mirror of
https://github.com/dogkeeper886/ollama37.git
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Sync with upstream ollama/ollama and restore Tesla K80 (compute 3.7) support
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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
This commit is contained in:
Shang Chieh Tseng
@@ -11,24 +11,24 @@ import (
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)
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func TestWriteGGUF(t *testing.T) {
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r := rand.New(rand.NewPCG(0, 0))
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b := bytes.NewBuffer(make([]byte, 2*3))
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for range 8 {
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t.Run("shuffle", func(t *testing.T) {
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t.Parallel()
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ts := []*Tensor{
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{Name: "token_embd.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
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{Name: "blk.0.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
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{Name: "blk.1.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
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{Name: "blk.2.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
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{Name: "blk.3.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
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{Name: "blk.4.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
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{Name: "blk.5.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
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{Name: "output_norm.weight", Shape: []uint64{3, 2}, WriterTo: bytes.NewBuffer(make([]byte, 3*2))},
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{Name: "output.weight", Shape: []uint64{3, 2}, WriterTo: bytes.NewBuffer(make([]byte, 3*2))},
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{Name: "token_embd.weight", Shape: []uint64{2, 3}, WriterTo: b},
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{Name: "blk.0.ffn_norm.weight", Shape: []uint64{2, 3}, WriterTo: b},
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{Name: "blk.0.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: b},
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{Name: "blk.1.ffn_up.weight", Shape: []uint64{2, 3}, WriterTo: b},
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{Name: "blk.2.ffn_norm.weight", Shape: []uint64{2, 3}, WriterTo: b},
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{Name: "blk.1.ffn_down.weight", Shape: []uint64{2, 3}, WriterTo: b},
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{Name: "blk.0.attn_k.weight", Shape: []uint64{2, 3}, WriterTo: b},
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{Name: "output_norm.weight", Shape: []uint64{3, 2}, WriterTo: b},
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{Name: "output.weight", Shape: []uint64{3, 2}, WriterTo: b},
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}
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r.Shuffle(len(ts), func(i, j int) {
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rand.Shuffle(len(ts), func(i, j int) {
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ts[i], ts[j] = ts[j], ts[i]
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})
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@@ -39,7 +39,12 @@ func TestWriteGGUF(t *testing.T) {
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defer w.Close()
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if err := WriteGGUF(w, KV{
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"general.alignment": uint32(16),
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"general.architecture": "test",
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"general.alignment": uint32(16),
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"test.key": "value",
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"attention.key": "value2",
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"tokenizer.key": "value3",
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"adapter.key": "value4",
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}, ts); err != nil {
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t.Fatal(err)
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}
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@@ -56,21 +61,26 @@ func TestWriteGGUF(t *testing.T) {
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}
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if diff := cmp.Diff(KV{
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"general.architecture": "test",
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"general.alignment": uint32(16),
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"general.parameter_count": uint64(54),
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"test.key": "value",
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"test.attention.key": "value2",
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"tokenizer.key": "value3",
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"adapter.key": "value4",
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}, ff.KV()); diff != "" {
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t.Errorf("Mismatch (-want +got):\n%s", diff)
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}
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if diff := cmp.Diff(Tensors{
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Offset: 608,
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Offset: 800,
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items: []*Tensor{
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{Name: "blk.0.attn_norm.weight", Offset: 0, Shape: []uint64{2, 3}},
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{Name: "blk.1.attn_norm.weight", Offset: 32, Shape: []uint64{2, 3}},
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{Name: "blk.2.attn_norm.weight", Offset: 64, Shape: []uint64{2, 3}},
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{Name: "blk.3.attn_norm.weight", Offset: 96, Shape: []uint64{2, 3}},
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{Name: "blk.4.attn_norm.weight", Offset: 128, Shape: []uint64{2, 3}},
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{Name: "blk.5.attn_norm.weight", Offset: 160, Shape: []uint64{2, 3}},
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{Name: "blk.0.attn_k.weight", Offset: 0, Shape: []uint64{2, 3}},
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{Name: "blk.0.attn_norm.weight", Offset: 32, Shape: []uint64{2, 3}},
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{Name: "blk.0.ffn_norm.weight", Offset: 64, Shape: []uint64{2, 3}},
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{Name: "blk.1.ffn_down.weight", Offset: 96, Shape: []uint64{2, 3}},
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{Name: "blk.1.ffn_up.weight", Offset: 128, Shape: []uint64{2, 3}},
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{Name: "blk.2.ffn_norm.weight", Offset: 160, Shape: []uint64{2, 3}},
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{Name: "output.weight", Offset: 192, Shape: []uint64{3, 2}},
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{Name: "output_norm.weight", Offset: 224, Shape: []uint64{3, 2}},
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{Name: "token_embd.weight", Offset: 256, Shape: []uint64{2, 3}},
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