mirror of
https://github.com/ollama/ollama.git
synced 2026-09-21 13:38:14 -05:00
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.
62 lines
2.4 KiB
Go
62 lines
2.4 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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// cohere2MoeImportTransform adjusts quantization for Cohere2 MoE imports
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// (Command A family / North models).
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type cohere2MoeImportTransform struct {
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numLayers int
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}
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func newCohere2MoeImportTransform(rawConfig json.RawMessage) (quantizePolicy, error) {
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var cfg struct {
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NumHiddenLayers int `json:"num_hidden_layers"`
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}
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if err := json.Unmarshal(rawConfig, &cfg); err != nil {
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return nil, fmt.Errorf("cohere2moe: parse config.json: %w", err)
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}
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return cohere2MoeImportTransform{numLayers: cfg.NumHiddenLayers}, nil
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}
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func (t cohere2MoeImportTransform) quantizationType(name string, shape []int32, quantize string) string {
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base := normalizeQuantType(quantize)
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// The embedding serves double duty: lookup (via QuantizedEmbedding) and the
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// tied lm_head projection (via AsLinear). With a 262k vocab the bf16
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// embedding dominates decode bandwidth through the lm_head matmul, so
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// quantize it to the 8-bit variant of the requested mode, or keep source
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// precision when that does not fit.
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if isEmbedTokensWeight(name) && len(shape) == 2 {
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return promoteEmbedding(shape, base)
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}
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// The MoE router picks the top-k expert set; quantization noise there can
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// flip expert selection and compound downstream. It is tiny, so keep it in
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// source precision. (GetTensorQuantization already skips "mlp.gate.weight";
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// kept explicit here so renames in the default policy cannot regress this.)
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if strings.HasSuffix(name, ".mlp.gate.weight") {
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return ""
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}
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// Sensitive tensors (v_proj, k_proj, down_proj) get higher precision only
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// at quantization-sensitive layer positions (useMoreBits) instead of the
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// default policy's blanket promotion. The blanket int8 down_proj costs
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// ~25% of decode bandwidth on a top-8 MoE; the layer-position heuristic
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// keeps the early/late layers (and every third in between) at 8 bits where
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// residual-stream error matters most.
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isSensitive := strings.Contains(name, ".v_proj") || strings.Contains(name, ".k_proj") || strings.Contains(name, "down_proj")
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if isSensitive && eightBit(base) != base && t.numLayers > 0 {
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if idx := layerIndex(name); idx >= 0 {
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// Bypass GetTensorQuantization's blanket promotion — the
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// layer-position heuristic is authoritative here.
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return sensitiveType(useMoreBits(idx, t.numLayers), shape, base)
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}
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}
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return GetTensorQuantization(name, shape, quantize)
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}
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