bugfix: add missing linear layer factory (#14289)

x/mlxrunner/model/linear.go

@@ -0,0 +1,92 @@
+//go:build mlx
+
+package model
+
+import (
+	"github.com/ollama/ollama/x/mlxrunner/mlx"
+	"github.com/ollama/ollama/x/models/nn"
+)
+
+// LinearFactory builds linear layers using shared tensor maps and quant defaults.
+type LinearFactory struct {
+	tensors          map[string]*mlx.Array
+	defaultGroupSize int
+	defaultBits      int
+	defaultMode      string
+	tensorQuant      map[string]*TensorQuantInfo
+}
+
+// NewLinearFactory creates a reusable constructor for model linear layers.
+func NewLinearFactory(
+	tensors map[string]*mlx.Array,
+	defaultGroupSize, defaultBits int,
+	defaultMode string,
+	tensorQuant map[string]*TensorQuantInfo,
+) LinearFactory {
+	return LinearFactory{
+		tensors:          tensors,
+		defaultGroupSize: defaultGroupSize,
+		defaultBits:      defaultBits,
+		defaultMode:      defaultMode,
+		tensorQuant:      tensorQuant,
+	}
+}
+
+// Make constructs a linear layer at path.
+func (f LinearFactory) Make(path string) nn.LinearLayer {
+	return MakeLinearLayer(
+		f.tensors,
+		path,
+		f.defaultGroupSize,
+		f.defaultBits,
+		f.defaultMode,
+		f.tensorQuant,
+	)
+}
+
+// MakeLinearLayer constructs a linear layer from a tensor map.
+//
+// For quantized tensors (path.weight + path.weight_scale), it resolves per-tensor
+// quant params via TensorQuant metadata (with shape-based affine fallback).
+// For non-quantized tensors, it returns a standard nn.Linear.
+func MakeLinearLayer(
+	tensors map[string]*mlx.Array,
+	path string,
+	defaultGroupSize, defaultBits int,
+	defaultMode string,
+	tensorQuant map[string]*TensorQuantInfo,
+) nn.LinearLayer {
+	w := tensors[path+".weight"]
+	if w == nil {
+		return nil
+	}
+
+	scales := tensors[path+".weight_scale"]
+	if scales != nil {
+		qbiases := tensors[path+".weight_qbias"]
+		bias := tensors[path+".bias"]
+
+		groupSize, bits, mode := ResolveLinearQuantParams(
+			defaultGroupSize,
+			defaultBits,
+			defaultMode,
+			tensorQuant,
+			path+".weight",
+			w,
+			scales,
+		)
+
+		return &nn.QuantizedLinear{
+			Weight:    w,
+			Scales:    scales,
+			QBiases:   qbiases,
+			Bias:      bias,
+			GroupSize: groupSize,
+			Bits:      bits,
+			Mode:      mode,
+		}
+	}
+
+	bias := tensors[path+".bias"]
+	return nn.NewLinear(w, bias)
+}