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#include "models.h"
ggml_cgraph * clip_graph_minicpmv::build() {
GGML_ASSERT(model.class_embedding == nullptr);
const int n_pos = n_patches;
const int n_embd_proj = n_mmproj_embd;
// position embeddings for the projector (not for ViT)
// see: https://huggingface.co/openbmb/MiniCPM-o-2_6/blob/main/resampler.py#L70
// base frequency omega
ggml_tensor * omega = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_embd_proj / 4);
ggml_set_name(omega, "omega");
ggml_set_input(omega);
// 2D input positions (using float for sinusoidal embeddings)
ggml_tensor * pos_h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos);
ggml_set_name(pos_h, "pos_h");
ggml_set_input(pos_h);
ggml_tensor * pos_w = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos);
ggml_set_name(pos_w, "pos_w");
ggml_set_input(pos_w);
// for selecting learned pos embd, used by ViT
struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
ggml_set_name(positions, "positions");
ggml_set_input(positions);
ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions);
ggml_tensor * inp = build_inp();
ggml_tensor * embeddings = build_vit(
inp, n_pos,
NORM_TYPE_NORMAL,
hparams.ffn_op,
learned_pos_embd,
nullptr);
// resampler projector (it is just another transformer)
ggml_tensor * q = model.mm_model_query;
ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings);
// norm
q = build_norm(q, model.mm_model_ln_q_w, model.mm_model_ln_q_b, NORM_TYPE_NORMAL, eps, -1);
v = build_norm(v, model.mm_model_ln_kv_w, model.mm_model_ln_kv_b, NORM_TYPE_NORMAL, eps, -1);
// calculate sinusoidal pos embd
ggml_tensor * pos_embed = nullptr;
{
// outer product
ggml_tensor * omega_b = ggml_repeat_4d(ctx0, omega, omega->ne[0], n_pos, 1, 1); // n_pos rows
ggml_tensor * theta_x = ggml_mul(ctx0, omega_b, pos_w);
ggml_tensor * theta_y = ggml_mul(ctx0, omega_b, pos_h);
// sin and cos
ggml_tensor * pos_embd_x = ggml_concat(
ctx0,
ggml_sin(ctx0, theta_x),
ggml_cos(ctx0, theta_x),
0 // concat on first dim
);
ggml_tensor * pos_embd_y = ggml_concat(
ctx0,
ggml_sin(ctx0, theta_y),
ggml_cos(ctx0, theta_y),
0 // concat on first dim
);
pos_embed = ggml_concat(ctx0, pos_embd_x, pos_embd_y, 0);
}
// k = v + pos_embed
ggml_tensor * k = ggml_add(ctx0, v, pos_embed);
// attention
{
const int d_head = 128;
int n_head = n_embd_proj/d_head;
// Use actual config value if available, otherwise fall back to hardcoded values
int num_query = hparams.minicpmv_query_num;
ggml_tensor * Q = ggml_add(ctx0,
ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q),
model.mm_model_attn_q_b);
ggml_tensor * K = ggml_add(ctx0,
ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k),
model.mm_model_attn_k_b);
ggml_tensor * V = ggml_add(ctx0,
ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v),
model.mm_model_attn_v_b);
Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_query);
K = ggml_reshape_3d(ctx0, K, d_head, n_head, n_pos);
V = ggml_reshape_3d(ctx0, V, d_head, n_head, n_pos);
cb(Q, "resampler_Q", -1);
cb(K, "resampler_K", -1);
cb(V, "resampler_V", -1);
float resampler_kq_scale = 1.0f/ sqrtf(float(d_head));
embeddings = build_attn(
model.mm_model_attn_o_w,
model.mm_model_attn_o_b,
Q, K, V, nullptr, resampler_kq_scale, -1);
cb(embeddings, "resampler_attn_out", -1);
}
// layernorm
embeddings = build_norm(embeddings, model.mm_model_ln_post_w, model.mm_model_ln_post_b, NORM_TYPE_NORMAL, eps, -1);
// projection
embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings);
// build the graph
ggml_build_forward_expand(gf, embeddings);
return gf;
}
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