upstream-archive/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2026-07-23 11:10:55 -05:00
Code Issues Packages Projects Releases Wiki Activity

DeepseekV4: reduce graph splits (#25702)

Browse Source
This commit is contained in:
Aman Gupta
2026-07-15 15:47:18 +08:00
committed by GitHub
parent d3fba0c79d
commit 33a75f41c3
1 changed files with 14 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files

26
src/models/deepseek4.cpp
View File

@@ -435,27 +435,29 @@ ggml_tensor * llama_model_deepseek4::graph::build_overlap_compressed_kv_from_sta
kv_state = dsv4_append_zero_row(ctx0, kv_state, false);
score_state = dsv4_append_zero_row(ctx0, score_state, true);
ggml_tensor * prev_idxs = dsv4_view_1d(ctx0, state_read_idxs, ratio*n_blocks, 0);
ggml_tensor * cur_idxs = dsv4_view_1d(ctx0, state_read_idxs, ratio*n_blocks, ratio*n_blocks);
const int64_t n_read = ratio*n_blocks;
ggml_tensor * kv_prev = ggml_get_rows(ctx0, kv_state, prev_idxs);
kv_prev = ggml_cont(ctx0, ggml_view_2d(ctx0, kv_prev, n_embd_head, ratio*n_blocks, kv_prev->nb[1], 0));
ggml_tensor * kv_rows = ggml_get_rows(ctx0, kv_state, state_read_idxs);
ggml_tensor * score_rows = ggml_get_rows(ctx0, score_state, state_read_idxs);
ggml_tensor * kv_prev = ggml_cont(ctx0,
ggml_view_2d(ctx0, kv_rows, n_embd_head, n_read, kv_rows->nb[1], 0));
kv_prev = ggml_reshape_3d(ctx0, kv_prev, n_embd_head, ratio, n_blocks);
cb(kv_prev, name, il);
ggml_tensor * score_prev = ggml_get_rows(ctx0, score_state, prev_idxs);
score_prev = ggml_cont(ctx0, ggml_view_2d(ctx0, score_prev, n_embd_head, ratio*n_blocks, score_prev->nb[1], 0));
ggml_tensor * score_prev = ggml_cont(ctx0,
ggml_view_2d(ctx0, score_rows, n_embd_head, n_read, score_rows->nb[1], 0));
score_prev = ggml_reshape_3d(ctx0, score_prev, n_embd_head, ratio, n_blocks);
cb(score_prev, name, il);
ggml_tensor * kv_cur = ggml_get_rows(ctx0, kv_state, cur_idxs);
kv_cur = ggml_cont(ctx0, ggml_view_2d(ctx0, kv_cur, n_embd_head, ratio*n_blocks, kv_cur->nb[1],
ggml_row_size(kv_cur->type, n_embd_head)));
ggml_tensor * kv_cur = ggml_cont(ctx0,
ggml_view_2d(ctx0, kv_rows, n_embd_head, n_read, kv_rows->nb[1],
n_read*kv_rows->nb[1] + ggml_row_size(kv_rows->type, n_embd_head)));
kv_cur = ggml_reshape_3d(ctx0, kv_cur, n_embd_head, ratio, n_blocks);
ggml_tensor * score_cur = ggml_get_rows(ctx0, score_state, cur_idxs);
score_cur = ggml_cont(ctx0, ggml_view_2d(ctx0, score_cur, n_embd_head, ratio*n_blocks, score_cur->nb[1],
ggml_row_size(score_cur->type, n_embd_head)));
ggml_tensor * score_cur = ggml_cont(ctx0,
ggml_view_2d(ctx0, score_rows, n_embd_head, n_read, score_rows->nb[1],
n_read*score_rows->nb[1] + ggml_row_size(score_rows->type, n_embd_head)));
score_cur = ggml_reshape_3d(ctx0, score_cur, n_embd_head, ratio, n_blocks);
ggml_tensor * values = ggml_concat(ctx0, kv_prev, kv_cur, 1);