Files
ollama37/openai/openai_encoding_format_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- Removed outdated CI/CD workflows (tesla-k80-*.yml)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

140 lines
3.9 KiB
Go

package openai
import (
"encoding/base64"
"math"
"testing"
"github.com/ollama/ollama/api"
)
func TestToEmbeddingList(t *testing.T) {
testCases := []struct {
name string
embeddings [][]float32
format string
expectType string // "float" or "base64"
expectBase64 []string
expectCount int
promptEval int
}{
{"float format", [][]float32{{0.1, -0.2, 0.3}}, "float", "float", nil, 1, 10},
{"base64 format", [][]float32{{0.1, -0.2, 0.3}}, "base64", "base64", []string{"zczMPc3MTL6amZk+"}, 1, 5},
{"default to float", [][]float32{{0.1, -0.2, 0.3}}, "", "float", nil, 1, 0},
{"invalid defaults to float", [][]float32{{0.1, -0.2, 0.3}}, "invalid", "float", nil, 1, 0},
{"multiple embeddings", [][]float32{{0.1, 0.2}, {0.3, 0.4}, {0.5, 0.6}}, "base64", "base64", []string{"zczMPc3MTD4=", "mpmZPs3MzD4=", "AAAAP5qZGT8="}, 3, 0},
{"empty embeddings", nil, "float", "", nil, 0, 0},
}
for _, tc := range testCases {
t.Run(tc.name, func(t *testing.T) {
resp := api.EmbedResponse{
Embeddings: tc.embeddings,
PromptEvalCount: tc.promptEval,
}
result := ToEmbeddingList("test-model", resp, tc.format)
if tc.expectCount == 0 {
if len(result.Data) != 0 {
t.Errorf("expected 0 embeddings, got %d", len(result.Data))
}
return
}
if len(result.Data) != tc.expectCount {
t.Fatalf("expected %d embeddings, got %d", tc.expectCount, len(result.Data))
}
if result.Model != "test-model" {
t.Errorf("expected model 'test-model', got %q", result.Model)
}
// Check type of first embedding
switch tc.expectType {
case "float":
if _, ok := result.Data[0].Embedding.([]float32); !ok {
t.Errorf("expected []float32, got %T", result.Data[0].Embedding)
}
case "base64":
for i, data := range result.Data {
embStr, ok := data.Embedding.(string)
if !ok {
t.Errorf("embedding %d: expected string, got %T", i, data.Embedding)
continue
}
// Verify it's valid base64
if _, err := base64.StdEncoding.DecodeString(embStr); err != nil {
t.Errorf("embedding %d: invalid base64: %v", i, err)
}
// Compare against expected base64 string if provided
if tc.expectBase64 != nil && i < len(tc.expectBase64) {
if embStr != tc.expectBase64[i] {
t.Errorf("embedding %d: expected base64 %q, got %q", i, tc.expectBase64[i], embStr)
}
}
}
}
// Check indices
for i := range result.Data {
if result.Data[i].Index != i {
t.Errorf("embedding %d: expected index %d, got %d", i, i, result.Data[i].Index)
}
}
if tc.promptEval > 0 && result.Usage.PromptTokens != tc.promptEval {
t.Errorf("expected %d prompt tokens, got %d", tc.promptEval, result.Usage.PromptTokens)
}
})
}
}
func TestFloatsToBase64(t *testing.T) {
floats := []float32{0.1, -0.2, 0.3, -0.4, 0.5}
result := floatsToBase64(floats)
// Verify it's valid base64
decoded, err := base64.StdEncoding.DecodeString(result)
if err != nil {
t.Fatalf("failed to decode base64: %v", err)
}
// Check length
expectedBytes := len(floats) * 4
if len(decoded) != expectedBytes {
t.Errorf("expected %d bytes, got %d", expectedBytes, len(decoded))
}
// Decode and verify values
for i, expected := range floats {
offset := i * 4
bits := uint32(decoded[offset]) |
uint32(decoded[offset+1])<<8 |
uint32(decoded[offset+2])<<16 |
uint32(decoded[offset+3])<<24
decodedFloat := math.Float32frombits(bits)
if math.Abs(float64(decodedFloat-expected)) > 1e-6 {
t.Errorf("float[%d]: expected %f, got %f", i, expected, decodedFloat)
}
}
}
func TestFloatsToBase64_EmptySlice(t *testing.T) {
result := floatsToBase64([]float32{})
// Should return valid base64 for empty slice
decoded, err := base64.StdEncoding.DecodeString(result)
if err != nil {
t.Fatalf("failed to decode base64: %v", err)
}
if len(decoded) != 0 {
t.Errorf("expected 0 bytes, got %d", len(decoded))
}
}