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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>
101 lines
2.7 KiB
Go
101 lines
2.7 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 gemmaModel struct {
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ModelParameters
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MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
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HiddenSize uint32 `json:"hidden_size"`
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HiddenLayers uint32 `json:"num_hidden_layers"`
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IntermediateSize uint32 `json:"intermediate_size"`
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NumAttentionHeads uint32 `json:"num_attention_heads"`
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NumKeyValueHeads uint32 `json:"num_key_value_heads"`
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RMSNormEPS float32 `json:"rms_norm_eps"`
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HeadDim uint32 `json:"head_dim"`
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}
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var _ ModelConverter = (*gemmaModel)(nil)
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func (p *gemmaModel) KV(t *Tokenizer) ggml.KV {
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kv := p.ModelParameters.KV(t)
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kv["general.architecture"] = "gemma"
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kv["gemma.context_length"] = p.MaxPositionEmbeddings
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kv["gemma.embedding_length"] = p.HiddenSize
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kv["gemma.block_count"] = p.HiddenLayers
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kv["gemma.feed_forward_length"] = p.IntermediateSize
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kv["gemma.attention.head_count"] = p.NumAttentionHeads
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kv["gemma.attention.head_count_kv"] = p.NumKeyValueHeads
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kv["gemma.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
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kv["gemma.attention.key_length"] = p.HeadDim
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kv["gemma.attention.value_length"] = p.HeadDim
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kv["tokenizer.ggml.eot_token_id"] = uint32(107)
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kv["tokenizer.ggml.middle_token_id"] = uint32(68)
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kv["tokenizer.ggml.prefix_token_id"] = uint32(67)
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kv["tokenizer.ggml.suffix_token_id"] = uint32(69)
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return kv
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}
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func (p *gemmaModel) 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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if strings.HasSuffix(t.Name(), "_norm.weight") {
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t.SetRepacker(p.addOne)
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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 *gemmaModel) Replacements() []string {
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return []string{
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"model.embed_tokens", "token_embd",
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"model.norm", "output_norm",
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"model.layers", "blk",
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"input_layernorm", "attn_norm",
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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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"post_attention_layernorm", "ffn_norm",
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}
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}
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func (*gemmaModel) addOne(_ string, data []float32, shape []uint64) ([]float32, error) {
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n := tensor.New(tensor.WithShape(int(shape[0])), tensor.WithBacking(data))
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ones := tensor.Ones(tensor.Float32, int(shape[0]))
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n, err := n.Add(ones)
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if err != nil {
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return nil, err
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}
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ts, err := native.SelectF32(n, 0)
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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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