std::vector<int32_t> image_grid_pinpoints;
int32_t image_crop_resolution;
std::unordered_set<int32_t> vision_feature_layer;
- int32_t attn_window_size;
- int32_t n_wa_pattern;
+ int32_t attn_window_size = 0;
+ int32_t n_wa_pattern = 0;
};
struct clip_layer {
float image_std[3];
bool use_gelu = false;
bool use_silu = false;
- int32_t ftype = 1;
gguf_context_ptr ctx_gguf;
ggml_context_ptr ctx_data;
const int image_size_width = imgs.entries[0]->nx;
const int image_size_height = imgs.entries[0]->ny;
- const bool use_mrope = ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL;
const bool use_window_attn = hparams.n_wa_pattern > 0;
const int n_wa_pattern = hparams.n_wa_pattern;
const int patches_w = image_size_width / patch_size;
const int patches_h = image_size_height / patch_size;
const int num_positions = num_patches + (model.class_embedding ? 1 : 0);
- const int num_position_ids = use_mrope ? num_positions * 4 : num_positions;
+ const int num_position_ids = num_positions * 4; // m-rope requires 4 dim per position
const int hidden_size = hparams.hidden_size;
const int n_head = hparams.n_head;
const int d_head = hidden_size / n_head;
+ const int n_layer = hparams.n_layer;
const float eps = hparams.eps;
int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
}
// loop over layers
- for (int il = 0; il < ctx->max_feature_layer; il++) {
+ for (int il = 0; il < n_layer; il++) {
struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
// rmsnorm1
if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) {
int pos_w = image_size_width/patch_size;
int pos_h = image_size_height/patch_size;
- if (ctx->minicpmv_version == 2) {
- pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 4096, pos_w * pos_h, 1);
- }
- else if (ctx->minicpmv_version == 3) {
- pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1);
- }
- else if (ctx->minicpmv_version == 4) {
- pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1);
- }
+ int n_output_dim = clip_n_mmproj_embd(ctx);
+ pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_output_dim, pos_w * pos_h, 1);
ggml_set_name(pos_embed, "pos_embed");
ggml_set_input(pos_embed);
}
}
{ // attention
- int hidden_size = 4096;
+ int hidden_size = clip_n_mmproj_embd(ctx);
const int d_head = 128;
int n_head = hidden_size/d_head;
int num_query = 96;
if (ctx->minicpmv_version == 2) {
- hidden_size = 4096;
- n_head = hidden_size/d_head;
num_query = 96;
}
else if (ctx->minicpmv_version == 3) {
- hidden_size = 3584;
- n_head = hidden_size/d_head;
num_query = 64;
}
else if (ctx->minicpmv_version == 4) {
- hidden_size = 3584;
- n_head = hidden_size/d_head;
num_query = 64;
}
LOG_INF("%s: projector: %s\n", __func__, proj_type.c_str());
LOG_INF("%s: has_llava_proj: %d\n", __func__, ctx_clip.has_llava_projector);
LOG_INF("%s: minicpmv_version: %d\n", __func__, ctx_clip.minicpmv_version);
+ LOG_INF("%s: proj_scale_factor: %d\n", __func__, hparams.proj_scale_factor);
+ LOG_INF("%s: n_wa_pattern: %d\n", __func__, hparams.n_wa_pattern);
LOG_INF("%s: model size: %.2f MiB\n", __func__, model_size / 1024.0 / 1024.0);
LOG_INF("%s: metadata size: %.2f MiB\n", __func__, ggml_get_mem_size(ctx_meta.get()) / 1024.0 / 1024.0);
}
const int patch_size = hparams.patch_size;
const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size));
const int num_positions = num_patches + (model.class_embedding ? 1 : 0);
- const int pos_w = ctx->load_image_size.width / patch_size;
+ const int pos_w = ctx->load_image_size.width / patch_size;
const int pos_h = ctx->load_image_size.height / patch_size;
const bool use_window_attn = hparams.n_wa_pattern > 0; // for qwen2.5vl
+ auto get_inp_tensor = [&gf](const char * name) {
+ struct ggml_tensor * inp = ggml_graph_get_tensor(gf, name);
+ if (inp == nullptr) {
+ GGML_ABORT("Failed to get tensor %s", name);
+ }
+ if (!(inp->flags & GGML_TENSOR_FLAG_INPUT)) {
+ GGML_ABORT("Tensor %s is not an input tensor", name);
+ }
+ return inp;
+ };
+
+ auto set_input_f32 = [&get_inp_tensor](const char * name, std::vector<float> & values) {
+ ggml_tensor * cur = get_inp_tensor(name);
+ GGML_ASSERT(cur->type == GGML_TYPE_F32);
+ GGML_ASSERT(ggml_nelements(cur) == (int64_t)values.size());
+ ggml_backend_tensor_set(cur, values.data(), 0, ggml_nbytes(cur));
+ };
+
+ auto set_input_i32 = [&get_inp_tensor](const char * name, std::vector<int32_t> & values) {
+ ggml_tensor * cur = get_inp_tensor(name);
+ GGML_ASSERT(cur->type == GGML_TYPE_I32);
+ GGML_ASSERT(ggml_nelements(cur) == (int64_t)values.size());
+ ggml_backend_tensor_set(cur, values.data(), 0, ggml_nbytes(cur));
+ };
+
+ // set input pixel values
{
- struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw");
- std::vector<float> inp_data(ggml_nelements(inp_raw));
- float * data = inp_data.data();
+ size_t nelem = 0;
+ for (const auto & img : imgs.entries) {
+ nelem += img->nx * img->ny * 3;
+ }
+ std::vector<float> inp_raw(nelem);
// layout of data (note: the channel dim is unrolled to better visualize the layout):
//
const int n = nx * ny;
for (int b = 0; b < batch_size; b++) {
- float * batch_entry = data + b * (3*n);
+ float * batch_entry = inp_raw.data() + b * (3*n);
for (int y = 0; y < ny; y++) {
for (int x = 0; x < nx; x++) {
size_t base_src = 3*(y * nx + x); // idx of the first channel
}
}
}
- ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw));
+ set_input_f32("inp_raw", inp_raw);
}
- if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) {
- {
- // inspired from siglip:
- // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit
- // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit/blob/d66538faeba44480d0bfaa42145eef26f9423199/modeling_siglip.py#L316
- struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
- std::vector<int> pos_data(ggml_nelements(positions));
- int * data = pos_data.data();
- int bucket_coords_h[1024];
- int bucket_coords_w[1024];
- for (int i = 0; i < pos_h; i++){
- bucket_coords_h[i] = std::floor(70.0*i/pos_h);
- }
- for (int i = 0; i < pos_w; i++){
- bucket_coords_w[i] = std::floor(70.0*i/pos_w);
- }
- for (int i = 0, id = 0; i < pos_h; i++){
- for (int j = 0; j < pos_w; j++){
- data[id++] = bucket_coords_h[i]*70 + bucket_coords_w[j];
+ // set input per projector
+ switch (ctx->proj_type) {
+ case PROJECTOR_TYPE_MINICPMV:
+ {
+ // inspired from siglip:
+ // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit
+ // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit/blob/d66538faeba44480d0bfaa42145eef26f9423199/modeling_siglip.py#L316
+ std::vector<int32_t> positions(pos_h * pos_w);
+ int bucket_coords_h[1024];
+ int bucket_coords_w[1024];
+ for (int i = 0; i < pos_h; i++){
+ bucket_coords_h[i] = std::floor(70.0*i/pos_h);
}
- }
- ggml_backend_tensor_set(positions, data, 0, ggml_nbytes(positions));
- }
+ for (int i = 0; i < pos_w; i++){
+ bucket_coords_w[i] = std::floor(70.0*i/pos_w);
+ }
+ for (int i = 0, id = 0; i < pos_h; i++){
+ for (int j = 0; j < pos_w; j++){
+ positions[id++] = bucket_coords_h[i]*70 + bucket_coords_w[j];
+ }
+ }
+ set_input_i32("positions", positions);
- {
- // inspired from resampler of Qwen-VL:
- // -> https://huggingface.co/Qwen/Qwen-VL/tree/main
- // -> https://huggingface.co/Qwen/Qwen-VL/blob/0547ed36a86561e2e42fecec8fd0c4f6953e33c4/visual.py#L23
- struct ggml_tensor * pos_embed = ggml_graph_get_tensor(gf, "pos_embed");
- int embed_dim = 4096;
- if (ctx->minicpmv_version == 2) {
- embed_dim = 4096;
- }
- else if (ctx->minicpmv_version == 3) {
- embed_dim = 3584;
- }
- else if (ctx->minicpmv_version == 4) {
- embed_dim = 3584;
- }
- else {
- GGML_ABORT("Unknown minicpmv version");
- }
+ // inspired from resampler of Qwen-VL:
+ // -> https://huggingface.co/Qwen/Qwen-VL/tree/main
+ // -> https://huggingface.co/Qwen/Qwen-VL/blob/0547ed36a86561e2e42fecec8fd0c4f6953e33c4/visual.py#L23
+ int embed_dim = clip_n_mmproj_embd(ctx);
- // TODO @ngxson : this is very inefficient, can we do this using ggml_sin and ggml_cos?
- auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h));
+ // TODO @ngxson : this is very inefficient, can we do this using ggml_sin and ggml_cos?
+ auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h));
- std::vector<float> pos_data(ggml_nelements(pos_embed));
- float * data = pos_data.data();
- for(int i = 0; i < pos_w * pos_h; ++i){
- for(int j = 0; j < embed_dim; ++j){
- data[i * embed_dim + j] = pos_embed_t[i][j];
+ std::vector<float> pos_embed(embed_dim * pos_w * pos_h);
+ for(int i = 0; i < pos_w * pos_h; ++i){
+ for(int j = 0; j < embed_dim; ++j){
+ pos_embed[i * embed_dim + j] = pos_embed_t[i][j];
+ }
}
- }
- ggml_backend_tensor_set(pos_embed, data, 0, ggml_nbytes(pos_embed));
- }
- }
- else {
- // non-minicpmv models
-
- if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL) {
- // pw * ph = number of tokens output by ViT after apply patch merger
- // ipw * ipw = number of vision token been processed inside ViT
- const int merge_ratio = 2;
- const int pw = image_size_width / patch_size / merge_ratio;
- const int ph = image_size_height / patch_size / merge_ratio;
- const int ipw = image_size_width / patch_size;
- const int iph = image_size_height / patch_size;
-
- std::vector<int> idx (ph * pw);
- std::vector<int> inv_idx(ph * pw);
-
- if (use_window_attn) {
- const int attn_window_size = 112;
- struct ggml_tensor * window_idx = ggml_graph_get_tensor(gf, "window_idx");
- struct ggml_tensor * inv_window_idx = ggml_graph_get_tensor(gf, "inv_window_idx");
- struct ggml_tensor * window_mask = ggml_graph_get_tensor(gf, "window_mask");
-
- const int grid_window = attn_window_size / patch_size / merge_ratio;
- int dst = 0;
- // [num_vision_tokens, num_vision_tokens] attention mask tensor
- std::vector<float> mask(pow(ipw * iph, 2), std::numeric_limits<float>::lowest());
- int mask_row = 0;
-
- for (int y = 0; y < ph; y += grid_window)
- {
- for (int x = 0; x < pw; x += grid_window)
- {
- const int win_h = std::min(grid_window, ph - y);
- const int win_w = std::min(grid_window, pw - x);
- const int dst_0 = dst;
- // group all tokens belong to the same window togather (to a continue range)
- for (int dy = 0; dy < win_h; dy++) {
- for (int dx = 0; dx < win_w; dx++) {
- const int src = (y + dy) * pw + (x + dx);
- assert(src < (int)idx.size());
- assert(dst < (int)inv_idx.size());
- idx [src] = dst;
- inv_idx[dst] = src;
- dst++;
+ set_input_f32("pos_embed", pos_embed);
+ } break;
+ case PROJECTOR_TYPE_QWEN2VL:
+ {
+ const int merge_ratio = 2;
+ const int pw = image_size_width / patch_size;
+ const int ph = image_size_height / patch_size;
+ std::vector<int> positions(num_positions * 4);
+ int ptr = 0;
+ for (int y = 0; y < ph; y += merge_ratio) {
+ for (int x = 0; x < pw; x += merge_ratio) {
+ for (int dy = 0; dy < 2; dy++) {
+ for (int dx = 0; dx < 2; dx++) {
+ positions[ ptr] = y + dy;
+ positions[ num_patches + ptr] = x + dx;
+ positions[2 * num_patches + ptr] = y + dy;
+ positions[3 * num_patches + ptr] = x + dx;
+ ptr++;
}
}
-
- for (int r=0; r < win_h * win_w * merge_ratio * merge_ratio; r++) {
- int row_offset = mask_row * (ipw * iph);
- std::fill(
- mask.begin() + row_offset + (dst_0 * merge_ratio * merge_ratio),
- mask.begin() + row_offset + (dst * merge_ratio * merge_ratio),
- 0.0);
- mask_row++;
- }
}
}
- ggml_backend_tensor_set(window_idx, idx.data(), 0, ggml_nbytes(window_idx));
- ggml_backend_tensor_set(inv_window_idx, inv_idx.data(), 0, ggml_nbytes(inv_window_idx));
- ggml_backend_tensor_set(window_mask, mask.data(), 0, ggml_nbytes(window_mask));
- } else {
- std::iota(idx.begin(), idx.end(), 0);
- std::iota(inv_idx.begin(), inv_idx.end(), 0);
- }
+ set_input_i32("positions", positions);
+ } break;
+ case PROJECTOR_TYPE_QWEN25VL:
+ {
+ // pw * ph = number of tokens output by ViT after apply patch merger
+ // ipw * ipw = number of vision token been processed inside ViT
+ const int merge_ratio = 2;
+ const int pw = image_size_width / patch_size / merge_ratio;
+ const int ph = image_size_height / patch_size / merge_ratio;
+ const int ipw = image_size_width / patch_size;
+ const int iph = image_size_height / patch_size;
+
+ std::vector<int> idx (ph * pw);
+ std::vector<int> inv_idx(ph * pw);
+
+ if (use_window_attn) {
+ const int attn_window_size = 112;
+ const int grid_window = attn_window_size / patch_size / merge_ratio;
+ int dst = 0;
+ // [num_vision_tokens, num_vision_tokens] attention mask tensor
+ std::vector<float> mask(pow(ipw * iph, 2), std::numeric_limits<float>::lowest());
+ int mask_row = 0;
+
+ for (int y = 0; y < ph; y += grid_window) {
+ for (int x = 0; x < pw; x += grid_window) {
+ const int win_h = std::min(grid_window, ph - y);
+ const int win_w = std::min(grid_window, pw - x);
+ const int dst_0 = dst;
+ // group all tokens belong to the same window togather (to a continue range)
+ for (int dy = 0; dy < win_h; dy++) {
+ for (int dx = 0; dx < win_w; dx++) {
+ const int src = (y + dy) * pw + (x + dx);
+ GGML_ASSERT(src < (int)idx.size());
+ GGML_ASSERT(dst < (int)inv_idx.size());
+ idx [src] = dst;
+ inv_idx[dst] = src;
+ dst++;
+ }
+ }
- struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
- const int mpow = merge_ratio * merge_ratio;
- std::vector<int> positions_data(ggml_nelements(positions));
- int * data = positions_data.data();
+ for (int r=0; r < win_h * win_w * merge_ratio * merge_ratio; r++) {
+ int row_offset = mask_row * (ipw * iph);
+ std::fill(
+ mask.begin() + row_offset + (dst_0 * merge_ratio * merge_ratio),
+ mask.begin() + row_offset + (dst * merge_ratio * merge_ratio),
+ 0.0);
+ mask_row++;
+ }
+ }
+ }
- int ptr = 0;
- for (int y = 0; y < iph; y += merge_ratio)
- {
- for (int x = 0; x < ipw; x += merge_ratio)
- {
- for (int dy = 0; dy < 2; dy++) {
- for (int dx = 0; dx < 2; dx++) {
- auto remap = idx[ptr / mpow];
- remap = remap * mpow + (ptr % mpow);
-
- data[ remap] = y + dy;
- data[ num_patches + remap] = x + dx;
- data[2 * num_patches + remap] = y + dy;
- data[3 * num_patches + remap] = x + dx;
- ptr++;
+ set_input_i32("window_idx", idx);
+ set_input_i32("inv_window_idx", inv_idx);
+ set_input_f32("window_mask", mask);
+ } else {
+ for (int i = 0; i < ph * pw; i++) {
+ idx[i] = i;
+ }
+ }
+
+ const int mpow = merge_ratio * merge_ratio;
+ std::vector<int> positions(num_positions * 4);
+
+ int ptr = 0;
+ for (int y = 0; y < iph; y += merge_ratio) {
+ for (int x = 0; x < ipw; x += merge_ratio) {
+ for (int dy = 0; dy < 2; dy++) {
+ for (int dx = 0; dx < 2; dx++) {
+ auto remap = idx[ptr / mpow];
+ remap = (remap * mpow) + (ptr % mpow);
+
+ positions[ remap] = y + dy;
+ positions[ num_patches + remap] = x + dx;
+ positions[2 * num_patches + remap] = y + dy;
+ positions[3 * num_patches + remap] = x + dx;
+ ptr++;
+ }
}
}
}
- }
- ggml_backend_tensor_set(positions, data, 0, ggml_nbytes(positions));
- }
- else if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
- // do nothing
- }
- else if (ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) {
- // do nothing
- }
- else if (ctx->proj_type == PROJECTOR_TYPE_PIXTRAL) {
- // set the 2D positions
- int n_patches_per_col = image_size_width / patch_size;
- std::vector<int> pos_data(num_positions);
- struct ggml_tensor * pos;
- // dimension H
- pos = ggml_graph_get_tensor(gf, "pos_h");
- for (int i = 0; i < num_positions; i++) {
- pos_data[i] = i / n_patches_per_col;
- }
- ggml_backend_tensor_set(pos, pos_data.data(), 0, ggml_nbytes(pos));
- // dimension W
- pos = ggml_graph_get_tensor(gf, "pos_w");
- for (int i = 0; i < num_positions; i++) {
- pos_data[i] = i % n_patches_per_col;
- }
- ggml_backend_tensor_set(pos, pos_data.data(), 0, ggml_nbytes(pos));
- }
- else {
+ set_input_i32("positions", positions);
+ } break;
+ case PROJECTOR_TYPE_PIXTRAL:
+ {
+ // set the 2D positions
+ int n_patches_per_col = image_size_width / patch_size;
+ std::vector<int> pos_data(num_positions);
+ // dimension H
+ for (int i = 0; i < num_positions; i++) {
+ pos_data[i] = i / n_patches_per_col;
+ }
+ set_input_i32("pos_h", pos_data);
+ // dimension W
+ for (int i = 0; i < num_positions; i++) {
+ pos_data[i] = i % n_patches_per_col;
+ }
+ set_input_i32("pos_w", pos_data);
+ } break;
+ case PROJECTOR_TYPE_GLM_EDGE:
+ {
// llava and other models
- struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
-
- int* positions_data = (int*)malloc(ggml_nbytes(positions));
+ std::vector<int32_t> positions(num_positions);
for (int i = 0; i < num_positions; i++) {
- positions_data[i] = i;
+ positions[i] = i;
}
- ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
- free(positions_data);
+ set_input_i32("positions", positions);
+ } break;
+ case PROJECTOR_TYPE_MLP:
+ case PROJECTOR_TYPE_MLP_NORM:
+ case PROJECTOR_TYPE_LDP:
+ case PROJECTOR_TYPE_LDPV2:
+ {
+ // llava and other models
+ std::vector<int32_t> positions(num_positions);
+ for (int i = 0; i < num_positions; i++) {
+ positions[i] = i;
+ }
+ set_input_i32("positions", positions);
- if (ctx->proj_type != PROJECTOR_TYPE_GLM_EDGE) {
- struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
// The patches vector is used to get rows to index into the embeds with;
// we should skip dim 0 only if we have CLS to avoid going out of bounds
// when retrieving the rows.
int patch_offset = model.class_embedding ? 1 : 0;
- int* patches_data = (int*)malloc(ggml_nbytes(patches));
+ std::vector<int32_t> patches(num_patches);
for (int i = 0; i < num_patches; i++) {
- patches_data[i] = i + patch_offset;
+ patches[i] = i + patch_offset;
}
- ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
- free(patches_data);
- }
- }
- }
-
- if (use_window_attn && (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL)) {
- struct ggml_tensor * window_idx = ggml_graph_get_tensor(gf, "window_idx");
- struct ggml_tensor * inv_window_idx = ggml_graph_get_tensor(gf, "inv_window_idx");
- struct ggml_tensor * window_mask = ggml_graph_get_tensor(gf, "window_mask");
-
- const int merge_ratio = 2;
- const int attn_window_size = 112;
- const int pw = image_size_width / patch_size / merge_ratio;
- const int ph = image_size_height / patch_size / merge_ratio;
- const int grid_window = attn_window_size / patch_size / merge_ratio;
- const int ipw = image_size_width / patch_size;
- const int iph = image_size_height / patch_size;
- /*
- pw * ph = number of tokens output by ViT after apply patch merger
- ipw * ipw = number of vision token been processed inside ViT
- */
-
- std::vector<int> idx(ph * pw);
- std::vector<int> inv_idx(ph * pw);
- int dst = 0;
- // [num_vision_tokens, num_vision_tokens] attention mask tensor
- std::vector<float> mask(pow(ipw * iph, 2), std::numeric_limits<float>::lowest());
- int mask_row = 0;
-
- for (int y = 0; y < ph; y+=grid_window)
- {
- for (int x = 0; x < pw; x+=grid_window)
+ set_input_i32("patches", patches);
+ } break;
+ case PROJECTOR_TYPE_GEMMA3:
+ case PROJECTOR_TYPE_IDEFICS3:
{
- const int win_h = std::min(grid_window, ph - y);
- const int win_w = std::min(grid_window, pw - x);
- const int dst_0 = dst;
- // group all tokens belong to the same window togather (to a continue range)
- for (int dy = 0; dy < win_h; dy++) {
- for (int dx = 0; dx < win_w; dx++) {
- const int src = (y + dy) * pw + (x + dx);
- assert(src < (int)idx.size());
- assert(dst < (int)inv_idx.size());
- idx[src] = dst;
- inv_idx[dst] = src;
- dst++;
- }
- }
-
- for (int r=0; r < win_h * win_w * merge_ratio * merge_ratio; r++) {
- int row_offset = mask_row * (ipw * iph);
- std::fill(
- mask.begin() + row_offset + (dst_0 * merge_ratio * merge_ratio),
- mask.begin() + row_offset + (dst * merge_ratio * merge_ratio),
- 0.0);
- mask_row++;
- }
- }
- }
-
- ggml_backend_tensor_set(window_idx, idx.data(), 0, ggml_nbytes(window_idx));
- ggml_backend_tensor_set(inv_window_idx, inv_idx.data(), 0, ggml_nbytes(inv_window_idx));
- ggml_backend_tensor_set(window_mask, mask.data(), 0, ggml_nbytes(window_mask));
+ // do nothing
+ } break;
+ default:
+ GGML_ABORT("Unknown projector type");
}
ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads);
}
bool clip_is_qwen2vl(const struct clip_ctx * ctx) {
- return ctx->proj_type == PROJECTOR_TYPE_QWEN2VL;
+ return ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL;
}
bool clip_is_llava(const struct clip_ctx * ctx) {
overview / pkg / ggml / sources / llama.cpp / commitdiff