Add experimental MLX backend and engine with imagegen support (#13648)

* WIP - MLX backend with gemma3

* MLX: add cmake and go tag build toggles

To build the new MLX backend code:
  cmake --preset MLX
  cmake --build --preset MLX --parallel
  cmake --install build --component MLX
  go build -tags mlx .

Note: the main.go entrypoint for the MLX engine will change in a follow up commit.

* add experimental image generation runtime

* add experimental image generation runtime

* MLX: wire up cuda build for linux

* MLX: get dependencies correct and dedup

This is still too large for a unified github artifact, but is now "correct" for the mlx_cuda_v13
directory.

* fix relative link bug in dedup

* Add darwin build and readme

* add go build tag for mlx dependent code and wire up build_darwin.sh

* lint cleanup

* macos: build mlx for x86

This will be CPU only.

* cuda build instructions and fix drift from mlx bump

* stale comment

* Delete agent helper doc

* Clean up readme.md

* Revise README for tokenizer clarity and details

Updated README to clarify tokenizer functionality and removed correctness section.

---------

Co-authored-by: jmorganca <jmorganca@gmail.com>

x/imagegen/cmd/engine/sample.go

@@ -0,0 +1,49 @@
+//go:build mlx
+
+package main
+
+import "github.com/ollama/ollama/x/imagegen/mlx"
+
+// sampleTopK samples from top-k logits using global random state
+func sampleTopK(scaledLogits *mlx.Array, k int) *mlx.Array {
+	neg := mlx.Neg(scaledLogits)
+	indices := mlx.Argpartition(neg, k-1, -1)
+	topKIdx := mlx.Slice(indices, []int32{0}, []int32{int32(k)})
+	values := mlx.TakeAlongAxis(scaledLogits, topKIdx, -1)
+	sampled := mlx.RandomCategorical(values, -1, 1)
+	return mlx.Take(topKIdx, sampled, -1)
+}
+
+// sampleTopP samples using nucleus sampling with global random state
+func sampleTopP(scaledLogits *mlx.Array, p float32, vocabSize int32) *mlx.Array {
+	sorted := mlx.Argsort(mlx.Neg(scaledLogits), -1)
+	sortedLogits := mlx.TakeAlongAxis(scaledLogits, sorted, -1)
+	probs := mlx.Softmax(sortedLogits, -1)
+	cumProbs := mlx.Cumsum(probs, -1)
+	mask := mlx.LessScalar(cumProbs, p)
+	negInf := mlx.FullDtype(float32(-1e9), scaledLogits.Dtype(), vocabSize)
+	masked := mlx.Where(mask, sortedLogits, negInf)
+	sampled := mlx.RandomCategorical(masked, -1, 1)
+	return mlx.Take(sorted, sampled, -1)
+}
+
+// sample samples from logits at the last position
+func sample(logits *mlx.Array, temp float32, topK int, topP float32, vocab int32) *mlx.Array {
+	// Get last position logits: [1, L, vocab] -> [vocab]
+	shape := logits.Shape()
+	seqLen := shape[1]
+	lastLogits := mlx.Slice(logits, []int32{0, seqLen - 1, 0}, []int32{1, seqLen, vocab})
+	lastLogits = mlx.Reshape(lastLogits, vocab)
+
+	if temp == 0 {
+		return mlx.Argmax(lastLogits, -1, false)
+	}
+	scaled := mlx.DivScalar(lastLogits, temp)
+	if topK > 0 && topK < int(vocab) {
+		return sampleTopK(scaled, topK)
+	}
+	if topP > 0 && topP < 1.0 {
+		return sampleTopP(scaled, topP, vocab)
+	}
+	return mlx.RandomCategorical(scaled, -1, 1)
+}