### Gated Activation Example

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

C++ code demonstrating the gated activation process within a WaveNet layer.

```cpp
if (this->_gating_mode == GatingMode::GATED) {
    auto input_block = this->_z.leftCols(num_frames);
    auto output_block = this->_z.topRows(bottleneck).leftCols(num_frames);
    this->_gating_activation->apply(input_block, output_block);
    if (this->_activation_post_film) {
        this->_activation_post_film->Process(this->_z.topRows(bottleneck), condition, num_frames);
        this->_z.topRows(bottleneck).leftCols(num_frames).noalias() =
            this->_activation_post_film->GetOutput().leftCols(num_frames);
    }
}
```

--------------------------------

### 1x1 Convolution Example

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

C++ code for the 1x1 convolution step, reducing bottleneck channels.

```cpp
_1x1.process_(this->_z.topRows(bottleneck), num_frames);
if (this->_1x1_post_film) {
    Eigen::MatrixXf& _1x1_output = this->_1x1.GetOutput();
    this->_1x1_post_film->Process_(_1x1_output, condition, num_frames);
}
```

--------------------------------

### Layer Processing Example

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

C++ code demonstrating the sequential processing of layers within a LayerArray.

```cpp
for (size_t i = 0; i < this->_layers.size(); i++) {
    if (i == 0) {
        // First layer consumes the rechannel output buffer
        this->_layers[i].Process(rechannel_output, condition, num_frames);
    } else {
        // Subsequent layers consume the residual output from the previous layer
        Eigen::MatrixXf& prev_output = this->_layers[i - 1].GetOutputNextLayer();
        this->_layers[i].Process(prev_output, condition, num_frames);
    }

    // Accumulate head output from this layer
    this->_head_inputs.leftCols(num_frames).noalias() +=
        this->_layers[i].GetOutputHead().leftCols(num_frames);
}
```

--------------------------------

### Rechanneling Example

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

C++ code snippet for the rechanneling step in LayerArray computation.

```cpp
this->_rechannel.process_(layer_inputs, num_frames);
Eigen::MatrixXf& rechannel_output = _rechannel.GetOutput();
```

--------------------------------

### Residual and Skip Connections Example

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

C++ code illustrating the computation of residual and skip connections.

```cpp
// Store output to next layer (residual connection)
this->_output_next_layer.leftCols(num_frames).noalias() =
    input.leftCols(num_frames) + _1x1.GetOutput().leftCols(num_frames);

// Store output to head (skip connection)
if (this->_head1x1) {
    this->_output_head.leftCols(num_frames).noalias() =
        this->_head1x1->GetOutput().leftCols(num_frames);
} else {
    this->_output_head.leftCols(num_frames).noalias() =
        this->_z.topRows(bottleneck).leftCols(num_frames);
}
```

--------------------------------

### Head 1x1 Convolution Example

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

C++ code for the optional head 1x1 convolution, processing the activated output for the skip connection.

```cpp
if (this->_head1x1) {
    this->_head1x1->process_(this->_z.topRows(bottleneck).leftCols(num_frames), num_frames);
    if (this->_head1x1_post_film) {
        Eigen::MatrixXf& head1x1_output = this->_head1x1->GetOutput();
        this->_head1x1_post_film->Process_(head1x1_output, condition, num_frames);
    }
    this->_output_head.leftCols(num_frames).noalias() =
        this->_head1x1->GetOutput().leftCols(num_frames);
}
```

--------------------------------

### Tool Definitions

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/tools/CMakeLists.txt

Sets up custom targets and executables for various tools.

```cmake
set(TOOLS benchmodel)

add_custom_target(tools ALL
	DEPENDS ${TOOLS} render)

add_executable(loadmodel loadmodel.cpp ${NAM_SOURCES})
add_executable(benchmodel benchmodel.cpp ${NAM_SOURCES})
add_executable(render render.cpp ${NAM_SOURCES} ${AUDIO_DSP_TOOLS_WAV_SOURCES})
target_compile_features(render PUBLIC cxx_std_20)
# AudioDSPTools wav.cpp has sign-compare issues; don't fail build
set_source_files_properties(${AUDIO_DSP_TOOLS_WAV_SOURCES} PROPERTIES COMPILE_FLAGS "-Wno-error")
set_target_properties(render PROPERTIES
	CXX_VISIBILITY_PRESET hidden
	INTERPROCEDURAL_OPTIMIZATION TRUE
	PREFIX ""
)
if (CMAKE_SYSTEM_NAME STREQUAL "Windows")
	target_compile_definitions(render PRIVATE NOMINMAX WIN32_LEAN_AND_MEAN)
endif()
if (MSVC)
	target_compile_options(render PRIVATE
		"$<$<CONFIG:DEBUG>:/W4>"
		"$<$<CONFIG:RELEASE>:/O2>"
	)
else()
	target_compile_options(render PRIVATE
		-Wall -Wextra -Wpedantic -Wstrict-aliasing -Wunreachable-code -Weffc++ -Wno-unused-parameter
		"$<$<CONFIG:DEBUG>:-Og;-ggdb;-Werror>"
		"$<$<CONFIG:RELEASE>:-Ofast>"
	)
endif()
add_executable(benchmodel_bufsize benchmodel_bufsize.cpp ${NAM_SOURCES})
add_executable(bench_a2_fast bench_a2_fast.cpp ${NAM_SOURCES})
target_compile_features(bench_a2_fast PUBLIC cxx_std_20)
set_target_properties(bench_a2_fast PROPERTIES
	CXX_VISIBILITY_PRESET hidden
	INTERPROCEDURAL_OPTIMIZATION TRUE
	PREFIX ""
)
if (MSVC)
	target_compile_options(bench_a2_fast PRIVATE
		"$<$<CONFIG:DEBUG>:/W4>"
		"$<$<CONFIG:RELEASE>:/O2>"
	)
else()
	target_compile_options(bench_a2_fast PRIVATE
		-Wall -Wextra -Wpedantic -Wstrict-aliasing -Wunreachable-code -Wno-unused-parameter
		"$<$<CONFIG:DEBUG>:-Og;-ggdb;-Werror>"
		"$<$<CONFIG:RELEASE>:-Ofast>"
	)
endif()
add_executable(run_tests run_tests.cpp test/allocation_tracking.cpp ${NAM_SOURCES})
# Compile run_tests without optimizations to ensure allocation tracking works correctly
# Also ensure assertions are enabled (NDEBUG is not defined) so tests actually run
set_target_properties(run_tests PROPERTIES COMPILE_OPTIONS "-O0")
# Ensure assertions are enabled for run_tests by removing NDEBUG if it was set
# Release/RelWithDebInfo/MinSizeRel build types automatically define NDEBUG
# We use a compile option to undefine it, which works on GCC, Clang, and MSVC
target_compile_options(run_tests PRIVATE
	$<$<OR:$<CONFIG:Release>,$<CONFIG:RelWithDebInfo>,$<CONFIG:MinSizeRel>>:-UNDEBUG>
)

# Option to enable ADDASSERT test (tests that assertions are actually enabled)
option(ENABLE_ASSERT_TEST "Enable ADDASSERT test to verify assertions are enabled" OFF)
if(ENABLE_ASSERT_TEST)
	target_compile_definitions(run_tests PRIVATE ADDASSERT)
endif()

source_group(NAM ${CMAKE_CURRENT_SOURCE_DIR} FILES ${NAM_SOURCES})

target_compile_features(${TOOLS} PUBLIC cxx_std_20)

set_target_properties(${TOOLS}
	PROPERTIES
	CXX_VISIBILITY_PRESET hidden
	INTERPROCEDURAL_OPTIMIZATION TRUE
	PREFIX ""
)

if (CMAKE_SYSTEM_NAME STREQUAL "Windows")
	target_compile_definitions(${TOOLS} PRIVATE NOMINMAX WIN32_LEAN_AND_MEAN)
endif()

if (MSVC)
	target_compile_options(${TOOLS} PRIVATE
		"$<$<CONFIG:DEBUG>:/W4>"
		"$<$<CONFIG:RELEASE>:/O2>"
	)
else()
	target_compile_options(${TOOLS} PRIVATE
		-Wall -Wextra -Wpedantic -Wstrict-aliasing -Wunreachable-code -Weffc++ -Wno-unused-parameter
		"$<$<CONFIG:DEBUG>:-Og;-ggdb;-Werror>"
		"$<$<CONFIG:RELEASE>:-Ofast>"
	)
endif()

# There's an error in eigen's
# /Users/steve/src/NeuralAmpModelerCore/Dependencies/eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h
# Don't let this break my build on debug:
set_source_files_properties(../NAM/dsp.cpp PROPERTIES COMPILE_FLAGS "-Wno-error")
set_source_files_properties(../NAM/conv1d.cpp PROPERTIES COMPILE_FLAGS "-Wno-error")
```

--------------------------------

### Project Definition

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/CMakeLists.txt

Defines the project name and version.

```cmake
# Make sure this matches ./NAM/version.h!
project(NAM VERSION 0.4.0)
```

--------------------------------

### Head Scaling and Output

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

Scales the final head output from the last LayerArray and writes it to the output buffers.

```cpp
Eigen::MatrixXf& final_head = this->_layer_arrays.back().GetHeadOutputs();
// Apply head scale and write to output buffers
// (implementation details in wavenet.cpp)
```

--------------------------------

### Layer Computation: Input Mixin

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

C++ code illustrating the input mixin step, handling optional pre-FiLM, input mixin convolution, and optional post-FiLM.

```cpp
if (this->_input_mixin_pre_film) {
    this->_input_mixin_pre_film->Process(condition, condition, num_frames);
    this->_input_mixin.process_(this->_input_mixin_pre_film->GetOutput(), num_frames);
} else {
    this->_input_mixin.process_(condition, num_frames);
}
if (this->_input_mixin_post_film) {
    Eigen::MatrixXf& input_mixin_output = this->_input_mixin.GetOutput();
    this->_input_mixin_post_film->Process_(input_mixin_output, condition, num_frames);
}
```

--------------------------------

### Layer Computation: Input Convolution

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

C++ code demonstrating the input convolution step within a Layer, including optional pre-FiLM, dilated convolution, and optional post-FiLM.

```cpp
if (this->_conv_pre_film) {
    this->_conv_pre_film->Process(input, condition, num_frames);
    this->_conv.Process(this->_conv_pre_film->GetOutput(), num_frames);
} else {
    this->_conv.Process(input, num_frames);
}
if (this->_conv_post_film) {
    Eigen::MatrixXf& conv_output = this->_conv.GetOutput();
    this->_conv_post_film->Process_(conv_output, condition, num_frames);
}
```

--------------------------------

### A2 Fast-Path WaveNet Option

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/CMakeLists.txt

Option to enable a specialized, hand-optimized WaveNet implementation for A2 models.

```cmake
# Build the specialized A2 fast-path WaveNet (A2 standard + A2 nano). When ON,
# models whose config matches the A2 shape signature are routed to a hand-optimized
# WaveNet implementation instead of the generic one. When OFF, all models go
# through the generic path.
option(NAM_ENABLE_A2_FAST "Build the A2 fast-path WaveNet" ON)
if(NAM_ENABLE_A2_FAST)
	add_compile_definitions(NAM_ENABLE_A2_FAST)
endif()
```

--------------------------------

### C++ Standard Configuration

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/CMakeLists.txt

Sets the C++ standard to C++20 and related requirements.

```cmake
set(CMAKE_CXX_STANDARD 20)
set(CMAKE_CXX_STANDARD_REQUIRED OFF)
set(CMAKE_CXX_EXTENSIONS OFF)
```

--------------------------------

### libc++ Option for Linux

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/CMakeLists.txt

Option to enable libc++ on Linux.

```cmake
option(NAM_USE_LIBCXX_LINUX "Use libc++ on Linux (Clang + libc++-dev packages; tests SlimmableWavenet _LIBCPP_VERSION path)" OFF)
```

--------------------------------

### Dependencies Path Configuration

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/CMakeLists.txt

Sets the path for project dependencies and includes them.

```cmake
set(NAM_DEPS_PATH "${CMAKE_CURRENT_SOURCE_DIR}/Dependencies")
include_directories(SYSTEM "${NAM_DEPS_PATH}/eigen")
```

--------------------------------

### Condition Processing

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

Processes the input through a condition DSP to generate the conditioning signal, or uses the input directly if no DSP is provided.

```cpp
void WaveNet::_process_condition(const int num_frames) {
    if (this->_condition_dsp != nullptr) {
        // Process input through condition DSP
        this->_condition_dsp->process(/* input */, /* output */, num_frames);
        // Copy output to condition buffer
    } else {
        // Use input directly as condition
        this->_condition_output = this->_condition_input;
    }
}
```

--------------------------------

### Module Path Configuration

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/CMakeLists.txt

Sets the path for CMake modules.

```cmake
set(CMAKE_MODULE_PATH "${CMAKE_SOURCE_DIR}/cmake")
```

--------------------------------

### Add Subdirectory for Tools

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/CMakeLists.txt

Includes the 'tools' subdirectory into the build.

```cmake
add_subdirectory(tools)
```

--------------------------------

### Layer Computation: Sum and Pre-Activation FiLM

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

C++ code showing the summation of convolution and input mixin outputs, followed by optional pre-activation FiLM modulation.

```cpp
this->_z.leftCols(num_frames).noalias() =
    _conv.GetOutput().leftCols(num_frames) + _input_mixin.GetOutput().leftCols(num_frames);
if (this->_activation_pre_film) {
    this->_activation_pre_film->Process_(this->_z, condition, num_frames);
}
```

--------------------------------

### CMake Minimum Required Version

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/CMakeLists.txt

Specifies the minimum version of CMake required to build the project.

```cmake
cmake_minimum_required(VERSION 3.10)
```

--------------------------------

### Head Rechanneling

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

Projects the accumulated head outputs to the final output dimension for the layer array.

```cpp
_head_rechannel.process_(this->_head_inputs, num_frames);
```

--------------------------------

### Source File Globbing

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/tools/CMakeLists.txt

Defines source files for the NAM library using globbing.

```cmake
file(GLOB NAM_SOURCES_TOP "${CMAKE_CURRENT_SOURCE_DIR}/../NAM/*.cpp" "${CMAKE_CURRENT_SOURCE_DIR}/../NAM/*.c")
file(GLOB NAM_SOURCES_SUB "${CMAKE_CURRENT_SOURCE_DIR}/../NAM/*/*.cpp")
set(NAM_SOURCES ${NAM_SOURCES_TOP} ${NAM_SOURCES_SUB})
```

--------------------------------

### Platform-Specific C++ Flags (Linux/Windows)

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/CMakeLists.txt

Configures C++ flags and libraries based on the operating system.

```cmake
if (CMAKE_SYSTEM_NAME STREQUAL "Darwin")
	include_directories(SYSTEM /usr/local/include)
elseif (CMAKE_SYSTEM_NAME STREQUAL "Linux")
	if (NAM_USE_LIBCXX_LINUX)
		set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libc++")
		# Clang links libc++/libc++abi; do not pull in libstdc++fs (libstdc++).
	else()
		link_libraries(stdc++fs)
	endif()
elseif (CMAKE_SYSTEM_NAME STREQUAL "Windows")
	add_compile_definitions(NOMINMAX WIN32_LEAN_AND_MEAN)
else()
	message(FATAL_ERROR "Unrecognized Platform!")
endif()
```

--------------------------------

### Platform-Specific C++ Flags (macOS)

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/CMakeLists.txt

Adds libc++ to CMAKE_CXX_FLAGS on macOS.

```cmake
if (CMAKE_SYSTEM_NAME STREQUAL "Darwin")
	set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libc++")
endif()
```

--------------------------------

### LayerArray Processing

Source: https://github.com/sdatkinson/neuralampmodelercore/blob/main/docs/wavenet_walkthrough.md

Processes the output of the previous LayerArray, accumulating head inputs. The first LayerArray processes the input with zeroed head inputs, while subsequent LayerArrays use the previous array's output and head inputs.

```cpp
// First layer array
this->_layer_arrays[0].Process(input, condition, num_frames);

// Subsequent layer arrays
for (size_t i = 1; i < this->_layer_arrays.size(); i++) {
    Eigen::MatrixXf& prev_output = this->_layer_arrays[i-1].GetLayerOutputs();
    Eigen::MatrixXf& prev_head = this->_layer_arrays[i-1].GetHeadOutputs();
    this->_layer_arrays[i].Process(prev_output, condition, prev_head, num_frames);
}
```

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