Lumpiasty/llama.cpp
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mtmd: support multi-row batching for deepseek-ocr (#26154)

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* mtmd: support multi-row batching for deepseek-ocr

* mtmd: weave deepseek-ocr rows in one shot instead of per row (#26615)

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Co-authored-by: Saba Fallah <sabafallah@gmail.com>
This commit is contained in:
Xuan-Son Nguyen
2026-08-05 13:34:52 +02:00
committed by GitHub
co-authored by Saba Fallah
parent 9a688e51e6
commit 717dad5c8e
3 changed files with 41 additions and 29 deletions
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tools/mtmd/models/deepseekocr.cpp
+29 -22
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@@ -253,6 +253,9 @@ ggml_cgraph * clip_graph_deepseekocr::build() {
bool is_overview = img.add_viewsep;
int n_tiles_per_row = 0;
// number of separate "row" images batched together in this graph call
// (captured now, before n_batch below gets repurposed as the SAM/ViT batch size)
const int n_rows_batch = n_batch;
// note: we expect either a batch of rows or a batch of overviews, but not a mix of both
@@ -272,16 +275,18 @@ ggml_cgraph * clip_graph_deepseekocr::build() {
GGML_ASSERT(img.ny() % img.nx() == 0);
n_tiles_per_row = img.ny() / img.nx();
// input shape: [tile_size, tile_size * n_tiles_per_row, 3]
// we want to reshape it to [tile_size, tile_size, 3, n_tiles_per_row]
inp_raw = ggml_reshape_4d(ctx0, inp_raw, img.nx(), img.nx(), n_tiles_per_row, 3);
inp_raw = ggml_cont(ctx0, ggml_permute(ctx0, inp_raw, 0, 1, 3, 2));
// each entry is one "row" image of shape [tile_size, tile_size * n_tiles_per_row, 3];
// merge the tile axis into the batch axis, giving a combined SAM input of shape
// [tile_size, tile_size, 3, n_tiles_per_row * n_rows_batch] (tile fast, row slow)
inp_raw = ggml_reshape_4d(ctx0, inp_raw, img.nx() * img.nx(), n_tiles_per_row, 3, n_rows_batch);
inp_raw = ggml_cont(ctx0, ggml_permute(ctx0, inp_raw, 0, 2, 1, 3));
inp_raw = ggml_reshape_4d(ctx0, inp_raw, img.nx(), img.nx(), 3, n_tiles_per_row * n_rows_batch);
}
ggml_tensor * sam_out = build_sam(inp_raw);
if (!is_overview) {
n_batch = n_tiles_per_row;
n_batch = n_tiles_per_row * n_rows_batch;
}
const int clip_n_patches = sam_out->ne[0] * sam_out->ne[1];
@@ -354,34 +359,36 @@ ggml_cgraph * clip_graph_deepseekocr::build() {
const auto w = h;
const auto n_dim = cur->ne[0];
ggml_tensor * imgnl = ggml_repeat_4d(ctx0, model.image_newline, n_dim, 1, h, 1);
cur = ggml_reshape_3d(ctx0, cur, n_dim, w, h);
cur = ggml_reshape_2d(ctx0, ggml_concat(ctx0, cur, imgnl, 1), n_dim, (w + 1) * h);
cur = ggml_concat(ctx0, cur, model.view_seperator, 1); // (n_dim, h*(w+1) + 1)
ggml_tensor * imgnl = ggml_repeat_4d(ctx0, model.image_newline, n_dim, 1, h, n_batch);
cur = ggml_reshape_4d(ctx0, cur, n_dim, w, h, n_batch);
cur = ggml_reshape_3d(ctx0, ggml_concat(ctx0, cur, imgnl, 1), n_dim, (w + 1) * h, n_batch);
ggml_tensor * vs = ggml_repeat_4d(ctx0, model.view_seperator, n_dim, 1, n_batch, 1);
cur = ggml_concat(ctx0, cur, vs, 1); // (n_dim, h*(w+1) + 1, n_batch)
} else {
// tile row: interleave tiles within each row, add newline per row
const int grid_x = static_cast<int>(std::sqrt(static_cast<float>(clip_n_patches)));
const int grid_y = grid_x;
const auto n_dim = cur->ne[0];
const int grid_x = static_cast<int>(std::sqrt(static_cast<float>(clip_n_patches)));
const int grid_y = grid_x;
const auto n_dim = cur->ne[0];
// (n_dim, clip_n_patches, n_batch) -> (n_dim, grid_x, grid_y, n_batch)
cur = ggml_reshape_4d(ctx0, cur, n_dim, grid_x, grid_y, n_batch);
// merge n_dim into the grid_x axis, freeing the 4th axis for n_rows_batch
// (n_dim, clip_n_patches, n_tiles_per_row * n_rows_batch) -> (n_dim*grid_x, grid_y, n_tiles_per_row, n_rows_batch)
cur = ggml_reshape_4d(ctx0, cur, n_dim * grid_x, grid_y, n_tiles_per_row, n_rows_batch);
// tiles: re-order from A.row0 A.row1 B.row0 B.row1 ...
// to A.row0 B.row0 A.row1 B.row1 ...
// then add nl: A.row0 B.row0 [nl] A.row1 B.row1 [nl] ...
// interleave tiles: (n_dim, grid_x, grid_y, n_batch) -> (n_dim, grid_x, n_batch, grid_y)
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 1, 3, 2));
// interleave tiles: -> (n_dim*grid_x, n_tiles_per_row, grid_y, n_rows_batch)
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3));
// merge: (n_dim, grid_x, n_batch, grid_y) -> (n_dim, grid_x*n_batch, grid_y, 1)
cur = ggml_reshape_4d(ctx0, cur, n_dim, grid_x * n_batch, grid_y, 1);
// merge: -> (n_dim, grid_x*n_tiles_per_row, grid_y, n_rows_batch)
cur = ggml_reshape_4d(ctx0, cur, n_dim, grid_x * n_tiles_per_row, grid_y, n_rows_batch);
// append newline per row: (n_dim, grid_x*n_batch+1, grid_y, 1)
ggml_tensor * imgnl = ggml_repeat_4d(ctx0, model.image_newline, n_dim, 1, grid_y, 1);
// append newline per row: (n_dim, grid_x*n_tiles_per_row+1, grid_y, n_rows_batch)
ggml_tensor * imgnl = ggml_repeat_4d(ctx0, model.image_newline, n_dim, 1, grid_y, n_rows_batch);
cur = ggml_concat(ctx0, cur, imgnl, 1);
// flatten: (n_dim, (grid_x*n_batch+1)*grid_y)
cur = ggml_reshape_2d(ctx0, cur, n_dim, (grid_x * n_batch + 1) * grid_y);
// flatten: (n_dim, (grid_x*n_tiles_per_row+1)*grid_y, n_rows_batch)
cur = ggml_reshape_3d(ctx0, cur, n_dim, (grid_x * n_tiles_per_row + 1) * grid_y, n_rows_batch);
}
cb(cur, "dsocr_output", -1);