Brunobkr/llama.cpp_AlgMor24_github

1// ABOUTME: Yasa2 vision encoder graph builder for ConvNeXt-based architecture.2// ABOUTME: Implements patch embedding, ConvNeXt stages with GRN, and adaptive pooling.3 4#include "models.h"5 6static ggml_tensor * add_channel_bias(7        ggml_context * ctx0,8        ggml_tensor * x_whcb,9        ggml_tensor * b_c) {10    if (!b_c) {11        return x_whcb;12    }13    ggml_tensor * b4 = ggml_reshape_4d(ctx0, b_c, 1, 1, b_c->ne[0], 1);14    return ggml_add(ctx0, x_whcb, b4);15}16 17static ggml_tensor * mul_channel_weight(18        ggml_context * ctx0,19        ggml_tensor * x_whcb,20        ggml_tensor * w_c) {21    if (!w_c) {22        return x_whcb;23    }24    ggml_tensor * w4 = ggml_reshape_4d(ctx0, w_c, 1, 1, w_c->ne[0], 1);25    return ggml_mul(ctx0, x_whcb, w4);26}27 28ggml_tensor * clip_graph_yasa2::layer_norm_channels(ggml_tensor * inp, ggml_tensor * w, ggml_tensor * b, float eps) {29    // Match HF ConvNextLayerNorm(channels_first):30    // u = mean_c(x), s = mean_c((x-u)^2), x = (x-u)/sqrt(s+eps)31    // cast back to input dtype before affine.32    ggml_tensor * cur = ggml_permute(ctx0, inp, 2, 1, 0, 3); // [W,H,C,B] -> [C,H,W,B]33    cur = ggml_cont(ctx0, cur);34 35    ggml_tensor * u = ggml_mean(ctx0, cur);                 // [1,H,W,B]36    ggml_tensor * xm = ggml_sub(ctx0, cur, u);              // [C,H,W,B]37 38    ggml_tensor * s = ggml_mul(ctx0, xm, xm);               // [C,H,W,B]39    s = ggml_mean(ctx0, s);                                 // [1,H,W,B]40    s = ggml_clamp(ctx0, s, eps, 1e30f);                    // avoid div-by-zero in no-alloc warmup41    s = ggml_sqrt(ctx0, s);                                 // [1,H,W,B]42 43    ggml_tensor * xhat = ggml_div(ctx0, xm, s);             // [C,H,W,B]44    xhat = ggml_permute(ctx0, xhat, 2, 1, 0, 3);            // [W,H,C,B]45    xhat = ggml_cont(ctx0, xhat);46    xhat = mul_channel_weight(ctx0, xhat, w);47    xhat = add_channel_bias(ctx0, xhat, b);48    return xhat;49}50 51ggml_tensor * clip_graph_yasa2::convnext_grn(ggml_tensor * inp, ggml_tensor * w, ggml_tensor * b) {52    // Exact ConvNeXtV2 GRN:53    // Gx = ||x||_2 over spatial dims (W,H), Nx = Gx / (mean_c(Gx) + eps)54    // y  = w * (x * Nx) + b + x55    const int64_t wdim = inp->ne[0];56    const int64_t hdim = inp->ne[1];57    const int64_t cdim = inp->ne[2];58    const int64_t bdim = inp->ne[3];59 60    // Keep GRN math in fp32 for stability; fp16/bf16 accumulation can drift.61    ggml_tensor * sq = ggml_mul(ctx0, inp, inp);62    ggml_tensor * sq_flat = ggml_reshape_4d(ctx0, sq, wdim * hdim, cdim, 1, bdim);   // [WH,C,1,B]63    ggml_tensor * gx = ggml_sum_rows(ctx0, sq_flat);                                   // [1,C,1,B]64    gx = ggml_sqrt(ctx0, gx);                                                           // [1,C,1,B]65 66    ggml_tensor * gx_ch_first = ggml_permute(ctx0, gx, 1, 0, 2, 3);                    // [C,1,1,B]67    gx_ch_first = ggml_cont(ctx0, gx_ch_first);68    ggml_tensor * gx_mean = ggml_mean(ctx0, gx_ch_first);                              // [1,1,1,B]69 70    gx_mean = ggml_clamp(ctx0, gx_mean, 1e-6f, 1e30f);                                  // approx +eps, warmup-safe71    ggml_tensor * nx = ggml_div(ctx0, gx, gx_mean);                                    // [1,C,1,B]72    nx = ggml_permute(ctx0, nx, 0, 2, 1, 3);                                            // [1,1,C,B]73    nx = ggml_cont(ctx0, nx);74 75    ggml_tensor * xnx = ggml_mul(ctx0, inp, nx);76    xnx = mul_channel_weight(ctx0, xnx, w);77    xnx = add_channel_bias(ctx0, xnx, b);78    return ggml_add(ctx0, inp, xnx);79}80 81ggml_cgraph * clip_graph_yasa2::build() {82    ggml_tensor * cur = build_inp_raw();83 84    // Patch embedding Conv2d(kernel=4, stride=4)85    cur = ggml_conv_2d(ctx0, model.yasa_patch_w, cur, patch_size, patch_size, 0, 0, 1, 1);86    cur = add_channel_bias(ctx0, cur, model.yasa_patch_b);87    ggml_set_name(cur, "yasa2_patch_conv_out");88    cb(cur, "yasa2_patch_conv_out", -1);89    cur = layer_norm_channels(cur, model.yasa_patch_ln_w, model.yasa_patch_ln_b, eps);90    ggml_set_name(cur, "yasa2_patch_ln_out");91    cb(cur, "yasa2_patch_ln_out", -1);92 93    // ConvNeXt stages94    for (size_t s = 0; s < model.yasa_stages.size(); ++s) {95        const auto & stage = model.yasa_stages[s];96 97        if (stage.down_conv_w) {98            cur = layer_norm_channels(cur, stage.down_ln_w, stage.down_ln_b, eps);99            cur = ggml_conv_2d(ctx0, stage.down_conv_w, cur, 2, 2, 0, 0, 1, 1);100            cur = add_channel_bias(ctx0, cur, stage.down_conv_b);101            ggml_format_name(cur, "yasa2_stage%zu_down_out", s);102        }103 104        for (size_t bi = 0; bi < stage.blocks.size(); ++bi) {105            const auto & blk = stage.blocks[bi];106            ggml_tensor * res = cur;107 108            ggml_tensor * x = ggml_conv_2d_dw(ctx0, blk.dw_w, cur, 1, 1, 3, 3, 1, 1);109            x = add_channel_bias(ctx0, x, blk.dw_b);110            x = layer_norm_channels(x, blk.ln_w, blk.ln_b, eps);111 112            // pwconv1/pwconv2 are HF Linear layers over channels; implement via matmul on tokens.113            const int64_t w = x->ne[0];114            const int64_t h = x->ne[1];115            const int64_t b = x->ne[3];116 117            ggml_tensor * tok = ggml_reshape_3d(ctx0, x, w * h, x->ne[2], b); // [T,C,B]118            tok = ggml_permute(ctx0, tok, 1, 0, 2, 3);                        // [C,T,B]119            tok = ggml_cont(ctx0, tok);120 121            tok = ggml_mul_mat(ctx0, blk.pw1_w, tok);                         // [4C,T,B]122            if (blk.pw1_b) {123                ggml_tensor * b1 = ggml_reshape_3d(ctx0, blk.pw1_b, blk.pw1_b->ne[0], 1, 1); // [4C,1,1]124                tok = ggml_add(ctx0, tok, b1);125            }126            x = ggml_permute(ctx0, tok, 1, 0, 2, 3);                         // [T,4C,B]127            x = ggml_cont(ctx0, x);128            x = ggml_reshape_4d(ctx0, x, w, h, tok->ne[0], b);               // [W,H,4C,B]129            x = ggml_gelu_erf(ctx0, x);130            x = convnext_grn(x, blk.grn_w, blk.grn_b);131 132            tok = ggml_reshape_3d(ctx0, x, w * h, x->ne[2], b);              // [T,4C,B]133            tok = ggml_permute(ctx0, tok, 1, 0, 2, 3);                       // [4C,T,B]134            tok = ggml_cont(ctx0, tok);135 136            tok = ggml_mul_mat(ctx0, blk.pw2_w, tok);                        // [C,T,B]137            if (blk.pw2_b) {138                ggml_tensor * b2 = ggml_reshape_3d(ctx0, blk.pw2_b, blk.pw2_b->ne[0], 1, 1); // [C,1,1]139                tok = ggml_add(ctx0, tok, b2);140            }141            x = ggml_permute(ctx0, tok, 1, 0, 2, 3);                         // [T,C,B]142            x = ggml_cont(ctx0, x);143            x = ggml_reshape_4d(ctx0, x, w, h, tok->ne[0], b);               // [W,H,C,B]144 145            cur = ggml_add(ctx0, res, x);146            ggml_format_name(cur, "yasa2_stage%zu_blk%zu_out", s, bi);147        }148    }149 150    // HF path adds vision position embeddings BEFORE adaptive pooling.151    const int64_t pre_w = cur->ne[0];152    const int64_t pre_h = cur->ne[1];153    ggml_tensor * tokens_pre = ggml_reshape_3d(ctx0, cur, pre_w * pre_h, cur->ne[2], cur->ne[3]); // [T,C,B]154    tokens_pre = ggml_permute(ctx0, tokens_pre, 1, 0, 2, 3); // [C,T,B]155    tokens_pre = ggml_cont(ctx0, tokens_pre);156    if (model.yasa_vision_pos_embed && tokens_pre->ne[1] == model.yasa_vision_pos_embed->ne[1]) {157        const int64_t n_ch = model.yasa_vision_pos_embed->ne[0];158        const int64_t n_tokens = model.yasa_vision_pos_embed->ne[1];159        ggml_tensor * pos = ggml_reshape_3d(ctx0, model.yasa_vision_pos_embed, (int) n_ch, (int) n_tokens, 1);160        tokens_pre = ggml_add(ctx0, tokens_pre, pos);161    }162    cur = ggml_permute(ctx0, tokens_pre, 1, 0, 2, 3); // [T,C,B]163    cur = ggml_cont(ctx0, cur);164    cur = ggml_reshape_4d(ctx0, cur, pre_w, pre_h, cur->ne[1], cur->ne[2]); // [W,H,C,B]165 166    // AdaptiveAvgPool2d target is 8x8 for real inputs, but warmup can use tiny images.167    const int pooled_w = std::min(8, (int) cur->ne[0]);168    const int pooled_h = std::min(8, (int) cur->ne[1]);169    const int kw = std::max(1, (int) cur->ne[0] / pooled_w);170    const int kh = std::max(1, (int) cur->ne[1] / pooled_h);171    cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, kw, kh, kw, kh, 0, 0);172 173    // [W,H,C,B] -> [C,T,B]174    ggml_tensor * tokens = ggml_reshape_3d(ctx0, cur, cur->ne[0] * cur->ne[1], cur->ne[2], cur->ne[3]);175    tokens = ggml_permute(ctx0, tokens, 1, 0, 2, 3);176    tokens = ggml_cont(ctx0, tokens);177    cb(tokens, "yasa2_tokens", -1);178 179    GGML_ASSERT(model.mm_0_w && model.mm_2_w);180    ggml_tensor * embeddings = build_ffn(181        tokens,182        model.mm_0_w, model.mm_0_b,183        nullptr, nullptr,184        model.mm_2_w, model.mm_2_b,185        FFN_GELU_ERF,186        -1);187    cb(embeddings, "yasa2_emb", -1);188 189    ggml_build_forward_expand(gf, embeddings);190    return gf;191}192