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llama.cpp
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reverting automatically removed spaces

This commit is contained in:
Saba Fallah 2025-12-04 16:12:41 +01:00
parent c73748ab5d
commit a661c52990
1 changed files with 18 additions and 18 deletions
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36
tools/mtmd/clip.cpp
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@ -664,13 +664,13 @@ struct clip_graph {
ggml_tensor * inp_raw = build_inp_raw();
ggml_tensor * sam_out = build_sam(inp_raw);
ggml_tensor * clip_out = build_dsocr_clip(sam_out);
int clip_n_patches = sam_out->ne[0] * sam_out->ne[1];
sam_out = ggml_cont(ctx0, ggml_permute(ctx0, sam_out, 1, 2, 0, 3));
sam_out = ggml_reshape_2d(ctx0, sam_out, sam_out->ne[0], clip_n_patches);
clip_out = ggml_view_2d(ctx0, clip_out, n_embd, clip_n_patches, clip_out->nb[1], clip_out->nb[1]);
ggml_tensor * cur;
cur = ggml_concat(ctx0, clip_out, sam_out, 0);
cur = ggml_reshape_2d(ctx0, cur, 2*n_embd,clip_n_patches);
@ -2703,11 +2703,11 @@ private:
const int d_heads = n_embd / n_heads;
ggml_tensor * inpL;
inpL = ggml_conv_2d_sk_p0(ctx0, model.patch_embed_proj_w, inp_raw);
inpL = ggml_add(ctx0, inpL, ggml_reshape_3d(ctx0, model.patch_embed_proj_b, 1, 1, n_embd));
inpL = ggml_cont(ctx0, ggml_permute(ctx0, inpL, 1, 2, 0, 3));
ggml_tensor * cur;
const auto tgt_size = inpL->ne[1];
const auto str_size = model.pos_embed->ne[1];
@ -2752,7 +2752,7 @@ private:
// self-attention
{
const int B = cur->ne[3];
cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
cur = ggml_add(ctx0, cur, layer.qkv_b);
cur = ggml_cont(ctx0, cur); // Ensure tensor is contiguous before reshape
@ -2832,7 +2832,7 @@ private:
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 1, 2, 0, 3));
cur = build_norm(cur, model.neck_1_w, model.neck_1_b, NORM_TYPE_NORMAL, hparams.eps, -1);
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 2, 0, 1, 3));
cur = ggml_conv_2d(ctx0, model.neck_2_w, cur, 1, 1, 1, 1, 1, 1);
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 1, 2, 0, 3));
cur = build_norm(cur, model.neck_3_w, model.neck_3_b, NORM_TYPE_NORMAL, hparams.eps, -1);
@ -2862,7 +2862,7 @@ private:
if (tgt_size != src_size) {
ggml_tensor * old_pos_embd;
ggml_tensor * cls_tok;
old_pos_embd = ggml_view_2d(
ctx0, new_pos_embd,
new_pos_embd->ne[0], src_size * src_size,
@ -2891,7 +2891,7 @@ private:
ggml_tensor * positions = ggml_cast(ctx0, ggml_arange(ctx0, 0, n_pos, 1), GGML_TYPE_I32);
ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, new_pos_embd, positions);
ggml_tensor * cur = build_vit(inp, n_pos, NORM_TYPE_NORMAL, ffn_op_type::FFN_GELU_QUICK,
ggml_tensor * cur = build_vit(inp, n_pos, NORM_TYPE_NORMAL, ffn_op_type::FFN_GELU_QUICK,
learned_pos_embd, nullptr); // shape [1024, 16, 16]
ggml_build_forward_expand(gf, cur);
@ -5167,11 +5167,11 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
const int orig_h = original_size.height;
const int orig_area = orig_h * orig_w;
std::array<uint8_t, 3u> color;
for (int i = 0; i < 3; i++) {
color[i] = (int)(255 * params.image_mean[i]);
}
int mode_i = 0;
int min_diff = orig_area;
@ -5186,7 +5186,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
if (mode_i < 2) {
/* Native Resolution (Tiny/Small) */
const int image_size = native_resolutions[mode_i];
// Just resize the image to image_size × image_size
clip_image_u8_ptr resized_img(clip_image_u8_init());
img_tool::resize(*img, *resized_img,
@ -5203,7 +5203,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
else if (mode_i < 4) {
/* Native Resolution (Base/Large) */
const int image_size = native_resolutions[mode_i];
// Resize maintaining aspect ratio, then pad to square
float scale = std::min(
static_cast<float>(image_size) / orig_w,
@ -5260,7 +5260,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
else {
GGML_ABORT("DeepSeek-OCR hasn't supported Gundam/Gundam-Master yet");
/* Dynamic Resolution (Gundam/Gundam-Master) */
// configurable, or read from params
const int min_num = 2;
const int max_num = 9;
@ -5269,10 +5269,10 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
// original image size
const int orig_w = original_size.width;
const int orig_h = original_size.height;
// create overview image (thumbnail)
clip_image_u8_ptr overview_img(clip_image_u8_init());
img_tool::resize(*img, *overview_img, { image_size, image_size },
img_tool::resize(*img, *overview_img, { image_size, image_size },
img_tool::RESIZE_ALGO_BICUBIC_PILLOW, true, color);
clip_image_f32_ptr overview_f32(clip_image_f32_init());
normalize_image_u8_to_f32(*overview_img, *overview_f32, params.image_mean, params.image_std);
@ -5280,7 +5280,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
// build candidate grids (cols, rows)
auto target_ratios = ds_build_target_ratios(min_num, max_num);
// pick the grid that best matches the original aspect ratio
const float aspect_ratio = static_cast<float>(orig_w) / static_cast<float>(orig_h);
auto best = ds_find_closest_ratio(aspect_ratio, target_ratios, orig_w, orig_h, image_size);
@ -5289,7 +5289,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
// resize to refined size (no padding, direct resize)
clip_image_u8_ptr refined_img(clip_image_u8_init());
img_tool::resize(*img, *refined_img, { image_size * grid_cols, image_size * grid_rows },
img_tool::resize(*img, *refined_img, { image_size * grid_cols, image_size * grid_rows },
img_tool::RESIZE_ALGO_BICUBIC_PILLOW, false);
// crop slices from the refined image