experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

[TF:XLA] Broadcast NextAfter arguments when needed.

Browse Source 
PiperOrigin-RevId: 251613307
This commit is contained in:
A. Unique TensorFlower 2019-06-05 03:47:33 -07:00 committed by TensorFlower Gardener
parent c47ad082f4
commit 58c796df1d
4 changed files with 46 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
tensorflow/compiler/tf2xla/kernels/BUILD
View File

@ -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",

4
tensorflow/compiler/tf2xla/kernels/binary_ops.cc
View File

@ -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 {

43
tensorflow/compiler/tf2xla/kernels/next_after_op.cc Normal file
View File

@ -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

1
tensorflow/python/ops/math_ops_test.py
View File

@ -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]: