use 2d pooling

convert/convert_gemma3.go

@@ -26,15 +26,16 @@ type gemma3Model struct {
 		NumChannels       uint32  `json:"num_channels"`        // num_channels 3
 		PatchSize         uint32  `json:"patch_size"`          // patch_size 14
 	} `json:"vision_config"`
-	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
-	NumAttentionHeads     uint32  `json:"num_attention_heads"`
-	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
-	RMSNormEPS            float32 `json:"rms_norm_eps"`
-	HeadDim               uint32  `json:"head_dim"`
-	FinalLogitSoftcap     float32 `json:"final_logit_softcapping"`
-	RopeLocalTheta        float32 `json:"rope_local_base_freq"`
-	RopeGlobalTheta       float32 `json:"rope_global_base_freq"`
-	SlidingWindow         uint32  `json:"sliding_window"`
+	MaxPositionEmbeddings    uint32  `json:"max_position_embeddings"`
+	NumAttentionHeads        uint32  `json:"num_attention_heads"`
+	NumKeyValueHeads         uint32  `json:"num_key_value_heads"`
+	RMSNormEPS               float32 `json:"rms_norm_eps"`
+	HeadDim                  uint32  `json:"head_dim"`
+	FinalLogitSoftcap        float32 `json:"final_logit_softcapping"`
+	RopeLocalTheta           float32 `json:"rope_local_base_freq"`
+	RopeGlobalTheta          float32 `json:"rope_global_base_freq"`
+	SlidingWindow            uint32  `json:"sliding_window"`
+	MultiModalTokensPerImage uint32  `json:"mm_tokens_per_image"`
 }
 
 const (
@@ -102,6 +103,10 @@ func (p *gemma3Model) KV(t *Tokenizer) ggml.KV {
 		kv["gemma3.attention.value_length"] = cmp.Or(p.TextModel.HeadDim, 256)
 	}
 
+	if p.MultiModalTokensPerImage > 0 {
+		kv["gemma3.mm.tokens_per_image"] = p.MultiModalTokensPerImage
+	}
+
 	return kv
 }
 

ml/backend.go

@@ -135,7 +135,7 @@ type Tensor interface {
 	RMSNorm(ctx Context, weight Tensor, eps float32) Tensor
 	Scale(ctx Context, s float64) Tensor
 
-	AvgPool1D(ctx Context, k, s, p int) Tensor
+	AvgPool2D(ctx Context, k, s int, p float32) Tensor
 	Conv2D(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
 
 	RoPE(ctx Context, positionIDs, ropeFactors Tensor, dim, ropeType uint32, base, scale float32) Tensor

ml/backend/ggml/ggml.go

@@ -247,7 +247,7 @@ func New(r *os.File, params ml.BackendParams) (ml.Backend, error) {
 			createTensor(tensor{source: t}, output.bts)
 		case strings.HasPrefix(t.Name, "v.") || strings.HasPrefix(t.Name, "mm."):
 			// TODO: assign vision tensors to the gpu if possible
-			createTensor(tensor{source: t}, input.bts)
+			createTensor(tensor{source: t}, output.bts)
 		case contains(t.Name, "rope_freqs", "rope_factors_long", "rope_factors_short"):
 			// these tensors should be repeated per layer
 			for i, layer := range layers {
@@ -952,10 +952,10 @@ func (t *Tensor) Conv2D(ctx ml.Context, t2 ml.Tensor, s0, s1, p0, p1, d0, d1 int
 	}
 }
 
-func (t *Tensor) AvgPool1D(ctx ml.Context, k, s, p int) ml.Tensor {
+func (t *Tensor) AvgPool2D(ctx ml.Context, k, s int, p float32) ml.Tensor {
 	return &Tensor{
 		b: t.b,
-		t: C.ggml_pool_1d(ctx.(*Context).ctx, t.t, C.GGML_OP_POOL_AVG, C.int(k), C.int(s), C.int(p)),
+		t: C.ggml_pool_2d(ctx.(*Context).ctx, t.t, C.GGML_OP_POOL_AVG, C.int(k), C.int(k), C.int(s), C.int(s), C.float(p), C.float(p)),
 	}
 }
 

model/models/gemma3/model.go

@@ -5,6 +5,7 @@ import (
 	"encoding/binary"
 	"hash/fnv"
 	"image"
+	"math"
 
 	"github.com/ollama/ollama/kvcache"
 	"github.com/ollama/ollama/ml"
@@ -30,9 +31,21 @@ var _ model.MultimodalProcessor = (*Model)(nil)
 type MultiModalProjector struct {
 	SoftEmbNorm     *nn.RMSNorm `gguf:"mm_soft_emb_norm"`
 	InputProjection *nn.Linear  `gguf:"mm_input_projection"`
+
+	tokensPerImage int
 }
 
-func (p *MultiModalProjector) Forward(ctx ml.Context, visionOutputs ml.Tensor, eps float32) ml.Tensor {
+func (p *MultiModalProjector) Forward(ctx ml.Context, visionOutputs ml.Tensor, imageSize, patchSize int, eps float32) ml.Tensor {
+	l := visionOutputs.Dim(0)
+
+	visionOutputs = visionOutputs.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
+	patchesPerImage := imageSize / patchSize
+	visionOutputs = visionOutputs.Reshape(ctx, patchesPerImage, patchesPerImage, l)
+
+	kernelSize := patchesPerImage / int(math.Sqrt(float64(p.tokensPerImage)))
+	visionOutputs = visionOutputs.AvgPool2D(ctx, kernelSize, kernelSize, 0)
+	visionOutputs = visionOutputs.Reshape(ctx, visionOutputs.Dim(0)*visionOutputs.Dim(1), l)
+	visionOutputs = visionOutputs.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
 	visionOutputs = p.SoftEmbNorm.Forward(ctx, visionOutputs, eps)
 
 	// TODO: inputProjection must be transposed since they're incompatible with visionOutputs
@@ -59,6 +72,9 @@ func New(c ml.Config) (model.Model, error) {
 		ImageProcessor: newImageProcessor(c),
 		VisionModel:    newVisionModel(c),
 		TextModel:      newTextModel(c),
+		MultiModalProjector: &MultiModalProjector{
+			tokensPerImage: int(c.Uint("mm_tokens_per_image", 256)),
+		},
 	}
 
 	slidingWindowLen := int32(c.Uint("attention.sliding_window"))
@@ -88,17 +104,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, er
 	}
 
 	visionOutputs := m.VisionModel.Forward(ctx, pixelValues)
-	visionOutputs = visionOutputs.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-	patchesPerImage := m.ImageProcessor.imageSize / m.ImageProcessor.patchSize
-
-	// TODO (jmorganca): read this from the model config
-	// it should instead be math.Sqrt(tokens per image)
-	tokensPerSide := 8
-	kernelSize := patchesPerImage / tokensPerSide
-	visionOutputs = visionOutputs.AvgPool1D(ctx, kernelSize, kernelSize, 0)
-
-	visionOutputs = visionOutputs.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-	visionOutputs = m.MultiModalProjector.Forward(ctx, visionOutputs, m.VisionModel.eps)
+	visionOutputs = m.MultiModalProjector.Forward(ctx, visionOutputs, m.imageSize, m.patchSize, m.VisionModel.eps)
 	return visionOutputs, nil
 }