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