You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
392 lines
14 KiB
392 lines
14 KiB
package qwen25vl
|
|
|
|
import (
|
|
"fmt"
|
|
"math"
|
|
"slices"
|
|
|
|
"github.com/ollama/ollama/fs"
|
|
"github.com/ollama/ollama/ml"
|
|
"github.com/ollama/ollama/ml/nn"
|
|
)
|
|
|
|
// We only support batch size of 1
|
|
var batchSize int = 1
|
|
|
|
func rotateHalf(ctx ml.Context, t ml.Tensor) ml.Tensor {
|
|
x1 := t.View(ctx, 0, t.Dim(0)/2, t.Stride(1), t.Dim(1), t.Stride(2), t.Dim(2), t.Stride(3), t.Dim(3))
|
|
x2 := t.View(ctx, t.Stride(0)*t.Dim(0)/2, t.Dim(0)/2, t.Stride(1), t.Dim(1), t.Stride(2), t.Dim(2), t.Stride(3), t.Dim(3)).Contiguous(ctx)
|
|
return x2.Neg(ctx).Concat(ctx, x1, 0)
|
|
}
|
|
|
|
func applyRotaryPositionalEmbedding(ctx ml.Context, t, cos, sin ml.Tensor) ml.Tensor {
|
|
return t.Mul(ctx, cos).Add(ctx, rotateHalf(ctx, t).Mul(ctx, sin))
|
|
}
|
|
|
|
func blockDiagonalMask(ctx ml.Context, seqLength int, bounds []int, numHeads int) ml.Tensor {
|
|
// Create a flat slice for the mask (all -inf initially to block all attention)
|
|
flat := make([]float32, seqLength*seqLength)
|
|
for i := range flat {
|
|
flat[i] = float32(math.Inf(-1)) // Negative infinity to block attention
|
|
}
|
|
|
|
// Fill in the mask with zeros for tokens that CAN attend to each other
|
|
for i := 1; i < len(bounds); i++ {
|
|
start := bounds[i-1]
|
|
end := bounds[i]
|
|
|
|
// Enable attention within this sequence block by setting values to 0
|
|
for row := start; row < end; row++ {
|
|
for col := start; col < end; col++ {
|
|
idx := row*seqLength + col
|
|
flat[idx] = 0.0 // 0 allows attention, -inf blocks it
|
|
}
|
|
}
|
|
}
|
|
|
|
mask, err := ctx.Input().FromFloatSlice(flat, seqLength, seqLength)
|
|
if err != nil {
|
|
panic(err)
|
|
}
|
|
// Reshape to match [seqLength, seqLength, 1] for broadcasting
|
|
mask = mask.Reshape(ctx, seqLength, seqLength, 1)
|
|
|
|
return mask
|
|
}
|
|
|
|
type VisionSelfAttention struct {
|
|
Query *nn.Linear `gguf:"attn_q"`
|
|
Key *nn.Linear `gguf:"attn_k"`
|
|
Value *nn.Linear `gguf:"attn_v"`
|
|
Output *nn.Linear `gguf:"attn_out"`
|
|
}
|
|
|
|
func (sa *VisionSelfAttention) Forward(ctx ml.Context, hiddenStates, cos, sin, mask ml.Tensor, opts *VisionModelOptions) ml.Tensor {
|
|
query := sa.Query.Forward(ctx, hiddenStates)
|
|
key := sa.Key.Forward(ctx, hiddenStates)
|
|
value := sa.Value.Forward(ctx, hiddenStates)
|
|
|
|
query = query.Reshape(ctx, opts.headDim, opts.numHeads, query.Dim(1), batchSize)
|
|
key = key.Reshape(ctx, opts.headDim, opts.numHeads, key.Dim(1), batchSize)
|
|
value = value.Reshape(ctx, opts.headDim, opts.numHeads, value.Dim(1), batchSize)
|
|
|
|
query = applyRotaryPositionalEmbedding(ctx, query, cos, sin)
|
|
key = applyRotaryPositionalEmbedding(ctx, key, cos, sin)
|
|
|
|
// Scale factor for scaled dot-product attention
|
|
scale := 1.0 / math.Sqrt(float64(opts.headDim))
|
|
|
|
// Scaled dot-product attention
|
|
query = query.Permute(ctx, 0, 2, 1, 3)
|
|
key = key.Permute(ctx, 0, 2, 1, 3)
|
|
value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
|
|
kq := key.MulmatFullPrec(ctx, query)
|
|
kq = kq.Scale(ctx, scale)
|
|
if mask != nil {
|
|
kq = kq.Add(ctx, mask)
|
|
}
|
|
kq = kq.Softmax(ctx)
|
|
kqv := value.Mulmat(ctx, kq)
|
|
attention := kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
|
|
attention = attention.Reshape(ctx, opts.hiddenSize, attention.Dim(2), batchSize)
|
|
|
|
return sa.Output.Forward(ctx, attention)
|
|
}
|
|
|
|
// VisionMLP implements the multi-layer perceptron
|
|
type VisionMLP struct {
|
|
Gate *nn.Linear `gguf:"ffn_gate"`
|
|
Up *nn.Linear `gguf:"ffn_up"`
|
|
Down *nn.Linear `gguf:"ffn_down"`
|
|
}
|
|
|
|
func (mlp *VisionMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *VisionModelOptions) ml.Tensor {
|
|
// Using activation as specified in config (likely GELU or SiLU/Swish)
|
|
gateOutput := mlp.Gate.Forward(ctx, hiddenStates)
|
|
upOutput := mlp.Up.Forward(ctx, hiddenStates)
|
|
hiddenStates = gateOutput.SILU(ctx).Mul(ctx, upOutput)
|
|
|
|
return mlp.Down.Forward(ctx, hiddenStates)
|
|
}
|
|
|
|
type VisionEncoderLayer struct {
|
|
Norm1 *nn.RMSNorm `gguf:"ln1"`
|
|
SelfAttention *VisionSelfAttention
|
|
Norm2 *nn.RMSNorm `gguf:"ln2"`
|
|
MLP *VisionMLP
|
|
}
|
|
|
|
func (e *VisionEncoderLayer) Forward(ctx ml.Context, hiddenStates, cos, sin, mask ml.Tensor, opts *VisionModelOptions) ml.Tensor {
|
|
residual := hiddenStates
|
|
hiddenStates = e.Norm1.Forward(ctx, hiddenStates, opts.eps)
|
|
hiddenStates = e.SelfAttention.Forward(ctx, hiddenStates, cos, sin, mask, opts)
|
|
hiddenStates = hiddenStates.Add(ctx, residual)
|
|
|
|
residual = hiddenStates
|
|
hiddenStates = e.Norm2.Forward(ctx, hiddenStates, opts.eps)
|
|
hiddenStates = e.MLP.Forward(ctx, hiddenStates, opts)
|
|
return hiddenStates.Add(ctx, residual)
|
|
}
|
|
|
|
// VisionModelOptions contains configuration options
|
|
type VisionModelOptions struct {
|
|
hiddenSize int
|
|
numHeads int
|
|
headDim int
|
|
patchSize int
|
|
numChannels int
|
|
eps float32
|
|
ropeTheta float32
|
|
spatialMergeSize int
|
|
windowSize int
|
|
fullAttnBlocks []int32
|
|
temporalPatchSize int
|
|
}
|
|
|
|
type PatchEmbedding struct {
|
|
PatchConv0 *nn.Conv2D `gguf:"patch_embd_0"`
|
|
PatchConv1 *nn.Conv2D `gguf:"patch_embd_1"`
|
|
}
|
|
|
|
func (pe *PatchEmbedding) Forward(ctx ml.Context, pixelValues ml.Tensor, opts *VisionModelOptions) ml.Tensor {
|
|
numPatches := pixelValues.Shape()[1]
|
|
|
|
// Reshape the input tensor to match the expected dimensions
|
|
pixelValues = pixelValues.Reshape(ctx, opts.patchSize*opts.patchSize, opts.temporalPatchSize, opts.numChannels, numPatches)
|
|
|
|
// Permute the tensor to bring the temporal dimension to the front
|
|
pixelValues = pixelValues.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
|
|
|
|
// Split the tensor into parts for the temporal convolutions
|
|
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)
|
|
in0 = in0.Reshape(ctx, opts.patchSize, opts.patchSize, opts.numChannels, numPatches)
|
|
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)
|
|
in1 = in1.Reshape(ctx, opts.patchSize, opts.patchSize, opts.numChannels, numPatches)
|
|
|
|
s0, s1 := opts.patchSize, opts.patchSize // Use full stride
|
|
p0, p1 := 0, 0 // padding
|
|
d0, d1 := 1, 1 // dilation
|
|
out0 := pe.PatchConv0.Forward(ctx, in0, s0, s1, p0, p1, d0, d1)
|
|
out1 := pe.PatchConv1.Forward(ctx, in1, s0, s1, p0, p1, d0, d1)
|
|
|
|
// Add the outputs from the two temporal convolutions
|
|
out := out0.Add(ctx, out1)
|
|
|
|
// Reshape the output tensor to match the expected dimensions
|
|
return out.Reshape(ctx, opts.hiddenSize, numPatches)
|
|
}
|
|
|
|
// VisionPatchMerger implements patch merging for the Qwen vision model
|
|
type VisionPatchMerger struct {
|
|
LNQ *nn.RMSNorm `gguf:"ln_q"`
|
|
MLP0 *nn.Linear `gguf:"mlp.0"`
|
|
MLP2 *nn.Linear `gguf:"mlp.2"`
|
|
}
|
|
|
|
// Forward computes patch merging for the vision model
|
|
func (pm *VisionPatchMerger) Forward(ctx ml.Context, visionOutputs ml.Tensor, opts *VisionModelOptions) ml.Tensor {
|
|
normalized := pm.LNQ.Forward(ctx, visionOutputs, opts.eps)
|
|
|
|
hiddenSize := visionOutputs.Dim(0) * (opts.spatialMergeSize * opts.spatialMergeSize)
|
|
|
|
// Reshape the normalized output to view the hidden size dimension
|
|
reshaped := normalized.Reshape(ctx, hiddenSize, normalized.Dim(1)/(opts.spatialMergeSize*opts.spatialMergeSize), batchSize)
|
|
hidden := pm.MLP0.Forward(ctx, reshaped)
|
|
activated := hidden.GELU(ctx)
|
|
|
|
output := pm.MLP2.Forward(ctx, activated)
|
|
|
|
return output
|
|
}
|
|
|
|
// VisionModel implements the Qwen vision model
|
|
type VisionModel struct {
|
|
PatchEmbedding *PatchEmbedding
|
|
Layers []VisionEncoderLayer `gguf:"blk"`
|
|
PatchMerger *VisionPatchMerger `gguf:"merger"`
|
|
|
|
*VisionModelOptions
|
|
}
|
|
|
|
// Forward computes the vision model for an input tensor
|
|
func (m *VisionModel) Forward(ctx ml.Context, pixelValues ml.Tensor, grid *Grid) ml.Tensor {
|
|
// Extract patch embeddings
|
|
hiddenStates := m.PatchEmbedding.Forward(ctx, pixelValues, m.VisionModelOptions)
|
|
|
|
positionEmbedding := m.PositionalEmbedding(ctx, grid)
|
|
|
|
windowIndex, bounds := m.WindowIndex(ctx, grid)
|
|
|
|
spatialMergeUnit := m.spatialMergeSize * m.spatialMergeSize
|
|
|
|
hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0)*spatialMergeUnit, hiddenStates.Dim(1)/spatialMergeUnit)
|
|
hiddenStates = hiddenStates.Rows(ctx, windowIndex)
|
|
hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0)/spatialMergeUnit, hiddenStates.Dim(1)*spatialMergeUnit)
|
|
|
|
positionEmbedding = positionEmbedding.Reshape(ctx, positionEmbedding.Dim(0)*spatialMergeUnit, positionEmbedding.Dim(1)/spatialMergeUnit)
|
|
positionEmbedding = positionEmbedding.Rows(ctx, windowIndex)
|
|
positionEmbedding = positionEmbedding.Reshape(ctx, positionEmbedding.Dim(0)/spatialMergeUnit, positionEmbedding.Dim(1)*spatialMergeUnit)
|
|
positionEmbedding = positionEmbedding.Concat(ctx, positionEmbedding, 0)
|
|
|
|
cos, sin := positionEmbedding.Cos(ctx), positionEmbedding.Sin(ctx)
|
|
cos = cos.Reshape(ctx, cos.Dim(0), 1, cos.Dim(1))
|
|
sin = sin.Reshape(ctx, sin.Dim(0), 1, sin.Dim(1))
|
|
|
|
mask := blockDiagonalMask(ctx, hiddenStates.Dim(1), bounds, m.VisionModelOptions.numHeads)
|
|
// Apply encoder layers
|
|
for i, layer := range m.Layers {
|
|
if slices.Contains(m.fullAttnBlocks, int32(i)) {
|
|
hiddenStates = layer.Forward(ctx, hiddenStates, cos, sin, nil, m.VisionModelOptions)
|
|
} else {
|
|
hiddenStates = layer.Forward(
|
|
ctx,
|
|
hiddenStates,
|
|
cos,
|
|
sin,
|
|
mask,
|
|
m.VisionModelOptions,
|
|
)
|
|
}
|
|
}
|
|
|
|
hiddenStates = m.PatchMerger.Forward(ctx, hiddenStates, m.VisionModelOptions)
|
|
reverseWindowIndex := windowIndex.Argsort(ctx)
|
|
return hiddenStates.Rows(ctx, reverseWindowIndex)
|
|
}
|
|
|
|
// WindowIndex divides the grid into windows and returns:
|
|
// 1. A tensor containing flattened indices of all grid points organized by windows
|
|
// 2. A slice of boundaries that mark where each window's data begins and ends
|
|
// in the flattened representation, scaled by spatialMergeSize squared
|
|
//
|
|
// The boundaries slice always starts with 0 and contains cumulative ending
|
|
// positions for each window, allowing downstream processing to identify
|
|
// window boundaries in the tensor data.
|
|
func (m *VisionModel) WindowIndex(ctx ml.Context, grid *Grid) (ml.Tensor, []int) {
|
|
vitMergerWindowSize := m.windowSize / m.spatialMergeSize / m.patchSize
|
|
|
|
llmGridH := grid.Height / m.spatialMergeSize
|
|
llmGridW := grid.Width / m.spatialMergeSize
|
|
|
|
// Calculate window parameters
|
|
numWindowsH := int(math.Ceil(float64(llmGridH) / float64(vitMergerWindowSize)))
|
|
numWindowsW := int(math.Ceil(float64(llmGridW) / float64(vitMergerWindowSize)))
|
|
|
|
// Initialize index_new slice
|
|
var index []int32
|
|
|
|
// Initialize bounds with the first element as 0
|
|
bounds := []int{0}
|
|
totalSeqLen := 0
|
|
|
|
// Process each window without padding
|
|
for wh := range numWindowsH {
|
|
for ww := range numWindowsW {
|
|
// Calculate window boundaries
|
|
hStart := wh * vitMergerWindowSize
|
|
wStart := ww * vitMergerWindowSize
|
|
hEnd := min(hStart+vitMergerWindowSize, llmGridH)
|
|
wEnd := min(wStart+vitMergerWindowSize, llmGridW)
|
|
|
|
// Calculate sequence length for this window
|
|
seqLen := (hEnd - hStart) * (wEnd - wStart)
|
|
|
|
// Collect indices for this window
|
|
for h := hStart; h < hEnd; h++ {
|
|
for w := wStart; w < wEnd; w++ {
|
|
index = append(index, int32(h*llmGridW+w))
|
|
}
|
|
}
|
|
|
|
totalSeqLen += seqLen
|
|
bounds = append(bounds, totalSeqLen*(m.spatialMergeSize*m.spatialMergeSize)+bounds[0])
|
|
}
|
|
}
|
|
|
|
t, err := ctx.Input().FromIntSlice(index, len(index))
|
|
if err != nil {
|
|
panic(err)
|
|
}
|
|
|
|
return t, bounds
|
|
}
|
|
|
|
// PositionalEmbedding generates rotary position embeddings for attention mechanisms
|
|
func (m *VisionModel) PositionalEmbedding(ctx ml.Context, grid *Grid) ml.Tensor {
|
|
dim := m.headDim / 2
|
|
freq := dim / 2
|
|
theta := float64(m.ropeTheta)
|
|
merge := m.spatialMergeSize
|
|
|
|
// Create frequency patterns for position encoding
|
|
maxGridSize := max(grid.Height, grid.Width)
|
|
freqVals := make([]float32, freq*maxGridSize)
|
|
for i := range maxGridSize {
|
|
for j := range freq {
|
|
freqVals[i*freq+j] = float32(i) / float32(math.Pow(theta, float64(j*2)/float64(dim)))
|
|
}
|
|
}
|
|
freqs, err := ctx.Input().FromFloatSlice(freqVals, freq, maxGridSize)
|
|
if err != nil {
|
|
panic(fmt.Errorf("failed to create tensor from frequencies: %w", err))
|
|
}
|
|
|
|
// Create position coordinates (y,x pairs) for the grid
|
|
// In PyTorch: Equivalent to generating position ids with torch.arange()
|
|
coords := make([]int32, 0, grid.Height*grid.Width*2)
|
|
for y := range grid.Height {
|
|
for x := range grid.Width {
|
|
coords = append(coords, int32(y), int32(x))
|
|
}
|
|
}
|
|
pos, err := ctx.Input().FromIntSlice(coords, 2, grid.Width, grid.Height)
|
|
if err != nil {
|
|
panic(fmt.Errorf("failed to create tensor from positions: %w", err))
|
|
}
|
|
|
|
// Reshape and permute positions to match spatial merging pattern
|
|
pos = pos.Reshape(ctx, 2, grid.Width, merge, grid.Height/merge)
|
|
pos = pos.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
|
|
pos = pos.Reshape(ctx, 2, merge, merge, grid.Width/merge*grid.Height/merge)
|
|
pos = pos.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
|
|
pos = pos.Reshape(ctx, 2*merge*merge*grid.Width/merge*grid.Height/merge)
|
|
|
|
// Use position indices to look up corresponding frequency values
|
|
positionalEmbedding := freqs.Rows(ctx, pos)
|
|
positionalEmbedding = positionalEmbedding.Reshape(ctx, positionalEmbedding.Dim(0)*2, positionalEmbedding.Dim(1)/2)
|
|
return positionalEmbedding
|
|
}
|
|
|
|
// newVisionModel creates a new instance of the Qwen vision model
|
|
func newVisionModel(c fs.Config) *VisionModel {
|
|
patchSize := int(c.Uint("vision.patch_size", 14))
|
|
hiddenSize := int(c.Uint("vision.embedding_length", 1280))
|
|
numHeads := int(c.Uint("vision.attention.head_count", 16))
|
|
numChannels := int(c.Uint("vision.num_channels", 3))
|
|
eps := c.Float("vision.attention.layer_norm_epsilon", 1e-6)
|
|
ropeTheta := c.Float("vision.rope.freq_base", 10000.0)
|
|
spatialMergeSize := int(c.Uint("vision.spatial_merge_size", 2))
|
|
windowSize := int(c.Uint("vision.window_size", 112))
|
|
fullAttnBlocks := c.Ints("qwen25vl.vision.fullatt_block_indexes", []int32{7, 15, 23, 31})
|
|
temporalPatchSize := int(c.Uint("vision.temporal_patch_size", 2))
|
|
|
|
model := &VisionModel{
|
|
Layers: make([]VisionEncoderLayer, c.Uint("vision.block_count", 32)),
|
|
VisionModelOptions: &VisionModelOptions{
|
|
hiddenSize: hiddenSize,
|
|
numHeads: numHeads,
|
|
headDim: hiddenSize / numHeads,
|
|
patchSize: patchSize,
|
|
numChannels: numChannels,
|
|
eps: eps,
|
|
ropeTheta: ropeTheta,
|
|
spatialMergeSize: spatialMergeSize,
|
|
windowSize: windowSize,
|
|
temporalPatchSize: temporalPatchSize,
|
|
fullAttnBlocks: fullAttnBlocks,
|
|
},
|
|
}
|
|
|
|
return model
|
|
}
|