cmake : add PCH and unity build to improve build times (#28091)

* scripts : add initial profiling script (wip) * src : add precompile headers (PCH) for models.h * common : add common.h as PCH * ggml : add PCH for ggml-impl.h * mtmd : use PCH for models.h * scripts : add script to build with Server/Tools/Tests * server : add PCH for common.h * docs: add profiling progress notes (wip) * ggml : add exclude for GCC + SVE on ARM Refs: https://github.com/ggml-org/llama.cpp/actions/runs/33393906061/job/99493756214?pr=28091 * ggml : attempt to fix use of std::hardware_destructive_inference_size Refs: https://github.com/ggml-org/llama.cpp/actions/runs/33396221677/job/99501265689?pr=28091 * squash! ggml : attempt to fix use of std::hardware_destructive_inference_size Add a version check for GCC 12 to conditionally apply the `-Winterference-size` pragma. * editorconfig : exclude profiling reports dir This directory will not be included in the merge later and this commit can be ignore at that point. Just fixing to keep CI happy. * ggml : skip PCH for gcc on non-x86 architectures * tests : add PCH for peg-parser/tests.h There are 7 peg-parser tests that can share one PCH instead of then each parsing the full tests.h. * common : add PCH for chat.h * docs : update linux build profiling full results Just updating after a number of PCH additions. These are not exact figures and will vary a bit from run to run, but they give a general idea of the performance impact of PCH. * cmake : introduce unity build for models This commit introduces a unity build for the models to improve compilation time. The improvements were roughly the following: ```console +------------------------+-----+------------+------------+------------+ | Build | TUs | Frontend | Backend | Total | +------------------------+-----+------------+------------+------------+ | Full, master | 396 | 811.0 s | 692.2 s | 1,503.2 s | | Full, with PCH | 405 | 380.0 s | 664.7 s | 1,044.7 s | | Full, with PCH + UB | 264 | 357.7 s | 635.7 s | 993.4 s | +------------------------+-----+------------+------------+------------+ TU = Translation Unit. Full = includes Server, Tools, and Tests. PCH = precompiled headers. UB = unity build for models. ``` * docs : update linux profiling table with unitiy build results * docs : update mac profiling results to include unity build [no ci] * docs: remove profiling reports * scripts : merge build profile scripts into one script I was lazy before and just copied the first script to enable Tests, Server, and Tools. This now merges them into a single script. * Revert "editorconfig : exclude profiling reports dir" [no ci] This reverts commit 2922a12118. * src : rename ggml_view_2d_slice to gemma3n_view_2d_slice This is to be consistent with the rename in gemma4.cpp which was required to avoid a name clash. * cmake : add build profile script for windows [no ci] This commit adds a port of the scripts/build-profile.sh script to windows powershell. This was developed on Windows on ARM but should work on X64 as well but needs to be tested there as well.
2026-09-12 07:59:01 +02:00 · 2026-09-11 13:01:29 +02:00
parent 1dfe94e048
commit 3bcfeb700f
12 changed files with 462 additions and 44 deletions
@@ -134,6 +134,8 @@ set_target_properties(${TARGET} PROPERTIES
 target_include_directories(${TARGET} PUBLIC .)
 target_link_libraries     (${TARGET} PUBLIC vendor::nlohmann vendor::sheredom)
 target_compile_features   (${TARGET} PUBLIC cxx_std_17)
+target_precompile_headers (${TARGET} PRIVATE common.h)
+target_precompile_headers (${TARGET} PRIVATE chat.h)

 if (LLAMA_SUBPROCESS)
    target_compile_definitions(${TARGET} PUBLIC LLAMA_SUBPROCESS)
@@ -0,0 +1,122 @@
+## Build profiling
+This page is a working document for analyzing the current build and try to
+identify ways to improve the build time.
+
+### Requirements
+The profiling script requires clang to be used as the compiler tool chain and
+also requires that ClangBuildAnalyzer is installed.
+
+Mac:
+```console
+brew install clang-build-analyzer
+```
+
+Linux:
+```console
+git clone https://github.com/aras-p/ClangBuildAnalyzer.git
+cd ClangBuildAnalyzer
+cmake -B build -DCMAKE_BUILD_TYPE=Release
+cmake --build build -j$(nproc)
+sudo cp build/ClangBuildAnalyzer /usr/local/bin/
+```
+
+Windows: install LLVM/clang and Ninja (e.g. via the
+[LLVM releases page](https://github.com/llvm/llvm-project/releases) and
+`winget install Ninja-build.Ninja`), then build ClangBuildAnalyzer the same
+way as on Linux:
+```console
+git clone https://github.com/aras-p/ClangBuildAnalyzer.git
+cd ClangBuildAnalyzer
+cmake -B build -G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
+cmake --build build --config Release
+```
+Then add `ClangBuildAnalyzer\build` to `PATH`.
+
+### Usage
+Mac/Linux:
+```console
+$ ./scripts/build-profile.sh
+```
+
+Windows:
+```console
+> .\scripts\build-profile.ps1
+```
+
+Both accept `--full`/`-Full` (include Server, Tools, and Tests) and a jobs
+override (`-jN` / `-Jobs N`).
+
+Note: on Windows, `cmake` defaults to the Visual Studio generator, which
+ignores `CMAKE_C_COMPILER`/`CMAKE_CXX_COMPILER` and silently falls back to
+MSVC. `build-profile.ps1` passes `-G Ninja` so clang is actually used, this
+is required on ARM64.
+
+### Linux (Ubuntu 24.04)
+
+Environment:
+- Clang:     18.1.3 (Ubuntu clang version 18.1.3 (1ubuntu1))
+- libstdc++: GCC 13.3.0 (Ubuntu 13.3.0-6ubuntu2~24.04.1)
+- Target:    x86_64-pc-linux-gnu
+
+```console
+------------------------+-----+------------+------------+------------+
+| Build                  | TUs | Frontend   | Backend    | Total      |
+------------------------+-----+------------+------------+------------+
+| Minimal, master        | 249 |   468.2 s  |   270.3 s  |   738.5 s  |
+| Minimal, with PCH      | 253 |   177.1 s  |   265.8 s  |   442.9 s  |
+| Full,    master        | 396 |   811.0 s  |   692.2 s  | 1,503.2 s  |
+| Full,    with PCH      | 405 |   380.0 s  |   664.7 s  | 1,044.7 s  |
+| Full,    with PCH + UB | 264 |   357.7 s  |   635.7 s  |   993.4 s  |
+------------------------+-----+------------+------------+------------+
+
+PCH  = precompiled header.
+Full = includes building Server, Tools, and Tests.
+UB   = unity build for models
+```
+Note that the number of translation units (TUs) increases when using precompiled
+headers — each PCH target adds one extra TU for the precompilation step itself.
+
+### Mac (Apple M3)
+
+Environment:
+- Clang:  Apple clang version 17.0.0 (clang-1700.3.19.1)
+- libc++: ships with Apple clang 17.0.0 (Xcode toolchain)
+- Target: arm64-apple-macosx15.6
+
+```console
+------------------------+-----+------------+------------+------------+
+| Build                  | TUs | Frontend   | Backend    | Total      |
+------------------------+-----+------------+------------+------------+
+| Minimal, master        | 256 |   154.5 s  |    94.8 s  |   249.3 s  |
+| Minimal, with PCH      | 261 |    65.9 s  |    90.0 s  |   155.9 s  |
+| Full,    master        | 407 |   265.7 s  |   209.7 s  |   475.4 s  |
+| Full,    with PCH      | 414 |   154.6 s  |   197.5 s  |   352.1 s  |
+| Full,    with PCH + UB | 274 |   143.0 s  |   192.2 s  |   335.2 s  |
+------------------------+-----+------------+------------+------------+
+
+PCH = precompiled header.
+Full = includes building Server, Tools, and Tests.
+UB   = unity build for models
+```
+
+### Windows (ARM64)
+
+Environment:
+- Clang:  clang version 22.1.8 (LLVM, `C:\Program Files\LLVM`)
+- STL:    MSVC STL (Visual Studio 2022 Build Tools 14.44.35207)
+- Target: aarch64-pc-windows-msvc
+
+```console
+------------------------+-----+------------+------------+------------+
+| Build                  | TUs | Frontend   | Backend    | Total      |
+------------------------+-----+------------+------------+------------+
+| Minimal, master        | 249 |   159.4 s  |    82.2 s  |   241.6 s  |
+| Full,    master        | 373 |   337.2 s  |   167.4 s  |   504.6 s  |
+| Minimal, with PCH + UB | 113 |    62.3 s  |    82.4 s  |   144.7 s  |
+| Full,    with PCH + UB | 240 |   233.0 s  |   185.1 s  |   418.1 s  |
+------------------------+-----+------------+------------+------------+
+
+PCH = precompiled header.
+Full = includes building Server, Tools, and Tests.
+UB   = unity build for models
+```
@@ -675,6 +675,12 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
    target_compile_options(${GGML_CPU_NAME} PRIVATE ${ARCH_FLAGS})
    target_compile_definitions(${GGML_CPU_NAME} PRIVATE ${ARCH_DEFINITIONS})

+    if (CMAKE_C_COMPILER_ID STREQUAL "GNU" AND NOT GGML_SYSTEM_ARCH STREQUAL "x86")
+        message(STATUS "Skipping PCH for ${GGML_CPU_NAME}: GCC PCH is only enabled for x86 (arch: ${GGML_SYSTEM_ARCH})")
+    else()
+        target_precompile_headers(${GGML_CPU_NAME} PRIVATE ggml-impl.h)
+    endif()
+
    if (EMSCRIPTEN)
        set_target_properties(${GGML_CPU_NAME} PROPERTIES COMPILE_FLAGS "-msimd128")
    endif()
@@ -18,7 +18,15 @@
 #endif
 #endif

+// -Winterference-size was introduced in GCC 12
+#if defined(__cplusplus) && defined(__GNUC__) && !defined(__clang__) && __GNUC__ >= 12
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Winterference-size"
+#endif
 static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);
+#if defined(__cplusplus) && defined(__GNUC__) && !defined(__clang__) && __GNUC__ >= 12
+#pragma GCC diagnostic pop
+#endif

 // Work buffer size for im2col operations in CONV2D
 #define GGML_IM2COL_WORK_SIZE (16 * 1024 * 1024)
@@ -0,0 +1,136 @@
+# Compile-time profiling using clang -ftime-trace + ClangBuildAnalyzer.
+#
+# Usage:
+#   .\scripts\build-profile.ps1 [-Full] [-Jobs N]
+#
+# -Full : include Server, Tools, and Tests (default: minimal build)
+# -Jobs :  number of parallel jobs (default: all cores)
+#
+# Requires ClangBuildAnalyzer:
+#   https://github.com/aras-p/ClangBuildAnalyzer
+
+param(
+    [switch]$Full,
+    [int]$Jobs = [Environment]::ProcessorCount
+)
+
+$ErrorActionPreference = "Stop"
+
+$ScriptDir = Split-Path -Parent $MyInvocation.MyCommand.Path
+$RootDir = Split-Path -Parent $ScriptDir
+
+if ($Full) {
+    $BuildDir = Join-Path $RootDir "build-profile-full"
+    $Report = Join-Path $BuildDir "profile-report-full.txt"
+} else {
+    $BuildDir = Join-Path $RootDir "build-profile-baseline"
+    $Report = Join-Path $BuildDir "profile-report.txt"
+}
+
+$OutputBin = Join-Path $BuildDir "clang_analysis.bin"
+
+if (-not (Get-Command clang++ -ErrorAction SilentlyContinue)) {
+    Write-Error "clang++ not found"
+    exit 1
+}
+
+if (-not (Get-Command ninja -ErrorAction SilentlyContinue)) {
+    Write-Error "ninja not found (required so cmake does not fall back to the Visual Studio/MSVC generator)"
+    exit 1
+}
+
+if (-not (Get-Command ClangBuildAnalyzer -ErrorAction SilentlyContinue)) {
+    Write-Error "ClangBuildAnalyzer not found`n  https://github.com/aras-p/ClangBuildAnalyzer/releases"
+    exit 1
+}
+
+$ClangVer = (clang++ --version | Select-Object -First 1)
+Write-Host "compiler : $ClangVer"
+Write-Host "build dir: $BuildDir"
+Write-Host "output   : $OutputBin"
+Write-Host "jobs     : $Jobs"
+Write-Host ""
+
+if (Get-Command ccache -ErrorAction SilentlyContinue) {
+    Write-Host "clearing ccache..."
+    ccache -C -z
+}
+
+$env:CCACHE_DISABLE = "1"
+
+$TestsFlag = if ($Full) { "ON" } else { "OFF" }
+$ToolsFlag = if ($Full) { "ON" } else { "OFF" }
+$ServerFlag = if ($Full) { "ON" } else { "OFF" }
+
+cmake --fresh `
+    -S $RootDir `
+    -B $BuildDir `
+    -G "Ninja" `
+    -DCMAKE_BUILD_TYPE=Release `
+    -DCMAKE_C_COMPILER=clang `
+    -DCMAKE_CXX_COMPILER=clang++ `
+    -DCMAKE_C_FLAGS="-ftime-trace" `
+    -DCMAKE_CXX_FLAGS="-ftime-trace" `
+    -DGGML_CCACHE=OFF `
+    -DGGML_OPENMP=ON `
+    -DGGML_NATIVE=OFF `
+    "-DLLAMA_BUILD_TESTS=$TestsFlag" `
+    -DLLAMA_BUILD_EXAMPLES=OFF `
+    "-DLLAMA_BUILD_TOOLS=$ToolsFlag" `
+    "-DLLAMA_BUILD_SERVER=$ServerFlag" `
+    -DLLAMA_BUILD_APP=OFF
+
+if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE }
+
+$StrayTrace = Join-Path $RootDir "-.json"
+if (Test-Path $StrayTrace) {
+    Remove-Item $StrayTrace -Force
+}
+
+Write-Host ""
+Write-Host "Initializing ClangBuildAnalyzer..."
+ClangBuildAnalyzer --start $BuildDir
+Write-Host ""
+
+Write-Host "building..."
+Write-Host ""
+
+$StartTime = Get-Date
+
+cmake --build $BuildDir --clean-first -j $Jobs
+
+if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE }
+
+$Elapsed = (Get-Date) - $StartTime
+
+Write-Host ""
+Write-Host ("build time: {0}s ({1}m {2}s)" -f [int]$Elapsed.TotalSeconds, [int]$Elapsed.TotalMinutes, $Elapsed.Seconds)
+Write-Host ""
+
+Write-Host "Aggregating profile metrics..."
+ClangBuildAnalyzer --stop $BuildDir $OutputBin | Out-Null
+
+Write-Host ""
+Write-Host ("=" * 80)
+
+$TUs = "?"
+if (Test-Path $Report) {
+    $Match = Select-String -Path $Report -Pattern "Compilation \((\d+)" | Select-Object -First 1
+    if ($Match) { $TUs = $Match.Matches[0].Groups[1].Value }
+}
+
+ClangBuildAnalyzer --analyze $OutputBin | Tee-Object -FilePath $Report
+
+Write-Host ""
+Write-Host "translation units: $TUs"
+Write-Host ""
+Write-Host "largest trace files (top 20 by size):"
+
+Get-ChildItem -Path $BuildDir -Recurse -Filter "*.json" |
+    Where-Object { $_.Name -ne "compile_commands.json" } |
+    Sort-Object Length -Descending |
+    Select-Object -First 20 |
+    ForEach-Object { "{0,8:F1} KB  {1}" -f ($_.Length / 1024), $_.FullName }
+
+Write-Host ""
+Write-Host "ClangBuildAnalyzer report was generated: $Report"
@@ -0,0 +1,122 @@
+#!/usr/bin/env bash
+# Compile-time profiling using clang -ftime-trace + ClangBuildAnalyzer.
+#
+# Usage:
+#   ./scripts/build-profile.sh [--full] [-jN]
+#
+# --full: include Server, Tools, and Tests (default: minimal build)
+# -jN   :  number of parallel jobs (default: all cores)
+#
+# Requires ClangBuildAnalyzer:
+#   macOS: brew install clang-build-analyzer
+#   Linux: https://github.com/aras-p/ClangBuildAnalyzer.git
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ROOT_DIR="$(cd "${SCRIPT_DIR}/.." && pwd)"
+
+FULL=0
+JOBS="-j$(nproc 2>/dev/null || sysctl -n hw.ncpu)"
+
+for arg in "$@"; do
+    case "${arg}" in
+        --full) FULL=1 ;;
+        -j*)    JOBS="${arg}" ;;
+        *) echo "error: unknown argument: ${arg}" >&2; exit 1 ;;
+    esac
+done
+
+if [ "${FULL}" -eq 1 ]; then
+    BUILD_DIR="${ROOT_DIR}/build-profile-full"
+    REPORT="${BUILD_DIR}/profile-report-full.txt"
+else
+    BUILD_DIR="${ROOT_DIR}/build-profile-baseline"
+    REPORT="${BUILD_DIR}/profile-report.txt"
+fi
+
+OUTPUT_BIN="${BUILD_DIR}/clang_analysis.bin"
+
+if ! command -v clang++ &>/dev/null; then
+    echo "error: clang++ not found" >&2
+    exit 1
+fi
+
+if ! command -v ClangBuildAnalyzer &>/dev/null; then
+    echo "error: ClangBuildAnalyzer not found" >&2
+    echo "  brew install clangbuildanalyzer  (macOS)" >&2
+    echo "  or: https://github.com/aras-p/ClangBuildAnalyzer/releases" >&2
+    exit 1
+fi
+
+CLANG_VER=$(clang++ --version | head -1)
+echo "compiler : ${CLANG_VER}"
+echo "build dir: ${BUILD_DIR}"
+echo "output   : ${OUTPUT_BIN}"
+echo "jobs     : ${JOBS}"
+echo
+
+if command -v ccache &>/dev/null; then
+    echo "clearing ccache..."
+    ccache -C -z
+fi
+
+export CCACHE_DISABLE=1
+
+cmake --fresh \
+    -S "${ROOT_DIR}" \
+    -B "${BUILD_DIR}" \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DCMAKE_C_COMPILER=clang \
+    -DCMAKE_CXX_COMPILER=clang++ \
+    -DCMAKE_C_FLAGS="-ftime-trace" \
+    -DCMAKE_CXX_FLAGS="-ftime-trace" \
+    -DGGML_CCACHE=OFF \
+    -DGGML_OPENMP=ON \
+    -DGGML_NATIVE=OFF \
+    -DLLAMA_BUILD_TESTS=$([ "${FULL}" -eq 1 ] && echo ON || echo OFF) \
+    -DLLAMA_BUILD_EXAMPLES=OFF \
+    -DLLAMA_BUILD_TOOLS=$([ "${FULL}" -eq 1 ] && echo ON || echo OFF) \
+    -DLLAMA_BUILD_SERVER=$([ "${FULL}" -eq 1 ] && echo ON || echo OFF) \
+    -DLLAMA_BUILD_APP=OFF
+
+echo
+
+echo "Initializing ClangBuildAnalyzer..."
+ClangBuildAnalyzer --start "${BUILD_DIR}"
+echo
+
+echo "building..."
+echo
+
+START=$(date +%s)
+
+cmake --build "${BUILD_DIR}" --clean-first "${JOBS}"
+
+END=$(date +%s)
+ELAPSED=$((END - START))
+
+echo
+printf "build time: %ds (%dm %ds)\n" "${ELAPSED}" "$((ELAPSED / 60))" "$((ELAPSED % 60))"
+echo
+
+echo "Aggregating profile metrics..."
+ClangBuildAnalyzer --stop "${BUILD_DIR}" "${OUTPUT_BIN}" > /dev/null
+
+echo
+echo "================================================================================"
+TUS=$(grep -oP "Compilation \(\K[0-9]+" "${REPORT}" 2>/dev/null || echo "?")
+ClangBuildAnalyzer --analyze "${OUTPUT_BIN}" | tee "${REPORT}"
+
+echo
+echo "translation units: ${TUS}"
+echo
+echo "largest trace files (top 20 by size):"
+find "${BUILD_DIR}" -name "*.json" ! -name "compile_commands.json" \
+    | xargs ls -l 2>/dev/null \
+    | awk 'NF>5 {print $5, $NF}' \
+    | sort -rn \
+    | awk 'NR<=20 {printf "%8.1f KB  %s\n", $1/1024, $2}'
+
+echo
+echo "ClangBuildAnalyzer report was generated: ${REPORT}"
@@ -8,40 +8,44 @@ llama_add_compile_flags()

 file(GLOB LLAMA_MODELS_SOURCES "models/*.cpp")

+set(LLAMA_CORE_SOURCES
+    llama.cpp
+    llama-adapter.cpp
+    llama-arch.cpp
+    llama-batch.cpp
+    llama-chat.cpp
+    llama-context.cpp
+    llama-cparams.cpp
+    llama-grammar.cpp
+    llama-graph.cpp
+    llama-hparams.cpp
+    llama-impl.cpp
+    llama-io.cpp
+    llama-kv-cache.cpp
+    llama-kv-cache-iswa.cpp
+    llama-kv-cache-dsa.cpp
+    llama-kv-cache-dsa-iswa.cpp
+    llama-kv-cache-msa.cpp
+    llama-kv-cache-dsv4.cpp
+    llama-memory.cpp
+    llama-memory-hybrid.cpp
+    llama-memory-hybrid-iswa.cpp
+    llama-memory-hybrid-idx.cpp
+    llama-memory-recurrent.cpp
+    llama-mmap.cpp
+    llama-model-loader.cpp
+    llama-model-saver.cpp
+    llama-model.cpp
+    llama-quant.cpp
+    llama-sampler.cpp
+    llama-vocab.cpp
+    unicode-data.cpp
+    unicode.cpp
+)
+
 add_library(llama
            ../include/llama.h
-            llama.cpp
-            llama-adapter.cpp
-            llama-arch.cpp
-            llama-batch.cpp
-            llama-chat.cpp
-            llama-context.cpp
-            llama-cparams.cpp
-            llama-grammar.cpp
-            llama-graph.cpp
-            llama-hparams.cpp
-            llama-impl.cpp
-            llama-io.cpp
-            llama-kv-cache.cpp
-            llama-kv-cache-iswa.cpp
-            llama-kv-cache-dsa.cpp
-            llama-kv-cache-dsa-iswa.cpp
-            llama-kv-cache-msa.cpp
-            llama-kv-cache-dsv4.cpp
-            llama-memory.cpp
-            llama-memory-hybrid.cpp
-            llama-memory-hybrid-iswa.cpp
-            llama-memory-hybrid-idx.cpp
-            llama-memory-recurrent.cpp
-            llama-mmap.cpp
-            llama-model-loader.cpp
-            llama-model-saver.cpp
-            llama-model.cpp
-            llama-quant.cpp
-            llama-sampler.cpp
-            llama-vocab.cpp
-            unicode-data.cpp
-            unicode.cpp
+            ${LLAMA_CORE_SOURCES}
            unicode.h
            ${LLAMA_MODELS_SOURCES}
            )
@@ -50,13 +54,20 @@ set_target_properties(llama PROPERTIES
    VERSION ${LLAMA_VERSION_BASE}
    SOVERSION ${LLAMA_VERSION_MAJOR}
    MACHO_CURRENT_VERSION 0 # keep macOS linker from seeing oversized version number
+    UNITY_BUILD ON
+    UNITY_BUILD_BATCH_SIZE 16
 )

+# exclude non-model sources from unity build
+set_source_files_properties(${LLAMA_CORE_SOURCES} ../include/llama.h unicode.h
+    PROPERTIES SKIP_UNITY_BUILD_INCLUSION ON)
+
 configure_file(llama-version.h.in ${CMAKE_CURRENT_BINARY_DIR}/llama-version.h @ONLY)

 target_include_directories(llama PRIVATE . ${CMAKE_CURRENT_BINARY_DIR})
 target_include_directories(llama PUBLIC ../include)
 target_compile_features   (llama PRIVATE cxx_std_17) # don't bump
+target_precompile_headers (llama PRIVATE models/models.h)

 target_link_libraries(llama PUBLIC ggml)

@@ -82,7 +82,7 @@ std::unique_ptr<llm_graph_context> llama_model_gemma3n::build_arch_graph(const l
 }

 // get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim
-static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, int idx) {
+static ggml_tensor * gemma3n_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, int idx) {
    GGML_ASSERT(idx < (int) x->ne[2]);
    return ggml_view_2d(ctx0, x, x->ne[0], x->ne[1], ggml_row_size(x->type, x->ne[0]),
                        idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
@@ -139,7 +139,7 @@ llama_model_gemma3n::graph::graph(const llama_model & model, const llm_graph_par
        ggml_tensor * predictions = altup_predict(cur, il);  // [n_embd, n_tokens, n_altup]

        // predicted value will go through self-attention and laurel
-        ggml_tensor * active_prediction = ggml_view_2d_slice(ctx0, predictions, i_altup_act);  // [n_embd, n_tokens]
+        ggml_tensor * active_prediction = gemma3n_view_2d_slice(ctx0, predictions, i_altup_act);  // [n_embd, n_tokens]
        cur = active_prediction;
        cb(cur, "active_prediction", il);

@@ -236,13 +236,13 @@ llama_model_gemma3n::graph::graph(const llama_model & model, const llm_graph_par

        ggml_tensor * first_prediction;                                                   // [n_embd, n_tokens]
        {
-            first_prediction = ggml_view_2d_slice(ctx0, corrected, i_altup_act);          // [n_embd, n_tokens]
+            first_prediction = gemma3n_view_2d_slice(ctx0, corrected, i_altup_act);       // [n_embd, n_tokens]
            first_prediction = ggml_mul(ctx0, first_prediction, model.layers[il].altup_correct_scale);
            first_prediction = build_lora_mm(model.layers[il].per_layer_inp_gate, first_prediction);
            first_prediction = ggml_gelu(ctx0, first_prediction);                 // [n_embd_altup, n_tokens]
            cb(first_prediction, "first_prediction_gated", il);

-            ggml_tensor * inp_this_layer = ggml_view_2d_slice(ctx0, inp_per_layer, il);   // [n_embd_altup, n_tokens]
+            ggml_tensor * inp_this_layer = gemma3n_view_2d_slice(ctx0, inp_per_layer, il);   // [n_embd_altup, n_tokens]
            first_prediction = ggml_mul(ctx0, first_prediction, inp_this_layer);  // [n_embd_altup, n_tokens]
            cb(first_prediction, "first_prediction_scaled", il);

@@ -253,7 +253,7 @@ llama_model_gemma3n::graph::graph(const llama_model & model, const llm_graph_par
        }
        // equivalent to python code: corrected_predictions[1:] += first_prediction
        {
-            ggml_tensor * slice_first = ggml_view_2d_slice(ctx0, corrected, 0);
+            ggml_tensor * slice_first = gemma3n_view_2d_slice(ctx0, corrected, 0);
            ggml_tensor * slice_rest  = ggml_view_3d(
                ctx0, corrected, n_embd, n_tokens, n_altup - 1, ggml_row_size(corrected->type, n_embd),
                ggml_row_size(corrected->type, n_embd * n_tokens), n_embd * n_tokens * ggml_element_size(corrected));
@@ -271,7 +271,7 @@ llama_model_gemma3n::graph::graph(const llama_model & model, const llm_graph_par

    // cur now has multiple altup(s), we want to merge them back to 1 altup
    {
-        ggml_tensor * target_magnitude = calc_magnitude(ggml_view_2d_slice(ctx0, cur, i_altup_act));  // [n_embd, n_tokens]
+        ggml_tensor * target_magnitude = calc_magnitude(gemma3n_view_2d_slice(ctx0, cur, i_altup_act));  // [n_embd, n_tokens]
        // do a view to skip the first slice (active altup)
        ggml_tensor * alt_slice =
            ggml_view_3d(ctx0, cur, n_embd, n_tokens, n_altup - 1, ggml_row_size(cur->type, n_embd),
@@ -283,9 +283,9 @@ llama_model_gemma3n::graph::graph(const llama_model & model, const llm_graph_par
        cb(altup_unembd, "altup_unembd", -1);

        // equivalent to torch.mean(hidden_states, dim=0)
-        cur = ggml_view_2d_slice(ctx0, cur, 0);  // [n_embd, n_tokens]
+        cur = gemma3n_view_2d_slice(ctx0, cur, 0);  // [n_embd, n_tokens]
        for (int i = 0; i < n_altup - 1; ++i) {
-            cur = ggml_add(ctx0, cur, ggml_view_2d_slice(ctx0, altup_unembd, i));
+            cur = ggml_add(ctx0, cur, gemma3n_view_2d_slice(ctx0, altup_unembd, i));
        }
        cur = ggml_scale(ctx0, cur, 1.0f / float(n_altup));  // [n_embd, n_tokens]
        cb(cur, "unembd_merged", -1);
@@ -419,7 +419,7 @@ ggml_tensor * llama_model_gemma3n::graph::altup_compute_router_modalities(ggml_t
 // input cur shape: [n_embd, n_tokens, n_altup]
 // output    shape: [n_embd, n_tokens, n_altup]
 ggml_tensor * llama_model_gemma3n::graph::altup_predict(ggml_tensor * cur, int il) {
-    ggml_tensor * activated  = ggml_view_2d_slice(ctx0, cur, i_altup_act);      // [n_embd, n_tokens]
+    ggml_tensor * activated  = gemma3n_view_2d_slice(ctx0, cur, i_altup_act);   // [n_embd, n_tokens]
    ggml_tensor * modalities = altup_compute_router_modalities(activated, il);  // [n_altup, n_tokens]
    cb(modalities, "modalities", il);

@@ -447,7 +447,7 @@ ggml_tensor * llama_model_gemma3n::graph::altup_correct(ggml_tensor * prediction
    ggml_tensor * modalities = altup_compute_router_modalities(activated, il);  // [n_altup, n_tokens]
    cb(modalities, "modalities", il);

-    ggml_tensor * active_prediction = ggml_view_2d_slice(ctx0, predictions, i_altup_act);
+    ggml_tensor * active_prediction = gemma3n_view_2d_slice(ctx0, predictions, i_altup_act);
    ggml_tensor * innovation        = ggml_sub(ctx0, activated, active_prediction);  // [n_embd, n_tokens]
    cb(innovation, "innovation", il);

@@ -145,7 +145,7 @@ std::unique_ptr<llm_graph_context> llama_model_gemma4::build_arch_graph(const ll
 }

 // get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim
-static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, int idx) {
+static ggml_tensor * gemma4_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, int idx) {
    GGML_ASSERT(idx < (int) x->ne[2]);
    return ggml_view_2d(ctx0, x, x->ne[0], x->ne[1], ggml_row_size(x->type, x->ne[0]),
                        idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
@@ -372,7 +372,7 @@ llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_para
            cur = build_lora_mm(model.layers[il].per_layer_inp_gate, cur); // [n_embd_per_layer, n_tokens]
            cur = ggml_gelu(ctx0, cur);

-            ggml_tensor * inp_this_layer = ggml_view_2d_slice(ctx0, inp_per_layer, il); // [n_embd_per_layer, n_tokens]
+            ggml_tensor * inp_this_layer = gemma4_view_2d_slice(ctx0, inp_per_layer, il); // [n_embd_per_layer, n_tokens]

            // TODO @ngxson : improve this
            if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
@@ -278,6 +278,8 @@ llama_build_and_test(
    peg-parser/test-unicode.cpp
    peg-parser/tests.h
 )
+target_precompile_headers(test-peg-parser PRIVATE peg-parser/tests.h)
+

 if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "s390x")
    set(MODEL_NAME "tinyllamas/stories15M-q4_0.gguf")
@@ -84,6 +84,13 @@ target_link_libraries     (mtmd PUBLIC ggml llama)
 target_link_libraries     (mtmd PRIVATE Threads::Threads vendor::hash vendor::miniaudio vendor::stb vendor::sheredom)
 target_include_directories(mtmd PUBLIC  .)
 target_compile_features   (mtmd PRIVATE cxx_std_17)
+target_precompile_headers (mtmd PRIVATE models/models.h)
+
+set_source_files_properties(
+    mtmd-helper.cpp
+    mtmd-helper-gen.cpp
+    PROPERTIES SKIP_PRECOMPILE_HEADERS ON
+)

 if (MTMD_VIDEO)
    target_compile_definitions(mtmd PRIVATE MTMD_VIDEO)
@@ -32,6 +32,7 @@ endif()
 target_include_directories(${TARGET} PRIVATE ../mtmd)
 target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR})
 target_link_libraries(${TARGET} PUBLIC llama-common mtmd ${CMAKE_THREAD_LIBS_INIT})
+target_precompile_headers(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR}/common/common.h)

 # llama-server-impl: server logic, reusable by app

@@ -49,6 +50,7 @@ set_target_properties(${TARGET} PROPERTIES WINDOWS_EXPORT_ALL_SYMBOLS ON)
 target_include_directories(${TARGET} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
 target_include_directories(${TARGET} PRIVATE ../mtmd ${CMAKE_SOURCE_DIR})
 target_link_libraries(${TARGET} PUBLIC server-context llama-ui cpp-httplib ${CMAKE_THREAD_LIBS_INIT})
+target_precompile_headers(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR}/common/common.h)

 add_dependencies(${TARGET} llama-ui-assets)