use common_batch_add, reuse llama_batch in loop

Author: ngxson

examples/imatrix/imatrix.cpp

@@ -496,6 +496,8 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
         // clear the KV cache
         llama_kv_cache_clear(ctx);
 
+        llama_batch batch = llama_batch_init(n_batch, 0, 1);
+
         for (int j = 0; j < num_batches; ++j) {
             const int batch_start = start + j * n_batch;
             const int batch_size  = std::min(end - batch_start, n_batch);
@@ -508,12 +510,9 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
                 tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
             }
 
-            llama_batch batch = llama_batch_init(batch_size, 0, 1);
+            common_batch_clear(batch);
             for (int i = 0; i < batch_size; i++) {
-                batch. token[i] = tokens[batch_start + i];
-                batch.   pos[i] = j*n_batch + i;
-                batch.logits[i] = true;
-                batch.seq_id[i][0] = 0;
+                common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true);
             }
 
             if (llama_decode(ctx, batch)) {
@@ -522,8 +521,6 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
                 return false;
             }
 
-            llama_batch_free(batch);
-
             // restore the original token in case it was set to BOS
             tokens[batch_start] = token_org;
 
@@ -533,6 +530,8 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
             }
         }
 
+        llama_batch_free(batch);
+
         const auto t_end = std::chrono::high_resolution_clock::now();
 
         if (i == 0) {

examples/perplexity/perplexity.cpp

@@ -1800,6 +1800,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) {
         // clear the KV cache
         llama_kv_cache_clear(ctx);
 
+        llama_batch batch = llama_batch_init(n_batch, 0, 1);
+
         for (int j = 0; j < num_batches; ++j) {
             const int batch_start = start + j * n_batch;
             const int batch_size  = std::min(end - batch_start, n_batch);
@@ -1812,12 +1814,9 @@ static void kl_divergence(llama_context * ctx, const common_params & params) {
                 tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
             }
 
-            llama_batch batch = llama_batch_init(batch_size, 0, 1);
+            common_batch_clear(batch);
             for (int i = 0; i < batch_size; i++) {
-                batch. token[i] = tokens[batch_start + i];
-                batch.   pos[i] = j*n_batch + i;
-                batch.logits[i] = true;
-                batch.seq_id[i][0] = 0;
+                common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true);
             }
 
             if (llama_decode(ctx, batch)) {
@@ -1826,8 +1825,6 @@ static void kl_divergence(llama_context * ctx, const common_params & params) {
                 return;
             }
 
-            llama_batch_free(batch);
-
             // restore the original token in case it was set to BOS
             tokens[batch_start] = token_org;
 
@@ -1837,6 +1834,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) {
             }
         }
 
+        llama_batch_free(batch);
+
         const auto t_end = std::chrono::high_resolution_clock::now();
 
         if (i == 0) {