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perf: build graph for next batch async to keep GPU busy (#11863)
* perf: build graph for next batch in parallel to keep GPU busy This refactors the main run loop of the ollama runner to perform the main GPU intensive tasks (Compute+Floats) in a go routine so we can prepare the next batch in parallel to reduce the amount of time the GPU stalls waiting for the next batch of work. * tests: tune integration tests for ollama engine This tunes the integration tests to focus more on models supported by the new engine.
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@@ -133,22 +133,22 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input
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// [IMG]...[IMG][IMG_BREAK][IMG]...[IMG][IMG_BREAK][IMG]...[IMG][IMG_END]
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// Each sequence of [IMG]...[IMG] is a set of patches of vision embeddings
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// that can be processed together.
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func (m *Model) PostTokenize(inputs []input.Input) ([]input.Input, error) {
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var result []input.Input
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func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
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var result []*input.Input
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for _, inp := range inputs {
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if len(inp.Multimodal) == 0 {
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result = append(result, inp)
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} else {
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for i, row := range inp.Multimodal {
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// [IMG]
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result = append(result, input.Input{Token: 10, Multimodal: []input.Multimodal{{Tensor: row.Tensor}}, MultimodalHash: inp.MultimodalHash, SameBatch: row.Tensor.Dim(1)})
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result = append(result, slices.Repeat([]input.Input{{Token: 10}}, row.Tensor.Dim(1)-1)...)
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result = append(result, &input.Input{Token: 10, Multimodal: []input.Multimodal{{Tensor: row.Tensor}}, MultimodalHash: inp.MultimodalHash, SameBatch: row.Tensor.Dim(1)})
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result = append(result, slices.Repeat([]*input.Input{{Token: 10}}, row.Tensor.Dim(1)-1)...)
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if i == len(inp.Multimodal)-1 {
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// [IMG_END]
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result = append(result, input.Input{Token: 13})
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result = append(result, &input.Input{Token: 13})
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} else {
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// [IMG_BREAK]
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result = append(result, input.Input{Token: 12})
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result = append(result, &input.Input{Token: 12})
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}
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}
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}
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