compiler/xla/service:compiler",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
         "@llvm//:support",
     ],
 )
@@ -159,6 +159,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:platform_util",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -186,6 +187,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/service:hlo_proto",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -212,6 +214,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:shape_inference",
         "//tensorflow/core:lib",
         "@com_google_absl//absl/algorithm:container",
+        "@com_google_absl//absl/memory",
     ],
 )
 

tensorflow/compiler/xla/client/client.cc

@@ -18,11 +18,11 @@ limitations under the License.
 #include <string>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/client/xla_computation.h"
 #include "tensorflow/compiler/xla/execution_options_util.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/core/lib/core/errors.h"
@@ -89,7 +89,7 @@ StatusOr<std::unique_ptr<GlobalData>> Client::TransferToServer(
         "TransferToServer request");
   }
 
-  return MakeUnique<GlobalData>(stub_, response.data());
+  return absl::make_unique<GlobalData>(stub_, response.data());
 }
 
 Status Client::TransferToInfeed(const LiteralSlice& literal, int64 replica_id,
@@ -248,7 +248,7 @@ StatusOr<std::unique_ptr<GlobalData>> Client::Execute(
     }
   }
 
-  return MakeUnique<GlobalData>(stub_, response.output());
+  return absl::make_unique<GlobalData>(stub_, response.output());
 }
 
 StatusOr<std::vector<std::unique_ptr<GlobalData>>> Client::ExecuteParallel(
@@ -278,7 +278,7 @@ StatusOr<std::vector<std::unique_ptr<GlobalData>>> Client::ExecuteParallel(
   std::vector<std::unique_ptr<GlobalData>> outputs;
   for (size_t i = 0; i < computations.size(); ++i) {
     outputs.push_back(
-        MakeUnique<GlobalData>(stub_, response.responses(i).output()));
+        absl::make_unique<GlobalData>(stub_, response.responses(i).output()));
     if (computations[i].execution_profile != nullptr) {
       *computations[i].execution_profile = response.responses(i).profile();
     }
@@ -340,7 +340,7 @@ StatusOr<std::vector<std::unique_ptr<GlobalData>>> Client::DeconstructTuple(
 
   std::vector<std::unique_ptr<GlobalData>> handles;
   for (auto& handle : response.element_handles()) {
-    handles.push_back(MakeUnique<GlobalData>(stub_, handle));
+    handles.push_back(absl::make_unique<GlobalData>(stub_, handle));
   }
   return std::move(handles);
 }
@@ -369,7 +369,7 @@ StatusOr<ComputationStats> Client::GetComputationStats(
 StatusOr<std::unique_ptr<ProgramShape>> Client::GetComputationShape(
     const XlaComputation& computation) {
   TF_ASSIGN_OR_RETURN(const auto& result, computation.GetProgramShape());
-  return MakeUnique<ProgramShape>(result);
+  return absl::make_unique<ProgramShape>(result);
 }
 
 StatusOr<Shape> Client::GetShape(const GlobalData& data) {

tensorflow/compiler/xla/client/client_library.cc

@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/client_library.h"
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/backend.h"
 #include "tensorflow/compiler/xla/service/platform_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -94,10 +95,10 @@ ClientLibrary::~ClientLibrary() = default;
   service_options.set_intra_op_parallelism_threads(
       options.intra_op_parallelism_threads());
 
-  auto instance = MakeUnique<LocalInstance>();
+  auto instance = absl::make_unique<LocalInstance>();
   TF_ASSIGN_OR_RETURN(instance->service,
                       LocalService::NewService(service_options));
-  instance->client = MakeUnique<LocalClient>(instance->service.get());
+  instance->client = absl::make_unique<LocalClient>(instance->service.get());
   LocalClient* cl = instance->client.get();
 
   client_library.local_instances_.insert(
@@ -134,10 +135,11 @@ ClientLibrary::GetOrCreateCompileOnlyClient(se::Platform* platform) {
     return it->second->client.get();
   }
 
-  auto instance = MakeUnique<CompileOnlyInstance>();
+  auto instance = absl::make_unique<CompileOnlyInstance>();
   TF_ASSIGN_OR_RETURN(instance->service,
                       CompileOnlyService::NewService(platform));
-  instance->client = MakeUnique<CompileOnlyClient>(instance->service.get());
+  instance->client =
+      absl::make_unique<CompileOnlyClient>(instance->service.get());
   CompileOnlyClient* cl = instance->client.get();
 
   client_library.compile_only_instances_.insert(

tensorflow/compiler/xla/client/compile_only_client.cc

@@ -15,8 +15,8 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/compile_only_client.h"
 
+#include "absl/memory/memory.h"
 #include "llvm/ADT/Triple.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 
 namespace xla {

tensorflow/compiler/xla/client/local_client.cc

@@ -17,9 +17,9 @@ limitations under the License.
 
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "llvm/ADT/Triple.h"
 #include "tensorflow/compiler/xla/client/xla_computation.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/backend.h"
 #include "tensorflow/compiler/xla/service/service_executable_run_options.h"
 #include "tensorflow/compiler/xla/service/source_map_util.h"
@@ -257,7 +257,7 @@ StatusOr<std::unique_ptr<LocalExecutable>> LocalClient::Compile(
   TF_ASSIGN_OR_RETURN(std::unique_ptr<Executable> executable,
                       local_service_->CompileExecutable(
                           computation, argument_layouts, updated_options));
-  return WrapUnique(new LocalExecutable(std::move(executable),
+  return absl::WrapUnique(new LocalExecutable(std::move(executable),
                                               local_service_->mutable_backend(),
                                               updated_options));
 }

tensorflow/compiler/xla/client/xla_builder.cc

@@ -22,6 +22,7 @@ limitations under the License.
 #include <utility>
 
 #include "absl/algorithm/container.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/client/sharding_builder.h"
 #include "tensorflow/compiler/xla/client/xla_computation.h"
 #include "tensorflow/compiler/xla/execution_options_util.h"
@@ -2297,7 +2298,7 @@ StatusOr<XlaComputation> XlaBuilder::BuildConstantSubGraph(
 
 std::unique_ptr<XlaBuilder> XlaBuilder::CreateSubBuilder(
     const string& computation_name) {
-  auto sub_builder = MakeUnique<XlaBuilder>(computation_name);
+  auto sub_builder = absl::make_unique<XlaBuilder>(computation_name);
   sub_builder->parent_builder_ = this;
   sub_builder->die_immediately_on_error_ = this->die_immediately_on_error_;
   return sub_builder;

tensorflow/compiler/xla/client/xla_computation.cc

@@ -17,7 +17,7 @@ limitations under the License.
 
 #include <utility>
 
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/util.h"
 
@@ -32,7 +32,7 @@ StatusOr<std::unique_ptr<HloSnapshot>> XlaComputation::Snapshot() const {
   if (IsNull()) {
     return InvalidArgument("Computation is invalid.");
   }
-  auto session = MakeUnique<HloSnapshot>();
+  auto session = absl::make_unique<HloSnapshot>();
   *session->mutable_hlo()->mutable_hlo_module() = proto_;
   return std::move(session);
 }

tensorflow/compiler/xla/iterator_util_test.cc

@@ -18,7 +18,7 @@ limitations under the License.
 #include <algorithm>
 #include <list>
 
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/test.h"
 
 namespace xla {
@@ -27,7 +27,7 @@ namespace {
 TEST(UnwrappingIteratorTest, Simple) {
   std::vector<std::unique_ptr<int>> v;
   for (int i = 0; i < 3; ++i) {
-    v.push_back(MakeUnique<int>(i));
+    v.push_back(absl::make_unique<int>(i));
   }
   int i = 0;
   for (auto iter = MakeUnwrappingIterator(v.begin());
@@ -51,7 +51,7 @@ TEST(UnwrappingIteratorTest, PostincrementOperator) {
 TEST(UnwrappingIteratorTest, StdFind) {
   std::list<std::unique_ptr<int>> l;
   for (int i = 0; i < 3; ++i) {
-    l.push_back(MakeUnique<int>(i));
+    l.push_back(absl::make_unique<int>(i));
   }
   EXPECT_EQ(l.begin()->get(),
             *std::find(MakeUnwrappingIterator(l.begin()),

tensorflow/compiler/xla/literal.cc

@@ -22,6 +22,7 @@ limitations under the License.
 #include <numeric>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/index_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -134,7 +135,7 @@ void Literal::SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays) {
 
 Literal::Literal(const Shape& shape, bool allocate_arrays)
     : MutableLiteralBase() {
-  shape_ = MakeUnique<Shape>(shape);
+  shape_ = absl::make_unique<Shape>(shape);
   CHECK(LayoutUtil::HasLayout(*shape_));
   root_piece_ = new Piece();
   root_piece_->set_subshape(shape_.get());
@@ -175,7 +176,7 @@ Literal& Literal::operator=(Literal&& other) {
 }
 
 std::unique_ptr<Literal> LiteralBase::CreateFromShape(const Shape& shape) {
-  auto literal = MakeUnique<Literal>(shape);
+  auto literal = absl::make_unique<Literal>(shape);
   literal->root_piece_->ForEachMutableSubpiece(
       [&](const ShapeIndex& index, Piece* piece) {
         if (ShapeUtil::IsArray(piece->subshape())) {
@@ -289,7 +290,7 @@ MutableLiteralBase::CreateFromProto(const LiteralProto& proto) {
     return InvalidArgument("LiteralProto has no layout");
   }
 
-  auto literal = MakeUnique<Literal>(proto.shape());
+  auto literal = absl::make_unique<Literal>(proto.shape());
 
   TF_RETURN_IF_ERROR(literal->root_piece_->ForEachMutableSubpieceWithStatus(
       [&](const ShapeIndex& index, Piece* piece) {
@@ -479,7 +480,7 @@ Status Literal::MoveFrom(Literal&& src_literal,
         dest_piece.set_sparse_indices(src_piece.sparse_indices());
       });
 
-  src_literal.shape_ = MakeUnique<Shape>(ShapeUtil::MakeNil());
+  src_literal.shape_ = absl::make_unique<Shape>(ShapeUtil::MakeNil());
   delete src_literal.root_piece_;
   src_literal.root_piece_ = new LiteralBase::Piece();
   src_literal.root_piece_->set_subshape(src_literal.shape_.get());
@@ -566,7 +567,7 @@ std::unique_ptr<Literal> LiteralBase::Relayout(
   Shape* subshape = ShapeUtil::GetMutableSubshape(&new_shape, shape_index);
   TF_CHECK_OK(LayoutUtil::ValidateLayoutForShape(new_layout, *subshape));
   *subshape->mutable_layout() = new_layout;
-  auto result = MakeUnique<Literal>(new_shape);
+  auto result = absl::make_unique<Literal>(new_shape);
   TF_CHECK_OK(result->CopyFrom(*this));
   return result;
 }
@@ -602,7 +603,7 @@ StatusOr<std::unique_ptr<Literal>> LiteralBase::Broadcast(
                  result_shape.dimensions(dimensions[i]));
   }
 
-  std::unique_ptr<Literal> result = MakeUnique<Literal>(result_shape);
+  std::unique_ptr<Literal> result = absl::make_unique<Literal>(result_shape);
 
   // scratch_source_index is temporary storage space for the computed index into
   // the input literal.  We put it here to avoid allocating an std::vector in
@@ -691,7 +692,7 @@ std::unique_ptr<Literal> LiteralBase::Transpose(
   for (auto index : LayoutUtil::MinorToMajor(shape())) {
     layout->add_minor_to_major(inverse_permutation[index]);
   }
-  auto new_literal = MakeUnique<Literal>(permuted_shape);
+  auto new_literal = absl::make_unique<Literal>(permuted_shape);
   DCHECK_EQ(ShapeUtil::ByteSizeOf(new_literal->shape()),
             ShapeUtil::ByteSizeOf(shape()));
   std::memcpy(new_literal->untyped_data(), untyped_data(), size_bytes());
@@ -702,7 +703,7 @@ template <typename NativeT>
 std::unique_ptr<Literal> LiteralBase::SliceInternal(
     const Shape& result_shape,
     tensorflow::gtl::ArraySlice<int64> start_indices) const {
-  auto result_literal = MakeUnique<Literal>(result_shape);
+  auto result_literal = absl::make_unique<Literal>(result_shape);
   DimensionVector new_indices(ShapeUtil::Rank(result_shape));
   result_literal->EachCell<NativeT>(
       [&](tensorflow::gtl::ArraySlice<int64> indices, NativeT /*value*/) {
@@ -756,7 +757,7 @@ Literal LiteralBase::Clone() const {
 }
 
 std::unique_ptr<Literal> LiteralBase::CloneToUnique() const {
-  auto result = MakeUnique<Literal>(shape());
+  auto result = absl::make_unique<Literal>(shape());
   TF_CHECK_OK(result->CopyFrom(*this));
   return result;
 }
@@ -1203,7 +1204,7 @@ template <typename NativeSrcT, typename NativeDestT, typename ConverterType>
 std::unique_ptr<Literal> ConvertBetweenNativeTypesWithConverter(
     const LiteralBase& src_literal, const ConverterType& converter) {
   CHECK(ShapeUtil::IsArray(src_literal.shape()));
-  auto result_literal = MakeUnique<Literal>(ShapeUtil::ChangeElementType(
+  auto result_literal = absl::make_unique<Literal>(ShapeUtil::ChangeElementType(
       src_literal.shape(),
       primitive_util::NativeToPrimitiveType<NativeDestT>()));
   auto src_data = src_literal.data<NativeSrcT>();
@@ -1249,7 +1250,7 @@ BitcastBetweenNativeTypes(const LiteralBase& src_literal) {
 template <PrimitiveType primitive_src_type>
 std::unique_ptr<Literal> ConvertToC64(const LiteralBase& src_literal) {
   CHECK(ShapeUtil::IsArray(src_literal.shape()));
-  auto result_literal = MakeUnique<Literal>(
+  auto result_literal = absl::make_unique<Literal>(
       ShapeUtil::ChangeElementType(src_literal.shape(), C64));
   using NativeSrcT =
       typename primitive_util::PrimitiveTypeToNative<primitive_src_type>::type;
@@ -1396,7 +1397,7 @@ StatusOr<std::unique_ptr<Literal>> LiteralBase::ConvertToShape(
         element.ConvertToShape(ShapeUtil::GetSubshape(dest_shape, {i})));
     elements.push_back(std::move(*new_element));
   }
-  auto converted = MakeUnique<Literal>();
+  auto converted = absl::make_unique<Literal>();
   *converted = MutableLiteralBase::MoveIntoTuple(&elements);
   return std::move(converted);
 }
@@ -1956,7 +1957,7 @@ MutableLiteralBase::~MutableLiteralBase() {}
 MutableBorrowingLiteral::MutableBorrowingLiteral(
     const MutableBorrowingLiteral& literal)
     : MutableLiteralBase() {
-  shape_ = MakeUnique<Shape>(literal.shape());
+  shape_ = absl::make_unique<Shape>(literal.shape());
   CHECK(LayoutUtil::HasLayout(*shape_));
 
   root_piece_ = new Piece();
@@ -1967,7 +1968,7 @@ MutableBorrowingLiteral::MutableBorrowingLiteral(
 
 MutableBorrowingLiteral& MutableBorrowingLiteral::operator=(
     const MutableBorrowingLiteral& literal) {
-  shape_ = MakeUnique<Shape>(literal.shape());
+  shape_ = absl::make_unique<Shape>(literal.shape());
   CHECK(LayoutUtil::HasLayout(*shape_));
 
   root_piece_ = new Piece();
@@ -1981,7 +1982,7 @@ MutableBorrowingLiteral& MutableBorrowingLiteral::operator=(
 MutableBorrowingLiteral::MutableBorrowingLiteral(
     const MutableLiteralBase& literal)
     : MutableLiteralBase() {
-  shape_ = MakeUnique<Shape>(literal.shape());
+  shape_ = absl::make_unique<Shape>(literal.shape());
   CHECK(LayoutUtil::HasLayout(*shape_));
 
   root_piece_ = new Piece();
@@ -1992,7 +1993,7 @@ MutableBorrowingLiteral::MutableBorrowingLiteral(
 
 MutableBorrowingLiteral::MutableBorrowingLiteral(MutableLiteralBase* literal)
     : MutableLiteralBase() {
-  shape_ = MakeUnique<Shape>(literal->shape());
+  shape_ = absl::make_unique<Shape>(literal->shape());
   CHECK(LayoutUtil::HasLayout(*shape_));
 
   root_piece_ = new Piece();
@@ -2004,7 +2005,7 @@ MutableBorrowingLiteral::MutableBorrowingLiteral(MutableLiteralBase* literal)
 MutableBorrowingLiteral::MutableBorrowingLiteral(
     MutableBorrowingLiteral literal, const ShapeIndex& view_root)
     : MutableLiteralBase() {
-  shape_ = MakeUnique<Shape>(literal.piece(view_root).subshape());
+  shape_ = absl::make_unique<Shape>(literal.piece(view_root).subshape());
   CHECK(LayoutUtil::HasLayout(*shape_));
 
   root_piece_ = new Piece();
@@ -2016,7 +2017,7 @@ MutableBorrowingLiteral::MutableBorrowingLiteral(
 MutableBorrowingLiteral::MutableBorrowingLiteral(const char* src_buf_ptr,
                                                  const Shape& shape)
     : MutableLiteralBase() {
-  shape_ = MakeUnique<Shape>(shape);
+  shape_ = absl::make_unique<Shape>(shape);
   CHECK(LayoutUtil::HasLayout(*shape_));
   CHECK(!ShapeUtil::IsTuple(*shape_));
 
@@ -2061,7 +2062,7 @@ void BorrowingLiteral::BuildPieceSubtree(const Shape& shape, Piece* piece) {
 }
 
 BorrowingLiteral::BorrowingLiteral(const char* src_buf_ptr, const Shape& shape)
-    : LiteralBase(), shape_(MakeUnique<Shape>(shape)) {
+    : LiteralBase(), shape_(absl::make_unique<Shape>(shape)) {
   CHECK(ShapeUtil::IsArray(*shape_));
   CHECK(LayoutUtil::HasLayout(*shape_));
 
@@ -2072,7 +2073,7 @@ BorrowingLiteral::BorrowingLiteral(const char* src_buf_ptr, const Shape& shape)
 
 BorrowingLiteral::BorrowingLiteral(
     tensorflow::gtl::ArraySlice<const char*> src_buf_ptrs, const Shape& shape)
-    : LiteralBase(), shape_(MakeUnique<Shape>(shape)) {
+    : LiteralBase(), shape_(absl::make_unique<Shape>(shape)) {
   CHECK(ShapeUtil::IsTuple(*shape_));
   CHECK(!ShapeUtil::IsNestedTuple(*shape_));
   CHECK_EQ(src_buf_ptrs.size(), ShapeUtil::TupleElementCount(*shape_));

tensorflow/compiler/xla/literal.h

@@ -25,13 +25,13 @@ limitations under the License.
 #include <type_traits>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/array2d.h"
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/array4d.h"
 #include "tensorflow/compiler/xla/index_util.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/sparse_index_array.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -312,7 +312,7 @@ class LiteralBase {
   // Note: It's an antipattern to use this method then immediately call
   // MutableLiteralBase::Populate on the result (since that results in zero
   // initialization, then reinitialization. Conside if a call to
-  // MakeUnique<Literal>(shape), followed by the call to
+  // absl::make_unique<Literal>(shape), followed by the call to
   // MutableLiteralBase::Populate can be used instead.
   static std::unique_ptr<Literal> CreateFromShape(const Shape& shape);
 
@@ -1154,8 +1154,8 @@ std::unique_ptr<Literal> LiteralBase::Replicate(int64 times) const {
   for (int64 bound : shape().dimensions()) {
     bounds.push_back(bound);
   }
-  auto literal =
+  auto literal = absl::make_unique<Literal>(
-      MakeUnique<Literal>(ShapeUtil::MakeShape(shape().element_type(), bounds));
+      ShapeUtil::MakeShape(shape().element_type(), bounds));
   int64 elements = ShapeUtil::ElementsIn(literal->shape());
   if (elements == 0) {
     return literal;

tensorflow/compiler/xla/literal_test.cc

@@ -17,6 +17,7 @@ limitations under the License.
 
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/tf2xla/shape_util.h"
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/array4d.h"
@@ -355,15 +356,15 @@ TEST_F(LiteralUtilTest, TokenEquality) {
 
 TEST_F(LiteralUtilTest, DifferentLayoutEquality) {
   // Test equality with literals which have different layouts.
-  auto colmajor =
+  auto colmajor = absl::make_unique<Literal>(
-      MakeUnique<Literal>(ShapeUtil::MakeShapeWithLayout(F32, {2, 2}, {0, 1}));
+      ShapeUtil::MakeShapeWithLayout(F32, {2, 2}, {0, 1}));
   colmajor->Set<float>({0, 0}, 1.0);
   colmajor->Set<float>({0, 1}, 2.0);
   colmajor->Set<float>({1, 0}, 3.0);
   colmajor->Set<float>({1, 1}, 4.0);
 
-  auto rowmajor =
+  auto rowmajor = absl::make_unique<Literal>(
-      MakeUnique<Literal>(ShapeUtil::MakeShapeWithLayout(F32, {2, 2}, {1, 0}));
+      ShapeUtil::MakeShapeWithLayout(F32, {2, 2}, {1, 0}));
   rowmajor->Set<float>({0, 0}, 1.0);
   rowmajor->Set<float>({0, 1}, 2.0);
   rowmajor->Set<float>({1, 0}, 3.0);
@@ -1089,7 +1090,7 @@ TEST_F(LiteralUtilTest, Populate) {
     Shape shape = ShapeUtil::MakeShapeWithLayout(
         primitive_util::NativeToPrimitiveType<uint32>(), data.dimensions,
         data.layout);
-    auto literal = MakeUnique<Literal>(shape);
+    auto literal = absl::make_unique<Literal>(shape);
     auto generator = [&](ArraySlice<int64> indexes) -> uint32 {
       // Offsets from linear index just to avoid R0 literals to be initialized
       // with zero.
@@ -1131,7 +1132,7 @@ TEST_F(LiteralUtilTest, PopulateParallel) {
     Shape shape = ShapeUtil::MakeShapeWithLayout(
         primitive_util::NativeToPrimitiveType<uint32>(), data.dimensions,
         data.layout);
-    auto literal = MakeUnique<Literal>(shape);
+    auto literal = absl::make_unique<Literal>(shape);
     auto generator = [&](ArraySlice<int64> indexes) -> uint32 {
       // Offsets from linear index just to avoid R0 literals to be initialized
       // with zero.

tensorflow/compiler/xla/literal_util.cc

@@ -22,6 +22,7 @@ limitations under the License.
 #include <numeric>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/index_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -57,7 +58,7 @@ std::unique_ptr<Literal> ConvertType(LiteralSlice literal) {
               primitive_util::NativeToPrimitiveType<ToNativeT>());
         }
       });
-  auto result = MakeUnique<Literal>(result_shape);
+  auto result = absl::make_unique<Literal>(result_shape);
 
   // Then copy over the data from 'literal' converting FromNativeT values to
   // ToNativeT values as necessary.
@@ -102,7 +103,7 @@ std::unique_ptr<Literal> ConvertType(LiteralSlice literal) {
 }
 
 /* static */ std::unique_ptr<Literal> LiteralUtil::CreateToken() {
-  return MakeUnique<Literal>(ShapeUtil::MakeTokenShape());
+  return absl::make_unique<Literal>(ShapeUtil::MakeTokenShape());
 }
 
 /* static */ Literal LiteralUtil::Zero(PrimitiveType primitive_type) {
@@ -279,7 +280,7 @@ std::unique_ptr<Literal> ConvertType(LiteralSlice literal) {
 
 /* static */ std::unique_ptr<Literal> LiteralUtil::CreateR1(
     const tensorflow::core::Bitmap& values) {
-  auto literal = MakeUnique<Literal>(
+  auto literal = absl::make_unique<Literal>(
       ShapeUtil::MakeShape(PRED, {static_cast<int64>(values.bits())}));
   literal->PopulateR1(values);
   return literal;
@@ -287,7 +288,7 @@ std::unique_ptr<Literal> ConvertType(LiteralSlice literal) {
 
 /* static */ std::unique_ptr<Literal> LiteralUtil::CreateR1U8(
     tensorflow::StringPiece value) {
-  auto literal = MakeUnique<Literal>(
+  auto literal = absl::make_unique<Literal>(
       ShapeUtil::MakeShape(U8, {static_cast<int64>(value.size())}));
   for (int i = 0; i < value.size(); ++i) {
     literal->Set<uint8>({i}, value[i]);
@@ -312,7 +313,7 @@ std::unique_ptr<Literal> ConvertType(LiteralSlice literal) {
   CHECK_EQ(ShapeUtil::ElementsIn(literal.shape()), new_num_elements);
   CHECK_EQ(new_dimensions.size(), minor_to_major.size());
 
-  auto new_literal = MakeUnique<Literal>(
+  auto new_literal = absl::make_unique<Literal>(
       ShapeUtil::MakeShape(literal.shape().element_type(), new_dimensions));
 
   // Create a new shape with the given minor-to-major layout. This shape is used
@@ -436,7 +437,8 @@ std::unique_ptr<Literal> ConvertType(LiteralSlice literal) {
   for (const auto* element : elements) {
     element_shapes.push_back(element->shape());
   }
-  auto literal = MakeUnique<Literal>(ShapeUtil::MakeTupleShape(element_shapes));
+  auto literal =
+      absl::make_unique<Literal>(ShapeUtil::MakeTupleShape(element_shapes));
   for (int i = 0; i < elements.size(); ++i) {
     TF_CHECK_OK(literal->CopyFrom(*elements[i], /*dest_shape_index=*/{i}));
   }
@@ -449,7 +451,8 @@ std::unique_ptr<Literal> ConvertType(LiteralSlice literal) {
   for (const auto& element : elements) {
     element_shapes.push_back(element.shape());
   }
-  auto literal = MakeUnique<Literal>(ShapeUtil::MakeTupleShape(element_shapes));
+  auto literal =
+      absl::make_unique<Literal>(ShapeUtil::MakeTupleShape(element_shapes));
   for (int i = 0; i < elements.size(); ++i) {
     TF_CHECK_OK(literal->CopyFrom(elements[i], /*dest_shape_index=*/{i}));
   }
@@ -463,7 +466,8 @@ std::unique_ptr<Literal> ConvertType(LiteralSlice literal) {
   for (const auto& element : elements) {
     element_shapes.push_back(element->shape());
   }
-  auto literal = MakeUnique<Literal>(ShapeUtil::MakeTupleShape(element_shapes));
+  auto literal =
+      absl::make_unique<Literal>(ShapeUtil::MakeTupleShape(element_shapes));
   for (int64 i = 0; i < elements.size(); ++i) {
     TF_CHECK_OK(
         literal->MoveFrom(std::move(*elements[i]), /*dest_shape_index=*/{i}));

tensorflow/compiler/xla/literal_util.h

@@ -27,6 +27,7 @@ limitations under the License.
 #include <type_traits>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/array2d.h"
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/array4d.h"
@@ -34,7 +35,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/sparse_index_array.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -327,7 +327,7 @@ std::ostream& operator<<(std::ostream& out, const Literal& literal);
 
 template <typename NativeT>
 /* static */ std::unique_ptr<Literal> LiteralUtil::CreateR0(NativeT value) {
-  auto literal = MakeUnique<Literal>(ShapeUtil::MakeShape(
+  auto literal = absl::make_unique<Literal>(ShapeUtil::MakeShape(
       primitive_util::NativeToPrimitiveType<NativeT>(), {}));
   literal->Set({}, value);
   return literal;
@@ -336,7 +336,7 @@ template <typename NativeT>
 template <typename NativeT>
 /* static */ std::unique_ptr<Literal> LiteralUtil::CreateR1(
     tensorflow::gtl::ArraySlice<NativeT> values) {
-  auto literal = MakeUnique<Literal>(
+  auto literal = absl::make_unique<Literal>(
       ShapeUtil::MakeShape(primitive_util::NativeToPrimitiveType<NativeT>(),
                            {static_cast<int64>(values.size())}));
   literal->PopulateR1(values);
@@ -347,7 +347,7 @@ template <typename NativeT>
 /* static */ std::unique_ptr<Literal> LiteralUtil::CreateR2WithLayout(
     std::initializer_list<std::initializer_list<NativeT>> values,
     const Layout& layout) {
-  auto literal = MakeUnique<Literal>(ShapeUtil::MakeShapeWithLayout(
+  auto literal = absl::make_unique<Literal>(ShapeUtil::MakeShapeWithLayout(
       primitive_util::NativeToPrimitiveType<NativeT>(),
       {static_cast<int64>(values.size()),
        static_cast<int64>(values.begin()->size())},
@@ -433,7 +433,8 @@ template <typename NativeT>
   int64 rank = dimensions.size();
   CHECK_EQ(num_elements, indices.index_count());
   CHECK_EQ(rank, indices.rank());
-  auto literal = MakeUnique<Literal>(ShapeUtil::MakeShapeWithSparseLayout(
+  auto literal =
+      absl::make_unique<Literal>(ShapeUtil::MakeShapeWithSparseLayout(
           primitive_util::NativeToPrimitiveType<NativeT>(), dimensions,
           indices.max_indices()));
   literal->PopulateSparse(indices, values, sort);
@@ -451,7 +452,7 @@ template <typename NativeT>
 template <typename NativeT>
 /* static */ std::unique_ptr<Literal> LiteralUtil::CreateFromArrayWithLayout(
     const Array<NativeT>& values, const Layout& layout) {
-  auto literal = MakeUnique<Literal>(ShapeUtil::MakeShapeWithLayout(
+  auto literal = absl::make_unique<Literal>(ShapeUtil::MakeShapeWithLayout(
       primitive_util::NativeToPrimitiveType<NativeT>(), values.dimensions(),
       AsInt64Slice(layout.minor_to_major())));
   literal->PopulateFromArray(values);
@@ -571,7 +572,8 @@ template <typename NativeT>
 /* static */ std::unique_ptr<Literal>
 LiteralUtil::CreateFullWithDescendingLayout(
     tensorflow::gtl::ArraySlice<int64> dimensions, NativeT value) {
-  auto literal = MakeUnique<Literal>(ShapeUtil::MakeShapeWithDescendingLayout(
+  auto literal =
+      absl::make_unique<Literal>(ShapeUtil::MakeShapeWithDescendingLayout(
           primitive_util::NativeToPrimitiveType<NativeT>(), dimensions));
   literal->PopulateWithValue(value);
   return literal;
@@ -584,7 +586,7 @@ LiteralUtil::CreateRandomLiteral(
     const std::function<T(tensorflow::gtl::ArraySlice<int64>)>& generator) {
   using NativeT = typename primitive_util::PrimitiveTypeToNative<type>::type;
   TF_RET_CHECK(shape.element_type() == type);
-  auto literal = MakeUnique<Literal>(shape);
+  auto literal = absl::make_unique<Literal>(shape);
   TF_RETURN_IF_ERROR(literal.get()->Populate<NativeT>(
       [&](tensorflow::gtl::ArraySlice<int64> indexes) {
         return generator(indexes);

tensorflow/compiler/xla/packed_literal_reader.cc

@@ -19,9 +19,9 @@ limitations under the License.
 #include <string>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -57,7 +57,7 @@ StatusOr<std::unique_ptr<Literal>> PackedLiteralReader::Read(
         PrimitiveType_Name(shape.element_type()).c_str());
   }
 
-  auto result = MakeUnique<Literal>(literal_shape);
+  auto result = absl::make_unique<Literal>(literal_shape);
   result->PopulateWithValue(std::numeric_limits<float>::quiet_NaN());
 
   int64 elements = ShapeUtil::ElementsIn(shape);

tensorflow/compiler/xla/ptr_util.h

@@ -1,35 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_PTR_UTIL_H_
-#define TENSORFLOW_COMPILER_XLA_PTR_UTIL_H_
-
-// As this was moved to tensorflow/core/util, provide indirections here to
-// maintain current functionality of the library.
-
-#include <stddef.h>
-
-#include <memory>
-#include <type_traits>
-#include <utility>
-
-#include "tensorflow/core/util/ptr_util.h"
-
-namespace xla {
-using tensorflow::MakeUnique;
-using tensorflow::WrapUnique;
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_PTR_UTIL_H_

tensorflow/compiler/xla/python/BUILD

@@ -59,6 +59,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:shaped_buffer",
         "//tensorflow/core:framework_lite",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 

tensorflow/compiler/xla/python/local_computation_builder.cc

@@ -14,10 +14,10 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/python/local_computation_builder.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/client/lib/math.h"
 #include "tensorflow/compiler/xla/client/xla_builder.h"
 #include "tensorflow/compiler/xla/executable_run_options.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/platform/thread_annotations.h"
 

tensorflow/compiler/xla/reference_util.cc

@@ -18,6 +18,7 @@ limitations under the License.
 #include <array>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/client/xla_builder.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h"
@@ -43,7 +44,7 @@ std::unique_ptr<Array2D<T>> MatmulArray2DImpl(
   int m = lhs.height();
   int n = rhs.width();
   int k = lhs.width();
-  auto result = MakeUnique<Array2D<T>>(m, n);
+  auto result = absl::make_unique<Array2D<T>>(m, n);
   // Because Eigen is a header-oriented library, make sure that the Eigen code
   // is the same as the code used by the CPU backend (otherwise the linker will
   // randomly pick *some* definition).
@@ -77,7 +78,8 @@ std::unique_ptr<Array2D<T>> MatmulArray2DImpl(
 
 /* static */ std::unique_ptr<Array2D<double>> ReferenceUtil::Array2DF32ToF64(
     const Array2D<float>& input) {
-  auto result = MakeUnique<Array2D<double>>(input.height(), input.width());
+  auto result =
+      absl::make_unique<Array2D<double>>(input.height(), input.width());
   for (int64 rowno = 0; rowno < input.height(); ++rowno) {
     for (int64 colno = 0; colno < input.height(); ++colno) {
       (*result)(rowno, colno) = input(rowno, colno);
@@ -126,8 +128,8 @@ ReferenceUtil::ConvArray3DGeneralDimensionsDilated(
       a4dlhs, a4drhs, {kernel_stride, 1}, padding, {lhs_dilation, 1},
       {rhs_dilation, 1}, dnums2d);
 
-  auto convr3 = MakeUnique<Array3D<float>>(convr4->planes(), convr4->depth(),
+  auto convr3 = absl::make_unique<Array3D<float>>(
-                                           convr4->height());
+      convr4->planes(), convr4->depth(), convr4->height());
   convr4->Each(
       [&](tensorflow::gtl::ArraySlice<int64> indices, float* value_ptr) {
         CHECK_EQ(indices[3], 0);
@@ -201,7 +203,7 @@ ReferenceUtil::ReduceWindow1DGeneric(
         window_util::StridedBound(padded_width, window[i], stride[i]);
     pad_low[i] = padding[i].first;
   }
-  auto result = MakeUnique<std::vector<float>>(window_counts[0]);
+  auto result = absl::make_unique<std::vector<float>>(window_counts[0]);
 
   // Do a full 1D reduce window.
   for (int64 i0 = 0; i0 < window_counts[0]; ++i0) {
@@ -247,7 +249,8 @@ ReferenceUtil::ReduceWindow2DGeneric(
         window_util::StridedBound(padded_width, window[i], stride[i]);
     pad_low[i] = padding[i].first;
   }
-  auto result = MakeUnique<Array2D<float>>(window_counts[0], window_counts[1]);
+  auto result =
+      absl::make_unique<Array2D<float>>(window_counts[0], window_counts[1]);
 
   // Do a full 2D reduce window.
   for (int64 i0 = 0; i0 < window_counts[0]; ++i0) {
@@ -296,8 +299,8 @@ ReferenceUtil::ReduceWindow2DGeneric(
         WindowCount(dim_lengths[i], window[i], stride[i], padding);
     pad_low[i] = padding_both[i].first;
   }
-  auto result = MakeUnique<Array3D<float>>(window_counts[0], window_counts[1],
+  auto result = absl::make_unique<Array3D<float>>(
-                                           window_counts[2]);
+      window_counts[0], window_counts[1], window_counts[2]);
 
   for (int64 i0 = 0; i0 < window_counts[0]; ++i0) {
     for (int64 i1 = 0; i1 < window_counts[1]; ++i1) {
@@ -358,8 +361,8 @@ ReferenceUtil::ReduceWindow4DGeneric(
         window_util::StridedBound(padded_width, window[i], stride[i]);
     pad_low[i] = padding[i].first;
   }
-  auto result = MakeUnique<Array4D<float>>(window_counts[0], window_counts[1],
+  auto result = absl::make_unique<Array4D<float>>(
-                                           window_counts[2], window_counts[3]);
+      window_counts[0], window_counts[1], window_counts[2], window_counts[3]);
   // Do a full 4D reduce window.
   for (int64 i0 = 0; i0 < window_counts[0]; ++i0) {
     for (int64 i1 = 0; i1 < window_counts[1]; ++i1) {
@@ -426,7 +429,7 @@ ReferenceUtil::SelectAndScatter4DGePlus(
     const tensorflow::gtl::ArraySlice<int64>& window,
     const tensorflow::gtl::ArraySlice<int64>& stride, bool same_padding) {
   Padding padding = same_padding ? Padding::kSame : Padding::kValid;
-  auto result = MakeUnique<Array4D<float>>(operand.n1(), operand.n2(),
+  auto result = absl::make_unique<Array4D<float>>(operand.n1(), operand.n2(),
                                                   operand.n3(), operand.n4());
   std::vector<int64> dim_lengths{operand.n1(), operand.n2(), operand.n3(),
                                  operand.n4()};
@@ -583,7 +586,7 @@ ReferenceUtil::ConvArray4DGeneralDimensionsDilated(
 
   CHECK_EQ(ShapeUtil::Rank(result_literal->shape()), 4);
   auto result =
-      MakeUnique<Array4D<float>>(result_literal->shape().dimensions(0),
+      absl::make_unique<Array4D<float>>(result_literal->shape().dimensions(0),
                                         result_literal->shape().dimensions(1),
                                         result_literal->shape().dimensions(2),
                                         result_literal->shape().dimensions(3));
@@ -601,7 +604,7 @@ ReferenceUtil::ReduceToColArray2D(
     const std::function<float(float, float)>& reduce_function) {
   int64 rows = matrix.height();
   int64 cols = matrix.width();
-  auto result = MakeUnique<std::vector<float>>();
+  auto result = absl::make_unique<std::vector<float>>();
   for (int64 i = 0; i < rows; ++i) {
     float acc = init;
     for (int64 j = 0; j < cols; ++j) {
@@ -618,7 +621,7 @@ ReferenceUtil::ReduceToRowArray2D(
     const std::function<float(float, float)>& reduce_function) {
   int64 rows = matrix.height();
   int64 cols = matrix.width();
-  auto result = MakeUnique<std::vector<float>>();
+  auto result = absl::make_unique<std::vector<float>>();
   for (int64 i = 0; i < cols; ++i) {
     float acc = init;
     for (int64 j = 0; j < rows; ++j) {
@@ -674,8 +677,8 @@ ReferenceUtil::ReduceToRowArray2D(
 /* static */ std::unique_ptr<Array4D<float>> ReferenceUtil::Broadcast1DTo4D(
     const std::vector<float>& array, const std::vector<int64>& bounds,
     int64 broadcast_from_dim) {
-  auto result =
+  auto result = absl::make_unique<Array4D<float>>(bounds[0], bounds[1],
-      MakeUnique<Array4D<float>>(bounds[0], bounds[1], bounds[2], bounds[3]);
+                                                  bounds[2], bounds[3]);
   for (int64 i = 0; i < result->n1(); ++i) {
     for (int64 j = 0; j < result->n2(); ++j) {
       for (int64 k = 0; k < result->n3(); ++k) {
@@ -710,7 +713,7 @@ ReferenceUtil::ReduceToRowArray2D(
   CHECK_EQ(dims.size(), 1);
   int64 rows = dims[0] == 0 ? array.n2() : array.n1();
   int64 cols = dims[0] == 2 ? array.n2() : array.n3();
-  auto result = MakeUnique<Array2D<float>>(rows, cols);
+  auto result = absl::make_unique<Array2D<float>>(rows, cols);
   result->Fill(init);
   for (int i0 = 0; i0 < array.n1(); ++i0) {
     for (int i1 = 0; i1 < array.n2(); ++i1) {
@@ -730,7 +733,7 @@ ReferenceUtil::ReduceToRowArray2D(
     const std::function<float(float)>& map_function) {
   int64 rows = matrix.height();
   int64 cols = matrix.width();
-  auto result = MakeUnique<Array2D<float>>(rows, cols);
+  auto result = absl::make_unique<Array2D<float>>(rows, cols);
   for (int64 i = 0; i < rows; ++i) {
     for (int64 j = 0; j < cols; ++j) {
       (*result)(i, j) = map_function(matrix(i, j));
@@ -746,7 +749,7 @@ ReferenceUtil::ReduceToRowArray2D(
   CHECK_EQ(lhs.width(), rhs.width());
   int64 rows = lhs.height();
   int64 cols = rhs.width();
-  auto result = MakeUnique<Array2D<float>>(rows, cols);
+  auto result = absl::make_unique<Array2D<float>>(rows, cols);
   for (int64 i = 0; i < rows; ++i) {
     for (int64 j = 0; j < cols; ++j) {
       (*result)(i, j) = map_function(lhs(i, j), rhs(i, j));
@@ -760,7 +763,7 @@ ReferenceUtil::ReduceToRowArray2D(
     const std::function<float(float, int64, int64)>& map_function) {
   int64 rows = matrix.height();
   int64 cols = matrix.width();
-  auto result = MakeUnique<Array2D<float>>(rows, cols);
+  auto result = absl::make_unique<Array2D<float>>(rows, cols);
   for (int64 i = 0; i < rows; ++i) {
     for (int64 j = 0; j < cols; ++j) {
       (*result)(i, j) = map_function(matrix(i, j), i, j);

tensorflow/compiler/xla/reference_util.h

@@ -22,11 +22,11 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/array2d.h"
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/array4d.h"
 #include "tensorflow/compiler/xla/client/padding.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
@@ -42,7 +42,8 @@ class ReferenceUtil {
   template <typename T>
   static std::unique_ptr<Array2D<T>> TransposeArray2D(
       const Array2D<T>& operand) {
-    auto result = MakeUnique<Array2D<T>>(operand.width(), operand.height());
+    auto result =
+        absl::make_unique<Array2D<T>>(operand.width(), operand.height());
     for (int64 w = 0; w < operand.width(); ++w) {
       for (int64 h = 0; h < operand.height(); ++h) {
         (*result)(w, h) = operand(h, w);
@@ -242,7 +243,7 @@ class ReferenceUtil {
                                               const Array2D<T>& rhs,
                                               int concatenate_dimension) {
     CHECK(0 <= concatenate_dimension && concatenate_dimension < 2);
-    auto result = MakeUnique<Array2D<T>>(
+    auto result = absl::make_unique<Array2D<T>>(
         concatenate_dimension == 0 ? lhs.n1() + rhs.n1() : lhs.n1(),
         concatenate_dimension == 1 ? lhs.n2() + rhs.n2() : lhs.n2());
     for (int64 i0 = 0; i0 < result->n1(); ++i0) {
@@ -276,7 +277,8 @@ class ReferenceUtil {
         out_dims[i] = lhs_dims[i] + rhs_dims[i];
       }
     }
-    auto result = MakeUnique<Array3D<T>>(out_dims[0], out_dims[1], out_dims[2]);
+    auto result =
+        absl::make_unique<Array3D<T>>(out_dims[0], out_dims[1], out_dims[2]);
     for (int64 i0 = 0; i0 < result->n1(); ++i0) {
       for (int64 i1 = 0; i1 < result->n2(); ++i1) {
         for (int64 i2 = 0; i2 < result->n3(); ++i2) {
@@ -310,8 +312,8 @@ class ReferenceUtil {
         out_dims[i] = lhs_dims[i] + rhs_dims[i];
       }
     }
-    auto result = MakeUnique<Array4D<T>>(out_dims[0], out_dims[1], out_dims[2],
+    auto result = absl::make_unique<Array4D<T>>(out_dims[0], out_dims[1],
-                                         out_dims[3]);
+                                                out_dims[2], out_dims[3]);
     for (int64 i0 = 0; i0 < result->n1(); ++i0) {
       for (int64 i1 = 0; i1 < result->n2(); ++i1) {
         for (int64 i2 = 0; i2 < result->n3(); ++i2) {
@@ -355,8 +357,8 @@ class ReferenceUtil {
     CHECK_LE(limits[1], input.n2());
     CHECK_GE(strides[0], 1);
     CHECK_GE(strides[1], 1);
-    auto result =
+    auto result = absl::make_unique<Array2D<T>>(
-        MakeUnique<Array2D<T>>(CeilOfRatio(limits[0] - starts[0], strides[0]),
+        CeilOfRatio(limits[0] - starts[0], strides[0]),
         CeilOfRatio(limits[1] - starts[1], strides[1]));
     for (int64 i0 = 0; i0 < result->n1(); ++i0) {
       for (int64 i1 = 0; i1 < result->n2(); ++i1) {
@@ -381,8 +383,8 @@ class ReferenceUtil {
     CHECK_GE(strides[0], 1);
     CHECK_GE(strides[1], 1);
     CHECK_GE(strides[2], 1);
-    auto result =
+    auto result = absl::make_unique<Array3D<T>>(
-        MakeUnique<Array3D<T>>(CeilOfRatio(limits[0] - starts[0], strides[0]),
+        CeilOfRatio(limits[0] - starts[0], strides[0]),
         CeilOfRatio(limits[1] - starts[1], strides[1]),
         CeilOfRatio(limits[2] - starts[2], strides[2]));
 
@@ -415,8 +417,8 @@ class ReferenceUtil {
     CHECK_GE(strides[1], 1);
     CHECK_GE(strides[2], 1);
     CHECK_GE(strides[3], 1);
-    auto result =
+    auto result = absl::make_unique<Array4D<T>>(
-        MakeUnique<Array4D<T>>(CeilOfRatio(limits[0] - starts[0], strides[0]),
+        CeilOfRatio(limits[0] - starts[0], strides[0]),
         CeilOfRatio(limits[1] - starts[1], strides[1]),
         CeilOfRatio(limits[2] - starts[2], strides[2]),
         CeilOfRatio(limits[3] - starts[3], strides[3]));
@@ -460,8 +462,8 @@ class ReferenceUtil {
   template <typename F>
   static std::unique_ptr<Array4D<float>> MapWithIndexArray4D(
       const Array4D<float>& input, F&& map_function) {
-    auto result = MakeUnique<Array4D<float>>(input.planes(), input.depth(),
+    auto result = absl::make_unique<Array4D<float>>(
-                                             input.height(), input.width());
+        input.planes(), input.depth(), input.height(), input.width());
     for (int64 plane = 0; plane < input.planes(); ++plane) {
       for (int64 depth = 0; depth < input.depth(); ++depth) {
         for (int64 height = 0; height < input.height(); ++height) {
@@ -495,7 +497,7 @@ class ReferenceUtil {
   template <typename F>
   static std::unique_ptr<Array4D<float>> MapWithIndexArray4D(
       const Array4D<float>& lhs, const Array4D<float>& rhs, F&& map_function) {
-    auto result = MakeUnique<Array4D<float>>(lhs.planes(), lhs.depth(),
+    auto result = absl::make_unique<Array4D<float>>(lhs.planes(), lhs.depth(),
                                                     lhs.height(), lhs.width());
     for (int64 plane = 0; plane < lhs.planes(); ++plane) {
       for (int64 depth = 0; depth < lhs.depth(); ++depth) {
@@ -530,7 +532,7 @@ class ReferenceUtil {
     int64 out1 =
         in1 + low_padding1 + high_padding1 + (in1 - 1) * interior_padding1;
 
-    auto result = MakeUnique<Array2D<NativeT>>(out0, out1);
+    auto result = absl::make_unique<Array2D<NativeT>>(out0, out1);
     result->Fill(pad);
     int64 o0 = low_padding0;
     for (int64 i0 = 0; i0 < in0; ++i0) {
@@ -669,7 +671,7 @@ class ReferenceUtil {
   static std::unique_ptr<Array2D<T1>> ApplyElementwise2D(
       F&& f, const Array2D<T1>& array1, const Array2D<Ts>&... arrays) {
     AssertSameSize2D(array1, arrays...);
-    auto result = MakeUnique<Array2D<T1>>(array1.n1(), array1.n2());
+    auto result = absl::make_unique<Array2D<T1>>(array1.n1(), array1.n2());
     for (int64 i = 0; i < array1.n1(); ++i) {
       for (int64 j = 0; j < array1.n2(); ++j) {
         (*result)(i, j) = f(array1(i, j), arrays(i, j)...);

tensorflow/compiler/xla/reference_util_test.cc

@@ -18,12 +18,12 @@ limitations under the License.
 #include <cmath>
 #include <memory>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/array2d.h"
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/array4d.h"
 #include "tensorflow/compiler/xla/client/padding.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/test.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
@@ -36,7 +36,7 @@ namespace {
 class ReferenceUtilTest : public ::testing::Test {
  protected:
   ReferenceUtilTest() {
-    matrix_ = MakeUnique<Array2D<float>>(rows_, cols_);
+    matrix_ = absl::make_unique<Array2D<float>>(rows_, cols_);
     // [1.f  2.f  3.f]
     // [4.f  5.f  6.f]
     for (int64 i = 0; i < rows_; ++i) {
@@ -112,7 +112,7 @@ TEST_F(ReferenceUtilTest, MapWithIndexArray2D) {
 }
 
 TEST_F(ReferenceUtilTest, MapArray4D) {
-  auto input = MakeUnique<Array4D<float>>(/*planes=*/2, /*depth=*/3,
+  auto input = absl::make_unique<Array4D<float>>(/*planes=*/2, /*depth=*/3,
                                                  /*height=*/4, /*width=*/5);
   input->FillWithMultiples(1.0f);
   auto multiply_by_two = [](float value) { return 2 * value; };
@@ -126,7 +126,7 @@ TEST_F(ReferenceUtilTest, MapArray4D) {
 }
 
 TEST_F(ReferenceUtilTest, MapWithIndexArray4D) {
-  auto input = MakeUnique<Array4D<float>>(/*planes=*/2, /*depth=*/3,
+  auto input = absl::make_unique<Array4D<float>>(/*planes=*/2, /*depth=*/3,
                                                  /*height=*/4, /*width=*/5);
   input->FillWithMultiples(1.0f);
   auto subtract_index = [](float value, int64 plane, int64 depth, int64 height,

tensorflow/compiler/xla/service/BUILD

@@ -239,6 +239,7 @@ cc_library(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
         "@com_google_absl//absl/algorithm:container",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -265,6 +266,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",  # fixdeps: keep
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -314,6 +316,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "@com_google_absl//absl/algorithm:container",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -452,6 +455,7 @@ cc_library(
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -520,6 +524,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -577,6 +582,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
         "//third_party/eigen3",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -618,6 +624,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:ptr_util",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
     alwayslink = 1,
 )
@@ -650,6 +657,7 @@ cc_library(
         "//tensorflow/compiler/xla/client:xla_computation",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -722,6 +730,7 @@ cc_library(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -739,6 +748,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:ptr_util",
         "//tensorflow/core:test",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -769,6 +779,7 @@ cc_library(
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:stream_executor_no_cuda",
         "//tensorflow/stream_executor",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -816,6 +827,7 @@ cc_library(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -834,6 +846,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -850,6 +863,7 @@ cc_library(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -867,6 +881,7 @@ cc_library(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -926,6 +941,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -953,6 +969,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -980,6 +997,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1034,6 +1052,7 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1052,6 +1071,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1068,6 +1088,7 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1085,6 +1106,7 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1186,6 +1208,7 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:util",
         "@com_google_absl//absl/algorithm:container",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1203,6 +1226,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:test",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1274,6 +1298,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1297,6 +1322,7 @@ cc_library(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
         "@com_google_absl//absl/algorithm:container",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1320,6 +1346,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",  # fixdeps: keep
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1421,6 +1448,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1610,6 +1638,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:hlo_verified_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1643,6 +1672,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1662,6 +1692,7 @@ cc_library(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
     alwayslink = True,  # Contains per-platform computation placer registration
 )
@@ -1753,6 +1784,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
         "@com_google_absl//absl/algorithm:container",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1798,6 +1830,7 @@ tf_cc_binary(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1814,6 +1847,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1873,6 +1907,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1891,6 +1926,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -1932,6 +1968,7 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2025,6 +2062,7 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2037,7 +2075,6 @@ cc_library(
         ":hlo_dataflow_analysis",
         ":logical_buffer",
         ":logical_buffer_analysis",
-        "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_tree",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:statusor",
@@ -2045,6 +2082,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2095,6 +2133,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2185,6 +2224,7 @@ cc_library(
         ":shape_inference",
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2267,6 +2307,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2348,6 +2389,7 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2385,6 +2427,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:test_utils",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2401,6 +2444,7 @@ cc_library(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2432,6 +2476,7 @@ cc_library(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2446,6 +2491,7 @@ cc_library(
         "//tensorflow/compiler/xla:shape_tree",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2506,6 +2552,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:hlo_verified_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:test",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2606,10 +2653,10 @@ cc_library(
         ":computation_layout",
         "//tensorflow/compiler/xla:shape_layout",
         "//tensorflow/compiler/xla:types",
-        "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla:xla_proto",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2790,9 +2837,9 @@ cc_library(
     hdrs = ["stream_pool.h"],
     deps = [
         "//tensorflow/compiler/xla:types",
-        "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -2890,6 +2937,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
         "//third_party/eigen3",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -3085,6 +3133,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "@com_google_absl//absl/algorithm:container",
+        "@com_google_absl//absl/memory",
     ],
 )
 

tensorflow/compiler/xla/service/algebraic_simplifier.cc

@@ -23,6 +23,7 @@ limitations under the License.
 #include <vector>
 
 #include "absl/algorithm/container.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/literal_util.h"
@@ -541,7 +542,7 @@ Status AlgebraicSimplifierVisitor::HandleConstant(HloInstruction* constant) {
   // If a literal is all the same element replace it with a scalar broadcast.
   if (ShapeUtil::ElementsIn(constant->shape()) > 1 &&
       constant->literal().IsAllFirst()) {
-    std::unique_ptr<Literal> unique_scalar = MakeUnique<Literal>(
+    std::unique_ptr<Literal> unique_scalar = absl::make_unique<Literal>(
         LiteralUtil::GetFirstScalarLiteral(constant->literal()));
     HloInstruction* scalar = computation_->AddInstruction(
         HloInstruction::CreateConstant(std::move(unique_scalar)));

tensorflow/compiler/xla/service/algebraic_simplifier_test.cc

@@ -18,9 +18,9 @@ limitations under the License.
 #include <memory>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_matchers.h"

tensorflow/compiler/xla/service/allocation_tracker.cc

@@ -17,8 +17,8 @@ limitations under the License.
 
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/map_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/device_memory_allocator.h"
 #include "tensorflow/compiler/xla/service/transfer_manager.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -91,7 +91,8 @@ StatusOr<GlobalDataHandle> AllocationTracker::RegisterInternal(
     // If ShapedBufferTy is ScopedShapedBuffer, release the ScopedShapedBuffer
     // into a regular ShapedBuffer, which is stored in
     // handle_to_shaped_buffers_.
-    handle_to_shaped_buffers_[handle].emplace_back(MakeUnique<ShapedBuffer>(
+    handle_to_shaped_buffers_[handle].emplace_back(
+        absl::make_unique<ShapedBuffer>(
             ReleaseIfScopedShapedBuffer(std::move(shaped_buffer))));
   }
 

tensorflow/compiler/xla/service/backend.cc

@@ -21,6 +21,7 @@ limitations under the License.
 #include <string>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/compiler/xla/service/compiler.h"
 #include "tensorflow/compiler/xla/service/platform_util.h"
@@ -127,8 +128,8 @@ Backend::Backend(
     }
   }
   // Create a memory allocator for the valid stream executors.
-  memory_allocator_ =
+  memory_allocator_ = absl::make_unique<StreamExecutorMemoryAllocator>(
-      MakeUnique<StreamExecutorMemoryAllocator>(platform, stream_executors);
+      platform, stream_executors);
   CHECK(!stream_executors_.empty())
       << "Service found no devices for backend " << platform_->Name() << '.';
 

tensorflow/compiler/xla/service/batchnorm_expander_test.cc

@@ -18,9 +18,9 @@ limitations under the License.
 #include <memory>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_matchers.h"

tensorflow/compiler/xla/service/buffer_assignment.cc

@@ -22,8 +22,8 @@ limitations under the License.
 #include <ostream>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/map_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/buffer_value_containers.h"
 #include "tensorflow/compiler/xla/service/heap_simulator.h"
 #include "tensorflow/compiler/xla/service/hlo.pb.h"
@@ -1100,8 +1100,8 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering(
       options.buffers_to_assign = &buffer_value_set;
       TF_ASSIGN_OR_RETURN(
           const HeapSimulator::Result result,
-          HeapSimulator::Run(MakeUnique<DecreasingSizeRunsHeap>(
+          HeapSimulator::Run(absl::make_unique<DecreasingSizeRunsHeap>(
-                                 MakeUnique<LazyBestFitHeap>(alignment)),
+                                 absl::make_unique<LazyBestFitHeap>(alignment)),
                              assignment->module(), module_sequence,
                              assignment->points_to_analysis(),
                              assignment->buffer_size_, options));
@@ -1130,11 +1130,12 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering(
         options.buffers_to_assign = &buffer_value_set;
         TF_ASSIGN_OR_RETURN(
             const HeapSimulator::Result result,
-            HeapSimulator::Run(MakeUnique<DecreasingSizeRunsHeap>(
+            HeapSimulator::Run(
-                                   MakeUnique<LazyBestFitHeap>(alignment)),
+                absl::make_unique<DecreasingSizeRunsHeap>(
+                    absl::make_unique<LazyBestFitHeap>(alignment)),
                 *computation, *instruction_sequence,
-                               assignment->points_to_analysis(),
+                assignment->points_to_analysis(), assignment->buffer_size_,
-                               assignment->buffer_size_, options));
+                options));
         AssignBuffersFromHeapSimulator(result, assignment,
                                        single_colored_set.first);
       }
@@ -1646,7 +1647,8 @@ StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::CreateAssignment(
   XLA_VLOG_LINES(3, liveness->ToString());
   XLA_VLOG_LINES(3, liveness->points_to_analysis().ToString());
 
-  // Can't use MakeUnique because BufferAssignment constructor is private.
+  // Can't use absl::make_unique because BufferAssignment constructor is
+  // private.
   std::unique_ptr<BufferAssignment> assignment(
       new BufferAssignment(module, std::move(liveness), std::move(buffer_size),
                            std::move(color_alignment)));

tensorflow/compiler/xla/service/buffer_assignment_test.cc

@@ -21,8 +21,8 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/buffer_value.h"
 #include "tensorflow/compiler/xla/service/call_graph.h"
 #include "tensorflow/compiler/xla/service/copy_insertion.h"
@@ -87,7 +87,7 @@ class BufferAssignmentTest : public HloTestBase {
   std::unique_ptr<BufferAssignment> RunBufferAssignment(HloModule* module,
                                                         int64 alignment = 1) {
     return BufferAssigner::Run(
-               module, xla::MakeUnique<DependencyHloOrdering>(module),
+               module, absl::make_unique<DependencyHloOrdering>(module),
                backend().compiler()->BufferSizeBytesFunction(),
                [alignment](LogicalBuffer::Color) { return alignment; },
                /*allow_input_output_aliasing=*/false,
@@ -98,7 +98,7 @@ class BufferAssignmentTest : public HloTestBase {
   std::unique_ptr<BufferAssignment> RunBufferAssignmentNoBuffersForConstants(
       HloModule* module, int64 alignment = 1) {
     return BufferAssigner::Run(
-               module, xla::MakeUnique<DependencyHloOrdering>(module),
+               module, absl::make_unique<DependencyHloOrdering>(module),
                backend().compiler()->BufferSizeBytesFunction(),
                [alignment](LogicalBuffer::Color) { return alignment; },
                /*allow_input_output_aliasing=*/false,
@@ -109,7 +109,7 @@ class BufferAssignmentTest : public HloTestBase {
   std::unique_ptr<BufferAssignment> RunColoredBufferAssignment(
       HloModule* module, BufferLiveness::Colorer colorer, int64 alignment = 1) {
     return BufferAssigner::Run(
-               module, xla::MakeUnique<DependencyHloOrdering>(module),
+               module, absl::make_unique<DependencyHloOrdering>(module),
                backend().compiler()->BufferSizeBytesFunction(),
                [alignment](LogicalBuffer::Color) { return alignment; },
                /*allow_input_output_aliasing=*/false,
@@ -127,7 +127,8 @@ class BufferAssignmentTest : public HloTestBase {
                                            instruction_sequence.end());
     return BufferAssigner::Run(
                module,
-               xla::MakeUnique<SequentialHloOrdering>(module, module_sequence),
+               absl::make_unique<SequentialHloOrdering>(module,
+                                                        module_sequence),
                backend().compiler()->BufferSizeBytesFunction(),
                [alignment](LogicalBuffer::Color) { return alignment; },
                /*allow_input_output_aliasing=*/false,
@@ -1769,7 +1770,8 @@ class WhileBufferAssignmentTest : public HloTestBase {
     auto sequence =
         ScheduleComputationsInModule(*module, ByteSizeOf).ConsumeValueOrDie();
     return BufferAssigner::Run(
-               module, xla::MakeUnique<SequentialHloOrdering>(module, sequence),
+               module,
+               absl::make_unique<SequentialHloOrdering>(module, sequence),
                ByteSizeOf,
                [alignment](LogicalBuffer::Color) { return alignment; },
                /*allow_input_output_aliasing=*/false,
@@ -2083,7 +2085,7 @@ TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) {
       auto assignment,
       BufferAssigner::Run(
           module.get(),
-          xla::MakeUnique<SequentialHloOrdering>(module.get(), sequence),
+          absl::make_unique<SequentialHloOrdering>(module.get(), sequence),
           backend().compiler()->BufferSizeBytesFunction(),
           [](LogicalBuffer::Color) { return 1; },
           /*allow_input_output_aliasing=*/false,
@@ -2340,7 +2342,7 @@ TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) {
   auto assignment =
       BufferAssigner::Run(
           module.get(),
-          xla::MakeUnique<SequentialHloOrdering>(module.get(), sequence),
+          absl::make_unique<SequentialHloOrdering>(module.get(), sequence),
           ByteSizeOf, [](LogicalBuffer::Color) { return 1; },
           /*allow_input_output_aliasing=*/false,
           /*allocate_buffers_for_constants=*/true)

tensorflow/compiler/xla/service/buffer_liveness_test.cc

@@ -18,7 +18,7 @@ limitations under the License.
 #include <memory>
 #include <string>
 
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
@@ -119,8 +119,8 @@ TEST_F(BufferLivenessTest, ElementwiseChain) {
   module->AddEntryComputation(builder.Build());
 
   auto liveness =
-      BufferLiveness::Run(module.get(),
+      BufferLiveness::Run(
-                          xla::MakeUnique<DependencyHloOrdering>(module.get()))
+          module.get(), absl::make_unique<DependencyHloOrdering>(module.get()))
           .ConsumeValueOrDie();
 
   EXPECT_FALSE(InstructionsMayInterfere(*liveness, param, negate));
@@ -167,8 +167,8 @@ TEST_F(BufferLivenessTest, MultipleEntryParameters_Sequential) {
 
   SequentialHloOrdering::HloModuleSequence sequence;
   sequence.insert({entry, {param0, negate, param1, exp, add}});
-  auto liveness =
+  auto liveness = BufferLiveness::Run(module.get(),
-      BufferLiveness::Run(module.get(), xla::MakeUnique<SequentialHloOrdering>(
+                                      absl::make_unique<SequentialHloOrdering>(
                                           module.get(), sequence))
                       .ConsumeValueOrDie();
 
@@ -215,8 +215,8 @@ TEST_F(BufferLivenessTest, NonElementwiseOperand) {
   module->AddEntryComputation(builder.Build());
 
   auto liveness =
-      BufferLiveness::Run(module.get(),
+      BufferLiveness::Run(
-                          xla::MakeUnique<DependencyHloOrdering>(module.get()))
+          module.get(), absl::make_unique<DependencyHloOrdering>(module.get()))
           .ConsumeValueOrDie();
 
   EXPECT_FALSE(InstructionsMayInterfere(*liveness, param, exp));
@@ -249,8 +249,8 @@ TEST_F(BufferLivenessTest, OverlappedBuffers) {
   module->AddEntryComputation(builder.Build());
 
   auto liveness =
-      BufferLiveness::Run(module.get(),
+      BufferLiveness::Run(
-                          xla::MakeUnique<DependencyHloOrdering>(module.get()))
+          module.get(), absl::make_unique<DependencyHloOrdering>(module.get()))
           .ConsumeValueOrDie();
 
   EXPECT_TRUE(InstructionsMayInterfere(*liveness, param, negate));
@@ -293,8 +293,8 @@ TEST_F(BufferLivenessTest, OverlappedBuffersSequentialOrder) {
   SequentialHloOrdering::HloModuleSequence module_sequence;
   std::vector<const HloInstruction*> order = {param, negate, exp, add};
   module_sequence.emplace(computation, order);
-  auto liveness =
+  auto liveness = BufferLiveness::Run(module.get(),
-      BufferLiveness::Run(module.get(), xla::MakeUnique<SequentialHloOrdering>(
+                                      absl::make_unique<SequentialHloOrdering>(
                                           module.get(), module_sequence))
                       .ConsumeValueOrDie();
 
@@ -342,8 +342,8 @@ TEST_F(BufferLivenessTest, RootInstructionIsNotLastInSequentialOrder) {
   std::vector<const HloInstruction*> order = {param,     add,  recv,
                                               recv_done, send, send_done};
   module_sequence.emplace(computation, order);
-  auto liveness =
+  auto liveness = BufferLiveness::Run(module.get(),
-      BufferLiveness::Run(module.get(), xla::MakeUnique<SequentialHloOrdering>(
+                                      absl::make_unique<SequentialHloOrdering>(
                                           module.get(), module_sequence))
                       .ConsumeValueOrDie();
 
@@ -376,8 +376,8 @@ TEST_F(BufferLivenessTest, TupleLiveOut) {
   module->AddEntryComputation(builder.Build());
 
   auto liveness =
-      BufferLiveness::Run(module.get(),
+      BufferLiveness::Run(
-                          xla::MakeUnique<DependencyHloOrdering>(module.get()))
+          module.get(), absl::make_unique<DependencyHloOrdering>(module.get()))
           .ConsumeValueOrDie();
 
   // All buffers should be live out except the param
@@ -412,8 +412,8 @@ TEST_F(BufferLivenessTest, EmbeddedComputation) {
   module->AddEntryComputation(builder.Build());
 
   auto liveness =
-      BufferLiveness::Run(module.get(),
+      BufferLiveness::Run(
-                          xla::MakeUnique<DependencyHloOrdering>(module.get()))
+          module.get(), absl::make_unique<DependencyHloOrdering>(module.get()))
           .ConsumeValueOrDie();
 
   // Buffers in different computations should always interfere.
@@ -453,8 +453,8 @@ TEST_F(BufferLivenessTest, TupleConstantLiveOut) {
   module->AddEntryComputation(builder.Build());
 
   auto liveness =
-      BufferLiveness::Run(module.get(),
+      BufferLiveness::Run(
-                          xla::MakeUnique<DependencyHloOrdering>(module.get()))
+          module.get(), absl::make_unique<DependencyHloOrdering>(module.get()))
           .ConsumeValueOrDie();
 
   // Only the element buffers of the tuple constant which are pointed to by
@@ -518,8 +518,8 @@ TEST_F(BufferLivenessTest, IndependentTupleElements) {
   module->AddEmbeddedComputation(builder.Build());
 
   auto liveness =
-      BufferLiveness::Run(module.get(),
+      BufferLiveness::Run(
-                          xla::MakeUnique<DependencyHloOrdering>(module.get()))
+          module.get(), absl::make_unique<DependencyHloOrdering>(module.get()))
           .ConsumeValueOrDie();
 
   // We compare tuple element pairs that are input/output to the computation:
@@ -580,8 +580,8 @@ TEST_F(BufferLivenessTest, DependentTupleElements) {
   module->AddEmbeddedComputation(builder.Build());
 
   auto liveness =
-      BufferLiveness::Run(module.get(),
+      BufferLiveness::Run(
-                          xla::MakeUnique<DependencyHloOrdering>(module.get()))
+          module.get(), absl::make_unique<DependencyHloOrdering>(module.get()))
           .ConsumeValueOrDie();
 
   // We compare tuple element pairs that are input/output to the computation:
@@ -668,9 +668,9 @@ class FusedDynamicUpdateSliceLivenessTest : public BufferLivenessTest {
     }
 
     // Run BufferLiveness on 'module'.
-    auto liveness =
+    auto liveness = BufferLiveness::Run(
-        BufferLiveness::Run(
+                        module.get(),
-            module.get(), xla::MakeUnique<DependencyHloOrdering>(module.get()))
+                        absl::make_unique<DependencyHloOrdering>(module.get()))
                         .ConsumeValueOrDie();
     // Return whether or not buffers interference is detected between
     // 'tuple_param0' and 'tuple_root' at shape index '{1}'.
@@ -780,9 +780,9 @@ class DynamicUpdateSliceLivenessTest : public BufferLivenessTest {
     module->AddEntryComputation(BuildDummyComputation());
     module->AddEmbeddedComputation(builder.Build());
     // Run BufferLiveness on 'module'.
-    auto liveness =
+    auto liveness = BufferLiveness::Run(
-        BufferLiveness::Run(
+                        module.get(),
-            module.get(), xla::MakeUnique<DependencyHloOrdering>(module.get()))
+                        absl::make_unique<DependencyHloOrdering>(module.get()))
                         .ConsumeValueOrDie();
     // Return whether or not buffers interference is detected between
     // 'tuple_param0' and 'tuple_root' at shape index '{1}'.

tensorflow/compiler/xla/service/call_graph.cc

@@ -17,8 +17,8 @@ limitations under the License.
 
 #include <queue>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/map_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/core/errors.h"
@@ -237,8 +237,8 @@ void CallGraph::SetCallContexts() {
 
 /* static */
 std::unique_ptr<CallGraph> CallGraph::Build(const HloModule* module) {
-  // Constructor for CallGraph is private so MakeUnique can't be used.
+  // Constructor for CallGraph is private so absl::make_unique can't be used.
-  auto call_graph = WrapUnique<CallGraph>(new CallGraph(module));
+  auto call_graph = absl::WrapUnique<CallGraph>(new CallGraph(module));
 
   VLOG(2) << "Building call graph for:";
   XLA_VLOG_LINES(2, module->ToString());

tensorflow/compiler/xla/service/call_inliner_test.cc

@@ -18,9 +18,9 @@ limitations under the License.
 #include <memory>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_matchers.h"

tensorflow/compiler/xla/service/channel_tracker.cc

@@ -15,7 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/channel_tracker.h"
 
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/status.h"

tensorflow/compiler/xla/service/computation_placer.cc

@@ -19,8 +19,8 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -60,8 +60,8 @@ DeviceAssignment::Deserialize(const DeviceAssignmentProto& proto) {
         "computation_count=%d",
         proto.replica_count(), proto.computation_count());
   }
-  auto assignment = MakeUnique<DeviceAssignment>(proto.replica_count(),
+  auto assignment = absl::make_unique<DeviceAssignment>(
-                                                 proto.computation_count());
+      proto.replica_count(), proto.computation_count());
   for (int computation = 0; computation < proto.computation_count();
        ++computation) {
     const auto& computation_device = proto.computation_devices(computation);
@@ -156,7 +156,7 @@ ComputationPlacer::GetPlatformComputationPlacers() {
 }  // namespace xla
 
 static std::unique_ptr<xla::ComputationPlacer> CreateComputationPlacer() {
-  return xla::MakeUnique<xla::ComputationPlacer>();
+  return absl::make_unique<xla::ComputationPlacer>();
 }
 
 static bool InitModule() {

tensorflow/compiler/xla/service/convolution_feature_group_converter.cc

@@ -18,9 +18,9 @@ limitations under the License.
 #include <memory>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/literal_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
@@ -214,7 +214,7 @@ Status ConvolutionVisitor::HandleConvolution(HloInstruction* convolution) {
     expanded_filter = add(HloInstruction::CreateConcatenate(
         expanded_filter_shape, concat_operands, input_feature_dim));
   }
-  auto zero = add(HloInstruction::CreateConstant(MakeUnique<Literal>(
+  auto zero = add(HloInstruction::CreateConstant(absl::make_unique<Literal>(
       LiteralUtil::Zero(expanded_filter_shape.element_type()))));
   auto zero_filter =
       add(HloInstruction::CreateBroadcast(expanded_filter_shape, zero, {}));

tensorflow/compiler/xla/service/cpu/BUILD

@@ -50,6 +50,7 @@ cc_library(
         "//tensorflow/compiler/xla/service/cpu:cpu_runtime",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
     alwayslink = True,  # Contains per-platform transfer manager registration
 )
@@ -85,6 +86,7 @@ cc_library(
         ":ir_emitter",
         ":parallel_task_assignment",
         ":simple_orc_jit",
+        "@com_google_absl//absl/memory",
         "//tensorflow/compiler/tf2xla:cpu_function_runtime",
         "//tensorflow/compiler/xla/service:scatter_expander",
         "//tensorflow/compiler/xla:literal",
@@ -178,6 +180,7 @@ cc_library(
         ":runtime_single_threaded_conv2d",
         ":runtime_single_threaded_fft",
         ":runtime_single_threaded_matmul",
+        "@com_google_absl//absl/memory",
         "@llvm//:execution_engine",
         "@llvm//:core",
         "@llvm//:mc",  # fixdeps: keep
@@ -418,6 +421,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:llvm_compiler",
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
         "@llvm//:analysis",
         "@llvm//:core",
         "@llvm//:ipo",
@@ -634,6 +638,7 @@ tf_cc_test(
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
         "//third_party/eigen3",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -810,6 +815,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_cost_analysis",
         "//tensorflow/compiler/xla/service:hlo_pass",
+        "@com_google_absl//absl/memory",
     ],
 )
 

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

@@ -22,6 +22,7 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "llvm/ADT/StringRef.h"
 #include "llvm/Analysis/TargetLibraryInfo.h"
 #include "llvm/Analysis/TargetTransformInfo.h"
@@ -35,7 +36,6 @@ limitations under the License.
 #include "llvm/Transforms/IPO.h"
 #include "llvm/Transforms/IPO/AlwaysInliner.h"
 #include "llvm/Transforms/IPO/PassManagerBuilder.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
@@ -205,7 +205,7 @@ void CompilerFunctor::AddTargetInfoPasses(
     llvm::legacy::PassManagerBase* passes) const {
   llvm::Triple target_triple(target_machine_->getTargetTriple());
   auto target_library_info_impl =
-      MakeUnique<llvm::TargetLibraryInfoImpl>(target_triple);
+      absl::make_unique<llvm::TargetLibraryInfoImpl>(target_triple);
   target_library_info_impl->addVectorizableFunctions(
       VectorFunctionsForTargetLibraryInfoImpl());
   passes->add(

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

@@ -26,6 +26,7 @@ limitations under the License.
 
 // IWYU pragma: no_include "llvm/Config/Disassemblers.def.inc"
 // IWYU pragma: no_include "llvm/Config/Targets.def.inc"
+#include "absl/memory/memory.h"
 #include "llvm/ADT/StringRef.h"
 #include "llvm/ADT/Triple.h"
 #include "llvm/IR/Function.h"
@@ -42,7 +43,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/protobuf_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/algebraic_simplifier.h"
 #include "tensorflow/compiler/xla/service/batch_dot_simplification.h"
 #include "tensorflow/compiler/xla/service/batchnorm_expander.h"
@@ -453,7 +453,7 @@ Status CreateHloProfilingArtifacts(
         computation_to_profile_idx,
     std::unique_ptr<HloProfileIndexMap>* hlo_profile_index_map,
     std::unique_ptr<HloProfilePrinterData>* hlo_profile_printer_data) {
-  *hlo_profile_index_map = MakeUnique<HloProfileIndexMap>(module);
+  *hlo_profile_index_map = absl::make_unique<HloProfileIndexMap>(module);
   const HloComputation& entry_computation = *module.entry_computation();
 
   TF_ASSIGN_OR_RETURN(
@@ -520,11 +520,11 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::RunBackend(
       &pre_optimization_ir_hook, &post_optimization_ir_hook));
 
   // Compile must be thread-safe so create a new LLVM context for the module.
-  auto llvm_context = xla::MakeUnique<llvm::LLVMContext>();
+  auto llvm_context = absl::make_unique<llvm::LLVMContext>();
   auto llvm_module =
-      xla::MakeUnique<llvm::Module>("__compute_module", *llvm_context);
+      absl::make_unique<llvm::Module>("__compute_module", *llvm_context);
 
-  auto jit = xla::MakeUnique<SimpleOrcJIT>(
+  auto jit = absl::make_unique<SimpleOrcJIT>(
       CompilerTargetOptions(module->config()),
       CodeGenOptLevel(module->config()),
       options::OptimizeForSizeRequested(module->config()),
@@ -566,9 +566,9 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::RunBackend(
   // temporary buffers are required to run the computation.
   TF_ASSIGN_OR_RETURN(
       std::unique_ptr<BufferAssignment> assignment,
-      BufferAssigner::Run(
+      BufferAssigner::Run(module.get(),
-          module.get(),
+                          absl::make_unique<SequentialHloOrdering>(
-          xla::MakeUnique<SequentialHloOrdering>(module.get(), module_sequence),
+                              module.get(), module_sequence),
                           BufferSizeBytesFunction(), memory_alignment,
                           /*allow_input_output_aliasing=*/false,
                           /*allocate_buffers_for_constants=*/true));
@@ -716,7 +716,7 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
   llvm::StringRef cpu_name = llvm_ir::AsStringRef(options.cpu_name());
   llvm::StringRef features = llvm_ir::AsStringRef(options.features());
   llvm::CodeGenOpt::Level opt_level = CodeGenOptLevel(modules[0]->config());
-  std::unique_ptr<llvm::TargetMachine> target_machine = WrapUnique(
+  std::unique_ptr<llvm::TargetMachine> target_machine = absl::WrapUnique(
       target->createTargetMachine(triple.getTriple(), cpu_name, features,
                                   CompilerTargetOptions(modules[0]->config()),
                                   reloc_model, llvm::None, opt_level));
@@ -757,7 +757,7 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
         std::unique_ptr<BufferAssignment> assignment,
         BufferAssigner::Run(
             module,
-            xla::MakeUnique<SequentialHloOrdering>(module, module_sequence),
+            absl::make_unique<SequentialHloOrdering>(module, module_sequence),
             BufferSizeBytesFunction(), memory_alignment,
             /*allow_input_output_aliasing=*/false,
             /*allocate_buffers_for_constants=*/true));
@@ -851,7 +851,7 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
     TF_ASSIGN_OR_RETURN(const BufferAllocation::Slice result_slice,
                         assignment->GetUniqueTopLevelOutputSlice());
 
-    results.emplace_back(MakeUnique<CpuAotCompilationResult>(
+    results.emplace_back(absl::make_unique<CpuAotCompilationResult>(
         std::move(object_file_data), std::move(buffer_infos),
         result_slice.index(), std::move(hlo_profile_printer_data)));
   }
@@ -874,7 +874,7 @@ HloCostAnalysis::ShapeSizeFunction CpuCompiler::ShapeSizeBytesFunction() const {
 static bool InitModule() {
   xla::Compiler::RegisterCompilerFactory(
       stream_executor::host::kHostPlatformId,
-      []() { return xla::MakeUnique<xla::cpu::CpuCompiler>(); });
+      []() { return absl::make_unique<xla::cpu::CpuCompiler>(); });
   return true;
 }
 static bool module_initialized = InitModule();

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

@@ -19,10 +19,10 @@ limitations under the License.
 #include <string>
 #include <tuple>
 
+#include "absl/memory/memory.h"
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/compiler/xla/array2d.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_matmul.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h"
@@ -46,7 +46,7 @@ std::unique_ptr<Array2D<float>> MaybeTransposeArray2D(const Array2D<T>& array,
   if (transpose) {
     std::swap(output_width, output_height);
   }
-  auto output = MakeUnique<Array2D<float>>(output_height, output_width);
+  auto output = absl::make_unique<Array2D<float>>(output_height, output_width);
   for (int y = 0; y < array.height(); y++) {
     for (int x = 0; x < array.width(); x++) {
       if (transpose) {
@@ -93,7 +93,7 @@ std::unique_ptr<Array2D<float>> EigenMatrixMultiply(const Array2D<float>& a,
 
   // Since we're going to transpose c before returning it. Swap the order of the
   // dimension sizes to ensure the returned array is properly dimensioned.
-  auto c_transpose = MakeUnique<Array2D<float>>(n, m);
+  auto c_transpose = absl::make_unique<Array2D<float>>(n, m);
   if (single_threaded) {
     __xla_cpu_runtime_EigenSingleThreadedMatMulF32(
         nullptr, c_transpose->data(), a_transpose->data(), b_transpose->data(),
@@ -204,7 +204,7 @@ std::unique_ptr<Array2D<float>> MKLMatrixMultiply(const Array2D<float>& a,
 
   // Since we're going to transpose c before returning it, swap the order of the
   // dimension sizes to ensure the returned array is properly dimensioned.
-  auto c_transpose = MakeUnique<Array2D<float>>(n, m);
+  auto c_transpose = absl::make_unique<Array2D<float>>(n, m);
   if (single_threaded) {
     __xla_cpu_runtime_MKLSingleThreadedMatMulF32(
         nullptr, c_transpose->data(), a_transpose->data(), b_transpose->data(),

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

@@ -19,6 +19,7 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
@@ -256,7 +257,7 @@ StatusOr<Shape> CpuTransferManager::TransferBuffersFromOutfeedInternal(
     VLOG(2)
         << "Enqueueing outfeed buffer (for the device to populate) of length "
         << size_32 << "B";
-    buffers.emplace_back(MakeUnique<CpuOutfeedBuffer>(b.first, size_32));
+    buffers.emplace_back(absl::make_unique<CpuOutfeedBuffer>(b.first, size_32));
   }
 
   std::vector<cpu::runtime::XfeedBuffer*> buffer_pointers;
@@ -283,7 +284,7 @@ StatusOr<Shape> CpuTransferManager::TransferBuffersFromOutfeedInternal(
 }  // namespace xla
 
 static std::unique_ptr<xla::TransferManager> CreateCpuTransferManager() {
-  return xla::MakeUnique<xla::CpuTransferManager>();
+  return absl::make_unique<xla::CpuTransferManager>();
 }
 
 static bool InitModule() {

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

@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/cpu/parallel_task_assignment.h"
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/cpu/dot_op_emitter.h"
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 #include "tensorflow/compiler/xla/service/cpu/shape_partition.h"
@@ -109,7 +110,7 @@ ParallelTaskAssignment::ParallelTaskAssignment(
     : target_machine_features_(*target_machine_features) {
   VLOG(1) << "ParallelTaskAssignment max_parallelism: " << max_parallelism;
   // Run cost analysis on 'module'.
-  auto cost_analysis = MakeUnique<HloCostAnalysis>(shape_size);
+  auto cost_analysis = absl::make_unique<HloCostAnalysis>(shape_size);
   HloComputation* computation = module->entry_computation();
   Status status = computation->root_instruction()->Accept(cost_analysis.get());
   if (status.ok()) {

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

@@ -20,13 +20,13 @@ limitations under the License.
 #include <list>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "llvm/ExecutionEngine/ExecutionEngine.h"
 #include "llvm/ExecutionEngine/JITSymbol.h"
 #include "llvm/ExecutionEngine/SectionMemoryManager.h"
 #include "llvm/IR/Mangler.h"
 #include "llvm/Support/CodeGen.h"
 #include "llvm/Support/Host.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
 #include "tensorflow/compiler/xla/service/cpu/custom_call_target_registry.h"
 #include "tensorflow/compiler/xla/service/cpu/orc_jit_memory_mapper.h"

tensorflow/compiler/xla/service/cpu/tests/BUILD

@@ -51,6 +51,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -94,6 +95,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:filecheck",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
+        "@com_google_absl//absl/memory",
         "@llvm//:core",
     ],
 )

tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc

@@ -17,8 +17,8 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"

tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc

@@ -16,9 +16,9 @@ limitations under the License.
 #include <memory>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "llvm/IR/Module.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
 #include "tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
@@ -62,7 +62,8 @@ TEST_F(CpuNoAliasTest, Concat) {
 
   // Now that we have an HLO module, build an llvm_ir::AliasAnalysis for it.
   auto status_or_buffer_assn = BufferAssigner::Run(
-      hlo_module.get(), MakeUnique<DependencyHloOrdering>(hlo_module.get()),
+      hlo_module.get(),
+      absl::make_unique<DependencyHloOrdering>(hlo_module.get()),
       backend().compiler()->BufferSizeBytesFunction(),
       [](LogicalBuffer::Color) { return /*alignment=*/1; });
   ASSERT_EQ(status_or_buffer_assn.status(), Status::OK());

tensorflow/compiler/xla/service/executable.cc

@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/executable.h"
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
 #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h"
 #include "tensorflow/compiler/xla/status.h"
@@ -76,7 +77,7 @@ StatusOr<ScopedShapedBuffer> Executable::ExecuteOnStreamWrapper(
   std::unique_ptr<HloExecutionProfile> profile_ptr =
       module_config().debug_options().xla_hlo_profile() &&
               hlo_profiling_enabled()
-          ? MakeUnique<HloExecutionProfile>(&hlo_profile_printer_data(),
+          ? absl::make_unique<HloExecutionProfile>(&hlo_profile_printer_data(),
                                                    &hlo_profile_index_map())
           : nullptr;
 

tensorflow/compiler/xla/service/execution_tracker.cc

@@ -17,7 +17,7 @@ limitations under the License.
 
 #include <utility>
 
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/stream_executor_no_cuda.h"
@@ -53,8 +53,8 @@ ExecutionHandle ExecutionTracker::Register(Backend* backend,
   tensorflow::mutex_lock lock(execution_mutex_);
   int64 handle = next_handle_++;
   auto inserted = handle_to_execution_.emplace(
-      handle,
+      handle, absl::make_unique<AsyncExecution>(backend, std::move(streams),
-      MakeUnique<AsyncExecution>(backend, std::move(streams), profile, result));
+                                                profile, result));
   CHECK(inserted.second);
 
   ExecutionHandle execution_handle;

tensorflow/compiler/xla/service/gpu/BUILD

@@ -56,6 +56,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -91,6 +92,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_reachability",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -107,6 +109,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -181,6 +184,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
         "@com_google_absl//absl/algorithm:container",
+        "@com_google_absl//absl/memory",
         "@llvm//:core",
         "@llvm//:support",
     ],
@@ -244,6 +248,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:device_memory_allocator",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -258,6 +263,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:ptr_util",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -338,6 +344,7 @@ cc_library(
         "//tensorflow/core/platform/default/build_config:cufft_plugin",
         "//tensorflow/core/platform/default/build_config:stream_executor_cuda",  # build_cleaner: keep
         "//tensorflow/stream_executor",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -547,6 +554,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:hlo_creation_utils",
         "//tensorflow/compiler/xla/service:hlo_pass",
         "//tensorflow/compiler/xla/service:shape_inference",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -603,6 +611,7 @@ cc_library(
         "//tensorflow/compiler/xla/service/gpu:infeed_manager",
         "//tensorflow/core:lib",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
         "@llvm//:core",
     ],
     alwayslink = True,  # Contains per-platform transfer manager registration
@@ -673,6 +682,7 @@ cc_library(
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:regexp_internal",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
         "@llvm//:core",
     ],
     alwayslink = True,  # Contains compiler registration
@@ -705,8 +715,8 @@ cc_library(
         ":xfeed_queue",
         "//tensorflow/compiler/xla:shape_tree",
         "//tensorflow/compiler/xla:types",
-        "//tensorflow/compiler/xla:util",
         "//tensorflow/core:stream_executor_no_cuda",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -721,6 +731,7 @@ cc_library(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -770,12 +781,12 @@ cc_library(
         ":stream_assignment",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
-        "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/service:buffer_value",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_ordering",
         "//tensorflow/compiler/xla/service:hlo_reachability",
         "//tensorflow/compiler/xla/service:hlo_scheduling",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -792,6 +803,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
+        "@com_google_absl//absl/memory",
     ],
 )
 

tensorflow/compiler/xla/service/gpu/buffer_allocations.cc

@@ -17,8 +17,8 @@ limitations under the License.
 
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/map_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/gpu/gpu_constants.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -40,7 +40,7 @@ StatusOr<std::unique_ptr<BufferAllocations>> BufferAllocations::Builder::Build(
     const BufferAssignment* buffer_assignment, int device_ordinal,
     DeviceMemoryAllocator* memory_allocator) {
   const int64 num_buffers = buffer_assignment->Allocations().size();
-  auto buffer_allocations = WrapUnique(new BufferAllocations(
+  auto buffer_allocations = absl::WrapUnique(new BufferAllocations(
       num_buffers, device_ordinal, memory_allocator, buffer_assignment));
 
   for (BufferAllocation::Index i = 0; i < num_buffers; ++i) {

tensorflow/compiler/xla/service/gpu/conditional_thunk.cc

@@ -15,7 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/gpu/conditional_thunk.h"
 
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/core/errors.h"

tensorflow/compiler/xla/service/gpu/for_thunk.cc

@@ -15,7 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/gpu/for_thunk.h"
 
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/core/errors.h"
@@ -28,7 +28,7 @@ ForThunk::ForThunk(const int64 loop_limit,
                    const HloInstruction* hlo)
     : Thunk(Kind::kWhile, hlo),
       loop_limit_(loop_limit),
-      body_thunk_sequence_(MakeUnique<SequentialThunk>(
+      body_thunk_sequence_(absl::make_unique<SequentialThunk>(
           // Pass nullptr as the HloInstruction* to the body_thunk_sequence_
           // constructor because this SequentialThunk is logically "part of"
           // this ForThunk, and shouldn't be profiled separately from it.

tensorflow/compiler/xla/service/gpu/gpu_executable.cc

@@ -19,8 +19,8 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/map_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h"
 #include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
@@ -144,7 +144,7 @@ Status GpuExecutable::ExecuteThunks(
     TF_RETURN_IF_ERROR(
         thunk->ExecuteOnStream(buffer_allocations, stream, &profiler));
     if (thunk_schedule_->Depended(thunk)) {
-      auto finish_event = MakeUnique<se::Event>(main_stream->parent());
+      auto finish_event = absl::make_unique<se::Event>(main_stream->parent());
       finish_event->Init();
       stream->ThenRecordEvent(finish_event.get());
       thunk_to_finish_event[thunk] = std::move(finish_event);

tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc

@@ -19,6 +19,7 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "llvm/IR/DataLayout.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/literal_util.h"
@@ -160,9 +161,10 @@ Status GpuTransferManager::TransferLiteralFromOutfeed(
         if (ShapeUtil::IsTuple(shape)) {
           return;
         }
-        *buffer = MakeUnique<gpu::OutfeedBuffer>(GetByteSizeRequirement(shape));
+        *buffer = absl::make_unique<gpu::OutfeedBuffer>(
+            GetByteSizeRequirement(shape));
         (*buffer)->set_destination(
-            MakeUnique<MutableBorrowingLiteral>(literal, index));
+            absl::make_unique<MutableBorrowingLiteral>(literal, index));
       });
 
   // Give the tree of buffers to the outfeed mananger. The device will fill it
@@ -179,7 +181,7 @@ Status GpuTransferManager::TransferLiteralFromOutfeed(
 }  // namespace xla
 
 static std::unique_ptr<xla::TransferManager> CreateNVPTXTransferManager() {
-  return xla::MakeUnique<xla::gpu::GpuTransferManager>(
+  return absl::make_unique<xla::gpu::GpuTransferManager>(
       /*id=*/stream_executor::cuda::kCudaPlatformId,
       /*pointer_size=*/llvm::DataLayout(xla::gpu::NVPTXCompiler::kDataLayout)
           .getPointerSize(0 /* default address space */));

tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc

@@ -20,6 +20,7 @@ limitations under the License.
 #include <unordered_set>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_execution_profile.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
@@ -33,7 +34,7 @@ namespace gpu {
 namespace {
 void InitAndStartTimer(std::stack<std::unique_ptr<se::Timer>>* timers,
                        se::Stream* stream) {
-  timers->push(MakeUnique<se::Timer>(stream->parent()));
+  timers->push(absl::make_unique<se::Timer>(stream->parent()));
   stream->InitTimer(timers->top().get()).ThenStartTimer(timers->top().get());
 }
 
@@ -115,7 +116,7 @@ HloExecutionProfiler::MakeScopedInstructionProfiler(
     CHECK(hlo_instructions_.insert(hlo_instruction).second)
         << hlo_instruction->name();
   }
-  return MakeUnique<ScopedInstructionProfiler>(this, hlo_instruction);
+  return absl::make_unique<ScopedInstructionProfiler>(this, hlo_instruction);
 }
 
 }  // namespace gpu

tensorflow/compiler/xla/service/gpu/hlo_schedule.cc

@@ -19,7 +19,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/gpu/hlo_schedule.h"
 
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/buffer_value.h"
 #include "tensorflow/compiler/xla/service/hlo_reachability.h"
 #include "tensorflow/compiler/xla/service/hlo_scheduling.h"
@@ -59,8 +59,8 @@ GpuHloOrdering::GpuHloOrdering(
     : PredecessorHloOrdering(module) {
   // The entry computation has a total order when there's only one stream.
   if (stream_assignment.StreamCount() == 1) {
-    entry_sequence_ =
+    entry_sequence_ = absl::make_unique<std::vector<const HloInstruction*>>(
-        MakeUnique<std::vector<const HloInstruction*>>(thunk_launch_order);
+        thunk_launch_order);
   }
 
   // The ordering of instructions for the entry computation is determined by the
@@ -75,7 +75,7 @@ GpuHloOrdering::GpuHloOrdering(
   // same-stream predecessors of each instruction.
 
   // Compute the set of all instructions we will want to set reachability on.
-  auto predecessor_map = MakeUnique<HloReachabilityMap>(
+  auto predecessor_map = absl::make_unique<HloReachabilityMap>(
       module->entry_computation()->MakeInstructionPostOrder());
 
   // The most recently visited instruction per stream.
@@ -208,7 +208,7 @@ StatusOr<std::unique_ptr<HloSchedule>> HloSchedule::Build(
     BFSLaunchOrder(entry_computation, &schedule->thunk_launch_order_);
   }
 
-  schedule->hlo_ordering_ = MakeUnique<GpuHloOrdering>(
+  schedule->hlo_ordering_ = absl::make_unique<GpuHloOrdering>(
       &module, stream_assignment, schedule->thunk_launch_order_);
 
   return std::move(schedule);

tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc

@@ -18,6 +18,7 @@ limitations under the License.
 #include <algorithm>
 #include <unordered_set>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/gpu/stream_assignment.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
@@ -47,7 +48,7 @@ class HloScheduleTest : public HloTestBase {
     auto debug_options = GetDebugOptionsForTest();
     debug_options.set_xla_gpu_disable_multi_streaming(false);
     config.set_debug_options(debug_options);
-    return MakeUnique<HloModule>("test_module", config);
+    return absl::make_unique<HloModule>("test_module", config);
   }
 
   HloVec RemoveHlo(const HloVec& input,

tensorflow/compiler/xla/service/gpu/infeed_manager.cc

@@ -15,7 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/gpu/infeed_manager.h"
 
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 
 namespace xla {
 namespace gpu {
@@ -24,7 +24,7 @@ se::Stream* InfeedManager::GetStream(se::StreamExecutor* executor) {
   tensorflow::mutex_lock l(host_to_device_stream_mu_);
   if (host_to_device_executor_ == nullptr) {
     host_to_device_executor_ = executor;
-    host_to_device_stream_ = MakeUnique<se::Stream>(executor);
+    host_to_device_stream_ = absl::make_unique<se::Stream>(executor);
     host_to_device_stream_->Init();
   }
 

tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc

@@ -22,6 +22,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h"
 
 #include "absl/algorithm/container.h"
+#include "absl/memory/memory.h"
 #include "llvm/ADT/StringRef.h"
 #include "llvm/IR/BasicBlock.h"
 #include "llvm/IR/Function.h"
@@ -30,7 +31,6 @@ limitations under the License.
 #include "llvm/IR/LLVMContext.h"
 #include "llvm/IR/Module.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
 #include "tensorflow/compiler/xla/service/dfs_hlo_visitor.h"
 #include "tensorflow/compiler/xla/service/gpu/backend_configs.pb.h"
@@ -384,7 +384,7 @@ Status IrEmitterUnnested::HandleCustomCall(HloInstruction* custom_call) {
     int64 feature_index_value = feature_index->literal().Get<int64>({});
 
     thunk_sequence_->emplace_back(
-        MakeUnique<CudnnBatchNormForwardInferenceThunk>(
+        absl::make_unique<CudnnBatchNormForwardInferenceThunk>(
             /*operand=*/GetAllocationSlice(*custom_call->operand(0)),
             /*scale=*/GetAllocationSlice(*custom_call->operand(1)),
             /*offset=*/GetAllocationSlice(*custom_call->operand(2)),
@@ -414,7 +414,7 @@ Status IrEmitterUnnested::HandleCustomCall(HloInstruction* custom_call) {
     auto output_mean = assn.GetUniqueSlice(custom_call, {1}).ValueOrDie();
     auto output_inv_stddev = assn.GetUniqueSlice(custom_call, {2}).ValueOrDie();
     thunk_sequence_->emplace_back(
-        MakeUnique<CudnnBatchNormForwardTrainingThunk>(
+        absl::make_unique<CudnnBatchNormForwardTrainingThunk>(
             /*operand=*/GetAllocationSlice(*custom_call->operand(0)),
             /*scale=*/GetAllocationSlice(*custom_call->operand(1)),
             /*offset=*/GetAllocationSlice(*custom_call->operand(2)),
@@ -444,7 +444,8 @@ Status IrEmitterUnnested::HandleCustomCall(HloInstruction* custom_call) {
     auto output_grad_scale = assn.GetUniqueSlice(custom_call, {1}).ValueOrDie();
     auto output_grad_offset =
         assn.GetUniqueSlice(custom_call, {2}).ValueOrDie();
-    thunk_sequence_->emplace_back(MakeUnique<CudnnBatchNormBackwardThunk>(
+    thunk_sequence_->emplace_back(
+        absl::make_unique<CudnnBatchNormBackwardThunk>(
             /*operand=*/GetAllocationSlice(*custom_call->operand(0)),
             /*scale=*/GetAllocationSlice(*custom_call->operand(1)),
             /*mean=*/GetAllocationSlice(*custom_call->operand(2)),
@@ -476,7 +477,7 @@ Status IrEmitterUnnested::HandleCustomCall(HloInstruction* custom_call) {
     const auto& target = custom_call->custom_call_target();
     std::unique_ptr<ConvolutionThunk> thunk;
     if (target == kCudnnConvForwardCallTarget) {
-      thunk = MakeUnique<ConvolutionThunk>(
+      thunk = absl::make_unique<ConvolutionThunk>(
           CudnnConvKind::kForward,
           /*input_buffer=*/lhs_slice,
           /*filter_buffer=*/rhs_slice,
@@ -490,7 +491,7 @@ Status IrEmitterUnnested::HandleCustomCall(HloInstruction* custom_call) {
           backend_config.algorithm(), backend_config.tensor_ops_enabled(),
           custom_call);
     } else if (target == kCudnnConvBackwardInputCallTarget) {
-      thunk = MakeUnique<ConvolutionThunk>(
+      thunk = absl::make_unique<ConvolutionThunk>(
           CudnnConvKind::kBackwardInput,
           /*input_buffer=*/conv_result_slice,
           /*filter_buffer=*/rhs_slice,
@@ -504,7 +505,7 @@ Status IrEmitterUnnested::HandleCustomCall(HloInstruction* custom_call) {
           backend_config.algorithm(), backend_config.tensor_ops_enabled(),
           custom_call);
     } else if (target == kCudnnConvBackwardFilterCallTarget) {
-      thunk = MakeUnique<ConvolutionThunk>(
+      thunk = absl::make_unique<ConvolutionThunk>(
           CudnnConvKind::kBackwardFilter,
           /*input_buffer=*/lhs_slice,
           /*filter_buffer=*/conv_result_slice,
@@ -577,7 +578,7 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) {
         thunks.push_back(
             BuildKernelThunk(fusion, /*implements_whole_instruction=*/false));
         thunk_sequence_->emplace_back(
-            MakeUnique<SequentialThunk>(std::move(thunks), fusion));
+            absl::make_unique<SequentialThunk>(std::move(thunks), fusion));
         std::vector<IrArray> parameter_arrays;
         for (HloInstruction* operand : fusion->operands()) {
           parameter_arrays.push_back(GetIrArray(*operand, *fusion));
@@ -1719,7 +1720,7 @@ Status IrEmitterUnnested::HandleReduce(HloInstruction* reduce) {
     thunks.push_back(
         BuildKernelThunk(reduce, /*implements_whole_instruction=*/false));
     thunk_sequence_->emplace_back(
-        MakeUnique<SequentialThunk>(std::move(thunks), reduce));
+        absl::make_unique<SequentialThunk>(std::move(thunks), reduce));
 
     return EmitReductionToVector(
         reduce, input->shape(), {[&](const IrArray::Index& index) {
@@ -1761,7 +1762,7 @@ Status IrEmitterUnnested::HandleTuple(HloInstruction* tuple) {
     for (const HloInstruction* tuple_element : tuple->operands()) {
       tuple_element_buffers.push_back(GetAllocationSlice(*tuple_element));
     }
-    thunk_sequence_->emplace_back(MakeUnique<TupleThunk>(
+    thunk_sequence_->emplace_back(absl::make_unique<TupleThunk>(
         tuple_element_buffers, GetAllocationSlice(*tuple), tuple));
     return Status::OK();
   }
@@ -1793,8 +1794,8 @@ Status IrEmitterUnnested::HandleSelectAndScatter(
   thunks.push_back(std::move(initializer_thunk));
   thunks.push_back(BuildKernelThunk(select_and_scatter,
                                     /*implements_whole_instruction=*/false));
-  thunk_sequence_->emplace_back(
+  thunk_sequence_->emplace_back(absl::make_unique<SequentialThunk>(
-      MakeUnique<SequentialThunk>(std::move(thunks), select_and_scatter));
+      std::move(thunks), select_and_scatter));
 
   // TODO(b/31410564): Implement dilation rate for select-and-scatter.
   if (window_util::HasDilation(window)) {
@@ -2019,7 +2020,7 @@ Status IrEmitterUnnested::HandleRng(HloInstruction* rng) {
   thunks.push_back(std::move(rng_thunk));
   thunks.push_back(std::move(increment_seed_thunk));
   thunk_sequence_->emplace_back(
-      MakeUnique<SequentialThunk>(std::move(thunks), rng));
+      absl::make_unique<SequentialThunk>(std::move(thunks), rng));
 
   return Status::OK();
 }
@@ -2044,7 +2045,7 @@ Status IrEmitterUnnested::HandleSort(HloInstruction* sort) {
   auto values_destination = GetAllocationSlice(*sort, values_shape_index);
 
   if (keys_destination != GetAllocationSlice(*keys)) {
-    thunks.push_back(MakeUnique<DeviceToDeviceCopyThunk>(
+    thunks.push_back(absl::make_unique<DeviceToDeviceCopyThunk>(
         /*source_address=*/GetAllocationSlice(*keys),
         /*destination_buffer=*/keys_destination,
         /*mem_size=*/ShapeUtil::ByteSizeOf(keys->shape()), nullptr));
@@ -2052,7 +2053,7 @@ Status IrEmitterUnnested::HandleSort(HloInstruction* sort) {
   if (values != nullptr && values_destination != GetAllocationSlice(*values)) {
     // TODO(b/26783907): Figure out why we never seem to share buffers for
     // key/value sort.
-    thunks.push_back(MakeUnique<DeviceToDeviceCopyThunk>(
+    thunks.push_back(absl::make_unique<DeviceToDeviceCopyThunk>(
         /*source_address=*/GetAllocationSlice(*values),
         /*destination_buffer=*/values_destination,
         /*mem_size=*/ShapeUtil::ByteSizeOf(values->shape()), nullptr));
@@ -2104,7 +2105,7 @@ Status IrEmitterUnnested::HandleSort(HloInstruction* sort) {
   }
 
   thunk_sequence_->emplace_back(
-      MakeUnique<SequentialThunk>(std::move(thunks), sort));
+      absl::make_unique<SequentialThunk>(std::move(thunks), sort));
   return Status::OK();
 }
 
@@ -2131,7 +2132,7 @@ Status IrEmitterUnnested::HandleCrossReplicaSum(HloInstruction* crs) {
   if (crs->operand_count() == 1) {
     CHECK(ShapeUtil::IsArray(crs->operand(0)->shape()))
         << "Operands to cross-replica-sum must be arrays: " << crs->ToString();
-    thunk_sequence_->push_back(MakeUnique<DeviceToDeviceCopyThunk>(
+    thunk_sequence_->push_back(absl::make_unique<DeviceToDeviceCopyThunk>(
         /*source_address=*/GetAllocationSlice(*crs->operand(0)),
         /*destination_buffer=*/GetAllocationSlice(*crs),
         /*mem_size=*/ShapeUtil::ByteSizeOf(crs->shape()), crs));
@@ -2146,17 +2147,17 @@ Status IrEmitterUnnested::HandleCrossReplicaSum(HloInstruction* crs) {
     tuple_element_buffers.push_back(ir_emitter_context_->buffer_assignment()
                                         .GetUniqueSlice(crs, {i})
                                         .ValueOrDie());
-    thunks.push_back(MakeUnique<DeviceToDeviceCopyThunk>(
+    thunks.push_back(absl::make_unique<DeviceToDeviceCopyThunk>(
         /*source_address=*/GetAllocationSlice(*crs->operand(i)),
         /*destination_buffer=*/tuple_element_buffers.back(),
         /*mem_size=*/ShapeUtil::ByteSizeOf(crs->operand(i)->shape()), nullptr));
   }
 
   // Output a tuple of the buffers above.
-  thunks.push_back(MakeUnique<TupleThunk>(tuple_element_buffers,
+  thunks.push_back(absl::make_unique<TupleThunk>(
-                                          GetAllocationSlice(*crs), nullptr));
+      tuple_element_buffers, GetAllocationSlice(*crs), nullptr));
   thunk_sequence_->push_back(
-      MakeUnique<SequentialThunk>(std::move(thunks), crs));
+      absl::make_unique<SequentialThunk>(std::move(thunks), crs));
   return Status::OK();
 }
 
@@ -2390,7 +2391,7 @@ std::unique_ptr<KernelThunk> IrEmitterUnnested::BuildKernelThunk(
         llvm::ConstantPointerNull::get(b_.getInt8PtrTy()));
   }
 
-  return MakeUnique<KernelThunk>(
+  return absl::make_unique<KernelThunk>(
       non_constant_buffers, llvm_ir::AsString(kernel->getName()),
       implements_whole_instruction ? inst : nullptr, unroll_factor);
 }
@@ -2399,7 +2400,7 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildHostToDeviceCopyThunk(
     const HloInstruction* inst) {
   const HloInstruction* operand = inst->operand(0);
   CHECK_EQ(HloOpcode::kConstant, operand->opcode());
-  return MakeUnique<HostToDeviceCopyThunk>(
+  return absl::make_unique<HostToDeviceCopyThunk>(
       /*source_address=*/operand->literal().untyped_data(),
       /*destination_buffer=*/GetAllocationSlice(*inst),
       /*mem_size=*/
@@ -2411,7 +2412,7 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildHostToDeviceCopyThunk(
 std::unique_ptr<Thunk> IrEmitterUnnested::BuildDeviceToDeviceCopyThunk(
     const HloInstruction* inst) {
   const HloInstruction* operand = inst->operand(0);
-  return MakeUnique<DeviceToDeviceCopyThunk>(
+  return absl::make_unique<DeviceToDeviceCopyThunk>(
       /*source_address=*/GetAllocationSlice(*operand),
       /*destination_buffer=*/GetAllocationSlice(*inst),
       /*mem_size=*/
@@ -2431,7 +2432,7 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildInfeedThunk(
                      .GetUniqueSlice(inst, index)
                      .ConsumeValueOrDie();
       });
-  return MakeUnique<InfeedThunk>(slices, inst);
+  return absl::make_unique<InfeedThunk>(slices, inst);
 }
 
 std::unique_ptr<Thunk> IrEmitterUnnested::BuildOutfeedThunk(
@@ -2448,7 +2449,7 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildOutfeedThunk(
           *slice = status_or_slice.ConsumeValueOrDie();
         }
       });
-  return MakeUnique<OutfeedThunk>(std::move(slices), inst);
+  return absl::make_unique<OutfeedThunk>(std::move(slices), inst);
 }
 
 namespace {
@@ -2471,7 +2472,7 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildGemmThunk(
   if (inst->opcode() == HloOpcode::kDot) {
     const HloInstruction* lhs = inst->operand(0);
     const HloInstruction* rhs = inst->operand(1);
-    return MakeUnique<GemmThunk>(
+    return absl::make_unique<GemmThunk>(
         GetAllocationSlice(*lhs),   // The buffer assigned to LHS.
         GetAllocationSlice(*rhs),   // The buffer assigned to RHS.
         GetAllocationSlice(*inst),  // The output buffer.
@@ -2513,7 +2514,7 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildGemmThunk(
     const HloInstruction* rhs =
         inst->operand(rhs_parameter->parameter_number());
 
-    return MakeUnique<GemmThunk>(
+    return absl::make_unique<GemmThunk>(
         GetAllocationSlice(*lhs),   // The buffer assigned to LHS.
         GetAllocationSlice(*rhs),   // The buffer assigned to RHS.
         GetAllocationSlice(*inst),  // The output buffer.
@@ -2530,7 +2531,8 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildGemmThunk(
 std::unique_ptr<Thunk> IrEmitterUnnested::BuildFftThunk(
     const HloInstruction* inst) {
   const HloInstruction* operand = inst->operand(0);
-  return MakeUnique<FftThunk>(inst->fft_type(), inst->fft_length(),
+  return absl::make_unique<FftThunk>(
+      inst->fft_type(), inst->fft_length(),
       /*input_buffer=*/GetAllocationSlice(*operand),
       /*output_buffer=*/GetAllocationSlice(*inst),
       /*input_shape=*/operand->shape(),
@@ -2584,8 +2586,8 @@ StatusOr<std::unique_ptr<Thunk>> IrEmitterUnnested::BuildInitializerThunk(
     ArraySlice<uint8> literal_bytes(
         reinterpret_cast<const uint8*>(literal.untyped_data()), num_bytes);
     if (absl::c_all_of(literal_bytes, [](uint8 byte) { return byte == 0; })) {
-      return {
+      return {absl::make_unique<MemzeroThunk>(GetAllocationSlice(*hlo, index),
-          MakeUnique<MemzeroThunk>(GetAllocationSlice(*hlo, index), nullptr)};
+                                              nullptr)};
     }
 
     // If the literal is 8 or 16 bits wide, we can emit a 32-bit memset by
@@ -2602,7 +2604,7 @@ StatusOr<std::unique_ptr<Thunk>> IrEmitterUnnested::BuildInitializerThunk(
         memcpy(&pattern16, literal_bytes.data(), sizeof(pattern16));
       }
       uint32 pattern32 = uint32{pattern16} | (uint32{pattern16} << 16);
-      return {MakeUnique<Memset32BitValueThunk>(
+      return {absl::make_unique<Memset32BitValueThunk>(
           pattern32, GetAllocationSlice(*hlo, index), nullptr)};
     }
 
@@ -2613,7 +2615,7 @@ StatusOr<std::unique_ptr<Thunk>> IrEmitterUnnested::BuildInitializerThunk(
                literal_bytes.size() - 4) == 0) {
       uint32 word;
       memcpy(&word, literal_bytes.data(), sizeof(word));
-      return {MakeUnique<Memset32BitValueThunk>(
+      return {absl::make_unique<Memset32BitValueThunk>(
           word, GetAllocationSlice(*hlo, index), nullptr)};
     }
   }
@@ -2765,7 +2767,7 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildWhileThunk(
                                     ir_emitter_context_);
   TF_CHECK_OK(body->Accept(&ir_emitter_body));
 
-  return MakeUnique<WhileThunk>(
+  return absl::make_unique<WhileThunk>(
       GetAllocationSlice(*condition->root_instruction()),  // cond result
       ir_emitter_condition.ConsumeThunkSequence(),
       ir_emitter_body.ConsumeThunkSequence(), hlo);
@@ -2783,8 +2785,8 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildForThunk(
                                     ir_emitter_context_);
   TF_CHECK_OK(body->Accept(&ir_emitter_body));
 
-  return MakeUnique<ForThunk>(loop_limit,
+  return absl::make_unique<ForThunk>(
-                              ir_emitter_body.ConsumeThunkSequence(), hlo);
+      loop_limit, ir_emitter_body.ConsumeThunkSequence(), hlo);
 }
 
 std::unique_ptr<Thunk> IrEmitterUnnested::BuildConditionalThunk(
@@ -2804,7 +2806,7 @@ std::unique_ptr<Thunk> IrEmitterUnnested::BuildConditionalThunk(
                                      ir_emitter_context_);
   TF_CHECK_OK(false_computation->Accept(&ir_emitter_false));
 
-  return MakeUnique<ConditionalThunk>(
+  return absl::make_unique<ConditionalThunk>(
       GetAllocationSlice(*hlo->operand(0)),
       GetAllocationSlice(*hlo->operand(1)),
       GetAllocationSlice(*hlo->operand(2)),

tensorflow/compiler/xla/service/gpu/kernel_thunk.cc

@@ -15,7 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/gpu/kernel_thunk.h"
 
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/gpu/gpu_executable.h"
 #include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -95,7 +95,7 @@ Status KernelThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations,
   VLOG(3) << "Launching " << kernel->name();
   // Launch the kernel with potentially multiple blocks and threads.
   static constexpr int kKernelArgsLimit = 1024;
-  auto kernel_args = MakeUnique<se::KernelArgsArray<kKernelArgsLimit>>();
+  auto kernel_args = absl::make_unique<se::KernelArgsArray<kKernelArgsLimit>>();
   for (const BufferAllocation* arg : args_) {
     const auto& buf = buffer_allocations.GetDeviceAddress(arg->index());
     kernel_args->add_device_memory_argument(buf);

tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD

@@ -34,6 +34,7 @@ cc_library(
         "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
+        "@com_google_absl//absl/memory",
         "@llvm//:amdgpu_code_gen",
         "@llvm//:analysis",
         "@llvm//:bit_reader",

tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc

@@ -20,7 +20,7 @@ limitations under the License.
 #include <string>
 #include <utility>
 
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/dump_ir_pass.h"
 #include "tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/utils.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
@@ -205,7 +205,7 @@ std::unique_ptr<llvm::TargetMachine> GetTargetMachine(
     default:
       codegen_opt_level = CodeGenOpt::None;
   }
-  return WrapUnique(target->createTargetMachine(
+  return absl::WrapUnique(target->createTargetMachine(
       triple.str(), llvm_ir::AsStringRef(cpu_name), "+ptx60", target_options,
       Optional<Reloc::Model>(RelocModel), Optional<CodeModel::Model>(CMModel),
       codegen_opt_level));

tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc

@@ -21,13 +21,13 @@ limitations under the License.
 #include <mutex>  // NOLINT(build/c++11): only using std::call_once, not mutex.
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "llvm/IR/DiagnosticInfo.h"
 #include "llvm/IR/DiagnosticPrinter.h"
 #include "llvm/IR/LLVMContext.h"
 #include "llvm/IR/Module.h"
 #include "llvm/IR/Verifier.h"
 #include "tensorflow/compiler/xla/protobuf_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/algebraic_simplifier.h"
 #include "tensorflow/compiler/xla/service/batchnorm_expander.h"
 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
@@ -690,7 +690,7 @@ StatusOr<std::unique_ptr<Executable>> NVPTXCompiler::RunBackend(
   const std::vector<uint8> cubin =
       CompilePtxOrGetCachedResult(ptx, cc_major, cc_minor);
 
-  auto thunk_schedule = MakeUnique<ThunkSchedule>(
+  auto thunk_schedule = absl::make_unique<ThunkSchedule>(
       ir_emitter.ConsumeThunkSequence(), std::move(stream_assignment),
       hlo_schedule->ThunkLaunchOrder());
   VLOG(2) << "Printing the thunk schedule...";
@@ -704,7 +704,7 @@ StatusOr<std::unique_ptr<Executable>> NVPTXCompiler::RunBackend(
     cost_analysis.set_bytes_per_second(
         stream_exec->GetDeviceDescription().memory_bandwidth());
     TF_RETURN_IF_ERROR(module->entry_computation()->Accept(&cost_analysis));
-    profile_index_map = MakeUnique<HloProfileIndexMap>(*module);
+    profile_index_map = absl::make_unique<HloProfileIndexMap>(*module);
     profile_printer =
         CreateHloProfilePrinterData(*profile_index_map, cost_analysis);
   }
@@ -813,7 +813,7 @@ se::Platform::Id NVPTXCompiler::PlatformId() const {
 static bool InitModule() {
   xla::Compiler::RegisterCompilerFactory(
       stream_executor::cuda::kCudaPlatformId,
-      []() { return xla::MakeUnique<xla::gpu::NVPTXCompiler>(); });
+      []() { return absl::make_unique<xla::gpu::NVPTXCompiler>(); });
   return true;
 }
 static bool module_initialized = InitModule();

tensorflow/compiler/xla/service/gpu/outfeed_manager.cc

@@ -15,8 +15,8 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/gpu/outfeed_manager.h"
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/map_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/core/platform/logging.h"
 

tensorflow/compiler/xla/service/gpu/pad_insertion.cc

@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/gpu/pad_insertion.h"
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h"
@@ -69,7 +70,7 @@ HloInstruction* MaybePaddedAndSlicedInput(
     PrimitiveType element_type = input->shape().element_type();
     HloInstruction* padding =
         computation->AddInstruction(HloInstruction::CreateConstant(
-            MakeUnique<Literal>(LiteralUtil::Zero(element_type))));
+            absl::make_unique<Literal>(LiteralUtil::Zero(element_type))));
     input = MakePadHlo(input, padding, padding_config).ValueOrDie();
   }
 
@@ -126,7 +127,7 @@ HloInstruction* MaybePaddedKernel(const Window& conv_window,
   PrimitiveType element_type = kernel->shape().element_type();
   HloInstruction* padding =
       computation->AddInstruction(HloInstruction::CreateConstant(
-          MakeUnique<Literal>(LiteralUtil::Zero(element_type))));
+          absl::make_unique<Literal>(LiteralUtil::Zero(element_type))));
   return MakePadHlo(kernel, padding, padding_config).ValueOrDie();
 }
 }  // namespace
@@ -236,7 +237,7 @@ bool PadInsertion::CanonicalizeBackwardFilterConvolution(
   HloComputation* computation = backward_conv->parent();
   HloInstruction* output = backward_conv->mutable_operand(1);
   HloInstruction* padding = computation->AddInstruction(
-      HloInstruction::CreateConstant(MakeUnique<Literal>(
+      HloInstruction::CreateConstant(absl::make_unique<Literal>(
           LiteralUtil::Zero(input->shape().element_type()))));
   HloInstruction* padded_input =
       MakePadHlo(input, padding, input_padding_config).ValueOrDie();

tensorflow/compiler/xla/service/gpu/partition_assignment.cc

@@ -18,8 +18,8 @@ limitations under the License.
 #include <ostream>
 #include <string>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/map_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
 #include "tensorflow/compiler/xla/shape_util.h"

tensorflow/compiler/xla/service/gpu/stream_assignment.cc

@@ -15,8 +15,8 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/gpu/stream_assignment.h"
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/map_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_reachability.h"
@@ -119,7 +119,7 @@ int ComputeStreamToAssign(
 }  // namespace
 
 std::unique_ptr<StreamAssignment> AssignStreams(const HloModule& module) {
-  auto stream_assignment = MakeUnique<StreamAssignment>();
+  auto stream_assignment = absl::make_unique<StreamAssignment>();
   const HloComputation& computation = *module.entry_computation();
   std::unique_ptr<HloReachabilityMap> reachability =
       computation.ComputeReachability();

tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc

@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/gpu/stream_assignment.h"
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
@@ -33,7 +34,7 @@ class StreamAssignmentTest : public HloTestBase {
     auto debug_options = GetDebugOptionsForTest();
     debug_options.set_xla_gpu_disable_multi_streaming(false);
     config.set_debug_options(debug_options);
-    return MakeUnique<HloModule>("test_module", config);
+    return absl::make_unique<HloModule>("test_module", config);
   }
 
   // Pre-canned shapes.

tensorflow/compiler/xla/service/gpu/tests/BUILD

@@ -35,13 +35,13 @@ cc_library(
         "requires-gpu-sm35",
     ],
     deps = [
-        "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
         "//tensorflow/compiler/xla/service:gpu_plugin",
         "//tensorflow/compiler/xla/service/gpu:gpu_executable",
         "//tensorflow/compiler/xla/tests:filecheck",
         "//tensorflow/compiler/xla/tests:llvm_irgen_test_base",
         "//tensorflow/core:lib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -60,6 +60,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -94,6 +95,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -150,6 +152,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
+        "@com_google_absl//absl/memory",
     ],
 )
 
@@ -168,6 +171,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
+        "@com_google_absl//absl/memory",
     ],
 )
 

tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.cc

@@ -14,8 +14,8 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/gpu/gpu_executable.h"
 #include "tensorflow/compiler/xla/tests/filecheck.h"
 #include "tensorflow/core/platform/logging.h"
@@ -32,7 +32,7 @@ std::unique_ptr<HloModule> GpuCodegenTest::CreateNewModuleWithFTZ(bool ftz) {
   debug_options.add_xla_disable_hlo_passes("constant_folding");
   config.set_debug_options(debug_options);
 
-  return MakeUnique<HloModule>(TestName(), config);
+  return absl::make_unique<HloModule>(TestName(), config);
 }
 
 void GpuCodegenTest::CompileAndVerifyPtx(std::unique_ptr<HloModule> hlo_module,

tensorflow/compiler/xla/service/gpu/tests/gpu_copy_test.cc

@@ -16,9 +16,9 @@ limitations under the License.
 #include <memory>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/literal_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"

tensorflow/compiler/xla/service/gpu/tests/gpu_index_test.cc

@@ -16,8 +16,8 @@ limitations under the License.
 #include <memory>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"

tensorflow/compiler/xla/service/gpu/tests/gpu_ldg_test.cc

@@ -20,8 +20,8 @@ limitations under the License.
 #include <memory>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"

tensorflow/compiler/xla/service/gpu/tests/gpu_noalias_test.cc

@@ -16,8 +16,8 @@ limitations under the License.
 #include <memory>
 #include <utility>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"

tensorflow/compiler/xla/service/gpu/tuple_thunk.cc

@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/gpu/tuple_thunk.h"
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h"
 #include "tensorflow/compiler/xla/util.h"
 
@@ -25,7 +26,7 @@ Status TupleThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations,
                                    se::Stream* stream,
                                    HloExecutionProfiler* profiler) {
   auto size = tuple_element_buffers_.size();
-  auto tuple_element_buffer_addresses = MakeUnique<void*[]>(size);
+  auto tuple_element_buffer_addresses = absl::make_unique<void*[]>(size);
   for (int i = 0; i != size; ++i) {
     tuple_element_buffer_addresses[i] =
         buffer_allocations.GetDeviceAddress(tuple_element_buffers_[i]).opaque();

tensorflow/compiler/xla/service/gpu/while_thunk.cc

@@ -15,7 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/gpu/while_thunk.h"
 
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/core/errors.h"
@@ -34,9 +34,9 @@ WhileThunk::WhileThunk(
       // and body_thunk_sequence_ constructors because these SequentialThunks
       // are logically "part of" this WhileThunk, and shouldn't be profiled
       // separately from it.
-      condition_thunk_sequence_(MakeUnique<SequentialThunk>(
+      condition_thunk_sequence_(absl::make_unique<SequentialThunk>(
           std::move(*condition_thunk_sequence), nullptr)),
-      body_thunk_sequence_(MakeUnique<SequentialThunk>(
+      body_thunk_sequence_(absl::make_unique<SequentialThunk>(
           std::move(*body_thunk_sequence), nullptr)) {}
 
 Status WhileThunk::Initialize(const GpuExecutable& executable,

tensorflow/compiler/xla/service/graphviz_example.cc

@@ -22,9 +22,9 @@ limitations under the License.
 #include <memory>
 #include <string>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/literal_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
@@ -84,7 +84,7 @@ HloComputation* CallForwardingComputation(HloComputation* computation,
 // the module.
 std::unique_ptr<HloModule> MakeBigGraph() {
   HloModuleConfig config;
-  auto module = MakeUnique<HloModule>("BigGraph", config);
+  auto module = absl::make_unique<HloModule>("BigGraph", config);
 
   auto builder = HloComputation::Builder("TestBigGraphvizGraph");
 

tensorflow/compiler/xla/service/heap_simulator.cc

@@ -18,6 +18,7 @@ limitations under the License.
 #include <algorithm>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/util.h"
 
@@ -45,7 +46,7 @@ StatusOr<int64> HeapSimulator::MinimumMemoryForModule(
   // bound, by minimizing the liveness of sub-computations.
   TF_ASSIGN_OR_RETURN(
       HeapSimulator::Result result,
-      HeapSimulator::Run(MakeUnique<NoFragmentationStatsHeap>(), *module,
+      HeapSimulator::Run(absl::make_unique<NoFragmentationStatsHeap>(), *module,
                          module_sequence, *points_to_analysis, size_function));
   return result.heap_size;
 }
@@ -60,9 +61,10 @@ StatusOr<int64> HeapSimulator::MinimumMemoryForComputation(
         memory_by_computation) {
   TF_ASSIGN_OR_RETURN(
       HeapSimulator::Result result,
-      HeapSimulator::Run(MakeUnique<NoFragmentationStatsHeap>(), computation,
+      HeapSimulator::Run(absl::make_unique<NoFragmentationStatsHeap>(),
-                         sequence, points_to_analysis, size_function,
+                         computation, sequence, points_to_analysis,
-                         HeapSimulator::Options(), memory_by_computation));
+                         size_function, HeapSimulator::Options(),
+                         memory_by_computation));
   return result.heap_size;
 }
 
@@ -344,7 +346,7 @@ HeapSimulator::HeapSimulator(
     const SequentialHloOrdering::HloModuleSequence* module_sequence,
     const tensorflow::gtl::FlatMap<const HloComputation*, int64>*
         memory_by_computation)
-    : no_fragmentation_stats_(MakeUnique<NoFragmentationStatsHeap>()),
+    : no_fragmentation_stats_(absl::make_unique<NoFragmentationStatsHeap>()),
       algorithm_(std::move(algorithm)),
       size_fn_(size_fn),
       options_(options),

tensorflow/compiler/xla/service/heap_simulator_test.cc

@@ -19,6 +19,7 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/service/buffer_value.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
@@ -137,7 +138,7 @@ class HeapSimulatorTracker {
       const string& name, std::unique_ptr<HloComputation> computation,
       const std::vector<const HloInstruction*>& instruction_sequence) {
     HloModuleConfig config;
-    module_ = MakeUnique<HloModule>(name, config);
+    module_ = absl::make_unique<HloModule>(name, config);
     module_->AddEntryComputation(std::move(computation));
     points_to_analysis_ =
         TuplePointsToAnalysis::Run(module_.get()).ConsumeValueOrDie();
@@ -146,8 +147,8 @@ class HeapSimulatorTracker {
     // the secondary sorting criteria of DecreasingSizeRunsHeap to sort calls by
     // buffer id, for determinism in the tests.
     auto zero_size = [](const BufferValue& buffer) { return 0; };
-    auto algorithm = MakeUnique<DecreasingSizeRunsHeap>(
+    auto algorithm = absl::make_unique<DecreasingSizeRunsHeap>(
-        MakeUnique<HeapCallRecorder>(&actual_calls_));
+        absl::make_unique<HeapCallRecorder>(&actual_calls_));
     result_ = HeapSimulator::Run(
                   std::move(algorithm), *module_->entry_computation(),
                   instruction_sequence, *points_to_analysis_, zero_size)
@@ -156,7 +157,7 @@ class HeapSimulatorTracker {
 
   explicit HeapSimulatorTracker(const string& name) {
     HloModuleConfig config;
-    module_ = MakeUnique<HloModule>(name, config);
+    module_ = absl::make_unique<HloModule>(name, config);
   }
 
   // Similar to the single entry computation constructor above, but runs the
@@ -182,8 +183,8 @@ class HeapSimulatorTracker {
     auto size_fn = [&reverse_position](const BufferValue& buffer) {
       return reverse_position[buffer.instruction()];
     };
-    auto algorithm = MakeUnique<DecreasingSizeRunsHeap>(
+    auto algorithm = absl::make_unique<DecreasingSizeRunsHeap>(
-        MakeUnique<HeapCallRecorder>(&actual_calls_));
+        absl::make_unique<HeapCallRecorder>(&actual_calls_));
     result_ = HeapSimulator::Run(std::move(algorithm), *module_,
                                  module_sequence, *points_to_analysis_, size_fn)
                   .ConsumeValueOrDie();
@@ -675,7 +676,8 @@ class HeapAlgorithmTestBase : public ::testing::Test {
     const BufferValue::Id id = buffers_.size();
     auto const0 = builder_.AddInstruction(
         HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0)));
-    buffers_.emplace_back(MakeUnique<HloValue>(id, const0, ShapeIndex{}));
+    buffers_.emplace_back(
+        absl::make_unique<HloValue>(id, const0, ShapeIndex{}));
     return buffers_.back().get();
   }
 
@@ -724,7 +726,8 @@ class DecreasingSizeRunsHeapTest : public HeapAlgorithmTestBase {};
 
 TEST_F(DecreasingSizeRunsHeapTest, Empty) {
   CallSequence call_sequence;
-  DecreasingSizeRunsHeap heap(MakeUnique<HeapCallRecorder>(&call_sequence));
+  DecreasingSizeRunsHeap heap(
+      absl::make_unique<HeapCallRecorder>(&call_sequence));
   heap.Finish();
   EXPECT_EQ(call_sequence, CallSequence({
                                {kFinish, nullptr},
@@ -733,7 +736,8 @@ TEST_F(DecreasingSizeRunsHeapTest, Empty) {
 
 TEST_F(DecreasingSizeRunsHeapTest, Simple) {
   CallSequence call_sequence;
-  DecreasingSizeRunsHeap heap(MakeUnique<HeapCallRecorder>(&call_sequence));
+  DecreasingSizeRunsHeap heap(
+      absl::make_unique<HeapCallRecorder>(&call_sequence));
   heap.Alloc(buffer_a_, 10);
   heap.Alloc(buffer_b_, 20);
   heap.Alloc(buffer_c_, 30);
@@ -760,7 +764,8 @@ TEST_F(DecreasingSizeRunsHeapTest, Simple) {
 
 TEST_F(DecreasingSizeRunsHeapTest, Mixed) {
   CallSequence call_sequence;
-  DecreasingSizeRunsHeap heap(MakeUnique<HeapCallRecorder>(&call_sequence));
+  DecreasingSizeRunsHeap heap(
+      absl::make_unique<HeapCallRecorder>(&call_sequence));
   heap.Alloc(buffer_a_, 10);
   heap.Alloc(buffer_b_, 20);
   heap.Free(buffer_b_, 20);

tensorflow/compiler/xla/service/hlo_alias_analysis.cc

@@ -457,7 +457,7 @@ StatusOr<std::unique_ptr<HloAliasAnalysis>> HloAliasAnalysis::Run(
   VLOG(2) << "HloAliasAnalysis::Run on module " << module->name();
   XLA_VLOG_LINES(2, module->ToString());
 
-  auto alias_analysis = WrapUnique(new HloAliasAnalysis(module));
+  auto alias_analysis = absl::WrapUnique(new HloAliasAnalysis(module));
   TF_ASSIGN_OR_RETURN(alias_analysis->dataflow_analysis_,
                       HloDataflowAnalysis::Run(*module, /*ssa_form=*/true,
                                                /*bitcast_defines_value=*/false,

tensorflow/compiler/xla/service/hlo_computation.cc

@@ -24,9 +24,9 @@ limitations under the License.
 #include <sstream>
 
 #include "absl/algorithm/container.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/map_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
@@ -57,8 +57,8 @@ std::unique_ptr<HloComputation> HloComputation::Builder::Build(
   HloInstruction* root =
       root_instruction ? root_instruction : last_added_instruction_;
   CHECK_NE(nullptr, root);
-  return WrapUnique(new HloComputation(name_, parameter_count, &instructions_,
+  return absl::WrapUnique(new HloComputation(
-                                       root, fusion_instruction_));
+      name_, parameter_count, &instructions_, root, fusion_instruction_));
 }
 
 HloComputation::HloComputation(
@@ -494,7 +494,7 @@ HloComputation::CreateFromProto(
               return to_proto_id[a.get()] < to_proto_id[b.get()];
             });
 
-  return WrapUnique(new HloComputation(proto.name(), parameter_count,
+  return absl::WrapUnique(new HloComputation(proto.name(), parameter_count,
                                              &instructions, root,
                                              /*fusion_instruction=*/nullptr));
 }
@@ -675,7 +675,7 @@ Status HloComputation::ReplaceInstruction(HloInstruction* old_instruction,
 std::unique_ptr<HloReachabilityMap> HloComputation::ComputeReachability()
     const {
   const auto& all = MakeInstructionPostOrder();
-  auto result = MakeUnique<HloReachabilityMap>(all);
+  auto result = absl::make_unique<HloReachabilityMap>(all);
 
   std::vector<HloInstruction*> inputs;
   for (const HloInstruction* hlo : all) {
@@ -830,7 +830,7 @@ std::unique_ptr<HloComputation> HloComputation::CloneWithReplacements(
     HloCloneContext* context, const string& suffix) {
   std::unique_ptr<HloCloneContext> context_ptr;
   if (context == nullptr) {
-    context_ptr = MakeUnique<HloCloneContext>(parent(), suffix);
+    context_ptr = absl::make_unique<HloCloneContext>(parent(), suffix);
     context = context_ptr.get();
   }
 

tensorflow/compiler/xla/service/hlo_constant_folding.cc

@@ -20,6 +20,7 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h"
@@ -38,7 +39,7 @@ StatusOr<bool> HloConstantFolding::Run(HloModule* module) {
   // Limit the constant folding to 0 iterations to skip folding loops. This
   // retains the behavior from before while loop support in HloEvaluator and may
   // be revised.
-  auto evaluator = MakeUnique<HloEvaluator>(/*max_loop_iterations=*/0);
+  auto evaluator = absl::make_unique<HloEvaluator>(/*max_loop_iterations=*/0);
 
   XLA_VLOG_LINES(2,
                  "HloConstantFolding::Run(), before:\n" + module->ToString());

tensorflow/compiler/xla/service/hlo_creation_utils.cc

@@ -15,9 +15,9 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/hlo_creation_utils.h"
 #include "absl/algorithm/container.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/literal_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/shape_inference.h"
 #include "tensorflow/compiler/xla/util.h"
 
@@ -319,7 +319,7 @@ StatusOr<HloInstruction*> PadVectorWithZeros(HloInstruction* operand,
   *padding_config.add_dimensions() = padding_config_dim;
 
   HloInstruction* zero = computation->AddInstruction(
-      HloInstruction::CreateConstant(MakeUnique<Literal>(
+      HloInstruction::CreateConstant(absl::make_unique<Literal>(
           LiteralUtil::Zero(operand->shape().element_type()))));
   return MakePadHlo(operand, zero, padding_config);
 }
@@ -329,7 +329,7 @@ StatusOr<HloInstruction*> BroadcastZeros(
     ArraySlice<int64> broadcast_dimensions) {
   HloInstruction* zero =
       computation->AddInstruction(HloInstruction::CreateConstant(
-          MakeUnique<Literal>(LiteralUtil::Zero(element_type))));
+          absl::make_unique<Literal>(LiteralUtil::Zero(element_type))));
   return MakeBroadcastHlo(zero, /*broadcast_dimensions=*/{},
                           /*result_shape_bounds=*/broadcast_dimensions);
 }

tensorflow/compiler/xla/service/hlo_creation_utils_test.cc

@@ -14,7 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/hlo_creation_utils.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/service/hlo_evaluator.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/shape_util.h"

tensorflow/compiler/xla/service/hlo_cse_test.cc

@@ -20,9 +20,9 @@ limitations under the License.
 #include <utility>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/literal.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_matchers.h"

tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc

@@ -19,8 +19,8 @@ limitations under the License.
 #include <queue>
 #include <vector>
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/xla/map_util.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
@@ -886,7 +886,7 @@ StatusOr<std::unique_ptr<HloDataflowAnalysis>> HloDataflowAnalysis::Run(
   VLOG(1) << "HloDataflowAnalysis::Run on module " << module.name();
   XLA_VLOG_LINES(2, module.ToString());
 
-  auto dataflow_analysis = WrapUnique(new HloDataflowAnalysis(
+  auto dataflow_analysis = absl::WrapUnique(new HloDataflowAnalysis(
       module, ssa_form, bitcast_defines_value, fusion_can_share_buffer));
 
   TF_RETURN_IF_ERROR(dataflow_analysis->InitializeInstructionValueSets());