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#include "models.h"
ggml_tensor * clip_graph_minimax_m3::apply_rope(
ggml_tensor * x, ggml_tensor * pos_h, ggml_tensor * pos_w) {
const int64_t Hn = x->ne[1];
const int64_t P = x->ne[2];
const size_t es = ggml_element_size(x);
const int dh = (int) x->ne[0];
const int axd = 2 * ((2 * (dh / 2) / 3) / 2);
GGML_ASSERT(x->nb[0] == es);
GGML_ASSERT(3 * axd <= dh);
const float th = hparams.rope_theta;
// layout of x is [t, h, w, pad]
// t is unrotated, h and w are rotated, pad is unrotated
// note: everything from n_dims onward untouched, so w and pad are rotated in one call.
auto sl = [&](int off, int n) {
return ggml_cont(ctx0, ggml_view_3d(ctx0, x, n, Hn, P, x->nb[1], x->nb[2], (size_t) off * es));
};
ggml_tensor * t = sl(0, axd);
ggml_tensor * h = sl(axd, axd);
ggml_tensor * w = sl(2 * axd, dh - 2 * axd); // w + pad
h = ggml_rope_ext(ctx0, h, pos_h, nullptr, axd, GGML_ROPE_TYPE_NEOX, 0, th, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
w = ggml_rope_ext(ctx0, w, pos_w, nullptr, axd, GGML_ROPE_TYPE_NEOX, 0, th, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
return ggml_concat(ctx0, ggml_concat(ctx0, t, h, 0), w, 0);
}
ggml_cgraph * clip_graph_minimax_m3::build() {
GGML_ASSERT(model.patch_bias == nullptr);
GGML_ASSERT(model.class_embedding == nullptr);
GGML_ASSERT(model.patch_embeddings_0 && model.patch_embeddings_1);
GGML_ASSERT(model.mm_1_w && model.mm_2_w);
GGML_ASSERT(model.mm_merger_fc1_w && model.mm_merger_fc2_w);
const int batch_size = 1;
const int n_pos = n_patches;
const int merge = hparams.n_merge;
// patch embedding
ggml_tensor * inp_raw = build_inp_raw();
ggml_tensor * inp = ggml_add(ctx0,
ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1),
ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1));
// spatial merge
{
inp = ggml_permute(ctx0, inp, 1, 2, 0, 3);
inp = ggml_cont_4d(ctx0, inp, n_embd * merge, n_patches_x / merge, n_patches_y, batch_size);
inp = ggml_reshape_4d(ctx0, inp, n_embd * merge, n_patches_x / merge, merge, batch_size * (n_patches_y / merge));
inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
inp = ggml_cont_3d(ctx0, inp, n_embd, n_patches_x * n_patches_y, batch_size);
}
// t (time axis) is always 0 for now, so we leave it unrotated
ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
ggml_set_name(pos_h, "minimax_pos_h"); ggml_set_input(pos_h);
ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
ggml_set_name(pos_w, "minimax_pos_w"); ggml_set_input(pos_w);
ggml_tensor * inpL = build_vit(
inp, n_pos, NORM_TYPE_NORMAL, FFN_GELU_ERF, nullptr,
[&](ggml_tensor * c, const clip_layer &) {
return apply_rope(c, pos_h, pos_w);
});
// projector
ggml_tensor * emb = inpL;
emb = build_ffn(emb, model.mm_1_w, model.mm_1_b,
nullptr, nullptr,
model.mm_2_w, model.mm_2_b, FFN_GELU_ERF, -1);
const int64_t proj = emb->ne[0];
emb = ggml_reshape_2d(ctx0, emb, proj * merge * merge, n_pos / (merge * merge));
emb = build_ffn(emb, model.mm_merger_fc1_w, model.mm_merger_fc1_b,
nullptr, nullptr,
model.mm_merger_fc2_w, model.mm_merger_fc2_b, FFN_GELU_ERF, -1);
ggml_build_forward_expand(gf, emb);
return gf;
}