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llama.cpp
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corrected combining of image encoders' results

This commit is contained in:
Saba Fallah 2025-11-18 05:59:37 +01:00
parent 1e08157134
commit 331cea8f8e
3 changed files with 20 additions and 8 deletions
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4
gguf-py/gguf/constants.py
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@ -1064,8 +1064,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.V_SAM_NECK: "v.sam.neck.{bid}",
MODEL_TENSOR.V_SAM_NET_2: "v.sam.net_2",
MODEL_TENSOR.V_SAM_NET_3: "v.sam.net_3",
MODEL_TENSOR.V_ENC_EMBD_IMGNL: "v.image_newline_embd", # Deepseek-OCR
MODEL_TENSOR.V_ENC_EMBD_VSEP: "v.view_separator_embd", # Deepseek-OCR
MODEL_TENSOR.V_ENC_EMBD_IMGNL: "model.image_newline", # Deepseek-OCR
MODEL_TENSOR.V_ENC_EMBD_VSEP: "model.view_seperator", # Deepseek-OCR
# audio (mtmd)
MODEL_TENSOR.A_ENC_EMBD_POS: "a.position_embd",
MODEL_TENSOR.A_ENC_CONV1D: "a.conv1d.{bid}",

1
tools/mtmd/clip-impl.h
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@ -86,6 +86,7 @@
#define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
#define TN_MVLM_PROJ_PEG "mm.model.peg.%d.%s"
#define TN_IMAGE_NEWLINE "model.image_newline"
#define TN_IMAGE_SEPERATOR "model.view_seperator"
#define TN_MM_INP_NORM "mm.input_norm.weight"
#define TN_MM_INP_NORM_B "mm.input_norm.bias"
#define TN_MM_INP_PROJ "mm.input_projection.weight" // gemma3

21
tools/mtmd/clip.cpp
View File

@ -835,7 +835,15 @@ struct clip_graph {
ggml_tensor * global_features_2 = build_dp_ocr_clip(inp_raw, global_features_1);
// torch global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1)
ggml_tensor * global_features = ggml_concat(ctx0, global_features_1, global_features_2, 0);
global_features_1 = ggml_permute(ctx0, global_features_1,2,1,0,3);
global_features_1 = ggml_cont(ctx0, global_features_1);
global_features_1 = ggml_reshape_2d(ctx0, global_features_1, n_embd, n_patches);
// remove CLS token
global_features_2 = ggml_view_2d(ctx0, global_features_2,
n_embd, n_patches,
ggml_row_size(global_features_2->type, n_embd), 0);
ggml_tensor * global_features = ggml_concat(ctx0, global_features_2, global_features_1, 1);
global_features = build_global_local_features(
ctx0,
global_features,
@ -843,6 +851,7 @@ struct clip_graph {
n_patches_x,
n_embd
);
ggml_build_forward_expand(gf, global_features);
return gf;
}
@ -858,8 +867,8 @@ struct clip_graph {
int n_dim) {
GGML_ASSERT(model.image_newline != nullptr);
GGML_ASSERT(model.view_seperator != nullptr);
GGML_ASSERT(global_features->ne[0] == (int64_t) n_dim);
GGML_ASSERT(global_features->ne[1] == (int64_t) (h * w));
GGML_ASSERT(global_features->ne[0] == static_cast<int64_t>(n_dim));
GGML_ASSERT(global_features->ne[1] == static_cast<int64_t>(2 * (h * w)));
// 1) global_features: [n_dim, h*w] -> [n_dim, w, h] -> [h, w, n_dim]
ggml_tensor * t = ggml_reshape_3d(ctx0, global_features, n_dim, w, h); // (n_dim, w, h)
@ -1552,8 +1561,7 @@ struct clip_graph {
// for selecting learned pos embd, used by ViT
struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
ggml_set_name(positions, "positions");
ggml_set_input(positions);
cb(positions, "positions", -1);
ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions);
@ -3607,6 +3615,9 @@ struct clip_model_loader {
model.net_2 = get_tensor(string_format(TN_SAM_NET, 2, "weight"));
model.net_3 = get_tensor(string_format(TN_SAM_NET, 3, "weight"));
}
model.image_newline = get_tensor(TN_IMAGE_NEWLINE, false);
model.view_seperator = get_tensor(TN_IMAGE_SEPERATOR, false);
break;
default:
GGML_ASSERT(false && "unknown projector type");