Enha (onnx): use gpu

rag/embedder.go

@@ -156,14 +156,22 @@ type ONNXEmbedder struct {
 var onnxInitOnce sync.Once
 var onnxReady bool
 var onnxLibPath string
+var cudaLibPath string
 
 var onnxLibPaths = []string{
 	"/usr/lib/libonnxruntime.so",
+	"/usr/lib/libonnxruntime.so.1.24.2",
 	"/usr/local/lib/libonnxruntime.so",
 	"/usr/lib/x86_64-linux-gnu/libonnxruntime.so",
 	"/opt/onnxruntime/lib/libonnxruntime.so",
 }
 
+var cudaLibPaths = []string{
+	"/usr/lib/libonnxruntime_providers_cuda.so",
+	"/usr/local/lib/libonnxruntime_providers_cuda.so",
+	"/opt/onnxruntime/lib/libonnxruntime_providers_cuda.so",
+}
+
 func findONNXLibrary() string {
 	for _, path := range onnxLibPaths {
 		if _, err := os.Stat(path); err == nil {
@@ -173,6 +181,15 @@ func findONNXLibrary() string {
 	return ""
 }
 
+func findCUDALibrary() string {
+	for _, path := range cudaLibPaths {
+		if _, err := os.Stat(path); err == nil {
+			return path
+		}
+	}
+	return ""
+}
+
 func NewONNXEmbedder(modelPath, tokenizerPath string, dims int, logger *slog.Logger) (*ONNXEmbedder, error) {
 	// Check if model and tokenizer files exist
 	if _, err := os.Stat(modelPath); err != nil {
@@ -188,6 +205,12 @@ func NewONNXEmbedder(modelPath, tokenizerPath string, dims int, logger *slog.Log
 		return nil, errors.New("ONNX runtime library not found in standard locations")
 	}
 
+	// Find CUDA provider library (optional)
+	cudaLibPath = findCUDALibrary()
+	if cudaLibPath == "" {
+		fmt.Println("WARNING: CUDA provider library not found, will use CPU")
+	}
+
 	emb := &ONNXEmbedder{
 		tokenizerPath: tokenizerPath,
 		dims:          dims,
@@ -223,16 +246,56 @@ func (e *ONNXEmbedder) ensureInitialized() error {
 			onnxReady = false
 			return
 		}
+		// Register CUDA provider if available
+		if cudaLibPath != "" {
+			if err := onnxruntime_go.RegisterExecutionProviderLibrary("CUDA", cudaLibPath); err != nil {
+				e.logger.Warn("failed to register CUDA provider", "error", err)
+			}
+		}
 		onnxReady = true
 	})
 	if !onnxReady {
 		return errors.New("ONNX runtime not ready")
 	}
+
+	// Create session options
+	opts, err := onnxruntime_go.NewSessionOptions()
+	if err != nil {
+		return fmt.Errorf("failed to create session options: %w", err)
+	}
+	defer opts.Destroy()
+
+	// Try to add CUDA provider
+	useCUDA := cudaLibPath != ""
+	if useCUDA {
+		cudaOpts, err := onnxruntime_go.NewCUDAProviderOptions()
+		if err != nil {
+			e.logger.Warn("failed to create CUDA provider options, falling back to CPU", "error", err)
+			useCUDA = false
+		} else {
+			defer cudaOpts.Destroy()
+			if err := cudaOpts.Update(map[string]string{"device_id": "0"}); err != nil {
+				e.logger.Warn("failed to update CUDA options, falling back to CPU", "error", err)
+				useCUDA = false
+			} else if err := opts.AppendExecutionProviderCUDA(cudaOpts); err != nil {
+				e.logger.Warn("failed to append CUDA provider, falling back to CPU", "error", err)
+				useCUDA = false
+			}
+		}
+	}
+
+	if useCUDA {
+		e.logger.Info("Using CUDA for ONNX inference")
+	} else {
+		e.logger.Info("Using CPU for ONNX inference")
+	}
+
+	// Create session with options
 	session, err := onnxruntime_go.NewDynamicAdvancedSession(
 		e.getModelPath(),
 		[]string{"input_ids", "attention_mask"},
 		[]string{"sentence_embedding"},
-		nil,
+		opts,
 	)
 	if err != nil {
 		return fmt.Errorf("failed to create ONNX session: %w", err)
@@ -304,6 +367,9 @@ func (e *ONNXEmbedder) Embed(text string) ([]float32, error) {
 }
 
 func (e *ONNXEmbedder) EmbedSlice(texts []string) ([][]float32, error) {
+	if err := e.ensureInitialized(); err != nil {
+		return nil, err
+	}
 	encodings := make([]*tokenizer.Encoding, len(texts))
 	maxLen := 0
 	for i, txt := range texts {