mirror of
https://github.com/dogkeeper886/ollama37.git
synced 2025-12-10 15:57:04 +00:00
ef14fb5b26
This commit represents a complete rework after pulling the latest changes from official ollama/ollama repository and re-applying Tesla K80 compatibility patches. ## Key Changes ### CUDA Compute Capability 3.7 Support (Tesla K80) - Added sm_37 (compute 3.7) to CMAKE_CUDA_ARCHITECTURES in CMakeLists.txt - Updated CMakePresets.json to include compute 3.7 in "CUDA 11" preset - Using 37-virtual (PTX with JIT compilation) for maximum compatibility ### Legacy Toolchain Compatibility - **NVIDIA Driver**: 470.256.02 (last version supporting Kepler/K80) - **CUDA Version**: 11.4.4 (last CUDA 11.x supporting compute 3.7) - **GCC Version**: 10.5.0 (required by CUDA 11.4 host_config.h) ### CPU Architecture Trade-offs Due to GCC 10.5 limitation, sacrificed newer CPU optimizations: - Alderlake CPU variant enabled WITHOUT AVX_VNNI (requires GCC 11+) - Still supports: SSE4.2, AVX, F16C, AVX2, BMI2, FMA - Performance impact: ~3-7% on newer CPUs (acceptable for K80 compatibility) ### Build System Updates - Modified ml/backend/ggml/ggml/src/ggml-cuda/CMakeLists.txt for compute 3.7 - Added -Wno-deprecated-gpu-targets flag to suppress warnings - Updated ml/backend/ggml/ggml/src/CMakeLists.txt for Alderlake without AVX_VNNI ### Upstream Sync Merged latest llama.cpp changes including: - Enhanced KV cache management with ISWA and hybrid memory support - Improved multi-modal support (mtmd framework) - New model architectures (Gemma3, Llama4, Qwen3, etc.) - GPU backend improvements for CUDA, Metal, and ROCm - Updated quantization support and GGUF format handling ### Documentation - Updated CLAUDE.md with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was dropped in official Ollama due to legacy driver/CUDA requirements. The toolchain constraint creates a deadlock: - K80 → Driver 470 → CUDA 11.4 → GCC 10 → No AVX_VNNI We accept the loss of cutting-edge CPU optimizations to enable running modern LLMs on legacy but still capable Tesla K80 hardware (12GB VRAM per GPU). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
57 lines
1.2 KiB
Go
57 lines
1.2 KiB
Go
package ml
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import (
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"os"
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"path/filepath"
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"runtime"
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)
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// LibPath is a path to lookup dynamic libraries
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// in development it's usually 'build/lib/ollama'
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// in distribution builds it's 'lib/ollama' on Windows
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// '../lib/ollama' on Linux and the executable's directory on macOS
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// note: distribution builds, additional GPU-specific libraries are
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// found in subdirectories of the returned path, such as
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// 'cuda_v12', 'rocm', etc.
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var LibOllamaPath string = func() string {
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exe, err := os.Executable()
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if err != nil {
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return ""
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}
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if eval, err := filepath.EvalSymlinks(exe); err == nil {
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exe = eval
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}
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var libPath string
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switch runtime.GOOS {
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case "windows":
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libPath = filepath.Join(filepath.Dir(exe), "lib", "ollama")
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case "linux":
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libPath = filepath.Join(filepath.Dir(exe), "..", "lib", "ollama")
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case "darwin":
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libPath = filepath.Dir(exe)
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}
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cwd, err := os.Getwd()
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if err != nil {
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return ""
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}
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paths := []string{
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libPath,
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// build paths for development
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filepath.Join(filepath.Dir(exe), "build", "lib", "ollama"),
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filepath.Join(cwd, "build", "lib", "ollama"),
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}
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for _, p := range paths {
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if _, err := os.Stat(p); err == nil {
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return p
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}
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}
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return filepath.Dir(exe)
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}()
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