[TF:XLA] Broadcast NextAfter arguments when needed.

PiperOrigin-RevId: 251613307

tensorflow/compiler/tf2xla/kernels/BUILD

@@ -56,6 +56,7 @@ tf_kernel_library(
         "matrix_set_diag_op.cc",
         "matrix_triangular_solve_op.cc",
         "mirror_pad_op.cc",
+        "next_after_op.cc",
         "no_op.cc",
         "one_hot_op.cc",
         "pack_op.cc",

tensorflow/compiler/tf2xla/kernels/binary_ops.cc

@@ -16,6 +16,8 @@ limitations under the License.
 // Native XLA implementations of simple binary Ops
 
 #include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h"
+#include "tensorflow/compiler/tf2xla/lib/broadcast.h"
+#include "tensorflow/compiler/tf2xla/shape_util.h"
 #include "tensorflow/compiler/tf2xla/xla_helpers.h"
 #include "tensorflow/compiler/tf2xla/xla_op_registry.h"
 #include "tensorflow/compiler/xla/client/client_library.h"
@@ -236,8 +238,6 @@ XLA_MAKE_BINARY(TanhGrad,
 
 XLA_MAKE_BINARY(Pow, xla::Pow(lhs, rhs, extend_dimensions));
 
-XLA_MAKE_BINARY(NextAfter, xla::NextAfter(lhs, rhs));
-
 #undef XLA_MAKE_BINARY
 
 class ApproximateEqualOp : public XlaOpKernel {

tensorflow/compiler/tf2xla/kernels/next_after_op.cc

@@ -0,0 +1,43 @@
+/* Copyright 2019 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.
+==============================================================================*/
+
+#include "tensorflow/compiler/tf2xla/lib/broadcast.h"
+#include "tensorflow/compiler/tf2xla/shape_util.h"
+#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
+#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
+#include "tensorflow/compiler/xla/client/lib/math.h"
+#include "tensorflow/compiler/xla/client/xla_builder.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/util/bcast.h"
+
+namespace tensorflow {
+namespace {
+
+class NextAfterOp : public XlaOpKernel {
+ public:
+  explicit NextAfterOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    auto lhs = ctx->Input(0);
+    auto rhs = ctx->Input(1);
+    OP_REQUIRES_OK(ctx, BroadcastOpsToSame(&lhs, &rhs));
+    ctx->SetOutput(0, xla::NextAfter(lhs, rhs));
+  }
+};
+REGISTER_XLA_OP(Name("NextAfter"), NextAfterOp);
+
+}  // namespace
+}  // namespace tensorflow

tensorflow/python/ops/math_ops_test.py

@@ -668,7 +668,6 @@ class NextAfterTest(test_util.TensorFlowTestCase):
       self.assertAllEqual(math_ops.nextafter(one, one), one)
 
   @test_util.run_in_graph_and_eager_modes
-  @test_util.disable_xla("Broadcasting not supported for XLA")
   def testBroadcasting(self):
 
     for dtype in [dtypes.float32, dtypes.float64]: