llm: Separate llamaServer and ollamaServer code paths

Originally, llamaServer represented old memory estimates, which
could be used with either the old or new engine. ollamaServer was
used only for the new estimates and new engine. Since these
implementations did not map directly to engine, there was engine-
specific code in common code paths.

Now that new estimates are always used for the new engine, there is
a direct mapping between server type and engine. This separates out
most of the engine-specific code into the correct implementation
to make things easier to understand.
This commit is contained in:
Jesse Gross
2025-11-06 10:31:08 -08:00
committed by Jesse Gross
parent f560bd077f
commit b13fbad0fe

View File

@@ -89,7 +89,6 @@ type llmServer struct {
done chan error // Channel to signal when the process exits done chan error // Channel to signal when the process exits
status *StatusWriter status *StatusWriter
options api.Options options api.Options
numParallel int
modelPath string modelPath string
loadRequest LoadRequest // Parameters used to initialize the runner loadRequest LoadRequest // Parameters used to initialize the runner
@@ -100,10 +99,6 @@ type llmServer struct {
llamaModel *llama.Model llamaModel *llama.Model
llamaModelLock *sync.Mutex llamaModelLock *sync.Mutex
// textProcessor handles text encoding/decoding for the model in the Ollama engine
// nil if this server is running the llama.cpp based engine
textProcessor model.TextProcessor
totalLayers uint64 totalLayers uint64
loadStart time.Time // Record how long it took the model to load loadStart time.Time // Record how long it took the model to load
loadProgress float32 loadProgress float32
@@ -119,6 +114,8 @@ type llamaServer struct {
type ollamaServer struct { type ollamaServer struct {
llmServer llmServer
textProcessor model.TextProcessor // textProcessor handles text encoding/decoding
} }
// LoadModel will load a model from disk. The model must be in the GGML format. // LoadModel will load a model from disk. The model must be in the GGML format.
@@ -242,8 +239,6 @@ func NewLlamaServer(systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, modelPath st
loadRequest: loadRequest, loadRequest: loadRequest,
llamaModel: llamaModel, llamaModel: llamaModel,
llamaModelLock: &sync.Mutex{}, llamaModelLock: &sync.Mutex{},
textProcessor: textProcessor,
numParallel: numParallel,
sem: semaphore.NewWeighted(int64(numParallel)), sem: semaphore.NewWeighted(int64(numParallel)),
totalLayers: f.KV().BlockCount() + 1, totalLayers: f.KV().BlockCount() + 1,
loadStart: time.Now(), loadStart: time.Now(),
@@ -278,7 +273,7 @@ func NewLlamaServer(systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, modelPath st
}() }()
if textProcessor != nil { if textProcessor != nil {
return &ollamaServer{llmServer: s}, nil return &ollamaServer{llmServer: s, textProcessor: textProcessor}, nil
} else { } else {
return &llamaServer{llmServer: s, ggml: f}, nil return &llamaServer{llmServer: s, ggml: f}, nil
} }
@@ -1681,68 +1676,59 @@ func (s *llmServer) Embedding(ctx context.Context, input string) ([]float32, err
return e.Embedding, nil return e.Embedding, nil
} }
type TokenizeRequest struct { func (s *llamaServer) Tokenize(ctx context.Context, content string) ([]int, error) {
Content string `json:"content"`
}
type TokenizeResponse struct {
Tokens []int `json:"tokens"`
}
func (s *llmServer) Tokenize(ctx context.Context, content string) ([]int, error) {
s.llamaModelLock.Lock() s.llamaModelLock.Lock()
defer s.llamaModelLock.Unlock() defer s.llamaModelLock.Unlock()
if s.llamaModel != nil { if s.llamaModel == nil {
return s.llamaModel.Tokenize(content, false, true) return nil, fmt.Errorf("no tokenizer configured")
} }
if s.textProcessor != nil {
return s.llamaModel.Tokenize(content, false, true)
}
func (s *ollamaServer) Tokenize(ctx context.Context, content string) ([]int, error) {
tokens, err := s.textProcessor.Encode(content, false) tokens, err := s.textProcessor.Encode(content, false)
if err != nil { if err != nil {
return nil, err return nil, err
} }
toks := make([]int, len(tokens)) toks := make([]int, len(tokens))
for i, t := range tokens { for i, t := range tokens {
toks[i] = int(t) toks[i] = int(t)
} }
return toks, nil return toks, nil
}
// not reached
return nil, fmt.Errorf("no tokenizer configured")
} }
type DetokenizeRequest struct { func (s *llamaServer) Detokenize(ctx context.Context, tokens []int) (string, error) {
Tokens []int `json:"tokens"`
}
type DetokenizeResponse struct {
Content string `json:"content"`
}
func (s *llmServer) Detokenize(ctx context.Context, tokens []int) (string, error) {
s.llamaModelLock.Lock() s.llamaModelLock.Lock()
defer s.llamaModelLock.Unlock() defer s.llamaModelLock.Unlock()
if s.llamaModel != nil { if s.llamaModel == nil {
return "", fmt.Errorf("no tokenizer configured")
}
var resp string var resp string
for _, token := range tokens { for _, token := range tokens {
resp += s.llamaModel.TokenToPiece(token) resp += s.llamaModel.TokenToPiece(token)
} }
return resp, nil return resp, nil
} }
if s.textProcessor != nil {
func (s *ollamaServer) Detokenize(ctx context.Context, tokens []int) (string, error) {
toks := make([]int32, len(tokens)) toks := make([]int32, len(tokens))
for i, t := range tokens { for i, t := range tokens {
toks[i] = int32(t) toks[i] = int32(t)
} }
content, err := s.textProcessor.Decode(toks) content, err := s.textProcessor.Decode(toks)
if err != nil { if err != nil {
return "", err return "", err
} }
return content, nil return content, nil
}
// not reached
return "", fmt.Errorf("no tokenizer configured")
} }
func (s *llmServer) Close() error { func (s *llmServer) Close() error {