ollama/convert/convert_qwen3vl.go

117 lines
3.4 KiB
Go

package convert
import (
"cmp"
"encoding/json"
"io/fs"
"slices"
"strings"
"github.com/ollama/ollama/fs/ggml"
)
type qwen3VLModel struct {
qwen3Model `json:"text_config"`
VisionModel struct {
Depth uint32 `json:"depth"`
HiddenSize uint32 `json:"hidden_size"`
NumHeads uint32 `json:"num_heads"`
InChannels uint32 `json:"in_channels"`
PatchSize uint32 `json:"patch_size"`
SpatialMergeSize uint32 `json:"spatial_merge_size"`
WindowSize uint32 `json:"window_size"`
RMSNormEps float32 `json:"layer_norm_epsilon"`
RopeTheta float32 `json:"rope_theta"`
TemporalPatchSize uint32 `json:"temporal_patch_size"`
DeepstackVisualIndexes []int32 `json:"deepstack_visual_indexes"`
Size struct {
ShortestEdge uint32 `json:"shortest_edge"`
LongestEdge uint32 `json:"longest_edge"`
} `json:"size"`
ImageMean []float32 `json:"image_mean"`
ImageStd []float32 `json:"image_std"`
} `json:"vision_config"`
}
func (m *qwen3VLModel) parseMore(fsys fs.FS) error {
bts, err := fs.ReadFile(fsys, "preprocessor_config.json")
if err != nil {
return err
}
return json.Unmarshal(bts, &m.VisionModel)
}
func (m *qwen3VLModel) KV(t *Tokenizer) ggml.KV {
kv := m.qwen3Model.KV(t)
arch := "qwen3vl"
if m.NumExperts > 0 {
arch += "moe"
}
// override architecture
kv["general.architecture"] = arch
kv["vision.block_count"] = cmp.Or(m.VisionModel.Depth, 32)
kv["vision.embedding_length"] = m.VisionModel.HiddenSize
kv["vision.attention.head_count"] = cmp.Or(m.VisionModel.NumHeads, 16)
kv["vision.num_channels"] = m.VisionModel.InChannels
kv["vision.patch_size"] = cmp.Or(m.VisionModel.PatchSize, 14)
kv["vision.spatial_merge_size"] = cmp.Or(m.VisionModel.SpatialMergeSize, 2)
kv["vision.attention.layer_norm_epsilon"] = cmp.Or(m.VisionModel.RMSNormEps, 1e-6)
kv["vision.rope.freq_base"] = cmp.Or(m.VisionModel.RopeTheta, 1e4)
kv["vision.temporal_patch_size"] = cmp.Or(m.VisionModel.TemporalPatchSize, 2)
kv["vision.deepstack_visual_indexes"] = m.VisionModel.DeepstackVisualIndexes
kv["vision.shortest_edge"] = m.VisionModel.Size.ShortestEdge
kv["vision.longest_edge"] = m.VisionModel.Size.LongestEdge
kv["vision.image_mean"] = m.VisionModel.ImageMean
kv["vision.image_std"] = m.VisionModel.ImageStd
return kv
}
func (m *qwen3VLModel) Tensors(ts []Tensor) []*ggml.Tensor {
var rest []Tensor
var out []*ggml.Tensor
for _, t := range ts {
switch {
case strings.Contains(t.Name(), "attn_qkv"):
out = append(out, slices.Collect(splitDim(t, 0,
split{Replacer: strings.NewReplacer("attn_qkv", "attn_q")},
split{Replacer: strings.NewReplacer("attn_qkv", "attn_k")},
split{Replacer: strings.NewReplacer("attn_qkv", "attn_v")},
))...)
case strings.Contains(t.Name(), "patch_embed") && strings.HasSuffix(t.Name(), "weight"):
shape := t.Shape()
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: append([]uint64{shape[0] * shape[1]}, shape[2:]...),
WriterTo: t,
})
default:
rest = append(rest, t)
}
}
return append(m.qwen3Model.Tensors(rest), out...)
}
func (m *qwen3VLModel) Replacements() []string {
return append(
m.qwen3Model.Replacements(),
"model.language_", "",
"model.visual", "v",
"patch_embed.proj", "patch_embed",
"blocks", "blk",
"attn.qkv", "attn_qkv",
"attn.proj", "attn_out",
"deepstack_merger_list", "deepstack_merger",
)
}