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fix: qwen2.5 vl rope (#13486)

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* qwen25vl: bump max pixels

* qwen25vl: mrope

fix qwen2.5vl window

* qwen25vl: vision rope
This commit is contained in:
Michael Yang
2025-12-15 17:30:33 -08:00
committed by GitHub
parent ffbe8e076d
commit 971d62595a
6 changed files with 195 additions and 216 deletions
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42
model/models/qwen25vl/model_text.go
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@@ -8,20 +8,17 @@ import (
"github.com/ollama/ollama/ml"
"github.com/ollama/ollama/ml/nn"
"github.com/ollama/ollama/ml/nn/rope"
"github.com/ollama/ollama/model/input"
)
type TextOptions struct {
hiddenSize, numHeads, numKVHeads int
ropeDim, originalContextLength int
eps, ropeBase, ropeScale float32
mropeSections []int
}
func (o TextOptions) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions ml.Tensor) ml.Tensor {
return nn.RoPE(ctx, states, positions, o.ropeDim, o.ropeBase, 1./o.ropeScale,
rope.WithOriginalContextLength(o.originalContextLength),
rope.WithTypeNeoX(),
)
return nn.RoPE(ctx, states, positions, o.ropeDim, o.ropeBase, 1./o.ropeScale, rope.WithMRoPE(o.mropeSections))
}
type TextModel struct {
@@ -31,6 +28,7 @@ type TextModel struct {
Output *nn.Linear `gguf:"output,alt:token_embd"`
*TextOptions
positionCache []int32
}
func NewTextModel(c fs.Config) *TextModel {
@@ -45,6 +43,14 @@ func NewTextModel(c fs.Config) *TextModel {
eps: c.Float("attention.layer_norm_rms_epsilon"),
ropeBase: c.Float("rope.freq_base"),
ropeScale: c.Float("rope.scaling.factor", 1),
mropeSections: func() []int {
sections := c.Ints("rope.mrope_section")
s := make([]int, len(sections))
for i, section := range sections {
s[i] = int(section)
}
return s
}(),
},
}
@@ -84,6 +90,7 @@ 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) {
m.positionCache = nil
return m.applyRotaryPositionEmbeddings(ctx, key, shift), nil
}
@@ -130,28 +137,3 @@ func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Ten
hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
return hiddenState.Add(ctx, residual)
}
func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, batch input.Batch, cache kvcache.Cache) (ml.Tensor, error) {
// Initial token embedding
hiddenStates := m.TokenEmbedding.Forward(ctx, inputs).Duplicate(ctx)
for _, mi := range batch.Multimodal {
img := mi.Multimodal[0].Tensor
ctx.Forward(img.Copy(ctx, hiddenStates.View(ctx, mi.Index*hiddenStates.Stride(1), img.Dim(0)*img.Dim(1))))
}
// Process through transformer layers
for i, layer := range m.Layers {
cache.SetLayer(i)
var lastLayerOutputs ml.Tensor
if i == len(m.Layers)-1 {
lastLayerOutputs = outputs
}
hiddenStates = layer.Forward(ctx, hiddenStates, positions, lastLayerOutputs, cache, m.TextOptions)
}
hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
return m.Output.Forward(ctx, hiddenStates), nil
}