mirror of https://github.com/ollama/ollama
qwen25vl: vision rope
This commit is contained in:
Michael Yang
parent
5fb395f9f9
commit
d47f63e425
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@ -7,48 +7,28 @@ import (
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"github.com/ollama/ollama/fs"
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"github.com/ollama/ollama/ml"
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"github.com/ollama/ollama/ml/nn"
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"github.com/ollama/ollama/ml/nn/rope"
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)
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// We only support batch size of 1
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var batchSize int = 1
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func rotateHalf(ctx ml.Context, t ml.Tensor) ml.Tensor {
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x1 := t.Slice(ctx, 0, 0, t.Dim(0)/2, 1)
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x2 := t.Slice(ctx, 0, t.Dim(0)/2, t.Dim(0), 1).Contiguous(ctx)
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return x2.Scale(ctx, -1).Concat(ctx, x1, 0)
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}
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func applyRotaryPositionEmbeddings(ctx ml.Context, states, cos, sin ml.Tensor) ml.Tensor {
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return states.Mul(ctx, cos).Add(ctx, rotateHalf(ctx, states).Mul(ctx, sin))
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}
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func blockDiagonalMask(ctx ml.Context, seqLength int, bounds []int, numHeads int) ml.Tensor {
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// Create a flat slice for the mask (all -inf initially to block all attention)
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flat := make([]float32, seqLength*seqLength)
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for i := range flat {
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flat[i] = float32(math.Inf(-1)) // Negative infinity to block attention
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func blockDiagonalMask(ctx ml.Context, seqLength int, bounds []int) ml.Tensor {
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// Initialize a 2D mask with -Inf
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s := make([][]float32, seqLength)
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for i := range s {
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s[i] = slices.Repeat([]float32{float32(math.Inf(-1))}, seqLength)
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}
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// Fill in the mask with zeros for tokens that CAN attend to each other
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for i := 1; i < len(bounds); i++ {
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start := bounds[i-1]
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end := bounds[i]
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// Enable attention within this sequence block by setting values to 0
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start, end := bounds[i-1], bounds[i]
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// Enable attention within this sequence block
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for row := start; row < end; row++ {
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for col := start; col < end; col++ {
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idx := row*seqLength + col
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flat[idx] = 0.0 // 0 allows attention, -inf blocks it
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s[row][col] = 0.0
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}
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}
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}
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mask := ctx.Input().FromFloats(flat, seqLength, seqLength)
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// Reshape to match [seqLength, seqLength, 1] for broadcasting
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mask = mask.Reshape(ctx, seqLength, seqLength, 1)
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return mask
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return ctx.Input().FromFloats(slices.Concat(s...), seqLength, seqLength)
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}
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type VisionSelfAttention struct {
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@ -58,17 +38,17 @@ type VisionSelfAttention struct {
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Output *nn.Linear `gguf:"attn_out"`
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}
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func (sa *VisionSelfAttention) Forward(ctx ml.Context, hiddenStates, cos, sin, mask ml.Tensor, opts *VisionModelOptions) ml.Tensor {
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func (sa *VisionSelfAttention) Forward(ctx ml.Context, hiddenStates, positions, mask ml.Tensor, opts *VisionModelOptions) ml.Tensor {
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query := sa.Query.Forward(ctx, hiddenStates)
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key := sa.Key.Forward(ctx, hiddenStates)
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value := sa.Value.Forward(ctx, hiddenStates)
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query = query.Reshape(ctx, opts.headDim, opts.numHeads, query.Dim(1), batchSize)
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key = key.Reshape(ctx, opts.headDim, opts.numHeads, key.Dim(1), batchSize)
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value = value.Reshape(ctx, opts.headDim, opts.numHeads, value.Dim(1), batchSize)
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query = query.Reshape(ctx, opts.headDim, opts.numHeads, query.Dim(1))
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key = key.Reshape(ctx, opts.headDim, opts.numHeads, key.Dim(1))
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value = value.Reshape(ctx, opts.headDim, opts.numHeads, value.Dim(1))
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query = applyRotaryPositionEmbeddings(ctx, query, cos, sin)
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key = applyRotaryPositionEmbeddings(ctx, key, cos, sin)
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query = opts.applyRotaryPositionEmbeddings(ctx, query, positions)
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key = opts.applyRotaryPositionEmbeddings(ctx, key, positions)
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// Scale factor for scaled dot-product attention
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scale := 1.0 / math.Sqrt(float64(opts.headDim))
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@ -77,6 +57,7 @@ func (sa *VisionSelfAttention) Forward(ctx ml.Context, hiddenStates, cos, sin, m
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query = query.Permute(ctx, 0, 2, 1, 3)
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key = key.Permute(ctx, 0, 2, 1, 3)
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value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
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kq := key.MulmatFullPrec(ctx, query)
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kq = kq.Scale(ctx, scale)
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if mask != nil {
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@ -85,7 +66,7 @@ func (sa *VisionSelfAttention) Forward(ctx ml.Context, hiddenStates, cos, sin, m
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kq = kq.Softmax(ctx)
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kqv := value.Mulmat(ctx, kq)
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attention := kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
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attention = attention.Reshape(ctx, opts.hiddenSize, attention.Dim(2), batchSize)
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attention = attention.Reshape(ctx, opts.hiddenSize, attention.Dim(2))
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return sa.Output.Forward(ctx, attention)
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}
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@ -98,10 +79,7 @@ type VisionMLP struct {
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}
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func (mlp *VisionMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *VisionModelOptions) ml.Tensor {
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// Using activation as specified in config (likely GELU or SiLU/Swish)
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gateOutput := mlp.Gate.Forward(ctx, hiddenStates)
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hiddenStates = gateOutput.SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
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hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
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return mlp.Down.Forward(ctx, hiddenStates)
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}
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@ -112,10 +90,10 @@ type VisionEncoderLayer struct {
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MLP *VisionMLP
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}
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func (e *VisionEncoderLayer) Forward(ctx ml.Context, hiddenStates, cos, sin, mask ml.Tensor, opts *VisionModelOptions) ml.Tensor {
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func (e *VisionEncoderLayer) Forward(ctx ml.Context, hiddenStates, positions, mask ml.Tensor, opts *VisionModelOptions) ml.Tensor {
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residual := hiddenStates
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hiddenStates = e.Norm1.Forward(ctx, hiddenStates, opts.eps)
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hiddenStates = e.SelfAttention.Forward(ctx, hiddenStates, cos, sin, mask, opts)
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hiddenStates = e.SelfAttention.Forward(ctx, hiddenStates, positions, mask, opts)
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hiddenStates = hiddenStates.Add(ctx, residual)
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residual = hiddenStates
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@ -139,6 +117,17 @@ type VisionModelOptions struct {
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temporalPatchSize int
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}
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func (o VisionModelOptions) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions ml.Tensor) ml.Tensor {
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return nn.RoPE(ctx, states, positions, o.headDim/2, o.ropeTheta, 1,
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rope.WithVision([]int{
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o.headDim / 4,
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o.headDim / 4,
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o.headDim / 4,
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o.headDim / 4,
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}),
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)
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}
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type PatchEmbedding struct {
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PatchConv0 *nn.Conv2D `gguf:"patch_embd_0"`
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PatchConv1 *nn.Conv2D `gguf:"patch_embd_1"`
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@ -186,7 +175,7 @@ func (pm *VisionPatchMerger) Forward(ctx ml.Context, visionOutputs ml.Tensor, op
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hiddenSize := visionOutputs.Dim(0) * (opts.spatialMergeSize * opts.spatialMergeSize)
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// Reshape the normalized output to view the hidden size dimension
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reshaped := normalized.Reshape(ctx, hiddenSize, normalized.Dim(1)/(opts.spatialMergeSize*opts.spatialMergeSize), batchSize)
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reshaped := normalized.Reshape(ctx, hiddenSize, normalized.Dim(1)/(opts.spatialMergeSize*opts.spatialMergeSize))
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hidden := pm.MLP0.Forward(ctx, reshaped)
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activated := hidden.GELU(ctx)
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@ -209,36 +198,53 @@ func (m *VisionModel) Forward(ctx ml.Context, pixelValues ml.Tensor, grid *Grid)
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// Extract patch embeddings
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hiddenStates := m.PatchEmbedding.Forward(ctx, pixelValues, m.VisionModelOptions)
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positionEmbedding := m.PositionalEmbedding(ctx, grid)
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windowIndex, bounds := m.WindowIndex(ctx, grid)
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index, bounds := m.windowIndex(grid)
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spatialMergeUnit := m.spatialMergeSize * m.spatialMergeSize
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windowIndex := ctx.Input().FromInts(index, len(index))
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hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0)*spatialMergeUnit, hiddenStates.Dim(1)/spatialMergeUnit)
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hiddenStates = hiddenStates.Rows(ctx, windowIndex)
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hiddenStates = hiddenStates.Rows(ctx, windowIndex.Argsort(ctx))
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hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0)/spatialMergeUnit, hiddenStates.Dim(1)*spatialMergeUnit)
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positionEmbedding = positionEmbedding.Reshape(ctx, positionEmbedding.Dim(0)*spatialMergeUnit, positionEmbedding.Dim(1)/spatialMergeUnit)
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positionEmbedding = positionEmbedding.Rows(ctx, windowIndex)
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positionEmbedding = positionEmbedding.Reshape(ctx, positionEmbedding.Dim(0)/spatialMergeUnit, positionEmbedding.Dim(1)*spatialMergeUnit)
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positionEmbedding = positionEmbedding.Concat(ctx, positionEmbedding, 0)
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positions := ctx.Input().FromInts(func() []int32 {
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s := [][]int32{
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make([]int32, grid.Height*grid.Width),
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make([]int32, grid.Height*grid.Width),
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make([]int32, grid.Height*grid.Width),
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make([]int32, grid.Height*grid.Width),
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}
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cos, sin := positionEmbedding.Cos(ctx), positionEmbedding.Sin(ctx)
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cos = cos.Reshape(ctx, cos.Dim(0), 1, cos.Dim(1))
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sin = sin.Reshape(ctx, sin.Dim(0), 1, sin.Dim(1))
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var cur int
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for y := 0; y < grid.Height; y += m.spatialMergeSize {
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for x := 0; x < grid.Width; x += m.spatialMergeSize {
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for dy := range 2 {
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for dx := range 2 {
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i := int(index[cur/spatialMergeUnit]) * spatialMergeUnit
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i += cur % spatialMergeUnit
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s[0][i] = int32(y + dy)
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s[1][i] = int32(x + dx)
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s[2][i] = int32(y + dy)
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s[3][i] = int32(x + dx)
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cur++
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}
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}
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}
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}
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return slices.Concat(s...)
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}(), grid.Height*grid.Width*4)
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mask := blockDiagonalMask(ctx, hiddenStates.Dim(1), bounds)
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mask := blockDiagonalMask(ctx, hiddenStates.Dim(1), bounds, m.VisionModelOptions.numHeads)
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// Apply encoder layers
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for i, layer := range m.Layers {
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if slices.Contains(m.fullAttnBlocks, int32(i)) {
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hiddenStates = layer.Forward(ctx, hiddenStates, cos, sin, nil, m.VisionModelOptions)
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hiddenStates = layer.Forward(ctx, hiddenStates, positions, nil, m.VisionModelOptions)
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} else {
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hiddenStates = layer.Forward(
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ctx,
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hiddenStates,
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cos,
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sin,
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positions,
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mask,
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m.VisionModelOptions,
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)
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@ -246,102 +252,43 @@ func (m *VisionModel) Forward(ctx ml.Context, pixelValues ml.Tensor, grid *Grid)
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}
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hiddenStates = m.PatchMerger.Forward(ctx, hiddenStates, m.VisionModelOptions)
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reverseWindowIndex := windowIndex.Argsort(ctx)
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return hiddenStates.Rows(ctx, reverseWindowIndex)
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return hiddenStates.Rows(ctx, windowIndex)
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}
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// WindowIndex divides the grid into windows and returns:
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// 1. A tensor containing flattened indices of all grid points organized by windows
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// windowIndex divides the grid into windows and returns:
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// 1. A slice of grid point indices organized by windows
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// 2. A slice of boundaries that mark where each window's data begins and ends
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// in the flattened representation, scaled by spatialMergeSize squared
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//
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// The boundaries slice always starts with 0 and contains cumulative ending
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// positions for each window, allowing downstream processing to identify
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// window boundaries in the tensor data.
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func (m *VisionModel) WindowIndex(ctx ml.Context, grid *Grid) (ml.Tensor, []int) {
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vitMergerWindowSize := m.windowSize / m.spatialMergeSize / m.patchSize
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func (m *VisionModel) windowIndex(grid *Grid) (index []int32, bounds []int) {
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height := grid.Height / m.spatialMergeSize
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width := grid.Width / m.spatialMergeSize
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window := m.windowSize / m.patchSize / m.spatialMergeSize
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llmGridH := grid.Height / m.spatialMergeSize
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llmGridW := grid.Width / m.spatialMergeSize
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index = make([]int32, height*width)
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// Calculate window parameters
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numWindowsH := int(math.Ceil(float64(llmGridH) / float64(vitMergerWindowSize)))
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numWindowsW := int(math.Ceil(float64(llmGridW) / float64(vitMergerWindowSize)))
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bounds = make([]int, 0, ((height+window-1)/window)*((width+window-1)/window)+1)
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bounds = append(bounds, 0)
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// Initialize index_new slice
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var index []int32
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// Initialize bounds with the first element as 0
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bounds := []int{0}
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totalSeqLen := 0
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// Process each window without padding
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for wh := range numWindowsH {
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for ww := range numWindowsW {
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// Calculate window boundaries
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hStart := wh * vitMergerWindowSize
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wStart := ww * vitMergerWindowSize
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hEnd := min(hStart+vitMergerWindowSize, llmGridH)
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wEnd := min(wStart+vitMergerWindowSize, llmGridW)
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// Calculate sequence length for this window
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seqLen := (hEnd - hStart) * (wEnd - wStart)
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// Collect indices for this window
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for h := hStart; h < hEnd; h++ {
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for w := wStart; w < wEnd; w++ {
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index = append(index, int32(h*llmGridW+w))
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var cur int32
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for y := 0; y < height; y += window {
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for x := 0; x < width; x += window {
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h1 := min(window, height-y)
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w1 := min(window, width-x)
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for dy := range h1 {
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for dx := range w1 {
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win := (y+dy)*width + (x + dx)
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index[win] = cur
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cur++
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}
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}
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totalSeqLen += seqLen
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bounds = append(bounds, totalSeqLen*(m.spatialMergeSize*m.spatialMergeSize)+bounds[0])
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bounds = append(bounds, int(cur)*window)
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}
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}
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t := ctx.Input().FromInts(index, len(index))
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return t, bounds
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}
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// PositionalEmbedding generates rotary position embeddings for attention mechanisms
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func (m *VisionModel) PositionalEmbedding(ctx ml.Context, grid *Grid) ml.Tensor {
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dim := m.headDim / 2
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freq := dim / 2
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theta := float64(m.ropeTheta)
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merge := m.spatialMergeSize
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// Create frequency patterns for position encoding
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maxGridSize := max(grid.Height, grid.Width)
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freqVals := make([]float32, freq*maxGridSize)
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for i := range maxGridSize {
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for j := range freq {
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freqVals[i*freq+j] = float32(i) / float32(math.Pow(theta, float64(j*2)/float64(dim)))
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}
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}
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freqs := ctx.Input().FromFloats(freqVals, freq, maxGridSize)
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// Create position coordinates (y,x pairs) for the grid
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// In PyTorch: Equivalent to generating position ids with torch.arange()
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coords := make([]int32, 0, grid.Height*grid.Width*2)
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for y := range grid.Height {
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for x := range grid.Width {
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coords = append(coords, int32(y), int32(x))
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}
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}
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pos := ctx.Input().FromInts(coords, 2, grid.Width, grid.Height)
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// Reshape and permute positions to match spatial merging pattern
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pos = pos.Reshape(ctx, 2, grid.Width, merge, grid.Height/merge)
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pos = pos.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
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pos = pos.Reshape(ctx, 2, merge, merge, grid.Width/merge*grid.Height/merge)
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pos = pos.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
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pos = pos.Reshape(ctx, 2*merge*merge*grid.Width/merge*grid.Height/merge)
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// Use position indices to look up corresponding frequency values
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positionalEmbedding := freqs.Rows(ctx, pos)
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positionalEmbedding = positionalEmbedding.Reshape(ctx, positionalEmbedding.Dim(0)*2, positionalEmbedding.Dim(1)/2)
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return positionalEmbedding
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return index, bounds
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}
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// newVisionModel creates a new instance of the Qwen vision model
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