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handle window attention inputs

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HimariO 2025-02-03 01:58:53 +08:00
parent 9c7cc6de9c
commit 9c827814e6
1 changed files with 61 additions and 0 deletions
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61
examples/llava/clip.cpp
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@ -183,6 +183,7 @@ struct clip_hparams {
std::vector<int32_t> image_grid_pinpoints;
int32_t image_crop_resolution;
std::unordered_set<int32_t> vision_feature_layer;
int32_t attn_window_size;
std::vector<int32_t> full_attn_layers;
};
@ -1301,6 +1302,7 @@ struct clip_model_loader {
get_u32(KEY_IMAGE_SIZE, hparams.image_size);
get_u32(KEY_PATCH_SIZE, hparams.patch_size);
get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false);
get_u32(KEY_ATTN_WINDOW_SIZE, hparams.attn_window_size, false);
get_arr_int(KEY_IMAGE_GRID_PINPOINTS, hparams.image_grid_pinpoints, false);
get_arr_int(KEY_FULLATTN_BLK_IDX, hparams.full_attn_layers, false);
@ -2605,6 +2607,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw));
free(data);
}
if (ctx->has_minicpmv_projector) {
{
// inspired from siglip:
@ -2723,6 +2726,64 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
}
}
if (hparams.attn_window_size > 0 && ctx->has_qwen2vl_merger) { // TODO: add use_window_attn?
struct ggml_tensor * window_idx = ggml_graph_get_tensor(gf, "window_idx");
struct ggml_tensor * inv_window_idx = ggml_graph_get_tensor(gf, "inv_window_idx");
struct ggml_tensor * window_mask = ggml_graph_get_tensor(gf, "window_mask");
const int merge_ratio = 2;
const int pw = image_size_width / patch_size / merge_ratio;
const int ph = image_size_height / patch_size / merge_ratio;
const int grid_window = hparams.attn_window_size / hparams.patch_size / merge_ratio;
const int ipw = image_size_width / patch_size;
const int iph = image_size_height / patch_size;
/*
pw * ph = number of tokens output by ViT after apply patch merger
ipw * ipw = number of vision token been processed inside ViT
*/
std::vector<int> idx(ph * pw);
std::vector<int> inv_idx(ph * pw);
int dst = 0;
// [num_vision_tokens, num_vision_tokens] attention mask tensor
std::vector<float> mask(pow(ipw * iph, 2), std::numeric_limits<float>::lowest());
int mask_row = 0;
for (int y = 0; y < ph; y+=grid_window)
{
for (int x = 0; x < pw; x+=grid_window)
{
const int win_h = std::min(grid_window, ph - y);
const int win_w = std::min(grid_window, pw - x);
const int dst_0 = dst;
// group all tokens belong to the same window togather (to a continue range)
for (int dy = 0; dy < win_h; dy++) {
for (int dx = 0; dx < win_w; dx++) {
const int src = (y + dy) * pw + (x + dx);
assert(src < (int)idx.size());
assert(dst < (int)inv_idx.size());
idx[src] = dst;
inv_idx[dst] = src;
dst++;
}
}
for (int r=0; r < win_h * win_w * merge_ratio * merge_ratio; r++) {
int row_offset = mask_row * (ipw * iph);
std::fill(
mask.begin() + row_offset + (dst_0 * merge_ratio * merge_ratio),
mask.begin() + row_offset + (dst * merge_ratio * merge_ratio),
0.0);
mask_row++;
}
}
}
ggml_backend_tensor_set(window_idx, idx.data(), 0, ggml_nbytes(window_idx));
ggml_backend_tensor_set(inv_window_idx, inv_idx.data(), 0, ggml_nbytes(inv_window_idx));
ggml_backend_tensor_set(window_mask, mask.data(), 0, ggml_nbytes(window_mask));
}
ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads);
auto status = ggml_backend_sched_graph_compute(ctx->sched.get(), gf);