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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
135 lines
3.4 KiB
Go
135 lines
3.4 KiB
Go
//go:build integration && models
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package integration
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import (
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"bytes"
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"context"
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"fmt"
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"log/slog"
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"strings"
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"testing"
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"time"
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"github.com/ollama/ollama/api"
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)
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func TestQuantization(t *testing.T) {
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sourceModels := []string{
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"qwen2.5:0.5b-instruct-fp16",
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}
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quantizations := []string{
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"Q8_0",
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"Q4_K_S",
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"Q4_K_M",
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"Q4_K",
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}
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softTimeout, hardTimeout := getTimeouts(t)
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started := time.Now()
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slog.Info("Setting timeouts", "soft", softTimeout, "hard", hardTimeout)
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ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
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defer cancel()
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client, _, cleanup := InitServerConnection(ctx, t)
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defer cleanup()
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for _, base := range sourceModels {
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if err := PullIfMissing(ctx, client, base); err != nil {
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t.Fatalf("pull failed %s", err)
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}
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for _, quant := range quantizations {
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newName := fmt.Sprintf("%s__%s", base, quant)
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t.Run(newName, func(t *testing.T) {
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if time.Now().Sub(started) > softTimeout {
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t.Skip("skipping remaining tests to avoid excessive runtime")
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}
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req := &api.CreateRequest{
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Model: newName,
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Quantization: quant,
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From: base,
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}
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fn := func(resp api.ProgressResponse) error {
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// fmt.Print(".")
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return nil
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}
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t.Logf("quantizing: %s -> %s", base, quant)
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if err := client.Create(ctx, req, fn); err != nil {
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t.Fatalf("create failed %s", err)
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}
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defer func() {
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req := &api.DeleteRequest{
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Model: newName,
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}
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t.Logf("deleting: %s -> %s", base, quant)
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if err := client.Delete(ctx, req); err != nil {
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t.Logf("failed to clean up %s: %s", req.Model, err)
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}
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}()
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// Check metadata on the model
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resp, err := client.Show(ctx, &api.ShowRequest{Name: newName})
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if err != nil {
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t.Fatalf("unable to show model: %s", err)
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}
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if !strings.Contains(resp.Details.QuantizationLevel, quant) {
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t.Fatalf("unexpected quantization for %s:\ngot: %s", newName, resp.Details.QuantizationLevel)
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}
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stream := true
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chatReq := api.ChatRequest{
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Model: newName,
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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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KeepAlive: &api.Duration{Duration: 3 * time.Second},
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Options: map[string]any{
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"seed": 42,
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"temperature": 0.0,
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},
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Stream: &stream,
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}
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t.Logf("verifying: %s -> %s", base, quant)
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// Some smaller quantizations can cause models to have poor quality
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// or get stuck in repetition loops, so we stop as soon as we have any matches
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reqCtx, reqCancel := context.WithCancel(ctx)
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atLeastOne := false
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var buf bytes.Buffer
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chatfn := func(response api.ChatResponse) error {
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buf.Write([]byte(response.Message.Content))
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fullResp := strings.ToLower(buf.String())
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for _, resp := range blueSkyExpected {
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if strings.Contains(fullResp, resp) {
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atLeastOne = true
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t.Log(fullResp)
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reqCancel()
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break
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}
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}
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return nil
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}
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done := make(chan int)
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var genErr error
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go func() {
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genErr = client.Chat(reqCtx, &chatReq, chatfn)
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done <- 0
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}()
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select {
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case <-done:
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if genErr != nil && !atLeastOne {
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t.Fatalf("failed with %s request prompt %s ", chatReq.Model, chatReq.Messages[0].Content)
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}
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case <-ctx.Done():
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t.Error("outer test context done while waiting for generate")
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}
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t.Logf("passed")
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})
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}
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}
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}
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