change batch.logits to batch.output

common/common.cpp

@@ -2666,7 +2666,7 @@ void llama_batch_add(
     for (size_t i = 0; i < seq_ids.size(); ++i) {
         batch.seq_id[batch.n_tokens][i] = seq_ids[i];
     }
-    batch.logits  [batch.n_tokens] = logits;
+    batch.output  [batch.n_tokens] = logits;
 
     batch.n_tokens++;
 }

common/log.h

@@ -686,7 +686,7 @@ inline std::string LOG_BATCH_TOSTR_PRETTY(const C & ctx, const B & batch)
             << ":pos " << std::to_string(batch.pos[i])
             << ":n_seq_id  " << std::to_string(batch.n_seq_id[i])
             << ":seq_id " << std::to_string(batch.seq_id[i][0])
-            << ":logits " << std::to_string(batch.logits[i]);
+            << ":logits " << std::to_string(batch.output[i]);
     }
     buf << " ]";
 

examples/batched-bench/batched-bench.cpp

@@ -94,7 +94,7 @@ int main(int argc, char ** argv) {
                 batch.pos      + i,
                 batch.n_seq_id + i,
                 batch.seq_id   + i,
-                batch.logits   + i,
+                batch.output   + i,
                 0, 0, 0, // unused
             };
 
@@ -149,7 +149,7 @@ int main(int argc, char ** argv) {
                         llama_batch_add(batch, 0, i, { j }, false);
                     }
                 }
-                batch.logits[batch.n_tokens - 1] = true;
+                batch.output[batch.n_tokens - 1] = true;
 
                 const auto t_pp_start = ggml_time_us();
 

examples/batched.swift/Sources/main.swift

@@ -86,11 +86,11 @@ for (i, token) in tokens.enumerated() {
     if let seq_id = batch.seq_id[i] {
         seq_id[0] = 0
     }
-    batch.logits[i] = 0
+    batch.output[i] = 0
 }
 
 // llama_decode will output logits only for the last token of the prompt
-batch.logits[Int(batch.n_tokens) - 1] = 1
+batch.output[Int(batch.n_tokens) - 1] = 1
 
 if llama_decode(context, batch) != 0 {
     print("llama_decode() failed")
@@ -178,7 +178,7 @@ while n_cur <= n_len {
         if let seq_id = batch.seq_id[Int(batch.n_tokens)] {
             seq_id[0] = Int32(i)
         }
-        batch.logits[Int(batch.n_tokens)] = 1
+        batch.output[Int(batch.n_tokens)] = 1
 
         i_batch[i] = batch.n_tokens
 

examples/batched/batched.cpp

@@ -122,7 +122,7 @@ int main(int argc, char ** argv) {
     }
 
     // llama_decode will output logits only for the last token of the prompt
-    batch.logits[batch.n_tokens - 1] = true;
+    batch.output[batch.n_tokens - 1] = true;
 
     if (llama_decode(ctx, batch) != 0) {
         LOG_TEE("%s: llama_decode() failed\n", __func__);

examples/embedding/embedding.cpp

@@ -52,7 +52,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
     }
 
     for (int i = 0; i < batch.n_tokens; i++) {
-        if (!batch.logits[i]) {
+        if (!batch.output[i]) {
             continue;
         }
 

examples/gritlm/gritlm.cpp

@@ -102,21 +102,21 @@ static std::string generate(llama_context * ctx, const std::string & prompt, boo
     llama_set_embeddings(ctx, false);
     llama_set_causal_attn(ctx, true);
 
-    llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
+    llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1);
 
     std::vector<llama_token> inputs = llama_tokenize(mdl, prompt, false, true);
     int32_t i_current_token = 0;
 
     while (true) {
-        llama_batch_clear(bat);
+        llama_batch_clear(batch);
         auto n_inputs = (int32_t)inputs.size();
         for (int32_t i = 0; i < n_inputs; i++) {
-            llama_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1);
+            llama_batch_add(batch, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1);
         }
         inputs.clear();
 
-        llama_decode(ctx, bat);
-        auto logits = llama_get_logits_ith(ctx, bat.n_tokens - 1);
+        llama_decode(ctx, batch);
+        auto logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
 
         auto candidates = std::vector<llama_token_data>(llama_n_vocab(mdl));
         auto n_candidates = (int32_t)candidates.size();
@@ -145,7 +145,7 @@ static std::string generate(llama_context * ctx, const std::string & prompt, boo
         std::printf("\n");
     }
 
-    llama_batch_free(bat);
+    llama_batch_free(batch);
 
     return result;
 }

examples/imatrix/imatrix.cpp

@@ -513,7 +513,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
                 tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
             }
 
-            // TODO: use batch.logits to save computations instead of relying on logits_all == true
+            // TODO: use batch.output to save computations instead of relying on logits_all == true
             if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
                 fprintf(stderr, "%s : failed to eval\n", __func__);
                 return false;

examples/llama.android/llama/src/main/cpp/llama-android.cpp

@@ -193,7 +193,7 @@ Java_android_llama_cpp_LLamaAndroid_bench_1model(
             llama_batch_add(*batch, 0, i, { 0 }, false);
         }
 
-        batch->logits[batch->n_tokens - 1] = true;
+        batch->output[batch->n_tokens - 1] = true;
         llama_kv_cache_clear(context);
 
         const auto t_pp_start = ggml_time_us();
@@ -306,7 +306,7 @@ Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens,
     for (int i = 0; i < n_tokens; ++i) {
         batch->seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
     }
-    batch->logits   = (int8_t *)        malloc(sizeof(int8_t)         * n_tokens);
+    batch->output   = (int8_t *)        malloc(sizeof(int8_t)         * n_tokens);
 
     return reinterpret_cast<jlong>(batch);
 }
@@ -363,7 +363,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init(
     }
 
     // llama_decode will output logits only for the last token of the prompt
-    batch->logits[batch->n_tokens - 1] = true;
+    batch->output[batch->n_tokens - 1] = true;
 
     if (llama_decode(context, *batch) != 0) {
         LOGe("llama_decode() failed");

examples/llama.swiftui/llama.cpp.swift/LibLlama.swift

@@ -16,7 +16,7 @@ func llama_batch_add(_ batch: inout llama_batch, _ id: llama_token, _ pos: llama
     for i in 0..<seq_ids.count {
         batch.seq_id[Int(batch.n_tokens)]![Int(i)] = seq_ids[i]
     }
-    batch.logits  [Int(batch.n_tokens)] = logits ? 1 : 0
+    batch.output  [Int(batch.n_tokens)] = logits ? 1 : 0
 
     batch.n_tokens += 1
 }
@@ -132,7 +132,7 @@ actor LlamaContext {
             let i = Int(i1)
             llama_batch_add(&batch, tokens_list[i], Int32(i), [0], false)
         }
-        batch.logits[Int(batch.n_tokens) - 1] = 1 // true
+        batch.output[Int(batch.n_tokens) - 1] = 1 // true
 
         if llama_decode(context, batch) != 0 {
             print("llama_decode() failed")
@@ -214,7 +214,7 @@ actor LlamaContext {
             for i in 0..<n_tokens {
                 llama_batch_add(&batch, 0, Int32(i), [0], false)
             }
-            batch.logits[Int(batch.n_tokens) - 1] = 1 // true
+            batch.output[Int(batch.n_tokens) - 1] = 1 // true
 
             llama_kv_cache_clear(context)
 

examples/parallel/parallel.cpp

@@ -265,7 +265,7 @@ int main(int argc, char ** argv) {
 
                     // extract the logits only for the last token
                     if (batch.n_tokens > 0) {
-                        batch.logits[batch.n_tokens - 1] = true;
+                        batch.output[batch.n_tokens - 1] = true;
                     }
 
                     client.n_prompt  = tokens_prompt.size();
@@ -308,7 +308,7 @@ int main(int argc, char ** argv) {
                 batch.pos      + i,
                 batch.n_seq_id + i,
                 batch.seq_id   + i,
-                batch.logits   + i,
+                batch.output   + i,
                 0, 0, 0, // unused
             };
 

examples/passkey/passkey.cpp

@@ -140,7 +140,7 @@ int main(int argc, char ** argv) {
         }
 
         if (i + n_batch >= n_tokens_all) {
-            batch.logits[batch.n_tokens - 1] = true;
+            batch.output[batch.n_tokens - 1] = true;
         }
 
         if (llama_decode(ctx, batch) != 0) {
@@ -174,7 +174,7 @@ int main(int argc, char ** argv) {
         }
 
         if (i + n_batch >= n_tokens_all) {
-            batch.logits[batch.n_tokens - 1] = true;
+            batch.output[batch.n_tokens - 1] = true;
         }
 
         if (llama_decode(ctx, batch) != 0) {

examples/perplexity/perplexity.cpp

@@ -407,7 +407,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
             const int batch_size  = std::min(end - batch_start, n_batch);
 
             //fprintf(stderr, "    Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
-            // TODO: use llama_batch.logits instead of relying on logits_all == true
+            // TODO: use llama_batch.output instead of relying on logits_all == true
             if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
                 //fprintf(stderr, "%s : failed to eval\n", __func__);
                 return {tokens, -1, logit_history, prob_history};
@@ -601,9 +601,9 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
                     batch.pos     [idx]    = j*n_batch + k;
                     batch.n_seq_id[idx]    = 1;
                     batch.seq_id  [idx][0] = seq;
-                    batch.logits  [idx]    = batch.pos[idx] >= first ? 1 : 0;
+                    batch.output  [idx]    = batch.pos[idx] >= first ? 1 : 0;
 
-                    n_outputs += batch.logits[idx] != 0;
+                    n_outputs += batch.output[idx] != 0;
                 }
                 batch.n_tokens += batch_size;
 
@@ -697,7 +697,7 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<
             batch.pos      + i,
             batch.n_seq_id + i,
             batch.seq_id   + i,
-            batch.logits   + i,
+            batch.output   + i,
             0, 0, 0, // unused
         };
 
@@ -709,7 +709,7 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<
 
         int n_outputs = 0;
         for (int i = 0; i < n_tokens; ++i) {
-            n_outputs += batch_view.logits[i] != 0;
+            n_outputs += batch_view.output[i] != 0;
         }
 
         memcpy(batch_logits.data() + prev_outputs*n_vocab, llama_get_logits(ctx), n_outputs*n_vocab*sizeof(float));
@@ -917,7 +917,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
             for (size_t i = 0; i < hs_cur.common_prefix; ++i) {
                 llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false);
             }
-            batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
+            batch.output[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
             n_logits += 1;
 
             for (int s = 0; s < 4; ++s) {
@@ -1196,7 +1196,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
             for (size_t i = 0; i < data[i1].common_prefix; ++i) {
                 llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false);
             }
-            batch.logits[batch.n_tokens - 1] = true;
+            batch.output[batch.n_tokens - 1] = true;
             n_logits += 1;
 
             for (int s = 0; s < 2; ++s) {
@@ -1565,7 +1565,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
                 //llama_batch_add(batch, cur_task.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
                 llama_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
             }
-            batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
+            batch.output[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
             n_logits += 1;
 
             for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
@@ -1794,7 +1794,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
                 tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
             }
 
-            // TODO: use llama_batch.logits instead of relying on logits_all == true
+            // TODO: use llama_batch.output instead of relying on logits_all == true
             if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
                 fprintf(stderr, "%s : failed to eval\n", __func__);
                 return;

examples/retrieval/retrieval.cpp

@@ -91,7 +91,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
     }
 
     for (int i = 0; i < batch.n_tokens; i++) {
-        if (!batch.logits[i]) {
+        if (!batch.output[i]) {
             continue;
         }
 

examples/server/server.cpp

@@ -1480,7 +1480,7 @@ struct server_context {
         std::vector<float> embd_res(n_embd, 0.0f);
 
         for (int i = 0; i < batch.n_tokens; ++i) {
-            if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) {
+            if (!batch.output[i] || batch.seq_id[i][0] != slot.id + 1) {
                 continue;
             }
 
@@ -2269,7 +2269,7 @@ struct server_context {
                         GGML_ASSERT(batch.n_tokens > 0);
 
                         // extract the logits only for the last token
-                        batch.logits[batch.n_tokens - 1] = true;
+                        batch.output[batch.n_tokens - 1] = true;
 
                         slot.n_decoded = 0;
                         slot.i_batch   = batch.n_tokens - 1;
@@ -2341,7 +2341,7 @@ struct server_context {
                 batch.pos      + i,
                 batch.n_seq_id + i,
                 batch.seq_id   + i,
-                batch.logits   + i,
+                batch.output   + i,
                 0, 0, 0, // unused
             };
 

examples/simple/simple.cpp

@@ -93,7 +93,7 @@ int main(int argc, char ** argv) {
     }
 
     // llama_decode will output logits only for the last token of the prompt
-    batch.logits[batch.n_tokens - 1] = true;
+    batch.output[batch.n_tokens - 1] = true;
 
     if (llama_decode(ctx, batch) != 0) {
         LOG_TEE("%s: llama_decode() failed\n", __func__);

include/llama.h

@@ -220,7 +220,7 @@ extern "C" {
     // - embd   : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
     // - pos    : the positions of the respective token in the sequence
     // - seq_id : the sequence to which the respective token belongs
-    // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output
+    // - output : if zero, the logits (and/or the embeddings) for the respective token will not be output
     //
     typedef struct llama_batch {
         int32_t n_tokens;
@@ -230,7 +230,7 @@ extern "C" {
         llama_pos    *  pos;
         int32_t      *  n_seq_id;
         llama_seq_id ** seq_id;
-        int8_t       *  logits; // TODO: rename this to "output"
+        int8_t       *  output; // Previously named 'logits', renamed to 'output' now.
 
         // NOTE: helpers for smooth API transition - can be deprecated in the future
         //       for future-proof code, use the above fields instead and ignore everything below
@@ -328,7 +328,7 @@ extern "C" {
         enum ggml_type type_v; // data type for V cache [EXPERIMENTAL]
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
-        bool logits_all;  // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
+        bool logits_all;  // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.output instead)
         bool embeddings;  // if true, extract embeddings (together with logits)
         bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
         bool flash_attn;  // whether to use flash attention [EXPERIMENTAL]
@@ -859,9 +859,9 @@ extern "C" {
     LLAMA_API void llama_synchronize(struct llama_context * ctx);
 
     // Token logits obtained from the last call to llama_decode()
-    // The logits for which llama_batch.logits[i] != 0 are stored contiguously
+    // The logits for which llama_batch.output[i] != 0 are stored contiguously
     // in the order they have appeared in the batch.
-    // Rows: number of tokens for which llama_batch.logits[i] != 0
+    // Rows: number of tokens for which llama_batch.output[i] != 0
     // Cols: n_vocab
     LLAMA_API float * llama_get_logits(struct llama_context * ctx);
 
@@ -873,7 +873,7 @@ extern "C" {
 
     // Get all output token embeddings.
     // when pooling_type == LLAMA_POOLING_TYPE_NONE or when using a generative model,
-    // the embeddings for which llama_batch.logits[i] != 0 are stored contiguously
+    // the embeddings for which llama_batch.output[i] != 0 are stored contiguously
     // in the order they have appeared in the batch.
     // shape: [n_outputs*n_embd]
     // Otherwise, returns NULL.