Files
ollama37/integration/quantization_test.go
Shang Chieh Tseng ef14fb5b26 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- Removed outdated CI/CD workflows (tesla-k80-*.yml)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>
2025-11-05 14:03:05 +08:00

135 lines
3.4 KiB
Go

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