mirror of
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Match the ollamarunner and OpenAI semantics: raw, full-vocab log-softmax with the top-K ranked by probability. Skipped on the GPU when the request doesn't ask for logprobs so decode doesn't pay for it otherwise.
161 lines
4.0 KiB
Go
161 lines
4.0 KiB
Go
package mlxrunner
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import (
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"context"
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"errors"
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"log/slog"
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"net"
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"net/http"
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"strings"
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"golang.org/x/sync/errgroup"
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"github.com/ollama/ollama/api"
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"github.com/ollama/ollama/x/mlxrunner/mlx"
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"github.com/ollama/ollama/x/mlxrunner/model"
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"github.com/ollama/ollama/x/mlxrunner/model/base"
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"github.com/ollama/ollama/x/mlxrunner/sample"
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"github.com/ollama/ollama/x/tokenizer"
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)
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type Request struct {
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CompletionRequest
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Responses chan CompletionResponse
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Pipeline func(Request) error
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Ctx context.Context
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Sampler *sample.Sampler
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}
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type Runner struct {
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Model base.Model
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Tokenizer *tokenizer.Tokenizer
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Requests chan Request
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cache kvCache
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contextLength int
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}
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func (r *Runner) Load(modelName string) error {
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root, err := model.Open(modelName)
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if err != nil {
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return err
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}
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defer root.Close()
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m, err := base.New(root)
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if err != nil {
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return err
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}
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// Load all tensor blobs from manifest
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tensors, err := loadTensorsFromManifest(root)
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if err != nil {
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return err
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}
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// Assign weights to model (model-specific logic)
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loadWeights := base.Weights(m)
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if err := loadWeights(tensors); err != nil {
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return err
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}
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r.Model = m
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r.Tokenizer = m.Tokenizer()
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r.contextLength = m.MaxContextLength()
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mlx.EnableCompile()
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return nil
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}
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// loadTensorsFromManifest loads all tensor blobs from the manifest into a
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// flat map, deduplicating by digest and remapping safetensors key suffixes.
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//
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// Uses a two-phase approach: first loads all raw tensors, then remaps
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// .bias → _qbias with complete knowledge of which base names have .scale
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// entries. This avoids a race condition where Go map iteration order could
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// cause .bias to be processed before .scale within the same blob.
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func loadTensorsFromManifest(root *model.Root) (map[string]*mlx.Array, error) {
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// Phase 1: Load all tensors raw from all blobs
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rawTensors := make(map[string]*mlx.Array)
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seen := make(map[string]bool)
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for _, layer := range root.Manifest.GetTensorLayers("") {
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if seen[layer.Digest] {
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continue
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}
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seen[layer.Digest] = true
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blobPath := root.Manifest.BlobPath(layer.Digest)
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for name, arr := range mlx.Load(blobPath) {
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rawTensors[name] = arr
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}
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}
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// Phase 2: Identify all base names that have .scale tensors and remap them
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scaleBaseNames := make(map[string]bool)
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allTensors := make(map[string]*mlx.Array, len(rawTensors))
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for name, arr := range rawTensors {
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if strings.HasSuffix(name, ".scale") {
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baseName := strings.TrimSuffix(name, ".scale")
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allTensors[baseName+"_scale"] = arr
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scaleBaseNames[baseName] = true
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}
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}
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// Phase 3: Process remaining tensors with complete scale knowledge
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for name, arr := range rawTensors {
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if strings.HasSuffix(name, ".scale") {
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continue // already handled
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}
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if strings.HasSuffix(name, ".bias") && !strings.HasSuffix(name, ".weight_qbias") {
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baseName := strings.TrimSuffix(name, ".bias")
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if scaleBaseNames[baseName] {
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allTensors[baseName+"_qbias"] = arr
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} else {
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allTensors[name] = arr
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}
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} else {
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allTensors[name] = arr
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}
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}
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slog.Info("Loaded tensors from manifest", "count", len(allTensors))
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return allTensors, nil
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}
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func (r *Runner) Run(host, port string, mux http.Handler) error {
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g, ctx := errgroup.WithContext(context.Background())
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g.Go(func() error {
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for {
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select {
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case <-ctx.Done():
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return nil
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case request := <-r.Requests:
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if err := request.Pipeline(request); err != nil {
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slog.Info("Request terminated", "error", err)
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var statusErr api.StatusError
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if !errors.As(err, &statusErr) {
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statusErr = api.StatusError{
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StatusCode: http.StatusInternalServerError,
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ErrorMessage: err.Error(),
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}
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}
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select {
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case request.Responses <- CompletionResponse{Error: &statusErr}:
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case <-request.Ctx.Done():
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}
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}
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close(request.Responses)
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}
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}
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})
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g.Go(func() error {
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slog.Info("Starting HTTP server", "host", host, "port", port)
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return http.ListenAndServe(net.JoinHostPort(host, port), mux)
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})
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return g.Wait()
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}
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