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* 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>
31 lines
792 B
Go
31 lines
792 B
Go
package nn
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import "github.com/ollama/ollama/x/ml"
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type Conv2D struct {
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Weight ml.Tensor `gguf:"weight"`
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Bias ml.Tensor `gguf:"bias"`
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}
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func (m *Conv2D) Forward(ctx ml.Context, t ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
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t = m.Weight.Conv2D(ctx, t, s0, s1, p0, p1, d0, d1, 1)
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if m.Bias != nil {
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// Bias shape is (out_channels,) while t shape is (width, height, out_channels, batch)
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t = t.Add(ctx, m.Bias.Reshape(ctx, 1, 1, -1))
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}
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return t
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}
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type Conv3D struct {
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Weight ml.Tensor `gguf:"weight"`
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Bias ml.Tensor `gguf:"bias"`
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}
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func (m *Conv3D) Forward(ctx ml.Context, t ml.Tensor, s0, s1, s2, p0, p1, p2, d0, d1, d2, g int) ml.Tensor {
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t = m.Weight.Conv3D(ctx, t, s0, s1, s2, p0, p1, p2, d0, d1, d2, g)
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if m.Bias != nil {
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t = t.Add(ctx, m.Bias)
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}
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return t
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}
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