server: add data: [DONE] to /chat/completions stream response (#9459)

* cmake : use list(APPEND ...) instead of set() + dedup linker

ggml-ci

* cmake : try fix sycl

* cmake : try to fix sycl 2

* cmake : fix sycl build (#9469)

* try fix sycl build

* use CMAKE_CXX_FLAGS as a string variable

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* one more CMAKE_CXX_FLAGS fix (#9471)

---------

Co-authored-by: Michael Podvitskiy <podvitskiymichael@gmail.com>

.github/workflows/build.yml

@@ -375,7 +375,7 @@ jobs:
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -401,7 +401,7 @@ jobs:
     continue-on-error: true
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
 
       - name: add oneAPI to apt
         shell: bash
@@ -442,7 +442,7 @@ jobs:
     continue-on-error: true
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
 
       - name: add oneAPI to apt
         shell: bash
@@ -546,7 +546,7 @@ jobs:
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v1
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -576,7 +576,7 @@ jobs:
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v1
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -610,7 +610,7 @@ jobs:
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v1
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -969,14 +969,14 @@ jobs:
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Install
         id: depends
         run: |
           $ErrorActionPreference = "Stop"
           write-host "Downloading AMD HIP SDK Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-23.Q4-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP SDK"
           Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
           write-host "Completed AMD HIP SDK installation"

.github/workflows/server.yml

@@ -173,6 +173,7 @@ jobs:
         if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
         run: |
           cd examples/server/tests
+          $env:PYTHONIOENCODING = ":replace"
           behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
 
       - name: Slow tests

CMakeLists.txt

@@ -139,10 +139,16 @@ set(LLAMA_BIN_INSTALL_DIR     ${CMAKE_INSTALL_BINDIR}     CACHE PATH "Location o
 # determining _precisely_ which defines are necessary for the llama-config
 # package.
 #
+set(GGML_TRANSIENT_DEFINES)
 get_target_property(GGML_DIRECTORY ggml SOURCE_DIR)
 get_directory_property(GGML_DIR_DEFINES DIRECTORY ${GGML_DIRECTORY} COMPILE_DEFINITIONS)
+if (GGML_DIR_DEFINES)
+    list(APPEND GGML_TRANSIENT_DEFINES ${GGML_DIR_DEFINES})
+endif()
 get_target_property(GGML_TARGET_DEFINES ggml COMPILE_DEFINITIONS)
-set(GGML_TRANSIENT_DEFINES ${GGML_TARGET_DEFINES} ${GGML_DIR_DEFINES})
+if (GGML_TARGET_DEFINES)
+    list(APPEND GGML_TRANSIENT_DEFINES ${GGML_TARGET_DEFINES})
+endif()
 get_target_property(GGML_LINK_LIBRARIES ggml LINK_LIBRARIES)
 
 set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h)

Makefile

@@ -434,7 +434,7 @@ endif
 # TODO: probably these flags need to be tweaked on some architectures
 #       feel free to update the Makefile for your architecture and send a pull request or issue
 
-ifndef RISCV
+ifndef RISCV_CROSS_COMPILE
 
 ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
 	# Use all CPU extensions that are available:
@@ -514,7 +514,12 @@ ifneq ($(filter loongarch64%,$(UNAME_M)),)
 	MK_CXXFLAGS += -mlasx
 endif
 
-else
+ifneq ($(filter riscv64%,$(UNAME_M)),)
+	MK_CFLAGS   += -march=rv64gcv -mabi=lp64d
+	MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d
+endif
+
+else # RISC-V CROSS COMPILATION
 	MK_CFLAGS   += -march=rv64gcv -mabi=lp64d
 	MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d
 endif
@@ -925,6 +930,7 @@ OBJ_LLAMA = \
 
 OBJ_COMMON = \
 	common/common.o \
+	common/arg.o \
 	common/console.o \
 	common/ngram-cache.o \
 	common/sampling.o \
@@ -1157,6 +1163,11 @@ common/common.o: \
 	include/llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
+common/arg.o: \
+	common/arg.cpp \
+	common/arg.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
 common/sampling.o: \
 	common/sampling.cpp \
 	common/sampling.h \
@@ -1429,6 +1440,7 @@ llama-server: \
 	examples/server/system-prompts.js.hpp \
 	examples/server/prompt-formats.js.hpp \
 	examples/server/json-schema-to-grammar.mjs.hpp \
+	examples/server/loading.html.hpp \
 	common/json.hpp \
 	common/stb_image.h \
 	$(OBJ_ALL)
@@ -1448,7 +1460,6 @@ llama-gen-docs: examples/gen-docs/gen-docs.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-	./llama-gen-docs
 
 libllava.a: examples/llava/llava.cpp \
 	examples/llava/llava.h \

README.md

@@ -89,6 +89,7 @@ Typically finetunes of the base models below are supported as well.
 - [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
 - [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
 - [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
+- [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
 
 (instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md))
 

common/CMakeLists.txt

@@ -54,6 +54,8 @@ add_library(${TARGET} STATIC
     base64.hpp
     common.h
     common.cpp
+    arg.h
+    arg.cpp
     sampling.h
     sampling.cpp
     console.h

common/arg.h

@@ -0,0 +1,77 @@
+#pragma once
+
+#include "common.h"
+
+#include <set>
+#include <string>
+#include <vector>
+
+//
+// CLI argument parsing
+//
+
+struct llama_arg {
+    std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
+    std::vector<const char *> args;
+    const char * value_hint   = nullptr; // help text or example for arg value
+    const char * value_hint_2 = nullptr; // for second arg value
+    const char * env          = nullptr;
+    std::string help;
+    bool is_sparam = false; // is current arg a sampling param?
+    void (*handler_void)   (gpt_params & params) = nullptr;
+    void (*handler_string) (gpt_params & params, const std::string &) = nullptr;
+    void (*handler_str_str)(gpt_params & params, const std::string &, const std::string &) = nullptr;
+    void (*handler_int)    (gpt_params & params, int) = nullptr;
+
+    llama_arg(
+        const std::initializer_list<const char *> & args,
+        const char * value_hint,
+        const std::string & help,
+        void (*handler)(gpt_params & params, const std::string &)
+    ) : args(args), value_hint(value_hint), help(help), handler_string(handler) {}
+
+    llama_arg(
+        const std::initializer_list<const char *> & args,
+        const char * value_hint,
+        const std::string & help,
+        void (*handler)(gpt_params & params, int)
+    ) : args(args), value_hint(value_hint), help(help), handler_int(handler) {}
+
+    llama_arg(
+        const std::initializer_list<const char *> & args,
+        const std::string & help,
+        void (*handler)(gpt_params & params)
+    ) : args(args), help(help), handler_void(handler) {}
+
+    // support 2 values for arg
+    llama_arg(
+        const std::initializer_list<const char *> & args,
+        const char * value_hint,
+        const char * value_hint_2,
+        const std::string & help,
+        void (*handler)(gpt_params & params, const std::string &, const std::string &)
+    ) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
+
+    llama_arg & set_examples(std::initializer_list<enum llama_example> examples);
+    llama_arg & set_env(const char * env);
+    llama_arg & set_sparam();
+    bool in_example(enum llama_example ex);
+    bool get_value_from_env(std::string & output);
+    bool has_value_from_env();
+    std::string to_string();
+};
+
+struct gpt_params_context {
+    enum llama_example ex = LLAMA_EXAMPLE_COMMON;
+    gpt_params & params;
+    std::vector<llama_arg> options;
+    void(*print_usage)(int, char **) = nullptr;
+    gpt_params_context(gpt_params & params) : params(params) {}
+};
+
+// parse input arguments from CLI
+// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
+bool gpt_params_parse(int argc, char ** argv, gpt_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
+
+// function to be used by test-arg-parser
+gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);

common/common.h

@@ -4,20 +4,11 @@
 
 #include "llama.h"
 
-#include "sampling.h"
-
 #define LOG_NO_FILE_LINE_FUNCTION
 #include "log.h"
 
-#include <cmath>
 #include <string>
 #include <vector>
-#include <random>
-#include <thread>
-#include <set>
-#include <unordered_map>
-#include <tuple>
-#include <functional>
 
 #ifdef _WIN32
 #define DIRECTORY_SEPARATOR '\\'
@@ -56,11 +47,20 @@ struct llama_control_vector_load_info;
 // CPU utils
 //
 
+struct cpu_params {
+    int      n_threads                   = -1;
+    bool     cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask.
+    bool     mask_valid                  = false;   // Default: any CPU
+    enum ggml_sched_priority  priority   = GGML_SCHED_PRIO_NORMAL;  // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime)
+    bool     strict_cpu                  = false;   // Use strict CPU placement
+    uint32_t poll                        = 50;      // Polling (busywait) level (0 - no polling, 100 - mostly polling)
+};
+
 int32_t cpu_get_num_physical_cores();
 int32_t cpu_get_num_math();
 
 //
-// CLI argument parsing
+// Common params
 //
 
 enum llama_example {
@@ -78,28 +78,72 @@ enum llama_example {
     LLAMA_EXAMPLE_CVECTOR_GENERATOR,
     LLAMA_EXAMPLE_EXPORT_LORA,
     LLAMA_EXAMPLE_LLAVA,
+    LLAMA_EXAMPLE_LOOKUP,
+    LLAMA_EXAMPLE_PARALLEL,
 
     LLAMA_EXAMPLE_COUNT,
 };
 
+enum gpt_sampler_type {
+    GPT_SAMPLER_TYPE_NONE        = 0,
+    GPT_SAMPLER_TYPE_TOP_K       = 1,
+    GPT_SAMPLER_TYPE_TOP_P       = 2,
+    GPT_SAMPLER_TYPE_MIN_P       = 3,
+    GPT_SAMPLER_TYPE_TFS_Z       = 4,
+    GPT_SAMPLER_TYPE_TYPICAL_P   = 5,
+    GPT_SAMPLER_TYPE_TEMPERATURE = 6,
+};
+
 // dimensionality reduction methods, used by cvector-generator
 enum dimre_method {
     DIMRE_METHOD_PCA,
     DIMRE_METHOD_MEAN,
 };
 
-struct cpu_params {
-    int      n_threads                   = -1;
-    bool     cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask.
-    bool     mask_valid                  = false;   // Default: any CPU
-    enum ggml_sched_priority  priority   = GGML_SCHED_PRIO_NORMAL;  // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime)
-    bool     strict_cpu                  = false;   // Use strict CPU placement
-    uint32_t poll                        = 50;      // Polling (busywait) level (0 - no polling, 100 - mostly polling)
+// sampler parameters
+struct gpt_sampler_params {
+    uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
+
+    int32_t n_prev            = 64;    // number of previous tokens to remember
+    int32_t n_probs           = 0;     // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t min_keep          = 0;     // 0 = disabled, otherwise samplers should return at least min_keep tokens
+    int32_t top_k             = 40;    // <= 0 to use vocab size
+    float   top_p             = 0.95f; // 1.0 = disabled
+    float   min_p             = 0.05f; // 0.0 = disabled
+    float   tfs_z             = 1.00f; // 1.0 = disabled
+    float   typ_p             = 1.00f; // typical_p, 1.0 = disabled
+    float   temp              = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
+    float   dynatemp_range    = 0.00f; // 0.0 = disabled
+    float   dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
+    int32_t penalty_last_n    = 64;    // last n tokens to penalize (0 = disable penalty, -1 = context size)
+    float   penalty_repeat    = 1.00f; // 1.0 = disabled
+    float   penalty_freq      = 0.00f; // 0.0 = disabled
+    float   penalty_present   = 0.00f; // 0.0 = disabled
+    int32_t mirostat          = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
+    float   mirostat_tau      = 5.00f; // target entropy
+    float   mirostat_eta      = 0.10f; // learning rate
+    bool    penalize_nl       = false; // consider newlines as a repeatable token
+    bool    ignore_eos        = false;
+    bool    no_perf           = false; // disable performance metrics
+
+    std::vector<enum gpt_sampler_type> samplers = {
+        GPT_SAMPLER_TYPE_TOP_K,
+        GPT_SAMPLER_TYPE_TFS_Z,
+        GPT_SAMPLER_TYPE_TYPICAL_P,
+        GPT_SAMPLER_TYPE_TOP_P,
+        GPT_SAMPLER_TYPE_MIN_P,
+        GPT_SAMPLER_TYPE_TEMPERATURE
+    };
+
+    std::string grammar; // optional BNF-like grammar to constrain sampling
+
+    std::vector<llama_logit_bias> logit_bias; // logit biases to apply
+
+    // print the parameters into a string
+    std::string print() const;
 };
 
 struct gpt_params {
-    enum llama_example curr_ex    = LLAMA_EXAMPLE_COMMON;
-
     int32_t n_predict             =    -1; // new tokens to predict
     int32_t n_ctx                 =     0; // context size
     int32_t n_batch               =  2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
@@ -143,23 +187,23 @@ struct gpt_params {
 
     struct gpt_sampler_params sparams;
 
-    std::string model                = ""; // model path
-    std::string model_draft          = ""; // draft model for speculative decoding
-    std::string model_alias          = "unknown"; // model alias
-    std::string model_url            = ""; // model url to download
-    std::string hf_token             = ""; // HF token
-    std::string hf_repo              = ""; // HF repo
-    std::string hf_file              = ""; // HF file
-    std::string prompt               = "";
-    std::string prompt_file          = ""; // store the external prompt file name
-    std::string path_prompt_cache    = ""; // path to file for saving/loading prompt eval state
-    std::string input_prefix         = ""; // string to prefix user inputs with
-    std::string input_suffix         = ""; // string to suffix user inputs with
-    std::string logdir               = ""; // directory in which to save YAML log files
-    std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding
-    std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding
-    std::string logits_file          = ""; // file for saving *all* logits
-    std::string rpc_servers          = ""; // comma separated list of RPC servers
+    std::string model                = ""; // model path                                                    // NOLINT
+    std::string model_draft          = ""; // draft model for speculative decoding                          // NOLINT
+    std::string model_alias          = "unknown"; // model alias                                            // NOLINT
+    std::string model_url            = ""; // model url to download                                         // NOLINT
+    std::string hf_token             = ""; // HF token                                                      // NOLINT
+    std::string hf_repo              = ""; // HF repo                                                       // NOLINT
+    std::string hf_file              = ""; // HF file                                                       // NOLINT
+    std::string prompt               = "";                                                                  // NOLINT
+    std::string prompt_file          = ""; // store the external prompt file name                           // NOLINT
+    std::string path_prompt_cache    = ""; // path to file for saving/loading prompt eval state             // NOLINT
+    std::string input_prefix         = ""; // string to prefix user inputs with                             // NOLINT
+    std::string input_suffix         = ""; // string to suffix user inputs with                             // NOLINT
+    std::string logdir               = ""; // directory in which to save YAML log files                     // NOLINT
+    std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding           // NOLINT
+    std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding          // NOLINT
+    std::string logits_file          = ""; // file for saving *all* logits                                  // NOLINT
+    std::string rpc_servers          = ""; // comma separated list of RPC servers                           // NOLINT
 
     std::vector<std::string> in_files;   // all input files
     std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
@@ -189,7 +233,6 @@ struct gpt_params {
 
     bool   kl_divergence    = false; // compute KL divergence
 
-    std::function<void(int, char **)> print_usage = nullptr; // print example-specific usage and example
     bool usage             = false; // print usage
     bool use_color         = false; // use color to distinguish generations and inputs
     bool special           = false; // enable special token output
@@ -204,6 +247,7 @@ struct gpt_params {
     bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
     bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
     bool flash_attn        = false; // flash attention
+    bool no_perf           = false; // disable performance metrics
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
     bool logits_all        = false; // return logits for all tokens in the batch
@@ -220,7 +264,7 @@ struct gpt_params {
     std::string cache_type_v = "f16"; // KV cache data type for the V
 
     // multimodal models (see examples/llava)
-    std::string mmproj = "";        // path to multimodal projector
+    std::string mmproj = "";        // path to multimodal projector                                         // NOLINT
     std::vector<std::string> image; // path to image file(s)
 
     // embedding
@@ -236,15 +280,15 @@ struct gpt_params {
     int     n_threads_http = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
 
     std::string hostname      = "127.0.0.1";
-    std::string public_path   = "";
-    std::string chat_template = "";
-    std::string system_prompt = "";
+    std::string public_path   = "";                                                                         // NOLINT
+    std::string chat_template = "";                                                                         // NOLINT
+    std::string system_prompt = "";                                                                         // NOLINT
     bool enable_chat_template = true;
 
     std::vector<std::string> api_keys;
 
-    std::string ssl_file_key  = "";
-    std::string ssl_file_cert = "";
+    std::string ssl_file_key  = "";                                                                         // NOLINT
+    std::string ssl_file_cert = "";                                                                         // NOLINT
 
     bool endpoint_slots   = true;
     bool endpoint_metrics = false;
@@ -299,92 +343,6 @@ struct gpt_params {
     bool batched_bench_output_jsonl = false;
 };
 
-struct llama_arg {
-    std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
-    std::vector<const char *> args;
-    const char * value_hint   = nullptr; // help text or example for arg value
-    const char * value_hint_2 = nullptr; // for second arg value
-    const char * env          = nullptr;
-    std::string help;
-    void (*handler_void)   (gpt_params & params) = nullptr;
-    void (*handler_string) (gpt_params & params, const std::string &) = nullptr;
-    void (*handler_str_str)(gpt_params & params, const std::string &, const std::string &) = nullptr;
-    void (*handler_int)    (gpt_params & params, int) = nullptr;
-
-    llama_arg(
-        const std::initializer_list<const char *> & args,
-        const char * value_hint,
-        const std::string & help,
-        void (*handler)(gpt_params & params, const std::string &)
-    ) : args(args), value_hint(value_hint), help(help), handler_string(handler) {}
-
-    llama_arg(
-        const std::initializer_list<const char *> & args,
-        const char * value_hint,
-        const std::string & help,
-        void (*handler)(gpt_params & params, int)
-    ) : args(args), value_hint(value_hint), help(help), handler_int(handler) {}
-
-    llama_arg(
-        const std::initializer_list<const char *> & args,
-        const std::string & help,
-        void (*handler)(gpt_params & params)
-    ) : args(args), help(help), handler_void(handler) {}
-
-    // support 2 values for arg
-    llama_arg(
-        const std::initializer_list<const char *> & args,
-        const char * value_hint,
-        const char * value_hint_2,
-        const std::string & help,
-        void (*handler)(gpt_params & params, const std::string &, const std::string &)
-    ) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
-
-    llama_arg & set_examples(std::initializer_list<enum llama_example> examples) {
-        this->examples = std::move(examples);
-        return *this;
-    }
-
-    llama_arg & set_env(const char * env) {
-        help = help + "\n(env: " + env + ")";
-        this->env = env;
-        return *this;
-    }
-
-    bool in_example(enum llama_example ex) {
-        return examples.find(ex) != examples.end();
-    }
-
-    bool get_value_from_env(std::string & output) const {
-        if (env == nullptr) return false;
-        char * value = std::getenv(env);
-        if (value) {
-            output = value;
-            return true;
-        }
-        return false;
-    }
-
-    bool has_value_from_env() const {
-        return env != nullptr && std::getenv(env);
-    }
-
-    std::string to_string();
-};
-
-// initialize list of options (arguments) that can be used by the current example
-std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex);
-// optionally, we can provide "print_usage" to print example usage
-std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex, std::function<void(int, char **)> print_usage);
-
-// parse input arguments from CLI
-// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
-bool gpt_params_parse   (int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options);
-bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options);
-
-// print full usage message; it will be called internally by gpt_params_parse() if "-h" is set
-void gpt_params_print_usage(gpt_params & params, std::vector<llama_arg> & options);
-
 std::string gpt_params_get_system_info(const gpt_params & params);
 
 bool parse_cpu_range(const std::string& range, bool(&boolmask)[GGML_MAX_N_THREADS]);

common/sampling.cpp

@@ -2,6 +2,9 @@
 
 #include "common.h"
 
+#include <cmath>
+#include <unordered_map>
+
 // the ring buffer works similarly to std::deque, but with a fixed capacity
 // TODO: deduplicate with llama-impl.h
 template<typename T>
@@ -139,7 +142,7 @@ std::string gpt_sampler_params::print() const {
 struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const struct gpt_sampler_params & params) {
     llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
 
-    lparams.no_perf = false; // TODO: control via params
+    lparams.no_perf = params.no_perf;
 
     auto * result = new gpt_sampler {
         /* .params = */ params,
@@ -254,10 +257,10 @@ void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler *
     // TODO: measure grammar performance
 
     if (gsmpl) {
-        llama_perf_print(gsmpl->chain, LLAMA_PERF_TYPE_SAMPLER_CHAIN);
+        llama_perf_sampler_print(gsmpl->chain);
     }
     if (ctx) {
-        llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+        llama_perf_context_print(ctx);
     }
 }
 
@@ -307,6 +310,10 @@ llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context
     return cur_p.data[cur_p.selected].id;
 }
 
+uint32_t gpt_sampler_get_seed(const struct gpt_sampler * gsmpl) {
+    return llama_sampler_get_seed(gsmpl->chain);
+}
+
 // helpers
 
 llama_token_data_array * gpt_sampler_get_candidates(struct gpt_sampler * gsmpl) {
@@ -420,7 +427,7 @@ std::vector<gpt_sampler_type> gpt_sampler_types_from_names(const std::vector<std
 }
 
 std::vector<gpt_sampler_type> gpt_sampler_types_from_chars(const std::string & chars) {
-    std::unordered_map<char, gpt_sampler_type> sampler_name_map {
+    std::unordered_map<char, gpt_sampler_type> sampler_name_map = {
         { gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TOP_K),       GPT_SAMPLER_TYPE_TOP_K },
         { gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TFS_Z),       GPT_SAMPLER_TYPE_TFS_Z },
         { gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TYPICAL_P),   GPT_SAMPLER_TYPE_TYPICAL_P },

common/sampling.h

@@ -2,61 +2,11 @@
 
 #include "llama.h"
 
+#include "common.h"
+
 #include <string>
 #include <vector>
 
-enum gpt_sampler_type {
-    GPT_SAMPLER_TYPE_NONE        = 0,
-    GPT_SAMPLER_TYPE_TOP_K       = 1,
-    GPT_SAMPLER_TYPE_TOP_P       = 2,
-    GPT_SAMPLER_TYPE_MIN_P       = 3,
-    GPT_SAMPLER_TYPE_TFS_Z       = 4,
-    GPT_SAMPLER_TYPE_TYPICAL_P   = 5,
-    GPT_SAMPLER_TYPE_TEMPERATURE = 6,
-};
-
-// sampling parameters
-struct gpt_sampler_params {
-    uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
-
-    int32_t n_prev            = 64;    // number of previous tokens to remember
-    int32_t n_probs           = 0;     // if greater than 0, output the probabilities of top n_probs tokens.
-    int32_t min_keep          = 0;     // 0 = disabled, otherwise samplers should return at least min_keep tokens
-    int32_t top_k             = 40;    // <= 0 to use vocab size
-    float   top_p             = 0.95f; // 1.0 = disabled
-    float   min_p             = 0.05f; // 0.0 = disabled
-    float   tfs_z             = 1.00f; // 1.0 = disabled
-    float   typ_p             = 1.00f; // typical_p, 1.0 = disabled
-    float   temp              = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
-    float   dynatemp_range    = 0.00f; // 0.0 = disabled
-    float   dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
-    int32_t penalty_last_n    = 64;    // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float   penalty_repeat    = 1.00f; // 1.0 = disabled
-    float   penalty_freq      = 0.00f; // 0.0 = disabled
-    float   penalty_present   = 0.00f; // 0.0 = disabled
-    int32_t mirostat          = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
-    float   mirostat_tau      = 5.00f; // target entropy
-    float   mirostat_eta      = 0.10f; // learning rate
-    bool    penalize_nl       = false; // consider newlines as a repeatable token
-    bool    ignore_eos        = false;
-
-    std::vector<enum gpt_sampler_type> samplers = {
-        GPT_SAMPLER_TYPE_TOP_K,
-        GPT_SAMPLER_TYPE_TFS_Z,
-        GPT_SAMPLER_TYPE_TYPICAL_P,
-        GPT_SAMPLER_TYPE_TOP_P,
-        GPT_SAMPLER_TYPE_MIN_P,
-        GPT_SAMPLER_TYPE_TEMPERATURE
-    };
-
-    std::string grammar; // optional BNF-like grammar to constrain sampling
-
-    std::vector<llama_logit_bias> logit_bias; // logit biases to apply
-
-    // print the parameters into a string
-    std::string print() const;
-};
-
 // gpt_sampler extends llama_sampler with additional functionality:
 //
 //  - grammar support
@@ -110,6 +60,8 @@ void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler *
 //
 llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
 
+uint32_t gpt_sampler_get_seed(const struct gpt_sampler * gsmpl);
+
 // helpers
 
 // access the internal list of current candidate tokens

convert_hf_to_gguf.py

@@ -302,6 +302,8 @@ class Model:
                             gguf.MODEL_TENSOR.TIME_MIX_FIRST,
                             gguf.MODEL_TENSOR.TIME_MIX_W1,
                             gguf.MODEL_TENSOR.TIME_MIX_W2,
+                            gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1,
+                            gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2,
                         )
                     )
                     or not new_name.endswith(".weight")
@@ -624,6 +626,9 @@ class Model:
         if chkhsh == "4e2b24cc4770243d65a2c9ec19770a72f08cffc161adbb73fcbb6b7dd45a0aae":
             # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct
             res = "exaone"
+        if chkhsh == "fcace8b9cac38ce847670c970cd5892031a753a1ef381abd1d9af00f713da085":
+            # ref: https://huggingface.co/microsoft/phi-2
+            res = "phi-2"
 
         if res is None:
             logger.warning("\n")
@@ -2769,6 +2774,8 @@ class Rwkv6Model(Model):
         self.gguf_writer.add_tokenizer_model("rwkv")
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_types(toktypes)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+        special_vocab.add_to_gguf(self.gguf_writer)
 
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]

convert_hf_to_gguf_update.py

@@ -31,6 +31,7 @@ import re
 import requests
 import sys
 import json
+import shutil
 
 from hashlib import sha256
 from enum import IntEnum, auto
@@ -97,6 +98,7 @@ models = [
     {'name': "bloom",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", },
     {'name': "gpt3-finnish",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", },
     {"name": "exaone",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", },
+    {"name": "phi-2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", },
 ]
 
 
@@ -125,12 +127,27 @@ def download_model(model):
     if tokt == TOKENIZER_TYPE.UGM:
         files.append("spiece.model")
 
-    for file in files:
-        save_path = f"models/tokenizers/{name}/{file}"
-        if os.path.isfile(save_path):
-            logger.info(f"{name}: File {save_path} already exists - skipping")
-            continue
-        download_file_with_auth(f"{repo}/resolve/main/{file}", token, save_path)
+    if os.path.isdir(repo):
+        # If repo is a path on the file system, copy the directory
+        for file in files:
+            src_path = os.path.join(repo, file)
+            dst_path = f"models/tokenizers/{name}/{file}"
+            if os.path.isfile(dst_path):
+                logger.info(f"{name}: File {dst_path} already exists - skipping")
+                continue
+            if os.path.isfile(src_path):
+                shutil.copy2(src_path, dst_path)
+                logger.info(f"{name}: Copied {src_path} to {dst_path}")
+            else:
+                logger.warning(f"{name}: Source file {src_path} does not exist")
+    else:
+        # If repo is a URL, download the files
+        for file in files:
+            save_path = f"models/tokenizers/{name}/{file}"
+            if os.path.isfile(save_path):
+                logger.info(f"{name}: File {save_path} already exists - skipping")
+                continue
+            download_file_with_auth(f"{repo}/resolve/main/{file}", token, save_path)
 
 
 for model in models:

convert_lora_to_gguf.py

@@ -363,7 +363,13 @@ if __name__ == '__main__':
                     yield (name, cast(torch.Tensor, LoraTorchTensor(tensor.A, tensor.B)))
 
             def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-                dest = super().modify_tensors(data_torch, name, bid)
+                dest = list(super().modify_tensors(data_torch, name, bid))
+                # some archs may have the same tensor for lm_head and output (tie word embeddings)
+                # in this case, adapters targeting lm_head will fail when using llama-export-lora
+                # therefore, we ignore them for now
+                # see: https://github.com/ggerganov/llama.cpp/issues/9065
+                if name == "lm_head.weight" and len(dest) == 0:
+                    raise ValueError("lm_head is present in adapter, but is ignored in base model")
                 for dest_name, dest_data in dest:
                     assert isinstance(dest_data, LoraTorchTensor)
                     lora_a, lora_b = dest_data.get_lora_A_B()

examples/batched-bench/batched-bench.cpp

@@ -1,33 +1,12 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
 #include <algorithm>
-#include <cmath>
 #include <cstdio>
 #include <string>
 #include <vector>
 
-// mutates the input string
-static std::vector<int> parse_list(char * p) {
-    std::vector<int> ret;
-
-    char * q = p;
-
-    while (*p) {
-        if (*p == ',') {
-            *p = '\0';
-            ret.push_back(std::atoi(q));
-            q = p + 1;
-        }
-
-        ++p;
-    }
-
-    ret.push_back(std::atoi(q));
-
-    return ret;
-}
-
 static void print_usage(int, char ** argv) {
     LOG_TEE("\nexample usage:\n");
     LOG_TEE("\n    %s -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]\n", argv[0]);
@@ -37,8 +16,7 @@ static void print_usage(int, char ** argv) {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_BENCH, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_BENCH, print_usage)) {
         return 1;
     }
 
@@ -209,7 +187,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_context_print(ctx);
 
     llama_batch_free(batch);
 

examples/batched.swift/Sources/main.swift

@@ -200,8 +200,8 @@ let t_main_end = ggml_time_us()
 
 print("decoded \(n_decode) tokens in \(String(format: "%.2f", Double(t_main_end - t_main_start) / 1_000_000.0)) s, speed: \(String(format: "%.2f", Double(n_decode) / (Double(t_main_end - t_main_start) / 1_000_000.0))) t/s\n\n")
 
-llama_perf_print(UnsafeRawPointer(context), LLAMA_PERF_TYPE_CONTEXT)
-llama_perf_print(UnsafeRawPointer(smpl),    LLAMA_PERF_TYPE_SAMPLER_CHAIN)
+llama_perf_sampler_print(smpl)
+llama_perf_context_print(context)
 
 private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
     let utf8Count = text.utf8.count

examples/batched/batched.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -18,8 +19,7 @@ int main(int argc, char ** argv) {
     params.prompt = "Hello my name is";
     params.n_predict = 32;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
         return 1;
     }
 
@@ -229,8 +229,8 @@ int main(int argc, char ** argv) {
             __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
 
     LOG_TEE("\n");
-    llama_perf_print(smpl, LLAMA_PERF_TYPE_SAMPLER_CHAIN);
-    llama_perf_print(ctx,  LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_sampler_print(smpl);
+    llama_perf_context_print(ctx);
 
     fprintf(stderr, "\n");
 

examples/benchmark/benchmark-matmult.cpp

@@ -183,7 +183,7 @@ int main(int argc, char ** argv)  {
 
     ggml_graph_compute_helper(work_buffer, gf, benchmark_params.n_threads);
 
-    TENSOR_DUMP(gf->nodes[0]);
+    TENSOR_DUMP(ggml_graph_node(gf, 0));
 
     printf("\n------ Test 2 - Matrix Mult via %s code\n", ggml_type_name(qtype));
 
@@ -224,7 +224,7 @@ int main(int argc, char ** argv)  {
 
 
     // Let's use the F32 result from above as a reference for the quantized multiplication
-    float sum_of_F32_reference = tensor_sum_elements(gf->nodes[0]);
+    float sum_of_F32_reference = tensor_sum_elements(ggml_graph_node(gf, 0));
 
     printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n");
     printf("=====================================================================================\n");
@@ -252,7 +252,7 @@ int main(int argc, char ** argv)  {
 
         // Check that the matrix multiplication result is in the right ballpark
         // We cannot use the exact value from the F32 multiplication because the quantizuation will be slightly different
-        float sum_of_Q4_result = tensor_sum_elements(gf31->nodes[0]);
+        float sum_of_Q4_result = tensor_sum_elements(ggml_graph_node(gf31, 0));
         float delta = std::abs(sum_of_Q4_result - sum_of_F32_reference);
         float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; //  Let's accept an epsilon of 10^-6
 

examples/cvector-generator/cvector-generator.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 #include "ggml.h"
@@ -388,8 +389,7 @@ static int prepare_entries(gpt_params & params, train_context & ctx_train) {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage)) {
         return 1;
     }
 

examples/cvector-generator/pca.hpp

@@ -12,12 +12,9 @@
 
 #include <cstdio>
 #include <ctime>
+#include <random>
 #include <string>
-#include <tuple>
 #include <vector>
-#include <algorithm>
-#include <iostream>
-#include <fstream>
 
 #define DEBUG_POS 5
 
@@ -229,8 +226,8 @@ static ggml_status compute_piter(
         result.eigenvectors.resize(params.n_batch);
         result.distances.resize(params.n_batch);
         // get output nodes
-        for (int i = 0; i < gf->n_nodes; ++i) {
-            auto node = gf->nodes[i];
+        for (int i = 0; i < ggml_graph_n_nodes(gf); ++i) {
+            auto node = ggml_graph_node(gf, i);
             int iter = -1;
             // find b_tensor (without copying data from device)
             if ((iter = extract_i("b_tensor_norm_", node->name)) > -1) {

examples/embedding/embedding.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -79,8 +80,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_EMBEDDING);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_EMBEDDING)) {
         return 1;
     }
 
@@ -90,8 +90,6 @@ int main(int argc, char ** argv) {
 
     print_build_info();
 
-    LOG_TEE("%s: seed = %u\n", __func__, params.sparams.seed);
-
     llama_backend_init();
     llama_numa_init(params.numa);
 
@@ -308,7 +306,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_context_print(ctx);
 
     // clean up
     llama_batch_free(batch);

examples/eval-callback/eval-callback.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 #include "ggml.h"
@@ -144,8 +145,7 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 
@@ -182,7 +182,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_context_print(ctx);
 
     llama_free(ctx);
     llama_free_model(model);

examples/export-lora/export-lora.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "ggml.h"
 #include "ggml-alloc.h"
@@ -369,7 +370,7 @@ struct lora_merge_ctx {
 
         // write data to output file
         {
-            auto result = gf->nodes[gf->n_nodes - 1];
+            auto * result = ggml_graph_node(gf, -1);
             size_t len = ggml_nbytes(result);
             if (read_buf.size() < len) {
                 read_buf.resize(len);
@@ -401,8 +402,7 @@ static void print_usage(int, char ** argv) {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage)) {
         return 1;
     }
 

examples/gen-docs/gen-docs.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 
 #include <fstream>
@@ -9,11 +10,11 @@ static void export_md(std::string fname, llama_example ex) {
     std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc);
 
     gpt_params params;
-    auto options = gpt_params_parser_init(params, ex);
+    auto ctx_arg = gpt_params_parser_init(params, ex);
 
     file << "| Argument | Explanation |\n";
     file << "| -------- | ----------- |\n";
-    for (auto & opt : options) {
+    for (auto & opt : ctx_arg.options) {
         file << "| `";
         // args
         for (const auto & arg : opt.args) {

examples/gritlm/gritlm.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -153,8 +154,7 @@ static std::string gritlm_instruction(const std::string & instruction) {
 int main(int argc, char * argv[]) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 

examples/imatrix/imatrix.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -577,8 +578,7 @@ int main(int argc, char ** argv) {
     params.logits_all = true;
     params.verbosity = 1;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_IMATRIX, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) {
         return 1;
     }
 
@@ -637,7 +637,7 @@ int main(int argc, char ** argv) {
     g_collector.save_imatrix();
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_context_print(ctx);
 
     llama_free(ctx);
     llama_free_model(model);

examples/infill/infill.cpp

@@ -1,6 +1,7 @@
+#include "arg.h"
 #include "common.h"
-
 #include "console.h"
+#include "sampling.h"
 #include "llama.h"
 
 #include <cassert>
@@ -105,8 +106,7 @@ int main(int argc, char ** argv) {
     gpt_params params;
     g_params = &params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_INFILL);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_INFILL)) {
         return 1;
     }
 
@@ -159,8 +159,6 @@ int main(int argc, char ** argv) {
 
     print_build_info();
 
-    LOG_TEE("%s: seed = %u\n", __func__, params.sparams.seed);
-
     LOG("%s: llama backend init\n", __func__);
     llama_backend_init();
     llama_numa_init(params.numa);
@@ -301,6 +299,9 @@ int main(int argc, char ** argv) {
             LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str());
         }
     }
+    smpl = gpt_sampler_init(model, sparams);
+
+    LOG_TEE("sampling seed: %u\n", gpt_sampler_get_seed(smpl));
     LOG_TEE("sampling: \n%s\n", sparams.print().c_str());
     LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
     LOG_TEE("\n\n");
@@ -340,8 +341,6 @@ int main(int argc, char ** argv) {
 
     std::vector<llama_token> embd;
 
-    smpl = gpt_sampler_init(model, sparams);
-
     while (n_remain != 0 || params.interactive) {
         // predict
         if (!embd.empty()) {

examples/llama-bench/llama-bench.cpp

@@ -1630,7 +1630,7 @@ int main(int argc, char ** argv) {
             fflush(p_err->fout);
         }
 
-        llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+        llama_perf_context_print(ctx);
 
         llama_free(ctx);
 

examples/llava/MobileVLM-README.md

@@ -39,7 +39,7 @@ python ./examples/llava/llava_surgery.py -m path/to/MobileVLM-1.7B
 3. Use `convert_image_encoder_to_gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
 
 ```sh
-python ./examples/llava/convert_image_encoder_to_gguf \
+python ./examples/llava/convert_image_encoder_to_gguf.py \
     -m path/to/clip-vit-large-patch14-336 \
     --llava-projector path/to/MobileVLM-1.7B/llava.projector \
     --output-dir path/to/MobileVLM-1.7B \
@@ -47,7 +47,7 @@ python ./examples/llava/convert_image_encoder_to_gguf \
 ```
 
 ```sh
-python ./examples/llava/convert_image_encoder_to_gguf \
+python ./examples/llava/convert_image_encoder_to_gguf.py \
     -m path/to/clip-vit-large-patch14-336 \
     --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \
     --output-dir path/to/MobileVLM-1.7B_V2 \
@@ -57,12 +57,12 @@ python ./examples/llava/convert_image_encoder_to_gguf \
 4. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF:
 
 ```sh
-python ./examples/convert_legacy_llama.py path/to/MobileVLM-1.7B
+python ./examples/convert_legacy_llama.py path/to/MobileVLM-1.7B --skip-unknown
 ```
 
-5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k`
+5. Use `quantize` to convert LLaMA part's DataType from `fp32` to `q4_k`
 ```sh
-./llama-quantize path/to/MobileVLM-1.7B/ggml-model-f16.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s
+./llama-quantize path/to/MobileVLM-1.7B/ggml-model-F32.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s
 ```
 
 Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directory.

examples/llava/clip.cpp

@@ -2449,7 +2449,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     ggml_backend_graph_compute(ctx->backend, gf);
 
     // the last node is the embedding tensor
-    struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 1];
+    struct ggml_tensor * embeddings = ggml_graph_node(gf, -1);
 
     // copy the embeddings to the location passed by the user
     ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));

examples/llava/llava-cli.cpp

@@ -1,11 +1,12 @@
-#include "ggml.h"
+#include "arg.h"
+#include "base64.hpp"
 #include "log.h"
 #include "common.h"
+#include "sampling.h"
 #include "clip.h"
 #include "llava.h"
 #include "llama.h"
-
-#include "base64.hpp"
+#include "ggml.h"
 
 #include <cstdio>
 #include <cstdlib>
@@ -278,8 +279,7 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_LLAVA, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
         return 1;
     }
 
@@ -308,7 +308,7 @@ int main(int argc, char ** argv) {
         // process the prompt
         process_prompt(ctx_llava, image_embed, &params, params.prompt);
 
-        llama_perf_print(ctx_llava->ctx_llama, LLAMA_PERF_TYPE_CONTEXT);
+        llama_perf_context_print(ctx_llava->ctx_llama);
         llava_image_embed_free(image_embed);
         ctx_llava->model = NULL;
         llava_free(ctx_llava);
@@ -325,7 +325,7 @@ int main(int argc, char ** argv) {
             // process the prompt
             process_prompt(ctx_llava, image_embed, &params, params.prompt);
 
-            llama_perf_print(ctx_llava->ctx_llama, LLAMA_PERF_TYPE_CONTEXT);
+            llama_perf_context_print(ctx_llava->ctx_llama);
             llava_image_embed_free(image_embed);
             ctx_llava->model = NULL;
             llava_free(ctx_llava);

examples/llava/llava.cpp

@@ -184,7 +184,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
     // ggml_tensor_printf(flatten,"flatten",__LINE__,false,false);
     ggml_build_forward_expand(gf, flatten);
     ggml_graph_compute_with_ctx(model.ctx, gf, 1);
-    struct ggml_tensor* result = gf->nodes[gf->n_nodes - 1];
+    struct ggml_tensor* result = ggml_graph_node(gf, -1);
 
     memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context
     // append without newline tokens (default behavior in llava_arch when not using unpad ):

examples/llava/minicpmv-cli.cpp

@@ -1,9 +1,11 @@
-#include "ggml.h"
+#include "arg.h"
 #include "log.h"
 #include "common.h"
+#include "sampling.h"
 #include "clip.h"
 #include "llava.h"
 #include "llama.h"
+#include "ggml.h"
 
 #include <cstdio>
 #include <cstdlib>
@@ -16,8 +18,8 @@ struct llava_context {
 };
 
 static void show_additional_info(int /*argc*/, char ** argv) {
-    LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    LOG_TEE("  note: a lower temperature value like 0.1 is recommended for better quality.\n");
+    LOG_TEE("\nexample usage:\n\n%s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    LOG_TEE("\nnote: a lower temperature value like 0.1 is recommended for better quality.\n");
 }
 
 static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
@@ -253,8 +255,7 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, show_additional_info);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) {
         return 1;
     }
 
@@ -318,7 +319,7 @@ int main(int argc, char ** argv) {
             }
         }
         printf("\n");
-        llama_perf_print(ctx_llava->ctx_llama, LLAMA_PERF_TYPE_CONTEXT);
+        llama_perf_context_print(ctx_llava->ctx_llama);
 
         ctx_llava->model = NULL;
         llava_free(ctx_llava);

examples/lookahead/lookahead.cpp

@@ -1,4 +1,6 @@
+#include "arg.h"
 #include "common.h"
+#include "sampling.h"
 #include "llama.h"
 
 #include <cstdio>
@@ -36,8 +38,7 @@ struct ngram_container {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 

examples/lookup/lookup-create.cpp

@@ -1,7 +1,8 @@
-#include "ggml.h"
-#include "llama.h"
+#include "arg.h"
 #include "common.h"
 #include "ngram-cache.h"
+#include "ggml.h"
+#include "llama.h"
 
 #include <cstdint>
 #include <fstream>
@@ -13,8 +14,7 @@
 int main(int argc, char ** argv){
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
         return 1;
     }
 
@@ -40,4 +40,6 @@ int main(int argc, char ** argv){
     fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str());
 
     llama_ngram_cache_save(ngram_cache, params.lookup_cache_static);
+
+    return 0;
 }

examples/lookup/lookup-stats.cpp

@@ -1,8 +1,9 @@
-#include "ggml.h"
+#include "arg.h"
 #include "common.h"
-#include "llama.h"
 #include "log.h"
 #include "ngram-cache.h"
+#include "llama.h"
+#include "ggml.h"
 
 #include <cmath>
 #include <cstdint>
@@ -15,8 +16,7 @@
 int main(int argc, char ** argv){
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
         return 1;
     }
 

examples/lookup/lookup.cpp

@@ -1,7 +1,9 @@
+#include "arg.h"
 #include "ggml.h"
-#include "llama.h"
 #include "common.h"
 #include "ngram-cache.h"
+#include "sampling.h"
+#include "llama.h"
 
 #include <cstdint>
 #include <cstdio>
@@ -12,8 +14,7 @@
 int main(int argc, char ** argv){
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
         return 1;
     }
 
@@ -239,8 +240,7 @@ int main(int argc, char ** argv){
     LOG_TEE("accept       = %.3f%%\n", 100.0f * n_accept / n_drafted);
 
     LOG_TEE("\ntarget:\n\n");
-    llama_perf_print(smpl, LLAMA_PERF_TYPE_SAMPLER_CHAIN);
-    llama_perf_print(ctx,  LLAMA_PERF_TYPE_CONTEXT);
+    gpt_perf_print(ctx, smpl);
 
     gpt_sampler_free(smpl);
 

examples/main/main.cpp

@@ -1,6 +1,7 @@
+#include "arg.h"
 #include "common.h"
-
 #include "console.h"
+#include "sampling.h"
 #include "llama.h"
 
 #include <cassert>
@@ -138,9 +139,7 @@ static std::string chat_add_and_format(struct llama_model * model, std::vector<l
 int main(int argc, char ** argv) {
     gpt_params params;
     g_params = &params;
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_MAIN, print_usage);
-
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_MAIN, print_usage)) {
         return 1;
     }
 
@@ -192,8 +191,6 @@ int main(int argc, char ** argv) {
 
     print_build_info();
 
-    LOG_TEE("%s: seed = %u\n", __func__, params.sparams.seed);
-
     LOG("%s: llama backend init\n", __func__);
     llama_backend_init();
     llama_numa_init(params.numa);
@@ -471,8 +468,10 @@ int main(int argc, char ** argv) {
         exit(1);
     }
 
+    LOG_TEE("sampling seed: %u\n", gpt_sampler_get_seed(smpl));
     LOG_TEE("sampling params: \n%s\n", sparams.print().c_str());
-    LOG_TEE(" sampler constr: \n%s\n", gpt_sampler_print(smpl).c_str());
+    LOG_TEE("sampler constr: \n%s\n", gpt_sampler_print(smpl).c_str());
+
     LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
 
     // group-attention state

examples/parallel/parallel.cpp

@@ -1,7 +1,9 @@
 // A basic application simulating a server with multiple clients.
 // The clients submit requests to the server and they are processed in parallel.
 
+#include "arg.h"
 #include "common.h"
+#include "sampling.h"
 #include "llama.h"
 
 #include <cmath>
@@ -100,8 +102,7 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PARALLEL)) {
         return 1;
     }
 
@@ -414,7 +415,7 @@ int main(int argc, char ** argv) {
     LOG_TEE("\n");
 
     // TODO: print sampling/grammar timings for all clients
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_context_print(ctx);
 
     llama_batch_free(batch);
 

examples/passkey/passkey.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -19,8 +20,7 @@ int main(int argc, char ** argv) {
     params.n_keep = 32;
     params.i_pos  = -1;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_PASSKEY, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PASSKEY, print_usage)) {
         return 1;
     }
 
@@ -256,7 +256,7 @@ int main(int argc, char ** argv) {
             __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_context_print(ctx);
 
     fprintf(stderr, "\n");
 

examples/perplexity/perplexity.cpp

@@ -1,18 +1,19 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
+#include <array>
+#include <atomic>
 #include <cmath>
 #include <cstdio>
 #include <cstring>
 #include <ctime>
+#include <fstream>
+#include <mutex>
+#include <random>
 #include <sstream>
 #include <thread>
-#include <mutex>
-#include <atomic>
 #include <vector>
-#include <array>
-#include <fstream>
-#include <sstream>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -1967,8 +1968,7 @@ int main(int argc, char ** argv) {
     params.n_ctx = 512;
     params.logits_all = true;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_PERPLEXITY);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
         return 1;
     }
 
@@ -2007,8 +2007,6 @@ int main(int argc, char ** argv) {
 
     print_build_info();
 
-    LOG_TEE("%s: seed = %u\n", __func__, params.sparams.seed);
-
     llama_backend_init();
     llama_numa_init(params.numa);
 
@@ -2049,7 +2047,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_context_print(ctx);
     write_logfile(ctx, params, model, results);
 
     llama_free(ctx);

examples/quantize/CMakeLists.txt

@@ -1,6 +1,6 @@
 set(TARGET llama-quantize)
 add_executable(${TARGET} quantize.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_include_directories(${TARGET} PRIVATE ../../common)
 target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/retrieval/retrieval.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -111,8 +112,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_RETRIEVAL, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_RETRIEVAL, print_usage)) {
         return 1;
     }
 
@@ -292,7 +292,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_context_print(ctx);
 
     // clean up
     llama_batch_free(query_batch);

examples/save-load-state/save-load-state.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -10,8 +11,7 @@ int main(int argc, char ** argv) {
     params.prompt = "The quick brown fox";
     params.sparams.seed = 1234;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 

examples/server/CMakeLists.txt

@@ -30,6 +30,7 @@ set(PUBLIC_ASSETS
     system-prompts.js
     prompt-formats.js
     json-schema-to-grammar.mjs
+    loading.html
 )
 
 foreach(asset ${PUBLIC_ASSETS})

examples/server/README.md

@@ -23,36 +23,32 @@ The project is under active development, and we are [looking for feedback and co
 | `--version` | show version and build info |
 | `-v, --verbose` | print verbose information |
 | `--verbosity N` | set specific verbosity level (default: 0) |
-| `--verbose-prompt` | print a verbose prompt before generation (default: false) |
-| `--no-display-prompt` | don't print prompt at generation (default: false) |
-| `-s, --seed SEED` | RNG seed (default: -1, use random seed for < 0) |
 | `-t, --threads N` | number of threads to use during generation (default: -1)<br/>(env: LLAMA_ARG_THREADS) |
 | `-tb, --threads-batch N` | number of threads to use during batch and prompt processing (default: same as --threads) |
 | `-C, --cpu-mask M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") |
 | `-Cr, --cpu-range lo-hi` | range of CPUs for affinity. Complements --cpu-mask |
 | `--cpu-strict <0\|1>` | use strict CPU placement (default: 0)<br/> |
+| `--prio N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)<br/> |
 | `--poll <0...100>` | use polling level to wait for work (0 - no polling, default: 50)<br/> |
 | `-Cb, --cpu-mask-batch M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask) |
 | `-Crb, --cpu-range-batch lo-hi` | ranges of CPUs for affinity. Complements --cpu-mask-batch |
 | `--cpu-strict-batch <0\|1>` | use strict CPU placement (default: same as --cpu-strict) |
+| `--prio-batch N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)<br/> |
 | `--poll-batch <0\|1>` | use polling to wait for work (default: same as --poll) |
-| `-lcs, --lookup-cache-static FNAME` | path to static lookup cache to use for lookup decoding (not updated by generation) |
-| `-lcd, --lookup-cache-dynamic FNAME` | path to dynamic lookup cache to use for lookup decoding (updated by generation) |
 | `-c, --ctx-size N` | size of the prompt context (default: 0, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE) |
 | `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled)<br/>(env: LLAMA_ARG_N_PREDICT) |
 | `-b, --batch-size N` | logical maximum batch size (default: 2048)<br/>(env: LLAMA_ARG_BATCH) |
 | `-ub, --ubatch-size N` | physical maximum batch size (default: 512)<br/>(env: LLAMA_ARG_UBATCH) |
 | `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) |
-| `--chunks N` | max number of chunks to process (default: -1, -1 = all) |
 | `-fa, --flash-attn` | enable Flash Attention (default: disabled)<br/>(env: LLAMA_ARG_FLASH_ATTN) |
 | `-p, --prompt PROMPT` | prompt to start generation with |
 | `-f, --file FNAME` | a file containing the prompt (default: none) |
-| `--in-file FNAME` | an input file (repeat to specify multiple files) |
 | `-bf, --binary-file FNAME` | binary file containing the prompt (default: none) |
 | `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) |
 | `--no-escape` | do not process escape sequences |
 | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
-| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;tfs_z;typical_p;top_p;min_p;temperature) |
+| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;tfs_z;typ_p;top_p;min_p;temperature) |
+| `-s, --seed SEED` | RNG seed (default: -1, use random seed for < 0) |
 | `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: kfypmt) |
 | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
 | `--penalize-nl` | penalize newline tokens (default: false) |
@@ -92,13 +88,12 @@ The project is under active development, and we are [looking for feedback and co
 | `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: -1.0, < 0 - disabled)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1) |
-| `-ns, --sequences N` | number of sequences to decode (default: 1) |
 | `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
 | `-nocb, --no-cont-batching` | disable continuous batching<br/>(env: LLAMA_ARG_NO_CONT_BATCHING) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing |
 | `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggerganov/llama.cpp/issues/1437 |
-| `-ngl, --gpu-layers N` | number of layers to store in VRAM<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
+| `-ngl, --gpu-layers, --n-gpu-layers N` | number of layers to store in VRAM<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
 | `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs |
 | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1 |
 | `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0) |
@@ -109,7 +104,7 @@ The project is under active development, and we are [looking for feedback and co
 | `--control-vector FNAME` | add a control vector<br/>note: this argument can be repeated to add multiple control vectors |
 | `--control-vector-scaled FNAME SCALE` | add a control vector with user defined scaling SCALE<br/>note: this argument can be repeated to add multiple scaled control vectors |
 | `--control-vector-layer-range START END` | layer range to apply the control vector(s) to, start and end inclusive |
-| `-a, --alias STRING` | set alias for model name (to be used by REST API)<br/>(env: LLAMA_ARG_MODEL) |
+| `-a, --alias STRING` | set alias for model name (to be used by REST API) |
 | `-m, --model FNAME` | model path (default: `models/$filename` with filename from `--hf-file` or `--model-url` if set, otherwise models/7B/ggml-model-f16.gguf)<br/>(env: LLAMA_ARG_MODEL) |
 | `-mu, --model-url MODEL_URL` | model download url (default: unused)<br/>(env: LLAMA_ARG_MODEL_URL) |
 | `-hfr, --hf-repo REPO` | Hugging Face model repository (default: unused)<br/>(env: LLAMA_ARG_HF_REPO) |
@@ -123,7 +118,7 @@ The project is under active development, and we are [looking for feedback and co
 | `--api-key-file FNAME` | path to file containing API keys (default: none) |
 | `--ssl-key-file FNAME` | path to file a PEM-encoded SSL private key |
 | `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate |
-| `--timeout N` | server read/write timeout in seconds (default: 600) |
+| `-to, --timeout N` | server read/write timeout in seconds (default: 600) |
 | `--threads-http N` | number of threads used to process HTTP requests (default: -1)<br/>(env: LLAMA_ARG_THREADS_HTTP) |
 | `-spf, --system-prompt-file FNAME` | set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications |
 | `--log-format {text, json}` | log output format: json or text (default: json) |
@@ -412,9 +407,44 @@ Notice that each `probs` is an array of length `n_probs`.
 
     *Options:*
 
-    `content`: Set the text to tokenize.
+    `content`: (Required) The text to tokenize.
 
-    `add_special`: Boolean indicating if special tokens, i.e. `BOS`, should be inserted.  Default: `false`
+    `add_special`: (Optional) Boolean indicating if special tokens, i.e. `BOS`, should be inserted.  Default: `false`
+
+    `with_pieces`: (Optional) Boolean indicating whether to return token pieces along with IDs.  Default: `false`
+
+**Response:**
+
+Returns a JSON object with a `tokens` field containing the tokenization result. The `tokens` array contains either just token IDs or objects with `id` and `piece` fields, depending on the `with_pieces` parameter. The piece field is a string if the piece is valid unicode or a list of bytes otherwise.
+
+
+If `with_pieces` is `false`:
+```json
+{
+  "tokens": [123, 456, 789]
+}
+```
+
+If `with_pieces` is `true`:
+```json
+{
+  "tokens": [
+    {"id": 123, "piece": "Hello"},
+    {"id": 456, "piece": " world"},
+    {"id": 789, "piece": "!"}
+  ]
+}
+```
+
+With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k
+```json
+{
+  "tokens": [
+    {"id": 198, "piece": [195]}, // hex C3
+    {"id": 164, "piece": [161]} // hex A1
+  ]
+}
+```
 
 ### POST `/detokenize`: Convert tokens to text
 

examples/server/public/loading.html

@@ -0,0 +1,12 @@
+<!DOCTYPE html>
+<html>
+    <head>
+        <meta http-equiv="refresh" content="5">
+    </head>
+    <body>
+        <div id="loading">
+            The model is loading. Please wait.<br/>
+            The user interface will appear soon.
+        </div>
+    </body>
+</html>

examples/server/server.cpp

@@ -1,6 +1,8 @@
 #include "utils.hpp"
 
+#include "arg.h"
 #include "common.h"
+#include "sampling.h"
 #include "json-schema-to-grammar.h"
 #include "llama.h"
 
@@ -26,6 +28,7 @@
 #include "system-prompts.js.hpp"
 #include "prompt-formats.js.hpp"
 #include "json-schema-to-grammar.mjs.hpp"
+#include "loading.html.hpp"
 
 #include <atomic>
 #include <chrono>
@@ -1264,6 +1267,7 @@ struct server_context {
             {"n_predict",                 slot.n_predict},     // Server configured n_predict
             {"model",                     params.model_alias},
             {"seed",                      slot.sparams.seed},
+            {"seed_cur",                  slot.smpl ? gpt_sampler_get_seed(slot.smpl) : 0},
             {"temperature",               slot.sparams.temp},
             {"dynatemp_range",            slot.sparams.dynatemp_range},
             {"dynatemp_exponent",         slot.sparams.dynatemp_exponent},
@@ -2423,8 +2427,7 @@ int main(int argc, char ** argv) {
     // own arguments required by this example
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_SERVER);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_SERVER)) {
         return 1;
     }
 
@@ -2590,10 +2593,16 @@ int main(int argc, char ** argv) {
         return false;
     };
 
-    auto middleware_server_state = [&res_error, &state](const httplib::Request &, httplib::Response & res) {
+    auto middleware_server_state = [&res_error, &state](const httplib::Request & req, httplib::Response & res) {
         server_state current_state = state.load();
         if (current_state == SERVER_STATE_LOADING_MODEL) {
-            res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE));
+            auto tmp = string_split(req.path, '.');
+            if (req.path == "/" || tmp.back() == "html") {
+                res.set_content(reinterpret_cast<const char*>(loading_html), loading_html_len, "text/html; charset=utf-8");
+                res.status = 503;
+            } else {
+                res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE));
+            }
             return false;
         }
         return true;
@@ -2984,6 +2993,8 @@ int main(int argc, char ** argv) {
                 }, [&](json error_data) {
                     server_sent_event(sink, "error", error_data);
                 });
+                static const std::string ev_done = "data: [DONE]\n\n";
+                sink.write(ev_done.data(), ev_done.size());
                 sink.done();
                 return true;
             };
@@ -3011,12 +3022,39 @@ int main(int argc, char ** argv) {
     const auto handle_tokenize = [&ctx_server, &res_ok](const httplib::Request & req, httplib::Response & res) {
         const json body = json::parse(req.body);
 
-        std::vector<llama_token> tokens;
+        json tokens_response = json::array();
         if (body.count("content") != 0) {
             const bool add_special = json_value(body, "add_special", false);
-            tokens = ctx_server.tokenize(body.at("content"), add_special);
+            const bool with_pieces = json_value(body, "with_pieces", false);
+            std::vector<llama_token> tokens = ctx_server.tokenize(body.at("content"), add_special);
+
+            if (with_pieces) {
+                for (const auto& token : tokens) {
+                    std::string piece = llama_token_to_piece(ctx_server.ctx, token);
+                    json piece_json;
+
+                    // Check if the piece is valid UTF-8
+                    if (is_valid_utf8(piece)) {
+                        piece_json = piece;
+                    } else {
+                        // If not valid UTF-8, store as array of byte values
+                        piece_json = json::array();
+                        for (unsigned char c : piece) {
+                            piece_json.push_back(static_cast<int>(c));
+                        }
+                    }
+
+                    tokens_response.push_back({
+                        {"id", token},
+                        {"piece", piece_json}
+                    });
+                }
+            } else {
+                tokens_response = tokens;
+            }
         }
-        const json data = format_tokenizer_response(tokens);
+
+        const json data = format_tokenizer_response(tokens_response);
         res_ok(res, data);
     };
 

examples/server/tests/features/server.feature

@@ -105,6 +105,14 @@ Feature: llama.cpp server
     Given first token is removed
     Then  tokens can be detokenized
 
+  Scenario: Tokenize with pieces
+    When  tokenizing with pieces:
+    """
+    What is the capital of Germany?
+    媽
+    """
+    Then  tokens are given with pieces
+
   Scenario: Models available
     Given available models
     Then  1 models are supported

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

@@ -1,3 +1,6 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
 import asyncio
 import json
 import os
@@ -697,6 +700,32 @@ def step_tokenize_set_add_special(context):
     context.tokenize_add_special = True
 
 
+@step("tokenizing with pieces")
+@async_run_until_complete
+async def step_tokenize_with_pieces(context):
+    context.tokenized_text = context_text(context)
+    async with aiohttp.ClientSession() as session:
+        tokenize_args = {"content": context.tokenized_text, "with_pieces": True}
+        if getattr(context, "tokenize_add_special", None) is not None:
+            tokenize_args["add_special"] = context.tokenize_add_special
+
+        async with session.post(
+            f"{context.base_url}/tokenize", json=tokenize_args
+        ) as response:
+            assert response.status == 200
+            tokenize_json = await response.json()
+            context.tokens_with_pieces = tokenize_json["tokens"]
+
+
+@step("tokens are given with pieces")
+@async_run_until_complete
+async def step_tokenize_with_pieces(context):
+    # Verify that the response contains both token IDs and pieces
+    assert all(
+        "id" in token and "piece" in token for token in context.tokens_with_pieces
+    )
+
+
 @step('tokenizing')
 @async_run_until_complete
 async def step_tokenize(context):
@@ -991,6 +1020,8 @@ async def oai_chat_completions(user_prompt,
                             event_data = line.split(': ', 1)
                             assert event_data[0] == 'data', f'Bad event code received: ```{event_data}```'
                             chunk_raw = event_data[1]
+                            if chunk_raw == '[DONE]':
+                                break
 
                             chunk = json.loads(chunk_raw)
                             assert len(chunk['choices']) == 1, f"no choices provided, line ```{line}```"

examples/server/utils.hpp

@@ -616,7 +616,40 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
     return res;
 }
 
-static json format_tokenizer_response(const std::vector<llama_token> & tokens) {
+static bool is_valid_utf8(const std::string & str) {
+    const unsigned char* bytes = reinterpret_cast<const unsigned char*>(str.data());
+    const unsigned char* end = bytes + str.length();
+
+    while (bytes < end) {
+        if (*bytes <= 0x7F) {
+            // 1-byte sequence (0xxxxxxx)
+            bytes++;
+        } else if ((*bytes & 0xE0) == 0xC0) {
+            // 2-byte sequence (110xxxxx 10xxxxxx)
+            if (end - bytes < 2 || (bytes[1] & 0xC0) != 0x80)
+                return false;
+            bytes += 2;
+        } else if ((*bytes & 0xF0) == 0xE0) {
+            // 3-byte sequence (1110xxxx 10xxxxxx 10xxxxxx)
+            if (end - bytes < 3 || (bytes[1] & 0xC0) != 0x80 || (bytes[2] & 0xC0) != 0x80)
+                return false;
+            bytes += 3;
+        } else if ((*bytes & 0xF8) == 0xF0) {
+            // 4-byte sequence (11110xxx 10xxxxxx 10xxxxxx 10xxxxxx)
+            if (end - bytes < 4 || (bytes[1] & 0xC0) != 0x80 ||
+                (bytes[2] & 0xC0) != 0x80 || (bytes[3] & 0xC0) != 0x80)
+                return false;
+            bytes += 4;
+        } else {
+            // Invalid UTF-8 lead byte
+            return false;
+        }
+    }
+
+    return true;
+}
+
+static json format_tokenizer_response(const json & tokens) {
     return json {
         {"tokens", tokens}
     };

examples/simple/simple.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -18,8 +19,7 @@ int main(int argc, char ** argv) {
     params.prompt = "Hello my name is";
     params.n_predict = 32;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
         return 1;
     }
 
@@ -154,8 +154,8 @@ int main(int argc, char ** argv) {
             __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
 
     LOG_TEE("\n");
-    llama_perf_print(smpl, LLAMA_PERF_TYPE_SAMPLER_CHAIN);
-    llama_perf_print(ctx,  LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_sampler_print(smpl);
+    llama_perf_context_print(ctx);
 
     fprintf(stderr, "\n");
 

examples/speculative/speculative.cpp

@@ -1,11 +1,13 @@
+#include "arg.h"
 #include "common.h"
+#include "sampling.h"
 #include "llama.h"
 
-#include <cmath>
 #include <cstdio>
 #include <string>
 #include <vector>
 #include <set>
+#include <random>
 
 #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE  100
 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
@@ -27,8 +29,7 @@ struct seq_draft {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_SPECULATIVE);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
         return 1;
     }
 
@@ -615,7 +616,7 @@ int main(int argc, char ** argv) {
 
     LOG_TEE("\ndraft:\n\n");
     // TODO: print sampling/grammar timings for all drafts
-    llama_perf_print(ctx_dft, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_context_print(ctx_dft);
 
     LOG_TEE("\ntarget:\n\n");
     gpt_perf_print(ctx_tgt, smpl);

examples/sycl/run-llama2.sh

@@ -4,33 +4,23 @@
 #  Copyright (C) 2024 Intel Corporation
 #  SPDX-License-Identifier: MIT
 
-INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 source /opt/intel/oneapi/setvars.sh
 
-if [ $# -gt 0 ]; then
-    GGML_SYCL_DEVICE=$1
-    GGML_SYCL_SINGLE_GPU=1
-else
-    GGML_SYCL_DEVICE=0
-    GGML_SYCL_SINGLE_GPU=0
-fi
-
 #export GGML_SYCL_DEBUG=1
 
-
 #ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer.
 
-if [ $GGML_SYCL_SINGLE_GPU -eq 1 ]; then
+INPUT_PROMPT="Building a website can be done in 10 simple steps:\nStep 1:"
+MODEL_FILE=llama-2-7b.Q4_0.gguf
+NGL=33
+
+if [ $# -gt 0 ]; then
+    GGML_SYCL_DEVICE=$1
     echo "use $GGML_SYCL_DEVICE as main GPU"
     #use signle GPU only
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -mg $GGML_SYCL_DEVICE -sm none
+
 else
     #use multiple GPUs with same max compute units
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0
 fi
-
-#use main GPU only
-#ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
-
-#use multiple GPUs with same max compute units
-#ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0

flake.lock

@@ -5,11 +5,11 @@
         "nixpkgs-lib": "nixpkgs-lib"
       },
       "locked": {
-        "lastModified": 1725024810,
-        "narHash": "sha256-ODYRm8zHfLTH3soTFWE452ydPYz2iTvr9T8ftDMUQ3E=",
+        "lastModified": 1725234343,
+        "narHash": "sha256-+ebgonl3NbiKD2UD0x4BszCZQ6sTfL4xioaM49o5B3Y=",
         "owner": "hercules-ci",
         "repo": "flake-parts",
-        "rev": "af510d4a62d071ea13925ce41c95e3dec816c01d",
+        "rev": "567b938d64d4b4112ee253b9274472dc3a346eb6",
         "type": "github"
       },
       "original": {
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1724819573,
-        "narHash": "sha256-GnR7/ibgIH1vhoy8cYdmXE6iyZqKqFxQSVkFgosBh6w=",
+        "lastModified": 1725634671,
+        "narHash": "sha256-v3rIhsJBOMLR8e/RNWxr828tB+WywYIoajrZKFM+0Gg=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "71e91c409d1e654808b2621f28a327acfdad8dc2",
+        "rev": "574d1eac1c200690e27b8eb4e24887f8df7ac27c",
         "type": "github"
       },
       "original": {
@@ -36,14 +36,14 @@
     },
     "nixpkgs-lib": {
       "locked": {
-        "lastModified": 1722555339,
-        "narHash": "sha256-uFf2QeW7eAHlYXuDktm9c25OxOyCoUOQmh5SZ9amE5Q=",
+        "lastModified": 1725233747,
+        "narHash": "sha256-Ss8QWLXdr2JCBPcYChJhz4xJm+h/xjl4G0c0XlP6a74=",
         "type": "tarball",
-        "url": "https://github.com/NixOS/nixpkgs/archive/a5d394176e64ab29c852d03346c1fc9b0b7d33eb.tar.gz"
+        "url": "https://github.com/NixOS/nixpkgs/archive/356624c12086a18f2ea2825fed34523d60ccc4e3.tar.gz"
       },
       "original": {
         "type": "tarball",
-        "url": "https://github.com/NixOS/nixpkgs/archive/a5d394176e64ab29c852d03346c1fc9b0b7d33eb.tar.gz"
+        "url": "https://github.com/NixOS/nixpkgs/archive/356624c12086a18f2ea2825fed34523d60ccc4e3.tar.gz"
       }
     },
     "root": {

ggml/include/ggml-cann.h

@@ -80,6 +80,13 @@ ggml_backend_cann_buffer_type(int32_t device);
  */
 GGML_API GGML_CALL int32_t ggml_backend_cann_get_device_count(void);
 
+/**
+ * @brief pinned host buffer for use with the CPU backend for faster copies between CPU and NPU.
+ *
+ * @return A pointer to the host buffer type interface.
+ */
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type(void);
+
 /**
  * @brief Retrieves the description of a specific CANN device.
  *

ggml/include/ggml.h

@@ -358,6 +358,7 @@ extern "C" {
 
     struct ggml_object;
     struct ggml_context;
+    struct ggml_cgraph;
 
     // NOTE: always add types at the end of the enum to keep backward compatibility
     enum ggml_type {
@@ -575,23 +576,9 @@ extern "C" {
         GGML_TENSOR_FLAG_PARAM  = 4,
     };
 
-    // ggml object
-    struct ggml_object {
-        size_t offs;
-        size_t size;
-
-        struct ggml_object * next;
-
-        enum ggml_object_type type;
-
-        char padding[4];
-    };
-
-    static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);
-
     // n-dimensional tensor
     struct ggml_tensor {
-        enum ggml_type         type;
+        enum ggml_type type;
 
         GGML_DEPRECATED(enum ggml_backend_type backend, "use the buffer type to find the storage location of the tensor");
 
@@ -655,7 +642,7 @@ extern "C" {
 
     struct ggml_threadpool;     // forward declaration, see ggml.c
 
-    typedef struct  ggml_threadpool * ggml_threadpool_t;
+    typedef struct ggml_threadpool * ggml_threadpool_t;
 
     // the compute plan that needs to be prepared for ggml_graph_compute()
     // since https://github.com/ggerganov/ggml/issues/287
@@ -671,35 +658,6 @@ extern "C" {
         void *              abort_callback_data;
     };
 
-    enum ggml_cgraph_eval_order {
-        GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT = 0,
-        GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT,
-        GGML_CGRAPH_EVAL_ORDER_COUNT
-    };
-
-    typedef uint32_t ggml_bitset_t;
-
-    struct ggml_hash_set {
-        size_t size;
-        ggml_bitset_t * used;       // whether or not the keys are in use i.e. set
-        struct ggml_tensor ** keys; // actual tensors in the set, keys[i] is only defined if ggml_bitset_get(used, i)
-    };
-
-    // computation graph
-    struct ggml_cgraph {
-        int size;
-        int n_nodes;
-        int n_leafs;
-
-        struct ggml_tensor ** nodes;
-        struct ggml_tensor ** grads;
-        struct ggml_tensor ** leafs;
-
-        struct ggml_hash_set visited_hash_set;
-
-        enum ggml_cgraph_eval_order order;
-    };
-
     // scratch buffer
     struct ggml_scratch {
         size_t offs;
@@ -2017,8 +1975,6 @@ extern "C" {
     typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
     typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);
 
-    #define GGML_N_TASKS_MAX -1
-
     GGML_API struct ggml_tensor * ggml_map_custom1(
             struct ggml_context   * ctx,
             struct ggml_tensor    * a,
@@ -2088,30 +2044,35 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * tensor);
 
-
     GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
     GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);
 
     // graph allocation in a context
-    GGML_API struct ggml_cgraph * ggml_new_graph         (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
-    GGML_API struct ggml_cgraph * ggml_new_graph_custom  (struct ggml_context * ctx, size_t size, bool grads);
-    GGML_API struct ggml_cgraph * ggml_graph_dup         (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
-    GGML_API struct ggml_cgraph   ggml_graph_view        (struct ggml_cgraph * cgraph, int i0, int i1);
-    GGML_API void                 ggml_graph_cpy         (struct ggml_cgraph * src, struct ggml_cgraph * dst);
-    GGML_API void                 ggml_graph_reset       (struct ggml_cgraph * cgraph);  // zero grads
-    GGML_API void                 ggml_graph_clear       (struct ggml_cgraph * cgraph);
+    GGML_API struct ggml_cgraph * ggml_new_graph       (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
+    GGML_API struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads);
+    GGML_API struct ggml_cgraph * ggml_graph_dup       (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
+    GGML_API void                 ggml_graph_cpy       (struct ggml_cgraph * src, struct ggml_cgraph * dst);
+    GGML_API void                 ggml_graph_reset     (struct ggml_cgraph * cgraph);  // zero grads
+    GGML_API void                 ggml_graph_clear     (struct ggml_cgraph * cgraph);
+
+    GGML_API int                   ggml_graph_size   (struct ggml_cgraph * cgraph);
+    GGML_API struct ggml_tensor *  ggml_graph_node   (struct ggml_cgraph * cgraph, int i); // if i < 0, returns nodes[n_nodes + i]
+    GGML_API struct ggml_tensor ** ggml_graph_nodes  (struct ggml_cgraph * cgraph);
+    GGML_API int                   ggml_graph_n_nodes(struct ggml_cgraph * cgraph);
+
+    GGML_API void   ggml_graph_add_node(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
 
     GGML_API size_t ggml_graph_overhead(void);
     GGML_API size_t ggml_graph_overhead_custom(size_t size, bool grads);
 
-    GGML_API struct ggml_threadpool_params   ggml_threadpool_params_default(int n_threads);
-    GGML_API void                            ggml_threadpool_params_init  (struct ggml_threadpool_params *p, int n_threads);
-    GGML_API bool                            ggml_threadpool_params_match (const struct ggml_threadpool_params *p0, const struct ggml_threadpool_params *p1);
-    GGML_API struct ggml_threadpool*         ggml_threadpool_new          (struct ggml_threadpool_params  * params);
-    GGML_API void                            ggml_threadpool_free         (struct ggml_threadpool * threadpool);
-    GGML_API int                             ggml_threadpool_get_n_threads(struct ggml_threadpool * threadpool);
-    GGML_API void                            ggml_threadpool_pause        (struct ggml_threadpool * threadpool);
-    GGML_API void                            ggml_threadpool_resume       (struct ggml_threadpool * threadpool);
+    GGML_API struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads);
+    GGML_API void                          ggml_threadpool_params_init   (struct ggml_threadpool_params * p, int n_threads);
+    GGML_API bool                          ggml_threadpool_params_match  (const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1);
+    GGML_API struct ggml_threadpool *      ggml_threadpool_new          (struct ggml_threadpool_params  * params);
+    GGML_API void                          ggml_threadpool_free         (struct ggml_threadpool * threadpool);
+    GGML_API int                           ggml_threadpool_get_n_threads(struct ggml_threadpool * threadpool);
+    GGML_API void                          ggml_threadpool_pause        (struct ggml_threadpool * threadpool);
+    GGML_API void                          ggml_threadpool_resume       (struct ggml_threadpool * threadpool);
 
     // ggml_graph_plan() has to be called before ggml_graph_compute()
     // when plan.work_size > 0, caller must allocate memory for plan.work_data
@@ -2509,6 +2470,7 @@ extern "C" {
     GGML_API int ggml_cpu_has_gpublas    (void);
     GGML_API int ggml_cpu_has_sse3       (void);
     GGML_API int ggml_cpu_has_ssse3      (void);
+    GGML_API int ggml_cpu_has_riscv_v    (void);
     GGML_API int ggml_cpu_has_sycl       (void);
     GGML_API int ggml_cpu_has_rpc        (void);
     GGML_API int ggml_cpu_has_vsx        (void);

ggml/src/CMakeLists.txt

@@ -26,6 +26,8 @@ if (NOT MSVC)
     endif()
 endif()
 
+unset(GGML_EXTRA_LIBS)
+
 if (APPLE AND GGML_ACCELERATE)
     find_library(ACCELERATE_FRAMEWORK Accelerate)
     if (ACCELERATE_FRAMEWORK)
@@ -35,7 +37,7 @@ if (APPLE AND GGML_ACCELERATE)
         add_compile_definitions(ACCELERATE_NEW_LAPACK)
         add_compile_definitions(ACCELERATE_LAPACK_ILP64)
 
-        set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} ${ACCELERATE_FRAMEWORK})
+        list(APPEND GGML_EXTRA_LIBS ${ACCELERATE_FRAMEWORK})
     else()
         message(WARNING "Accelerate framework not found")
     endif()
@@ -87,7 +89,7 @@ if (GGML_METAL)
             COMMENT "Generate assembly for embedded Metal library"
         )
 
-        set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${METALLIB_EMBED_ASM})
+    list(APPEND GGML_SOURCES_METAL ${METALLIB_EMBED_ASM})
     else()
         if (GGML_METAL_SHADER_DEBUG)
             # custom command to do the following:
@@ -132,7 +134,7 @@ if (GGML_METAL)
             )
     endif() # GGML_METAL_EMBED_LIBRARY
 
-    set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS}
+    list(APPEND GGML_EXTRA_LIBS
         ${FOUNDATION_LIBRARY}
         ${METAL_FRAMEWORK}
         ${METALKIT_FRAMEWORK}
@@ -157,11 +159,11 @@ if (GGML_OPENMP)
 
         add_compile_definitions(GGML_USE_OPENMP)
 
-        set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
+        list(APPEND GGML_EXTRA_LIBS OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
 
         if (GGML_MUSA)
-            set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} "/usr/lib/llvm-10/include/openmp")
-            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} "/usr/lib/llvm-10/lib/libomp.so")
+            list(APPEND GGML_EXTRA_INCLUDES "/usr/lib/llvm-10/include/openmp")
+            list(APPEND GGML_EXTRA_LIBS     "/usr/lib/llvm-10/lib/libomp.so")
         endif()
     else()
         message(WARNING "OpenMP not found")
@@ -244,8 +246,8 @@ if (GGML_BLAS)
         set(GGML_HEADERS_BLAS ../include/ggml-blas.h)
         set(GGML_SOURCES_BLAS ggml-blas.cpp)
 
-        set(GGML_EXTRA_LIBS     ${GGML_EXTRA_LIBS}     ${BLAS_LIBRARIES})
-        set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS})
+        list(APPEND GGML_EXTRA_LIBS     ${BLAS_LIBRARIES})
+        list(APPEND GGML_EXTRA_INCLUDES ${BLAS_INCLUDE_DIRS})
     else()
         message(WARNING "BLAS not found, please refer to "
         "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
@@ -368,19 +370,19 @@ if (GGML_CUDA)
         if (GGML_STATIC)
             if (WIN32)
                 # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library
-                set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt)
+                list(APPEND GGML_EXTRA_LIBS CUDA::cudart_static CUDA::cublas CUDA::cublasLt)
             else ()
                 if (GGML_MUSA)
-                    set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} MUSA::musart_static MUSA::mublas_static)
+                    list(APPEND GGML_EXTRA_LIBS MUSA::musart_static MUSA::mublas_static)
                 else()
-                    set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
+                    list(APPEND GGML_EXTRA_LIBS CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
                 endif()
             endif()
         else()
             if (GGML_MUSA)
-                set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} MUSA::musart MUSA::mublas)
+                list(APPEND GGML_EXTRA_LIBS MUSA::musart MUSA::mublas)
             else()
-                set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
+                list(APPEND GGML_EXTRA_LIBS CUDA::cudart CUDA::cublas CUDA::cublasLt)
             endif()
         endif()
 
@@ -388,9 +390,9 @@ if (GGML_CUDA)
             # No VMM requested, no need to link directly with the cuda driver lib (libcuda.so)
         else()
             if (GGML_MUSA)
-                set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} MUSA::musa_driver) # required by muDeviceGetAttribute(), muMemGetAllocationGranularity(...), ...
+                list(APPEND GGML_EXTRA_LIBS MUSA::musa_driver) # required by muDeviceGetAttribute(), muMemGetAllocationGranularity(...), ...
             else()
-                set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ...
+                list(APPEND GGML_EXTRA_LIBS CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ...
             endif()
         endif()
     else()
@@ -495,7 +497,7 @@ if (GGML_HIPBLAS)
 
     if (CXX_IS_HIPCC)
         set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX)
-        set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} hip::device)
+        list(APPEND GGML_EXTRA_LIBS hip::device)
     else()
         set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE HIP)
     endif()
@@ -504,7 +506,8 @@ if (GGML_HIPBLAS)
         message(FATAL_ERROR "Static linking not supported for HIP/ROCm")
     endif()
 
-    set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} PUBLIC hip::host roc::rocblas roc::hipblas)
+    # TODO: this "PUBLIC" here seems wrong
+    list(APPEND GGML_EXTRA_LIBS PUBLIC hip::host roc::rocblas roc::hipblas)
 endif()
 
 if (GGML_SYCL)
@@ -513,7 +516,8 @@ if (GGML_SYCL)
     endif()
 
     check_cxx_compiler_flag("-fsycl" SUPPORTS_SYCL)
-    if ( DEFINED ENV{ONEAPI_ROOT})
+
+    if (DEFINED ENV{ONEAPI_ROOT})
         message(STATUS "Using oneAPI Release SYCL compiler (icpx).")
     elseif(SUPPORTS_SYCL)
         message(WARNING "Using open-source SYCL compiler (clang++). Didn't detect ENV {ONEAPI_ROOT}.
@@ -551,21 +555,27 @@ if (GGML_SYCL)
 
     find_package(DNNL)
     message("-- DNNL found:" ${DNNL_FOUND})
+
     if (GGML_SYCL_TARGET STREQUAL "INTEL")
         add_compile_definitions(GGML_SYCL_DNNL=${DNNL_FOUND})
     else()
         add_compile_definitions(GGML_SYCL_DNNL=0)
     endif()
+
+    if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL")
+        list(APPEND GGML_EXTRA_LIBS DNNL::dnnl)
+    endif()
+
     if (WIN32)
         find_package(IntelSYCL REQUIRED)
         find_package(MKL REQUIRED)
-        set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
+        list(APPEND GGML_EXTRA_LIBS IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
     else()
         if (GGML_SYCL_TARGET STREQUAL "INTEL")
-            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
+            list(APPEND GGML_EXTRA_LIBS OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
         elseif (GGML_SYCL_TARGET STREQUAL "NVIDIA")
             set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
-            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} -fsycl pthread m dl onemkl)
+            list(APPEND GGML_EXTRA_LIBS pthread m dl onemkl)
         endif()
     endif()
     if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL")
@@ -579,7 +589,7 @@ if (GGML_RPC)
     list(APPEND GGML_CDEF_PUBLIC GGML_USE_RPC)
 
     if (WIN32)
-        set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} ws2_32)
+        list(APPEND GGML_EXTRA_LIBS ws2_32)
     endif()
 
     set(GGML_HEADERS_RPC ../include/ggml-rpc.h)
@@ -657,8 +667,8 @@ if (GGML_VULKAN)
         set(GGML_HEADERS_VULKAN ${CMAKE_CURRENT_SOURCE_DIR}/../include/ggml-vulkan.h ${_ggml_vk_header})
         set(GGML_SOURCES_VULKAN ggml-vulkan.cpp ${_ggml_vk_source})
 
-        set(GGML_EXTRA_LIBS     ${GGML_EXTRA_LIBS} Vulkan::Vulkan)
-        set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} ${CMAKE_CURRENT_BINARY_DIR})
+        list(APPEND GGML_EXTRA_LIBS     Vulkan::Vulkan)
+        list(APPEND GGML_EXTRA_INCLUDES ${CMAKE_CURRENT_BINARY_DIR})
     else()
         message(WARNING "Vulkan not found")
     endif()
@@ -817,8 +827,8 @@ if (GGML_KOMPUTE)
 
         list(APPEND GGML_CDEF_PUBLIC GGML_USE_KOMPUTE)
 
-        set(GGML_EXTRA_LIBS     ${GGML_EXTRA_LIBS}     kompute)
-        set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} ${CMAKE_CURRENT_BINARY_DIR})
+        list(APPEND GGML_EXTRA_LIBS     kompute)
+        list(APPEND GGML_EXTRA_INCLUDES ${CMAKE_CURRENT_BINARY_DIR})
     else()
         message(WARNING "Kompute not found")
     endif()
@@ -883,9 +893,10 @@ if (GGML_CANN)
             message(STATUS "CANN: CANN_INCLUDE_DIRS =  ${CANN_INCLUDE_DIRS}")
             message(STATUS "CANN: CANN_LIBRARIES =  ${CANN_LIBRARIES}")
 
-            set(GGML_EXTRA_LIBS     ${GGML_EXTRA_LIBS}     ${CANN_LIBRARIES} )
-            set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} ${CANN_INCLUDE_DIRS})
-            set(GGML_EXTRA_LIBDIRS  ${GGML_EXTRA_LIBDIRS}  ${CANN_INSTALL_DIR}/lib64)
+            list(APPEND GGML_EXTRA_LIBS     ${CANN_LIBRARIES} )
+            list(APPEND GGML_EXTRA_INCLUDES ${CANN_INCLUDE_DIRS})
+            list(APPEND GGML_EXTRA_LIBDIRS  ${CANN_INSTALL_DIR}/lib64)
+
             list(APPEND GGML_CDEF_PUBLIC GGML_USE_CANN)
         endif()
     else()
@@ -1322,12 +1333,14 @@ if (EMSCRIPTEN)
     set_target_properties(ggml PROPERTIES COMPILE_FLAGS "-msimd128")
 endif()
 
-target_compile_definitions(ggml PUBLIC  ${GGML_CDEF_PUBLIC})
-target_include_directories(ggml PUBLIC ../include)
+target_compile_definitions(ggml PUBLIC    ${GGML_CDEF_PUBLIC})
+target_include_directories(ggml PUBLIC  ../include)
 target_include_directories(ggml PRIVATE . ${GGML_EXTRA_INCLUDES})
-target_link_directories(ggml PRIVATE ${GGML_EXTRA_LIBDIRS})
+target_link_directories   (ggml PRIVATE   ${GGML_EXTRA_LIBDIRS})
 target_compile_features   (ggml PRIVATE c_std_11) # don't bump
 
+list(REMOVE_DUPLICATES GGML_EXTRA_LIBS)
+
 target_link_libraries(ggml PRIVATE Threads::Threads ${GGML_EXTRA_LIBS})
 
 find_library(MATH_LIBRARY m)

ggml/src/ggml-blas.cpp

@@ -1,3 +1,4 @@
+#include "ggml-impl.h"
 #include "ggml-blas.h"
 #include "ggml-backend-impl.h"
 

ggml/src/ggml-cann.cpp

@@ -30,6 +30,7 @@
 #include <cstring>
 #include <mutex>
 
+#include "ggml-impl.h"
 #include "ggml-backend-impl.h"
 #include "ggml-cann/aclnn_ops.h"
 #include "ggml-cann/common.h"
@@ -1220,6 +1221,116 @@ ggml_backend_cann_buffer_type(int32_t device) {
     return &ggml_backend_cann_buffer_types[device];
 }
 
+/**
+ * @brief Retrieves the name associated with a CANN host buffer type.
+ *
+ * This function returns the descriptive name associated with the specified
+ * CANN host buffer type context.
+ *
+ * @param buft Pointer to the host buffer type context.
+ * @return Const pointer to the C-style string containing the name.
+ */
+GGML_CALL static const char * ggml_backend_cann_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return "CANN_Host";
+
+    GGML_UNUSED(buft);
+}
+
+/**
+ * @brief Retrieves the name associated with a CANN host buffer.
+ *
+ * This function returns the descriptive name associated with the specified
+ * CANN host buffer context.
+ *
+ * @param buft Pointer to the host buffer context.
+ * @return Const pointer to the C-style string containing the name.
+ */
+GGML_CALL static const char * ggml_backend_cann_host_buffer_name(ggml_backend_buffer_t buffer) {
+    return "CANN_Host";
+
+    GGML_UNUSED(buffer);
+}
+
+/**
+ * @brief Free resources associated with a CANN host buffer.
+ *
+ * This function frees the resources associated with a CANN host buffer, including
+ * its context.
+ *
+ * @param buffer The CANN host buffer to free.
+ */
+GGML_CALL static void ggml_backend_cann_host_buffer_free(ggml_backend_buffer_t buffer) {
+    ACL_CHECK(aclrtFreeHost(buffer->context));
+}
+
+/**
+ * @brief Allocates a new CANN host buffer of the specified size.
+ *
+ * This function allocates a new CANN host buffer with the given size.
+ * @param size Size in bytes of the host buffer to allocate.
+ * @return Pointer to the allocated host buffer, or nullptr if allocation fails.
+ */
+static void * ggml_cann_host_malloc(size_t size) {
+    if (getenv("GGML_CANN_NO_PINNED") != nullptr) {
+        return nullptr;
+    }
+
+    void * hostPtr = nullptr;
+    aclError err = aclrtMallocHost((void **) &hostPtr, size);
+    if (err != ACL_SUCCESS) {
+
+        GGML_CANN_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
+                           size / 1024.0 / 1024.0, aclGetRecentErrMsg());
+        return nullptr;
+    }
+    return hostPtr;
+}
+
+/**
+ * @brief Allocates a new CANN host buffer of the specified type and size.
+ *
+ * @param buft Pointer to the host buffer type context.
+ * @param size Size in bytes of the host buffer to allocate.
+ * @return Pointer to the allocated host buffer, or CPU buffer pointer if allocation fails.
+ */
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    void * hostPtr = ggml_cann_host_malloc(size);
+
+    if (hostPtr == nullptr) {
+        // fallback to cpu buffer
+        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
+    }
+
+    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;
+}
+
+/**
+ * @brief Interface for managing CANN host buffer types in the GGML backend.
+ *
+ * Provides function pointers for allocating, querying properties, and managing
+ * memory for CANN buffer types in the GGML backend.
+ */
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type() {
+    static struct ggml_backend_buffer_type ggml_backend_cann_buffer_type_host = {
+        /* .iface    = */ {
+            /* .get_name         = */ ggml_backend_cann_host_buffer_type_name,
+            /* .alloc_buffer     = */ ggml_backend_cann_host_buffer_type_alloc_buffer,
+            /* .get_alignment    = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
+            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
+            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
+        },
+        /* .context  = */ nullptr,
+    };
+
+    return &ggml_backend_cann_buffer_type_host;
+}
+
 /**
  * @brief Computes the forward operation for a given tensor using CANN
  * operations.
@@ -1942,7 +2053,7 @@ GGML_CALL ggml_backend_t ggml_backend_cann_init(int32_t device) {
         GGML_CANN_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
         return nullptr;
     }
-
+    ggml_cann_set_device(ctx->device);
     ggml_backend_t cann_backend =
         new ggml_backend{/* .guid      = */ ggml_backend_cann_guid(),
                          /* .interface = */ ggml_backend_cann_interface,

ggml/src/ggml-cuda.cu

@@ -1,5 +1,5 @@
 #include "ggml-cuda.h"
-#include "ggml.h"
+#include "ggml-impl.h"
 #include "ggml-backend-impl.h"
 
 #include "ggml-cuda/common.cuh"

ggml/src/ggml-cuda/mmq.cu

@@ -26,7 +26,11 @@ void ggml_cuda_op_mul_mat_q(
     // nrows_dst == nrows of the matrix that the kernel writes into
     const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
 
-    const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst};
+    // The stream-k decomposition is only faster for recent NVIDIA GPUs.
+    // Also its fixup needs to allocate a temporary buffer in the memory pool.
+    // There are multiple parallel CUDA streams for src1_ncols != ne11 which would introduce a race condition for this buffer.
+    const bool use_stream_k = compute_capability >= CC_VOLTA && compute_capability < CC_OFFSET_AMD && src1_ncols == ne11;
+    const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst, use_stream_k};
 
     switch (src0->type) {
         case GGML_TYPE_Q4_0:

ggml/src/ggml-cuda/mmq.cuh

@@ -2742,6 +2742,7 @@ struct mmq_args {
     int64_t ne00; int64_t ne01; int64_t stride01;
     int64_t ne10; int64_t ne11; int64_t stride11;
     int64_t ne0;
+    bool use_stream_k;
 };
 
 template<ggml_type type>
@@ -2777,8 +2778,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
     const int ntx = (args.ne11 + mmq_x - 1) / mmq_x;
     const dim3 block_nums_xy_tiling(nty, ntx, 1);
 
-    const bool use_stream_k = cc >= CC_VOLTA && cc < CC_OFFSET_AMD;
-    if (!use_stream_k) {
+    if (!args.use_stream_k) {
         if (args.ne01 % mmq_y == 0) {
             constexpr bool need_check = false;
             mul_mat_q<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_xy_tiling, block_dims, shmem, stream>>>

ggml/src/ggml-cuda/vendors/musa.h

@@ -130,42 +130,3 @@
 #define cudaKernelNodeParams musaKernelNodeParams
 #define cudaStreamCaptureModeRelaxed musaStreamCaptureModeRelaxed
 #define cudaStreamEndCapture musaStreamEndCapture
-
-// XXX: Clang builtins mapping
-#define __vsub4   __vsub4_musa
-#define __vcmpeq4 __vcmpeq4_musa
-#define __vcmpne4 __vcmpne4_musa
-
-#ifndef __has_builtin
-    #define __has_builtin(x) 0
-#endif
-
-typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
-
-static __device__ __forceinline__ int __vsub4_musa(const int a, const int b) {
-    return __vsubss4(a, b);
-}
-
-static __device__ __forceinline__ unsigned int __vcmpeq4_musa(unsigned int a, unsigned int b) {
-    const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
-    const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
-    unsigned int c;
-    uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
-#pragma unroll
-    for (int i = 0; i < 4; ++i) {
-        vc[i] = va[i] == vb[i] ? 0xff : 0x00;
-    }
-    return c;
-}
-
-static __device__ __forceinline__ unsigned int __vcmpne4_musa(unsigned int a, unsigned int b) {
-    const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
-    const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
-    unsigned int c;
-    uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
-#pragma unroll
-    for (int i = 0; i < 4; ++i) {
-        vc[i] = va[i] == vb[i] ? 0x00 : 0xff;
-    }
-    return c;
-}

ggml/src/ggml-impl.h

@@ -629,8 +629,16 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
 #define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
 #endif
 
+enum ggml_cgraph_eval_order {
+    GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT = 0,
+    GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT,
+    GGML_CGRAPH_EVAL_ORDER_COUNT
+};
+
 // bitset
 
+typedef uint32_t ggml_bitset_t;
+
 static_assert(sizeof(ggml_bitset_t) == 4, "bitset_t constants must be updated");
 #define BITSET_SHR 5 // log2(sizeof(ggml_bitset_t)*8)
 #define BITSET_MASK (sizeof(ggml_bitset_t)*8 - 1)
@@ -656,6 +664,12 @@ static inline void ggml_bitset_clear(ggml_bitset_t * bitset, size_t i) {
 #define GGML_HASHSET_FULL ((size_t)-1)
 #define GGML_HASHSET_ALREADY_EXISTS ((size_t)-2)
 
+struct ggml_hash_set {
+    size_t size;
+    ggml_bitset_t * used;       // whether or not the keys are in use i.e. set
+    struct ggml_tensor ** keys; // actual tensors in the set, keys[i] is only defined if ggml_bitset_get(used, i)
+};
+
 struct ggml_hash_set ggml_hash_set_new(size_t size);
 void                 ggml_hash_set_free(struct ggml_hash_set * hash_set);
 
@@ -745,6 +759,24 @@ static size_t ggml_hash_find_or_insert(struct ggml_hash_set * hash_set, struct g
     GGML_ABORT("fatal error");
 }
 
+// computation graph
+
+struct ggml_cgraph {
+    int size;
+    int n_nodes;
+    int n_leafs;
+
+    struct ggml_tensor ** nodes;
+    struct ggml_tensor ** grads;
+    struct ggml_tensor ** leafs;
+
+    struct ggml_hash_set visited_hash_set;
+
+    enum ggml_cgraph_eval_order order;
+};
+
+struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1);
+
 #ifdef __cplusplus
 }
 #endif

ggml/src/ggml-kompute.cpp

@@ -1,4 +1,4 @@
-#include "ggml.h"
+#include "ggml-impl.h"
 #include "ggml-backend.h"
 #include "ggml-backend-impl.h"
 #include "ggml-kompute.h"

ggml/src/ggml-metal.m

@@ -1,7 +1,7 @@
 #import "ggml-metal.h"
 
+#import "ggml-impl.h"
 #import "ggml-backend-impl.h"
-#import "ggml.h"
 
 #import <Foundation/Foundation.h>
 
@@ -17,8 +17,8 @@
 #define GGML_METAL_LOG_WARN(...)
 #define GGML_METAL_LOG_ERROR(...)
 #else
-#define GGML_METAL_LOG_INFO(...)  ggml_metal_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__)
-#define GGML_METAL_LOG_WARN(...)  ggml_metal_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__)
+#define GGML_METAL_LOG_INFO(...)  ggml_metal_log(GGML_LOG_LEVEL_INFO,  __VA_ARGS__)
+#define GGML_METAL_LOG_WARN(...)  ggml_metal_log(GGML_LOG_LEVEL_WARN,  __VA_ARGS__)
 #define GGML_METAL_LOG_ERROR(...) ggml_metal_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
 #endif
 
@@ -882,7 +882,7 @@ static enum ggml_status ggml_metal_graph_compute(
     // create multiple command buffers and enqueue them
     // then, we encode the graph into the command buffers in parallel
 
-    const int n_nodes  = gf->n_nodes;
+    const int n_nodes = gf->n_nodes;
     const int n_cb = ctx->n_cb;
     const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb;
 
@@ -3039,8 +3039,7 @@ static enum ggml_status ggml_metal_graph_compute(
         if (status != MTLCommandBufferStatusCompleted) {
             GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
             if (status == MTLCommandBufferStatusError) {
-                NSString * error_code = [command_buffer error].localizedDescription;
-                GGML_METAL_LOG_INFO("error: %s\n", [error_code UTF8String]);
+                GGML_METAL_LOG_INFO("error: %s\n", [[command_buffer error].localizedDescription UTF8String]);
             }
 
             return GGML_STATUS_FAILED;

ggml/src/ggml-rpc.cpp

@@ -1,5 +1,5 @@
 #include "ggml-rpc.h"
-#include "ggml.h"
+#include "ggml-impl.h"
 #include "ggml-backend-impl.h"
 
 #include <cinttypes>

ggml/src/ggml-sycl.cpp

@@ -33,7 +33,7 @@
 #include <sycl/half_type.hpp>
 
 #include "ggml-sycl.h"
-#include "ggml.h"
+#include "ggml-impl.h"
 #include "ggml-backend-impl.h"
 
 #include "ggml-sycl/backend.hpp"
@@ -5137,13 +5137,17 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons
         case GGML_OP_SCALE:
         case GGML_OP_SQR:
         case GGML_OP_CLAMP:
+            return true;
         case GGML_OP_CONT:
+            return op->src[0]->type != GGML_TYPE_BF16;
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
             return true;
         case GGML_OP_ROPE:
             return ggml_is_contiguous(op->src[0]);
         case GGML_OP_IM2COL:
+            // TODO: add support for the new F32 operations
+            return op->src[0]->type == GGML_TYPE_F16;
         case GGML_OP_POOL_2D:
         case GGML_OP_SUM_ROWS:
         case GGML_OP_ARGSORT:

ggml/src/ggml-vulkan.cpp

@@ -21,7 +21,7 @@
 #include <memory>
 #include <mutex>
 
-#include "ggml.h"
+#include "ggml-impl.h"
 #include "ggml-backend-impl.h"
 
 #include "ggml-vulkan-shaders.hpp"

ggml/src/ggml.c

@@ -287,6 +287,7 @@ void ggml_abort(const char * file, int line, const char * fmt, ...) {
 #define GGML_DEBUG 0
 #define GGML_GELU_FP16
 #define GGML_GELU_QUICK_FP16
+#define GGML_N_TASKS_MAX (-1)
 
 #define GGML_SOFT_MAX_UNROLL 4
 #define GGML_VEC_DOT_UNROLL  2
@@ -1120,21 +1121,21 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
 #define GGML_F32x4_ADD          vaddq_f32
 #define GGML_F32x4_MUL          vmulq_f32
 #define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x)
-#define GGML_F32x4_REDUCE(res, x)              \
-{                                              \
-    int offset = GGML_F32_ARR >> 1;            \
-    for (int i = 0; i < offset; ++i) {         \
-        x[i] = vaddq_f32(x[i], x[offset+i]);   \
-    }                                          \
-    offset >>= 1;                              \
-    for (int i = 0; i < offset; ++i) {         \
-        x[i] = vaddq_f32(x[i], x[offset+i]);   \
-    }                                          \
-    offset >>= 1;                              \
-    for (int i = 0; i < offset; ++i) {         \
-        x[i] = vaddq_f32(x[i], x[offset+i]);   \
-    }                                          \
-    res = GGML_F32x4_REDUCE_ONE(x[0]);         \
+#define GGML_F32x4_REDUCE(res, x)                  \
+{                                                  \
+    int offset = GGML_F32_ARR >> 1;                \
+    for (int i = 0; i < offset; ++i) {             \
+        (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \
+    }                                              \
+    offset >>= 1;                                  \
+    for (int i = 0; i < offset; ++i) {             \
+        (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \
+    }                                              \
+    offset >>= 1;                                  \
+    for (int i = 0; i < offset; ++i) {             \
+        (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \
+    }                                              \
+    (res) = GGML_F32x4_REDUCE_ONE((x)[0]);         \
 }
 
 #define GGML_F32_VEC        GGML_F32x4
@@ -1161,30 +1162,30 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
     #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c)
     #define GGML_F16x8_ADD          vaddq_f16
     #define GGML_F16x8_MUL          vmulq_f16
-    #define GGML_F16x8_REDUCE(res, x)                             \
-    do {                                                          \
-        int offset = GGML_F16_ARR >> 1;                           \
-        for (int i = 0; i < offset; ++i) {                        \
-            x[i] = vaddq_f16(x[i], x[offset+i]);                  \
-        }                                                         \
-        offset >>= 1;                                             \
-        for (int i = 0; i < offset; ++i) {                        \
-            x[i] = vaddq_f16(x[i], x[offset+i]);                  \
-        }                                                         \
-        offset >>= 1;                                             \
-        for (int i = 0; i < offset; ++i) {                        \
-            x[i] = vaddq_f16(x[i], x[offset+i]);                  \
-        }                                                         \
-        const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 (x[0])); \
-        const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0])); \
-        res = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1));         \
+    #define GGML_F16x8_REDUCE(res, x)                               \
+    do {                                                            \
+        int offset = GGML_F16_ARR >> 1;                             \
+        for (int i = 0; i < offset; ++i) {                          \
+            (x)[i] = vaddq_f16((x)[i], (x)[offset+i]);              \
+        }                                                           \
+        offset >>= 1;                                               \
+        for (int i = 0; i < offset; ++i) {                          \
+            (x)[i] = vaddq_f16((x)[i], (x)[offset+i]);              \
+        }                                                           \
+        offset >>= 1;                                               \
+        for (int i = 0; i < offset; ++i) {                          \
+            (x)[i] = vaddq_f16((x)[i], (x)[offset+i]);              \
+        }                                                           \
+        const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 ((x)[0])); \
+        const float32x4_t t1 = vcvt_f32_f16(vget_high_f16((x)[0])); \
+        (res) = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1));         \
     } while (0)
 
     #define GGML_F16_VEC                GGML_F16x8
     #define GGML_F16_VEC_ZERO           GGML_F16x8_ZERO
     #define GGML_F16_VEC_SET1           GGML_F16x8_SET1
     #define GGML_F16_VEC_LOAD(p, i)     GGML_F16x8_LOAD(p)
-    #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), r[i])
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), (r)[i])
     #define GGML_F16_VEC_FMA            GGML_F16x8_FMA
     #define GGML_F16_VEC_ADD            GGML_F16x8_ADD
     #define GGML_F16_VEC_MUL            GGML_F16x8_MUL
@@ -1893,6 +1894,23 @@ static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) {
 #define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR)
 #endif
 
+//
+// ggml object
+//
+
+struct ggml_object {
+    size_t offs;
+    size_t size;
+
+    struct ggml_object * next;
+
+    enum ggml_object_type type;
+
+    char padding[4];
+};
+
+static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);
+
 //
 // ggml context
 //
@@ -19161,6 +19179,34 @@ void ggml_graph_clear(struct ggml_cgraph * cgraph) {
     ggml_hash_set_reset(&cgraph->visited_hash_set);
 }
 
+int ggml_graph_size(struct ggml_cgraph * cgraph) {
+    return cgraph->size;
+}
+
+struct ggml_tensor * ggml_graph_node(struct ggml_cgraph * cgraph, int i) {
+    if (i < 0) {
+        GGML_ASSERT(cgraph->n_nodes + i >= 0);
+        return cgraph->nodes[cgraph->n_nodes + i];
+    }
+
+    GGML_ASSERT(i < cgraph->n_nodes);
+    return cgraph->nodes[i];
+}
+
+struct ggml_tensor ** ggml_graph_nodes(struct ggml_cgraph * cgraph) {
+    return cgraph->nodes;
+}
+
+int ggml_graph_n_nodes(struct ggml_cgraph * cgraph) {
+    return cgraph->n_nodes;
+}
+
+void ggml_graph_add_node(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor) {
+    GGML_ASSERT(cgraph->size > cgraph->n_nodes);
+    cgraph->nodes[cgraph->n_nodes] = tensor;
+    cgraph->n_nodes++;
+}
+
 // Android's libc implementation "bionic" does not support setting affinity
 #if defined(__gnu_linux__)
 static void set_numa_thread_affinity(int thread_n) {
@@ -23242,6 +23288,14 @@ int ggml_cpu_has_arm_fma(void) {
 #endif
 }
 
+int ggml_cpu_has_riscv_v(void) {
+#if defined(__riscv_v_intrinsic)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
 int ggml_cpu_has_metal(void) {
 #if defined(GGML_USE_METAL)
     return 1;

include/llama.h

@@ -343,7 +343,7 @@ extern "C" {
         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]
-      //bool no_perf;     // whether to measure performance timings, TODO: implement
+        bool no_perf;     // whether to measure performance timings
 
         // Abort callback
         // if it returns true, execution of llama_decode() will be aborted
@@ -1056,6 +1056,9 @@ extern "C" {
     LLAMA_API struct llama_sampler * llama_sampler_chain_get(const struct llama_sampler * chain, int32_t i);
     LLAMA_API int                    llama_sampler_chain_n  (const struct llama_sampler * chain);
 
+    // after removing a sampler, the chain will no longer own it, and it will not be freed when the chain is freed
+    LLAMA_API struct llama_sampler * llama_sampler_chain_remove(   struct llama_sampler * chain, int32_t i);
+
     // available samplers:
 
     LLAMA_API struct llama_sampler * llama_sampler_init_greedy     (void);
@@ -1127,6 +1130,10 @@ extern "C" {
                              int32_t   n_logit_bias,
               const llama_logit_bias * logit_bias);
 
+
+    // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise
+    LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl);
+
     /// @details Sample and accept a token from the idx-th output of the last evaluation
     //
     // Shorthand for:
@@ -1169,13 +1176,30 @@ extern "C" {
     // NOTE: Used by llama.cpp examples, avoid using in third-party apps. Instead, do your own performance measurements.
     //
 
-    enum llama_perf_type {
-        LLAMA_PERF_TYPE_CONTEXT       = 0,
-        LLAMA_PERF_TYPE_SAMPLER_CHAIN = 1,
+    struct llama_perf_context_data {
+        double t_start_ms;
+        double t_load_ms;
+        double t_p_eval_ms;
+        double t_eval_ms;
+
+        int32_t n_p_eval;
+        int32_t n_eval;
     };
 
-    LLAMA_API void llama_perf_print(const void * ctx, enum llama_perf_type type);
-    LLAMA_API void llama_perf_reset(      void * ctx, enum llama_perf_type type);
+    struct llama_perf_sampler_data {
+        double t_sample_ms;
+
+        int32_t n_sample;
+    };
+
+    LLAMA_API struct llama_perf_context_data llama_perf_context      (const struct llama_context * ctx);
+    LLAMA_API void                           llama_perf_context_print(const struct llama_context * ctx);
+    LLAMA_API void                           llama_perf_context_reset(      struct llama_context * ctx);
+
+    // NOTE: the following work only with samplers constructed via llama_sampler_chain_init
+    LLAMA_API struct llama_perf_sampler_data llama_perf_sampler      (const struct llama_sampler * chain);
+    LLAMA_API void                           llama_perf_sampler_print(const struct llama_sampler * chain);
+    LLAMA_API void                           llama_perf_sampler_reset(      struct llama_sampler * chain);
 
     LLAMA_API void llama_perf_dump_yaml(FILE * stream, const struct llama_context * ctx);
 

src/llama-sampling.cpp

@@ -8,6 +8,7 @@
 #include <cstring>
 #include <ctime>
 #include <cfloat>
+#include <chrono>
 #include <cmath>
 #include <numeric>
 #include <random>
@@ -162,6 +163,19 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k)
     cur_p->size = k;
 }
 
+static uint32_t get_rng_seed(uint32_t seed) {
+    if (seed == LLAMA_DEFAULT_SEED) {
+        // use system clock if std::random_device is not a true RNG
+        static bool is_rd_prng = std::random_device().entropy() == 0;
+        if (is_rd_prng) {
+            return (uint32_t) std::chrono::system_clock::now().time_since_epoch().count();
+        }
+        std::random_device rd;
+        return rd();
+    }
+    return seed;
+}
+
 // llama_sampler API
 
 const char * llama_sampler_name(const struct llama_sampler * smpl) {
@@ -335,13 +349,26 @@ void llama_sampler_chain_add(struct llama_sampler * chain, struct llama_sampler
 struct llama_sampler * llama_sampler_chain_get(const struct llama_sampler * chain, int32_t i) {
     const auto * p = (const llama_sampler_chain *) chain->ctx;
 
-    if (i < 0 || i >= (int32_t) p->samplers.size()) {
+    if (i < 0 || (size_t) i >= p->samplers.size()) {
         return nullptr;
     }
 
     return p->samplers[i];
 }
 
+struct llama_sampler * llama_sampler_chain_remove(struct llama_sampler * chain, int32_t i) {
+    auto * p = (llama_sampler_chain *) chain->ctx;
+
+    if (i < 0 || (size_t) i >= p->samplers.size()) {
+        return nullptr;
+    }
+
+    auto * result = p->samplers[i];
+    p->samplers.erase(p->samplers.begin() + i);
+
+    return result;
+}
+
 int llama_sampler_chain_n(const struct llama_sampler * chain) {
     const auto * p = (const llama_sampler_chain *) chain->ctx;
 
@@ -387,6 +414,7 @@ struct llama_sampler * llama_sampler_init_greedy() {
 
 struct llama_sampler_dist {
     const uint32_t seed;
+          uint32_t seed_cur;
 
     std::mt19937 rng;
 };
@@ -416,7 +444,8 @@ static struct llama_sampler * llama_sampler_dist_clone(const struct llama_sample
 
 static void llama_sampler_dist_reset(struct llama_sampler * smpl) {
     auto * ctx = (llama_sampler_dist *) smpl->ctx;
-    ctx->rng = std::mt19937(ctx->seed);
+    ctx->seed_cur = get_rng_seed(ctx->seed);
+    ctx->rng.seed(ctx->seed_cur);
 }
 
 static void llama_sampler_dist_free(struct llama_sampler * smpl) {
@@ -433,11 +462,13 @@ static struct llama_sampler_i llama_sampler_dist_i = {
 };
 
 struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
+    auto seed_cur = get_rng_seed(seed);
     return new llama_sampler {
         /* .iface = */ &llama_sampler_dist_i,
         /* .ctx   = */ new llama_sampler_dist {
-            /* .seed = */ seed,
-            /* .rng  = */ std::mt19937(seed),
+            /* .seed     = */ seed,
+            /* .seed_cur = */ seed_cur,
+            /* .rng      = */ std::mt19937(seed_cur),
         },
     };
 }
@@ -1032,6 +1063,7 @@ struct llama_sampler_mirostat {
     const int32_t n_vocab;
 
     const uint32_t seed;
+          uint32_t seed_cur;
 
     const float tau;
     const float eta;
@@ -1100,7 +1132,8 @@ static struct llama_sampler * llama_sampler_mirostat_clone(const struct llama_sa
 static void llama_sampler_mirostat_reset(struct llama_sampler * smpl) {
     auto * ctx = (llama_sampler_mirostat *) smpl->ctx;
     ctx->mu = 2.0f*ctx->tau;
-    ctx->rng = std::mt19937(ctx->seed);
+    ctx->seed_cur = get_rng_seed(ctx->seed);
+    ctx->rng.seed(ctx->seed_cur);
 }
 
 static void llama_sampler_mirostat_free(struct llama_sampler * smpl) {
@@ -1117,16 +1150,18 @@ static struct llama_sampler_i llama_sampler_mirostat_i = {
 };
 
 struct llama_sampler * llama_sampler_init_mirostat(int32_t n_vocab, uint32_t seed, float tau, float eta, int32_t m) {
+    auto seed_cur = get_rng_seed(seed);
     return new llama_sampler {
         /* .iface = */ &llama_sampler_mirostat_i,
         /* .ctx   = */ new llama_sampler_mirostat {
-            /* .n_vocab = */ n_vocab,
-            /* .seed    = */ seed,
-            /* .tau     = */ tau,
-            /* .eta     = */ eta,
-            /* .m       = */ m,
-            /* .mu      = */ 2.0f*tau,
-            /* .rng     = */ std::mt19937(seed),
+            /* .n_vocab  = */ n_vocab,
+            /* .seed     = */ seed,
+            /* .seed_cur = */ seed_cur,
+            /* .tau      = */ tau,
+            /* .eta      = */ eta,
+            /* .m        = */ m,
+            /* .mu       = */ 2.0f*tau,
+            /* .rng      = */ std::mt19937(seed_cur),
         },
     };
 }
@@ -1135,6 +1170,7 @@ struct llama_sampler * llama_sampler_init_mirostat(int32_t n_vocab, uint32_t see
 
 struct llama_sampler_mirostat_v2 {
     const uint32_t seed;
+          uint32_t seed_cur;
 
     const float tau;
     const float eta;
@@ -1179,7 +1215,8 @@ static void llama_sampler_mirostat_v2_apply(struct llama_sampler * smpl, llama_t
 static void llama_sampler_mirostat_v2_reset(struct llama_sampler * smpl) {
     auto * ctx = (llama_sampler_mirostat_v2 *) smpl->ctx;
     ctx->mu = 2.0f*ctx->tau;
-    ctx->rng = std::mt19937(ctx->seed);
+    ctx->seed_cur = get_rng_seed(ctx->seed);
+    ctx->rng.seed(ctx->seed_cur);
 }
 
 static struct llama_sampler * llama_sampler_mirostat_v2_clone(const struct llama_sampler * smpl) {
@@ -1212,14 +1249,16 @@ static struct llama_sampler_i llama_sampler_mirostat_v2_i = {
 };
 
 struct llama_sampler * llama_sampler_init_mirostat_v2(uint32_t seed, float tau, float eta) {
+    auto seed_cur = get_rng_seed(seed);
     return new llama_sampler {
         /* .iface = */ &llama_sampler_mirostat_v2_i,
         /* .ctx   = */ new llama_sampler_mirostat_v2 {
-            /* .seed  = */ seed,
-            /* .tau   = */ tau,
-            /* .eta   = */ eta,
-            /* .mu    = */ 2.0f*tau,
-            /* .rng   = */ std::mt19937(seed),
+            /* .seed     = */ seed,
+            /* .seed_cur = */ seed_cur,
+            /* .tau      = */ tau,
+            /* .eta      = */ eta,
+            /* .mu       = */ 2.0f*tau,
+            /* .rng      = */ std::mt19937(seed_cur),
         },
     };
 }
@@ -1505,6 +1544,8 @@ struct llama_sampler * llama_sampler_init_penalties(
         ignore_eos = false;
     }
 
+    penalty_last_n = std::max(penalty_last_n, 0);
+
     return new llama_sampler {
         /* .iface = */ &llama_sampler_penalties_i,
         /* .ctx   = */ new llama_sampler_penalties {
@@ -1568,6 +1609,7 @@ static void llama_sampler_logit_bias_apply(struct llama_sampler * smpl, llama_to
         }
     }
 }
+
 static struct llama_sampler * llama_sampler_logit_bias_clone(const struct llama_sampler * smpl) {
     const auto * ctx = (const llama_sampler_logit_bias *) smpl->ctx;
     return llama_sampler_init_logit_bias(ctx->n_vocab, ctx->logit_bias.size(), ctx->logit_bias.data());
@@ -1599,3 +1641,65 @@ struct llama_sampler * llama_sampler_init_logit_bias(
         },
     };
 }
+
+// utils
+
+uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl) {
+    if (smpl->iface == &llama_sampler_dist_i) {
+        return ((const llama_sampler_dist *) smpl->ctx)->seed_cur;
+    }
+
+    if (smpl->iface == &llama_sampler_mirostat_i) {
+        return ((const llama_sampler_mirostat *) smpl->ctx)->seed_cur;
+    }
+
+    if (smpl->iface == &llama_sampler_mirostat_v2_i) {
+        return ((const llama_sampler_mirostat_v2 *) smpl->ctx)->seed_cur;
+    }
+
+    if (smpl->iface == &llama_sampler_chain_i) {
+        const auto * ctx = (const llama_sampler_chain *) smpl->ctx;
+        for (auto it = ctx->samplers.rbegin(); it != ctx->samplers.rend(); ++it) {
+            const uint32_t seed = llama_sampler_get_seed(*it);
+            if (seed != LLAMA_DEFAULT_SEED) {
+                return seed;
+            }
+        }
+    }
+
+    return LLAMA_DEFAULT_SEED;
+}
+
+// perf
+
+struct llama_perf_sampler_data llama_perf_sampler(const struct llama_sampler * chain) {
+    struct llama_perf_sampler_data data = {};
+
+    if (chain == nullptr || chain->iface != &llama_sampler_chain_i) {
+        GGML_ABORT("%s: invalid sampler passed - requires a sampler created with llama_sampler_chain_init()\n", __func__);
+    }
+
+    const auto * ctx = (const struct llama_sampler_chain *) chain->ctx;
+
+    data.t_sample_ms = 1e-3 * ctx->t_sample_us;
+    data.n_sample    = std::max(0, ctx->n_sample);
+
+    return data;
+}
+
+void llama_perf_sampler_print(const struct llama_sampler * chain) {
+    const auto data = llama_perf_sampler(chain);
+
+    LLAMA_LOG_INFO("%s:    sampling time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
+            __func__, data.t_sample_ms, data.n_sample, data.t_sample_ms / data.n_sample, 1e3 / data.t_sample_ms * data.n_sample);
+}
+
+void llama_perf_sampler_reset(struct llama_sampler * chain) {
+    if (chain == nullptr || chain->iface != &llama_sampler_chain_i) {
+        GGML_ABORT("%s: invalid sampler passed - requires a sampler created with llama_sampler_chain_init()\n", __func__);
+    }
+
+    auto * ctx = (struct llama_sampler_chain *) chain->ctx;
+
+    ctx->t_sample_us = ctx->n_sample = 0;
+}

src/llama.cpp

@@ -2156,6 +2156,10 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer
     if (host_buffer) {
         buft = ggml_backend_sycl_host_buffer_type();
     }
+#elif defined(GGML_USE_CANN)
+    if (host_buffer) {
+        buft = ggml_backend_cann_host_buffer_type();
+    }
 #elif defined(GGML_USE_CPU_HBM)
     buft = ggml_backend_cpu_hbm_buffer_type();
 #elif defined(GGML_USE_VULKAN)
@@ -2482,6 +2486,7 @@ struct llama_cparams {
     bool causal_attn;
     bool offload_kqv;
     bool flash_attn;
+    bool no_perf;
 
     enum llama_pooling_type pooling_type;
 
@@ -6657,8 +6662,6 @@ static bool llm_load_tensors(
         bool use_mlock,
         llama_progress_callback progress_callback,
         void * progress_callback_user_data) {
-    model.t_start_us = ggml_time_us();
-
     auto & hparams = model.hparams;
 
     model.split_mode   = split_mode;
@@ -8589,14 +8592,13 @@ static bool llm_load_tensors(
         }
     }
 
-    // loading time will be recalculate after the first eval, so
-    // we take page faults deferred by mmap() into consideration
-    model.t_load_us = ggml_time_us() - model.t_start_us;
     return true;
 }
 
 // Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
 static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
+    model.t_start_us = ggml_time_us();
+
     try {
         llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides);
 
@@ -8658,6 +8660,10 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
         return -1;
     }
 
+    // loading time will be recalculate after the first eval, so
+    // we take page faults deferred by mmap() into consideration
+    model.t_load_us = ggml_time_us() - model.t_start_us;
+
     return 0;
 }
 
@@ -9258,7 +9264,7 @@ static struct ggml_tensor * llm_build_copy_mask_state(
     // FIXME: zero-out NANs?
     states = ggml_mul(ctx, states, state_mask);
 
-    // copy states which won't be changed further (between n_seqs and n_rs)
+    // copy states which won't be changed further (between n_seqs and n_kv)
     ggml_build_forward_expand(graph,
         ggml_cpy(ctx,
             ggml_view_1d(ctx, states, n_state*(n_kv - n_seqs), n_seqs*n_state*ggml_element_size(states)),
@@ -9877,8 +9883,8 @@ struct llm_build_context {
     struct ggml_cgraph * append_pooling(struct ggml_cgraph * gf) {
         // find result_norm tensor for input
         struct ggml_tensor * inp = nullptr;
-        for (int i = gf->n_nodes - 1; i >= 0; --i) {
-            inp = gf->nodes[i];
+        for (int i = ggml_graph_n_nodes(gf) - 1; i >= 0; --i) {
+            inp = ggml_graph_node(gf, i);
             if (strcmp(inp->name, "result_norm") == 0 || strcmp(inp->name, "result_embd") == 0) {
                 break;
             } else {
@@ -15820,7 +15826,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
 
             // clear unused states
             for (int i = 0; i < n_kv; ++i) {
-                uint32_t        cell_id = i + kv_self.head;
+                const uint32_t  cell_id = i + kv_self.head;
                 llama_kv_cell & kv_cell = lctx.kv_self.cells[cell_id];
 
                 data[i] = (float) (kv_cell.src >= 0);
@@ -16076,19 +16082,21 @@ static int llama_decode_internal(
         return -1;
     }
 
-    for (uint32_t i = 0; i < n_tokens_all; ++i) {
-        if (batch_all.token[i] < 0 || (uint32_t)batch_all.token[i] >= lctx.model.vocab.n_vocab) {
-            LLAMA_LOG_ERROR("%s: invalid token[%d] = %d", __func__, i, batch_all.token[i]);
-            return -1;
-        }
-    }
-
     const auto & model   = lctx.model;
     const auto & hparams = model.hparams;
     const auto & cparams = lctx.cparams;
 
     GGML_ASSERT((!batch_all.token && batch_all.embd) || (batch_all.token && !batch_all.embd)); // NOLINT
 
+    if (batch_all.token) {
+        for (uint32_t i = 0; i < n_tokens_all; ++i) {
+            if (batch_all.token[i] < 0 || (uint32_t)batch_all.token[i] >= model.vocab.n_vocab) {
+                LLAMA_LOG_ERROR("%s: invalid token[%d] = %d", __func__, i, batch_all.token[i]);
+                return -1;
+            }
+        }
+    }
+
     GGML_ASSERT(n_tokens_all <= cparams.n_batch);
 
     GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
@@ -16205,8 +16213,8 @@ static int llama_decode_internal(
         ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false);
 
         // the output is always the last tensor in the graph
-        struct ggml_tensor * res  = gf->nodes[gf->n_nodes - 1];
-        struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2];
+        struct ggml_tensor * res  = ggml_graph_node(gf, -1);
+        struct ggml_tensor * embd = ggml_graph_node(gf, -2);
 
         if (lctx.n_outputs == 0) {
             // no output
@@ -16215,9 +16223,9 @@ static int llama_decode_internal(
         } else if (cparams.embeddings) {
             res  = nullptr; // do not extract logits for embedding case
             embd = nullptr;
-            for (int i = gf->n_nodes - 1; i >= 0; --i) {
-                if (strcmp(gf->nodes[i]->name, "result_embd_pooled") == 0) {
-                    embd = gf->nodes[i];
+            for (int i = ggml_graph_n_nodes(gf) - 1; i >= 0; --i) {
+                if (strcmp(ggml_graph_node(gf, i)->name, "result_embd_pooled") == 0) {
+                    embd = ggml_graph_node(gf, i);
                     break;
                 }
             }
@@ -16375,19 +16383,21 @@ static int llama_encode_internal(
         return -1;
     }
 
-    for (uint32_t i = 0; i < n_tokens; ++i) {
-        if (batch.token[i] < 0 || (uint32_t)batch.token[i] >= lctx.model.vocab.n_vocab) {
-            LLAMA_LOG_ERROR("%s: invalid token[%d] = %d", __func__, i, batch.token[i]);
-            return -1;
-        }
-    }
-
     const auto & model   = lctx.model;
     const auto & hparams = model.hparams;
     const auto & cparams = lctx.cparams;
 
     GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
 
+    if (batch.token) {
+        for (uint32_t i = 0; i < n_tokens; ++i) {
+            if (batch.token[i] < 0 || (uint32_t)batch.token[i] >= model.vocab.n_vocab) {
+                LLAMA_LOG_ERROR("%s: invalid token[%d] = %d", __func__, i, batch.token[i]);
+                return -1;
+            }
+        }
+    }
+
     // micro-batching is not possible for non-causal encoding, so we process the batch in a single shot
     GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens");
 
@@ -16432,15 +16442,15 @@ static int llama_encode_internal(
     // there are two cases here
     if (llama_model_has_decoder(&lctx.model)) {
         // first case is an encoder-decoder T5 model where embeddings are passed to decoder
-        embd = gf->nodes[gf->n_nodes - 1];
+        embd = ggml_graph_node(gf, -1);
         GGML_ASSERT(strcmp(embd->name, "result_norm") == 0 && "missing result_output tensor");
     } else {
         // second case is an encoder-only T5 model
         if (cparams.embeddings) {
             // only output embeddings if required
-            embd = gf->nodes[gf->n_nodes - 1];
+            embd = ggml_graph_node(gf, -1);
             if (strcmp(embd->name, "result_embd_pooled") != 0) {
-                embd = gf->nodes[gf->n_nodes - 2];
+                embd = ggml_graph_node(gf, -2);
             }
             GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor");
         }
@@ -17530,6 +17540,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         quantize &= name.find("time_mix_first.weight") == std::string::npos;
         quantize &= name.find("time_mix_w1.weight") == std::string::npos;
         quantize &= name.find("time_mix_w2.weight") == std::string::npos;
+        quantize &= name.find("time_mix_decay_w1.weight") == std::string::npos;
+        quantize &= name.find("time_mix_decay_w2.weight") == std::string::npos;
 
         // do not quantize relative position bias (T5)
         quantize &= name.find("attn_rel_b.weight") == std::string::npos;
@@ -17939,6 +17951,7 @@ struct llama_context_params llama_context_default_params() {
         /*.embeddings                  =*/ false,
         /*.offload_kqv                 =*/ true,
         /*.flash_attn                  =*/ false,
+        /*.no_perf                     =*/ true,
         /*.abort_callback              =*/ nullptr,
         /*.abort_callback_data         =*/ nullptr,
     };
@@ -18149,6 +18162,7 @@ struct llama_context * llama_new_context_with_model(
     cparams.embeddings       = params.embeddings;
     cparams.offload_kqv      = params.offload_kqv;
     cparams.flash_attn       = params.flash_attn;
+    cparams.no_perf          = params.no_perf;
     cparams.pooling_type     = params.pooling_type;
 
     cparams.n_ctx            = params.n_ctx           == 0    ? hparams.n_ctx_train           : params.n_ctx;
@@ -18486,7 +18500,7 @@ struct llama_context * llama_new_context_with_model(
 
             // note: the number of splits during measure is higher than during inference due to the kv shift
             int n_splits = ggml_backend_sched_get_n_splits(ctx->sched);
-            LLAMA_LOG_INFO("%s: graph nodes  = %d\n", __func__, gf->n_nodes);
+            LLAMA_LOG_INFO("%s: graph nodes  = %d\n", __func__, ggml_graph_n_nodes(gf));
             LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits);
         }
     }
@@ -20067,10 +20081,14 @@ void llama_synchronize(struct llama_context * ctx) {
 
     // add the evaluation to the stats
     if (ctx->n_queued_tokens == 1) {
-        ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+        if (!ctx->cparams.no_perf) {
+            ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+        }
         ctx->n_eval++;
     } else if (ctx->n_queued_tokens > 1) {
-        ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+        if (!ctx->cparams.no_perf) {
+            ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+        }
         ctx->n_p_eval += ctx->n_queued_tokens;
     }
 
@@ -20666,6 +20684,7 @@ const char * llama_print_system_info(void) {
     s += "ARM_FMA = "     + std::to_string(ggml_cpu_has_arm_fma())     + " | ";
     s += "F16C = "        + std::to_string(ggml_cpu_has_f16c())        + " | ";
     s += "FP16_VA = "     + std::to_string(ggml_cpu_has_fp16_va())     + " | ";
+    s += "RISCV_VECT = "  + std::to_string(ggml_cpu_has_riscv_v())     + " | ";
     s += "WASM_SIMD = "   + std::to_string(ggml_cpu_has_wasm_simd())   + " | ";
     s += "BLAS = "        + std::to_string(ggml_cpu_has_blas())        + " | ";
     s += "SSE3 = "        + std::to_string(ggml_cpu_has_sse3())        + " | ";
@@ -20677,65 +20696,40 @@ const char * llama_print_system_info(void) {
     return s.c_str();
 }
 
-void llama_perf_print(const void * ctx, enum llama_perf_type type) {
-    switch (type) {
-        case LLAMA_PERF_TYPE_CONTEXT:
-            {
-                const auto * p = (const struct llama_context *) ctx;
+struct llama_perf_context_data llama_perf_context(const struct llama_context * ctx) {
+    struct llama_perf_context_data data = {};
 
-                const double t_start_ms   = 1e-3 * p->t_start_us;
-                const double t_end_ms     = 1.00 * ggml_time_ms();
-                const double t_load_ms    = 1e-3 * p->t_load_us;
-                const double t_p_eval_ms  = 1e-3 * p->t_p_eval_us;
-                const double t_eval_ms    = 1e-3 * p->t_eval_us;
-
-                const int32_t n_p_eval  = std::max(0, p->n_p_eval);
-                const int32_t n_eval    = std::max(1, p->n_eval);
-
-                LLAMA_LOG_INFO("%s:        load time = %10.2f ms\n", __func__, t_load_ms);
-                LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
-                        __func__, t_p_eval_ms, n_p_eval, t_p_eval_ms / n_p_eval, 1e3 / t_p_eval_ms * n_p_eval);
-                LLAMA_LOG_INFO("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-                        __func__, t_eval_ms, n_eval, t_eval_ms / n_eval, 1e3 / t_eval_ms * n_eval);
-                LLAMA_LOG_INFO("%s:       total time = %10.2f ms / %5d tokens\n", __func__, (t_end_ms - t_start_ms), (n_p_eval + n_eval));
-            } break;
-        case LLAMA_PERF_TYPE_SAMPLER_CHAIN:
-            {
-                const auto * smpl = (const struct llama_sampler *) ctx;
-                const auto * p = (const struct llama_sampler_chain *) smpl->ctx;
-
-                const double t_sampler_ms = 1e-3 * p->t_sample_us;
-
-                const int32_t n_sampler = std::max(0, p->n_sample);
-
-                LLAMA_LOG_INFO("%s:    sampling time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-                        __func__, t_sampler_ms, n_sampler, t_sampler_ms / n_sampler, 1e3 / t_sampler_ms * n_sampler);
-            } break;
-        default:
-            GGML_ABORT("invalid perf type");
+    if (ctx == nullptr) {
+        return data;
     }
+
+    data.t_start_ms  = 1e-3 * ctx->t_start_us;
+    data.t_load_ms   = 1e-3 * ctx->t_load_us;
+    data.t_p_eval_ms = 1e-3 * ctx->t_p_eval_us;
+    data.t_eval_ms   = 1e-3 * ctx->t_eval_us;
+    data.n_p_eval    = std::max(1, ctx->n_p_eval);
+    data.n_eval      = std::max(1, ctx->n_eval);
+
+    return data;
 }
 
-void llama_perf_reset(void * ctx, enum llama_perf_type type) {
-    switch (type) {
-        case LLAMA_PERF_TYPE_CONTEXT:
-            {
-                auto * p = (struct llama_context *) ctx;
+void llama_perf_context_print(const struct llama_context * ctx) {
+    const auto data = llama_perf_context(ctx);
 
-                p->t_start_us  = ggml_time_us();
-                p->t_eval_us   = p->n_eval = 0;
-                p->t_p_eval_us = p->n_p_eval = 0;
-            } break;
-        case LLAMA_PERF_TYPE_SAMPLER_CHAIN:
-            {
-                auto * smpl = (struct llama_sampler *) ctx;
-                auto * p = (struct llama_sampler_chain *) smpl->ctx;
+    const double t_end_ms = 1e-3 * ggml_time_us();
 
-                p->t_sample_us = p->n_sample = 0;
-            } break;
-        default:
-            GGML_ABORT("invalid perf type");
-    }
+    LLAMA_LOG_INFO("%s:        load time = %10.2f ms\n", __func__, data.t_load_ms);
+    LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
+            __func__, data.t_p_eval_ms, data.n_p_eval, data.t_p_eval_ms / data.n_p_eval, 1e3 / data.t_p_eval_ms * data.n_p_eval);
+    LLAMA_LOG_INFO("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
+            __func__, data.t_eval_ms, data.n_eval, data.t_eval_ms / data.n_eval, 1e3 / data.t_eval_ms * data.n_eval);
+    LLAMA_LOG_INFO("%s:       total time = %10.2f ms / %5d tokens\n", __func__, (t_end_ms - data.t_start_ms), (data.n_p_eval + data.n_eval));
+}
+
+void llama_perf_context_reset(struct llama_context * ctx) {
+    ctx->t_start_us  = ggml_time_us();
+    ctx->t_eval_us   = ctx->n_eval = 0;
+    ctx->t_p_eval_us = ctx->n_p_eval = 0;
 }
 
 void llama_perf_dump_yaml(FILE * stream, const llama_context * ctx) {

tests/test-arg-parser.cpp

@@ -1,3 +1,6 @@
+#include "arg.h"
+#include "common.h"
+
 #include <string>
 #include <vector>
 #include <sstream>
@@ -6,18 +9,16 @@
 #undef NDEBUG
 #include <cassert>
 
-#include "common.h"
-
 int main(void) {
     gpt_params params;
 
     printf("test-arg-parser: make sure there is no duplicated arguments in any examples\n\n");
     for (int ex = 0; ex < LLAMA_EXAMPLE_COUNT; ex++) {
         try {
-            auto options = gpt_params_parser_init(params, (enum llama_example)ex);
+            auto ctx_arg = gpt_params_parser_init(params, (enum llama_example)ex);
             std::unordered_set<std::string> seen_args;
             std::unordered_set<std::string> seen_env_vars;
-            for (const auto & opt : options) {
+            for (const auto & opt : ctx_arg.options) {
                 // check for args duplications
                 for (const auto & arg : opt.args) {
                     if (seen_args.find(arg) == seen_args.end()) {
@@ -52,40 +53,51 @@ int main(void) {
     };
 
     std::vector<std::string> argv;
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
 
     printf("test-arg-parser: test invalid usage\n\n");
 
+    // missing value
     argv = {"binary_name", "-m"};
-    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
 
+    // wrong value (int)
     argv = {"binary_name", "-ngl", "hello"};
-    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
 
+    // wrong value (enum)
     argv = {"binary_name", "-sm", "hello"};
-    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+
+    // non-existence arg in specific example (--draft cannot be used outside llama-speculative)
+    argv = {"binary_name", "--draft", "123"};
+    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SERVER));
 
 
     printf("test-arg-parser: test valid usage\n\n");
 
     argv = {"binary_name", "-m", "model_file.gguf"};
-    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
     assert(params.model == "model_file.gguf");
 
     argv = {"binary_name", "-t", "1234"};
-    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
     assert(params.cpuparams.n_threads == 1234);
 
     argv = {"binary_name", "--verbose"};
-    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
     assert(params.verbosity == 1);
 
     argv = {"binary_name", "-m", "abc.gguf", "--predict", "6789", "--batch-size", "9090"};
-    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
     assert(params.model == "abc.gguf");
     assert(params.n_predict == 6789);
     assert(params.n_batch == 9090);
 
+    // --draft cannot be used outside llama-speculative
+    argv = {"binary_name", "--draft", "123"};
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SPECULATIVE));
+    assert(params.n_draft == 123);
+
 // skip this part on windows, because setenv is not supported
 #ifdef _WIN32
     printf("test-arg-parser: skip on windows build\n");
@@ -94,12 +106,12 @@ int main(void) {
 
     setenv("LLAMA_ARG_THREADS", "blah", true);
     argv = {"binary_name"};
-    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
 
     setenv("LLAMA_ARG_MODEL", "blah.gguf", true);
     setenv("LLAMA_ARG_THREADS", "1010", true);
     argv = {"binary_name"};
-    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
     assert(params.model == "blah.gguf");
     assert(params.cpuparams.n_threads == 1010);
 
@@ -109,7 +121,7 @@ int main(void) {
     setenv("LLAMA_ARG_MODEL", "blah.gguf", true);
     setenv("LLAMA_ARG_THREADS", "1010", true);
     argv = {"binary_name", "-m", "overwritten.gguf"};
-    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
     assert(params.model == "overwritten.gguf");
     assert(params.cpuparams.n_threads == 1010);
 #endif // _WIN32

tests/test-backend-ops.cpp

@@ -519,7 +519,7 @@ struct test_case {
 
         // add sentinels as graph nodes so that they are checked in the callback
         for (ggml_tensor * sentinel : sentinels) {
-            gf->nodes[gf->n_nodes++] = sentinel;
+            ggml_graph_add_node(gf, sentinel);
         }
 
         // randomize tensors
@@ -679,9 +679,9 @@ struct test_case {
 
         // duplicate the op
         size_t target_size = ggml_backend_is_cpu(backend) ? 1ULL << 33 : 1ULL << 35; // 8 GB CPU, 32 GB GPU
-        int n_runs = std::min((size_t)gf->size - gf->n_nodes, target_size / op_size(out)) + 1;
+        int n_runs = std::min((size_t) ggml_graph_size(gf) - ggml_graph_n_nodes(gf), target_size / op_size(out)) + 1;
         for (int i = 1; i < n_runs; i++) {
-            gf->nodes[gf->n_nodes++] = out;
+            ggml_graph_add_node(gf, out);
         }
 
         // calculate memory
@@ -696,11 +696,11 @@ struct test_case {
             }
             return size;
         };
-        for (int i = 0; i < gf->n_nodes; i++) {
-            if (ggml_is_view_op(gf->nodes[i]->op) || gf->nodes[i] == out) {
+        for (int i = 0; i < ggml_graph_n_nodes(gf); ++i) {
+            if (ggml_is_view_op(ggml_graph_node(gf, i)->op) || ggml_graph_node(gf, i) == out) {
                 continue;
             }
-            mem += tensor_op_size(gf->nodes[i]);
+            mem += tensor_op_size(ggml_graph_node(gf, i));
         }
 
         // run
@@ -804,7 +804,7 @@ struct test_case {
         ggml_graph_cpy(gf, gb);
         ggml_build_backward_expand(ctx, gf, gb, false);
         if (expect.size() != 1 || expect[0] != 0.0f) {
-            GGML_ASSERT(gb->n_nodes > gf->n_nodes);
+            GGML_ASSERT(ggml_graph_n_nodes(gb) > ggml_graph_n_nodes(gf));
             for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
                 GGML_ASSERT(!(t->flags & GGML_TENSOR_FLAG_PARAM) || t->grad->op != GGML_OP_NONE);
             }