Temp

.github/workflows/build.yml

@@ -63,6 +63,7 @@ env:
 
 jobs:
 
+
     # CUDA Release
 
     ubuntu-latest-cmake:

common/common.cpp

@@ -1003,6 +1003,9 @@ static ggml_type kv_cache_type_from_str(const std::string & s) {
     if (s == "f16") {
         return GGML_TYPE_F16;
     }
+    if (s == "bf16") {
+        return GGML_TYPE_BF16;
+    }
     if (s == "q8_0") {
         return GGML_TYPE_Q8_0;
     }

examples/server/README.md

@@ -692,7 +692,10 @@ Given a ChatML-formatted json description in `messages`, it returns the predicte
 
 ### GET `/slots`: Returns the current slots processing state
 
-This endpoint can be disabled with `--no-slots`
+> [!WARNING]
+> This endpoint is intended for debugging and may be modified in future versions. For security reasons, we strongly advise against enabling it in production environments.
+
+This endpoint is disabled by default and can be enabled with `--slots`
 
 If query param `?fail_on_no_slot=1` is set, this endpoint will respond with status code 503 if there is no available slots.
 
@@ -709,6 +712,7 @@ Example:
         "grammar": "",
         "id": 0,
         "ignore_eos": false,
+        "is_processing": false,
         "logit_bias": [],
         "min_p": 0.05000000074505806,
         "mirostat": 0,
@@ -741,7 +745,6 @@ Example:
             "temperature"
         ],
         "seed": 42,
-        "state": 1,
         "stop": [
             "\n"
         ],
@@ -755,10 +758,6 @@ Example:
 ]
 ```
 
-Possible values for `slot[i].state` are:
-- `0`: SLOT_STATE_IDLE
-- `1`: SLOT_STATE_PROCESSING
-
 ### GET `/metrics`: Prometheus compatible metrics exporter
 
 This endpoint is only accessible if `--metrics` is set.

examples/server/server.cpp

@@ -378,8 +378,8 @@ struct server_queue {
     std::condition_variable condition_tasks;
 
     // callback functions
-    std::function<void(server_task&)> callback_new_task;
-    std::function<void(void)>         callback_update_slots;
+    std::function<void(server_task)> callback_new_task;
+    std::function<void(void)>        callback_update_slots;
 
     // Add a new task to the end of the queue
     int post(server_task task, bool front = false) {
@@ -431,7 +431,7 @@ struct server_queue {
     }
 
     // Register function to process a new task
-    void on_new_task(std::function<void(server_task &)> callback) {
+    void on_new_task(std::function<void(server_task)> callback) {
         callback_new_task = std::move(callback);
     }
 
@@ -481,7 +481,7 @@ struct server_queue {
                 lock.unlock();
 
                 QUE_DBG("processing task, id = %d\n", task.id);
-                callback_new_task(task);
+                callback_new_task(std::move(task));
             }
 
             // all tasks in the current loop is processed, slots data is now ready
@@ -644,17 +644,12 @@ struct server_context {
     bool load_model(const common_params & params_) {
         params = params_;
 
-        // reserve one extra sequence (seq_id == 0) for extra features
-        params.n_parallel += 1;
-
         common_init_result llama_init = common_init_from_params(params);
 
         model = llama_init.model;
         ctx   = llama_init.context;
         loras = llama_init.lora_adapters;
 
-        params.n_parallel -= 1; // but be sneaky about it
-
         if (model == nullptr) {
             SRV_ERR("failed to load model, '%s'\n", params.model.c_str());
             return false;
@@ -1288,16 +1283,16 @@ struct server_context {
 
     void send_embedding(const server_slot & slot, const llama_batch & batch) {
         server_task_result res;
-        res.id       = slot.id_task;
-        res.error    = false;
-        res.stop     = true;
+        res.id    = slot.id_task;
+        res.error = false;
+        res.stop  = true;
 
         const int n_embd = llama_n_embd(model);
 
         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.logits[i] || batch.seq_id[i][0] != slot.id) {
                 continue;
             }
 
@@ -1332,12 +1327,12 @@ struct server_context {
 
     void send_rerank(const server_slot & slot, const llama_batch & batch) {
         server_task_result res;
-        res.id       = slot.id_task;
-        res.error    = false;
-        res.stop     = true;
+        res.id    = slot.id_task;
+        res.error = false;
+        res.stop  = true;
 
         for (int i = 0; i < batch.n_tokens; ++i) {
-            if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) {
+            if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
                 continue;
             }
 
@@ -1510,7 +1505,7 @@ struct server_context {
     // Functions to process the task
     //
 
-    void process_single_task(const server_task & task) {
+    void process_single_task(server_task task) {
         switch (task.type) {
             case SERVER_TASK_TYPE_INFERENCE:
                 {
@@ -1566,11 +1561,11 @@ struct server_context {
 
                     for (server_slot & slot : slots) {
                         json slot_data = get_formated_generation(slot);
-                        slot_data["id"]         = slot.id;
-                        slot_data["id_task"]    = slot.id_task;
-                        slot_data["state"]      = slot.state;
-                        slot_data["prompt"]     = common_detokenize(ctx, slot.prompt_tokens);
-                        slot_data["next_token"] = {
+                        slot_data["id"]            = slot.id;
+                        slot_data["id_task"]       = slot.id_task;
+                        slot_data["is_processing"] = slot.is_processing();
+                        slot_data["prompt"]        = common_detokenize(ctx, slot.prompt_tokens);
+                        slot_data["next_token"]    = {
                             {"has_next_token", slot.has_next_token},
                             {"has_new_line",   slot.has_new_line},
                             {"n_remain",       slot.n_remaining},
@@ -1581,10 +1576,10 @@ struct server_context {
                             {"stopping_word",  slot.stopping_word},
                         };
 
-                        if (slot_data["state"] == SLOT_STATE_IDLE) {
-                            n_idle_slots++;
-                        } else {
+                        if (slot.is_processing()) {
                             n_processing_slots++;
+                        } else {
+                            n_idle_slots++;
                         }
 
                         slots_data.push_back(slot_data);
@@ -1646,7 +1641,7 @@ struct server_context {
                     std::string filename = task.data.at("filename");
                     std::string filepath = task.data.at("filepath");
 
-                    const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count);
+                    const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), token_count);
 
                     const int64_t t_end = ggml_time_us();
                     const double t_save_ms = (t_end - t_start) / 1000.0;
@@ -1688,7 +1683,7 @@ struct server_context {
 
                     slot->cache_tokens.resize(slot->n_ctx);
                     size_t token_count = 0;
-                    size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count);
+                    size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count);
                     if (nread == 0) {
                         slot->cache_tokens.resize(0);
                         send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST);
@@ -1731,7 +1726,7 @@ struct server_context {
 
                     // Erase token cache
                     const size_t n_erased = slot->cache_tokens.size();
-                    llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1);
+                    llama_kv_cache_seq_rm(ctx, slot->id, -1, -1);
                     slot->cache_tokens.clear();
 
                     server_task_result result;
@@ -1808,8 +1803,8 @@ struct server_context {
 
                 SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard);
 
-                llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep            , n_keep + n_discard);
-                llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, slot.n_past,        -n_discard);
+                llama_kv_cache_seq_rm (ctx, slot.id, n_keep            , n_keep + n_discard);
+                llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, slot.n_past,        -n_discard);
 
                 if (slot.params.cache_prompt) {
                     for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) {
@@ -1836,7 +1831,7 @@ struct server_context {
 
             slot.i_batch = batch.n_tokens;
 
-            common_batch_add(batch, slot.sampled, slot.n_past, { slot.id + 1 }, true);
+            common_batch_add(batch, slot.sampled, slot.n_past, { slot.id }, true);
 
             slot.n_past += 1;
 
@@ -1983,8 +1978,8 @@ struct server_context {
 
                                             const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c;
 
-                                            llama_kv_cache_seq_rm (ctx, slot.id + 1, head_p, head_c);
-                                            llama_kv_cache_seq_add(ctx, slot.id + 1, head_c, -1,     kv_shift);
+                                            llama_kv_cache_seq_rm (ctx, slot.id, head_p, head_c);
+                                            llama_kv_cache_seq_add(ctx, slot.id, head_c, -1,     kv_shift);
 
                                             for (size_t i = 0; i < n_match; i++) {
                                                 slot.cache_tokens[head_p + i] = slot.cache_tokens[head_c + i];
@@ -2033,9 +2028,9 @@ struct server_context {
                     }
 
                     // keep only the common part
-                    if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, slot.n_past, -1)) {
+                    if (!llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1)) {
                         // could not partially delete (likely using a non-Transformer model)
-                        llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1);
+                        llama_kv_cache_seq_rm(ctx, slot.id, -1, -1);
 
                         // there is no common part left
                         slot.n_past = 0;
@@ -2048,7 +2043,7 @@ struct server_context {
 
                     // add prompt tokens for processing in the current batch
                     while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) {
-                        common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id + 1 }, false);
+                        common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, false);
 
                         if (slot.params.cache_prompt) {
                             slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);

examples/server/tests/features/steps/steps.py

@@ -260,13 +260,13 @@ async def step_wait_for_server_status(context, expecting_status: Literal['health
 async def step_all_slots_status(context, expected_slot_status_string: Literal['idle', 'busy'] | str):
     match expected_slot_status_string:
         case 'idle':
-            expected_slot_status = 0
+            expected_slot_status = False
         case 'busy':
-            expected_slot_status = 1
+            expected_slot_status = True
         case _:
             assert False, "unknown status"
 
-    expected_slots = [{'id': slot_id, 'state': expected_slot_status}
+    expected_slots = [{'id': slot_id, 'is_processing': expected_slot_status}
                       for slot_id in range(context.n_slots)]
     await request_slots_status(context, expected_slots)
 
@@ -1354,8 +1354,8 @@ async def wait_for_slots_status(context,
                 if status_code == 503 and status_code == expected_http_status_code:
                     return
                 if status_code == 200 and status_code == expected_http_status_code:
-                    n_slots_idle = sum(1 if slot["state"] == 0 else 0 for slot in slots)
-                    n_slots_processing = sum(1 if slot["state"] != 0 else 0 for slot in slots)
+                    n_slots_idle = sum(1 if not slot["is_processing"] else 0 for slot in slots)
+                    n_slots_processing = sum(1 if slot["is_processing"] else 0 for slot in slots)
                     if ((slots_idle is None or slots_idle == n_slots_idle)
                         and (slots_processing is None or slots_processing == n_slots_processing)):
                         return

ggml/src/CMakeLists.txt

@@ -1406,7 +1406,7 @@ if (EMSCRIPTEN)
 endif()
 
 target_compile_definitions(ggml PUBLIC    ${GGML_CDEF_PUBLIC})
-target_include_directories(ggml PUBLIC  ../include)
+target_include_directories(ggml PUBLIC    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../include> $<INSTALL_INTERFACE:include>)
 target_include_directories(ggml PRIVATE . ${GGML_EXTRA_INCLUDES})
 target_link_directories   (ggml PRIVATE   ${GGML_EXTRA_LIBDIRS})
 target_compile_features   (ggml PRIVATE c_std_11) # don't bump

ggml/src/ggml-cann.cpp

@@ -1227,7 +1227,6 @@ static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggm
 
     ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(hostPtr, size);
     buffer->buft = buft;
-    buffer->iface.get_name = ggml_backend_cann_host_buffer_name;
     buffer->iface.free_buffer = ggml_backend_cann_host_buffer_free;
 
     return buffer;

ggml/src/ggml-cpu.c

@@ -304,6 +304,7 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
         .nrows                    = 1,
     },
     [GGML_TYPE_Q8_0] = {
+        .from_float_to_mat        = quantize_mat_q8_0,
         .vec_dot                  = ggml_vec_dot_q8_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
 #if defined (__ARM_FEATURE_MATMUL_INT8)
@@ -13677,31 +13678,35 @@ int ggml_cpu_get_sve_cnt(void) {
 }
 
 void ggml_cpu_init(void) {
+    // needed to initialize f16 tables
+    {
+        struct ggml_init_params params = { 0, NULL, false };
+        struct ggml_context * ctx = ggml_init(params);
+        ggml_free(ctx);
+    }
+
     ggml_critical_section_start();
 
     static bool is_first_call = true;
 
     if (is_first_call) {
         // initialize GELU, Quick GELU, SILU and EXP F32 tables
         {
-            // FIXME: this may be called before ggml_init
-            //const uint64_t t_start = ggml_time_us(); UNUSED(t_start);
+            const uint64_t t_start = ggml_time_us(); UNUSED(t_start);
 
             for (int i = 0; i < (1 << 16); ++i) {
                 union {
                     uint16_t u16;
                     ggml_fp16_t fp16;
                 } u = {i};
-                // FIXME: this table is used in conversion functions outside of compute
-                // current code depends on ggml_init initializing this table
-                float f = ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(u.fp16);
+                float f = GGML_FP16_TO_FP32(u.fp16);
                 ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f));
                 ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f));
             }
 
-            //const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
+            const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
 
-            //GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0);
+            GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0);
         }
 
 #if defined(__ARM_ARCH)

ggml/src/ggml-cuda.cu

@@ -1297,11 +1297,17 @@ static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) {
                     cudaError_t err = cudaDeviceEnablePeerAccess(id_other, 0);
                     if (err != cudaErrorPeerAccessAlreadyEnabled) {
                         CUDA_CHECK(err);
+                    } else {
+                        // reset the error
+                        cudaGetLastError();
                     }
                 } else {
                     cudaError_t err = cudaDeviceDisablePeerAccess(id_other);
                     if (err != cudaErrorPeerAccessNotEnabled) {
                         CUDA_CHECK(err);
+                    } else {
+                        // reset the error
+                        cudaGetLastError();
                     }
                 }
             }