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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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:
@@ -11,23 +11,27 @@ import (
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"time"
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"github.com/ollama/ollama/api"
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"github.com/stretchr/testify/require"
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)
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func TestBlueSky(t *testing.T) {
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ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
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defer cancel()
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// Set up the test data
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req := api.GenerateRequest{
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Model: smol,
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Prompt: "why is the sky blue?",
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req := api.ChatRequest{
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Model: smol,
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Messages: []api.Message{
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{
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Role: "user",
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Content: blueSkyPrompt,
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},
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},
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Stream: &stream,
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Options: map[string]any{
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"temperature": 0,
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"seed": 123,
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},
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}
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GenerateTestHelper(ctx, t, req, []string{"rayleigh", "scattering"})
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ChatTestHelper(ctx, t, req, blueSkyExpected)
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}
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func TestUnicode(t *testing.T) {
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@@ -35,10 +39,15 @@ func TestUnicode(t *testing.T) {
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ctx, cancel := context.WithTimeout(context.Background(), 3*time.Minute)
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defer cancel()
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// Set up the test data
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req := api.GenerateRequest{
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req := api.ChatRequest{
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// DeepSeek has a Unicode tokenizer regex, making it a unicode torture test
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Model: "deepseek-coder-v2:16b-lite-instruct-q2_K",
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Prompt: "天空为什么是蓝色的?",
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Model: "deepseek-coder-v2:16b-lite-instruct-q2_K", // TODO is there an ollama-engine model we can switch to and keep the coverage?
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Messages: []api.Message{
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{
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Role: "user",
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Content: "天空为什么是蓝色的?", // Why is the sky blue?
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},
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},
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Stream: &stream,
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Options: map[string]any{
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"temperature": 0,
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@@ -50,17 +59,39 @@ func TestUnicode(t *testing.T) {
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}
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client, _, cleanup := InitServerConnection(ctx, t)
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defer cleanup()
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require.NoError(t, PullIfMissing(ctx, client, req.Model))
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DoGenerate(ctx, t, client, req, []string{"散射", "频率"}, 120*time.Second, 120*time.Second)
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if err := PullIfMissing(ctx, client, req.Model); err != nil {
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t.Fatal(err)
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}
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slog.Info("loading", "model", req.Model)
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err := client.Generate(ctx, &api.GenerateRequest{Model: req.Model}, func(response api.GenerateResponse) error { return nil })
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if err != nil {
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t.Fatalf("failed to load model %s: %s", req.Model, err)
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}
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defer func() {
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// best effort unload once we're done with the model
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client.Generate(ctx, &api.GenerateRequest{Model: req.Model, KeepAlive: &api.Duration{Duration: 0}}, func(rsp api.GenerateResponse) error { return nil })
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}()
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skipIfNotGPULoaded(ctx, t, client, req.Model, 100)
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DoChat(ctx, t, client, req, []string{
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"散射", // scattering
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"频率", // frequency
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}, 120*time.Second, 120*time.Second)
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}
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func TestExtendedUnicodeOutput(t *testing.T) {
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ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
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defer cancel()
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// Set up the test data
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req := api.GenerateRequest{
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Model: "gemma2:2b",
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Prompt: "Output some smily face emoji",
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req := api.ChatRequest{
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Model: "gemma2:2b",
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Messages: []api.Message{
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{
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Role: "user",
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Content: "Output some smily face emoji",
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},
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},
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Stream: &stream,
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Options: map[string]any{
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"temperature": 0,
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@@ -69,8 +100,10 @@ func TestExtendedUnicodeOutput(t *testing.T) {
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}
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client, _, cleanup := InitServerConnection(ctx, t)
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defer cleanup()
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require.NoError(t, PullIfMissing(ctx, client, req.Model))
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DoGenerate(ctx, t, client, req, []string{"😀", "😊", "😁", "😂", "😄", "😃"}, 120*time.Second, 120*time.Second)
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if err := PullIfMissing(ctx, client, req.Model); err != nil {
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t.Fatal(err)
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}
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DoChat(ctx, t, client, req, []string{"😀", "😊", "😁", "😂", "😄", "😃"}, 120*time.Second, 120*time.Second)
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}
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func TestUnicodeModelDir(t *testing.T) {
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@@ -84,7 +117,9 @@ func TestUnicodeModelDir(t *testing.T) {
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}
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modelDir, err := os.MkdirTemp("", "ollama_埃")
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require.NoError(t, err)
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if err != nil {
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t.Fatal(err)
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}
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defer os.RemoveAll(modelDir)
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slog.Info("unicode", "OLLAMA_MODELS", modelDir)
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@@ -93,14 +128,19 @@ func TestUnicodeModelDir(t *testing.T) {
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ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
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defer cancel()
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req := api.GenerateRequest{
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Model: smol,
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Prompt: "why is the sky blue?",
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req := api.ChatRequest{
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Model: smol,
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Messages: []api.Message{
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{
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Role: "user",
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Content: blueSkyPrompt,
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},
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},
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Stream: &stream,
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Options: map[string]any{
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"temperature": 0,
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"seed": 123,
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},
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}
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GenerateTestHelper(ctx, t, req, []string{"rayleigh", "scattering"})
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ChatTestHelper(ctx, t, req, blueSkyExpected)
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}
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