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llama.cpp
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Merge branch 'sf/deepseek-ocr' of github.com:sfallah/llama.cpp into sf/deepseek-ocr

This commit is contained in:
bluebread 2025-11-21 15:36:45 +00:00
parent 8bce66d5f2
commit 5e6cf3c6a8
1 changed files with 103 additions and 6 deletions
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109
tools/mtmd/clip.cpp
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@ -734,8 +734,8 @@ struct clip_graph {
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, 1.0f / sqrtf(enc_d_heads));
struct ggml_tensor * rw = ggml_get_rel_pos(ctx0, layer.rel_pos_w, W, W);
struct ggml_tensor * rh = ggml_get_rel_pos(ctx0, layer.rel_pos_h, H, H);
struct ggml_tensor * rw = get_rel_pos(ctx0, layer.rel_pos_w, W, W);
struct ggml_tensor * rh = get_rel_pos(ctx0, layer.rel_pos_h, H, H);
struct ggml_tensor * q_r = ggml_reshape_4d(ctx0, Qcur, enc_d_heads, W, H, B * enc_n_heads);
@ -745,7 +745,7 @@ struct clip_graph {
2, 1, 3));
struct ggml_tensor * rel_h = ggml_mul_mat(ctx0, rh, q_r);
struct ggml_tensor * attn = ggml_add_rel_pos_inplace(ctx0, KQ_scaled, rel_w, rel_h);
struct ggml_tensor * attn = add_rel_pos_inplace(ctx0, KQ_scaled, rel_w, rel_h, W);
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, attn);
@ -837,9 +837,9 @@ struct clip_graph {
ggml_tensor * global_features_2 = build_dp_ocr_clip(global_features_1);
// torch global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1)
global_features_1 = ggml_permute(ctx0, global_features_1,2,1,0,3);
global_features_1 = ggml_cont(ctx0, global_features_1);
global_features_1 = ggml_cont(ctx0,ggml_permute(ctx0, global_features_1,2,1,0,3));
global_features_1 = ggml_reshape_2d(ctx0, global_features_1, n_embd, n_patches);
// remove CLS token
global_features_2 = ggml_view_2d(ctx0, global_features_2,
n_embd, n_patches,
@ -850,6 +850,7 @@ struct clip_graph {
global_features = ggml_cont(ctx0, global_features);
global_features = ggml_mul_mat(ctx0, model.fc_w, global_features);
global_features = ggml_add(ctx0, global_features, model.fc_b);
global_features = build_global_local_features(ctx0,global_features);
ggml_build_forward_expand(gf, global_features);
return gf;
@ -869,7 +870,6 @@ struct clip_graph {
t = ggml_cont(ctx0, ggml_permute(ctx0, t, 2, 1, 0, 3)); // (h, w, n_dim)
ggml_tensor * nl = ggml_cont(ctx0,ggml_permute(ctx0, model.image_newline, 2, 1, 0, 3));
nl = ggml_repeat_4d(ctx0, nl, 64, 1, 1280, 1); // n_pos rows
nl = ggml_cont(ctx0, nl);
// 2) image_newline: [n_dim] -> [1, 1, n_dim] -> repeat to [h, 1, n_dim]
@ -2466,6 +2466,103 @@ private:
return inpL;
}
// attn: [k_h*k_w, q_h*q_w]
// rel_h: [q_h, q_w, k_h]
// rel_w: [q_h, q_w, k_w]
static ggml_tensor * add_rel_pos_inplace(
ggml_context * ctx,
ggml_tensor * attn,
ggml_tensor * rel_w,
ggml_tensor * rel_h,
int q_size
) {
ggml_tensor *attn_4d =
ggml_reshape_4d(ctx, attn, q_size,q_size, attn->ne[1], attn->ne[2]);
ggml_tensor *rel_h_4d =
ggml_reshape_4d(ctx, rel_h, 1, q_size, attn->ne[1], attn->ne[2]);
ggml_tensor *rel_h_rep = ggml_repeat(ctx, rel_h_4d, attn_4d); // now same shape as attn_5d
ggml_tensor *rel_w_4d =
ggml_reshape_4d(ctx, rel_w, q_size, 1, attn->ne[1], attn->ne[2]);
ggml_tensor *rel_w_rep = ggml_repeat(ctx, rel_w_4d, attn_4d); // now same shape as attn_5d
ggml_tensor * result = ggml_add(ctx, attn_4d, ggml_add(ctx, rel_h_rep, rel_w_rep));
result = ggml_reshape_3d(ctx, result, attn->ne[0], attn->ne[1], attn->ne[2]);
return result;
}
static ggml_tensor * get_rel_pos(
ggml_context * ctx,
ggml_tensor * rel_pos, // [L, C]
int q_size,
int k_size
) {
const auto dtype = rel_pos->type;
const int64_t L = rel_pos->ne[0]; // length
const int64_t C = rel_pos->ne[1]; // channels
// -------------------------------------------------
// 1) q_idx ← arange(0..q_size-1) [q_size]
// 2) k_idx ← arange(0..k_size-1) [k_size]
// -------------------------------------------------
ggml_tensor * q_coord = ggml_cast(ctx,
ggml_arange(ctx, 0.0f, static_cast<float>(q_size), 1.0f),
GGML_TYPE_F32); // [q_size]
ggml_tensor * k_coord = ggml_cast(ctx,
ggml_arange(ctx, 0.0f, static_cast<float>(k_size), 1.0f),
GGML_TYPE_F32); // [k_size]
ggml_tensor * rel = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, q_size, k_size);
q_coord = ggml_cont(ctx,ggml_repeat(ctx, q_coord, rel)); // [q_size, k_size]
// broadcast reshape:
k_coord = ggml_reshape_2d(ctx, k_coord, 1, k_size); // [1, k_size]
k_coord = ggml_cont(ctx,ggml_repeat(ctx, k_coord, rel)); // [q_size, k_size]
// -------------------------------------------------
// relative_coords = q - k + (k_size - 1) // SAME as PyTorch when no scaling
// -------------------------------------------------
rel = ggml_sub(ctx, k_coord, q_coord); // [q_size, k_size]
rel = ggml_scale_bias(ctx, rel, 1.0f, static_cast<float>(k_size) - 1.0f); // [q_size, k_size]
// -------------------------------------------------
// clamp to [0, L-1] and cast to int32 (for ggml_get_rows)
// -------------------------------------------------
ggml_tensor * rel_clamped = ggml_clamp(ctx, rel, 0, static_cast<float>(L - 1));
ggml_tensor * idx_2d = ggml_cast(ctx, rel_clamped, GGML_TYPE_I32); // [q_size, k_size]
// flatten to 1D for ggml_get_rows
const int64_t qk = static_cast<int64_t>(q_size) * static_cast<int64_t>(k_size);
ggml_tensor * idx_flat = ggml_reshape_1d(ctx, idx_2d, qk); // [qk]
// -------------------------------------------------
// Gather from rel_pos → [qk, C]
// -------------------------------------------------
ggml_tensor * gathered = ggml_get_rows(ctx, rel_pos, idx_flat); // [qk, C]
// reshape to final output → [q_size, k_size, C]
ggml_tensor * out = ggml_reshape_3d(ctx, gathered,rel_pos->ne[0],
q_size,
k_size);
return out; // [q_size, k_size, C]
}
// Implementation based on approach suggested by Acly
// See: https://github.com/ggml-org/llama.cpp/pull/17383#issuecomment-3554227091
static ggml_tensor* window_partition(ggml_context* ctx, ggml_tensor* x, int window) {