grid refactor

model/models/qwen25vl/model.go

@@ -64,9 +64,7 @@ func New(c fs.Config) (model.Model, error) {
 
 type imageFeatures struct {
 	Tensor ml.Tensor
-	GridT  int
+	Grid   *Grid
-	GridH  int
-	GridW  int
 }
 
 func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, error) {
@@ -79,7 +77,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, er
 		return nil, err
 	}
 
-	f32s, gridT, gridH, gridW, err := m.ImageProcessor.ProcessImage(image)
+	f32s, grid, err := m.ImageProcessor.ProcessImage(image)
 	if err != nil {
 		return nil, err
 	}
@@ -87,7 +85,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, er
 	// Calculate tensor dimensions
 	patchDim := m.ImageProcessor.numChannels * m.ImageProcessor.temporalPatchSize *
 		m.ImageProcessor.patchSize * m.ImageProcessor.patchSize
-	numPatches := gridT * gridH * gridW
+	numPatches := grid.Temporal * grid.Height * grid.Width
 
 	pixelValues, err := ctx.Input().FromFloatSlice(f32s, patchDim, numPatches)
 	if err != nil {
@@ -99,9 +97,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, er
 
 	return &imageFeatures{
 		Tensor: visionOutputs,
-		GridT:  gridT,
+		Grid:   grid,
-		GridH:  gridH,
-		GridW:  gridW,
 	}, nil
 }
 
@@ -125,14 +121,9 @@ func (m *Model) PostTokenize(inputs []input.Input) ([]input.Input, error) {
 			// This is an image token with multimodal data
 			features := inp.Multimodal.(*imageFeatures)
 
-			// Get grid dimensions from the features
-			gridT := features.GridT
-			gridH := features.GridH
-			gridW := features.GridW
-
 			// Calculate tokens per grid based on grid dimensions
 			mergeLength := mergeSize * mergeSize
-			gridProduct := gridT * gridH * gridW
+			gridProduct := features.Grid.Temporal * features.Grid.Height * features.Grid.Width
 			tokensPerGrid := gridProduct / mergeLength
 
 			// First add the vision start token

model/models/qwen25vl/model_vision.go

@@ -177,7 +177,7 @@ type VisionModel struct {
 }
 
 // Forward computes the vision model for an input tensor
-func (m *VisionModel) Forward(ctx ml.Context, pixelValues ml.Tensor) ml.Tensor {
+func (m *VisionModel) Forward(ctx ml.Context, pixelValues ml.Tensor, grid *Grid) ml.Tensor {
 	// Calculate position IDs for 2D RoPE
 	numPatchesH := pixelValues.Dim(0) / m.patchSize
 	numPatchesW := pixelValues.Dim(1) / m.patchSize
@@ -193,14 +193,12 @@ func (m *VisionModel) Forward(ctx ml.Context, pixelValues ml.Tensor) ml.Tensor {
 	)
 
 	// Create position IDs - for Qwen2VL mRoPE we need 4 values per position
-	// The format needed is specified in the C++ code as "mrope expecting 4 position ids per token"
 	positions := make([]int32, numPatches*4)
 
 	for h := 0; h < numPatchesH; h++ {
 		for w := 0; w < numPatchesW; w++ {
 			idx := h*numPatchesW + w
 			// For each position, store both h and w coordinates twice
-			// This matches the pattern seen in the C++ implementation
 			positions[idx*4] = int32(h)   // y coordinate
 			positions[idx*4+1] = int32(w) // x coordinate
 			positions[idx*4+2] = int32(h) // y coordinate (repeated)

model/models/qwen25vl/process_image.go

@@ -77,7 +77,13 @@ func (p *ImageProcessor) SmartResize(height, width int) (int, int) {
 	return hBar, wBar
 }
 
-func (p *ImageProcessor) ProcessImage(img image.Image) ([]float32, int, int, int, error) {
+type Grid struct {
+	Height   int
+	Width    int
+	Temporal int
+}
+
+func (p *ImageProcessor) ProcessImage(img image.Image) ([]float32, *Grid, error) {
 	origWidth := img.Bounds().Dx()
 	origHeight := img.Bounds().Dy()
 
@@ -96,27 +102,29 @@ func (p *ImageProcessor) ProcessImage(img image.Image) ([]float32, int, int, int
 	)
 
 	// Calculate grid dimensions
-	gridH := resizedHeight / p.patchSize
+	grid := &Grid{
-	gridW := resizedWidth / p.patchSize
+		Height:   resizedHeight / p.patchSize,
-	gridT := 1 // For single images, temporal dimension is 1
+		Width:    resizedWidth / p.patchSize,
+		Temporal: 1, // For single images, temporal dimension is 1
+	}
 
-	patches, err := p.createPatches(normalizedPixels, resizedHeight, resizedWidth, gridH, gridW, gridT)
+	patches, err := p.createPatches(normalizedPixels, resizedHeight, resizedWidth, grid)
 	if err != nil {
-		return nil, 0, 0, 0, fmt.Errorf("failed to create patches: %v", err)
+		return nil, nil, fmt.Errorf("failed to create patches: %v", err)
 	}
 
 	// Return patches and grid dimensions
-	return patches, gridT, gridH, gridW, nil
+	return patches, grid, nil
 }
 
-func (p *ImageProcessor) createPatches(pixels []float32, height, width, gridH, gridW, gridT int) ([]float32, error) {
+func (p *ImageProcessor) createPatches(pixels []float32, height, width int, grid *Grid) ([]float32, error) {
 	channels := p.numChannels
 	patchSize := p.patchSize
 	mergeSize := p.mergeSize
 	temporalPatchSize := p.temporalPatchSize
 
 	// Calculate output dimensions
-	numPatches := gridT * gridH * gridW
+	numPatches := grid.Temporal * grid.Height * grid.Width
 	patchDim := channels * temporalPatchSize * patchSize * patchSize
 
 	// Create output tensor
@@ -126,10 +134,10 @@ func (p *ImageProcessor) createPatches(pixels []float32, height, width, gridH, g
 	// in the format expected by the forward pass
 	patchIndex := 0
 
-	for t := 0; t < gridT; t++ {
+	for t := 0; t < grid.Temporal; t++ {
 		// For each patch in the grid
-		for h := 0; h < gridH; h += mergeSize {
+		for h := 0; h < grid.Height; h += mergeSize {
-			for w := 0; w < gridW; w += mergeSize {
+			for w := 0; w < grid.Width; w += mergeSize {
 				// Handle the 2x2 merged patches
 				for mh := 0; mh < mergeSize; mh++ {
 					for mw := 0; mw < mergeSize; mw++ {