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llama.cpp
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minor editorconfig-check fixes

This commit is contained in:
Saba Fallah 2025-12-05 13:18:15 +01:00
parent 1c88647ec6
commit d981f19e9d
2 changed files with 37 additions and 35 deletions
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10
tools/mtmd/clip-impl.h
View File

@ -457,7 +457,7 @@ static std::string to_ne_string(const ggml_tensor * t) {
static void print_tensor_info(ggml_tensor * t) { static void print_tensor_info(ggml_tensor * t) {
const struct ggml_tensor * src0 = t->src[0]; const struct ggml_tensor * src0 = t->src[0];
const struct ggml_tensor * src1 = t->src[1]; const struct ggml_tensor * src1 = t->src[1];
char src1_str[128] = {0}; char src1_str[128] = {0};
if (src1) { if (src1) {
snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, to_ne_string(src1).c_str()); snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, to_ne_string(src1).c_str());
@ -643,7 +643,7 @@ static void save_tensor_to_file(const struct ggml_tensor * tensor, const uint8_t
append_str(" ["); append_str(" [");
for (int64_t i0 = 0; i0 < ne[0]; i0++) { for (int64_t i0 = 0; i0 < ne[0]; i0++) {
size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0]; size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
float v; float v;
if (type == GGML_TYPE_F16) { if (type == GGML_TYPE_F16) {
v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]); v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]);
} else if (type == GGML_TYPE_F32) { } else if (type == GGML_TYPE_F32) {
@ -659,13 +659,15 @@ static void save_tensor_to_file(const struct ggml_tensor * tensor, const uint8_t
} }
int len = snprintf(num_buf, sizeof(num_buf), "%8.4f", v); int len = snprintf(num_buf, sizeof(num_buf), "%8.4f", v);
append(num_buf, len); append(num_buf, len);
if (i0 < ne[0] - 1) append_str(", "); if (i0 < ne[0] - 1) {
append_str(", ");
}
} }
append_str("],\n"); append_str("],\n");
} }
append_str(" ],\n"); append_str(" ],\n");
} }
append_str(" ]"); // End of batch append_str(" ]"); // End of batch
if (i3 < ne[3] - 1) { if (i3 < ne[3] - 1) {
append_str(",\n"); // Comma between batches append_str(",\n"); // Comma between batches
} else { } else {

62
tools/mtmd/clip.cpp
View File

@ -662,19 +662,19 @@ struct clip_graph {
ggml_cgraph * build_deepseek_ocr() { ggml_cgraph * build_deepseek_ocr() {
//patch embedding //patch embedding
ggml_tensor * inp_raw = build_inp_raw(); ggml_tensor * inp_raw = build_inp_raw();
ggml_tensor * sam_out = build_sam(inp_raw); ggml_tensor * sam_out = build_sam(inp_raw);
ggml_tensor * clip_out = build_dsocr_clip(sam_out); ggml_tensor * clip_out = build_dsocr_clip(sam_out);
int clip_n_patches = sam_out->ne[0] * sam_out->ne[1]; 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_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); 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]); clip_out = ggml_view_2d(ctx0, clip_out, n_embd, clip_n_patches, clip_out->nb[1], clip_out->nb[1]);
ggml_tensor * cur; ggml_tensor * cur;
cur = ggml_concat(ctx0, clip_out, sam_out, 0); cur = ggml_concat(ctx0, clip_out, sam_out, 0);
cur = ggml_reshape_2d(ctx0, cur, 2*n_embd,clip_n_patches); cur = ggml_reshape_2d(ctx0, cur, 2 * n_embd, clip_n_patches);
cur = ggml_cont(ctx0, cur); cur = ggml_cont(ctx0, cur);
cur = ggml_mul_mat(ctx0, model.fc_w, cur); cur = ggml_mul_mat(ctx0, model.fc_w, cur);
cur = ggml_add(ctx0, cur, model.fc_b); cur = ggml_add(ctx0, cur, model.fc_b);
@ -687,10 +687,10 @@ struct clip_graph {
ggml_tensor * vs; ggml_tensor * vs;
imgnl = ggml_repeat_4d(ctx0, model.image_newline, n_dim, 1, h, 1); imgnl = ggml_repeat_4d(ctx0, model.image_newline, n_dim, 1, h, 1);
vs = ggml_reshape_2d(ctx0, model.view_seperator, n_dim, 1); // (n_dim, 1) vs = ggml_reshape_2d(ctx0, model.view_seperator, n_dim, 1); // (n_dim, 1)
cur = ggml_reshape_3d(ctx0, cur, n_dim, w, h); cur = ggml_reshape_3d(ctx0, cur, n_dim, w, h);
cur = ggml_reshape_2d(ctx0, ggml_concat(ctx0, cur, imgnl, 1), n_dim, (w+1)*h); cur = ggml_reshape_2d(ctx0, ggml_concat(ctx0, cur, imgnl, 1), n_dim, (w + 1) * h);
cur = ggml_concat(ctx0, cur, vs, 1); // (n_dim, h*(w+1) + 1) cur = ggml_concat(ctx0, cur, vs, 1); // (n_dim, h*(w+1) + 1)
cb(cur, "dsocr_output", -1); cb(cur, "dsocr_output", -1);
@ -2156,7 +2156,7 @@ private:
ggml_tensor * Qcur; ggml_tensor * Qcur;
ggml_tensor * Kcur; ggml_tensor * Kcur;
ggml_tensor * Vcur; ggml_tensor * Vcur;
if (layer.qkv_w) { if (layer.qkv_w) {
ggml_tensor * QKV; ggml_tensor * QKV;
@ -2181,12 +2181,12 @@ private:
if (layer.q_b) { if (layer.q_b) {
Qcur = ggml_add(ctx0, Qcur, layer.q_b); Qcur = ggml_add(ctx0, Qcur, layer.q_b);
} }
Kcur = ggml_mul_mat(ctx0, layer.k_w, cur); Kcur = ggml_mul_mat(ctx0, layer.k_w, cur);
if (layer.k_b) { if (layer.k_b) {
Kcur = ggml_add(ctx0, Kcur, layer.k_b); Kcur = ggml_add(ctx0, Kcur, layer.k_b);
} }
Vcur = ggml_mul_mat(ctx0, layer.v_w, cur); Vcur = ggml_mul_mat(ctx0, layer.v_w, cur);
if (layer.v_b) { if (layer.v_b) {
Vcur = ggml_add(ctx0, Vcur, layer.v_b); Vcur = ggml_add(ctx0, Vcur, layer.v_b);
@ -2591,7 +2591,7 @@ private:
} else { } else {
ggml_tensor * v = ggml_permute(ctx0, v_cur, 1, 2, 0, 3); ggml_tensor * v = ggml_permute(ctx0, v_cur, 1, 2, 0, 3);
v = ggml_cont(ctx0, v); v = ggml_cont(ctx0, v);
ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
// F32 may not needed for vision encoders? // F32 may not needed for vision encoders?
// ggml_mul_mat_set_prec(kq, GGML_PREC_F32); // ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
@ -2727,11 +2727,11 @@ private:
const int d_heads = n_embd / n_heads; const int d_heads = n_embd / n_heads;
ggml_tensor * inpL; ggml_tensor * inpL;
inpL = ggml_conv_2d_sk_p0(ctx0, model.patch_embed_proj_w, inp_raw); 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_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)); inpL = ggml_cont(ctx0, ggml_permute(ctx0, inpL, 1, 2, 0, 3));
ggml_tensor * cur; ggml_tensor * cur;
const auto tgt_size = inpL->ne[1]; const auto tgt_size = inpL->ne[1];
const auto str_size = model.pos_embed->ne[1]; const auto str_size = model.pos_embed->ne[1];
@ -2776,7 +2776,7 @@ private:
// self-attention // self-attention
{ {
const int B = cur->ne[3]; const int B = cur->ne[3];
cur = ggml_mul_mat(ctx0, layer.qkv_w, cur); cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
cur = ggml_add(ctx0, cur, layer.qkv_b); cur = ggml_add(ctx0, cur, layer.qkv_b);
cur = ggml_cont(ctx0, cur); // Ensure tensor is contiguous before reshape cur = ggml_cont(ctx0, cur); // Ensure tensor is contiguous before reshape
@ -2853,7 +2853,7 @@ private:
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 1, 2, 0, 3)); 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 = 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_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_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 = 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); cur = build_norm(cur, model.neck_3_w, model.neck_3_b, NORM_TYPE_NORMAL, hparams.eps, -1);
@ -2883,7 +2883,7 @@ private:
if (tgt_size != src_size) { if (tgt_size != src_size) {
ggml_tensor * old_pos_embd; ggml_tensor * old_pos_embd;
ggml_tensor * cls_tok; ggml_tensor * cls_tok;
old_pos_embd = ggml_view_2d( old_pos_embd = ggml_view_2d(
ctx0, new_pos_embd, ctx0, new_pos_embd,
new_pos_embd->ne[0], src_size * src_size, new_pos_embd->ne[0], src_size * src_size,
@ -2912,7 +2912,7 @@ private:
ggml_tensor * positions = ggml_cast(ctx0, ggml_arange(ctx0, 0, n_pos, 1), GGML_TYPE_I32); 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 * 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] learned_pos_embd, nullptr); // shape [1024, 16, 16]
ggml_build_forward_expand(gf, cur); ggml_build_forward_expand(gf, cur);
@ -5193,11 +5193,11 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
const int orig_h = original_size.height; const int orig_h = original_size.height;
const int orig_area = orig_h * orig_w; const int orig_area = orig_h * orig_w;
std::array<uint8_t, 3u> color; std::array<uint8_t, 3u> color;
for (int i = 0; i < 3; i++) { for (int i = 0; i < 3; i++) {
color[i] = (int)(255 * params.image_mean[i]); color[i] = (int)(255 * params.image_mean[i]);
} }
int mode_i = 0; int mode_i = 0;
int min_diff = orig_area; int min_diff = orig_area;
@ -5212,7 +5212,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
if (mode_i < 2) { if (mode_i < 2) {
/* Native Resolution (Tiny/Small) */ /* Native Resolution (Tiny/Small) */
const int image_size = native_resolutions[mode_i]; const int image_size = native_resolutions[mode_i];
// Just resize the image to image_size × image_size // Just resize the image to image_size × image_size
clip_image_u8_ptr resized_img(clip_image_u8_init()); clip_image_u8_ptr resized_img(clip_image_u8_init());
img_tool::resize(*img, *resized_img, img_tool::resize(*img, *resized_img,
@ -5229,7 +5229,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
else if (mode_i < 4) { else if (mode_i < 4) {
/* Native Resolution (Base/Large) */ /* Native Resolution (Base/Large) */
const int image_size = native_resolutions[mode_i]; const int image_size = native_resolutions[mode_i];
// Resize maintaining aspect ratio, then pad to square // Resize maintaining aspect ratio, then pad to square
float scale = std::min( float scale = std::min(
static_cast<float>(image_size) / orig_w, static_cast<float>(image_size) / orig_w,
@ -5286,7 +5286,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
else { else {
GGML_ABORT("DeepSeek-OCR hasn't supported Gundam/Gundam-Master yet"); GGML_ABORT("DeepSeek-OCR hasn't supported Gundam/Gundam-Master yet");
/* Dynamic Resolution (Gundam/Gundam-Master) */ /* Dynamic Resolution (Gundam/Gundam-Master) */
// configurable, or read from params // configurable, or read from params
const int min_num = 2; const int min_num = 2;
const int max_num = 9; const int max_num = 9;
@ -5295,10 +5295,10 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
// original image size // original image size
const int orig_w = original_size.width; const int orig_w = original_size.width;
const int orig_h = original_size.height; const int orig_h = original_size.height;
// create overview image (thumbnail) // create overview image (thumbnail)
clip_image_u8_ptr overview_img(clip_image_u8_init()); 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); img_tool::RESIZE_ALGO_BICUBIC_PILLOW, true, color);
clip_image_f32_ptr overview_f32(clip_image_f32_init()); 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); normalize_image_u8_to_f32(*overview_img, *overview_f32, params.image_mean, params.image_std);
@ -5306,7 +5306,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
// build candidate grids (cols, rows) // build candidate grids (cols, rows)
auto target_ratios = ds_build_target_ratios(min_num, max_num); auto target_ratios = ds_build_target_ratios(min_num, max_num);
// pick the grid that best matches the original aspect ratio // pick the grid that best matches the original aspect ratio
const float aspect_ratio = static_cast<float>(orig_w) / static_cast<float>(orig_h); 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); auto best = ds_find_closest_ratio(aspect_ratio, target_ratios, orig_w, orig_h, image_size);
@ -5315,7 +5315,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
// resize to refined size (no padding, direct resize) // resize to refined size (no padding, direct resize)
clip_image_u8_ptr refined_img(clip_image_u8_init()); 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); img_tool::RESIZE_ALGO_BICUBIC_PILLOW, false);
// crop slices from the refined image // crop slices from the refined image