naming : normalize the name of callback-related identifiers

ggml-ci

common/common.cpp

@@ -1018,7 +1018,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     cparams.attention_type    = params.attention_type;
     cparams.defrag_thold      = params.defrag_thold;
     cparams.cb_eval           = params.cb_eval;
-    cparams.cb_eval_user_data = params.cb_eval_user_data;
+    cparams.cb_eval_ctx       = params.cb_eval_ctx;
     cparams.offload_kqv       = !params.no_kv_offload;
     cparams.flash_attn        = params.flash_attn;
     cparams.no_perf           = params.no_perf;

common/common.h

@@ -173,8 +173,8 @@ struct gpt_params {
     struct cpu_params draft_cpuparams;
     struct cpu_params draft_cpuparams_batch;
 
-    ggml_backend_sched_eval_callback cb_eval = nullptr;
-    void * cb_eval_user_data                 = nullptr;
+    ggml_backend_sched_eval_callback cb_eval     = nullptr;
+    void *                           cb_eval_ctx = nullptr;
 
     ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
 

examples/cvector-generator/cvector-generator.cpp

@@ -50,7 +50,7 @@ static void print_usage(int, char ** argv) {
 
 
 // cb_eval is reused for each pair of positive - negative prompt
-struct callback_data {
+struct callback_context {
     ggml_context * ctx_ggml = nullptr;   // holds v_pos, v_neg, v_diff_filtered
 
     int n_layers = 0;
@@ -155,7 +155,7 @@ struct callback_data {
         return diff_filtered;
     }
 
-    // we don't implement destructor, because we want to reuse callback_data. we just want to free the tensors
+    // we don't implement destructor, because we want to reuse callback_context. we just want to free the tensors
     void reset() {
         for (auto ptr : v_pos) free(ptr->data);
         for (auto ptr : v_neg) free(ptr->data);
@@ -320,20 +320,20 @@ static std::vector<std::string> ctrlvec_load_prompt_file(std::string path, bool
 //////////////////////////////////////////////////
 
 static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
-    auto * cb_data = (callback_data *) user_data;
+    auto * cb_ctx = (callback_context *) user_data;
     static const char * l_out_name = "l_out";
     const bool is_l_out = strncmp(t->name, l_out_name, strlen(l_out_name)) == 0;
 
     if (ask) {
         return is_l_out;
     }
 
-    if (!is_l_out || t->ne[1] != cb_data->n_tokens) {
+    if (!is_l_out || t->ne[1] != cb_ctx->n_tokens) {
         return true;
     }
 
     // save the tensor to current context
-    cb_data->save_tensor_for_layer(t);
+    cb_ctx->save_tensor_for_layer(t);
     return true;
 }
 
@@ -400,12 +400,12 @@ int main(int argc, char ** argv) {
     }
 
 
-    callback_data cb_data;
+    callback_context cb_ctx;
 
     // pass the callback to the backend scheduler
     // it will be executed for each node during the graph computation
     params.cb_eval = cb_eval;
-    params.cb_eval_user_data = &cb_data;
+    params.cb_eval_ctx = &cb_ctx;
     params.warmup = false;
 
     print_build_info();
@@ -445,31 +445,31 @@ int main(int argc, char ** argv) {
     for(size_t i = 0; i < ctx_train.positive_entries.size(); ++i) {
         bool success = false;
         tokenized_prompt t = tokenized_prompts[i];
-        cb_data.n_layers = n_layers;
-        cb_data.n_tokens = t.max_seq_len;
+        cb_ctx.n_layers = n_layers;
+        cb_ctx.n_tokens = t.max_seq_len;
 
         printf("Evaluating prompt[%d/%d]: \"%s\" - \"%s\" (%d tokens)\n",
             (int) i+1, (int) ctx_train.positive_entries.size(),
             tokens_to_str(ctx, t.tokens_pos.cbegin(), t.tokens_pos.cend()).c_str(),
             tokens_to_str(ctx, t.tokens_neg.cbegin(), t.tokens_neg.cend()).c_str(),
             (int) t.max_seq_len);
 
-        cb_data.is_eval_pos = true;
+        cb_ctx.is_eval_pos = true;
         success = get_hidden_layers(ctx, t.tokens_pos);
         if (!success) break;
 
-        cb_data.is_eval_pos = false;
+        cb_ctx.is_eval_pos = false;
         success = get_hidden_layers(ctx, t.tokens_neg);
         if (!success) break;
 
         // calculate diff and remove all zero rows
-        auto v_diff_filtered = cb_data.calc_diff();
+        auto v_diff_filtered = cb_ctx.calc_diff();
 
         // save & concat the filtered v_diff to ctx_train
         ctx_train.concat_diff_tmp(v_diff_filtered);
 
         // reset for next iteration
-        cb_data.reset();
+        cb_ctx.reset();
     }
 
     // done with the model, we can now free it to make gain some memory

examples/eval-callback/eval-callback.cpp

@@ -12,7 +12,7 @@
  * This the arbitrary data which will be passed to each callback.
  * Later on we can for example add operation or tensor name filter from the CLI arg, or a file descriptor to dump the tensor.
  */
-struct callback_data {
+struct callback_context {
     std::vector<uint8_t> data;
 };
 
@@ -27,7 +27,7 @@ static std::string ggml_ne_string(const ggml_tensor * t) {
     return str;
 }
 
-static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
+static void ggml_print_tensor(const uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
     GGML_ASSERT(n > 0);
     float sum = 0;
     for (int64_t i3 = 0; i3 < ne[3]; i3++) {
@@ -52,15 +52,15 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne
                     size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
                     float v;
                     if (type == GGML_TYPE_F16) {
-                        v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]);
+                        v = ggml_fp16_to_fp32(*(const ggml_fp16_t *) &data[i]);
                     } else if (type == GGML_TYPE_F32) {
-                        v = *(float *) &data[i];
+                        v = *(const float *) &data[i];
                     } else if (type == GGML_TYPE_I32) {
-                        v = (float) *(int32_t *) &data[i];
+                        v = (float) *(const int32_t *) &data[i];
                     } else if (type == GGML_TYPE_I16) {
-                        v = (float) *(int16_t *) &data[i];
+                        v = (float) *(const int16_t *) &data[i];
                     } else if (type == GGML_TYPE_I8) {
-                        v = (float) *(int8_t *) &data[i];
+                        v = (float) *(const int8_t *) &data[i];
                     } else {
                         GGML_ABORT("fatal error");
                     }
@@ -88,7 +88,7 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne
  * @return true to receive data or continue the graph, false otherwise
  */
 static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
-    auto * cb_data = (callback_data *) user_data;
+    auto * cb_ctx = (callback_context *) user_data;
 
     const struct ggml_tensor * src0 = t->src[0];
     const struct ggml_tensor * src1 = t->src[1];
@@ -114,12 +114,12 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
 
     if (!is_host) {
         auto n_bytes = ggml_nbytes(t);
-        cb_data->data.resize(n_bytes);
-        ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
+        cb_ctx->data.resize(n_bytes);
+        ggml_backend_tensor_get(t, cb_ctx->data.data(), 0, n_bytes);
     }
 
     if (!ggml_is_quantized(t->type)) {
-        uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
+        uint8_t * data = is_host ? (uint8_t *) t->data : cb_ctx->data.data();
         ggml_print_tensor(data, t->type, t->ne, t->nb, 3);
     }
 
@@ -140,7 +140,7 @@ static bool run(llama_context * ctx, const gpt_params & params) {
 }
 
 int main(int argc, char ** argv) {
-    callback_data cb_data;
+    callback_context cb_ctx;
 
     gpt_params params;
 
@@ -156,7 +156,7 @@ int main(int argc, char ** argv) {
     // pass the callback to the backend scheduler
     // it will be executed for each node during the graph computation
     params.cb_eval = ggml_debug;
-    params.cb_eval_user_data = &cb_data;
+    params.cb_eval_ctx = &cb_ctx;
     params.warmup = false;
 
     // init

examples/imatrix/imatrix.cpp

@@ -602,7 +602,7 @@ int main(int argc, char ** argv) {
     // pass the callback to the backend scheduler
     // it will be executed for each node during the graph computation
     params.cb_eval = ik_collect_imatrix;
-    params.cb_eval_user_data = NULL;
+    params.cb_eval_ctx = NULL;
     params.warmup = false;
 
     // init

ggml/include/ggml-backend.h

@@ -104,7 +104,7 @@ extern "C" {
     GGML_API GGML_CALL bool ggml_backend_is_cpu                (ggml_backend_t backend);
     GGML_API           void ggml_backend_cpu_set_n_threads     (ggml_backend_t backend_cpu, int n_threads);
     GGML_API           void ggml_backend_cpu_set_threadpool    (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool);
-    GGML_API           void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data);
+    GGML_API           void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback cb, void * cb_ctx);
 
     // Create a backend buffer from an existing pointer
     GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
@@ -177,7 +177,7 @@ extern "C" {
     // when ask == false, the scheduler is passing the node tensor to the user for observation
     // if the user returns false, the scheduler will cancel the graph compute
     //
-    typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
+    typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * cb_ctx);
 
     // Initialize a backend scheduler
     GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
@@ -208,7 +208,7 @@ extern "C" {
     GGML_API void                 ggml_backend_sched_reset(ggml_backend_sched_t sched);
 
     // Set a callback to be called for each resulting node during graph compute
-    GGML_API void                 ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
+    GGML_API void                 ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback cb, void * cb_ctx);
 
     //
     // Utils
@@ -225,10 +225,10 @@ extern "C" {
     GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
     GGML_API void                           ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
 
-    typedef bool (*GGML_CALL ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
+    typedef bool (*GGML_CALL ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * cb_ctx);
 
     // Compare the output of two backends
-    GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
+    GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback cb_eval, void * cb_eval_ctx);
 
     // Tensor initialization
     GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);

ggml/include/ggml-metal.h

@@ -40,7 +40,7 @@ extern "C" {
 // user-code should use only these functions
 //
 
-GGML_API void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data);
+GGML_API void ggml_backend_metal_log_set_callback(ggml_log_callback cb, void * cb_ctx);
 
 GGML_API ggml_backend_t ggml_backend_metal_init(void);
 
@@ -50,7 +50,7 @@ GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void
 
 GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
 
-GGML_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data);
+GGML_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback cb, void * cb_ctx);
 
 GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
 

ggml/include/ggml.h

@@ -620,7 +620,7 @@ extern "C" {
     // Abort callback
     // If not NULL, called before ggml computation
     // If it returns true, the computation is aborted
-    typedef bool (*ggml_abort_callback)(void * data);
+    typedef bool (*ggml_abort_callback)(void * cb_ctx);
 
     // Scheduling priorities
     enum ggml_sched_priority {
@@ -655,8 +655,8 @@ extern "C" {
         struct ggml_threadpool * threadpool;
 
         // abort ggml_graph_compute when true
-        ggml_abort_callback abort_callback;
-        void *              abort_callback_data;
+        ggml_abort_callback cb_abort;
+        void *              cb_abort_ctx;
     };
 
     // scratch buffer
@@ -2143,8 +2143,8 @@ extern "C" {
         GGML_LINESEARCH_INVALID_PARAMETERS,
     };
 
-    typedef void (*ggml_opt_callback)(void * data, int accum_step, float * sched, bool * cancel);
-    typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * user_data);
+    typedef void (*ggml_opt_callback)(void * cb_ctx, int accum_step, float * sched, bool * cancel);
+    typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * cb_ctx);
 
     // optimization parameters
     //
@@ -2281,8 +2281,8 @@ extern "C" {
             struct ggml_tensor * f,
             struct ggml_cgraph * gf,
             struct ggml_cgraph * gb,
-            ggml_opt_callback callback,
-            void * callback_data);
+            ggml_opt_callback cb_opt,
+            void * cb_opt_ctx);
 
     //
     // tensor flags