100 lines
3.5 KiB
C++
100 lines
3.5 KiB
C++
#include "models.h"
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#include <cstring>
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#include <cmath>
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// note: this is similar to clip_graph::resize_position_embeddings, major difference is having
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// the w/h in ne[1] and ne[2] instead of assuming with sqrt. Could try storing the tensor in 2D instead
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// with a w*h? Also the permute is a bit different at (2, 1, 0, 3) instead of (2, 0, 1, 3).
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ggml_tensor * clip_graph_kimik25::resize_position_embeddings_3d(uint32_t interpolation_mode) {
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ggml_tensor * pos_embd = model.position_embeddings;
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const int height = img.ny / patch_size;
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const int width = img.nx / patch_size;
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const uint32_t mode = interpolation_mode;
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GGML_ASSERT(pos_embd);
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const int64_t stored_c = pos_embd->ne[0]; // C = 1152
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const int64_t orig_w = pos_embd->ne[1]; // W = 64
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const int64_t orig_h = pos_embd->ne[2]; // H = 64
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GGML_ASSERT(stored_c == n_embd);
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if (height == (int)orig_h && width == (int)orig_w) {
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// No interpolation needed, just flatten to [C, H*W]
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return ggml_cont_2d(ctx0, pos_embd, n_embd, width * height);
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}
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pos_embd = ggml_permute(ctx0, pos_embd, 2, 1, 0, 3);
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pos_embd = ggml_interpolate(ctx0, pos_embd, height, width, n_embd, 1, mode);
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pos_embd = ggml_permute(ctx0, pos_embd, 1, 2, 0, 3);
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pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height);
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return pos_embd;
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}
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ggml_cgraph * clip_graph_kimik25::build() {
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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_3d(GGML_SCALE_MODE_BICUBIC);
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// Kimi-K2.5 uses INTERLEAVED frequency pattern: [x_freq0, y_freq0, x_freq1, y_freq1, ...]
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auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
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return build_rope_2d_interleaved(ctx0, cur, pos_w, pos_h, hparams.rope_theta);
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};
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ggml_tensor * inp = build_inp();
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// I don't know why, but doing this in the build_vit lead to the ggml_add not occurring?
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// Doing it manually here does work.
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inp = ggml_add(ctx0, inp, learned_pos_embd);
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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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nullptr,
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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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int n_merged_patches = cur->ne[1];
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cur = ggml_view_2d(ctx0, cur,
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n_embd, n_merged_patches * 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, hparams.eps);
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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, n_merged_patches,
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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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