mirror of
https://github.com/dogkeeper886/ollama37.git
synced 2025-12-11 08:17:03 +00:00
gemma2 impl
This commit is contained in:
Patrick Devine
committed by
Michael Yang
Michael Yang
parent
4dcf80167a
commit
5f74d1fd47
206
model/models/gemma2/model.go
Normal file
206
model/models/gemma2/model.go
Normal file
@@ -0,0 +1,206 @@
|
||||
package gemma2
|
||||
|
||||
import (
|
||||
"math"
|
||||
|
||||
"github.com/ollama/ollama/kvcache"
|
||||
"github.com/ollama/ollama/ml"
|
||||
"github.com/ollama/ollama/ml/nn"
|
||||
"github.com/ollama/ollama/model"
|
||||
"github.com/ollama/ollama/model/input"
|
||||
)
|
||||
|
||||
type Options struct {
|
||||
hiddenSize, numHeads, numKVHeads int
|
||||
attnKeyLen, attnValLen int
|
||||
eps, ropeBase, ropeScale float32
|
||||
attnLogitSoftcap float32
|
||||
finalLogitSoftcap float32
|
||||
largeModelScaling bool
|
||||
}
|
||||
|
||||
type Model struct {
|
||||
model.Base
|
||||
model.SentencePieceModel
|
||||
|
||||
TokenEmbedding *nn.Embedding `gguf:"token_embd"`
|
||||
Layers []Layer `gguf:"blk"`
|
||||
OutputNorm *nn.RMSNorm `gguf:"output_norm"`
|
||||
Output *nn.Linear `gguf:"output,alt:token_embd"` // just set to token_embd?
|
||||
|
||||
*Options
|
||||
}
|
||||
|
||||
const (
|
||||
gemma27BLayerCount = 46
|
||||
)
|
||||
|
||||
func New(c ml.Config) (model.Model, error) {
|
||||
m := Model{
|
||||
SentencePieceModel: model.NewSentencePieceModel(
|
||||
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"),
|
||||
Scores: c.Floats("tokenizer.ggml.scores"),
|
||||
Types: c.Uints("tokenizer.ggml.token_type"),
|
||||
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")),
|
||||
attnKeyLen: int(c.Uint("attention.key_length")),
|
||||
attnValLen: int(c.Uint("attention.value_length")),
|
||||
eps: c.Float("attention.layer_norm_rms_epsilon"),
|
||||
ropeBase: c.Float("rope.freq_base", 10000.0),
|
||||
ropeScale: c.Float("rope.freq_scale", 1.0),
|
||||
attnLogitSoftcap: c.Float("attn_logit_softcapping"),
|
||||
finalLogitSoftcap: c.Float("final_logit_softcapping"),
|
||||
},
|
||||
}
|
||||
|
||||
slidingWindowLen := int32(c.Uint("attention.sliding_window"))
|
||||
m.Cache = kvcache.NewWrapperCache(kvcache.NewSWACache(slidingWindowLen, m.Shift), 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)
|
||||
ropeType := uint32(2)
|
||||
|
||||
q := sa.Query.Forward(ctx, hiddenState)
|
||||
q = q.Reshape(ctx, opts.attnKeyLen, opts.numHeads, batchSize)
|
||||
q = q.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, opts.ropeBase, opts.ropeScale)
|
||||
|
||||
if opts.largeModelScaling {
|
||||
q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.hiddenSize / opts.numHeads)))
|
||||
} else {
|
||||
q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.attnKeyLen)))
|
||||
}
|
||||
|
||||
k := sa.Key.Forward(ctx, hiddenState)
|
||||
k = k.Reshape(ctx, opts.attnKeyLen, opts.numKVHeads, batchSize)
|
||||
k = k.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, opts.ropeBase, opts.ropeScale)
|
||||
|
||||
v := sa.Value.Forward(ctx, hiddenState)
|
||||
v = v.Reshape(ctx, opts.attnValLen, 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.Mulmat(ctx, q)
|
||||
|
||||
// logit softcap
|
||||
kq = kq.Scale(ctx, 1.0/float64(opts.attnLogitSoftcap))
|
||||
kq = kq.Tanh(ctx)
|
||||
kq = kq.Scale(ctx, float64(opts.attnLogitSoftcap))
|
||||
|
||||
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.attnValLen*opts.numHeads, 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, shift, nil, uint32(m.Options.attnKeyLen), uint32(2), m.Options.ropeBase, 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).GELU(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
|
||||
PostAttentionNorm *nn.RMSNorm `gguf:"post_attention_norm"`
|
||||
MLPNorm *nn.RMSNorm `gguf:"ffn_norm"`
|
||||
MLP *MLP
|
||||
PostMLPNorm *nn.RMSNorm `gguf:"post_ffw_norm"`
|
||||
}
|
||||
|
||||
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 = l.PostAttentionNorm.Forward(ctx, hiddenState, opts.eps)
|
||||
hiddenState = hiddenState.Add(ctx, residual)
|
||||
residual = hiddenState
|
||||
|
||||
hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
|
||||
hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
|
||||
hiddenState = l.PostMLPNorm.Forward(ctx, hiddenState, opts.eps)
|
||||
return hiddenState.Add(ctx, residual)
|
||||
}
|
||||
|
||||
func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) {
|
||||
inputs, err := ctx.Input().FromIntSlice(opts.Inputs, len(opts.Inputs))
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
positions, err := ctx.Input().FromIntSlice(opts.Positions, len(opts.Positions))
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
|
||||
hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.Options.hiddenSize)))
|
||||
|
||||
if len(m.Layers) == gemma27BLayerCount {
|
||||
m.Options.largeModelScaling = true
|
||||
}
|
||||
|
||||
for i, layer := range m.Layers {
|
||||
cacheType := i % 2
|
||||
m.Cache.SetLayer(i)
|
||||
wc := m.Cache.(*kvcache.WrapperCache)
|
||||
wc.SetLayerType(cacheType)
|
||||
hiddenState = layer.Forward(ctx, hiddenState, positions, m.Cache, m.Options)
|
||||
}
|
||||
|
||||
hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
|
||||
hiddenState = m.Output.Forward(ctx, hiddenState)
|
||||
|
||||
// final logit softcap
|
||||
hiddenState = hiddenState.Scale(ctx, 1.0/float64(m.Options.finalLogitSoftcap))
|
||||
hiddenState = hiddenState.Tanh(ctx)
|
||||
hiddenState = hiddenState.Scale(ctx, float64(m.Options.finalLogitSoftcap))
|
||||
|
||||
outputs, err := ctx.Output().FromIntSlice(opts.Outputs, len(opts.Outputs))
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
return hiddenState.Rows(ctx, outputs), nil
|
||||
}
|
||||
|
||||
func init() {
|
||||
model.Register("gemma2", New)
|
||||
}
|
||||
Reference in New Issue
Block a user