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https://github.com/ollama/ollama.git
synced 2026-11-12 03:46:14 -06:00
Jesse Gross
7343e22764
nn.go held every layer type in the package apart from attention, recurrence and rope: the Linear and Embedding interfaces with their dense and quantized types, Conv1d, RMSNorm, LayerNorm and MultiLinear, with one test file to match. Finding a layer meant scanning the file named after the package. Each layer kind gets its own file: linear.go and embedding.go hold the interface and the dense and quantized types, conv.go, norm.go and multilinear.go take the rest, and nn_test.go splits the same way. The Layer and MultiLinearLayer interfaces go; nothing implemented or accepted them. No code changes otherwise.
38 lines
708 B
Go
38 lines
708 B
Go
package nn
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import "github.com/ollama/ollama/mlx"
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// Conv1d applies 1D convolution over NLC input.
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type Conv1d struct {
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Weight *mlx.Array
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Bias *mlx.Array
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Stride int32
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Padding int32
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Dilation int32
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Groups int32
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}
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func NewConv1d(weight, bias *mlx.Array, stride, padding, dilation, groups int32) *Conv1d {
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if stride <= 0 {
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stride = 1
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}
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if dilation <= 0 {
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dilation = 1
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}
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if groups <= 0 {
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groups = 1
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}
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return &Conv1d{
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Weight: weight,
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Bias: bias,
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Stride: stride,
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Padding: padding,
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Dilation: dilation,
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Groups: groups,
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}
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}
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func (c *Conv1d) Forward(x *mlx.Array) *mlx.Array {
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return mlx.Conv1d(x, c.Weight, c.Bias, c.Stride, c.Padding, c.Dilation, c.Groups)
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}
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