llama : apply suggestions

Co-authored-by: Georgi Gerganov <[email protected]>

Author: compilade

src/llama.cpp

@@ -3018,7 +3018,7 @@ struct llama_sbatch {
             return;
         }
         std::sort(ids.begin(), ids.end(),
-            [batch](size_t a, size_t b) {
+            [&batch](size_t a, size_t b) {
                 int32_t n_seq_a = batch.n_seq_id ? batch.n_seq_id[a] : 1;
                 int32_t n_seq_b = batch.n_seq_id ? batch.n_seq_id[b] : 1;
                 // sort by seq_id, then by pos
@@ -3050,7 +3050,6 @@ struct llama_sbatch {
         if (batch.n_seq_id != nullptr && batch.seq_id != nullptr) {
             for (size_t i = 0; i < n_tokens; ++i) {
                 const size_t bi = ids[i];
-                const size_t s_len = seq.size();
                 const int32_t n_seqs = batch.n_seq_id[bi];
                 llama_seq_id * seq_ids = batch.seq_id[bi];
                 if (last_seq != nullptr) {
@@ -3067,7 +3066,7 @@ struct llama_sbatch {
                 }
                 llama_sbatch_seq new_seq = {n_seqs, seq_ids, i, 1, batch.all_seq_id};
                 seq.push_back(new_seq);
-                last_seq = &seq[s_len];
+                last_seq = &seq.back();
             }
         } else {
             llama_sbatch_seq new_seq = {1, nullptr, 0, n_tokens, batch.all_seq_id};
@@ -15089,8 +15088,8 @@ static int llama_decode_internal(
 
     while (lctx.sbatch.n_tokens > 0) {
         // For now, only use equal splits for recurrent model architectures
-        llama_ubatch u_batch = kv_self.recurrent ? lctx.sbatch.split_equal(n_ubatch) : lctx.sbatch.split_simple(n_ubatch);
-        const uint32_t n_tokens = u_batch.n_tokens;
+        llama_ubatch ubatch = kv_self.recurrent ? lctx.sbatch.split_equal(n_ubatch) : lctx.sbatch.split_simple(n_ubatch);
+        const uint32_t n_tokens = ubatch.n_tokens;
 
         // count the outputs in this u_batch
         {
@@ -15099,9 +15098,9 @@ static int llama_decode_internal(
             if (n_outputs == n_tokens_all) {
                 n_outputs_new = n_tokens;
             } else {
-                GGML_ASSERT(u_batch.output);
+                GGML_ASSERT(ubatch.output);
                 for (uint32_t i = 0; i < n_tokens; i++) {
-                    n_outputs_new += (int32_t) (u_batch.output[i] != 0);
+                    n_outputs_new += (int32_t) (ubatch.output[i] != 0);
                 }
             }
 
@@ -15122,7 +15121,7 @@ static int llama_decode_internal(
                 kv_self.head = 0;
             }
 
-            if (!llama_kv_cache_find_slot(kv_self, u_batch)) {
+            if (!llama_kv_cache_find_slot(kv_self, ubatch)) {
                 return 1;
             }
 
@@ -15141,7 +15140,7 @@ static int llama_decode_internal(
         ggml_backend_sched_reset(lctx.sched);
         ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
 
-        ggml_cgraph * gf = llama_build_graph(lctx, u_batch, false);
+        ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false);
 
         // the output is always the last tensor in the graph
         struct ggml_tensor * res  = gf->nodes[gf->n_nodes - 1];
@@ -15166,7 +15165,7 @@ static int llama_decode_internal(
 
         ggml_backend_sched_alloc_graph(lctx.sched, gf);
 
-        llama_set_inputs(lctx, u_batch);
+        llama_set_inputs(lctx, ubatch);
 
         llama_graph_compute(lctx, gf, n_threads);
 
@@ -15229,7 +15228,7 @@ static int llama_decode_internal(
                         embd_seq_out.clear();
 
                         for (uint32_t i = 0; i < n_tokens; i++) {
-                            const llama_seq_id seq_id = u_batch.seq_id[i][0];
+                            const llama_seq_id seq_id = ubatch.seq_id[i][0];
                             if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
                                 continue;
                             }