clip : Add Qwen2.5VL support (#12402)
* implment vision model architecture, gguf convertor * handle window attention inputs * add debug utils * fix few incorrect tensor memory layout * move position id remap out of ggml to avoid int32 cuda operations * cleaning up * ignore transformers Qwen2_5_xxx type check * remove not so often use `qwen2vl-cli` debug functions * remove commented-out code blocks * fix attn weight scaling after rebase * add `PROJECTOR_TYPE_QWEN2_5_VL` * remove `KEY_USE_GLU_MLP`, `KEY_USE_RMS_NORM` * replace `KEY_FULLATTN_BLK_IDX` with `KEY_WIN_ATTN_PATTERN` * remove `attn_window_size` from gguf * fix model conversion * clean up * fix merging problem * add test --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
This commit is contained in:
HimariO
GitHub
parent
2d451c8059
commit
ca2bb89eac
6 changed files with 594 additions and 102 deletions
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@ -28,6 +28,7 @@
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#include <cinttypes>
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#include <limits>
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#include <array>
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#include <numeric>
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struct clip_logger_state g_logger_state = {GGML_LOG_LEVEL_CONT, clip_log_callback_default, NULL};
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@ -169,6 +170,8 @@ struct clip_hparams {
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std::vector<int32_t> image_grid_pinpoints;
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int32_t image_crop_resolution;
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std::unordered_set<int32_t> vision_feature_layer;
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int32_t attn_window_size;
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int32_t n_wa_pattern;
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};
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struct clip_layer {
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@ -200,6 +203,9 @@ struct clip_layer {
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struct ggml_tensor * ff_down_w = nullptr;
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struct ggml_tensor * ff_down_b = nullptr;
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struct ggml_tensor * ff_g_w = NULL;
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struct ggml_tensor * ff_g_b = NULL;
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// layernorm 2
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struct ggml_tensor * ln_2_w = nullptr;
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struct ggml_tensor * ln_2_b = nullptr;
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@ -319,6 +325,7 @@ struct clip_ctx {
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float image_std[3];
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bool use_gelu = false;
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bool use_silu = false;
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int32_t ftype = 1;
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gguf_context_ptr ctx_gguf;
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ggml_context_ptr ctx_data;
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@ -762,6 +769,236 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i
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return gf;
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}
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static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_image_f32_batch & imgs) {
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const auto & model = ctx->vision_model;
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const auto & hparams = model.hparams;
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const int image_size_width = imgs.entries[0]->nx;
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const int image_size_height = imgs.entries[0]->ny;
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const bool use_mrope = ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL;
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const bool use_window_attn = hparams.n_wa_pattern > 0;
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const int n_wa_pattern = hparams.n_wa_pattern;
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const int patch_size = hparams.patch_size;
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const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size));
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const int patches_w = image_size_width / patch_size;
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const int patches_h = image_size_height / patch_size;
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const int num_positions = num_patches + (model.class_embedding ? 1 : 0);
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const int num_position_ids = use_mrope ? num_positions * 4 : num_positions;
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const int hidden_size = hparams.hidden_size;
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const int n_head = hparams.n_head;
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const int d_head = hidden_size / n_head;
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const float eps = hparams.eps;
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int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
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const int batch_size = imgs.entries.size();
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GGML_ASSERT(batch_size == 1);
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struct ggml_init_params params = {
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/*.mem_size =*/ ctx->buf_compute_meta.size(),
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/*.mem_buffer =*/ ctx->buf_compute_meta.data(),
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/*.no_alloc =*/ true,
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};
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ggml_context_ptr ctx0_ptr(ggml_init(params));
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auto ctx0 = ctx0_ptr.get();
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struct ggml_cgraph * gf = ggml_new_graph(ctx0);
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struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3, batch_size);
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ggml_set_name(inp_raw, "inp_raw");
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ggml_set_input(inp_raw);
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struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
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GGML_ASSERT(image_size_width % (patch_size * 2) == 0);
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GGML_ASSERT(image_size_height % (patch_size * 2) == 0);
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auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
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inp = ggml_add(ctx0, inp, inp_1);
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inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3)); // [w, h, c, b] -> [c, w, h, b]
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inp = ggml_reshape_4d(
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ctx0, inp,
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hidden_size * 2, patches_w / 2, patches_h, batch_size);
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inp = ggml_reshape_4d(
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ctx0, inp,
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hidden_size * 2, patches_w / 2, 2, batch_size * (patches_h / 2));
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inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3));
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inp = ggml_reshape_3d(
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ctx0, inp,
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hidden_size, patches_w * patches_h, batch_size);
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if (model.patch_bias) {
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// inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
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inp = ggml_add(ctx0, inp, model.patch_bias);
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}
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struct ggml_tensor * embeddings = inp;
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struct ggml_tensor * window_mask = nullptr;
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struct ggml_tensor * window_idx = nullptr;
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struct ggml_tensor * inv_window_idx = nullptr;
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struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
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ggml_set_name(positions, "positions");
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ggml_set_input(positions);
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// pre-layernorm
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if (model.pre_ln_w) {
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embeddings = ggml_rms_norm(ctx0, embeddings, eps);
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ggml_set_name(embeddings, "pre_ln");
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embeddings = ggml_mul(ctx0, embeddings, model.pre_ln_w);
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}
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if (use_window_attn) {
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// handle window attention inputs
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inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4);
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ggml_set_name(inv_window_idx, "inv_window_idx");
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ggml_set_input(inv_window_idx);
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// mask for window attention
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window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, num_positions, num_positions);
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ggml_set_name(window_mask, "window_mask");
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ggml_set_input(window_mask);
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// embeddings shape: [hidden_size, patches_w * patches_h, batch_size]
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GGML_ASSERT(batch_size == 1);
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embeddings = ggml_reshape_2d(ctx0, embeddings, hidden_size * 4, patches_w * patches_h * batch_size / 4);
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embeddings = ggml_get_rows(ctx0, embeddings, inv_window_idx);
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embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size, patches_w * patches_h, batch_size);
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}
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// loop over layers
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for (int il = 0; il < ctx->max_feature_layer; il++) {
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struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
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// rmsnorm1
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cur = ggml_rms_norm(ctx0, cur, eps);
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cur = ggml_mul(ctx0, cur, model.layers[il].ln_1_w);
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// self-attention
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{
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struct ggml_tensor * Q =
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ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b);
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Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size);
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Q = ggml_rope_multi(
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ctx0, Q, positions, nullptr,
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d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
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Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
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Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size);
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struct ggml_tensor * K =
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ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b);
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K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);
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K = ggml_rope_multi(
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ctx0, K, positions, nullptr,
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d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
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K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
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K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);
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struct ggml_tensor * V =
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ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b);
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V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size);
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V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
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V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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const bool full_attn = use_window_attn ? (il + 1) % n_wa_pattern == 0 : true;
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if (full_attn) {
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KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f);
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} else {
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KQ = ggml_soft_max_ext(ctx0, KQ, window_mask, 1.0f / sqrtf((float)d_head), 0.0f);
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}
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struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
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KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size);
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KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
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cur = ggml_cont_3d(ctx0, KQV, hidden_size, num_positions, batch_size);
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}
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// attention output
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cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b);
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// re-add the layer input, e.g., residual
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cur = ggml_add(ctx0, cur, embeddings);
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embeddings = cur; // embeddings = residual, cur = hidden_states
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// rms norm2
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cur = ggml_rms_norm(ctx0, cur, eps);
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cur = ggml_mul(ctx0, cur, model.layers[il].ln_2_w);
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// mlp
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// ffn_up
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auto cur_up = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
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cur_up = ggml_add(ctx0, cur_up, model.layers[il].ff_o_b);
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auto cur_gate = ggml_mul_mat(ctx0, model.layers[il].ff_g_w, cur);
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cur_gate = ggml_add(ctx0, cur_gate, model.layers[il].ff_g_b);
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// TODO : only 2 of these 3 are actually used, should we remove one of them?
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if (ctx->use_gelu) {
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cur_gate = ggml_gelu_inplace(ctx0, cur_gate);
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} else if (ctx->use_silu) {
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cur_gate = ggml_silu_inplace(ctx0, cur_gate);
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} else {
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cur_gate = ggml_gelu_quick_inplace(ctx0, cur_gate);
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}
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cur = ggml_mul(ctx0, cur_gate, cur_up);
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// ffn_down
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cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
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cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
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// residual 2
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cur = ggml_add(ctx0, embeddings, cur);
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embeddings = cur;
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}
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// post-layernorm
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if (model.post_ln_w) {
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embeddings = ggml_rms_norm(ctx0, embeddings, eps);
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ggml_set_name(embeddings, "post_ln");
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embeddings = ggml_mul(ctx0, embeddings, model.post_ln_w);
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}
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embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size);
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embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
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embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
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// GELU activation
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embeddings = ggml_gelu(ctx0, embeddings);
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// Second linear layer
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embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings);
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embeddings = ggml_add(ctx0, embeddings, model.mm_1_b);
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if (use_window_attn) {
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window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4);
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ggml_set_name(window_idx, "window_idx");
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ggml_set_input(window_idx);
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// embeddings shape: [hidden_size, patches_w * patches_h, batch_size]
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GGML_ASSERT(batch_size == 1);
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embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4);
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embeddings = ggml_get_rows(ctx0, embeddings, window_idx);
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embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4, batch_size);
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}
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// build the graph
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ggml_build_forward_expand(gf, embeddings);
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return gf;
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}
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static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_image_f32_batch & imgs, struct clip_image_size load_image_size, bool is_inf = false) {
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const auto & model = ctx->vision_model;
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const auto & hparams = model.hparams;
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@ -1331,6 +1568,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
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GGML_ASSERT(imgs.entries.size() == 1);
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res = clip_image_build_graph_pixtral(ctx, *imgs.entries[0]);
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} break;
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case PROJECTOR_TYPE_QWEN25VL:
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{
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res = clip_image_build_graph_qwen25vl(ctx, imgs);
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} break;
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default:
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{
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// TODO: we should have one build_* function per model
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@ -1507,6 +1748,10 @@ struct clip_model_loader {
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{
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hparams.rope_theta = 10000.0f;
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} break;
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case PROJECTOR_TYPE_QWEN25VL:
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{
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get_u32(KEY_WIN_ATTN_PATTERN, hparams.n_wa_pattern);
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} break;
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default:
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break;
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}
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@ -1600,8 +1845,10 @@ struct clip_model_loader {
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// legacy naming (the in and out is reversed! don't ask me why)
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layer.ff_i_w = layer.ff_down_w;
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layer.ff_o_w = layer.ff_up_w;
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layer.ff_g_w = layer.ff_gate_w;
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layer.ff_i_b = layer.ff_down_b;
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layer.ff_o_b = layer.ff_up_b;
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layer.ff_g_b = layer.ff_gate_b;
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}
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switch (ctx_clip.proj_type) {
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@ -1700,6 +1947,7 @@ struct clip_model_loader {
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vision_model.mm_model_mlp_3_w = get_tensor(string_format(TN_GLM_ADAPTER_D_4H_2_H,"weight"));
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} break;
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case PROJECTOR_TYPE_QWEN2VL:
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case PROJECTOR_TYPE_QWEN25VL:
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{
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vision_model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
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vision_model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"));
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@ -2651,7 +2899,7 @@ int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * i
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else {
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GGML_ABORT("Unknown minicpmv version");
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}
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} else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) {
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} else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL) {
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int patch_size = params.patch_size * 2;
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int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0);
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int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0);
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@ -2792,6 +3040,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
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const int pos_w = ctx->load_image_size.width / patch_size;
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const int pos_h = ctx->load_image_size.height / patch_size;
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const bool use_window_attn = hparams.n_wa_pattern > 0; // for qwen2.5vl
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{
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struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw");
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std::vector<float> inp_data(ggml_nelements(inp_raw));
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@ -2890,31 +3140,93 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
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// non-minicpmv models
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if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) {
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struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
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// pw * ph = number of tokens output by ViT after apply patch merger
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// ipw * ipw = number of vision token been processed inside ViT
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const int merge_ratio = 2;
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const int pw = image_size_width / patch_size / merge_ratio;
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const int ph = image_size_height / patch_size / merge_ratio;
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const int ipw = image_size_width / patch_size;
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const int iph = image_size_height / patch_size;
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const int pw = image_size_width / patch_size;
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const int ph = image_size_height / patch_size;
|
||||
int* positions_data = (int*)malloc(ggml_nbytes(positions));
|
||||
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++;
|
||||
}
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
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();
|
||||
|
||||
int ptr = 0;
|
||||
for (int y = 0; y < ph; y+=2)
|
||||
for (int y = 0; y < iph; y += merge_ratio)
|
||||
{
|
||||
for (int x = 0; x < pw; x+=2)
|
||||
for (int x = 0; x < ipw; x += merge_ratio)
|
||||
{
|
||||
for (int dy = 0; dy < 2; dy++) {
|
||||
for (int dx = 0; dx < 2; dx++) {
|
||||
positions_data[ptr] = y + dy;
|
||||
positions_data[num_patches + ptr] = x + dx;
|
||||
positions_data[num_patches * 2 + ptr] = y + dy;
|
||||
positions_data[num_patches * 3 + ptr] = x + 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++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
|
||||
free(positions_data);
|
||||
ggml_backend_tensor_set(positions, data, 0, ggml_nbytes(positions));
|
||||
}
|
||||
else if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
|
||||
// do nothing
|
||||
|
|
@ -2967,6 +3279,65 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
|||
}
|
||||
}
|
||||
|
||||
if (use_window_attn && 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)
|
||||
{
|
||||
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));
|
||||
}
|
||||
|
||||
ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads);
|
||||
|
||||
auto status = ggml_backend_sched_graph_compute(ctx->sched.get(), gf);
|
||||
|
|
@ -3142,6 +3513,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
|
|||
case PROJECTOR_TYPE_GLM_EDGE:
|
||||
return ctx->vision_model.mm_model_mlp_3_w->ne[1];
|
||||
case PROJECTOR_TYPE_QWEN2VL:
|
||||
case PROJECTOR_TYPE_QWEN25VL:
|
||||
return ctx->vision_model.mm_1_b->ne[0];
|
||||
case PROJECTOR_TYPE_GEMMA3:
|
||||
return ctx->vision_model.mm_input_proj_w->ne[0];
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue