Brunobkr/llama.cpp_AlgMor24_github

1#include "models.h"2 3ggml_cgraph * clip_graph_minicpmv::build() {4    GGML_ASSERT(model.class_embedding == nullptr);5    const int n_pos       = n_patches;6    const int n_embd_proj = n_mmproj_embd;7 8    // position embeddings for the projector (not for ViT)9    // see: https://huggingface.co/openbmb/MiniCPM-o-2_6/blob/main/resampler.py#L7010    // base frequency omega11    ggml_tensor * omega = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_embd_proj / 4);12    ggml_set_name(omega, "omega");13    ggml_set_input(omega);14 15    // 2D input positions (using float for sinusoidal embeddings)16    ggml_tensor * pos_h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos);17    ggml_set_name(pos_h, "pos_h");18    ggml_set_input(pos_h);19    ggml_tensor * pos_w = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos);20    ggml_set_name(pos_w, "pos_w");21    ggml_set_input(pos_w);22 23    // for selecting learned pos embd, used by ViT24    struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);25    ggml_set_name(positions, "positions");26    ggml_set_input(positions);27 28    ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions);29 30    ggml_tensor * inp = build_inp();31    ggml_tensor * embeddings = build_vit(32                            inp, n_pos,33                            NORM_TYPE_NORMAL,34                            hparams.ffn_op,35                            learned_pos_embd,36                            nullptr);37 38    // resampler projector (it is just another transformer)39 40    ggml_tensor * q = model.mm_model_query;41    ggml_tensor * v = build_mm(model.mm_model_kv_proj, embeddings);42 43    // norm44    q = build_norm(q, model.mm_model_ln_q_w,  model.mm_model_ln_q_b,  NORM_TYPE_NORMAL, eps, -1);45    v = build_norm(v, model.mm_model_ln_kv_w, model.mm_model_ln_kv_b, NORM_TYPE_NORMAL, eps, -1);46 47    // calculate sinusoidal pos embd48    ggml_tensor * pos_embed = nullptr;49    {50        // outer product51        ggml_tensor * omega_b = ggml_repeat_4d(ctx0, omega, omega->ne[0], n_pos, 1, 1); // n_pos rows52        ggml_tensor * theta_x = ggml_mul(ctx0, omega_b, pos_w);53        ggml_tensor * theta_y = ggml_mul(ctx0, omega_b, pos_h);54        // sin and cos55        ggml_tensor * pos_embd_x = ggml_concat(56            ctx0,57            ggml_sin(ctx0, theta_x),58            ggml_cos(ctx0, theta_x),59            0 // concat on first dim60        );61        ggml_tensor * pos_embd_y = ggml_concat(62            ctx0,63            ggml_sin(ctx0, theta_y),64            ggml_cos(ctx0, theta_y),65            0 // concat on first dim66        );67        pos_embed = ggml_concat(ctx0, pos_embd_x, pos_embd_y, 0);68    }69 70    // k = v + pos_embed71    ggml_tensor * k = ggml_add(ctx0, v, pos_embed);72 73    // attention74    {75        const int d_head = 128;76        int n_head = n_embd_proj/d_head;77        // Use actual config value if available, otherwise fall back to hardcoded values78        int num_query = hparams.minicpmv_query_num;79        ggml_tensor * Q = ggml_add(ctx0,80            build_mm(model.mm_model_attn_q_w, q),81            model.mm_model_attn_q_b);82        ggml_tensor * K = ggml_add(ctx0,83            build_mm(model.mm_model_attn_k_w, k),84            model.mm_model_attn_k_b);85        ggml_tensor * V = ggml_add(ctx0,86            build_mm(model.mm_model_attn_v_w, v),87            model.mm_model_attn_v_b);88 89        Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_query);90        K = ggml_reshape_3d(ctx0, K, d_head, n_head, n_pos);91        V = ggml_reshape_3d(ctx0, V, d_head, n_head, n_pos);92 93        cb(Q, "resampler_Q", -1);94        cb(K, "resampler_K", -1);95        cb(V, "resampler_V", -1);96 97        float resampler_kq_scale = 1.0f/ sqrtf(float(d_head));98        embeddings = build_attn(99            model.mm_model_attn_o_w,100            model.mm_model_attn_o_b,101            Q, K, V, nullptr, resampler_kq_scale, -1);102        cb(embeddings, "resampler_attn_out", -1);103    }104    // layernorm105    embeddings = build_norm(embeddings, model.mm_model_ln_post_w, model.mm_model_ln_post_b, NORM_TYPE_NORMAL, eps, -1);106 107    // projection108    embeddings = build_mm(model.mm_model_proj, embeddings);109 110    // build the graph111    ggml_build_forward_expand(gf, embeddings);112 113    return gf;114}115 116ggml_cgraph * clip_graph_minicpmv4_6::build() {117    const bool is_4x = hparams.n_merge == 2;118    const int n_pos  = n_patches;119    const int half_h = n_patches_y / 2;120    const int half_w = n_patches_x / 2;121    const int n_ds   = half_h * half_w;122    const int n_out  = is_4x ? n_ds : (half_h / 2) * (half_w / 2);123 124    auto add_i32_input = [&](const char * name, int n) {125        ggml_tensor * t = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n);126        ggml_set_name(t, name);127        ggml_set_input(t);128        return t;129    };130 131    // position indices for ViT learned positional embeddings132    ggml_tensor * positions = add_i32_input("positions", n_pos);133    ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions);134 135    ggml_tensor * vit_merger_window_idx     = nullptr;136    ggml_tensor * vit_merger_inv_window_idx = nullptr;137    ggml_tensor * vit_merger_window_mask    = nullptr;138    ggml_tensor * vit_merger_ds_idx_0       = nullptr;139    ggml_tensor * vit_merger_ds_idx_1       = nullptr;140    ggml_tensor * vit_merger_ds_idx_2       = nullptr;141    ggml_tensor * vit_merger_ds_idx_3       = nullptr;142 143    if (!is_4x) {144        // ViT merger window reorder indices + block-diagonal mask145        // (mask layout follows qwen2vl: -inf except for 4x4 blocks on the diagonal,146        // so each window-major group of 4 tokens only attends to itself)147        vit_merger_window_idx     = add_i32_input("vit_merger_window_idx", n_pos);148        vit_merger_inv_window_idx = add_i32_input("vit_merger_inv_window_idx", n_pos);149        vit_merger_window_mask    = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos);150        ggml_set_name(vit_merger_window_mask, "vit_merger_window_mask");151        ggml_set_input(vit_merger_window_mask);152        if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {153            vit_merger_window_mask = ggml_cast(ctx0, vit_merger_window_mask, GGML_TYPE_F16);154        }155 156        // ViT merger 2x2 downsample gather indices157        vit_merger_ds_idx_0 = add_i32_input("vit_merger_ds_idx_0", n_ds);158        vit_merger_ds_idx_1 = add_i32_input("vit_merger_ds_idx_1", n_ds);159        vit_merger_ds_idx_2 = add_i32_input("vit_merger_ds_idx_2", n_ds);160        vit_merger_ds_idx_3 = add_i32_input("vit_merger_ds_idx_3", n_ds);161    }162 163    // final merger 2x2 downsample gather indices164    ggml_tensor * merger_ds_idx_0 = add_i32_input("merger_ds_idx_0", n_out);165    ggml_tensor * merger_ds_idx_1 = add_i32_input("merger_ds_idx_1", n_out);166    ggml_tensor * merger_ds_idx_2 = add_i32_input("merger_ds_idx_2", n_out);167    ggml_tensor * merger_ds_idx_3 = add_i32_input("merger_ds_idx_3", n_out);168 169    // patch embedding + positional embedding170    ggml_tensor * inp = build_inp();171    inp = ggml_add(ctx0, inp, learned_pos_embd);172    cb(inp, "pos_embed", -1);173 174    ggml_tensor * inpL = inp;175    if (model.pre_ln_w) {176        inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, NORM_TYPE_NORMAL, eps, -1);177        cb(inpL, "pre_ln", -1);178    }179 180    auto build_vit_layers = [&](ggml_tensor * input, int il_begin, int il_end, int64_t n_pos_layer) {181        for (int il = il_begin; il < il_end; il++) {182            auto & layer = model.layers[il];183            ggml_tensor * cur = input;184 185            cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);186            cb(cur, "layer_inp_normed", il);187 188            {189                ggml_tensor * Qcur = build_mm(layer.q_w, cur);190                if (layer.q_b) {191                    Qcur = ggml_add(ctx0, Qcur, layer.q_b);192                }193                ggml_tensor * Kcur = build_mm(layer.k_w, cur);194                if (layer.k_b) {195                    Kcur = ggml_add(ctx0, Kcur, layer.k_b);196                }197                ggml_tensor * Vcur = build_mm(layer.v_w, cur);198                if (layer.v_b) {199                    Vcur = ggml_add(ctx0, Vcur, layer.v_b);200                }201 202                Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos_layer);203                Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos_layer);204                Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos_layer);205                cb(Qcur, "Qcur", il);206                cb(Kcur, "Kcur", il);207                cb(Vcur, "Vcur", il);208 209                cur = build_attn(layer.o_w, layer.o_b, Qcur, Kcur, Vcur, nullptr, kq_scale, il);210                cb(cur, "attn_out", il);211            }212 213            if (layer.ls_1_w) {214                cur = ggml_mul(ctx0, cur, layer.ls_1_w);215                cb(cur, "attn_out_scaled", il);216            }217            cur = ggml_add(ctx0, cur, input);218            input = cur;219            cb(cur, "ffn_inp", il);220 221            cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);222            cb(cur, "ffn_inp_normed", il);223 224            cur = build_ffn(cur, layer.ff_up_w, layer.ff_up_b, layer.ff_gate_w, layer.ff_gate_b,225                            layer.ff_down_w, layer.ff_down_b, hparams.ffn_op, il);226            cb(cur, "ffn_out", il);227 228            if (layer.ls_2_w) {229                cur = ggml_mul(ctx0, cur, layer.ls_2_w);230                cb(cur, "ffn_out_scaled", il);231            }232            input = ggml_add(ctx0, input, cur);233            cb(input, "layer_out", il);234        }235        return input;236    };237 238    if (!is_4x) {239        const int insert_lid = hparams.insert_layer_id;240 241        inpL = build_vit_layers(inpL, 0, insert_lid + 1, n_pos);242 243        // ViT merger: window self-attention244        // Tokens are reordered to window-major (4 tokens per window are contiguous),245        // and a block-diagonal mask restricts attention to within each window. This246        // mirrors the qwen2vl windowed-attention pattern so build_attn() can pick the247        // flash-attention path when available.248        {249            ggml_tensor * residual = inpL;250            ggml_tensor * cur = build_norm(inpL,251                model.vit_merger_ln1_w, model.vit_merger_ln1_b,252                NORM_TYPE_NORMAL, eps, -1);253            cb(cur, "vit_merger_attn_inp_normed", -1);254 255            cur = ggml_get_rows(ctx0, cur, vit_merger_window_idx);256            cb(cur, "vit_merger_window_reorder", -1);257 258            ggml_tensor * Qcur = build_mm(model.vit_merger_attn_q_w, cur);259            if (model.vit_merger_attn_q_b) {260                Qcur = ggml_add(ctx0, Qcur, model.vit_merger_attn_q_b);261            }262            ggml_tensor * Kcur = build_mm(model.vit_merger_attn_k_w, cur);263            if (model.vit_merger_attn_k_b) {264                Kcur = ggml_add(ctx0, Kcur, model.vit_merger_attn_k_b);265            }266            ggml_tensor * Vcur = build_mm(model.vit_merger_attn_v_w, cur);267            if (model.vit_merger_attn_v_b) {268                Vcur = ggml_add(ctx0, Vcur, model.vit_merger_attn_v_b);269            }270 271            Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);272            Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);273            Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);274            cb(Qcur, "vit_merger_Qcur", -1);275            cb(Kcur, "vit_merger_Kcur", -1);276            cb(Vcur, "vit_merger_Vcur", -1);277 278            cur = build_attn(model.vit_merger_attn_o_w, model.vit_merger_attn_o_b,279                             Qcur, Kcur, Vcur, vit_merger_window_mask, kq_scale, -1);280            cb(cur, "vit_merger_attn_out", -1);281 282            cur = ggml_get_rows(ctx0, cur, vit_merger_inv_window_idx);283            inpL = ggml_add(ctx0, cur, residual);284            cb(inpL, "vit_merger_attn_residual", -1);285        }286 287        // ViT merger: 2x2 spatial downsample + MLP (4 tokens -> 1)288        {289            ggml_tensor * p0 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_0);290            ggml_tensor * p1 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_1);291            ggml_tensor * p2 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_2);292            ggml_tensor * p3 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_3);293 294            ggml_tensor * mean_res = ggml_add(ctx0, p0, p1);295            mean_res = ggml_add(ctx0, mean_res, p2);296            mean_res = ggml_add(ctx0, mean_res, p3);297            mean_res = ggml_scale(ctx0, mean_res, 0.25f);298            cb(mean_res, "vit_merger_ds_mean_res", -1);299 300            ggml_tensor * cat = ggml_concat(ctx0, p0, p1, 0);301            cat = ggml_concat(ctx0, cat, p2, 0);302            cat = ggml_concat(ctx0, cat, p3, 0);303 304            ggml_tensor * cur = build_norm(cat,305                model.vit_merger_ds_ln_w, model.vit_merger_ds_ln_b,306                NORM_TYPE_NORMAL, eps, -1);307            cb(cur, "vit_merger_ds_normed", -1);308 309            // ViTWindowAttentionMerger downsample MLP uses gelu_pytorch_tanh (FFN_GELU)310            cur = build_ffn(cur,311                model.vit_merger_ds_up_w,   model.vit_merger_ds_up_b,312                nullptr, nullptr,313                model.vit_merger_ds_down_w, model.vit_merger_ds_down_b,314                FFN_GELU, -1);315            cb(cur, "vit_merger_ds_mlp_out", -1);316 317            inpL = ggml_add(ctx0, cur, mean_res);318            cb(inpL, "vit_merger_ds_out", -1);319        }320 321        inpL = build_vit_layers(inpL, insert_lid + 1, n_layer, n_ds);322    } else {323        inpL = build_vit_layers(inpL, 0, n_layer, n_pos);324    }325 326    if (model.post_ln_w) {327        inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, eps, -1);328        cb(inpL, "post_ln", -1);329    }330 331    // Final Merger (DownsampleMLP): another 2x2 spatial merge -> projector embedding332    {333        ggml_tensor * p0 = ggml_get_rows(ctx0, inpL, merger_ds_idx_0);334        ggml_tensor * p1 = ggml_get_rows(ctx0, inpL, merger_ds_idx_1);335        ggml_tensor * p2 = ggml_get_rows(ctx0, inpL, merger_ds_idx_2);336        ggml_tensor * p3 = ggml_get_rows(ctx0, inpL, merger_ds_idx_3);337 338        ggml_tensor * cat = ggml_concat(ctx0, p0, p1, 0);339        cat = ggml_concat(ctx0, cat, p2, 0);340        cat = ggml_concat(ctx0, cat, p3, 0);341 342        ggml_tensor * cur = build_norm(cat,343            model.mm_input_norm_w, model.mm_input_norm_b,344            NORM_TYPE_NORMAL, eps, -1);345        cb(cur, "merger_normed", -1);346 347        // MiniCPMV4_6DownsampleMLP uses nn.GELU() (erf-based, FFN_GELU_ERF)348        cur = build_ffn(cur,349            model.mm_ffn_up_w,   model.mm_ffn_up_b,350            nullptr, nullptr,351            model.mm_ffn_down_w, model.mm_ffn_down_b,352            FFN_GELU_ERF, -1);353        cb(cur, "merger_out", -1);354 355        inpL = cur;356    }357 358    ggml_build_forward_expand(gf, inpL);359    return gf;360}361