minor spelling tweaks

tensorflow/compiler/mlir/lite/ir/tfl_ops.td

@@ -3397,7 +3397,7 @@ def TFL_BidirectionalSequenceLSTMOp :
   let summary = "Bidirectional sequence lstm operator";
 
   let description = [{
-    Bidirectional lstm is essentiallay two lstms, one running forward & the
+    Bidirectional lstm is essentially two lstms, one running forward & the
     other running backward. And the output is the concatenation of the two
     lstms.
   }];

tensorflow/compiler/mlir/xla/ir/hlo_client_ops.td

@@ -51,7 +51,7 @@ class HLOClient_Op<string mnemonic, list<OpTrait> traits> :
 // broadcasting (via the broadcast_dimensions attribute) and implicit degenerate
 // shape broadcasting.
 //
-// These have 1:1 correspondance with same-named ops in the xla_hlo dialect;
+// These have 1:1 correspondence with same-named ops in the xla_hlo dialect;
 // however, those operations do not support broadcasting.
 //
 // See:

tensorflow/compiler/mlir/xla/transforms/legalize_tf_patterns.td

@@ -382,7 +382,7 @@ class createIotaOp<string dim>: NativeCodeCall<
 def createConvertOp: NativeCodeCall<
   "CreateConvertOp(&($_builder), $0.getOwner()->getLoc(), $1, $2)">;
 
-// Performs a substitution of MatrixBandPartOp for XLA HLO ops. Psuedocode is
+// Performs a substitution of MatrixBandPartOp for XLA HLO ops. Pseudocode is
 // shown below, given a tensor `input` with k dimensions [I, J, K, ..., M, N]
 // and two integers, `num_lower` and `num_upper`:
 //
@@ -454,14 +454,14 @@ def : Pat<(TF_ConstOp:$res ElementsAttr:$value), (HLO_ConstOp $value),
 // TODO(hinsu): Make these patterns to TF to TF lowering. Relu6 lowering will
 // require HLO canonicalization of min and max on a tensor to ClampOp.
 
-// TODO(hinsu): Lower unsinged and quantized types after supporting
+// TODO(hinsu): Lower unsigned and quantized types after supporting
 // them in GetScalarOfType.
 def : Pat<(TF_ReluOp AnyRankedTensor:$input),
           (HLO_MaxOp (HLO_ConstOp:$zero (GetScalarOfType<0> $input)), $input,
                      (BinBroadcastDimensions $zero, $input)),
           [(TF_SintOrFpTensor $input)]>;
 
-// TODO(hinsu): Lower unsinged and quantized types after supporting
+// TODO(hinsu): Lower unsigned and quantized types after supporting
 // them in GetScalarOfType.
 def : Pat<(TF_Relu6Op AnyRankedTensor:$input),
           (HLO_ClampOp (HLO_ConstOp (GetScalarOfType<0> $input)), $input,

tensorflow/lite/delegates/xnnpack/README.md

@@ -40,7 +40,7 @@ interpreter->Invoke()
 
 ...
 
-// IMPORTANT: release the interpreter before destroing the delegate
+// IMPORTANT: release the interpreter before destroying the delegate
 interpreter.reset();
 TfLiteXNNPackDelegateDelete(xnnpack_delegate);
 ```

tensorflow/lite/experimental/ruy/profiler/README.md

@@ -133,7 +133,7 @@ But also the following advantages:
 The philosophy underlying this profiler is that software performance depends on
 software engineers profiling often, and a key factor limiting that in practice
 is the difficulty or cumbersome aspects of profiling with more serious profilers
-such as Linux's "perf", espectially in embedded/mobile development: multiple
+such as Linux's "perf", especially in embedded/mobile development: multiple
 command lines are involved to copy symbol files to devices, retrieve profile
 data from the device, etc. In that context, it is useful to make profiling as
 easy as benchmarking, even on embedded targets, even if the price to pay for

tensorflow/lite/g3doc/convert/python_api.md

@@ -171,7 +171,7 @@ TensorFlow Lite metadata provides a standard for model descriptions. The
 metadata is an important source of knowledge about what the model does and its
 input / output information. This makes it easier for other developers to
 understand the best practices and for code generators to create platform
-specific wrapper code. For more infomation, please refer to the
+specific wrapper code. For more information, please refer to the
 [TensorFlow Lite Metadata](metadata.md) section.
 
 ## Installing TensorFlow <a name="versioning"></a>
@@ -192,7 +192,7 @@ either install the nightly build with
 [Docker](https://www.tensorflow.org/install/docker), or
 [build the pip package from source](https://www.tensorflow.org/install/source).
 
-### Custom ops in the experimenal new converter
+### Custom ops in the experimental new converter
 
 There is a behavior change in how models containing
 [custom ops](https://www.tensorflow.org/lite/guide/ops_custom) (those for which

tensorflow/lite/g3doc/performance/best_practices.md

@@ -52,7 +52,7 @@ operator is executed. Check out our
 
 Model optimization aims to create smaller models that are generally faster and
 more energy efficient, so that they can be deployed on mobile devices. There are
-multiple optimization techniques suppored by TensorFlow Lite, such as
+multiple optimization techniques supported by TensorFlow Lite, such as
 quantization.
 
 Check out our [model optimization docs](model_optimization.md) for details.

tensorflow/lite/micro/examples/micro_speech/README.md

@@ -420,7 +420,7 @@ using [ARM Mbed](https://github.com/ARMmbed/mbed-cli).
     ```
     mbed compile --target K66F --toolchain GCC_ARM --profile release
     ```
-8.  For some mbed compliers, you may get compile error in mbed_rtc_time.cpp.
+8.  For some mbed compilers, you may get compile error in mbed_rtc_time.cpp.
     Go to `mbed-os/platform/mbed_rtc_time.h` and comment line 32 and line 37:
 
     ```

tensorflow/lite/micro/examples/person_detection/README.md

@@ -202,7 +202,7 @@ The next steps assume that the
 
 *   The `IDF_PATH` environment variable is set
 *   `idf.py` and Xtensa-esp32 tools (e.g. `xtensa-esp32-elf-gcc`) are in `$PATH`
-*   `esp32-camera` should be downloaded in `comopnents/` dir of example as
+*   `esp32-camera` should be downloaded in `components/` dir of example as
     explained in `Building the example`(below)
 
 ### Generate the examples

tensorflow/lite/micro/examples/person_detection/esp/README_ESP.md

@@ -16,7 +16,7 @@ The next steps assume that the
 [IDF environment variables are set](https://docs.espressif.com/projects/esp-idf/en/latest/get-started/index.html#step-4-set-up-the-environment-variables) :
 * The `IDF_PATH` environment variable is set. * `idf.py` and Xtensa-esp32 tools
 (e.g., `xtensa-esp32-elf-gcc`) are in `$PATH`. * `esp32-camera` should be
-downloaded in `comopnents/` dir of example as explained in `Build the
+downloaded in `components/` dir of example as explained in `Build the
 example`(below)
 
 ## Build the example

tensorflow/lite/tools/benchmark/android/README.md

@@ -37,7 +37,7 @@ bazel build -c opt \
 adb install -r -d -g bazel-bin/tensorflow/lite/tools/benchmark/android/benchmark_model.apk
 ```
 Note: Make sure to install with "-g" option to grant the permission for reading
-extenal storage.
+external storage.
 
 (3) Push the compute graph that you need to test.
 
@@ -119,6 +119,6 @@ a trace file,
 between tracing formats and
 [create](https://developer.android.com/topic/performance/tracing/on-device#create-html-report)
 an HTML report.
-Note that, the catured tracing file format is either in Perfetto format or in
+Note that, the captured tracing file format is either in Perfetto format or in
 Systrace format depending on the Android version of your device. Select the
 appropriate method to handle the generated file.

tensorflow/tools/ci_build/README.md

@@ -83,7 +83,7 @@ this UI, to see the logs for a failed build:
 
 *   Submit special pull request (PR) comment to trigger CI: **bot:mlx:test**
 *   Test session is run automatically.
-*   Test results and artefacts (log files) are reported via PR comments
+*   Test results and artifacts (log files) are reported via PR comments
 
 ##### CI Steps