deps(llama.cpp): update, support Gemma models (#1734)

mudler · web-flow · commit 8292781045a9 · 2024-02-21T17:23:38.000+01:00
deps(llama.cpp): update

Signed-off-by: Ettore Di Giacinto &lt;mudler@localai.io&gt;
diff --git a/Makefile b/Makefile
@@ -8,7 +8,7 @@ GOLLAMA_VERSION?=aeba71ee842819da681ea537e78846dc75949ac0
 
 GOLLAMA_STABLE_VERSION?=50cee7712066d9e38306eccadcfbb44ea87df4b7
 
-CPPLLAMA_VERSION?=9350a1cf21b1492c69b20175b73a419b897d6a3a
+CPPLLAMA_VERSION?=88c46cbdac05cebd936511b1d3c74112e721615f
 
 # gpt4all version
 GPT4ALL_REPO?=https://github.com/nomic-ai/gpt4all
diff --git a/backend/cpp/llama/CMakeLists.txt b/backend/cpp/llama/CMakeLists.txt
@@ -2,16 +2,20 @@
 ## XXX: In some versions of CMake clip wasn't being built before llama.
 ## This is an hack for now, but it should be fixed in the future.
 set(TARGET myclip)
-add_library(${TARGET} clip.cpp clip.h)
+add_library(${TARGET} clip.cpp clip.h llava.cpp llava.h)
 install(TARGETS ${TARGET} LIBRARY)
-target_link_libraries(${TARGET} PRIVATE common ggml ${CMAKE_THREAD_LIBS_INIT})
+target_include_directories(myclip PUBLIC .)
+target_include_directories(myclip PUBLIC ../..)
+target_include_directories(myclip PUBLIC ../../common)
+target_link_libraries(${TARGET} PRIVATE common ggml llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if (NOT MSVC)
     target_compile_options(${TARGET} PRIVATE -Wno-cast-qual) # stb_image.h
 endif()
+# END CLIP hack
+
 
 set(TARGET grpc-server)
-# END CLIP hack
 set(CMAKE_CXX_STANDARD 17)
 cmake_minimum_required(VERSION 3.15)
 set(TARGET grpc-server)
diff --git a/backend/cpp/llama/Makefile b/backend/cpp/llama/Makefile
@@ -45,6 +45,9 @@ llama.cpp/examples/grpc-server:
 ## XXX: In some versions of CMake clip wasn't being built before llama.
 ## This is an hack for now, but it should be fixed in the future.
 	cp -rfv llama.cpp/examples/llava/clip.h llama.cpp/examples/grpc-server/clip.h
+	cp -rfv llama.cpp/examples/llava/llava.cpp llama.cpp/examples/grpc-server/llava.cpp
+	echo '#include "llama.h"' > llama.cpp/examples/grpc-server/llava.h
+	cat llama.cpp/examples/llava/llava.h >> llama.cpp/examples/grpc-server/llava.h
 	cp -rfv llama.cpp/examples/llava/clip.cpp llama.cpp/examples/grpc-server/clip.cpp
 
 rebuild:
diff --git a/backend/cpp/llama/grpc-server.cpp b/backend/cpp/llama/grpc-server.cpp
@@ -11,7 +11,8 @@
 #include <memory>
 #include <string>
 #include <getopt.h>
-#include "../llava/clip.h"
+#include "clip.h"
+#include "llava.h"
 #include "stb_image.h"
 #include "common.h"
 #include "json.hpp"
@@ -32,6 +33,7 @@
 #include <grpcpp/grpcpp.h>
 #include <grpcpp/health_check_service_interface.h>
 #include <atomic>
+#include <signal.h>
 
 using grpc::Server;
 using grpc::ServerBuilder;
@@ -51,10 +53,11 @@ struct server_params
     std::string hostname = "127.0.0.1";
     std::vector<std::string> api_keys;
     std::string public_path = "examples/server/public";
-    std::string chat_template = "chatml";
+    std::string chat_template = "";
     int32_t port = 8080;
     int32_t read_timeout = 600;
     int32_t write_timeout = 600;
+    bool slots_endpoint = true;
 };
 
 bool server_verbose = false;
@@ -173,6 +176,7 @@ struct llama_client_slot
     int32_t n_decoded   = 0;
     int32_t n_remaining = -1;
     int32_t i_batch     = -1;
+    int32_t n_predict   = -1;
 
     int32_t num_prompt_tokens           = 0;
     int32_t num_prompt_tokens_processed = 0;
@@ -424,6 +428,7 @@ struct llama_server_context
 
             slot.id = i;
             slot.n_ctx = n_ctx_slot;
+            slot.n_predict = params.n_predict;
 
             LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot);
 
@@ -451,10 +456,6 @@ struct llama_server_context
         default_generation_settings_for_props["seed"] = -1;
 
         batch = llama_batch_init(n_ctx, 0, params.n_parallel);
-
-        // empty system prompt
-        system_prompt = "";
-        system_tokens.clear();
     }
 
     std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
@@ -531,7 +532,7 @@ struct llama_server_context
     bool launch_slot_with_data(llama_client_slot* &slot, json data) {
         slot_params default_params;
         llama_sampling_params default_sparams;
-
+ 
         slot->params.stream             = json_value(data, "stream",            false);
         slot->params.cache_prompt       = json_value(data, "cache_prompt",      false);
         slot->params.n_predict          = json_value(data, "n_predict",         default_params.n_predict);
@@ -555,6 +556,16 @@ struct llama_server_context
         slot->params.seed               = json_value(data, "seed",              default_params.seed);
         slot->sparams.grammar           = json_value(data, "grammar",           default_sparams.grammar);
         slot->sparams.n_probs           = json_value(data, "n_probs",           default_sparams.n_probs);
+        slot->sparams.min_keep          = json_value(data, "min_keep",          default_sparams.min_keep);
+
+        if (slot->n_predict > 0 && slot->params.n_predict > slot->n_predict) {
+            // Might be better to reject the request with a 400 ?
+            LOG_WARNING("Max tokens to predict exceeds server configuration", {
+                {"params.n_predict", slot->params.n_predict},
+                {"slot.n_predict", slot->n_predict},
+            });
+            slot->params.n_predict = slot->n_predict;
+        }
 
         // infill
         if (data.count("input_prefix") != 0)
@@ -683,6 +694,24 @@ struct llama_server_context
             }
         }
 
+        const auto &samplers_sequence = data.find("samplers");
+        if (samplers_sequence != data.end() && samplers_sequence->is_array())
+        {
+            std::vector<std::string> sampler_names;
+            for (const auto &sampler_name : *samplers_sequence)
+            {
+                if (sampler_name.is_string())
+                {
+                    sampler_names.emplace_back(sampler_name);
+                }
+            }
+            slot->sparams.samplers_sequence = sampler_types_from_names(sampler_names, false);
+        }
+        else
+        {
+            slot->sparams.samplers_sequence = default_sparams.samplers_sequence;
+        }
+
         if (multimodal)
         {
             const auto &images_data = data.find("image_data");
@@ -772,27 +801,30 @@ struct llama_server_context
     }
 
     void update_system_prompt() {
-        system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);
+        kv_cache_clear();
+        system_tokens.clear();
 
-        llama_batch_clear(batch);
+        if (!system_prompt.empty()) {
+            system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);
 
-        kv_cache_clear();
+            llama_batch_clear(batch);
 
-        for (int i = 0; i < (int) system_tokens.size(); ++i)
-        {
-            llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
-        }
+            for (int i = 0; i < (int)system_tokens.size(); ++i)
+            {
+                llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
+            }
 
-        if (llama_decode(ctx, batch) != 0)
-        {
-            LOG_TEE("%s: llama_decode() failed\n", __func__);
-            return;
-        }
+            if (llama_decode(ctx, batch) != 0)
+            {
+                LOG_TEE("%s: llama_decode() failed\n", __func__);
+                return;
+            }
 
-        // assign the system KV cache to all parallel sequences
-        for (int32_t i = 1; i < params.n_parallel; ++i)
-        {
-            llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size());
+            // assign the system KV cache to all parallel sequences
+            for (int32_t i = 1; i < params.n_parallel; ++i)
+            {
+                llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size());
+            }
         }
 
         LOG_TEE("system prompt updated\n");
@@ -814,10 +846,8 @@ struct llama_server_context
         name_user      = sys_props.value("anti_prompt", "");
         name_assistant = sys_props.value("assistant_name", "");
 
-        if (slots.size() > 0)
-        {
-            notify_system_prompt_changed();
-        }
+
+        notify_system_prompt_changed();
     }
 
     static size_t find_stopping_strings(const std::string &text, const size_t last_token_size,
@@ -975,44 +1005,12 @@ struct llama_server_context
             {
                 continue;
             }
-            clip_image_f32_batch img_res_v;
-            img_res_v.size = 0;
-            img_res_v.data = nullptr;
-            if (!clip_image_preprocess(clp_ctx, img.img_data, img_res_v))
-            {
-                LOG_TEE("Error processing the given image");
-                clip_free(clp_ctx);
-                clip_image_f32_batch_free(img_res_v);
-                return false;
-            }
-            if (img_res_v.size == 0)
-            {
-                LOG_TEE("Error processing the given image");
-                return false;
-            }
 
-            // note: assumes only one image was returned by clip_image_preprocess
-            clip_image_f32 * img_res = img_res_v.data;
-
-            img.image_tokens = clip_n_patches(clp_ctx);
-            img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx));
-            if (!img.image_embedding)
-            {
-                LOG_TEE("Unable to allocate memory for image embeddings\n");
-                clip_image_f32_batch_free(img_res_v);
-                clip_free(clp_ctx);
-                return false;
-            }
-            LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id);
-            if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding))
-            {
-                LOG_TEE("Unable to encode image\n");
-                clip_image_f32_batch_free(img_res_v);
+            if (!llava_image_embed_make_with_clip_img(clp_ctx, params.n_threads, img.img_data, &img.image_embedding, &img.image_tokens)) {
+                LOG_TEE("Error processing the given image");
                 return false;
             }
 
-            clip_image_f32_batch_free(img_res_v);
-
             img.request_encode_image = false;
         }
 
@@ -1036,8 +1034,15 @@ struct llama_server_context
         const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
         const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
                                 eos_bias->second < 0.0f && std::isinf(eos_bias->second);
+        std::vector<std::string> samplers_sequence;
+        for (const auto &sampler_type : slot.sparams.samplers_sequence)
+        {
+            samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type));
+        }
+
         return json {
             {"n_ctx",             slot.n_ctx},
+            {"n_predict",         slot.n_predict},
             {"model",             params.model_alias},
             {"seed",              slot.params.seed},
             {"temperature",       slot.sparams.temp},
@@ -1065,7 +1070,9 @@ struct llama_server_context
             {"stream",            slot.params.stream},
             {"logit_bias",        slot.sparams.logit_bias},
             {"n_probs",           slot.sparams.n_probs},
+            {"min_keep",          slot.sparams.min_keep},
             {"grammar",           slot.sparams.grammar},
+            {"samplers",          samplers_sequence}
         };
     }
 
@@ -1877,6 +1884,9 @@ static void append_to_generated_text_from_generated_token_probs(llama_server_con
     }
 }
 
+std::function<void(int)> shutdown_handler;
+inline void signal_handler(int signal) { shutdown_handler(signal); }
+
 /////////////////////////////////
 ////////////////////////////////
 //////// LOCALAI code starts below here
@@ -2147,7 +2157,8 @@ class BackendServiceImpl final : public backend::Backend::Service {
     gpt_params params;
     params_parse(request, params);
 
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     // load the model
     if (!llama.load_model(params))
