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10e51c5177
* prefer rocm v6 on windows Avoid building with v7 - more changes are needed * MLX: add header vendoring and remove go build tag This switches to using a vendoring approach for the mlx-c headers so that Go can build without requiring a cmake first. This enables building the new MLX based code by default. Every time cmake runs, the headers are refreshed, so we can easily keep them in sync when we bump mlx versions. Basic Windows and Linux support are verified. * ci: harden for flaky choco repo servers CI sometimes fails due to choco not actually installing cache. Since it just speeds up the build, we can proceed without. * review comments
48 lines
1.7 KiB
Go
48 lines
1.7 KiB
Go
package main
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import "github.com/ollama/ollama/x/imagegen/mlx"
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// sampleTopK samples from top-k logits using global random state
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func sampleTopK(scaledLogits *mlx.Array, k int) *mlx.Array {
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neg := mlx.Neg(scaledLogits)
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indices := mlx.Argpartition(neg, k-1, -1)
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topKIdx := mlx.Slice(indices, []int32{0}, []int32{int32(k)})
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values := mlx.TakeAlongAxis(scaledLogits, topKIdx, -1)
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sampled := mlx.RandomCategorical(values, -1, 1)
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return mlx.Take(topKIdx, sampled, -1)
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}
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// sampleTopP samples using nucleus sampling with global random state
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func sampleTopP(scaledLogits *mlx.Array, p float32, vocabSize int32) *mlx.Array {
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sorted := mlx.Argsort(mlx.Neg(scaledLogits), -1)
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sortedLogits := mlx.TakeAlongAxis(scaledLogits, sorted, -1)
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probs := mlx.Softmax(sortedLogits, -1)
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cumProbs := mlx.Cumsum(probs, -1)
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mask := mlx.LessScalar(cumProbs, p)
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negInf := mlx.FullDtype(float32(-1e9), scaledLogits.Dtype(), vocabSize)
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masked := mlx.Where(mask, sortedLogits, negInf)
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sampled := mlx.RandomCategorical(masked, -1, 1)
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return mlx.Take(sorted, sampled, -1)
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}
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// sample samples from logits at the last position
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func sample(logits *mlx.Array, temp float32, topK int, topP float32, vocab int32) *mlx.Array {
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// Get last position logits: [1, L, vocab] -> [vocab]
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shape := logits.Shape()
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seqLen := shape[1]
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lastLogits := mlx.Slice(logits, []int32{0, seqLen - 1, 0}, []int32{1, seqLen, vocab})
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lastLogits = mlx.Reshape(lastLogits, vocab)
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if temp == 0 {
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return mlx.Argmax(lastLogits, -1, false)
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}
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scaled := mlx.DivScalar(lastLogits, temp)
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if topK > 0 && topK < int(vocab) {
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return sampleTopK(scaled, topK)
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}
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if topP > 0 && topP < 1.0 {
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return sampleTopP(scaled, topP, vocab)
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}
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return mlx.RandomCategorical(scaled, -1, 1)
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}
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