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https://github.com/ollama/ollama.git
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58245413f4
feat: add new Ollama engine using ggml through cgo This change introduces a new way to run pretrained models. It introduces 3 high level interfaces and a bunch of smaller helper interfaces to facilitate this. - `model.Model` defines the interface for a model architecture. Models such as `llama` and `mllama`, which are provided as examples, can implement the model's forward propagation in the `Forward` method. This method will be called to generate completions. This interface can be found in `model/model.go` - `ml.Backend` defines the interface for a backend tensor library, in this case `ggml`. Among other things, a Backend is responsible for loading a pretrained model into hardware (GPU, CPU, etc) and providing an interface for Models to access loaded tensors. This interface can be found in `ml/backend.go` - `ml.Tensor` defines the interface for a tensor and tensor operations This is the first implementation of the new engine. Follow up PRs will implement more features: - non-greedy sampling (#8410) - integration with Ollama and KV caching (#8301) - more model support (#9080) with more coming soon Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
92 lines
1.9 KiB
Go
92 lines
1.9 KiB
Go
package convert
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import (
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"strings"
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"github.com/pdevine/tensor"
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"github.com/pdevine/tensor/native"
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"github.com/ollama/ollama/fs/ggml"
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)
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type gemma2Adapter struct {
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AdapterParameters
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}
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var _ AdapterConverter = (*gemma2Adapter)(nil)
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func (p *gemma2Adapter) KV(baseKV ggml.KV) ggml.KV {
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kv := p.AdapterParameters.KV()
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kv["general.architecture"] = "gemma2"
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return kv
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}
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func (p *gemma2Adapter) Tensors(ts []Tensor) []ggml.Tensor {
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var out []ggml.Tensor
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for _, t := range ts {
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shape := t.Shape()
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if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
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(strings.HasSuffix(t.Name(), "weight.lora_b") && shape[0] < shape[1]) {
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shape[0], shape[1] = shape[1], shape[0]
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t.SetRepacker(p.repack)
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}
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out = append(out, ggml.Tensor{
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Name: t.Name(),
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Kind: t.Kind(),
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Shape: t.Shape(),
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WriterTo: t,
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})
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}
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return out
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}
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func (p *gemma2Adapter) Replacements() []string {
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return []string{
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"base_model.model.", "",
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"model.layers", "blk",
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"self_attn.q_proj", "attn_q",
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"self_attn.k_proj", "attn_k",
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"self_attn.v_proj", "attn_v",
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"self_attn.o_proj", "attn_output",
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"mlp.gate_proj", "ffn_gate",
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"mlp.down_proj", "ffn_down",
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"mlp.up_proj", "ffn_up",
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"lora_A.weight", "weight.lora_a",
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"lora_B.weight", "weight.lora_b",
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"lora_a", "weight.lora_a",
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"lora_b", "weight.lora_b",
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}
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}
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func (p *gemma2Adapter) repack(name string, data []float32, shape []uint64) ([]float32, error) {
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dims := []int{int(shape[1]), int(shape[0])}
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n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
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if err := n.T(1, 0); err != nil {
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return nil, err
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}
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if err := n.Reshape(dims...); err != nil {
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return nil, err
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}
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if err := n.Transpose(); err != nil {
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return nil, err
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}
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ts, err := native.SelectF32(n, 1)
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if err != nil {
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return nil, err
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}
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var f32s []float32
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for _, t := range ts {
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f32s = append(f32s, t...)
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}
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return f32s, nil
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}
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