ollama/model/models/llama/model.go

package llama

import (
	"math"

	"github.com/ollama/ollama/kvcache"
	"github.com/ollama/ollama/ml"
	"github.com/ollama/ollama/ml/nn"
	"github.com/ollama/ollama/model"
)

type Options struct {
	RopeFactors                              ml.Tensor `gguf:"rope_freqs.weight"`
	ctxLen, hiddenSize, numHeads, numKVHeads int
	eps, ropeBase, ropeScale                 float32
	ropeDim                                  uint32
}

type Model struct {
	model.Base
	model.BytePairEncoding

	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 ml.Config) (model.Model, error) {
	m := Model{
		BytePairEncoding: model.NewBytePairEncoding(
			c.String("tokenizer.ggml.pretokenizer", `(?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+`),
			&model.Vocabulary{
				Values: c.Strings("tokenizer.ggml.tokens"),
				Types:  c.Uints("tokenizer.ggml.token_type"),
				Merges: c.Strings("tokenizer.ggml.merges"),
				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
				EOS:    int32(c.Uint("tokenizer.ggml.eos_token_id")),
			},
		),
		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")),
			eps:        c.Float("attention.layer_norm_rms_epsilon"),
			ctxLen:     int(c.Uint("context_length")),
			ropeBase:   c.Float("rope.freq_base"),
			ropeScale:  c.Float("rope.freq_scale", 1),
			ropeDim:    c.Uint("rope.dimension_count"),
		},
	}

	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"`
}

func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
	batchSize := hiddenState.Dim(1)
	headDim := opts.hiddenSize / opts.numHeads
	rc := ml.RopeConfig{
		PositionIDs: positionIDs,
		RopeFactors: opts.RopeFactors,
		RopeDim:     opts.ropeDim,
		RopeType:    ml.RopeTypeStandard,
		OrigCtxLen:  opts.ctxLen,
		RopeBase:    opts.ropeBase,
		RopeScale:   opts.ropeScale,
	}

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

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

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

	cache.Put(ctx, k, v)
	k, v, mask := cache.Get(ctx)

	q = q.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
	k = k.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
	v = v.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)

	kq := k.MulmatFullPrec(ctx, q)
	kq = kq.Scale(ctx, 1.0/math.Sqrt(float64(headDim)))
	kq = kq.Add(ctx, mask)
	kq = kq.Softmax(ctx)

	kqv := v.Mulmat(ctx, kq)
	kqv = kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
	kqv = kqv.Reshape(ctx, opts.hiddenSize, batchSize)

	return sa.Output.Forward(ctx, kqv)
}

func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
	return key.RoPE(
		ctx,
		ml.RopeConfig{
			PositionIDs: shift,
			RopeFactors: m.Options.RopeFactors,
			RopeDim:     m.Options.ropeDim,
			RopeType:    ml.RopeTypeStandard,
			OrigCtxLen:  m.Options.ctxLen,
			RopeBase:    m.Options.ropeBase,
			RopeScale:   m.Options.ropeScale,
		},
	), 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).Mul(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, positionIDs 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, positionIDs, cache, opts)
	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, opts model.Options) (ml.Tensor, error) {
	inputs, err := ctx.FromIntSlice(opts.Inputs, len(opts.Inputs))
	if err != nil {
		return nil, err
	}

	positions, err := ctx.FromIntSlice(opts.Positions, len(opts.Positions))
	if err != nil {
		return nil, err
	}

	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)

	for i, layer := range m.Layers {
		m.Cache.SetLayer(i)
		hiddenState = layer.Forward(ctx, hiddenState, positions, m.Cache, m.Options)
	}

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

	outputs, err := ctx.FromIntSlice(opts.Outputs, len(opts.Outputs))
	if err != nil {
		return nil, err
	}

	return hiddenState.Rows(ctx, outputs), nil
}

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