Support returning resource handles from function in XLA

There are a couple of reasons to do this:
 - resource handle are regular tensors part of a public API that
 can potentially be returned from a function.
 - When tfe.defun is executed under GradientTape, it generates a
 function returning resource handles in certain cases.

This CL adds support for returning resource handles from an XLA
compiled function. These resource handles must have been passed as
arguments to the function. In other words, we don't yet support
returning resources created inside the function. tfe.defun never
makes functions that create resources.

PiperOrigin-RevId: 210442856

1 files changed, 26 insertions, 21 deletions

diff --git a/tensorflow/compiler/jit/xla_launch_util.cc b/tensorflow/compiler/jit/xla_launch_util.cc
index 2ffce9298d..affeab4a8c 100644
--- a/tensorflow/compiler/jit/xla_launch_util.cc
+++ b/tensorflow/compiler/jit/xla_launch_util.cc
@@ -271,31 +271,36 @@ Status XlaComputationLaunchContext::PopulateOutputs(
       }
     } else {
       const TensorShape& shape = kernel->outputs[i].shape;
-      VLOG(2) << "Retval " << i << " shape " << shape.DebugString();
-
-      se::DeviceMemoryBase buffer = output.buffer({output_num});
-      if (allocate_xla_tensors_) {
-        Tensor* output_tensor;
-        TF_RETURN_IF_ERROR(ctx->allocate_output(i, shape, &output_tensor));
-        XlaTensor* xla_tensor = XlaTensor::FromTensor(output_tensor);
-        if (xla_tensor) {
-          xla_tensor->set_shaped_buffer(ScopedShapedBuffer(
-              ExtractSubShapedBuffer(&output, output_num, xla_allocator_)));
-          if (use_multiple_streams_) {
-            xla_tensor->SetDefinedOn(stream, definition_event);
+      const DataType& type = kernel->outputs[i].type;
+      VLOG(2) << "Retval " << i << " shape " << shape.DebugString() << " type "
+              << DataTypeString(type);
+      if (type == DT_RESOURCE) {
+        ctx->set_output(i, ctx->input(kernel->outputs[i].input_index));
+      } else {
+        se::DeviceMemoryBase buffer = output.buffer({output_num});
+        if (allocate_xla_tensors_) {
+          Tensor* output_tensor;
+          TF_RETURN_IF_ERROR(ctx->allocate_output(i, shape, &output_tensor));
+          XlaTensor* xla_tensor = XlaTensor::FromTensor(output_tensor);
+          if (xla_tensor) {
+            xla_tensor->set_shaped_buffer(ScopedShapedBuffer(
+                ExtractSubShapedBuffer(&output, output_num, xla_allocator_)));
+            if (use_multiple_streams_) {
+              xla_tensor->SetDefinedOn(stream, definition_event);
+            }
+          } else {
+            // xla_tensor wasn't valid, which must mean this is a zero-element
+            // tensor.
+            CHECK_EQ(output_tensor->TotalBytes(), 0);
           }
         } else {
-          // xla_tensor wasn't valid, which must mean this is a zero-element
-          // tensor.
-          CHECK_EQ(output_tensor->TotalBytes(), 0);
+          Tensor output_tensor = XlaTensorBuffer::MakeTensor(
+              ctx->expected_output_dtype(i), shape, buffer, allocator);
+          output.set_buffer(xla::OwningDeviceMemory(), {output_num});
+          ctx->set_output(i, output_tensor);
         }
-      } else {
-        Tensor output_tensor = XlaTensorBuffer::MakeTensor(
-            ctx->expected_output_dtype(i), shape, buffer, allocator);
-        output.set_buffer(xla::OwningDeviceMemory(), {output_num});
-        ctx->set_output(i, output_tensor);
+        ++output_num;
       }
-      ++output_num;
     }
 
     if (VLOG_IS_ON(3)) {