[XLA] Rollback of rollback of "Implement LocalClient::Run which supports buffer donation"

PiperOrigin-RevId: 317400695
Change-Id: I56f1f8df347d5a3b2bad9526c7315c63ad6ddadb

tensorflow/compiler/xla/client/local_client.cc

@@ -176,15 +176,23 @@ StatusOr<ScopedShapedBuffer> LocalExecutable::Run(
   for (const ShapedBuffer* const arg : arguments) {
     argument_shapes.push_back(&arg->on_host_shape());
   }
-  TF_ASSIGN_OR_RETURN(auto options_and_stream,
-                      RunHelper(argument_shapes, run_options));
-  ExecutableRunOptions options = options_and_stream.first.run_options();
-  options.set_device_ordinal(-1);
-  auto result = RunAsync(arguments, options);
-  Status block_status = options.stream()->BlockHostUntilDone();
-  TF_RETURN_IF_ERROR(result.status());
-  TF_RETURN_IF_ERROR(block_status);
-  return result;
+  return AsyncCallAndBlockHostUntilDone<xla::ScopedShapedBuffer>(
+      argument_shapes, run_options, [&](const ExecutableRunOptions& options) {
+        return RunAsync(arguments, options);
+      });
+}
+
+StatusOr<ExecutionOutput> LocalExecutable::Run(
+    std::vector<ExecutionInput> arguments, ExecutableRunOptions run_options) {
+  std::vector<const Shape*> argument_shapes;
+  argument_shapes.reserve(arguments.size());
+  for (const ExecutionInput& arg : arguments) {
+    argument_shapes.push_back(&arg.shape());
+  }
+  return AsyncCallAndBlockHostUntilDone<ExecutionOutput>(
+      argument_shapes, run_options, [&](const ExecutableRunOptions& options) {
+        return RunAsync(argument_shapes, std::move(arguments), options);
+      });
 }
 
 static std::shared_ptr<HloSnapshot> DumpArguments(

tensorflow/compiler/xla/client/local_client.h

@@ -51,6 +51,11 @@ class LocalExecutable {
       const absl::Span<const ShapedBuffer* const> arguments,
       ExecutableRunOptions run_options);
 
+  // Similar to Run(), but allows for donating argument buffers to the
+  // executable.
+  StatusOr<ExecutionOutput> Run(std::vector<ExecutionInput> arguments,
+                                ExecutableRunOptions run_options);
+
   // Similar to Run(), but need not block the host waiting for the computation
   // to complete before returning.
   StatusOr<ScopedShapedBuffer> RunAsync(
@@ -90,6 +95,22 @@ class LocalExecutable {
   // Backend::devices_equivalent).
   int build_device_ordinal() const { return build_options_.device_ordinal(); }
 
+  template <typename T>
+  StatusOr<T> AsyncCallAndBlockHostUntilDone(
+      absl::Span<Shape const* const> argument_shapes,
+      const ExecutableRunOptions& run_options,
+      std::function<StatusOr<T>(const ExecutableRunOptions&)> async_callback) {
+    TF_ASSIGN_OR_RETURN(auto options_and_stream,
+                        RunHelper(argument_shapes, run_options));
+    ExecutableRunOptions options = options_and_stream.first.run_options();
+    options.set_device_ordinal(-1);
+    StatusOr<T> result = async_callback(options);
+    Status block_status = options.stream()->BlockHostUntilDone();
+    TF_RETURN_IF_ERROR(result.status());
+    TF_RETURN_IF_ERROR(block_status);
+    return result;
+  }
+
   // Compiled computation.
   std::unique_ptr<Executable> executable_;
 

tensorflow/compiler/xla/tests/multiple_devices_on_host_test.cc

@@ -45,7 +45,8 @@ void CompileAndExecute(
       xla::ClientLibrary::GetXlaService(client->platform())
           ->backend()
           .memory_allocator());
-  StatusOr<ScopedShapedBuffer> result = executable->Run({}, execute_options);
+  StatusOr<ScopedShapedBuffer> result =
+      executable->Run(absl::Span<const ShapedBuffer* const>(), execute_options);
   {
     absl::MutexLock lock(results_mutex);
     results->emplace_back(device_ordinal, std::move(result));

tensorflow/compiler/xla/tests/while_test.cc

@@ -1324,14 +1324,16 @@ void BM_WhileLoop(int num_iters) {
   options.set_allocator(&allocator);
   const int kWarmups = 2;
   for (int i = 0; i < kWarmups; ++i) {
-    auto result = executable->Run({}, options);
+    auto result =
+        executable->Run(absl::Span<const ShapedBuffer* const>(), options);
     ASSERT_TRUE(result.ok());
   }
 
   // Run benchmark.
   tensorflow::testing::StartTiming();
   for (int i = 0; i < num_iters; ++i) {
-    auto result = executable->Run({}, options);
+    auto result =
+        executable->Run(absl::Span<const ShapedBuffer* const>(), options);
     ASSERT_TRUE(result.ok());
   }
 }