8ee0c4bc4e
CGo wrapper for mlx-c providing zero-Python Metal GPU inference. Includes Gemma 3 model architecture, BPE tokenizer, KV cache, composable sampling, and OpenAI-compatible serve command. Build-tagged (darwin && arm64 && mlx) with stubs for cross-platform. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
59 lines
1.6 KiB
Go
59 lines
1.6 KiB
Go
//go:build darwin && arm64 && mlx
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package mlx
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// Linear is a fully-connected layer: y = x @ W.T + bias.
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type Linear struct {
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Weight *Array `weight:"weight"`
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Bias *Array `weight:"bias"`
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}
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// NewLinear creates a Linear layer with optional bias.
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func NewLinear(weight, bias *Array) *Linear {
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return &Linear{Weight: weight, Bias: bias}
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}
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// Forward computes the linear transformation.
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func (l *Linear) Forward(x *Array) *Array {
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out := Matmul(x, Transpose(l.Weight))
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if l.Bias != nil && l.Bias.Valid() {
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out = Add(out, l.Bias)
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}
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return out
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}
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// Embedding is a lookup table for token embeddings.
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type Embedding struct {
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Weight *Array `weight:"weight"`
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}
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// Forward looks up embeddings for the given token indices.
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func (e *Embedding) Forward(indices *Array) *Array {
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return Take(e.Weight, indices, 0)
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}
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// RMSNormModule is an RMS normalization layer wrapping the fused kernel.
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type RMSNormModule struct {
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Weight *Array `weight:"weight"`
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}
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// Forward applies RMS normalization.
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func (r *RMSNormModule) Forward(x *Array, eps float32) *Array {
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return RMSNorm(x, r.Weight, eps)
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}
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// RepeatKV repeats key/value heads for grouped-query attention.
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// Input shape: [B, num_kv_heads, L, D]
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// Output shape: [B, num_kv_heads * factor, L, D]
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func RepeatKV(x *Array, factor int32) *Array {
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if factor <= 1 {
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return x
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}
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shape := x.Shape()
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B, H, L, D := shape[0], shape[1], shape[2], shape[3]
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// Expand: [B, H, 1, L, D] then broadcast to [B, H, factor, L, D]
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expanded := ExpandDims(x, 2)
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expanded = BroadcastTo(expanded, []int32{B, H, factor, L, D})
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return Reshape(expanded, B, H*factor, L, D)
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}
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