99 lines
3.0 KiB
C++
99 lines
3.0 KiB
C++
#include "models.h"
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ggml_cgraph * clip_graph_cogvlm::build() {
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GGML_ASSERT(model.class_embedding != nullptr);
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GGML_ASSERT(model.position_embeddings != nullptr);
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const int n_pos = n_patches + 1; // +1 for [CLS]
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// build input and concatenate class embedding
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ggml_tensor * inp = build_inp();
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inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
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inp = ggml_add(ctx0, inp, model.position_embeddings);
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cb(inp, "inp_pos", -1);
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ggml_tensor * inpL = inp;
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for (int il = 0; il < n_layer; il++) {
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auto & layer = model.layers[il];
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ggml_tensor * cur = inpL;
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cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
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cur = ggml_add(ctx0, cur, layer.qkv_b);
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ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
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cur->nb[1], 0);
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ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
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cur->nb[1], n_embd * sizeof(float));
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ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
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cur->nb[1], 2 * n_embd * sizeof(float));
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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cur = build_attn(layer.o_w, layer.o_b,
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Qcur, Kcur, Vcur, nullptr, kq_scale, il);
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cb(cur, "attn_out", il);
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cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);
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cb(cur, "attn_post_norm", il);
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cur = ggml_add(ctx0, cur, inpL);
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inpL = cur;
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cur = build_ffn(cur,
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layer.ff_up_w, layer.ff_up_b,
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layer.ff_gate_w, layer.ff_gate_b,
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layer.ff_down_w, layer.ff_down_b,
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hparams.ffn_op, il);
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cb(cur, "ffn_out", il);
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cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);
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cb(cur, "ffn_post_norm", il);
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cur = ggml_add(ctx0, cur, inpL);
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cb(cur, "layer_out", il);
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inpL = cur;
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}
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// remove CLS token (like build_llama4 does)
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ggml_tensor * cur = ggml_view_2d(ctx0, inpL,
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n_embd, n_patches,
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ggml_row_size(inpL->type, n_embd), 0);
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// Multiply with mm_model_proj
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cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur);
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// Apply layernorm, weight, bias
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cur = build_norm(cur, model.mm_post_fc_norm_w, model.mm_post_fc_norm_b, NORM_TYPE_NORMAL, 1e-5, -1);
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// Apply GELU
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cur = ggml_gelu_inplace(ctx0, cur);
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// Branch 1: multiply with mm_h_to_4h_w
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ggml_tensor * h_to_4h = ggml_mul_mat(ctx0, model.mm_h_to_4h_w, cur);
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// Branch 2: multiply with mm_gate_w
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ggml_tensor * gate = ggml_mul_mat(ctx0, model.mm_gate_w, cur);
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// Apply silu
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gate = ggml_swiglu_split(ctx0, gate, h_to_4h);
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// Apply mm_4h_to_h_w
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cur = ggml_mul_mat(ctx0, model.mm_4h_to_h_w, gate);
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// Concatenate with boi and eoi
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cur = ggml_concat(ctx0, model.mm_boi, cur, 1);
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cur = ggml_concat(ctx0, cur, model.mm_eoi, 1);
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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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