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ml: add more rope options (#10775)

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This commit is contained in:
Michael Yang
2025-05-20 15:51:08 -07:00
committed by GitHub
parent e6a800ca11
commit 9ed8bf14cb
11 changed files with 116 additions and 83 deletions
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9
model/models/gemma2/model.go
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@@ -7,6 +7,8 @@ import (
"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"
)
@@ -83,11 +85,10 @@ type SelfAttention struct {
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)
q = fast.RoPE(ctx, q, positionIDs, opts.attnKeyLen, opts.ropeBase, opts.ropeScale, rope.WithTypeNeoX())
if opts.largeModelScaling {
q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.hiddenSize/opts.numHeads)))
@@ -97,7 +98,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
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)
k = fast.RoPE(ctx, k, positionIDs, opts.attnKeyLen, opts.ropeBase, opts.ropeScale, rope.WithTypeNeoX())
v := sa.Value.Forward(ctx, hiddenState)
v = v.Reshape(ctx, opts.attnValLen, opts.numKVHeads, batchSize)
@@ -127,7 +128,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
}
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
return fast.RoPE(ctx, key, shift, m.Options.attnKeyLen, m.Options.ropeBase, m.Options.ropeScale, rope.WithTypeNeoX()), nil
}
type MLP struct {

9
model/models/gemma3/model_text.go
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@@ -7,6 +7,8 @@ import (
"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/input"
)
@@ -73,7 +75,6 @@ type TextSelfAttention struct {
func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextConfig) ml.Tensor {
batchSize := hiddenState.Dim(1)
ropeType := uint32(2)
ropeBase := opts.ropeLocalBase
if (layer+1)%gemmaGlobalCacheCount == 0 {
@@ -83,7 +84,7 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, pos
q := sa.Query.Forward(ctx, hiddenState)
q = q.Reshape(ctx, opts.attnKeyLen, opts.numHeads, batchSize)
q = sa.QueryNorm.Forward(ctx, q, opts.eps)
q = q.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, ropeBase, opts.ropeScale)
q = fast.RoPE(ctx, q, positionIDs, opts.attnKeyLen, ropeBase, opts.ropeScale, rope.WithTypeNeoX())
if opts.largeModelScaling {
q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.hiddenSize/opts.numHeads)))
@@ -94,7 +95,7 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, pos
k := sa.Key.Forward(ctx, hiddenState)
k = k.Reshape(ctx, opts.attnKeyLen, opts.numKVHeads, batchSize)
k = sa.KeyNorm.Forward(ctx, k, opts.eps)
k = k.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, ropeBase, opts.ropeScale)
k = fast.RoPE(ctx, k, positionIDs, opts.attnKeyLen, ropeBase, opts.ropeScale, rope.WithTypeNeoX())
v := sa.Value.Forward(ctx, hiddenState)
v = v.Reshape(ctx, opts.attnValLen, opts.numKVHeads, batchSize)
@@ -112,7 +113,7 @@ func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.T
ropeBase = m.TextConfig.ropeGlobalBase
}
return key.RoPE(ctx, shift, nil, uint32(m.TextConfig.attnKeyLen), uint32(2), ropeBase, m.TextConfig.ropeScale), nil
return fast.RoPE(ctx, key, shift, m.TextConfig.attnKeyLen, ropeBase, m.TextConfig.ropeScale, rope.WithTypeNeoX()), nil
}
type TextMLP struct {

17
model/models/llama/model.go
View File

@@ -8,14 +8,16 @@ import (
"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, headDim int
eps, ropeBase, ropeScale float32
ropeDim uint32
hiddenSize, numHeads, numKVHeads int
headDim, ropeDim int
eps, ropeBase, ropeScale float32
}
type Model struct {
@@ -53,10 +55,10 @@ func New(c fs.Config) (model.Model, error) {
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"),
ropeScale: c.Float("rope.freq_scale", 1),
ropeDim: c.Uint("rope.dimension_count"),
},
}
@@ -76,15 +78,14 @@ type SelfAttention struct {
func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
batchSize := hiddenState.Dim(1)
headDim := cmp.Or(opts.headDim, opts.hiddenSize/opts.numHeads)
ropeType := uint32(0)
q := sa.Query.Forward(ctx, hiddenState)
q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
q = q.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
q = fast.RoPE(ctx, q, positionIDs, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithFactors(sa.RopeFactors))
k := sa.Key.Forward(ctx, hiddenState)
k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
k = k.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
k = fast.RoPE(ctx, k, positionIDs, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithFactors(sa.RopeFactors))
v := sa.Value.Forward(ctx, hiddenState)
v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
@@ -97,7 +98,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
}
func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
return key.RoPE(ctx, shift, m.Layers[layer].SelfAttention.RopeFactors, uint32(0), m.ropeDim, m.ropeBase, m.ropeScale), nil
return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, m.ropeScale, rope.WithFactors(m.Layers[layer].SelfAttention.RopeFactors)), nil
}
type MLP struct {

8
model/models/llama4/model_text.go
View File

@@ -8,6 +8,8 @@ import (
"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/input"
)
@@ -31,8 +33,8 @@ func (sa *TextAttention) Forward(ctx ml.Context, hiddenStates, positions, attent
value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
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)
query = fast.RoPE(ctx, query, positions, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithFactors(sa.RopeFactors))
key = fast.RoPE(ctx, key, positions, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithFactors(sa.RopeFactors))
}
if opts.useQKNorm {
@@ -250,5 +252,5 @@ func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor
}
func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
return key.RoPE(ctx, shift, m.Layers[layer].Attention.RopeFactors, uint32(0), uint32(m.ropeDim), m.ropeBase, m.ropeScale), nil
return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, m.ropeScale, rope.WithFactors(m.Layers[layer].Attention.RopeFactors)), nil
}

16
model/models/mistral3/model_text.go
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@@ -8,13 +8,14 @@ import (
"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/model/input"
)
type TextOptions struct {
hiddenSize, numHeads, numKVHeads, headDim int
eps, ropeBase, ropeScale float32
ropeDim uint32
hiddenSize, numHeads, numKVHeads int
headDim, ropeDim int
eps, ropeBase, ropeScale float32
}
type TextModel struct {
@@ -35,16 +36,15 @@ type SelfAttention struct {
func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
batchSize := hiddenState.Dim(1)
ropeType := uint32(0)
headDim := cmp.Or(opts.headDim, opts.hiddenSize/opts.numHeads)
q := sa.Query.Forward(ctx, hiddenState)
q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
q = q.RoPE(ctx, positionIDs, nil, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
q = fast.RoPE(ctx, q, positionIDs, opts.ropeDim, opts.ropeBase, opts.ropeScale)
k := sa.Key.Forward(ctx, hiddenState)
k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
k = k.RoPE(ctx, positionIDs, nil, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
k = fast.RoPE(ctx, k, positionIDs, opts.ropeDim, opts.ropeBase, opts.ropeScale)
v := sa.Value.Forward(ctx, hiddenState)
v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
@@ -55,7 +55,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
}
func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
return key.RoPE(ctx, shift, nil, uint32(0), m.ropeDim, m.ropeBase, m.ropeScale), nil
return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, m.ropeScale), nil
}
type MLP struct {
@@ -129,10 +129,10 @@ func newTextModel(c fs.Config) *TextModel {
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"),
ropeScale: c.Float("rope.freq_scale", 1),
ropeDim: c.Uint("rope.dimension_count"),
},
}
}

13
model/models/mllama/model_text.go
View File

@@ -8,6 +8,8 @@ import (
"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"
)
type TextSelfAttention struct {
@@ -21,15 +23,14 @@ type TextSelfAttention struct {
func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
batchSize := hiddenState.Dim(1)
headDim := opts.hiddenSize / opts.numHeads
ropeType := uint32(0)
query := sa.Query.Forward(ctx, hiddenState)
query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)
query = query.RoPE(ctx, positions, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
query = fast.RoPE(ctx, query, positions, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithFactors(sa.RopeFactors))
key := sa.Key.Forward(ctx, hiddenState)
key = key.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
key = key.RoPE(ctx, positions, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
key = fast.RoPE(ctx, key, positions, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithFactors(sa.RopeFactors))
value := sa.Value.Forward(ctx, hiddenState)
value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
@@ -44,7 +45,7 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions ml.T
func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
// This will only get called for layers in the cache, which are just the self attention layers
if sa, ok := m.Transformer.Layers[layer].(*TextSelfAttentionDecoderLayer); ok {
return key.RoPE(ctx, shift, sa.SelfAttention.RopeFactors, m.ropeDim, uint32(0), m.ropeBase, m.ropeScale), nil
return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, m.ropeScale, rope.WithFactors(sa.SelfAttention.RopeFactors)), nil
}
return key, nil
@@ -199,8 +200,8 @@ func (d *TextDecoder) Forward(ctx ml.Context, hiddenState, positionIDs, outputs,
type TextModelOptions struct {
hiddenSize, numHeads, numKVHeads int
ropeDim int
eps, ropeBase, ropeScale float32
ropeDim uint32
crossAttentionLayers []int32
}
@@ -240,10 +241,10 @@ func newTextModel(c fs.Config) *TextModel {
hiddenSize: int(c.Uint("embedding_length")),
numHeads: int(c.Uint("attention.head_count")),
numKVHeads: int(c.Uint("attention.head_count_kv")),
ropeDim: int(c.Uint("rope.dimension_count")),
eps: c.Float("attention.layer_norm_rms_epsilon"),
ropeBase: c.Float("rope.freq_base"),
ropeScale: c.Float("rope.freq_scale", 1),
ropeDim: c.Uint("rope.dimension_count"),
crossAttentionLayers: c.Ints("attention.cross_attention_layers"),
},
}

31
model/models/qwen25vl/model_text.go
View File

@@ -7,13 +7,15 @@ import (
"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/input"
)
type TextOptions struct {
ctxLen, hiddenSize, numHeads, numKVHeads int
eps, ropeBase, ropeScale float32
ropeDim, defaultContextLen uint32
hiddenSize, numHeads, numKVHeads int
ropeDim, originalContextLength int
eps, ropeBase, ropeScale float32
}
type TextModel struct {
@@ -29,15 +31,14 @@ func NewTextModel(c fs.Config) *TextModel {
m := TextModel{
Layers: make([]Layer, c.Uint("block_count")),
TextOptions: &TextOptions{
ctxLen: int(c.Uint("context_length")),
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"),
ropeBase: c.Float("rope.freq_base"),
ropeScale: c.Float("rope.freq_scale", 1),
ropeDim: c.Uint("rope.dimension_count", 128),
defaultContextLen: c.Uint("context_length", 128000),
hiddenSize: int(c.Uint("embedding_length")),
numHeads: int(c.Uint("attention.head_count")),
numKVHeads: int(c.Uint("attention.head_count_kv")),
ropeDim: int(c.Uint("rope.dimension_count", 128)),
originalContextLength: int(c.Uint("context_length", 128000)),
eps: c.Float("attention.layer_norm_rms_epsilon"),
ropeBase: c.Float("rope.freq_base"),
ropeScale: c.Float("rope.freq_scale", 1),
},
}
@@ -59,11 +60,11 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
q := sa.Query.Forward(ctx, hiddenState)
q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
q = q.RoPE(ctx, positionIDs, nil, opts.ropeDim, 2, opts.ropeBase, opts.ropeScale, ml.WithContextLen(opts.defaultContextLen))
q = fast.RoPE(ctx, q, positionIDs, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithOriginalContextLength(opts.originalContextLength), rope.WithTypeNeoX())
k := sa.Key.Forward(ctx, hiddenState)
k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
k = k.RoPE(ctx, positionIDs, nil, opts.ropeDim, 2, opts.ropeBase, opts.ropeScale, ml.WithContextLen(opts.defaultContextLen))
k = fast.RoPE(ctx, k, positionIDs, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithOriginalContextLength(opts.originalContextLength), rope.WithTypeNeoX())
v := sa.Value.Forward(ctx, hiddenState)
v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
@@ -77,7 +78,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
// Shift applies rotary position embeddings to the key tensor for causal attention caching
func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
return key.RoPE(ctx, shift, nil, m.ropeDim, 2, m.ropeBase, m.ropeScale, ml.WithContextLen(m.defaultContextLen)), nil
return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, m.ropeScale, rope.WithOriginalContextLength(m.originalContextLength), rope.WithTypeNeoX()), nil
}
// MLP implements the feed-forward network component with SwiGLU activation