ollama source for Momentry Core verification
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Accusys
325
model/models/qwen25vl/model_vision.go
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325
model/models/qwen25vl/model_vision.go
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package qwen25vl
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import (
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"math"
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"slices"
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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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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, 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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s[row][col] = 0.0
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}
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}
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}
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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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Query *nn.Linear `gguf:"attn_q"`
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Key *nn.Linear `gguf:"attn_k"`
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Value *nn.Linear `gguf:"attn_v"`
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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, 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))
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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 = 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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// Scaled dot-product attention
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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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kq = kq.Add(ctx, mask)
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}
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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))
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return sa.Output.Forward(ctx, attention)
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}
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// VisionMLP implements the multi-layer perceptron
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type VisionMLP struct {
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Gate *nn.Linear `gguf:"ffn_gate"`
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Up *nn.Linear `gguf:"ffn_up"`
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Down *nn.Linear `gguf:"ffn_down"`
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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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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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type VisionEncoderLayer struct {
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Norm1 *nn.RMSNorm `gguf:"ln1"`
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SelfAttention *VisionSelfAttention
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Norm2 *nn.RMSNorm `gguf:"ln2"`
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MLP *VisionMLP
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}
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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, positions, mask, opts)
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hiddenStates = hiddenStates.Add(ctx, residual)
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residual = hiddenStates
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hiddenStates = e.Norm2.Forward(ctx, hiddenStates, opts.eps)
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hiddenStates = e.MLP.Forward(ctx, hiddenStates, opts)
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return hiddenStates.Add(ctx, residual)
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}
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// VisionModelOptions contains configuration options
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type VisionModelOptions struct {
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hiddenSize int
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numHeads int
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headDim int
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patchSize int
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numChannels int
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eps float32
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ropeTheta float32
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spatialMergeSize int
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windowSize int
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fullAttnBlocks []int32
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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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}
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func (pe *PatchEmbedding) Forward(ctx ml.Context, pixelValues ml.Tensor, opts *VisionModelOptions) ml.Tensor {
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numPatches := pixelValues.Shape()[1]
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// Reshape the input tensor to match the expected dimensions
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pixelValues = pixelValues.Reshape(ctx, opts.patchSize*opts.patchSize, opts.temporalPatchSize, opts.numChannels, numPatches)
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// Permute the tensor to bring the temporal dimension to the front
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pixelValues = pixelValues.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
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// Split the tensor into parts for the temporal convolutions
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in0 := pixelValues.View(ctx, 0, 1, pixelValues.Stride(1), pixelValues.Dim(1), pixelValues.Stride(2), pixelValues.Dim(2), pixelValues.Stride(3), pixelValues.Dim(3)).Contiguous(ctx)
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in0 = in0.Reshape(ctx, opts.patchSize, opts.patchSize, opts.numChannels, numPatches)
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in1 := pixelValues.View(ctx, pixelValues.Stride(0), 1, pixelValues.Stride(1), pixelValues.Dim(1), pixelValues.Stride(2), pixelValues.Dim(2), pixelValues.Stride(3), pixelValues.Dim(3)).Contiguous(ctx)
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in1 = in1.Reshape(ctx, opts.patchSize, opts.patchSize, opts.numChannels, numPatches)
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s0, s1 := opts.patchSize, opts.patchSize // Use full stride
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p0, p1 := 0, 0 // padding
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d0, d1 := 1, 1 // dilation
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out0 := pe.PatchConv0.Forward(ctx, in0, s0, s1, p0, p1, d0, d1)
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out1 := pe.PatchConv1.Forward(ctx, in1, s0, s1, p0, p1, d0, d1)
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// Add the outputs from the two temporal convolutions
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out := out0.Add(ctx, out1)
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// Reshape the output tensor to match the expected dimensions
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return out.Reshape(ctx, opts.hiddenSize, numPatches)
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}
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// VisionPatchMerger implements patch merging for the Qwen vision model
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type VisionPatchMerger struct {
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LNQ *nn.RMSNorm `gguf:"ln_q"`
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MLP0 *nn.Linear `gguf:"mlp.0"`
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MLP2 *nn.Linear `gguf:"mlp.2"`
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}
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// Forward computes patch merging for the vision model
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func (pm *VisionPatchMerger) Forward(ctx ml.Context, visionOutputs ml.Tensor, opts *VisionModelOptions) ml.Tensor {
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normalized := pm.LNQ.Forward(ctx, visionOutputs, opts.eps)
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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))
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hidden := pm.MLP0.Forward(ctx, reshaped)
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activated := hidden.GELU(ctx)
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output := pm.MLP2.Forward(ctx, activated)
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return output
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}
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// VisionModel implements the Qwen vision model
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type VisionModel struct {
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PatchEmbedding *PatchEmbedding
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Layers []VisionEncoderLayer `gguf:"blk"`
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PatchMerger *VisionPatchMerger `gguf:"merger"`
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*VisionModelOptions
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}
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// Forward computes the vision model for an input tensor
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func (m *VisionModel) Forward(ctx ml.Context, pixelValues ml.Tensor, grid *Grid) ml.Tensor {
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// Extract patch embeddings
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hiddenStates := m.PatchEmbedding.Forward(ctx, pixelValues, m.VisionModelOptions)
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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.Argsort(ctx))
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hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0)/spatialMergeUnit, hiddenStates.Dim(1)*spatialMergeUnit)
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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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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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// 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, 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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positions,
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mask,
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m.VisionModelOptions,
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)
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}
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}
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hiddenStates = m.PatchMerger.Forward(ctx, hiddenStates, m.VisionModelOptions)
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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 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(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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index = make([]int32, height*width)
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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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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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bounds = append(bounds, int(cur)*window)
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}
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}
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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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func newVisionModel(c fs.Config) *VisionModel {
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patchSize := int(c.Uint("vision.patch_size", 14))
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hiddenSize := int(c.Uint("vision.embedding_length", 1280))
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numHeads := int(c.Uint("vision.attention.head_count", 16))
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numChannels := int(c.Uint("vision.num_channels", 3))
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eps := c.Float("vision.attention.layer_norm_epsilon", 1e-6)
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ropeTheta := c.Float("vision.rope.freq_base", 10000.0)
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spatialMergeSize := int(c.Uint("vision.spatial_merge_size", 2))
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windowSize := int(c.Uint("vision.window_size", 112))
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fullAttnBlocks := c.Ints("qwen25vl.vision.fullatt_block_indexes", []int32{7, 15, 23, 31})
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temporalPatchSize := int(c.Uint("vision.temporal_patch_size", 2))
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model := &VisionModel{
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Layers: make([]VisionEncoderLayer, c.Uint("vision.block_count", 32)),
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VisionModelOptions: &VisionModelOptions{
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hiddenSize: hiddenSize,
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numHeads: numHeads,
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headDim: hiddenSize / numHeads,
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patchSize: patchSize,
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numChannels: numChannels,
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eps: eps,
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ropeTheta: ropeTheta,
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spatialMergeSize: spatialMergeSize,
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windowSize: windowSize,
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temporalPatchSize: temporalPatchSize,
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fullAttnBlocks: fullAttnBlocks,
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},
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}
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return model
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}
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