mirror of
https://github.com/ollama/ollama.git
synced 2026-11-10 15:02:13 -06:00
Jesse Gross
2e036e7cdf
The MLX runner is the only Go inference runner left and is no longer experimental, so its packages leave x/. The bindings become a top-level mlx package beside the carried patches in mlx/compat, mirroring how llama/ holds the llama.cpp integration, and the runner becomes mlxrunner with the architectures nested under the package they implement. Subpackages move with their parent unless listed. x/mlxrunner/mlx mlx x/internal/mlxthread mlx/mlxthread x/internal/mlxthreadtest mlx/mlxthread/mlxthreadtest x/internal/mlxtest mlx/mlxtest x/quant mlx/quant mlx/compat/*.patch mlx/compat/mlx-c (MLX patches go in mlx/compat/mlx) x/mlxrunner mlxrunner x/models/nn mlxrunner/nn x/models/<arch> mlxrunner/model/<arch> x/mlxrunner/imports.go mlxrunner/model/architectures (new package) x/create create x/safetensors fs/safetensors x/tokenizer mlxrunner/tokenizer Every package keeps its name, so the Go changes are the import path rewrites the moves force, and the CMake, Dockerfile, CI cache keys, drift check and Darwin payload script follow the new paths. Four edits are not paths: the runner's blank architecture imports become the package mlxrunner/model/architectures, so the list to extend for a new model sits beside the architecture directories; a depguard rule keeps the two test harnesses out of non-test code, as the x/internal placement used to; the CI change filter's two entries for the long-deleted x/imagegen/mlx now name the bindings' CMake project and the carried patches, so a change to either builds the payload; and the tokenizer parity test reads its fixtures from its own testdata instead of walking out of x/. x/server and x/imagegen/manifest stay for the next two commits.
96 lines
3.0 KiB
Go
96 lines
3.0 KiB
Go
package create
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import (
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"encoding/json"
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"fmt"
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"strings"
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)
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type gemma4ImportTransform struct {
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numLayers int
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numExperts int
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}
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// gemma4Config is a minimal subset of the Gemma 4 config.json used for quant decisions.
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type gemma4Config struct {
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NumHiddenLayers int `json:"num_hidden_layers"`
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NumExperts int `json:"num_experts"`
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TextConfig struct {
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NumHiddenLayers int `json:"num_hidden_layers"`
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NumExperts int `json:"num_experts"`
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} `json:"text_config"`
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}
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func newGemma4ImportTransform(rawConfig json.RawMessage) (quantizePolicy, error) {
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var cfg gemma4Config
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if err := json.Unmarshal(rawConfig, &cfg); err != nil {
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return nil, fmt.Errorf("gemma4: parse config.json: %w", err)
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}
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numLayers := cfg.NumHiddenLayers
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if numLayers == 0 {
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numLayers = cfg.TextConfig.NumHiddenLayers
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}
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numExperts := cfg.NumExperts
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if numExperts == 0 {
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numExperts = cfg.TextConfig.NumExperts
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}
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return gemma4ImportTransform{numLayers: numLayers, numExperts: numExperts}, nil
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}
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func (t gemma4ImportTransform) quantizationType(name string, shape []int32, quantize string) string {
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base := normalizeQuantType(quantize)
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switch {
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case isEmbedTokensWeight(name):
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// The embedding doubles as the lm_head projection; an 8-bit type keeps
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// quality close to bf16 (matching GGUF Q6_K) while saving bandwidth.
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// Fall back to the base type when 8-bit does not fit the vocab shape.
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if e := promoteEmbedding(shape, base); e != "" {
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return e
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}
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if isAligned(shape, base) {
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return base
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}
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return ""
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case t.isSensitiveProjection(name) && eightBit(base) != base:
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return sensitiveType(t.promoteSensitive(name), shape, base)
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default:
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// Routing gates, norms, embeddings, etc. are handled by the generic
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// policy; everything else quantizes at the requested type.
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return GetTensorQuantization(name, shape, quantize)
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}
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}
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// isSensitiveProjection reports the value/key/down projections whose precision
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// most affects quality — attention output (v/k) and the residual stream
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// (down). Audio and vision tensors are excluded and follow the generic
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// policy.
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func (t gemma4ImportTransform) isSensitiveProjection(name string) bool {
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if isVision(name) || isAudioTower(name) {
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return false
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}
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return strings.Contains(name, ".v_proj") ||
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strings.Contains(name, ".k_proj") ||
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strings.Contains(name, "down_proj")
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}
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// promoteSensitive decides whether a sensitive projection uses 8-bit precision.
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// 8-expert models share very few KV heads, so their k/v projections are always
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// promoted; otherwise v/down projections are promoted at the input and output
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// layers and periodically between (useMoreBits), where residual-stream error
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// accumulates most.
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func (t gemma4ImportTransform) promoteSensitive(name string) bool {
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if t.numLayers == 0 {
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return false
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}
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if t.numExperts == 8 && (strings.Contains(name, ".v_proj") || strings.Contains(name, ".k_proj")) {
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return true
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}
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if strings.Contains(name, ".k_proj") {
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return false // k_proj is promoted only via the 8-expert path
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}
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layer := layerIndex(name)
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return layer >= 0 && useMoreBits(layer, t.numLayers)
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}
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