Compress PR comments to essentials (comment-only; AST/token-verified)
Comment-compression sweep over comments this PR added, mirroring the sweep already done on main. No non-comment token changed: .py verified by AST equality (docstrings normalized), .sh by non-comment-line equality + bash -n, .ps1 by token-stream equality minus comments. test_spark_oom_guard.py: 13 passed before and after. Files touched: - install.ps1 - install.sh - scripts/uninstall.ps1 - studio/backend/core/training/worker.py - studio/scripts/provision_llama_cuda.sh - studio/setup.sh - unsloth/kernels/flex_attention.py - unsloth/models/_utils.py Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
Daniel Han
parent
b359acc74b
commit
42e69031b9
8 changed files with 222 additions and 366 deletions
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@ -1,14 +1,11 @@
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#!/usr/bin/env bash
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# Build a CUDA llama.cpp for Unsloth Studio GGUF *inference* into
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# ~/.unsloth/llama.cpp (resolver checks <dir>/build/bin/llama-server).
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# Idempotent, best-effort: safe to re-run, always exits 0.
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#
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# Needed because no aarch64+CUDA llama.cpp prebuilt exists for NVIDIA ARM hosts
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# (DGX Spark / GB10, N1X "RTX" laptops). Handles the platform gotchas:
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# * nvcc rejects gcc-15 -> force gcc-14 / g++-14 as the host compiler
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# * glibc >= 2.41 vs CUDA < 13.3 -> install CUDA 13.3 (rsqrt header clash)
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# * sm_121 (Blackwell) GPUs -> derive arch from the GPU's compute_cap
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#
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# Build CUDA llama.cpp for Studio GGUF *inference* into ~/.unsloth/llama.cpp
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# (resolver checks <dir>/build/bin/llama-server). Idempotent, best-effort, always
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# exits 0. Exists because no aarch64+CUDA prebuilt covers NVIDIA ARM hosts
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# (DGX Spark / GB10, N1X "RTX" laptops). Platform gotchas handled:
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# * nvcc rejects gcc-15 -> force gcc-14 / g++-14 host compiler
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# * glibc >= 2.41 vs CUDA < 13.3 -> install CUDA 13.3 (rsqrt header clash)
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# * sm_121 (Blackwell) -> derive arch from the GPU's compute_cap
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# Opt out with UNSLOTH_NO_LLAMA_CUDA=1 (handled by the caller).
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set -uo pipefail
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@ -16,10 +13,9 @@ LLAMA_DIR="${UNSLOTH_LLAMA_CPP_PATH:-$HOME/.unsloth/llama.cpp}"
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SERVER="$LLAMA_DIR/build/bin/llama-server"
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log() { printf ' - %s\n' "$*"; }
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# CUDA-capable in two layouts: old monolithic (libggml-cuda is a direct ldd dep)
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# or current split build (CUDA is a dlopen-ed backend libggml-cuda.so* beside the
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# binary, not shown by ldd). ldd alone false-negatives on current llama.cpp; a
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# CPU-only build has no libggml-cuda.so, so its presence is the reliable signal.
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# CUDA shows up two ways: old monolithic (libggml-cuda in ldd) or current split
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# build (dlopen-ed libggml-cuda.so* beside the binary, missed by ldd). CPU-only
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# builds ship no libggml-cuda.so, so its presence is the reliable signal.
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is_cuda_server() {
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[ -x "$1" ] || return 1
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ldd "$1" 2>/dev/null | grep -qi 'libggml-cuda' && return 0
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@ -42,16 +38,13 @@ fi
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SUDO=""; [ "$(id -u)" -ne 0 ] && SUDO="sudo"
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HAVE_APT=0; command -v apt-get >/dev/null 2>&1 && HAVE_APT=1
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# 2. Base toolchain (must succeed) THEN gcc-14 (best-effort, separate transaction).
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# gcc-14 is preferred because nvcc rejects gcc-15, but it isn't in the default apt
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# sources on Ubuntu 22.04 / Debian 12 -- installing it in the SAME transaction as
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# cmake/git/curl would make apt abort the whole transaction there, leaving the box
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# without the basic build tools needed to clone/configure llama.cpp.
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# 2. Base toolchain first, then gcc-14 (nvcc rejects gcc-15) in a SEPARATE apt
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# transaction: gcc-14 is absent from default Ubuntu 22.04 / Debian 12 sources,
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# which would abort a combined transaction and lose the base build tools too.
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if [ "$HAVE_APT" -eq 1 ]; then
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$SUDO apt-get update -y >/dev/null 2>&1 || true
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# libcurl4-openssl-dev: _cmake_configure forces -DLLAMA_CURL=ON, and on the WSL
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# deferred path this script is the only build path -- setup.sh's GGUF dep install
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# (which covers libcurl) was skipped, so configure would fail without the headers.
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# libcurl4-openssl-dev: -DLLAMA_CURL=ON needs it, and on the WSL deferred path
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# setup.sh's GGUF dep install (which covers libcurl) was skipped.
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$SUDO apt-get install -y --no-install-recommends \
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build-essential cmake git curl ca-certificates libcurl4-openssl-dev >/dev/null 2>&1 || true
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$SUDO apt-get install -y --no-install-recommends gcc-14 g++-14 >/dev/null 2>&1 || true
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@ -95,9 +88,8 @@ if [ -z "$NVCC" ]; then
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fi
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CUDA_HOME="$(dirname "$(dirname "$NVCC")")"
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# CUDA toolkit + Linux dirs FIRST so the build uses Linux cmake/gcc/git, not a
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# Windows tool leaked into PATH via WSL interop (/mnt/c, also has spaces). Keep
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# the original PATH after so nvidia-smi etc. still resolve.
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# CUDA + Linux dirs FIRST so the build uses Linux cmake/gcc/git, not Windows tools
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# leaked in via WSL interop (/mnt/c); original PATH kept so nvidia-smi resolves.
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export PATH="$CUDA_HOME/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:$PATH"
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export CUDAToolkit_ROOT="$CUDA_HOME"
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@ -110,14 +102,12 @@ export CC="$HCC" CXX="$HCXX" CUDAHOSTCXX="$HCXX"
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CC_CAP="$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader 2>/dev/null | head -1 | tr -d ' .')"
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if [ -n "$CC_CAP" ]; then CUDA_ARCH="$CC_CAP"; else CUDA_ARCH="native"; fi
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# 6. Clone + build into ~/.unsloth/llama.cpp. Honor a pinned llama.cpp ref
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# (UNSLOTH_LLAMA_TAG, the same var setup.sh uses) so a provisioner-built tree matches
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# the user's request instead of always tracking ggml-org main.
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# 6. Clone + build into ~/.unsloth/llama.cpp, honoring a UNSLOTH_LLAMA_TAG pin
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# (same var setup.sh uses) instead of always tracking ggml-org main.
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mkdir -p "$(dirname "$LLAMA_DIR")"
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_LLAMA_REF="${UNSLOTH_LLAMA_TAG:-}"
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# Preserve any existing (e.g. CPU-only) llama.cpp so a failed clone OR a failed CUDA
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# build doesn't leave the user with NO server: the backup is restored on any failure
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# exit and only dropped once a server from the fresh build is confirmed.
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# Back up any existing (e.g. CPU-only) llama.cpp: restored on any failure exit,
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# dropped only once the fresh build yields a server -- never leave NO server.
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_LLAMA_BAK=""
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_restore_prev() {
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if [ -n "$_LLAMA_BAK" ] && [ -e "$_LLAMA_BAK" ]; then
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@ -143,9 +133,8 @@ if [ ! -d "$LLAMA_DIR/.git" ]; then
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_restore_prev
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exit 0
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fi
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# Honor a llama.cpp PR pin (UNSLOTH_LLAMA_PR, the same var setup.sh supports)
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# so a provisioned tree matches the user's request instead of silently building
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# the default branch. Best-effort: a failed fetch keeps the default branch.
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# Honor a UNSLOTH_LLAMA_PR pin (same var setup.sh supports); best-effort --
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# a failed fetch keeps the default branch.
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case "${UNSLOTH_LLAMA_PR:-}" in
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''|*[!0-9]*) ;;
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*)
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@ -168,22 +157,17 @@ _cmake_configure() {
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-DCMAKE_CUDA_HOST_COMPILER="$HCXX" \
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-DLLAMA_CURL=ON >/dev/null 2>&1
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}
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# A pre-existing build/ may carry an incompatible CMake cache (e.g. the installer
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# relocates a versioned build dir here, leaving stale absolute paths + GGML_CUDA=OFF),
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# making CUDA configure fail. Try to reuse build/ first (fast incremental resume);
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# only wipe and configure clean if that fails.
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# A pre-existing build/ may carry a stale CMake cache (relocated dir: bad absolute
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# paths + GGML_CUDA=OFF). Reuse it first (fast incremental); wipe only on failure.
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if ! _cmake_configure; then
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log "stale/incompatible CMake cache detected; wiping build dir for a clean CUDA configure"
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rm -rf build
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_cmake_configure || { log "cmake configure failed"; cd /; _restore_prev; exit 0; }
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fi
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# Build the full target set unsloth-zoo's GGUF exporter also needs (llama-mtmd-cli,
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# llama-gguf-split) so one build serves both Studio inference and save_pretrained_gguf.
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# Parallelism default = ~half the cores: much faster than a tiny -j4, but leaves
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# thermal/power headroom -- a full -j(nproc) CUDA build trips shutdowns on
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# thermally constrained NVIDIA-ARM laptops (e.g. N1X "RTX Spark"). Also cap by RAM
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# (~1.5 GB per nvcc job) to avoid OOM. Tune with UNSLOTH_LLAMA_BUILD_JOBS=N (raise
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# on a well-cooled box, lower if it still trips). Incremental: a re-run resumes.
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# Also builds the targets unsloth-zoo's GGUF exporter needs (llama-mtmd-cli,
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# llama-gguf-split). Jobs default to ~half the cores -- full -j(nproc) CUDA builds
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# trip thermal shutdowns on NVIDIA-ARM laptops (N1X "RTX Spark") -- and are
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# RAM-capped (~1.5 GB/nvcc job). Tune: UNSLOTH_LLAMA_BUILD_JOBS=N; re-runs resume.
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_ncpu="$(nproc 2>/dev/null || echo 4)"
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# Honor a valid positive-int override; ignore junk/0 (cmake reads -j0 as "all cores").
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if [ -n "${UNSLOTH_LLAMA_BUILD_JOBS:-}" ] && [ "${UNSLOTH_LLAMA_BUILD_JOBS}" -ge 1 ] 2>/dev/null; then
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@ -198,15 +182,13 @@ else
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if [ "$_memjobs" -lt "$JOBS" ]; then JOBS="$_memjobs"; fi
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fi
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log "building with -j${JOBS} (cores=${_ncpu})"
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# Lowest CPU + idle I/O priority so this background build keeps full speed when the
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# box is idle but instantly yields to a foreground `unsloth studio` / training run.
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# nice/ionice: full speed when idle, yields to foreground Studio/training runs.
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_NICE=""
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command -v nice >/dev/null 2>&1 && _NICE="nice -n 19"
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command -v ionice >/dev/null 2>&1 && _NICE="$_NICE ionice -c 3"
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_cmake_build() {
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# Only llama-server is REQUIRED (mirrors setup.sh's source path): an older
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# UNSLOTH_LLAMA_TAG pin may predate newer helper targets (llama-mtmd-cli,
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# llama-gguf-split), and those missing must not fail the whole provision.
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# Only llama-server is REQUIRED: an old UNSLOTH_LLAMA_TAG pin may predate the
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# helper targets, and those missing must not fail the whole provision.
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$_NICE cmake --build build -j"$JOBS" --target llama-server >/dev/null 2>&1
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}
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_cmake_build_extras() {
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@ -216,10 +198,9 @@ _cmake_build_extras() {
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done
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}
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if ! _cmake_build; then
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# An interrupted build (e.g. a thermal/power shutdown mid-compile, which this
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# machine class is prone to) can leave a partially-linked libggml-cuda.so that
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# then fails to link llama-server on resume (undefined ggml_cuda_op_* refs).
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# Wipe build/ and rebuild clean once before giving up.
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# An interrupted build (thermal/power shutdown -- this machine class is prone)
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# can leave a half-linked libggml-cuda.so that breaks the resume link
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# (undefined ggml_cuda_op_* refs); wipe and rebuild clean once.
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log "build failed (likely interrupted/partial); wiping build dir and rebuilding clean"
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rm -rf build
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_cmake_configure || { log "cmake configure failed"; cd /; _restore_prev; exit 0; }
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