mirror of
https://github.com/dogkeeper886/ollama37.git
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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
150 lines
3.0 KiB
Go
150 lines
3.0 KiB
Go
package llamarunner
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import (
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"errors"
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"fmt"
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"hash/maphash"
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"log/slog"
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"sync"
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"time"
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"github.com/ollama/ollama/llama"
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)
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const imageCacheSize = 4
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type ImageContext struct {
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// mu is required to be held when generating embeddings or accessing the cache
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mu sync.Mutex
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mtmd *llama.MtmdContext
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// cache of images to embeddings
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images []imageCache
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imageHash maphash.Hash
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}
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func NewImageContext(llamaContext *llama.Context, modelPath string) (*ImageContext, error) {
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arch, err := llama.GetModelArch(modelPath)
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if err != nil {
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return nil, fmt.Errorf("unable to determine vision architecture: %w (%s)", err, modelPath)
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}
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var c ImageContext
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if arch == "clip" {
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c.mtmd, err = llama.NewMtmdContext(llamaContext, modelPath)
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} else {
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return nil, fmt.Errorf("unknown vision model architecture: %s", arch)
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}
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if err != nil {
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return nil, err
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}
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c.images = make([]imageCache, imageCacheSize)
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return &c, nil
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}
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func (c *ImageContext) Free(modelPath string) {
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if c == nil {
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return
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}
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if c.mtmd != nil {
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c.mtmd.Free()
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}
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}
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func (c *ImageContext) MultimodalTokenize(llamaContext *llama.Context, data []byte) ([]llama.MtmdChunk, error) {
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if c == nil {
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return nil, nil
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}
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if len(data) <= 0 {
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return nil, errors.New("received zero length image")
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}
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hash := c.hashImage(data)
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c.mu.Lock()
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defer c.mu.Unlock()
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chunks, err := c.findImage(hash)
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if err != nil {
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if c.mtmd != nil {
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chunks, err = c.mtmd.MultimodalTokenize(llamaContext, data)
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if err != nil {
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return nil, err
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}
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} else {
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return nil, errors.New("received image but vision model not loaded")
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}
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c.addImage(hash, chunks)
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}
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return chunks, nil
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}
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func (c *ImageContext) BatchSize(configuredBatchSize int) int {
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// If images are not supported, we don't need to allocate embedding batches
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if c == nil {
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return 0
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}
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return configuredBatchSize
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}
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func (c *ImageContext) EmbedSize(llamaContext *llama.Context) int {
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return llamaContext.Model().NEmbd()
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}
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type imageCache struct {
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key uint64
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val []llama.MtmdChunk
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lastUsed time.Time
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}
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func (c *ImageContext) hashImage(image []byte) uint64 {
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c.imageHash.Reset()
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_, _ = c.imageHash.Write(image)
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return c.imageHash.Sum64()
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}
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var errImageNotFound = errors.New("image not found in cache")
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func (c *ImageContext) findImage(hash uint64) ([]llama.MtmdChunk, error) {
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for i := range c.images {
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if c.images[i].key == hash {
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slog.Debug("loading image embeddings from cache", "entry", i)
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c.images[i].lastUsed = time.Now()
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return c.images[i].val, nil
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}
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}
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return nil, errImageNotFound
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}
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func (c *ImageContext) addImage(hash uint64, embed []llama.MtmdChunk) {
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best := time.Now()
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var bestImage int
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for i := range c.images {
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if c.images[i].key == hash {
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bestImage = i
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break
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}
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if c.images[i].lastUsed.Compare(best) < 0 {
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best = c.images[i].lastUsed
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bestImage = i
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}
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}
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slog.Debug("storing image embeddings in cache", "entry", bestImage, "used", c.images[bestImage].lastUsed)
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c.images[bestImage].key = hash
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c.images[bestImage].val = embed
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c.images[bestImage].lastUsed = time.Now()
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}
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