pre-commit: enable codespell; fix typos and cryptic names (#9019)

Author: alexreinking

.pre-commit-config.yaml

@@ -47,6 +47,21 @@ repos:
             | \.xcassets/
           )
 
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.4.1
+    hooks:
+      - id: codespell
+        args: [ --ignore-words-list, "CreateOr,implementors,PADD,re-use,re-used,re-using,subtile,subtiles" ]
+        exclude: |
+          (?x)(
+            ^src/autoschedulers/common/cmdline\.h$
+            | ^src/runtime/hexagon_remote/bin/src/
+            | ^src/runtime/mini_cuda\.h$
+            | ^src/runtime/mini_d3d12\.h$
+            | ^apps/HelloAndroid.*/gradlew\.bat$
+            | \.pgm$
+          )
+
   - repo: https://github.com/gitleaks/gitleaks
     rev: v8.9.0
     hooks:

Makefile

@@ -1745,7 +1745,7 @@ $(FILTERS_DIR)/nested_externs_%.a: $(BIN_DIR)/nested_externs.generator
 	$(CURDIR)/$< -g nested_externs_$* $(GEN_AOT_OUTPUTS) -o $(CURDIR)/$(FILTERS_DIR) target=$(TARGET)-no_runtime-user_context-c_plus_plus_name_mangling
 
 # Similarly, gpu_multi needs two different kernels to test compilation caching.
-# Also requies user-context.
+# Also requires user-context.
 $(FILTERS_DIR)/gpu_multi_context_threaded_%.a: $(BIN_DIR)/gpu_multi_context_threaded.generator
 	@mkdir -p $(@D)
 	$(CURDIR)/$< -g gpu_multi_context_threaded_$* $(GEN_AOT_OUTPUTS) -o $(CURDIR)/$(FILTERS_DIR) target=$(TARGET)-no_runtime-user_context

apps/HelloAndroid/README.md

@@ -26,7 +26,7 @@ In order to build, the following will be required:
   PATH.
 
 If everything is setup correctly, running the build.sh script in this directory,
-with the current directory set to here, whould build the HelloAndroid apk and
+with the current directory set to here, would build the HelloAndroid apk and
 install it on a connected Android device.
 
 # Gradle

apps/HelloAndroidCamera2/README.md

@@ -44,7 +44,7 @@ In order to build, the following will be required:
   PATH.
 
 If everything is setup correctly, running the build.sh script in this directory,
-with the current directory set to here, whould build the HelloAndroidCamera2 apk
+with the current directory set to here, would build the HelloAndroidCamera2 apk
 and install it on a connected Android device.
 
 # Gradle

apps/HelloBaremetal/cmake-external_project/CMakeLists.txt

@@ -41,7 +41,7 @@ add_executable(${GEN_EXE} IMPORTED)
 # Specify the location of exe file to import
 set_property(TARGET ${GEN_EXE}
              PROPERTY IMPORTED_LOCATION ${GEN_BINARY_DIR}/${GEN_EXE})
-# Associate with external project so that it invokes build porcess
+# Associate with external project so that it invokes build process
 add_dependencies(${GEN_EXE} gen_project)
 
 # Generate filter function

apps/HelloBaremetal/cmake/toolchain.noos-arm32-sample.cmake

@@ -12,7 +12,7 @@ set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
 set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
 set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY)
 
-# For newlib (standard C libraray) and semihosting mode
+# For newlib (standard C library) and semihosting mode
 set(C_COMMON_FLAGS "-specs=rdimon.specs")
 
 # Target CPU dependent flags to use NEON. Please modify for your target.

apps/hannk/compare_vs_tflite.cpp

@@ -9,7 +9,7 @@ int main(int argc, char **argv) {
 
     ModelRunner runner;
 
-    // Default the exernal delegate to disabled, since it may
+    // Default the external delegate to disabled, since it may
     // need extra setup to work (eg LD_LIBRARY_PATH or --external_delegate_path)
     runner.do_run[ModelRunner::kExternalDelegate] = false;
 

apps/hannk/delegate/hannk_delegate.cpp

@@ -271,7 +271,7 @@ class HannkDelegateKernel final {
             HLOG(INFO) << "Delegate " << (void *)this << " Init nodes: " << node_indices << "\n";
         }
 
-        // Pre-emptively map *all* the TFLiteTensors into our Tensor type.
+        // Preemptively map *all* the TFLiteTensors into our Tensor type.
         for (size_t tensor_id = 0; tensor_id < context->tensors_size; tensor_id++) {
             const TfLiteTensor &tensor = context->tensors[tensor_id];
             if (tensor.dims == nullptr) {

apps/hannk/interpreter/elementwise_program.h

@@ -66,7 +66,7 @@ class ElementwiseAssembler {
     }
 
     // Assemble the current program. The return value is the buffer
-    // fromt his assembler cropped to the region needed for the program.
+    // from this assembler cropped to the region needed for the program.
     Halide::Runtime::Buffer<int16_t, 2> assemble(std::initializer_list<Slot> outputs);
 
     // Write the current program to the given stream.

apps/hannk/interpreter/lower.cpp

@@ -10,15 +10,15 @@ namespace hannk {
 // and remove the 'LstmElementwiseOp' op.
 OpPtr lower_tflite_lstm(TensorPtr data_input, TensorPtr prev_activ_input, TensorPtr weights_input, TensorPtr biases_input, TensorPtr prev_state_input,
                         TensorPtr activ_output, TensorPtr state_output, TensorPtr concat_temp, TensorPtr activ_temp,
-                        ActivationFunction activation) {
+                        ActivationFunction activation_fn) {
     std::vector<TensorPtr> inputs = {data_input, prev_activ_input, weights_input, biases_input, prev_state_input};
     std::vector<TensorPtr> outputs = {activ_output, state_output};
 
     std::vector<OpPtr> ops;
 
     std::vector<TensorPtr> concat_inputs = {data_input, prev_activ_input};
     ops.push_back(make_op<ConcatenationOp>(concat_inputs, concat_temp, 0));
-    ops.push_back(lower_tflite_fullyconnected(concat_temp, weights_input, biases_input, activ_temp, activation));
+    ops.push_back(lower_tflite_fullyconnected(concat_temp, weights_input, biases_input, activ_temp, activation_fn));
 
     // Split activ_temp into the 4 ops we need.
     Box elementwise_bounds = activ_temp->bounds();
@@ -61,12 +61,12 @@ OpPtr lower_tflite_lstm(TensorPtr data_input, TensorPtr prev_activ_input, Tensor
     auto prev_state_times_forget_state = p.mul(forget_gate_output, prev_state);
 
     auto state = p.add(input_times_input_modulation, prev_state_times_forget_state);
-    auto activ = p.mul_add(output_gate_output, p.tanh(7, state, q - 4), 128);
+    auto activation = p.mul_add(output_gate_output, p.tanh(7, state, q - 4), 128);
     // Reload new_state so it's in the right place for the outputs.
     // TODO: Make the assembler smart enough to do this itself.
     state = p.add(state, 0);
 
-    auto program_buf = p.assemble({activ, state});
+    auto program_buf = p.assemble({activation, state});
     program_buf = program_buf.copy();
 
     ops.push_back(make_op<ElementwiseProgramOp>(elementwise_inputs, elementwise_outputs, program_buf));