llama : prompt processing optimizations in DeepSeek V2

Author: sszymczy

src/llama.cpp

@@ -6403,6 +6403,10 @@ struct llm_build_context {
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
         struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
+        // whether to use n_tokens as the matrix dimension during multiplication or n_head
+        // n_tokens is higher during prompt processing, this allows to optimize for this case
+        bool pp_opt = n_tokens > n_head;
+
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
 
@@ -6535,35 +6539,45 @@ struct llm_build_context {
                 struct ggml_tensor * q_nope2 = ggml_mul_mat(ctx0, wk_b, q_nope_perm);
                 cb(q_nope2, "q_nope2", il);
 
-                struct ggml_tensor * q_nope2_perm = ggml_permute(ctx0, q_nope2, 0, 2, 1, 3);
-                cb(q_nope2_perm, "q_nope2_perm", il);
+                if (!pp_opt) {
+                    q_nope2 = ggml_permute(ctx0, q_nope2, 0, 2, 1, 3);
+                    cb(q_nope2, "q_nope2_perm", il);
+                }
 
-                struct ggml_tensor * kq_nope = ggml_mul_mat(ctx0, kv_cache, q_nope2_perm);
+                struct ggml_tensor * kq_nope = ggml_mul_mat(ctx0, kv_cache, q_nope2);
                 cb(kq_nope, "kq_nope", il);
 
-                struct ggml_tensor * q_pe_perm = ggml_permute(ctx0, q_pe, 0, 3, 2, 1);
-                cb(q_pe_perm, "q_pe_perm", il);
+                if (pp_opt) {
+                    q_pe = ggml_permute(ctx0, q_pe, 0, 2, 1, 3);
+                    cb(q_pe, "q_pe_perm", il);
+                }
 
                 struct ggml_tensor * kq_pe = ggml_mul_mat(ctx0, kr_cache, q_pe);
                 cb(kq_pe, "kq_pe", il);
 
                 struct ggml_tensor * kq = ggml_add(ctx0, kq_nope, kq_pe);
                 cb(kq, "kq", il);
 
-                kq = ggml_cont(ctx0, ggml_permute(ctx0, kq, 0, 2, 1, 3));
-                cb(kq, "kq_perm", il);
+                if (!pp_opt) {
+                    kq = ggml_cont(ctx0, ggml_permute(ctx0, kq, 0, 2, 1, 3));
+                    cb(kq, "kq_perm", il);
+                }
 
                 kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, kq_scale, hparams.f_max_alibi_bias);
                 cb(kq, "kq_soft_max_ext", il);
 
-                struct ggml_tensor * kq_perm = ggml_permute(ctx0, kq, 0, 2, 1, 3);
-                cb(kq_perm, "kq_soft_max_ext_perm", il);
+                if (!pp_opt) {
+                    kq = ggml_permute(ctx0, kq, 0, 2, 1, 3);
+                    cb(kq, "kq_soft_max_ext_perm", il);
+                }
 
-                struct ggml_tensor * kqv_compressed = ggml_mul_mat(ctx0, kv_cache_trans, kq_perm);
+                struct ggml_tensor * kqv_compressed = ggml_mul_mat(ctx0, kv_cache_trans, kq);
                 cb(kqv_compressed, "kqv_compressed", il);
 
-                kqv_compressed = ggml_permute(ctx0, kqv_compressed, 0, 2, 1, 3);
-                cb(kqv_compressed, "kqv_compressed_perm", il);
+                if (!pp_opt) {
+                    kqv_compressed = ggml_permute(ctx0, kqv_compressed, 0, 2, 1, 3);
+                    cb(kqv_compressed, "kqv_compressed_perm", il);
+                }
 
                 struct ggml_tensor * wv_b = ggml_view_3d(ctx0, model.layers[il].wv_b, kv_lora_rank, n_embd_head_v, n_head, ggml_row_size(model.layers[il].wv_b->type, kv_lora_rank), ggml_row_size(model.layers[il].wv_b->type, kv_lora_rank * n_embd_head_v), 0);
                 cb(wv_b, "wv_b", il);