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PR #26235: typos in tensorflow/core fixed

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Imported from GitHub PR #26235

Copybara import of the project:

  - d2d879abd683dab3dfdab2414109231b4d1c9452 Update tfprof_op.cc by Siju <siju.samuel@huawei.com>
  - 58ed5984d2b6d94f6b5ddf898c06e6d90ba90dfe Update dynamic_stitch_op.cc by Siju <siju.samuel@huawei.com>
  - 5287c0c63593cf4d4f6e361839b9c97b6ca657cc Update graph_constructor_test.cc by Siju <siju.samuel@huawei.com>
  - 815e3faf355f6951d2a7ccd448b7b82b1b4fc201 Update mkl_concat_op.cc by Siju <siju.samuel@huawei.com>
  - 07f6b58e642ff943d963b380094ac7e29a151435 Update mkl_layout_pass_test.cc by Siju <siju.samuel@huawei.com>
  - b31b8b1c6cc6068718a2b53879fd926f2266801d Update eigen_benchmark_cpu_test.cc by Siju <siju.samuel@huawei.com>
  - be9a9cef0fb49e6626ff9d4df090f29d4107cd9f Update node_def_builder_test.cc by Siju <siju.samuel@huawei.com>
  - c8e931b0dbb42525249825735d5ff505c2bcfa47 Update crop_and_resize_op_gpu.cu.cc by Siju <siju.samuel@huawei.com>
  - 93fbb1651ce4da38f9bc0bac1ecb1233cdd44db4 Update tensor_flag_utils.h by Siju <siju.samuel@huawei.com>
  - 8e06ffe61af6a094176b77d1d60dccf7a8ba2d9d Update descriptors.cc by Siju <siju.samuel@huawei.com>
  - e387eb2db20387597ba75c096d40a529598b0a9c Update dependency_optimizer.cc by Siju <siju.samuel@huawei.com>
  - b44146b60e0176cbf21657ea3af9748b4e6843ef Update device_tracer.cc by Siju <siju.samuel@huawei.com>
  - e6378de52869584bfaef4f0fef447a322fbad38f Update nccl_manager.h by Siju <siju.samuel@huawei.com>
  - e84e0fa13a6982b9983eb8b07643d578d17d877a Update hexagon_control_wrapper.h by Siju <siju.samuel@huawei.com>
  - c4c12ce562d50def5c0fd4657749aa516b024d04 Update string_split_fuzz.cc by Siju <siju.samuel@huawei.com>
  - 9924f805777ab92d2eb23f8b5de9a422e11a681c Update collective_order.cc by Siju <siju.samuel@huawei.com>
  - d3916ed593ae10da9f99e6f63f514ea4b046a8df Update indexed_dataset_op.cc by Siju <siju.samuel@huawei.com>
  - c9da842bb575210469343cb6c036e8ebdb4b6715 Update mkl_conv_ops.cc by Siju <siju.samuel@huawei.com>
  - 15fee4b6eb8ba2a7a023373c3bf4184f41fbe0ec Update strong_hash.h by Siju <siju.samuel@huawei.com>
  - a5b911e6ec1f8481ad58b8394d7f578cec80e3a6 Update debug_ops_test.cc by Siju <siju.samuel@huawei.com>
  - abcedcb7ae06c7c673f187f24011bba50b1714c5 Update mkl_util.h by Siju <siju.samuel@huawei.com>
  - c3354ff5ae2cf6ca6225ca01b58c45d1eda423c9 Merge branch 'master' into patch-44 by Siju <sijusamuel@gmail.com>
  - 9f35087704751435dff37a18326ece224e273c37 Merge c3354ff5ae2cf6ca6225ca01b58c45d1eda423c9 into e82c1... by Siju <sijusamuel@gmail.com>

COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/26235 from siju-samuel:patch-44 c3354ff5ae2cf6ca6225ca01b58c45d1eda423c9
PiperOrigin-RevId: 237521323
This commit is contained in:
A. Unique TensorFlower 2019-03-08 14:35:05 -08:00 committed by TensorFlower Gardener
parent d3b9ce5b4b
commit 249644e6e0
20 changed files with 21 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
tensorflow/core/framework/node_def_builder_test.cc
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@ -235,7 +235,7 @@ TEST_F(NodeDefBuilderTest, Polymorphic) {
op: "Polymorphic" input: "a" op: "Polymorphic" input: "a"
attr { key: "T" value { type: DT_BOOL } } )proto"); attr { key: "T" value { type: DT_BOOL } } )proto");
// Conficting Attr() // Conflicting Attr()
ExpectFailure(Builder().Input(FakeInput(DT_BOOL)).Attr("T", DT_STRING), ExpectFailure(Builder().Input(FakeInput(DT_BOOL)).Attr("T", DT_STRING),
"Inconsistent values for attr 'T' DT_BOOL vs. DT_STRING while"); "Inconsistent values for attr 'T' DT_BOOL vs. DT_STRING while");

2
tensorflow/core/graph/collective_order.cc
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@ -143,7 +143,7 @@ Status CreateControlDependencies(
// Insert control dependencies defined by `dependency_edges` in `graph`. If // Insert control dependencies defined by `dependency_edges` in `graph`. If
// `order_type` is `kEdges`, insert explicit control edges, else if `order_type` // `order_type` is `kEdges`, insert explicit control edges, else if `order_type`
// is `kAttrs`, encode depdencies as an attribute on collective node. // is `kAttrs`, encode dependencies as an attribute on collective node.
Status InsertControlDependencies( Status InsertControlDependencies(
Graph* graph, GraphCollectiveOrder order_type, Graph* graph, GraphCollectiveOrder order_type,
const absl::flat_hash_map<Node*, absl::flat_hash_set<Node*>>& const absl::flat_hash_map<Node*, absl::flat_hash_set<Node*>>&

2
tensorflow/core/graph/graph_constructor_test.cc
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@ -951,7 +951,7 @@ TEST_F(GraphConstructorTest, ImportGraphDef) {
EXPECT_TRUE(HasControlEdge("D", sink)); EXPECT_TRUE(HasControlEdge("D", sink));
EXPECT_EQ(9, graph_.num_edges()); EXPECT_EQ(9, graph_.num_edges());
// Importing again should fail because of node name collissions. // Importing again should fail because of node name collisions.
s = ImportGraphDef(opts, def, &graph_, nullptr); s = ImportGraphDef(opts, def, &graph_, nullptr);
EXPECT_TRUE(errors::IsInvalidArgument(s)) << s; EXPECT_TRUE(errors::IsInvalidArgument(s)) << s;

4
tensorflow/core/graph/mkl_layout_pass_test.cc
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@ -572,7 +572,7 @@ TEST_F(MklLayoutPassTest, Input_ControlEdge_PadWithConv2D_Positive) {
// Test if output control edges does not duplicate after merge. // Test if output control edges does not duplicate after merge.
// If both the merging ops have output control edge to a common op, // If both the merging ops have output control edge to a common op,
// then after merge, the merged op will have only one control edge // then after merge, the merged op will have only one control edge
// to that commom op. // to that common op.
// padding is VALID type // padding is VALID type
// A = input(image), B = input(paddings), C= Pad = input of conv2D, // A = input(image), B = input(paddings), C= Pad = input of conv2D,
// D=input(filter), E = Conv2D, Z = Zeta // D=input(filter), E = Conv2D, Z = Zeta
@ -1501,7 +1501,7 @@ TEST_F(MklLayoutPassTest, Input_ControlEdge_PadWithFusedConv2D_Positive) {
// ts that there are no duplicate output control edges after merge. // ts that there are no duplicate output control edges after merge.
// If both the merging ops have output control edge to a common op, // If both the merging ops have output control edge to a common op,
// then after merge, the merged op will have only one control edge // then after merge, the merged op will have only one control edge
// to that commom op. This test only add additional output control edge check // to that common op. This test only add additional output control edge check
// based on the previous test NodeMerge_PadWithFusedConv2D_Positive1 // based on the previous test NodeMerge_PadWithFusedConv2D_Positive1
// padding is VALID type // padding is VALID type
// A = input(image), B = input(paddings), C = Pad(A, B) = input of conv2D, // A = input(image), B = input(paddings), C = Pad(A, B) = input of conv2D,

2
tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc
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@ -411,7 +411,7 @@ struct CropAndResizeBackpropImage<GPUDevice, T> {
d.stream(), config.virtual_thread_count, grads_image.data())); d.stream(), config.virtual_thread_count, grads_image.data()));
} }
// Configurate interpolation method. // Configure interpolation method.
InterpolationMethod method = BILINEAR; InterpolationMethod method = BILINEAR;
if (method_name == "nearest") { if (method_name == "nearest") {
method = NEAREST; method = NEAREST;

2
tensorflow/core/kernels/data/experimental/indexed_dataset_op.cc
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@ -149,7 +149,7 @@ class MaterializedDatasetResource : public ResourceBase {
// A wrapper class for storing an `IndexedDataset` instance in a DT_VARIANT // A wrapper class for storing an `IndexedDataset` instance in a DT_VARIANT
// tensor. Objects of the wrapper class own a reference on an instance of an // tensor. Objects of the wrapper class own a reference on an instance of an
// `IndexedTensor` and the wrapper's copy constructor and desctructor take care // `IndexedTensor` and the wrapper's copy constructor and destructor take care
// of managing the reference count. // of managing the reference count.
// //
// NOTE: This is not a feature-complete implementation of the DT_VARIANT // NOTE: This is not a feature-complete implementation of the DT_VARIANT

2
tensorflow/core/kernels/debug_ops_test.cc
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@ -364,7 +364,7 @@ TEST_F(DebugNumericSummaryOpTest, Float_only_valid_values) {
7.33333333333, // variance of non-inf and non-nan elements. 7.33333333333, // variance of non-inf and non-nan elements.
static_cast<double>(DT_FLOAT), // dtype static_cast<double>(DT_FLOAT), // dtype
2.0, // Number of dimensions. 2.0, // Number of dimensions.
2.0, 3.0}); // Dimensoin sizes. 2.0, 3.0}); // Dimension sizes.
test::ExpectTensorNear<double>(expected, *GetOutput(0), 1e-8); test::ExpectTensorNear<double>(expected, *GetOutput(0), 1e-8);
} }

2
tensorflow/core/kernels/dynamic_stitch_op.cc
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@ -167,7 +167,7 @@ class DynamicStitchOpGPU : public DynamicStitchOpImplBase<T> {
// merged that aren't covered by an index in indices. What should we do? // merged that aren't covered by an index in indices. What should we do?
if (first_dim_size > 0) { if (first_dim_size > 0) {
// because the collision requirements, we have to deal with // because the collision requirements, we have to deal with
// collion first before send data to gpu kernel. // collision first before send data to gpu kernel.
// TODO(ekelsen): Instead of doing a serial scan on the CPU to pick the // TODO(ekelsen): Instead of doing a serial scan on the CPU to pick the
// last of duplicated indices, it could instead be done of the GPU // last of duplicated indices, it could instead be done of the GPU
// implicitly using atomics to make sure the last index is the final // implicitly using atomics to make sure the last index is the final

2
tensorflow/core/kernels/eigen_benchmark_cpu_test.cc
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@ -8,7 +8,7 @@ You may obtain a copy of the License at
Unless required by applicable law or agreed to in writing, software Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONT OF ANY KIND, either express or implied. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
==============================================================================*/ ==============================================================================*/

2
tensorflow/core/kernels/fuzzing/string_split_fuzz.cc
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@ -34,7 +34,7 @@ class FuzzStringSplit : public FuzzSession {
Tensor delimiter_tensor(tensorflow::DT_STRING, TensorShape({})); Tensor delimiter_tensor(tensorflow::DT_STRING, TensorShape({}));
if (size > 0) { if (size > 0) {
// The spec for split is that the delimeter should be 0 or 1 characters. // The spec for split is that the delimiter should be 0 or 1 characters.
// Naturally, fuzz it with something larger. (This omits the possibility // Naturally, fuzz it with something larger. (This omits the possibility
// of handing it a > int32_max size string, which should be tested for in // of handing it a > int32_max size string, which should be tested for in
// an explicit test). // an explicit test).

2
tensorflow/core/kernels/hexagon/hexagon_control_wrapper.h
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@ -76,7 +76,7 @@ class HexagonControlWrapper final : public IRemoteFusedGraphExecutor {
// TODO(satok): Use actual data passed by FillInputNode and remove // TODO(satok): Use actual data passed by FillInputNode and remove
// std::vector<float> dummy_input_float_{}; // std::vector<float> dummy_input_float_{};
std::unordered_map<int, std::vector<uint8>> input_tensor_data_{}; std::unordered_map<int, std::vector<uint8>> input_tensor_data_{};
// Dummy byte array for cosnt node. // Dummy byte array for const node.
// TODO(satok): Remove // TODO(satok): Remove
std::unordered_map<int, std::vector<uint8>> dummy_const_data_{}; std::unordered_map<int, std::vector<uint8>> dummy_const_data_{};

2
tensorflow/core/kernels/mkl_concat_op.cc
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@ -484,7 +484,7 @@ class MklConcatOp : public OpKernel {
output_tensor->flat<uint8>().size() * sizeof(uint8)); output_tensor->flat<uint8>().size() * sizeof(uint8));
} }
// This method finds the most commom format across all MKL inputs // This method finds the most common format across all MKL inputs
// Inputs: // Inputs:
// 1. input_shapes: shapes of input (MKL) tensors. // 1. input_shapes: shapes of input (MKL) tensors.
// 2. concat_dim: concat dimension. // 2. concat_dim: concat dimension.

2
tensorflow/core/kernels/mkl_conv_ops.cc
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@ -96,7 +96,7 @@ struct MklConvFwdParams {
typedef mkldnn::convolution_forward::primitive_desc ConvFwdPd; typedef mkldnn::convolution_forward::primitive_desc ConvFwdPd;
// With quantization, input, filter, and output can have different types // With quantization, input, filter, and output can have different types
// so we use differnt template parameter for each type // so we use different template parameter for each type
template <typename T, typename Tinput, typename Tfilter, typename Tbias, template <typename T, typename Tinput, typename Tfilter, typename Tbias,
typename Toutput> typename Toutput>
class MklConvFwdPrimitive : public MklPrimitive { class MklConvFwdPrimitive : public MklPrimitive {

2
tensorflow/core/kernels/tensor_flag_utils.h
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@ -36,7 +36,7 @@ std::vector<Tindices> ParseRowStartIndices(
// Returns Status::OK() if and only if config is a float scalar or a matrix with // Returns Status::OK() if and only if config is a float scalar or a matrix with
// dimensions M x 3. If config is a scalar then config must be in the range // dimensions M x 3. If config is a scalar then config must be in the range
// [0, 1.0). If confix is a matrix then config must have shape M x 3, all of // [0, 1.0). If config is a matrix then config must have shape M x 3, all of
// its entries must be positive, and entries in the last column may not // its entries must be positive, and entries in the last column may not
// exceed 1.0. If config is a matrix then it may not be empty. // exceed 1.0. If config is a matrix then it may not be empty.
Status ValidateSparseMatrixShardingConfig(const Tensor& config); Status ValidateSparseMatrixShardingConfig(const Tensor& config);

2
tensorflow/core/nccl/nccl_manager.h
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@ -189,7 +189,7 @@ class NcclManager {
// the corresponding NCCL/CUDA error string. // the corresponding NCCL/CUDA error string.
Status GetCommunicator(Collective* collective, Communicator** communicator); Status GetCommunicator(Collective* collective, Communicator** communicator);
// Adds a participant device to the local `Collective` instance correponding // Adds a participant device to the local `Collective` instance corresponding
// to `collective_key`. Launches the `Collective` if it is ready, which it // to `collective_key`. Launches the `Collective` if it is ready, which it
// checks by calling `CheckReady()`. Also performs consistency and sanity // checks by calling `CheckReady()`. Also performs consistency and sanity
// checks before launching. // checks before launching.

2
tensorflow/core/platform/default/device_tracer.cc
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@ -560,7 +560,7 @@ void DeviceTracerImpl::AddCorrelationId(uint32 correlation_id,
auto *params = reinterpret_cast<const cuLaunchKernel_params *>( auto *params = reinterpret_cast<const cuLaunchKernel_params *>(
cbInfo->functionParams); cbInfo->functionParams);
if (VLOG_IS_ON(2)) { if (VLOG_IS_ON(2)) {
VLOG(2) << "LAUNCH stream " << params->hStream << " correllation " VLOG(2) << "LAUNCH stream " << params->hStream << " correlation "
<< cbInfo->correlationId << " kernel " << cbInfo->symbolName; << cbInfo->correlationId << " kernel " << cbInfo->symbolName;
} }
const string annotation = const string annotation =

2
tensorflow/core/platform/strong_hash.h
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@ -24,7 +24,7 @@ namespace tensorflow {
// This is a strong keyed hash function interface for strings. // This is a strong keyed hash function interface for strings.
// The hash function is deterministic on the content of the string within the // The hash function is deterministic on the content of the string within the
// process. The key of the hash is an array of 2 uint64 elements. // process. The key of the hash is an array of 2 uint64 elements.
// A strong hash make it dificult, if not infeasible, to compute inputs that // A strong hash make it difficult, if not infeasible, to compute inputs that
// hash to the same bucket. // hash to the same bucket.
// //
// Usage: // Usage:

2
tensorflow/core/profiler/internal/tfprof_op.cc
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@ -182,7 +182,7 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts,
// TODO(xpan): Is it the right choice? // TODO(xpan): Is it the right choice?
root_->formatted_str = display_str; root_->formatted_str = display_str;
} }
// Populate the chidren field. // Populate the children field.
auto* pre_pb = root_->mutable_proto(); auto* pre_pb = root_->mutable_proto();
for (auto& show_node : show_nodes) { for (auto& show_node : show_nodes) {
pre_pb->clear_children(); pre_pb->clear_children();

2
tensorflow/core/util/mkl_util.h
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@ -1581,7 +1581,7 @@ inline TensorShape MklDnnDimsToTFShape(const memory::dims& dims) {
/// Function to calculate strides given tensor shape in Tensorflow order /// Function to calculate strides given tensor shape in Tensorflow order
/// E.g., if dims_tf_order is {1, 2, 3, 4}, then as per Tensorflow convention, /// E.g., if dims_tf_order is {1, 2, 3, 4}, then as per Tensorflow convention,
/// dimesion with size 1 is outermost dimension; while dimension with size 4 is /// dimension with size 1 is outermost dimension; while dimension with size 4 is
/// innermost dimension. So strides for this tensor would be {4 * 3 * 2, /// innermost dimension. So strides for this tensor would be {4 * 3 * 2,
/// 4 * 3, 4, 1}, i.e., {24, 12, 4, 1}. /// 4 * 3, 4, 1}, i.e., {24, 12, 4, 1}.
/// ///

2
tensorflow/core/util/proto/descriptors.cc
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@ -25,7 +25,7 @@ namespace {
// Build a `DescriptorPool` from the named file or URI. The file or URI // Build a `DescriptorPool` from the named file or URI. The file or URI
// must be available to the current TensorFlow environment. // must be available to the current TensorFlow environment.
// //
// The file must contiain a serialized `FileDescriptorSet`. See // The file must contain a serialized `FileDescriptorSet`. See
// `GetDescriptorPool()` for more information. // `GetDescriptorPool()` for more information.
Status GetDescriptorPoolFromFile( Status GetDescriptorPoolFromFile(
tensorflow::Env* env, const string& filename, tensorflow::Env* env, const string& filename,