mirror of
https://github.com/dogkeeper886/ollama37.git
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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
49 lines
1.1 KiB
Go
49 lines
1.1 KiB
Go
package logutil
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import (
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"context"
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"io"
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"log/slog"
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"path/filepath"
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"runtime"
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"time"
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)
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const LevelTrace slog.Level = -8
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func NewLogger(w io.Writer, level slog.Level) *slog.Logger {
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return slog.New(slog.NewTextHandler(w, &slog.HandlerOptions{
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Level: level,
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AddSource: true,
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ReplaceAttr: func(_ []string, attr slog.Attr) slog.Attr {
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switch attr.Key {
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case slog.LevelKey:
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switch attr.Value.Any().(slog.Level) {
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case LevelTrace:
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attr.Value = slog.StringValue("TRACE")
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}
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case slog.SourceKey:
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source := attr.Value.Any().(*slog.Source)
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source.File = filepath.Base(source.File)
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}
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return attr
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},
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}))
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}
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type key string
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func Trace(msg string, args ...any) {
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TraceContext(context.WithValue(context.TODO(), key("skip"), 1), msg, args...)
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}
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func TraceContext(ctx context.Context, msg string, args ...any) {
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if logger := slog.Default(); logger.Enabled(ctx, LevelTrace) {
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skip, _ := ctx.Value(key("skip")).(int)
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pc, _, _, _ := runtime.Caller(1 + skip)
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record := slog.NewRecord(time.Now(), LevelTrace, msg, pc)
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record.Add(args...)
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logger.Handler().Handle(ctx, record)
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}
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}
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