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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>
52 lines
1.8 KiB
Go
52 lines
1.8 KiB
Go
package convert
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import "github.com/ollama/ollama/fs/ggml"
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type gemma2Model struct {
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gemmaModel
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SlidingWindow uint32 `json:"sliding_window"`
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AttentionLogitSoftcap float32 `json:"attn_logit_softcapping"`
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FinalLogitSoftcap float32 `json:"final_logit_softcapping"`
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}
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func (p *gemma2Model) KV(t *Tokenizer) ggml.KV {
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kv := p.ModelParameters.KV(t)
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kv["general.architecture"] = "gemma2"
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kv["gemma2.context_length"] = p.MaxPositionEmbeddings
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kv["gemma2.embedding_length"] = p.HiddenSize
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kv["gemma2.block_count"] = p.HiddenLayers
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kv["gemma2.feed_forward_length"] = p.IntermediateSize
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kv["gemma2.attention.head_count"] = p.NumAttentionHeads
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kv["gemma2.attention.head_count_kv"] = p.NumKeyValueHeads
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kv["gemma2.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
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kv["gemma2.attention.key_length"] = p.HeadDim
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kv["gemma2.attention.value_length"] = p.HeadDim
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kv["gemma2.attention.sliding_window"] = p.SlidingWindow
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kv["gemma2.attn_logit_softcapping"] = p.AttentionLogitSoftcap
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kv["gemma2.final_logit_softcapping"] = p.FinalLogitSoftcap
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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 *gemma2Model) 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", "post_attention_norm",
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"pre_feedforward_layernorm", "ffn_norm",
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"post_feedforward_layernorm", "post_ffw_norm",
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}
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}
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