echodict/llama.cpp

1#include "models.h"2#include <cstring>3#include <cmath>4 5// note: this is similar to clip_graph::resize_position_embeddings, major difference is having6// the w/h in ne[1] and ne[2] instead of assuming with sqrt. Could try storing the tensor in 2D instead7// with a w*h? Also the permute is a bit different at (2, 1, 0, 3) instead of (2, 0, 1, 3).8ggml_tensor * clip_graph_kimik25::resize_position_embeddings_3d(uint32_t interpolation_mode) {9    ggml_tensor * pos_embd = model.position_embeddings;10    const int height       = img.ny / patch_size;11    const int width        = img.nx / patch_size;12    const uint32_t mode    = interpolation_mode;13 14    GGML_ASSERT(pos_embd);15 16    const int64_t stored_c = pos_embd->ne[0];  // C = 115217    const int64_t orig_w = pos_embd->ne[1];    // W = 6418    const int64_t orig_h = pos_embd->ne[2];    // H = 6419 20    GGML_ASSERT(stored_c == n_embd);21 22    if (height == (int)orig_h && width == (int)orig_w) {23        // No interpolation needed, just flatten to [C, H*W]24        return ggml_cont_2d(ctx0, pos_embd, n_embd, width * height);25    }26 27    pos_embd = ggml_permute(ctx0, pos_embd, 2, 1, 0, 3);28    pos_embd = ggml_interpolate(ctx0, pos_embd, height, width, n_embd, 1, mode);29    pos_embd = ggml_permute(ctx0, pos_embd, 2, 1, 0, 3);30    pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height);31    return pos_embd;32}33 34ggml_cgraph * clip_graph_kimik25::build() {35    ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);36    ggml_set_name(pos_h, "pos_h");37    ggml_set_input(pos_h);38 39    ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);40    ggml_set_name(pos_w, "pos_w");41    ggml_set_input(pos_w);42 43    ggml_tensor * learned_pos_embd = resize_position_embeddings_3d(GGML_SCALE_MODE_BICUBIC);44 45    // Kimi-K2.5 uses interleaved 2D RoPE pattern natively, but46    // Q / K are permuted during conversion to use split format.47    auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {48        cur = build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false);49        return cur;50    };51 52    ggml_tensor * inp = build_inp();53 54    // I don't know why, but doing this in the build_vit lead to the ggml_add not occurring?55    // Doing it manually here does work.56    inp = ggml_add(ctx0, inp, learned_pos_embd);57 58    ggml_tensor * cur = build_vit(59                            inp, n_patches,60                            NORM_TYPE_NORMAL,61                            hparams.ffn_op,62                            nullptr,63                            add_pos);64 65    cb(cur, "vit_out", -1);66 67    {68        // patch_merger69        const int scale_factor = model.hparams.n_merge;70        cur = build_patch_merge_permute(cur, scale_factor);71 72        // projection norm73        int proj_inp_dim = cur->ne[0];74        int n_merged_patches = cur->ne[1];75        cur = ggml_view_2d(ctx0, cur,76            n_embd, n_merged_patches * scale_factor * scale_factor,77            ggml_row_size(cur->type, n_embd), 0);78        cur = ggml_norm(ctx0, cur, hparams.eps);79        cur = ggml_mul(ctx0, cur, model.mm_input_norm_w);80        cur = ggml_add(ctx0, cur, model.mm_input_norm_b);81        cur = ggml_view_2d(ctx0, cur,82            proj_inp_dim, n_merged_patches,83            ggml_row_size(cur->type, proj_inp_dim), 0);84        cb(cur, "proj_inp_normed", -1);85 86        // projection mlp87        cur = build_ffn(cur,88            model.mm_1_w, model.mm_1_b,89            nullptr, nullptr,90            model.mm_2_w, model.mm_2_b,91            FFN_GELU,92            -1);93 94        cb(cur, "proj_out", -1);95    }96 97    // build the graph98    ggml_build_forward_expand(gf, cur);99 100    return gf;101}102