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Tobyandaetherbird
f830688e91
* model : fold Ling 3.0 VL into the BailingMoeV3 architecture Assisted-by: Scout * model : keep shared NORM rope list intact when gating bailingmoe3 on mrope sections --------- Co-authored-by: aetherbird <aetherbird@users.noreply.github.com>
87 lines
3.2 KiB
C++
87 lines
3.2 KiB
C++
#include "models.h"
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ggml_cgraph * clip_graph_ling3vl::build() {
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// same vision tower as qwen3vl, but the merger is norm-only (no fc1/fc2) and
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// the projector MLP lives at the top level (mm.0 / mm.2)
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GGML_ASSERT(model.class_embedding == nullptr);
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GGML_ASSERT(model.mm_input_norm_w != nullptr); // merger norm (pre spatial merge)
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const int batch_size = 1;
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const int n_pos = n_patches;
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norm_type norm_t = NORM_TYPE_NORMAL;
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// vision M-RoPE, same layout as qwen3vl: [row, col, row, col] quarters
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int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
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ggml_tensor * inp = build_inp_with_temporal_merge();
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// spatial merge
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{
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inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w, h, c, b] -> [c, w, h, b]
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inp = ggml_cont_4d(
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ctx0, inp,
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n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
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inp = ggml_reshape_4d(
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ctx0, inp,
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n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
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inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
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inp = ggml_cont_3d(
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ctx0, inp,
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n_embd, n_patches_x * n_patches_y, batch_size);
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}
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// add patch bias
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if (model.patch_bias != nullptr) {
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inp = ggml_add(ctx0, inp, model.patch_bias);
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cb(inp, "patch_bias", -1);
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}
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// calculate absolute position embedding and apply
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ggml_tensor * learned_pos_embd = resize_position_embeddings(GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ALIGN_CORNERS);
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learned_pos_embd = ggml_cont_4d(
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ctx0, learned_pos_embd,
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n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
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learned_pos_embd = ggml_reshape_4d(
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ctx0, learned_pos_embd,
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n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
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learned_pos_embd = ggml_permute(ctx0, learned_pos_embd, 0, 2, 1, 3);
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learned_pos_embd = ggml_cont_3d(
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ctx0, learned_pos_embd,
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n_embd, n_patches_x * n_patches_y, batch_size);
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const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position
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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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ggml_tensor * inpL = build_vit(
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inp, n_pos, norm_t, hparams.ffn_op, learned_pos_embd,
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[&](ggml_tensor * c, const clip_layer &) {
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return ggml_rope_multi(
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ctx0, c, 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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});
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// multimodal projection (linear_proj MLP over the merged patches)
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ggml_tensor * embeddings = inpL;
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// per-patch merger norm, applied post-blocks before the 2x2 merge
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// (merger.norm, LayerNorm over n_embd)
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embeddings = build_norm(embeddings, model.mm_input_norm_w, model.mm_input_norm_b, norm_t, eps, -1);
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cb(embeddings, "merger_norm", -1);
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embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size);
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embeddings = build_ffn(embeddings,
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model.mm_0_w, model.mm_0_b,
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nullptr, nullptr,
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model.mm_1_w, model.mm_1_b,
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ffn_op_type::FFN_GELU, -1);
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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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