ollama/model/models/llama/model.go

package llama

import (
	"cmp"
	"math"

	"github.com/ollama/ollama/fs"
	"github.com/ollama/ollama/kvcache"
	"github.com/ollama/ollama/ml"
	"github.com/ollama/ollama/ml/nn"
	"github.com/ollama/ollama/ml/nn/fast"
	"github.com/ollama/ollama/ml/nn/rope"
	"github.com/ollama/ollama/model"
	"github.com/ollama/ollama/model/input"
)

type Options struct {
	hiddenSize, numHeads, numKVHeads int
	headDim, ropeDim                 int
	eps, ropeBase, ropeScale         float32
}

type Model struct {
	model.Base
	model.TextProcessor

	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
	Layers         []Layer       `gguf:"blk"`
	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
	Output         *nn.Linear    `gguf:"output,alt:token_embd"`

	Options
}

func New(c fs.Config) (model.Model, error) {
	if c.Uint("expert_count") > 0 {
		// TODO: support mixtures of experts
		return nil, model.ErrUnsupportedModel
	}

	var processor model.TextProcessor
	vocabulary := model.Vocabulary{
		Values: c.Strings("tokenizer.ggml.tokens"),
		Scores: c.Floats("tokenizer.ggml.scores"),
		Types:  c.Ints("tokenizer.ggml.token_type"),
		Merges: c.Strings("tokenizer.ggml.merges"),
		AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
		BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
		AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
		EOS: append(
			[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
			c.Ints("tokenizer.ggml.eos_token_ids")...,
		),
	}
	switch c.String("tokenizer.ggml.model") {
	case "gpt2":
		processor = model.NewBytePairEncoding(
			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
			&vocabulary,
		)
	case "llama":
		processor = model.NewSentencePiece(&vocabulary)
	default:
		return nil, model.ErrUnsupportedTokenizer
	}

	m := Model{
		TextProcessor: processor,
		Layers:        make([]Layer, c.Uint("block_count")),
		Options: Options{
			hiddenSize: int(c.Uint("embedding_length")),
			numHeads:   int(c.Uint("attention.head_count")),
			numKVHeads: int(c.Uint("attention.head_count_kv")),
			headDim:    int(c.Uint("attention.key_length")),
			ropeDim:    int(c.Uint("rope.dimension_count")),
			eps:        c.Float("attention.layer_norm_rms_epsilon"),
			ropeBase:   c.Float("rope.freq_base", 1e5),
			ropeScale:  c.Float("rope.scaling.factor", 1),
		},
	}

	m.Cache = kvcache.NewCausalCache(m.Shift)

	return &m, nil
}

type SelfAttention struct {
	Query       *nn.Linear `gguf:"attn_q"`
	Key         *nn.Linear `gguf:"attn_k"`
	Value       *nn.Linear `gguf:"attn_v"`
	Output      *nn.Linear `gguf:"attn_output"`
	RopeFactors ml.Tensor  `gguf:"rope_freqs.weight"`
}

func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positions ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
	batchSize := hiddenState.Dim(1)
	headDim := cmp.Or(opts.headDim, opts.hiddenSize/opts.numHeads)
	ropeDim := cmp.Or(opts.ropeDim, headDim)

	query := sa.Query.Forward(ctx, hiddenState)
	query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)

	key := sa.Key.Forward(ctx, hiddenState)
	key = key.Reshape(ctx, headDim, opts.numKVHeads, batchSize)

	value := sa.Value.Forward(ctx, hiddenState)
	value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)

	query = fast.RoPE(ctx, query, positions, ropeDim, opts.ropeBase, 1./opts.ropeScale, rope.WithFactors(sa.RopeFactors))
	key = fast.RoPE(ctx, key, positions, ropeDim, opts.ropeBase, 1./opts.ropeScale, rope.WithFactors(sa.RopeFactors))

	attention := nn.Attention(ctx, query, key, value, 1.0/math.Sqrt(float64(headDim)), cache)
	attention = attention.Reshape(ctx, headDim*opts.numHeads, batchSize)
	return sa.Output.Forward(ctx, attention)
}

func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
	ropeDim := cmp.Or(m.ropeDim, m.hiddenSize/m.numHeads)
	return fast.RoPE(ctx, key, shift, ropeDim, m.ropeBase, 1./m.ropeScale, rope.WithFactors(m.Layers[layer].SelfAttention.RopeFactors)), nil
}

type MLP struct {
	Up   *nn.Linear `gguf:"ffn_up"`
	Down *nn.Linear `gguf:"ffn_down"`
	Gate *nn.Linear `gguf:"ffn_gate"`
}

func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx, mlp.Up.Forward(ctx, hiddenState))
	return mlp.Down.Forward(ctx, hiddenState)
}

type Layer struct {
	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
	SelfAttention *SelfAttention
	MLPNorm       *nn.RMSNorm `gguf:"ffn_norm"`
	MLP           *MLP
}

func (l *Layer) Forward(ctx ml.Context, hiddenState, positions, outputs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
	residual := hiddenState

	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
	hiddenState = l.SelfAttention.Forward(ctx, hiddenState, positions, cache, opts)

	// In the final layer (outputs != nil), optimize by pruning to just the token positions
	// we need logits for.
	if outputs != nil {
		hiddenState = hiddenState.Rows(ctx, outputs)
		residual = residual.Rows(ctx, outputs)
	}

	hiddenState = hiddenState.Add(ctx, residual)
	residual = hiddenState

	hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
	hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
	return hiddenState.Add(ctx, residual)
}

func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))

	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)

	for i, layer := range m.Layers {
		m.Cache.SetLayer(i)

		var outputs ml.Tensor
		if i == len(m.Layers)-1 {
			outputs = batch.Outputs
		}

		hiddenState = layer.Forward(ctx, hiddenState, positions, outputs, m.Cache, &m.Options)
	}

	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
	return m.Output.Forward(ctx, hiddenState), nil
}

func init() {
	model.Register("llama", New)
}