rwkv: skip computing output for unused tokens for hybrid models

Signed-off-by: Molly Sophia <[email protected]>

Author: MollySophia

src/llama.cpp

@@ -7760,7 +7760,18 @@ struct llm_build_context {
                 ggml_view_3d(ctx0, x_norm_ffn, n_embd, n_seq_tokens - 1, n_seqs, x_norm_ffn->nb[1], x_norm_ffn->nb[2], 0),
                 1
             );
-            cur = ggml_add(ctx0, cur, llm_build_rwkv6_channel_mix(lctx, ctx0, layer, x_norm_ffn, x_prev));
+
+            struct ggml_tensor * inp_ffn = x_norm_ffn;
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                inp_ffn = ggml_get_rows(ctx0, x_norm_ffn, inp_out_ids);
+                x_prev  = ggml_get_rows(ctx0, x_prev,     inp_out_ids);
+                cur     = ggml_get_rows(ctx0, cur,        inp_out_ids);
+            }
+
+            cur = ggml_add(ctx0, cur, llm_build_rwkv6_channel_mix(lctx, ctx0, layer, inp_ffn, x_prev));
             ggml_build_forward_expand(gf, cur);
 
             struct ggml_tensor * last_norm_att = ggml_view_3d(ctx0, x_norm_att, n_embd, 1, n_seqs, x_norm_att->nb[1], x_norm_att->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(x_norm_att));
@@ -7789,9 +7800,8 @@ struct llm_build_context {
         }
 
         cur = inpL;
-        struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-        cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
-        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+        // struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+        // cur = ggml_get_rows(ctx0, cur, inp_out_ids);
 
         cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, model.output_norm_b, LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
@@ -7874,6 +7884,13 @@ struct llm_build_context {
 
             cb(ffn_inp, "ffn_inp", il);
 
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur     = ggml_get_rows(ctx0, cur, inp_out_ids);
+                ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+            }
+
             // feed-forward network
             cur = llm_build_norm(ctx0, ffn_inp, hparams,
                     model.layers[il].ffn_norm, NULL,
@@ -7897,10 +7914,6 @@ struct llm_build_context {
         }
 
         cur = inpL;
-        struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-        cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
-        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
-
         cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, model.output_norm_b, LLM_NORM_RMS, cb, -1);
         cb(cur, "result_norm", -1);
 
@@ -7981,7 +7994,18 @@ struct llm_build_context {
                 ggml_view_3d(ctx0, x_norm_ffn, n_embd, n_seq_tokens - 1, n_seqs, x_norm_ffn->nb[1], x_norm_ffn->nb[2], 0),
                 1
             );
-            cur = ggml_add(ctx0, cur, llm_build_rwkv7_channel_mix(lctx, ctx0, layer, x_norm_ffn, x_prev));
+
+            struct ggml_tensor * inp_ffn = x_norm_ffn;
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                inp_ffn = ggml_get_rows(ctx0, x_norm_ffn, inp_out_ids);
+                x_prev  = ggml_get_rows(ctx0, x_prev,     inp_out_ids);
+                cur     = ggml_get_rows(ctx0, cur,        inp_out_ids);
+            }
+
+            cur = ggml_add(ctx0, cur, llm_build_rwkv7_channel_mix(lctx, ctx0, layer, inp_ffn, x_prev));
             ggml_build_forward_expand(gf, cur);
 
             struct ggml_tensor * last_norm_att = ggml_view_3d(ctx0, x_norm_att, n_embd, 1, n_seqs, x_norm_att->nb[1], x_norm_att->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(x_norm_att));
@@ -8010,10 +8034,6 @@ struct llm_build_context {
         }
 
         cur = inpL;
-        struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-        cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
-        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
-
         cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, model.output_norm_b, LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
@@ -8095,6 +8115,13 @@ struct llm_build_context {
 
             cb(ffn_inp, "ffn_inp", il);
 
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur     = ggml_get_rows(ctx0, cur, inp_out_ids);
+                ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+            }
+
             // feed-forward network
             cur = llm_build_norm(ctx0, ffn_inp, hparams,
                     model.layers[il].ffn_norm, NULL,
@@ -8118,10 +8145,6 @@ struct llm_build_context {
         }
 
         cur = inpL;
-        struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-        cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
-        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
-
         cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, model.output_norm_b, LLM_NORM_RMS, cb, -1);
         cb(cur, "result_norm", -1);