Merge changes from github followup.
PiperOrigin-RevId: 160183498
diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 62ae5ae..6e76db0 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -208,6 +208,7 @@
"//tensorflow/compiler/jit/kernels:all_files",
"//tensorflow/compiler/jit/legacy_flags:all_files",
"//tensorflow/compiler/jit/ops:all_files",
+ "//tensorflow/compiler/plugin/executor:all_files",
"//tensorflow/compiler/tests:all_files",
"//tensorflow/compiler/tf2xla:all_files",
"//tensorflow/compiler/tf2xla/cc:all_files",
diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index 306e704..7ebd842 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -15,7 +15,10 @@
)
package(
- default_visibility = [":internal"],
+ default_visibility = [
+ ":internal",
+ "//tensorflow/compiler/plugin/executor:__pkg__",
+ ],
)
load("//tensorflow:tensorflow.bzl", "cc_header_only_library")
diff --git a/tensorflow/compiler/jit/kernels/BUILD b/tensorflow/compiler/jit/kernels/BUILD
index ed204b8..97f3512 100644
--- a/tensorflow/compiler/jit/kernels/BUILD
+++ b/tensorflow/compiler/jit/kernels/BUILD
@@ -2,6 +2,7 @@
package(
default_visibility = [
+ "//tensorflow/compiler/plugin/executor:__pkg__",
"//tensorflow/compiler/tf2xla:internal",
],
)
diff --git a/tensorflow/compiler/plugin/executor/BUILD b/tensorflow/compiler/plugin/executor/BUILD
index 9bc706a..2e58757 100644
--- a/tensorflow/compiler/plugin/executor/BUILD
+++ b/tensorflow/compiler/plugin/executor/BUILD
@@ -11,9 +11,11 @@
"*.h",
]),
deps = [
+ "//tensorflow/compiler/jit:xla_device",
"//tensorflow/compiler/jit:xla_jit_headers_lib",
+ "//tensorflow/compiler/tf2xla:xla_compiler",
"//tensorflow/compiler/xla:xla_headers_lib",
- "//tensorflow/compiler/xla/service:hlo_evaluator",
+ "//tensorflow/compiler/xla/service",
"//third_party/eigen3",
"@local_config_cuda//cuda:cuda_headers",
"@protobuf//:protobuf_headers",
diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 044857d..432b247 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -175,6 +175,11 @@
name = "slice_ops_test",
size = "small",
srcs = ["slice_ops_test.py"],
+ # TODO(b/62962492): Test fails with assertion error.
+ tags = [
+ "manual",
+ "notap",
+ ],
deps = [
":xla_test",
"//tensorflow/python:array_ops",
@@ -456,6 +461,11 @@
"//tensorflow/python:math_ops",
"//tensorflow/python:nn_ops",
],
+ # TODO(b/62961789): Test fails with SIGABRT
+ tags = [
+ "manual",
+ "notap",
+ ],
)
cc_library(
@@ -524,8 +534,12 @@
# --dump_graph_dir, and the config file was written by hand.
#
# Run the following to build a minimal benchmark of the computation on Android:
-# $ bazel build -c opt --config=android_arm \
-# third_party/tensorflow/compiler/tests:lstm_layer_inference_benchmark
+# $ bazel build -c opt --cxxopt='-std=c++11' --linkopt='-lm' \
+# --cpu=armeabi-v7a \
+# --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
+# --crosstool_top=//external:android/crosstool \
+# //tensorflow/compiler/tests:lstm_layer_inference_benchmark
+
#
# Currently the resulting binary size is ~190KB
tf_library(
diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py
index ce85182..f53f38f 100644
--- a/tensorflow/contrib/lookup/lookup_ops.py
+++ b/tensorflow/contrib/lookup/lookup_ops.py
@@ -260,11 +260,7 @@
For example:
```python
-<<<<<<< HEAD
mapping_string = tf.constant(["emerson", "lake", "palmer"])
-=======
- mapping_string = tf.constant(["emerson", "lake", "palmer")
->>>>>>> 338a7ead4475d6b97b420d6d1c56ff66815e3e7b
indices = tf.constant([1, 5], tf.int64)
values = tf.contrib.lookup.index_to_string(
indices, mapping=mapping_string, default_value="UNKNOWN")
diff --git a/tensorflow/core/kernels/sparse_reduce_sum_op.cc b/tensorflow/core/kernels/sparse_reduce_sum_op.cc
deleted file mode 100644
index 074aab9..0000000
--- a/tensorflow/core/kernels/sparse_reduce_sum_op.cc
+++ /dev/null
@@ -1,305 +0,0 @@
-/* Copyright 2016 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.
-==============================================================================*/
-
-// See docs in ../ops/sparse_ops.cc.
-
-#define EIGEN_USE_THREADS
-
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/register_types.h"
-#include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/framework/tensor_util.h"
-#include "tensorflow/core/framework/types.h"
-#include "tensorflow/core/util/sparse/sparse_tensor.h"
-
-// TODO(b/31496047): Fix non-standard include order.
-#include <numeric> // clang-format off
-
-using tensorflow::sparse::SparseTensor;
-using tensorflow::gtl::ArraySlice;
-
-namespace tensorflow {
-
-struct ReduceDetails {
- // The dimensions to call Reorder() with.
- std::vector<int64> reorder_dims;
-
- // The dimensions to call group() with after Reorder().
- std::vector<int64> group_by_dims;
-
- // The shape after reduction.
- TensorShape reduced_shape;
-};
-
-// Compute common reduce parameters that'll be used for SparseTensor
-// reductions. Usage:
-// ReduceDetails reduction = SparseTensorReduceHelper(sp, axes, keep_dims);
-// sp.Reorder(reduction.reorder_dims);
-// for (const auto& g : sp.group(reduction.group_by_dims)) {
-// ...
-// }
-// // Set output shape to reduction.reduced_shape.
-ReduceDetails SparseTensorReduceHelper(const SparseTensor &sp,
- gtl::ArraySlice<int32> axes_slice,
- bool keep_dims) {
- ReduceDetails reduction;
-
- std::vector<int32> reduction_axes(axes_slice.begin(), axes_slice.end());
- int ndims = sp.dims();
- for (int64 i = 0; i < reduction_axes.size(); ++i) {
- reduction_axes[i] = (reduction_axes[i] + ndims) % ndims;
- }
- std::sort(reduction_axes.begin(), reduction_axes.end());
-
- // (0) Calculate the grouping dimensions:
- // group_by_dims == {0, .., NDIMS-1} \ reduction_axes.
- std::vector<int64> perm(ndims);
- std::iota(perm.begin(), perm.end(), 0);
-
- // Requires perm and reduction_axes_ be sorted; group_by_dims will be
- // sorted as well.
- std::set_difference(
- perm.begin(), perm.end(), reduction_axes.begin(), reduction_axes.end(),
- std::inserter(reduction.group_by_dims, reduction.group_by_dims.begin()));
-
- // Now append the rest of the axes (the complement of group_by_dims_);
- // result is used by Reorder().
- reduction.reorder_dims = reduction.group_by_dims;
- std::set_difference(perm.begin(), perm.end(), reduction.group_by_dims.begin(),
- reduction.group_by_dims.end(),
- std::back_inserter(reduction.reorder_dims));
-
- // (1) Calculate the shape after reduction.
- auto sp_shape = sp.shape();
- std::vector<int64> out_dim_sizes;
- if (keep_dims) {
- out_dim_sizes.reserve(ndims);
- auto beg = reduction.group_by_dims.begin();
- auto end = reduction.group_by_dims.end();
- for (int d = 0; d < ndims; ++d) {
- if (std::find(beg, end, d) == end) {
- out_dim_sizes.push_back(1); // A reduced axis.
- } else {
- out_dim_sizes.push_back(sp_shape[d]);
- }
- }
- } else {
- out_dim_sizes = sp.PickDims(reduction.group_by_dims);
- }
-
- reduction.reduced_shape = TensorShape(out_dim_sizes);
- return reduction;
-}
-
-Status ValidateInputs(const Tensor *shape_t, const Tensor *reduction_axes_t) {
- // indices and values are validated in SparseTensor ctor.
- if (!TensorShapeUtils::IsVector(shape_t->shape())) {
- return errors::InvalidArgument(
- "Expected input_shape to be a vector; got shape: ",
- shape_t->shape().DebugString());
- }
- if (!TensorShapeUtils::IsScalar(reduction_axes_t->shape()) &&
- !TensorShapeUtils::IsVector(reduction_axes_t->shape())) {
- return errors::InvalidArgument(
- "Expected reduction_axes to be a scalar or a vector; got shape: ",
- reduction_axes_t->shape().DebugString());
- }
-
- const auto reduction_axes_flat = reduction_axes_t->flat<int32>();
- for (int64 i = 0; i < reduction_axes_flat.size(); i++) {
- int32 axis = reduction_axes_flat(i);
- if (axis < -shape_t->NumElements() || axis >= shape_t->NumElements()) {
- return errors::InvalidArgument("Invalid reduction dimension ", axis,
- ", for input with ",
- shape_t->NumElements(), " dimensions.");
- }
- }
-
- return Status::OK();
-}
-
-template <typename T>
-class SparseReduceSumOp : public OpKernel {
- public:
- explicit SparseReduceSumOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
- OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
- }
-
- void Compute(OpKernelContext *ctx) override {
- const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t;
- OP_REQUIRES_OK(ctx, ctx->input("input_indices", &indices_t));
- OP_REQUIRES_OK(ctx, ctx->input("input_values", &values_t));
- OP_REQUIRES_OK(ctx, ctx->input("input_shape", &shape_t));
- OP_REQUIRES_OK(ctx, ctx->input("reduction_axes", &reduction_axes_t));
-
- OP_REQUIRES_OK(ctx, ValidateInputs(shape_t, reduction_axes_t));
-
- // TODO(zongheng): we will call Reorder() below, which will modify
- // in-place the underlying indices and values buffers. To avoid
- // surprises of this kernel being stateful, we work around the above by
- // making deep copies here. Remove this if/when we change Reorder()'s
- // semantics.
- const auto shape_vec = shape_t->vec<int64>();
- SparseTensor sp(tensor::DeepCopy(*indices_t), tensor::DeepCopy(*values_t),
- TensorShape(shape_vec));
- ReduceDetails reduction = SparseTensorReduceHelper(
- sp, reduction_axes_t->flat<int32>(), keep_dims_);
-
- Tensor *out_values;
- OP_REQUIRES_OK(
- ctx, ctx->allocate_output(0, reduction.reduced_shape, &out_values));
- auto out_flat = out_values->flat<T>();
- out_flat.setZero();
-
- Tensor tmp_group_sum;
- OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
- TensorShape({}), &tmp_group_sum));
- auto group_sum = tmp_group_sum.scalar<T>();
-
- // Compute strides, and use it to convert coords to flat index. The
- // coordinates returned by .group() have the same ndims as group_by_dims.
- gtl::InlinedVector<int64, 8> output_strides(reduction.group_by_dims.size());
- if (!output_strides.empty()) { // Do this iff we don't reduce all.
- output_strides.back() = 1;
- for (int d = output_strides.size() - 2; d >= 0; --d) {
- output_strides[d] =
- output_strides[d + 1] * shape_vec(reduction.group_by_dims[d + 1]);
- }
- }
-
- auto CoordinatesToFlatIndex = [](ArraySlice<int64> coords,
- ArraySlice<int64> strides) {
- if (strides.empty()) { // Reduce all.
- return 0LL;
- }
- CHECK_EQ(coords.size(), strides.size());
- int64 idx = 0;
- for (int i = 0; i < coords.size(); ++i) {
- idx += coords[i] * strides[i];
- }
- return idx;
- };
-
- // Each group maps one-on-one onto a value in the reduced tensor.
- // g.group() provides the coordinates of a particular reduced value.
- sp.Reorder<T>(reduction.reorder_dims);
- for (const auto &g : sp.group(reduction.group_by_dims)) {
- group_sum.device(ctx->eigen_cpu_device()) = g.template values<T>().sum();
- const int64 idx = CoordinatesToFlatIndex(g.group(), output_strides);
- out_flat(idx) = group_sum();
- VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
- << "; idx: " << idx << "; group sum: " << group_sum();
- }
- }
-
- private:
- // True if the number of dimensions should be maintained.
- bool keep_dims_;
-};
-
-#define REGISTER_KERNELS(T) \
- REGISTER_KERNEL_BUILDER( \
- Name("SparseReduceSum").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
- SparseReduceSumOp<T>)
-TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
-#undef REGISTER_KERNELS
-
-template <typename T>
-class SparseReduceSumSparseOp : public OpKernel {
- public:
- explicit SparseReduceSumSparseOp(OpKernelConstruction *ctx) : OpKernel(ctx) {
- OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
- }
-
- void Compute(OpKernelContext *ctx) override {
- const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t;
- OP_REQUIRES_OK(ctx, ctx->input("input_indices", &indices_t));
- OP_REQUIRES_OK(ctx, ctx->input("input_values", &values_t));
- OP_REQUIRES_OK(ctx, ctx->input("input_shape", &shape_t));
- OP_REQUIRES_OK(ctx, ctx->input("reduction_axes", &reduction_axes_t));
-
- OP_REQUIRES_OK(ctx, ValidateInputs(shape_t, reduction_axes_t));
-
- SparseTensor sp(tensor::DeepCopy(*indices_t), tensor::DeepCopy(*values_t),
- TensorShape(shape_t->vec<int64>()));
- ReduceDetails reduction = SparseTensorReduceHelper(
- sp, reduction_axes_t->flat<int32>(), keep_dims_);
-
- sp.Reorder<T>(reduction.reorder_dims);
- // Count nnzs in the output SparseTensor.
- int64 nnz = 0;
- auto iter = sp.group(reduction.group_by_dims);
- for (auto it = iter.begin(); it != iter.end(); ++it) {
- nnz++;
- }
-
- Tensor *out_indices_t;
- OP_REQUIRES_OK(ctx,
- ctx->allocate_output(
- 0, TensorShape({nnz, reduction.reduced_shape.dims()}),
- &out_indices_t));
- typename TTypes<int64>::Matrix out_indices_mat =
- out_indices_t->matrix<int64>();
- // For keep_dims. We don't explicitly set dim fields for reduced dims below.
- out_indices_mat.setZero();
-
- Tensor *out_values_t;
- OP_REQUIRES_OK(ctx,
- ctx->allocate_output(1, TensorShape({nnz}), &out_values_t));
- auto out_flat = out_values_t->flat<T>();
-
- Tensor tmp_group_sum;
- OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
- TensorShape({}), &tmp_group_sum));
- auto group_sum = tmp_group_sum.scalar<T>();
- int64 i = 0;
- for (const auto &g : sp.group(reduction.group_by_dims)) {
- group_sum.device(ctx->eigen_cpu_device()) = g.template values<T>().sum();
- std::vector<int64> group = g.group();
- for (int64 j = 0; j < group.size(); j++) {
- if (keep_dims_) {
- out_indices_mat(i, reduction.group_by_dims[j]) = group[j];
- } else {
- out_indices_mat(i, j) = group[j];
- }
- }
- out_flat(i) = group_sum();
- i++;
- VLOG(2) << "coords: " << str_util::Join(g.group(), ",")
- << "; group sum: " << group_sum();
- }
-
- Tensor *out_shape_t;
- OP_REQUIRES_OK(ctx, ctx->allocate_output(
- 2, TensorShape({reduction.reduced_shape.dims()}),
- &out_shape_t));
- auto out_shape_flat = out_shape_t->flat<int64>();
- auto out_dim_sizes = reduction.reduced_shape.dim_sizes();
- std::copy(out_dim_sizes.begin(), out_dim_sizes.end(), &out_shape_flat(0));
- }
-
- private:
- // True if the number of dimensions should be maintained.
- bool keep_dims_;
-};
-
-#define REGISTER_KERNELS(T) \
- REGISTER_KERNEL_BUILDER( \
- Name("SparseReduceSumSparse").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
- SparseReduceSumSparseOp<T>)
-TF_CALL_NUMBER_TYPES(REGISTER_KERNELS);
-#undef REGISTER_KERNELS
-
-} // namespace tensorflow
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 22b18b9..936348e 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3056,6 +3056,7 @@
srcs = ["client/session_clusterspec_prop_test.py"],
srcs_version = "PY2AND3",
tags = [
+ "no_gpu",
"no_pip_gpu",
],
deps = [
@@ -3080,6 +3081,7 @@
srcs = ["client/session_list_devices_test.py"],
srcs_version = "PY2AND3",
tags = [
+ "no_gpu",
"no_pip_gpu",
],
deps = [
diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index 14eb2cb..6664362 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -19,7 +19,6 @@
from __future__ import print_function
import numpy as np
-import unittest
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
@@ -606,7 +605,6 @@
self._compare(sp_t, reduction_axes, ndims, True, False)
self._compare(sp_t, reduction_axes, ndims, True, True)
- @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
def testSimpleAndRandomInputs(self):
if np.__version__ == "1.13.0":
self.skipTest("numpy 1.13.0 bug")
@@ -646,7 +644,6 @@
with self.assertRaisesOpError("Invalid reduction dimension 2"):
sparse_ops.sparse_reduce_max(sp_t, 2).eval()
- @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
def testGradient(self):
if np.__version__ == "1.13.0":
self.skipTest("numpy 1.13.0 bug")