cmake : fix for builds without GGML_CDEF_PUBLIC (#9338)

* `GGML_TARGET_DEFINES-NOTFOUND` fix for builds without `GGML_CDEF_PUBLIC`

* Update CMakeLists.txt, spaces fix

.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"

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
@@ -1454,7 +1459,6 @@ 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/arg.cpp

@@ -173,7 +173,6 @@ static bool gpt_params_parse_ex(int argc, char ** argv, gpt_params_context & ctx
     std::string arg;
     const std::string arg_prefix = "--";
     gpt_params & params = ctx_arg.params;
-    gpt_sampler_params & sparams = params.sparams;
 
     std::unordered_map<std::string, llama_arg *> arg_to_options;
     for (auto & opt : ctx_arg.options) {
@@ -283,10 +282,6 @@ static bool gpt_params_parse_ex(int argc, char ** argv, gpt_params_context & ctx
         params.kv_overrides.back().key[0] = 0;
     }
 
-    if (sparams.seed == LLAMA_DEFAULT_SEED) {
-        sparams.seed = time(NULL);
-    }
-
     return true;
 }
 
@@ -823,7 +818,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         [](gpt_params & params) {
             params.special = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
     add_opt(llama_arg(
         {"-cnv", "--conversation"},
         format(
@@ -909,7 +904,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
     ).set_sparam());
     add_opt(llama_arg(
         {"-s", "--seed"}, "SEED",
-        format("RNG seed (default: %d, use random seed for < 0)", params.sparams.seed),
+        format("RNG seed (default: %u, use random seed for %u)", params.sparams.seed, LLAMA_DEFAULT_SEED),
         [](gpt_params & params, const std::string & value) {
             params.sparams.seed = std::stoul(value);
         }
@@ -1422,20 +1417,18 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
                 params.split_mode = LLAMA_SPLIT_MODE_NONE;
             } else if (arg_next == "layer") {
                 params.split_mode = LLAMA_SPLIT_MODE_LAYER;
-            }
-            else if (arg_next == "row") {
+            } else if (arg_next == "row") {
 #ifdef GGML_USE_SYCL
                 fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
                 exit(1);
 #endif // GGML_USE_SYCL
                 params.split_mode = LLAMA_SPLIT_MODE_ROW;
-            }
-            else {
+            } else {
                 throw std::invalid_argument("invalid value");
             }
-#ifndef GGML_USE_CUDA_SYCL_VULKAN
-            fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
-#endif // GGML_USE_CUDA_SYCL_VULKAN
+            if (!llama_supports_gpu_offload()) {
+                fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the split mode has no effect.\n");
+            }
         }
     ));
     add_opt(llama_arg(
@@ -1455,14 +1448,14 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
             }
             for (size_t i = 0; i < llama_max_devices(); ++i) {
                 if (i < split_arg.size()) {
-                        params.tensor_split[i] = std::stof(split_arg[i]);
+                    params.tensor_split[i] = std::stof(split_arg[i]);
                 } else {
-                        params.tensor_split[i] = 0.0f;
+                    params.tensor_split[i] = 0.0f;
                 }
             }
-#ifndef GGML_USE_CUDA_SYCL_VULKAN
-            fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
-#endif // GGML_USE_CUDA_SYCL_VULKAN
+            if (!llama_supports_gpu_offload()) {
+                fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting a tensor split has no effect.\n");
+            }
         }
     ));
     add_opt(llama_arg(
@@ -1470,9 +1463,9 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         format("the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu),
         [](gpt_params & params, int value) {
             params.main_gpu = value;
-#ifndef GGML_USE_CUDA_SYCL_VULKAN
-            fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
-#endif // GGML_USE_CUDA_SYCL_VULKAN
+            if (!llama_supports_gpu_offload()) {
+                fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the main GPU has no effect.\n");
+            }
         }
     ));
     add_opt(llama_arg(

common/common.cpp

@@ -56,14 +56,6 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL))
-#define GGML_USE_CUDA_SYCL
-#endif
-
-#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
-#define GGML_USE_CUDA_SYCL_VULKAN
-#endif
-
 #if defined(LLAMA_USE_CURL)
 #ifdef __linux__
 #include <linux/limits.h>
@@ -949,11 +941,37 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p
 
 #ifdef LLAMA_USE_CURL
 
+#define CURL_MAX_RETRY 3
+#define CURL_RETRY_DELAY_SECONDS 2
+
+
 static bool starts_with(const std::string & str, const std::string & prefix) {
     // While we wait for C++20's std::string::starts_with...
     return str.rfind(prefix, 0) == 0;
 }
 
+static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) {
+    int remaining_attempts = max_attempts;
+
+    while (remaining_attempts > 0) {
+        fprintf(stderr, "%s: Trying to download from %s (attempt %d of %d)...\n", __func__ , url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
+
+        CURLcode res = curl_easy_perform(curl);
+        if (res == CURLE_OK) {
+            return true;
+        }
+
+        int exponential_backoff_delay = std::pow(retry_delay_seconds, max_attempts - remaining_attempts) * 1000;
+        fprintf(stderr, "%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay);
+
+        remaining_attempts--;
+        std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
+    }
+
+    fprintf(stderr, "%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
+    return false;
+}
+
 static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
 
     // Initialize libcurl
@@ -1057,9 +1075,8 @@ static bool llama_download_file(const std::string & url, const std::string & pat
         curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
         curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
 
-        CURLcode res = curl_easy_perform(curl.get());
-        if (res != CURLE_OK) {
-            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
+        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
+        if (!was_perform_successful) {
             return false;
         }
 
@@ -1134,11 +1151,10 @@ static bool llama_download_file(const std::string & url, const std::string & pat
         };
 
         // start the download
-        fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
-                llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
-        auto res = curl_easy_perform(curl.get());
-        if (res != CURLE_OK) {
-            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
+        fprintf(stderr, "%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
+            llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
+        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
+        if (!was_perform_successful) {
             return false;
         }
 
@@ -1812,6 +1828,7 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "cpu_has_sve: %s\n",         ggml_cpu_has_sve()         ? "true" : "false");
     fprintf(stream, "cpu_has_f16c: %s\n",        ggml_cpu_has_f16c()        ? "true" : "false");
     fprintf(stream, "cpu_has_fp16_va: %s\n",     ggml_cpu_has_fp16_va()     ? "true" : "false");
+    fprintf(stream, "cpu_has_riscv_v: %s\n",     ggml_cpu_has_riscv_v()     ? "true" : "false");
     fprintf(stream, "cpu_has_wasm_simd: %s\n",   ggml_cpu_has_wasm_simd()   ? "true" : "false");
     fprintf(stream, "cpu_has_blas: %s\n",        ggml_cpu_has_blas()        ? "true" : "false");
     fprintf(stream, "cpu_has_sse3: %s\n",        ggml_cpu_has_sse3()        ? "true" : "false");

common/sampling.cpp

@@ -310,6 +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) {

common/sampling.h

@@ -60,6 +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:

examples/batched-bench/batched-bench.cpp

@@ -3,32 +3,10 @@
 #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]);

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/pca.hpp

@@ -226,8 +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

@@ -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);
 

examples/export-lora/export-lora.cpp

@@ -370,7 +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);

examples/infill/infill.cpp

@@ -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/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.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

@@ -18,8 +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) {
@@ -255,7 +255,7 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, show_additional_info)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) {
         return 1;
     }
 

examples/main/main.cpp

@@ -191,8 +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);
@@ -470,8 +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/perplexity/perplexity.cpp

@@ -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);
 

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/server/server.cpp

@@ -1266,6 +1266,7 @@ 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},

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.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/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"
@@ -1942,7 +1943,7 @@ 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

@@ -1127,6 +1127,10 @@ 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:

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) {
@@ -387,6 +401,7 @@ struct llama_sampler * llama_sampler_init_greedy() {
 
 struct llama_sampler_dist {
     const uint32_t seed;
+          uint32_t seed_cur;
 
     std::mt19937 rng;
 };
@@ -416,7 +431,8 @@ 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 +449,13 @@ 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 +1050,7 @@ 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 +1119,8 @@ 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 +1137,18 @@ 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 +1157,7 @@ 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 +1202,8 @@ 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 +1236,16 @@ 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 +1531,8 @@ 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 +1596,7 @@ 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 +1628,31 @@ 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;
+}

src/llama.cpp

@@ -9258,7 +9258,7 @@ 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 +9877,8 @@ 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 {
@@ -16076,19 +16076,21 @@ 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 +16207,8 @@ 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 +16217,9 @@ 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 +16377,21 @@ 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 +16436,15 @@ 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 +17534,8 @@ 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;
@@ -18486,7 +18492,7 @@ 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);
         }
     }
@@ -20666,6 +20672,7 @@ 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())        + " | ";

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);
             }