File size: 3,512 Bytes
8efb28e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | #include "models.h"
ggml_cgraph * clip_graph_pixtral::build() {
const int n_merge = hparams.n_merge;
// 2D input positions
ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
ggml_set_name(pos_h, "pos_h");
ggml_set_input(pos_h);
ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
ggml_set_name(pos_w, "pos_w");
ggml_set_input(pos_w);
auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
return build_rope_2d(ctx0, cur, pos_h, pos_w, hparams.rope_theta, true);
};
ggml_tensor * inp = build_inp();
ggml_tensor * cur = build_vit(
inp, n_patches,
NORM_TYPE_RMS,
hparams.ffn_op,
nullptr, // no learned pos embd
add_pos);
// mistral small 3.1 patch merger
// ref: https://github.com/huggingface/transformers/blob/7a3e208892c06a5e278144eaf38c8599a42f53e7/src/transformers/models/mistral3/modeling_mistral3.py#L67
if (model.mm_patch_merger_w) {
GGML_ASSERT(hparams.n_merge > 0);
cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.mm_input_norm_w);
// reshape image tokens to 2D grid
cur = ggml_reshape_3d(ctx0, cur, n_embd, n_patches_x, n_patches_y);
cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); // [x, y, n_embd]
cur = ggml_cont(ctx0, cur);
// torch.nn.functional.unfold is just an im2col under the hood
// we just need a dummy kernel to make it work
ggml_tensor * kernel = ggml_view_3d(ctx0, cur, n_merge, n_merge, cur->ne[2], 0, 0, 0);
cur = ggml_im2col(ctx0, kernel, cur, n_merge, n_merge, 0, 0, 1, 1, true, inp->type);
// project to n_embd
cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]);
cur = build_mm(model.mm_patch_merger_w, cur);
}
// LlavaMultiModalProjector (always using GELU activation)
{
cur = build_ffn(cur,
model.mm_1_w, model.mm_1_b,
nullptr, nullptr,
model.mm_2_w, model.mm_2_b,
FFN_GELU,
-1);
}
// arrangement of the [IMG_BREAK] token
if (model.token_embd_img_break) {
// not efficient, but works
// the trick is to view the embeddings as a 3D tensor with shape [n_embd, n_patches_per_row, n_rows]
// and then concatenate the [IMG_BREAK] token to the end of each row, aka n_patches_per_row dimension
// after the concatenation, we have a tensor with shape [n_embd, n_patches_per_row + 1, n_rows]
const int p_y = n_patches_y / n_merge;
const int p_x = n_patches_x / n_merge;
const int p_total = p_x * p_y;
const int n_embd_text = cur->ne[0];
const int n_tokens_output = p_total + p_y - 1; // one [IMG_BREAK] per row, except the last row
ggml_tensor * tmp = ggml_reshape_3d(ctx0, cur, n_embd_text, p_x, p_y);
ggml_tensor * tok = ggml_new_tensor_3d(ctx0, tmp->type, n_embd_text, 1, p_y);
tok = ggml_scale(ctx0, tok, 0.0); // clear the tensor
tok = ggml_add(ctx0, tok, model.token_embd_img_break);
tmp = ggml_concat(ctx0, tmp, tok, 1);
cur = ggml_view_2d(ctx0, tmp,
n_embd_text, n_tokens_output,
ggml_row_size(tmp->type, n_embd_text), 0);
}
// build the graph
ggml_build_forward_expand(gf, cur);
return gf;
}
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