typos in tensorflow/core fixed

tensorflow/core/framework/node_def_builder_test.cc

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

tensorflow/core/graph/collective_order.cc

@@ -143,7 +143,7 @@ Status CreateControlDependencies(
 
 // Insert control dependencies defined by `dependency_edges` in `graph`.  If
 // `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(
     Graph* graph, GraphCollectiveOrder order_type,
     const absl::flat_hash_map<Node*, absl::flat_hash_set<Node*>>&

tensorflow/core/graph/graph_constructor_test.cc

@@ -951,7 +951,7 @@ TEST_F(GraphConstructorTest, ImportGraphDef) {
   EXPECT_TRUE(HasControlEdge("D", sink));
   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);
   EXPECT_TRUE(errors::IsInvalidArgument(s)) << s;
 

tensorflow/core/graph/mkl_layout_pass_test.cc

@@ -572,7 +572,7 @@ TEST_F(MklLayoutPassTest, Input_ControlEdge_PadWithConv2D_Positive) {
 // Test if output control edges does not duplicate after merge.
 // 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
-// to that commom op.
+// to that common op.
 // padding is VALID type
 // A = input(image), B = input(paddings), C= Pad = input of conv2D,
 // 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.
 // 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
-// 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
 // padding is VALID type
 // A = input(image), B = input(paddings), C = Pad(A, B) = input of conv2D,

tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc

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

tensorflow/core/kernels/data/experimental/indexed_dataset_op.cc

@@ -149,7 +149,7 @@ class MaterializedDatasetResource : public ResourceBase {
 
 // 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
-// `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.
 //
 // NOTE: This is not a feature-complete implementation of the DT_VARIANT

tensorflow/core/kernels/debug_ops_test.cc

@@ -364,7 +364,7 @@ TEST_F(DebugNumericSummaryOpTest, Float_only_valid_values) {
        7.33333333333,  // variance of non-inf and non-nan elements.
        static_cast<double>(DT_FLOAT),  // dtype
        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);
 }

tensorflow/core/kernels/dynamic_stitch_op.cc

@@ -167,7 +167,7 @@ class DynamicStitchOpGPU : public DynamicStitchOpImplBase<T> {
     // merged that aren't covered by an index in indices.  What should we do?
     if (first_dim_size > 0) {
       // 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
       // last of duplicated indices, it could instead be done of the GPU
       // implicitly using atomics to make sure the last index is the final

tensorflow/core/kernels/eigen_benchmark_cpu_test.cc

@@ -8,7 +8,7 @@ You may obtain a copy of the License at
 
 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 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
 limitations under the License.
 ==============================================================================*/

tensorflow/core/kernels/fuzzing/string_split_fuzz.cc

@@ -34,7 +34,7 @@ class FuzzStringSplit : public FuzzSession {
     Tensor delimiter_tensor(tensorflow::DT_STRING, TensorShape({}));
 
     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
       // of handing it a > int32_max size string, which should be tested for in
       // an explicit test).

tensorflow/core/kernels/hexagon/hexagon_control_wrapper.h

@@ -76,7 +76,7 @@ class HexagonControlWrapper final : public IRemoteFusedGraphExecutor {
   // TODO(satok): Use actual data passed by FillInputNode and remove
   // std::vector<float> dummy_input_float_{};
   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
   std::unordered_map<int, std::vector<uint8>> dummy_const_data_{};
 

tensorflow/core/kernels/mkl_concat_op.cc

@@ -484,7 +484,7 @@ class MklConcatOp : public OpKernel {
         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:
   //   1. input_shapes: shapes of input (MKL) tensors.
   //   2. concat_dim: concat dimension.

tensorflow/core/kernels/mkl_conv_ops.cc

@@ -96,7 +96,7 @@ struct MklConvFwdParams {
 typedef mkldnn::convolution_forward::primitive_desc ConvFwdPd;
 
 // 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,
           typename Toutput>
 class MklConvFwdPrimitive : public MklPrimitive {

tensorflow/core/kernels/tensor_flag_utils.h

@@ -36,7 +36,7 @@ std::vector<Tindices> ParseRowStartIndices(
 
 // 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
-// [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
 // exceed 1.0. If config is a matrix then it may not be empty.
 Status ValidateSparseMatrixShardingConfig(const Tensor& config);

tensorflow/core/nccl/nccl_manager.h

@@ -189,7 +189,7 @@ class NcclManager {
   // the corresponding NCCL/CUDA error string.
   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
   // checks by calling `CheckReady()`.  Also performs consistency and sanity
   // checks before launching.

tensorflow/core/platform/default/device_tracer.cc

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

tensorflow/core/platform/strong_hash.h

@@ -24,7 +24,7 @@ namespace tensorflow {
 // This is a strong keyed hash function interface for strings.
 // 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.
-// 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.
 //
 // Usage:

tensorflow/core/profiler/internal/tfprof_op.cc

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

tensorflow/core/util/mkl_util.h

@@ -1581,7 +1581,7 @@ inline TensorShape MklDnnDimsToTFShape(const memory::dims& dims) {
 
 /// 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,
-/// 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,
 /// 4 * 3, 4, 1}, i.e., {24, 12, 4, 1}.
 ///

tensorflow/core/util/proto/descriptors.cc

@@ -25,7 +25,7 @@ namespace {
 // Build a `DescriptorPool` from the named file or URI. The file or URI
 // 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.
 Status GetDescriptorPoolFromFile(
     tensorflow::Env* env, const string& filename,