NFC - minor spelling tweaks under lite/toco directory

tensorflow/lite/toco/dump_graphviz.cc

@@ -647,7 +647,7 @@ void DumpNode(const Model& model, string* output_file, const string& node_name,
 
   for (const auto& child : node.children) {
     if (!child.second->array_id.empty()) {
-      // Dump array if this node posesses one.
+      // Dump array if this node possesses one.
       DumpArray(model, output_file, child.second->array_id);
     }
     // Note that it is always possible to have children. Unlike a filesystem,

tensorflow/lite/toco/graph_transformations/convert_trivial_transpose_to_reshape.cc

@@ -67,7 +67,7 @@ ::tensorflow::Status ConvertTrivialTransposeToReshape::Run(Model* model,
   }
   // Note: We can assume we have error checked inputs in PropagateFixedSizes.
 
-  // Check that the permutation has propogated.
+  // Check that the permutation has propagated.
   std::vector<int> const& perm = transpose_op->perm;
   if (perm.empty()) {
     return ::tensorflow::Status::OK();

tensorflow/lite/toco/graph_transformations/graph_transformations.h

@@ -159,7 +159,7 @@ DECLARE_GRAPH_TRANSFORMATION(PropagateFixedSizes)
 DECLARE_GRAPH_TRANSFORMATION(HardcodeMinMax)
 DECLARE_GRAPH_TRANSFORMATION(Quantize)
 DECLARE_GRAPH_TRANSFORMATION(RemoveFinalDequantizeOp)
-DECLARE_GRAPH_TRANSFORMATION(RemoveSuccesiveTranspose)
+DECLARE_GRAPH_TRANSFORMATION(RemoveSuccessiveTranspose)
 DECLARE_GRAPH_TRANSFORMATION(RemoveTensorFlowAssert)
 DECLARE_GRAPH_TRANSFORMATION(RemoveTensorFlowIdentity)
 DECLARE_GRAPH_TRANSFORMATION(RemoveTrivialBinaryOperator)

tensorflow/lite/toco/graph_transformations/identify_dilated_conv.cc

@@ -225,8 +225,8 @@ ::tensorflow::Status IdentifyDilatedConv::Run(Model* model,
         dilation_factor);
     if (changed) {
       LOG(INFO)
-          << "Replaced sub-netork with Dilated DepthwiseConv2D op outputting \""
-          << conv_base_op->outputs[0] << "\".";
+          << "Replaced sub-network with Dilated DepthwiseConv2D op outputting "
+          << "\"" << conv_base_op->outputs[0] << "\".";
     }
   }
 

tensorflow/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc

@@ -245,7 +245,7 @@ bool RecursivelyForwardPropagateDataType(GraphTransformation* transformation,
 // This can be thought of as a bidirectional flood-fill of the num_bits implied
 // final_data_type that terminates at other FakeQuant ops (and a few others as
 // determined by DoesOpBlockBackwardPropagation/DoesOpBlockForwardPropagation).
-// Once all FakeQuant ops have been visted the arrays should all have
+// Once all FakeQuant ops have been visited the arrays should all have
 // appropriate final_data_types if the source graph was annotated with the
 // proper FakeQuant ops.
 //

tensorflow/lite/toco/graph_transformations/propagate_fixed_sizes.cc

@@ -671,7 +671,7 @@ void ProcessConcatenationOperator(Model* model, ConcatenationOperator* op) {
       break;
     }
   }
-  // Determine the concat size, and enfore that all inputs have
+  // Determine the concat size, and enforce that all inputs have
   // the same dimensions count.
   int concat_size = 0;
   for (const auto& input_name : op->inputs) {
@@ -1098,7 +1098,7 @@ void ProcessUnidirectionalSequenceLstmOperator(
   constexpr int kInputActivationStateTensor = 18;
   constexpr int kInputCellStateTensor = 19;
 
-  // TFlite intepreter does not support array which is variable and contains a
+  // TFlite interpreter does not support array which is variable and contains a
   // buffer (see b/115961645 for more discussion).
   // The follow block remove buffer from the array to work around the
   // restriction, as a consequence, downstream applications should not
@@ -1142,7 +1142,7 @@ void ProcessUnidirectionalSequenceRnnOperator(
   }
 
   constexpr int kHiddenStateTensor = 4;
-  // TFlite intepreter does not support array which is variable and contains a
+  // TFlite interpreter does not support array which is variable and contains a
   // buffer (see b/115961645 for more discussion).
   // The follow block remove buffer from the array to work around the
   // restriction, as a consequence, downstream applications should not
@@ -1658,7 +1658,7 @@ void ProcessStridedSliceOperator(Model* model, StridedSliceOperator* op) {
   }
 
   if (op->ellipsis_mask != 0) {
-    // Something like LOG_FIRST_N(WARNING, 10) would be prefferable to reduce
+    // Something like LOG_FIRST_N(WARNING, 10) would be preferable to reduce
     // log noise. However, the TensorFlow logging library does not appear to
     // support this.
     LOG(WARNING) << "Skipping StridedSlice op with output \"" << op->outputs[0]
@@ -2434,7 +2434,7 @@ ::tensorflow::Status PropagateFixedSizes::Run(Model* model,
       break;
     case OperatorType::kCTCBeamSearchDecoder:
       // The sizes of the outputs are only known in runtime based on the input.
-      // Ignore shape progapation here and defer that to the interpreter.
+      // Ignore shape propagation here and defer that to the interpreter.
       break;
     case OperatorType::kMatrixSetDiagV2:
       // MatrixSetDiagV2 operators are converted to MatrixSetDiag,

tensorflow/lite/toco/graph_transformations/quantization_util.cc

@@ -229,7 +229,7 @@ bool IsArrayQuantizedRangeSubset(GraphTransformation* transformation,
       ChooseQuantizationParamsForArrayAndQuantizedDataType(
           array, quantized_data_type, &quantization_params);
       transformation->AddMessageF(
-          "No quantization params - infering from data type %s with minmax "
+          "No quantization params - inferring from data type %s with minmax "
           "%g,%g as zero_point=%g, scale=%g",
           ArrayDataTypeName(quantized_data_type), array.minmax->min,
           array.minmax->max, quantization_params.zero_point,

tensorflow/lite/toco/graph_transformations/remove_successive_transpose.cc

@@ -56,9 +56,9 @@ void ReplaceOpInputsWith(Model* model, const string& lookfor,
 
 }  // namespace
 
-::tensorflow::Status RemoveSuccesiveTranspose::Run(Model* model,
-                                                   std::size_t op_index,
-                                                   bool* modified) {
+::tensorflow::Status RemoveSuccessiveTranspose::Run(Model* model,
+                                                    std::size_t op_index,
+                                                    bool* modified) {
   *modified = false;
   auto op = model->operators.begin() + op_index;
   if (op->get()->type != OperatorType::kTranspose) {

tensorflow/lite/toco/graph_transformations/tests/remove_successive_transpose_test.cc

@@ -94,7 +94,7 @@ TEST_F(RemoveSuccessiveTransposeTest, RemoveTranspose) {
   // Creating a model.
   CreateGraph({1, 0}, {1, 0});
 
-  toco::RemoveSuccesiveTranspose transformation;
+  toco::RemoveSuccessiveTranspose transformation;
   bool modified;
   ASSERT_TRUE(transformation.Run(model_.get(), /*op_index=*/1, &modified).ok());
   EXPECT_TRUE(modified);
@@ -109,7 +109,7 @@ TEST_F(RemoveSuccessiveTransposeTest, DontRemoveNotIdentityTranspose) {
   // Creating a model.
   CreateGraph({0, 2, 1}, {1, 0, 2});
 
-  toco::RemoveSuccesiveTranspose transformation;
+  toco::RemoveSuccessiveTranspose transformation;
   bool modified;
   ASSERT_TRUE(transformation.Run(model_.get(), /*op_index=*/1, &modified).ok());
   EXPECT_FALSE(modified);
@@ -139,7 +139,7 @@ TEST_F(RemoveSuccessiveTransposeTest, DontRemoveTransposeOutputUnused) {
   transpose2_op->outputs = {"InputTransposeTranspose"};
   model_->operators.push_back(std::unique_ptr<toco::Operator>(transpose2_op));
 
-  toco::RemoveSuccesiveTranspose transformation;
+  toco::RemoveSuccessiveTranspose transformation;
   bool modified;
   ASSERT_TRUE(transformation.Run(model_.get(), /*op_index=*/1, &modified).ok());
   EXPECT_FALSE(modified);

tensorflow/lite/toco/graph_transformations/unroll_batch_matmul.cc

@@ -177,7 +177,7 @@ ::tensorflow::Status UnrollBatchMatMul::Run(Model* model, std::size_t op_index,
 
   CHECK_EQ(input_array_a.shape().dims(dims_a - 1),
            input_array_b.shape().dims(dims_b - 2))
-      << "Input dimensions must be compatible for multipication. shape a = ["
+      << "Input dimensions must be compatible for multiplication. shape a = ["
       << absl::StrJoin(input_array_a.shape().dims(), ", ") << "], shape b = ["
       << absl::StrJoin(input_array_b.shape().dims(), ", ") << "]";
 

tensorflow/lite/toco/logging/gen_html.py

@@ -136,7 +136,7 @@ def generate(self,
       dot_after: A string, the dot representation of the model after
         the conversion.
       toco_err_log: A string, the logs emitted by TOCO during conversion. Caller
-        need to ensure that this string is properly anoynimized (any kind of
+        need to ensure that this string is properly anonymized (any kind of
         user data should be eliminated).
       tflite_graph_path: A string, the filepath to the converted TFLite model.
 

tensorflow/lite/toco/model.h

@@ -490,7 +490,7 @@ struct ConvOperator : Operator {
 //   inputs[4]: optional: merge repeated.
 //
 //  Outputs:
-//    outputs[0]: deocoded.
+//    outputs[0]: decoded.
 //    outputs[1]: log probability.
 //
 // TensorFlow equivalent: CTCBeamSearchDecoder
@@ -1258,7 +1258,7 @@ struct ExpandDimsOperator : Operator {
   ExpandDimsOperator() : Operator(OperatorType::kExpandDims) {}
 };
 
-// Ceates a tensor of shape dims and fills it with the given scalar value.
+// Creates a tensor of shape dims and fills it with the given scalar value.
 // Output type will be the same as the given scalar value.
 //
 // Inputs:

tensorflow/lite/toco/python/toco_from_protos.py

@@ -21,7 +21,7 @@
 import sys
 
 # We need to import pywrap_tensorflow prior to the toco wrapper.
-# pylint: disable=invalud-import-order,g-bad-import-order
+# pylint: disable=invalid-import-order,g-bad-import-order
 from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
 from tensorflow.python import _pywrap_toco_api
 from tensorflow.python.platform import app

tensorflow/lite/toco/python/toco_from_protos_test.py

@@ -85,7 +85,7 @@ def test_toco(self):
       val = img + tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.])
       out = tf.identity(val, name="out")
       out2 = tf.sin(val, name="out2")
-      # This is a valid mdoel
+      # This is a valid model
       self._run(sess, img, out, True)
       # This uses an invalid function.
       # TODO(aselle): Check to make sure a warning is included.

tensorflow/lite/toco/tflite/export.cc

@@ -53,7 +53,7 @@ namespace {
 
 // Check if a TensorFlow Op is a control flow op by its name.
 bool IsControlFlowOp(const string& tensorflow_op) {
-  // Technically this is equalivent to `::tensorflow::Node::IsControlFlow()`.
+  // Technically this is equivalent to `::tensorflow::Node::IsControlFlow()`.
   // It requires to construct a `::tensorflow::Graph` to use that helper
   // function, so we simply hardcode the list of control flow ops here.
   if (tensorflow_op == "Switch" || tensorflow_op == "RefSwitch" ||
@@ -477,7 +477,8 @@ tensorflow::Status Export(
   for (const string& input_array : model.GetInvalidInputArrays()) {
     if (model.HasArray(input_array)) {
       return tensorflow::errors::InvalidArgument(absl::StrCat(
-          "Placeholder ", input_array, " should be specied by input_arrays."));
+          "Placeholder ", input_array, " should be specified by "
+          "input_arrays."));
     }
   }
 

tensorflow/lite/toco/tflite/operator_test.cc

@@ -37,7 +37,7 @@ class OperatorTest : public ::testing::Test {
     static auto* by_name = new OpsByName(BuildOperatorByNameMap());
     static auto* by_type = new OpsByType(BuildOperatorByTypeMap());
 
-    // Make sure the two maps were consitently built.
+    // Make sure the two maps were consistently built.
     CHECK(by_name->count(name)) << "No operator for '" << name << "'.";
     BaseOperator* op1 = by_name->at(name).get();
     CHECK(op1->type() == type) << "while verifying '" << name << "'.";

tensorflow/lite/toco/toco_cmdline_flags.cc

@@ -171,7 +171,7 @@ bool ParseTocoFlagsFromCommandLineFlags(
            "Ignored if the output format is not TFLite."),
       Flag("quantize_to_float16", parsed_flags.quantize_to_float16.bind(),
            parsed_flags.quantize_to_float16.default_value(),
-           "Used in conjuction with post_training_quantize. Specifies that "
+           "Used in conjunction with post_training_quantize. Specifies that "
            "the weights should be quantized to fp16 instead of the default "
            "(int8)"),
       Flag("quantize_weights", parsed_flags.quantize_weights.bind(),

tensorflow/lite/toco/toco_convert_test.cc

@@ -39,7 +39,7 @@ TEST(TocoTest, BadInputFormat) {
                "Unhandled input_format='FILE_FORMAT_UNKNOWN'");
 }
 
-TEST(TocoTest, MissingOuputArrays) {
+TEST(TocoTest, MissingOutputArrays) {
   TocoFlags toco_flags;
   ModelFlags model_flags;
 

tensorflow/lite/toco/toco_tooling.cc

@@ -67,7 +67,7 @@ void MakeGeneralGraphTransformationsSet(
   transformations->Add(new PropagateActivationFunctionIntoConstants);
   transformations->Add(new PropagateArrayDataTypes);
   transformations->Add(new PropagateFixedSizes);
-  transformations->Add(new RemoveSuccesiveTranspose);
+  transformations->Add(new RemoveSuccessiveTranspose);
   transformations->Add(new RemoveTensorFlowAssert);
   transformations->Add(new RemoveTensorFlowIdentity);
   transformations->Add(new RemoveTrivialConcatenation);
@@ -415,10 +415,10 @@ tensorflow::Status TransformWithStatus(const TocoFlags& toco_flags,
   // is:
   //    Input [1, 20, 1, 20, 1, 64] * ones [1, 3, 1, 3, 1, 1]
   // The problem is if the input is quantized, then the quantization parameters
-  // will be slightly different for the input and the output. (althought the
+  // will be slightly different for the input and the output. (although the
   // difference is really small).
   // But, since we're changing this pattern to be pack-based which enforce
-  // the quantization paramters to be exactly the same.
+  // the quantization parameters to be exactly the same.
   // So we have to wait for all quantization parameters being resolved and
   // propagated and create our own.
   // We may need to revisit this logic later.

tensorflow/lite/toco/tooling_util.cc

@@ -929,7 +929,7 @@ void CheckNonExistentIOArrays(const Model& model) {
   }
   static constexpr char general_comment[] =
       "Is it a typo? This should not happen. If you trigger this error "
-      "please send a bug report (with code to reporduce this error), to the "
+      "please send a bug report (with code to reproduce this error), to the "
       "TensorFlow Lite team.";
   for (const string& output_array : model.flags.output_arrays()) {
     if (IsConstantParameterArray(model, output_array)) {