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fixes for maverick

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This commit is contained in:
Michael Yang 2025-04-21 10:45:56 -07:00 committed by Michael Yang
parent 8bf11b84c1
commit 7ba9fa9c7d
3 changed files with 53 additions and 26 deletions
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4
convert/convert_llama4.go
View File

@ -45,8 +45,8 @@ func (p *llama4Model) KV(t *Tokenizer) ggml.KV {
}
}
kv["llama4.intermediate_size"] = p.TextModel.IntermediateSizeMLP
kv["llama4.intermediate_size_moe"] = p.TextModel.IntermediateSize
kv["llama4.feed_forward_length"] = p.TextModel.IntermediateSizeMLP
kv["llama4.expert_feed_forward_length"] = p.TextModel.IntermediateSize
kv["llama4.expert_count"] = p.TextModel.NumLocalExperts
kv["llama4.expert_used_count"] = p.TextModel.NumExpertsPerToken

5
model/models/llama4/model.go
View File

@ -35,7 +35,8 @@ func (p *Projector) Forward(ctx ml.Context, visionOutputs ml.Tensor) ml.Tensor {
func New(c fs.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+`),
c.String("tokenizer.ggml.pretokenizer",
`[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\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"),
@ -52,7 +53,7 @@ func New(c fs.Config) (model.Model, error) {
}
m.Cache = kvcache.NewWrapperCache(
kvcache.NewChunkedAttentionCache(int32(c.Uint("attention.chunk_size")), m.Shift),
kvcache.NewChunkedAttentionCache(int32(c.Uint("attention.chunk_size", 8192)), m.Shift),
kvcache.NewCausalCache(m.Shift),
)

70
model/models/llama4/model_text.go
View File

@ -19,7 +19,7 @@ type TextAttention struct {
RopeFactors ml.Tensor `gguf:"rope_factors"`
}
func (sa *TextAttention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache kvcache.Cache, useRope bool, opts *TextOptions) ml.Tensor {
func (sa *TextAttention) Forward(ctx ml.Context, hiddenStates, positions, attentionScales ml.Tensor, cache kvcache.Cache, useRope bool, opts *TextOptions) ml.Tensor {
batchSize, headDim := hiddenStates.Dim(1), cmp.Or(opts.headDim, opts.hiddenSize/opts.numHeads)
query := sa.Query.Forward(ctx, hiddenStates)
@ -33,11 +33,15 @@ func (sa *TextAttention) Forward(ctx ml.Context, hiddenStates, positions ml.Tens
if useRope {
query = query.RoPE(ctx, positions, sa.RopeFactors, uint32(opts.ropeDim), uint32(0), opts.ropeBase, opts.ropeScale)
key = key.RoPE(ctx, positions, sa.RopeFactors, uint32(opts.ropeDim), uint32(0), opts.ropeBase, opts.ropeScale)
}
if opts.useQKNorm {
query = query.RMSNorm(ctx, nil, opts.eps)
key = key.RMSNorm(ctx, nil, opts.eps)
}
if opts.useQKNorm {
query = query.RMSNorm(ctx, nil, opts.eps)
key = key.RMSNorm(ctx, nil, opts.eps)
}
if attentionScales != nil && !useRope {
query = query.Mul(ctx, attentionScales)
}
attention := nn.Attention(ctx, query, key, value, 1./math.Sqrt(float64(headDim)), cache)
@ -82,7 +86,7 @@ func (e *TextExperts) Forward(ctx ml.Context, hiddenStates, routerLogits ml.Tens
return nextStates
}
// TextSharedExpert is TextMLP with different names
// TextSharedExpert is TextMLP with different tensor names
type TextSharedExpert struct {
Gate *nn.Linear `gguf:"ffn_gate_shexp"`
Up *nn.Linear `gguf:"ffn_up_shexp"`
@ -122,12 +126,12 @@ type TextLayer struct {
FeedForward TextFeedForward
}
func (d *TextLayer) Forward(ctx ml.Context, hiddenStates, positions, outputs ml.Tensor, cache kvcache.Cache, useRope bool, opts *TextOptions) ml.Tensor {
func (d *TextLayer) Forward(ctx ml.Context, hiddenStates, positions, attentionScales, outputs ml.Tensor, cache kvcache.Cache, useRope bool, opts *TextOptions) ml.Tensor {
residual := hiddenStates
// self attention
hiddenStates = d.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
hiddenStates = d.Attention.Forward(ctx, hiddenStates, positions, cache, useRope, opts)
hiddenStates = d.Attention.Forward(ctx, hiddenStates, positions, attentionScales, cache, useRope, opts)
if outputs != nil {
hiddenStates = hiddenStates.Rows(ctx, outputs)
@ -151,7 +155,11 @@ type TextOptions struct {
ropeBase, ropeScale float32
eps float32
interleaveLayerStep int
noRopeInterval int
useQKNorm bool
attentionTemperatureTuning bool
attentionScale float64
attentionFloorScale float64
}
type TextModel struct {
@ -178,18 +186,22 @@ func newTextModel(c fs.Config) *TextModel {
return &TextModel{
Layers: layers,
TextOptions: &TextOptions{
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.head_dim", 128)),
numExperts: int(c.Uint("expert_count")),
numExpertsUsed: int(c.Uint("expert_used_count")),
ropeDim: int(c.Uint("rope.dimension_count")),
ropeBase: c.Float("rope.freq_base"),
ropeScale: c.Float("rope.freq_scale", 1),
eps: c.Float("attention.layer_norm_rms_epsilon"),
interleaveLayerStep: int(c.Uint("interleave_moe_layer_step", 1)),
useQKNorm: c.Bool("use_qk_norm", true),
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.head_dim", 128)),
numExperts: int(c.Uint("expert_count")),
numExpertsUsed: int(c.Uint("expert_used_count")),
ropeDim: int(c.Uint("rope.dimension_count")),
ropeBase: c.Float("rope.freq_base"),
ropeScale: c.Float("rope.freq_scale", 1),
eps: c.Float("attention.layer_norm_rms_epsilon"),
interleaveLayerStep: int(c.Uint("interleave_moe_layer_step", 1)),
noRopeInterval: int(c.Uint("no_rope_interval", 4)),
useQKNorm: c.Bool("use_qk_norm", true),
attentionTemperatureTuning: c.Bool("attention.temperature_tuning", true),
attentionScale: float64(c.Float("attention.scale", 0.1)),
attentionFloorScale: float64(c.Float("attention.floor_scale", 8192)),
},
}
}
@ -207,11 +219,25 @@ func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor
ctx.Forward(img.Copy(ctx, hiddenStates.View(ctx, mi.Index*hiddenStates.Stride(1), img.Dim(0)*img.Dim(1))))
}
var attentionScales ml.Tensor
if m.attentionTemperatureTuning {
scales := make([]float32, len(batch.Positions))
for i, p := range batch.Positions {
scales[i] = float32(math.Log(math.Floor(((float64(p)+1.0)/float64(m.attentionFloorScale))+1.0))*m.attentionScale + 1.0)
}
var err error
attentionScales, err = ctx.Input().FromFloatSlice(scales, 1, 1, len(scales))
if err != nil {
panic(err)
}
}
for i, layer := range m.Layers {
cache.SetLayer(i)
wc := cache.(*kvcache.WrapperCache)
wc.SetLayerType(1)
useChunkedAttention := (i+1)%4 != 0
useChunkedAttention := (i+1)%m.noRopeInterval != 0
if useChunkedAttention {
wc.SetLayerType(0)
}
@ -221,7 +247,7 @@ func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor
lastLayerOutputs = outputs
}
hiddenStates = layer.Forward(ctx, hiddenStates, positions, lastLayerOutputs, cache, useChunkedAttention, m.TextOptions)
hiddenStates = layer.Forward(ctx, hiddenStates, positions, attentionScales, lastLayerOutputs, cache, useChunkedAttention, m.TextOptions)
}
hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)