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- Removed outdated CI/CD workflows (tesla-k80-*.yml)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

221 lines
5.7 KiB
Go

package middleware
import (
"encoding/base64"
"encoding/json"
"net/http"
"net/http/httptest"
"strings"
"testing"
"github.com/gin-gonic/gin"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/openai"
)
func TestEmbeddingsMiddleware_EncodingFormats(t *testing.T) {
testCases := []struct {
name string
encodingFormat string
expectType string // "array" or "string"
verifyBase64 bool
}{
{"float format", "float", "array", false},
{"base64 format", "base64", "string", true},
{"default format", "", "array", false},
}
gin.SetMode(gin.TestMode)
endpoint := func(c *gin.Context) {
resp := api.EmbedResponse{
Embeddings: [][]float32{{0.1, -0.2, 0.3}},
PromptEvalCount: 5,
}
c.JSON(http.StatusOK, resp)
}
router := gin.New()
router.Use(EmbeddingsMiddleware())
router.Handle(http.MethodPost, "/api/embed", endpoint)
for _, tc := range testCases {
t.Run(tc.name, func(t *testing.T) {
body := `{"input": "test", "model": "test-model"`
if tc.encodingFormat != "" {
body += `, "encoding_format": "` + tc.encodingFormat + `"`
}
body += `}`
req, _ := http.NewRequest(http.MethodPost, "/api/embed", strings.NewReader(body))
req.Header.Set("Content-Type", "application/json")
resp := httptest.NewRecorder()
router.ServeHTTP(resp, req)
if resp.Code != http.StatusOK {
t.Fatalf("expected status 200, got %d", resp.Code)
}
var result openai.EmbeddingList
if err := json.Unmarshal(resp.Body.Bytes(), &result); err != nil {
t.Fatalf("failed to unmarshal response: %v", err)
}
if len(result.Data) != 1 {
t.Fatalf("expected 1 embedding, got %d", len(result.Data))
}
switch tc.expectType {
case "array":
if _, ok := result.Data[0].Embedding.([]interface{}); !ok {
t.Errorf("expected array, got %T", result.Data[0].Embedding)
}
case "string":
embStr, ok := result.Data[0].Embedding.(string)
if !ok {
t.Errorf("expected string, got %T", result.Data[0].Embedding)
} else if tc.verifyBase64 {
decoded, err := base64.StdEncoding.DecodeString(embStr)
if err != nil {
t.Errorf("invalid base64: %v", err)
} else if len(decoded) != 12 {
t.Errorf("expected 12 bytes, got %d", len(decoded))
}
}
}
})
}
}
func TestEmbeddingsMiddleware_BatchWithBase64(t *testing.T) {
gin.SetMode(gin.TestMode)
endpoint := func(c *gin.Context) {
resp := api.EmbedResponse{
Embeddings: [][]float32{
{0.1, 0.2},
{0.3, 0.4},
{0.5, 0.6},
},
PromptEvalCount: 10,
}
c.JSON(http.StatusOK, resp)
}
router := gin.New()
router.Use(EmbeddingsMiddleware())
router.Handle(http.MethodPost, "/api/embed", endpoint)
body := `{
"input": ["hello", "world", "test"],
"model": "test-model",
"encoding_format": "base64"
}`
req, _ := http.NewRequest(http.MethodPost, "/api/embed", strings.NewReader(body))
req.Header.Set("Content-Type", "application/json")
resp := httptest.NewRecorder()
router.ServeHTTP(resp, req)
if resp.Code != http.StatusOK {
t.Fatalf("expected status 200, got %d", resp.Code)
}
var result openai.EmbeddingList
if err := json.Unmarshal(resp.Body.Bytes(), &result); err != nil {
t.Fatalf("failed to unmarshal response: %v", err)
}
if len(result.Data) != 3 {
t.Fatalf("expected 3 embeddings, got %d", len(result.Data))
}
// All should be base64 strings
for i := range 3 {
embeddingStr, ok := result.Data[i].Embedding.(string)
if !ok {
t.Errorf("embedding %d: expected string, got %T", i, result.Data[i].Embedding)
continue
}
// Verify it's valid base64
if _, err := base64.StdEncoding.DecodeString(embeddingStr); err != nil {
t.Errorf("embedding %d: invalid base64: %v", i, err)
}
// Check index
if result.Data[i].Index != i {
t.Errorf("embedding %d: expected index %d, got %d", i, i, result.Data[i].Index)
}
}
}
func TestEmbeddingsMiddleware_InvalidEncodingFormat(t *testing.T) {
gin.SetMode(gin.TestMode)
endpoint := func(c *gin.Context) {
c.Status(http.StatusOK)
}
router := gin.New()
router.Use(EmbeddingsMiddleware())
router.Handle(http.MethodPost, "/api/embed", endpoint)
testCases := []struct {
name string
encodingFormat string
shouldFail bool
}{
{"valid: float", "float", false},
{"valid: base64", "base64", false},
{"valid: FLOAT (uppercase)", "FLOAT", false},
{"valid: BASE64 (uppercase)", "BASE64", false},
{"valid: Float (mixed)", "Float", false},
{"valid: Base64 (mixed)", "Base64", false},
{"invalid: json", "json", true},
{"invalid: hex", "hex", true},
{"invalid: invalid_format", "invalid_format", true},
}
for _, tc := range testCases {
t.Run(tc.name, func(t *testing.T) {
body := `{
"input": "test",
"model": "test-model",
"encoding_format": "` + tc.encodingFormat + `"
}`
req, _ := http.NewRequest(http.MethodPost, "/api/embed", strings.NewReader(body))
req.Header.Set("Content-Type", "application/json")
resp := httptest.NewRecorder()
router.ServeHTTP(resp, req)
if tc.shouldFail {
if resp.Code != http.StatusBadRequest {
t.Errorf("expected status 400, got %d", resp.Code)
}
var errResp openai.ErrorResponse
if err := json.Unmarshal(resp.Body.Bytes(), &errResp); err != nil {
t.Fatalf("failed to unmarshal error response: %v", err)
}
if errResp.Error.Type != "invalid_request_error" {
t.Errorf("expected error type 'invalid_request_error', got %q", errResp.Error.Type)
}
if !strings.Contains(errResp.Error.Message, "encoding_format") {
t.Errorf("expected error message to mention encoding_format, got %q", errResp.Error.Message)
}
} else {
if resp.Code != http.StatusOK {
t.Errorf("expected status 200, got %d: %s", resp.Code, resp.Body.String())
}
}
})
}
}