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- #include "models.h"
- ggml_cgraph * clip_graph_kimivl::build() {
- // 2D input positions
- ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
- ggml_set_name(pos_h, "pos_h");
- ggml_set_input(pos_h);
- ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
- ggml_set_name(pos_w, "pos_w");
- ggml_set_input(pos_w);
- ggml_tensor * learned_pos_embd = resize_position_embeddings();
- // build ViT with 2D position embeddings
- auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
- // first half is X axis and second half is Y axis
- return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false);
- };
- ggml_tensor * inp = build_inp();
- ggml_tensor * cur = build_vit(
- inp, n_patches,
- NORM_TYPE_NORMAL,
- hparams.ffn_op,
- learned_pos_embd,
- add_pos);
- cb(cur, "vit_out", -1);
- {
- // patch_merger
- const int scale_factor = model.hparams.n_merge;
- cur = build_patch_merge_permute(cur, scale_factor);
- // projection norm
- int proj_inp_dim = cur->ne[0];
- cur = ggml_view_2d(ctx0, cur,
- n_embd, cur->ne[1] * scale_factor * scale_factor,
- ggml_row_size(cur->type, n_embd), 0);
- cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm
- cur = ggml_mul(ctx0, cur, model.mm_input_norm_w);
- cur = ggml_add(ctx0, cur, model.mm_input_norm_b);
- cur = ggml_view_2d(ctx0, cur,
- proj_inp_dim, cur->ne[1] / scale_factor / scale_factor,
- ggml_row_size(cur->type, proj_inp_dim), 0);
- cb(cur, "proj_inp_normed", -1);
- // projection mlp
- cur = build_ffn(cur,
- model.mm_1_w, model.mm_1_b,
- nullptr, nullptr,
- model.mm_2_w, model.mm_2_b,
- FFN_GELU,
- -1);
- cb(cur, "proj_out", -1);
- }
- // build the graph
- ggml_build_forward_expand(gf, cur);
- return gf;
- }
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