sampling : fix off-by-one in tail-free sampling

ggml-ci

.dockerignore

@@ -1,7 +1,7 @@
 *.o
 *.a
 .cache/
-# Do not ignore .git directory, otherwise the reported build number will always be 0
+.git/
 .github/
 .gitignore
 .vs/

.github/workflows/build.yml

@@ -956,7 +956,6 @@ jobs:
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl7.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/svml_dispmd.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libmmd.dll" ./build/bin
-          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libiomp5md.dll" ./build/bin
           echo "cp oneAPI running time dll files to ./build/bin done"
           7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip ./build/bin/*
 

.github/workflows/docker.yml

@@ -15,17 +15,11 @@ on:
     branches:
       - master
     paths: ['.github/workflows/docker.yml', '.devops/*.Dockerfile', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
-  workflow_dispatch: # allows manual triggering, useful for debugging
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
   cancel-in-progress: true
 
-# Fine-grant permission
-# https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token
-permissions:
-  packages: write
-
 jobs:
   push_to_registry:
     name: Push Docker image to Docker Hub
@@ -52,8 +46,6 @@ jobs:
     steps:
       - name: Check out the repo
         uses: actions/checkout@v4
-        with:
-          fetch-depth: 0 # preserve git history, so we can determine the build number
 
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v2
@@ -68,34 +60,6 @@ jobs:
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      - name: Determine tag name
-        id: tag
-        shell: bash
-        run: |
-          BUILD_NUMBER="$(git rev-list --count HEAD)"
-          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
-          REPO_OWNER="${GITHUB_REPOSITORY_OWNER@L}"  # to lower case
-          REPO_NAME="${{ github.event.repository.name }}"
-
-          # determine tag name postfix (build number, commit hash)
-          if [[ "${{ env.GITHUB_BRANCH_NAME }}" == "master" ]]; then
-            TAG_POSTFIX="b${BUILD_NUMBER}"
-          else
-            SAFE_NAME=$(echo "${{ env.GITHUB_BRANCH_NAME }}" | tr '/' '-')
-            TAG_POSTFIX="${SAFE_NAME}-${SHORT_HASH}"
-          fi
-
-          # list all tags possible
-          TAGS=""
-          TAGS="${TAGS}ghcr.io/${REPO_OWNER}/${REPO_NAME}:${{ matrix.config.tag }},"
-          TAGS="${TAGS}ghcr.io/${REPO_OWNER}/${REPO_NAME}:${{ matrix.config.tag }}-${TAG_POSTFIX}"
-
-          echo "output_tags=$TAGS" >> $GITHUB_OUTPUT
-          echo "output_tags=$TAGS"  # print out for debugging
-        env:
-          GITHUB_BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
-          GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
-
       # https://github.com/jlumbroso/free-disk-space/tree/54081f138730dfa15788a46383842cd2f914a1be#example
       - name: Free Disk Space (Ubuntu)
         uses: jlumbroso/free-disk-space@main
@@ -113,6 +77,25 @@ jobs:
           docker-images: true
           swap-storage: true
 
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+          if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+            echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+          else
+            SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+            echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Downcase github.repository_owner
+        run: |
+          echo "repository_owner_lowercase=${GITHUB_REPOSITORY_OWNER@L}" >> $GITHUB_ENV
+        env:
+          GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
+
       - name: Build and push Docker image (tagged + versioned)
         if: github.event_name == 'push'
         uses: docker/build-push-action@v6
@@ -120,6 +103,5 @@ jobs:
           context: .
           push: true
           platforms: ${{ matrix.config.platforms }}
-          # tag list is generated from step above
-          tags: ${{ steps.tag.outputs.output_tags }}
+          tags: "ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }},ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}"
           file: ${{ matrix.config.dockerfile }}

CMakeLists.txt

@@ -62,9 +62,6 @@ option(LLAMA_SANITIZE_THREAD    "llama: enable thread sanitizer"    OFF)
 option(LLAMA_SANITIZE_ADDRESS   "llama: enable address sanitizer"   OFF)
 option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF)
 
-# utils
-option(LLAMA_BUILD_COMMON "llama: build common utils library" ON)
-
 # extra artifacts
 option(LLAMA_BUILD_TESTS    "llama: build tests"          ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES "llama: build examples"       ${LLAMA_STANDALONE})
@@ -194,17 +191,15 @@ install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
         DESTINATION lib/pkgconfig)
 
 #
-# utils, programs, examples and tests
+# programs, examples and tests
 #
 
-if (LLAMA_BUILD_COMMON)
-    add_subdirectory(common)
-endif()
+add_subdirectory(common)
 
 if (LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
     include(CTest)
     add_subdirectory(tests)
-endif()
+endif ()
 
 if (LLAMA_BUILD_EXAMPLES)
     add_subdirectory(examples)

CONTRIBUTING.md

@@ -27,8 +27,3 @@
 
 ![matmul](media/matmul.png)
 
-# Resources
-
-The Github issues, PRs and discussions contain a lot of information that can be useful to get familiar with the codebase. For convenience, some of the more important information is referenced from Github projects:
-
-https://github.com/ggerganov/llama.cpp/projects

README.md

@@ -17,8 +17,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 ## Hot topics
 
-- **Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggerganov/llama.cpp/discussions/9669**
-- Hugging Face GGUF editor: [discussion](https://github.com/ggerganov/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor)
+- Huggingface GGUF editor: [discussion](https://github.com/ggerganov/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor)
 
 ----
 
@@ -113,7 +112,6 @@ Typically finetunes of the base models below are supported as well.
 - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
 - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
 - JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp)
-- JS/TS (Programmable Prompt Engine CLI): [offline-ai/cli](https://github.com/offline-ai/cli)
 - JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm)
 - Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama)
 - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
@@ -174,7 +172,6 @@ Unless otherwise noted these projects are open-source with permissive licensing:
 **Tools:**
 
 - [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML
-- [akx/ollama-dl](https://github.com/akx/ollama-dl) – download models from the Ollama library to be used directly with llama.cpp
 - [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
 - [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage
 - [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with prebuild Mobile and Web platform wrappers and a model example)
@@ -443,7 +440,7 @@ To learn more how to measure perplexity using llama.cpp, [read this documentatio
 - Contributors can open PRs
 - Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
 - Collaborators will be invited based on contributions
-- Any help with managing issues, PRs and projects is very appreciated!
+- Any help with managing issues and PRs is very appreciated!
 - See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
 - Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information
 - Make sure to read this: [Inference at the edge](https://github.com/ggerganov/llama.cpp/discussions/205)

ci/run.sh

@@ -712,81 +712,6 @@ function gg_run_embd_bge_small {
     set +e
 }
 
-function gg_sum_embd_bge_small {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'BGE Small (BERT):\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
-    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
-}
-
-# rerank_tiny
-
-function gg_run_rerank_tiny {
-    cd ${SRC}
-
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/config.json
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/tokenizer.json
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/tokenizer_config.json
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/special_tokens_map.json
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/resolve/main/pytorch_model.bin
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/sentence_bert_config.json
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/vocab.txt
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/modules.json
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/config.json
-
-    gg_wget models-mnt/rerank-tiny/1_Pooling https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/1_Pooling/config.json
-
-    path_models="../models-mnt/rerank-tiny"
-
-    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
-
-    set -e
-
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
-
-    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
-
-    model_f16="${path_models}/ggml-model-f16.gguf"
-
-    (time ./bin/llama-embedding --model ${model_f16}  -p "what is panda?</s><s>hi\nwhat is panda?</s><s>it's a bear\nwhat is panda?</s><s>The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log
-
-    # sample output
-    # rerank score 0:    0.029
-    # rerank score 1:    0.029
-    # rerank score 2:    0.135
-
-    # check that the score is in the range [$3, $4]
-    function check_score {
-        qnt="$1"
-        score=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
-
-        if [ $(echo "$score < $3" | bc) -eq 1 ] || [ $(echo "$score > $4" | bc) -eq 1 ]; then
-            printf '  - %s @ %s (FAIL: score not in range [%s, %s])\n' "$qnt" "$score" "$3" "$4"
-            return 20
-        fi
-
-        printf '  - %s @ %s OK\n' "$qnt" "$score"
-        return 0
-    }
-
-    check_score "rerank score 0" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 0")" "0.00" "0.05" | tee -a $OUT/${ci}-rk-f16.log
-    check_score "rerank score 1" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 1")" "0.00" "0.05" | tee -a $OUT/${ci}-rk-f16.log
-    check_score "rerank score 2" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 2")" "0.10" "0.15" | tee -a $OUT/${ci}-rk-f16.log
-
-    set +e
-}
-
-function gg_sum_rerank_tiny {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Rerank Tiny (Jina):\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-rk-f16.log)"
-}
-
 function gg_check_build_requirements {
     if ! command -v cmake &> /dev/null; then
         gg_printf 'cmake not found, please install'
@@ -801,6 +726,15 @@ function gg_check_build_requirements {
     fi
 }
 
+function gg_sum_embd_bge_small {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'BGE Small (BERT):\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
+    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
+}
+
 ## main
 
 export LLAMA_LOG_PREFIX=1
@@ -828,7 +762,6 @@ test $ret -eq 0 && gg_run ctest_release
 
 if [ -z ${GG_BUILD_LOW_PERF} ]; then
     test $ret -eq 0 && gg_run embd_bge_small
-    test $ret -eq 0 && gg_run rerank_tiny
 
     if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then
         test $ret -eq 0 && gg_run test_scripts_debug

common/arg.cpp

@@ -284,10 +284,6 @@ static bool gpt_params_parse_ex(int argc, char ** argv, gpt_params_context & ctx
         params.kv_overrides.back().key[0] = 0;
     }
 
-    if (params.reranking && params.embedding) {
-        throw std::invalid_argument("error: either --embedding or --reranking can be specified, but not both");
-    }
-
     return true;
 }
 
@@ -395,7 +391,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         [](gpt_params & params) {
             params.verbose_prompt = true;
         }
-    ));
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(llama_arg(
         {"--no-display-prompt"},
         format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
@@ -695,7 +691,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         [](gpt_params & params) {
             params.ctx_shift = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(llama_arg(
         {"--chunks"}, "N",
         format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
@@ -967,7 +963,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         }
     ).set_sparam());
     add_opt(llama_arg(
-        {"--tfs"}, "N",
+        {"--tfs", "--tfs-z"}, "Z",
         format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z),
         [](gpt_params & params, const std::string & value) {
             params.sparams.tfs_z = std::stof(value);
@@ -1097,17 +1093,16 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         }
     ).set_sparam());
     add_opt(llama_arg(
-        {"--pooling"}, "{none,mean,cls,last,rank}",
+        {"--pooling"}, "{none,mean,cls,last}",
         "pooling type for embeddings, use model default if unspecified",
         [](gpt_params & params, const std::string & value) {
             /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
             else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
-            else if (value == "cls")  { params.pooling_type = LLAMA_POOLING_TYPE_CLS;  }
+            else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
             else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
-            else if (value == "rank") { params.pooling_type = LLAMA_POOLING_TYPE_RANK; }
             else { throw std::invalid_argument("invalid value"); }
         }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_POOLING"));
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
     add_opt(llama_arg(
         {"--attention"}, "{causal,non,causal}",
         "attention type for embeddings, use model default if unspecified",
@@ -1126,77 +1121,77 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
             else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
             else { throw std::invalid_argument("invalid value"); }
         }
-    ).set_env("LLAMA_ARG_ROPE_SCALING_TYPE"));
+    ));
     add_opt(llama_arg(
         {"--rope-scale"}, "N",
         "RoPE context scaling factor, expands context by a factor of N",
         [](gpt_params & params, const std::string & value) {
             params.rope_freq_scale = 1.0f / std::stof(value);
         }
-    ).set_env("LLAMA_ARG_ROPE_SCALE"));
+    ));
     add_opt(llama_arg(
         {"--rope-freq-base"}, "N",
         "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)",
         [](gpt_params & params, const std::string & value) {
             params.rope_freq_base = std::stof(value);
         }
-    ).set_env("LLAMA_ARG_ROPE_FREQ_BASE"));
+    ));
     add_opt(llama_arg(
         {"--rope-freq-scale"}, "N",
         "RoPE frequency scaling factor, expands context by a factor of 1/N",
         [](gpt_params & params, const std::string & value) {
             params.rope_freq_scale = std::stof(value);
         }
-    ).set_env("LLAMA_ARG_ROPE_FREQ_SCALE"));
+    ));
     add_opt(llama_arg(
         {"--yarn-orig-ctx"}, "N",
         format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx),
         [](gpt_params & params, int value) {
             params.yarn_orig_ctx = value;
         }
-    ).set_env("LLAMA_ARG_YARN_ORIG_CTX"));
+    ));
     add_opt(llama_arg(
         {"--yarn-ext-factor"}, "N",
         format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor),
         [](gpt_params & params, const std::string & value) {
             params.yarn_ext_factor = std::stof(value);
         }
-    ).set_env("LLAMA_ARG_YARN_EXT_FACTOR"));
+    ));
     add_opt(llama_arg(
         {"--yarn-attn-factor"}, "N",
         format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor),
         [](gpt_params & params, const std::string & value) {
             params.yarn_attn_factor = std::stof(value);
         }
-    ).set_env("LLAMA_ARG_YARN_ATTN_FACTOR"));
+    ));
     add_opt(llama_arg(
         {"--yarn-beta-slow"}, "N",
         format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow),
         [](gpt_params & params, const std::string & value) {
             params.yarn_beta_slow = std::stof(value);
         }
-    ).set_env("LLAMA_ARG_YARN_BETA_SLOW"));
+    ));
     add_opt(llama_arg(
         {"--yarn-beta-fast"}, "N",
         format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast),
         [](gpt_params & params, const std::string & value) {
             params.yarn_beta_fast = std::stof(value);
         }
-    ).set_env("LLAMA_ARG_YARN_BETA_FAST"));
+    ));
     add_opt(llama_arg(
         {"-gan", "--grp-attn-n"}, "N",
         format("group-attention factor (default: %d)", params.grp_attn_n),
         [](gpt_params & params, int value) {
             params.grp_attn_n = value;
         }
-    ).set_env("LLAMA_ARG_GRP_ATTN_N"));
+    ));
     add_opt(llama_arg(
         {"-gaw", "--grp-attn-w"}, "N",
         format("group-attention width (default: %.1f)", (double)params.grp_attn_w),
         [](gpt_params & params, int value) {
             params.grp_attn_w = value;
         }
-    ).set_env("LLAMA_ARG_GRP_ATTN_W"));
+    ));
     add_opt(llama_arg(
         {"-dkvc", "--dump-kv-cache"},
         "verbose print of the KV cache",
@@ -1210,7 +1205,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         [](gpt_params & params) {
             params.no_kv_offload = true;
         }
-    ).set_env("LLAMA_ARG_NO_KV_OFFLOAD"));
+    ));
     add_opt(llama_arg(
         {"-ctk", "--cache-type-k"}, "TYPE",
         format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
@@ -1218,7 +1213,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
             // TODO: get the type right here
             params.cache_type_k = value;
         }
-    ).set_env("LLAMA_ARG_CACHE_TYPE_K"));
+    ));
     add_opt(llama_arg(
         {"-ctv", "--cache-type-v"}, "TYPE",
         format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
@@ -1226,7 +1221,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
             // TODO: get the type right here
             params.cache_type_v = value;
         }
-    ).set_env("LLAMA_ARG_CACHE_TYPE_V"));
+    ));
     add_opt(llama_arg(
         {"--perplexity", "--all-logits"},
         format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"),
@@ -1360,7 +1355,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         [](gpt_params & params, const std::string & value) {
             params.rpc_servers = value;
         }
-    ).set_env("LLAMA_ARG_RPC"));
+    ));
 #endif
     add_opt(llama_arg(
         {"--mlock"},
@@ -1368,14 +1363,14 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         [](gpt_params & params) {
             params.use_mlock = true;
         }
-    ).set_env("LLAMA_ARG_MLOCK"));
+    ));
     add_opt(llama_arg(
         {"--no-mmap"},
         "do not memory-map model (slower load but may reduce pageouts if not using mlock)",
         [](gpt_params & params) {
             params.use_mmap = false;
         }
-    ).set_env("LLAMA_ARG_NO_MMAP"));
+    ));
     add_opt(llama_arg(
         {"--numa"}, "TYPE",
         "attempt optimizations that help on some NUMA systems\n"
@@ -1390,7 +1385,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
             else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
             else { throw std::invalid_argument("invalid value"); }
         }
-    ).set_env("LLAMA_ARG_NUMA"));
+    ));
     add_opt(llama_arg(
         {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
         "number of layers to store in VRAM",
@@ -1438,7 +1433,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
                 fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the split mode has no effect.\n");
             }
         }
-    ).set_env("LLAMA_ARG_SPLIT_MODE"));
+    ));
     add_opt(llama_arg(
         {"-ts", "--tensor-split"}, "N0,N1,N2,...",
         "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1",
@@ -1465,7 +1460,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
                 fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting a tensor split has no effect.\n");
             }
         }
-    ).set_env("LLAMA_ARG_TENSOR_SPLIT"));
+    ));
     add_opt(llama_arg(
         {"-mg", "--main-gpu"}, "INDEX",
         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),
@@ -1475,7 +1470,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
                 fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the main GPU has no effect.\n");
             }
         }
-    ).set_env("LLAMA_ARG_MAIN_GPU"));
+    ));
     add_opt(llama_arg(
         {"--check-tensors"},
         format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"),
@@ -1538,7 +1533,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         [](gpt_params & params, const std::string & value) {
             params.model_alias = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ALIAS"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(llama_arg(
         {"-m", "--model"}, "FNAME",
         ex == LLAMA_EXAMPLE_EXPORT_LORA
@@ -1746,7 +1741,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         [](gpt_params & params, const std::string & value) {
             params.public_path = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_STATIC_PATH"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(llama_arg(
         {"--embedding", "--embeddings"},
         format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
@@ -1754,13 +1749,6 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
             params.embedding = true;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
-    add_opt(llama_arg(
-        {"--reranking", "--rerank"},
-        format("enable reranking endpoint on server (default: %s)", params.reranking ? "enabled" : "disabled"),
-        [](gpt_params & params) {
-            params.reranking = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_RERANKING"));
     add_opt(llama_arg(
         {"--api-key"}, "KEY",
         "API key to use for authentication (default: none)",
@@ -1791,14 +1779,14 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         [](gpt_params & params, const std::string & value) {
             params.ssl_file_key = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_KEY_FILE"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(llama_arg(
         {"--ssl-cert-file"}, "FNAME",
         "path to file a PEM-encoded SSL certificate",
         [](gpt_params & params, const std::string & value) {
             params.ssl_file_cert = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_CERT_FILE"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(llama_arg(
         {"-to", "--timeout"}, "N",
         format("server read/write timeout in seconds (default: %d)", params.timeout_read),
@@ -1806,7 +1794,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
             params.timeout_read  = value;
             params.timeout_write = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TIMEOUT"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(llama_arg(
         {"--threads-http"}, "N",
         format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http),

common/common.cpp

@@ -1023,11 +1023,6 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     cparams.flash_attn        = params.flash_attn;
     cparams.no_perf           = params.no_perf;
 
-    if (params.reranking) {
-        cparams.embeddings    = true;
-        cparams.pooling_type  = LLAMA_POOLING_TYPE_RANK;
-    }
-
     cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
     cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
 
@@ -1437,8 +1432,6 @@ void llama_batch_add(
                           llama_pos   pos,
     const std::vector<llama_seq_id> & seq_ids,
                                bool   logits) {
-    GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded");
-
     batch.token   [batch.n_tokens] = id;
     batch.pos     [batch.n_tokens] = pos;
     batch.n_seq_id[batch.n_tokens] = seq_ids.size();

common/common.h

@@ -271,7 +271,6 @@ struct gpt_params {
     int32_t embd_normalize = 2;     // normalisation for embendings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
     std::string embd_out   = "";    // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix
     std::string embd_sep   = "\n";  // separator of embendings
-    bool reranking         = false; // enable reranking support on server
 
     // server params
     int32_t port           = 8080;         // server listens on this network port

common/log.cpp

@@ -82,7 +82,7 @@ struct gpt_log_entry {
             }
         }
 
-        if (level != GGML_LOG_LEVEL_NONE && level != GGML_LOG_LEVEL_CONT && prefix) {
+        if (level != GGML_LOG_LEVEL_NONE && prefix) {
             if (timestamp) {
                 // [M.s.ms.us]
                 fprintf(fcur, "%s%d.%02d.%03d.%03d%s ",

common/log.h

@@ -83,10 +83,8 @@ void gpt_log_set_timestamps(struct gpt_log * log,       bool   timestamps); // w
 #define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  0,                 __VA_ARGS__)
 #define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, 0,                 __VA_ARGS__)
 #define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, LOG_DEFAULT_DEBUG, __VA_ARGS__)
-#define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT,  0,                 __VA_ARGS__)
 
 #define LOG_INFV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_INFO,  verbosity, __VA_ARGS__)
 #define LOG_WRNV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  verbosity, __VA_ARGS__)
 #define LOG_ERRV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, verbosity, __VA_ARGS__)
 #define LOG_DBGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, verbosity, __VA_ARGS__)
-#define LOG_CNTV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_CONT,  verbosity, __VA_ARGS__)

common/sampling.cpp

@@ -209,15 +209,7 @@ struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const st
             GGML_ASSERT(false && "unknown mirostat version");
         }
     } else {
-        if (params.n_probs > 0) {
-            // some use cases require to sample greedily, but still obtain the probabilities of the top tokens
-            // ref: https://github.com/ggerganov/llama.cpp/pull/9605
-            //
-            // the following will not produce exactly the same probs as applyging softmax to the full vocabulary, but
-            // it is much faster, since we avoid sorting all tokens and should give a good approximation
-            llama_sampler_chain_add(result->chain, llama_sampler_init_top_k(params.n_probs));
-            llama_sampler_chain_add(result->chain, llama_sampler_init_softmax());
-        }
+        llama_sampler_chain_add(result->chain, llama_sampler_init_softmax());
         llama_sampler_chain_add(result->chain, llama_sampler_init_greedy());
     }
 

convert_hf_to_gguf.py

@@ -291,13 +291,8 @@ class Model:
                     bid = int(part)
                     break
 
-            for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
-                data = data_torch.squeeze().numpy()
-
-                # if data ends up empty, it means data_torch was a scalar tensor -> restore
-                if len(data.shape) == 0:
-                    data = data_torch.numpy()
-
+            for new_name, data in ((n, d.squeeze().numpy()) for n, d in self.modify_tensors(data_torch, name, bid)):
+                data: np.ndarray  # type hint
                 n_dims = len(data.shape)
                 data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims)
 
@@ -597,9 +592,6 @@ class Model:
         if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e":
             # ref: https://huggingface.co/databricks/dbrx-base
             res = "dbrx"
-        if chkhsh == "c7699093ba4255a91e702aa38a596aa81669f3525dae06c2953267dde580f448":
-            # ref: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
-            res = "jina-v1-en"
         if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
             # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en
             res = "jina-v2-en"
@@ -648,9 +640,6 @@ class Model:
         if chkhsh == "fcace8b9cac38ce847670c970cd5892031a753a1ef381abd1d9af00f713da085":
             # ref: https://huggingface.co/microsoft/phi-2
             res = "phi-2"
-        if chkhsh == "60824e3c0d9401f89943cbb2fff727f0e2d4c545ba4df2d6e4f09a6db0f5b450":
-            # ref: https://huggingface.co/facebook/chameleon-7b
-            res = "chameleon"
 
         if res is None:
             logger.warning("\n")
@@ -2609,7 +2598,7 @@ class NomicBertModel(BertModel):
         self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
 
 
-@Model.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
+@Model.register("XLMRobertaModel")
 class XLMRobertaModel(BertModel):
     model_arch = gguf.MODEL_ARCH.BERT
 
@@ -2707,11 +2696,6 @@ class XLMRobertaModel(BertModel):
         self.gguf_writer.add_add_eos_token(True)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # if name starts with "roberta.", remove the prefix
-        # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main
-        if name.startswith("roberta."):
-            name = name[8:]
-
         # position embeddings start at pad_token_id + 1, so just chop down the weight tensor
         if name == "embeddings.position_embeddings.weight":
             if self._position_offset is not None:
@@ -3123,14 +3107,6 @@ class JinaBertV2Model(BertModel):
         self.gguf_writer.add_add_bos_token(True)
         self.gguf_writer.add_add_eos_token(True)
 
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # if name starts with "bert.", remove the prefix
-        # e.g. https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
-        if name.startswith("bert."):
-            name = name[5:]
-
-        return super().modify_tensors(data_torch, name, bid)
-
 
 @Model.register("OpenELMForCausalLM")
 class OpenELMModel(Model):
@@ -4126,87 +4102,16 @@ class GraniteModel(LlamaModel):
         #   consistency
         if attention_scale := self.hparams.get("attention_multiplier"):
             self.gguf_writer.add_attention_scale(attention_scale)
-            logger.info("gguf: (granite) attention_scale = %s", attention_scale)
         if embedding_scale := self.hparams.get("embedding_multiplier"):
             self.gguf_writer.add_embedding_scale(embedding_scale)
-            logger.info("gguf: (granite) embedding_scale = %s", embedding_scale)
         if residual_scale := self.hparams.get("residual_multiplier"):
             self.gguf_writer.add_residual_scale(residual_scale)
-            logger.info("gguf: (granite) residual_scale = %s", residual_scale)
-        if logits_scale := self.hparams.get("logits_scaling"):
-            self.gguf_writer.add_logit_scale(logits_scale)
-            logger.info("gguf: (granite) logits_scale = %s", logits_scale)
-
-
-@Model.register("GraniteMoeForCausalLM")
-class GraniteMoeModel(GraniteModel):
-    """Conversion for IBM's GraniteMoeForCausalLM"""
-    model_arch = gguf.MODEL_ARCH.GRANITE_MOE
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        """In modeling_granitemoe, the JetMoe implementation of parallel experts
-        is used. This essentially merges w1 and w3 into a single tensor with 2x
-        the hidden size that is then split during forward. To keep compatibility
-        with existing mixtral support, we pull them apart here.
-        """
-
-        if name.endswith("block_sparse_moe.input_linear.weight"):
-            ffn_dim = self.hparams["intermediate_size"]
-            assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size"
-            gate, up = data_torch[..., :ffn_dim, :], data_torch[..., ffn_dim:, :]
-            return [
-                (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), gate),
-                (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), up),
-            ]
-
-        return super().modify_tensors(data_torch, name, bid)
-
-
-@Model.register("ChameleonForConditionalGeneration")
-@Model.register("ChameleonForCausalLM")  # obsolete
-class ChameleonModel(Model):
-    model_arch = gguf.MODEL_ARCH.CHAMELEON
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_swin_norm(self.hparams.get("swin_norm", False))
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # ignore image tokenizer for now
-        # TODO: remove this once image support is implemented for Chameleon
-        if name.startswith("model.vqmodel"):
-            return []
-
-        n_head = self.hparams["num_attention_heads"]
-        n_kv_head = self.hparams.get("num_key_value_heads")
-        hidden_dim = self.hparams.get("hidden_size")
-
-        if name.endswith(("q_proj.weight", "q_proj.bias")):
-            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
-        if name.endswith(("k_proj.weight", "k_proj.bias")):
-            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
-        if name.endswith(("q_norm.weight", "q_norm.bias")):
-            data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_head, hidden_dim)
-        if name.endswith(("k_norm.weight", "k_norm.bias")):
-            data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_kv_head, hidden_dim)
-
-        return [(self.map_tensor_name(name), data_torch)]
-
-    # see: https://github.com/huggingface/transformers/blob/72fb02c47dbbe1999ae105319f24631cad6e2e00/src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py#L176-L203
-    @staticmethod
-    def _reverse_hf_permute(data_torch, n_heads, hidden_dim):
-        head_dim = hidden_dim // n_heads
-        data_torch = data_torch[0].view(2, head_dim // 2).t().reshape(1, -1)
-        data_torch = data_torch.repeat_interleave(n_heads, 0)
-        return data_torch
+        if logits_scaling := self.hparams.get("logits_scaling"):
+            self.gguf_writer.add_logit_scale(logits_scaling)
 
 
 ###### CONVERSION LOGIC ######
 
-
 # tree of lazy tensors
 class LazyTorchTensor(gguf.LazyBase):
     _tensor_type = torch.Tensor

convert_hf_to_gguf_update.py

@@ -81,7 +81,6 @@ models = [
     {"name": "qwen2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
     {"name": "olmo",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
     {"name": "dbrx",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
-    {"name": "jina-v1-en",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-reranker-v1-tiny-en", },
     {"name": "jina-v2-en",     "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
     {"name": "jina-v2-es",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
     {"name": "jina-v2-de",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
@@ -100,7 +99,6 @@ models = [
     {'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", },
-    {"name": "chameleon",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
 ]
 
 

examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp

@@ -201,7 +201,7 @@ static void print_sample_weights(TransformerWeights *w){
 
 //////////////////////////////////////// ggml structs and functions required to load models, configs and save the model.
 
-struct my_llama_vocab {
+struct llama_vocab {
     using id    = int32_t;
     using token = std::string;
     using ttype = llama_token_type;
@@ -525,7 +525,7 @@ static std::string llama_escape_whitespaces(const std::string & text) {
     return out.str();
 }
 
-static void load_vocab(const char * filename, const Config * config, struct my_llama_vocab * vocab) {
+static void load_vocab(const char * filename, const Config * config, struct llama_vocab * vocab) {
     if (is_ggml_file(filename)) {
         LOG_INF("%s: Loading vocabulary from gguf file %s\n", __func__, filename);
         struct ggml_context * ctx_data = NULL;
@@ -583,13 +583,13 @@ static void load_vocab(const char * filename, const Config * config, struct my_l
         const int  n_vocab = config->vocab_size;
         /* uint32_t max_token_length =  */ file.read_u32(); // unused
         vocab->id_to_token.resize(n_vocab);
-        for (my_llama_vocab::id id=0; id<n_vocab; ++id) {
+        for (llama_vocab::id id=0; id<n_vocab; ++id) {
             float_t score = file.read_f32();
             uint32_t len = file.read_u32();
             std::string text = file.read_string(len);
 
             unsigned char byte_val;
-            my_llama_vocab::ttype type = LLAMA_TOKEN_TYPE_NORMAL;
+            llama_vocab::ttype type = LLAMA_TOKEN_TYPE_NORMAL;
             if (id == UNKNOWN_TOKEN_ID) {
                 text = "<unk>";
                 type = LLAMA_TOKEN_TYPE_UNKNOWN;
@@ -631,7 +631,7 @@ static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const floa
 }
 
 static void save_as_llama_model(
-    struct my_llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename
+    struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename
 ) {
     // convert AK weights into GG weights one by one.
     // w->token_embedding_table -> model->tok_embeddings
@@ -671,7 +671,7 @@ static void save_as_llama_model(
     std::vector<const char*> tokens;
     std::vector<float> scores;
     std::vector<llama_token_type> token_types;
-    for (const my_llama_vocab::token_data & token_data : vocab->id_to_token) {
+    for (const llama_vocab::token_data & token_data : vocab->id_to_token) {
         tokens.push_back(token_data.text.c_str());
         scores.push_back(token_data.score);
         token_types.push_back(token_data.type);
@@ -905,7 +905,7 @@ int main(int argc, char ** argv) {
         fclose(file);
     }
 
-    struct my_llama_vocab vocab;
+    struct llama_vocab vocab;
     load_vocab(params.fn_vocab_model, &config, &vocab);
 
     struct my_llama_model model;

examples/embedding/embedding.cpp

@@ -135,7 +135,7 @@ int main(int argc, char ** argv) {
     // tokenize the prompts and trim
     std::vector<std::vector<int32_t>> inputs;
     for (const auto & prompt : prompts) {
-        auto inp = ::llama_tokenize(ctx, prompt, true, true);
+        auto inp = ::llama_tokenize(ctx, prompt, true, false);
         if (inp.size() > n_batch) {
             LOG_ERR("%s: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
                     __func__, (long long int) inp.size(), (long long int) n_batch);
@@ -234,11 +234,6 @@ int main(int argc, char ** argv) {
                 }
                 LOG("\n");
             }
-        } else if (pooling_type == LLAMA_POOLING_TYPE_RANK) {
-            for (int j = 0; j < n_embd_count; j++) {
-                // NOTE: if you change this log - update the tests in ci/run.sh
-                LOG("rerank score %d: %8.3f\n", j, emb[j * n_embd]);
-            }
         } else {
             // print the first part of the embeddings or for a single prompt, the full embedding
             for (int j = 0; j < n_prompts; j++) {

examples/gen-docs/gen-docs.cpp

@@ -6,73 +6,42 @@
 
 // Export usage message (-h) to markdown format
 
-static void write_table_header(std::ofstream & file) {
-    file << "| Argument | Explanation |\n";
-    file << "| -------- | ----------- |\n";
-}
-
-static void write_table_entry(std::ofstream & file, const llama_arg & opt) {
-    file << "| `";
-    // args
-    for (const auto & arg : opt.args) {
-    if (arg == opt.args.front()) {
-            file << arg;
-            if (opt.args.size() > 1) file << ", ";
-        } else {
-            file << arg << (arg != opt.args.back() ? ", " : "");
-        }
-    }
-    // value hint
-    if (opt.value_hint) {
-        std::string md_value_hint(opt.value_hint);
-        string_replace_all(md_value_hint, "|", "\\|");
-        file << " " << md_value_hint;
-    }
-    if (opt.value_hint_2) {
-        std::string md_value_hint_2(opt.value_hint_2);
-        string_replace_all(md_value_hint_2, "|", "\\|");
-        file << " " << md_value_hint_2;
-    }
-    // help text
-    std::string md_help(opt.help);
-    string_replace_all(md_help, "\n", "<br/>");
-    string_replace_all(md_help, "|", "\\|");
-    file << "` | " << md_help << " |\n";
-}
-
-static void write_table(std::ofstream & file, std::vector<llama_arg *> & opts) {
-    write_table_header(file);
-    for (const auto & opt : opts) {
-        write_table_entry(file, *opt);
-    }
-}
-
 static void export_md(std::string fname, llama_example ex) {
     std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc);
 
     gpt_params params;
     auto ctx_arg = gpt_params_parser_init(params, ex);
 
-    std::vector<llama_arg *> common_options;
-    std::vector<llama_arg *> sparam_options;
-    std::vector<llama_arg *> specific_options;
+    file << "| Argument | Explanation |\n";
+    file << "| -------- | ----------- |\n";
     for (auto & opt : ctx_arg.options) {
-        // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example
-        if (opt.is_sparam) {
-            sparam_options.push_back(&opt);
-        } else if (opt.in_example(ctx_arg.ex)) {
-            specific_options.push_back(&opt);
-        } else {
-            common_options.push_back(&opt);
+        file << "| `";
+        // args
+        for (const auto & arg : opt.args) {
+        if (arg == opt.args.front()) {
+                file << arg;
+                if (opt.args.size() > 1) file << ", ";
+            } else {
+                file << arg << (arg != opt.args.back() ? ", " : "");
+            }
         }
+        // value hint
+        if (opt.value_hint) {
+            std::string md_value_hint(opt.value_hint);
+            string_replace_all(md_value_hint, "|", "\\|");
+            file << " " << md_value_hint;
+        }
+        if (opt.value_hint_2) {
+            std::string md_value_hint_2(opt.value_hint_2);
+            string_replace_all(md_value_hint_2, "|", "\\|");
+            file << " " << md_value_hint_2;
+        }
+        // help text
+        std::string md_help(opt.help);
+        string_replace_all(md_help, "\n", "<br/>");
+        string_replace_all(md_help, "|", "\\|");
+        file << "` | " << md_help << " |\n";
     }
-
-    file << "**Common params**\n\n";
-    write_table(file, common_options);
-    file << "\n\n**Sampling params**\n\n";
-    write_table(file, sparam_options);
-    file << "\n\n**Example-specific params**\n\n";
-    write_table(file, specific_options);
 }
 
 int main(int, char **) {

examples/infill/infill.cpp

@@ -263,9 +263,9 @@ int main(int argc, char ** argv) {
         if (params.n_keep > 0) {
         LOG_INF("%s: static prompt based on n_keep: '", __func__);
             for (int i = 0; i < params.n_keep; i++) {
-                LOG_CNT("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
+                LOG("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
             }
-            LOG_CNT("'\n");
+            LOG("'\n");
         }
         LOG_INF("\n");
     }
@@ -306,8 +306,8 @@ int main(int argc, char ** argv) {
 
     LOG_INF("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_INF("\n");
-    LOG_INF("\n#####  Infill mode  #####\n\n");
+    LOG("\n");
+    LOG("\n#####  Infill mode  #####\n\n");
     if (params.interactive) {
         const char *control_message;
         if (params.multiline_input) {
@@ -318,11 +318,11 @@ int main(int argc, char ** argv) {
                               " - To return control without starting a new line, end your input with '/'.\n"
                               " - If you want to submit another line, end your input with '\\'.\n";
         }
-        LOG_INF("== Running in interactive mode. ==\n");
+        LOG("== Running in interactive mode. ==\n");
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
-        LOG_INF(       " - Press Ctrl+C to interject at any time.\n");
+        LOG(       " - Press Ctrl+C to interject at any time.\n");
 #endif
-        LOG_INF(       "%s\n", control_message);
+        LOG(       "%s\n", control_message);
 
         is_interacting = params.interactive_first;
     }

examples/main/main.cpp

@@ -385,9 +385,9 @@ int main(int argc, char ** argv) {
         if (params.n_keep > add_bos) {
             LOG_INF("%s: static prompt based on n_keep: '", __func__);
             for (int i = 0; i < params.n_keep; i++) {
-                LOG_CNT("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
+                LOG("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
             }
-            LOG_CNT("'\n");
+            LOG("'\n");
         }
         LOG_INF("\n");
     }
@@ -409,40 +409,40 @@ int main(int argc, char ** argv) {
     }
 
     if (params.interactive) {
-        LOG_INF("%s: interactive mode on.\n", __func__);
+        LOG("%s: interactive mode on.\n", __func__);
 
         if (!params.antiprompt.empty()) {
             for (const auto & antiprompt : params.antiprompt) {
-                LOG_INF("Reverse prompt: '%s'\n", antiprompt.c_str());
+                LOG("Reverse prompt: '%s'\n", antiprompt.c_str());
                 if (params.verbose_prompt) {
                     auto tmp = ::llama_tokenize(ctx, antiprompt, false, true);
                     for (int i = 0; i < (int) tmp.size(); i++) {
-                        LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+                        LOG("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
                     }
                 }
             }
         }
 
         if (params.input_prefix_bos) {
-            LOG_INF("Input prefix with BOS\n");
+            LOG("Input prefix with BOS\n");
         }
 
         if (!params.input_prefix.empty()) {
-            LOG_INF("Input prefix: '%s'\n", params.input_prefix.c_str());
+            LOG("Input prefix: '%s'\n", params.input_prefix.c_str());
             if (params.verbose_prompt) {
                 auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true);
                 for (int i = 0; i < (int) tmp.size(); i++) {
-                    LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+                    LOG("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
                 }
             }
         }
 
         if (!params.input_suffix.empty()) {
-            LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str());
+            LOG("Input suffix: '%s'\n", params.input_suffix.c_str());
             if (params.verbose_prompt) {
                 auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true);
                 for (int i = 0; i < (int) tmp.size(); i++) {
-                    LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+                    LOG("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
                 }
             }
         }
@@ -474,7 +474,7 @@ int main(int argc, char ** argv) {
       //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT
         LOG_INF("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w);
     }
-    LOG_INF("\n");
+    LOG("\n");
 
     if (params.interactive) {
         const char * control_message;
@@ -486,11 +486,11 @@ int main(int argc, char ** argv) {
                               " - To return control without starting a new line, end your input with '/'.\n"
                               " - If you want to submit another line, end your input with '\\'.\n";
         }
-        LOG_INF("== Running in interactive mode. ==\n");
+        LOG("== Running in interactive mode. ==\n");
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
-        LOG_INF(       " - Press Ctrl+C to interject at any time.\n");
+        LOG(       " - Press Ctrl+C to interject at any time.\n");
 #endif
-        LOG_INF(       "%s\n", control_message);
+        LOG(       "%s\n", control_message);
 
         is_interacting = params.interactive_first;
     }

examples/server/README.md

@@ -7,7 +7,6 @@ Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
 **Features:**
  * LLM inference of F16 and quantized models on GPU and CPU
  * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes
- * Reranking endoint (WIP: https://github.com/ggerganov/llama.cpp/pull/9510)
  * Parallel decoding with multi-user support
  * Continuous batching
  * Multimodal (wip)
@@ -18,13 +17,12 @@ The project is under active development, and we are [looking for feedback and co
 
 ## Usage
 
-**Common params**
-
 | Argument | Explanation |
 | -------- | ----------- |
 | `-h, --help, --usage` | print usage and exit |
 | `--version` | show version and build info |
-| `--verbose-prompt` | print a verbose prompt before generation (default: false) |
+| `-v, --verbose` | print verbose information |
+| `--verbosity N` | set specific verbosity level (default: 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: "") |
@@ -44,63 +42,13 @@ The project is under active development, and we are [looking for feedback and co
 | `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) |
 | `-fa, --flash-attn` | enable Flash Attention (default: disabled)<br/>(env: LLAMA_ARG_FLASH_ATTN) |
 | `-p, --prompt PROMPT` | prompt to start generation with |
-| `--no-perf` | disable internal libllama performance timings (default: false)<br/>(env: LLAMA_ARG_NO_PERF) |
 | `-f, --file FNAME` | a file containing the prompt (default: none) |
 | `-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 |
-| `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model<br/>(env: LLAMA_ARG_ROPE_SCALING_TYPE) |
-| `--rope-scale N` | RoPE context scaling factor, expands context by a factor of N<br/>(env: LLAMA_ARG_ROPE_SCALE) |
-| `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model)<br/>(env: LLAMA_ARG_ROPE_FREQ_BASE) |
-| `--rope-freq-scale N` | RoPE frequency scaling factor, expands context by a factor of 1/N<br/>(env: LLAMA_ARG_ROPE_FREQ_SCALE) |
-| `--yarn-orig-ctx N` | YaRN: original context size of model (default: 0 = model training context size)<br/>(env: LLAMA_ARG_YARN_ORIG_CTX) |
-| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation)<br/>(env: LLAMA_ARG_YARN_EXT_FACTOR) |
-| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: 1.0)<br/>(env: LLAMA_ARG_YARN_ATTN_FACTOR) |
-| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: 1.0)<br/>(env: LLAMA_ARG_YARN_BETA_SLOW) |
-| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0)<br/>(env: LLAMA_ARG_YARN_BETA_FAST) |
-| `-gan, --grp-attn-n N` | group-attention factor (default: 1)<br/>(env: LLAMA_ARG_GRP_ATTN_N) |
-| `-gaw, --grp-attn-w N` | group-attention width (default: 512.0)<br/>(env: LLAMA_ARG_GRP_ATTN_W) |
-| `-dkvc, --dump-kv-cache` | verbose print of the KV cache |
-| `-nkvo, --no-kv-offload` | disable KV offload<br/>(env: LLAMA_ARG_NO_KV_OFFLOAD) |
-| `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
-| `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
-| `-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)<br/>(env: LLAMA_ARG_N_PARALLEL) |
-| `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
-| `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_NO_MMAP) |
-| `--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<br/>(env: LLAMA_ARG_NUMA) |
-| `-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<br/>(env: LLAMA_ARG_SPLIT_MODE) |
-| `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1<br/>(env: LLAMA_ARG_TENSOR_SPLIT) |
-| `-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)<br/>(env: LLAMA_ARG_MAIN_GPU) |
-| `--check-tensors` | check model tensor data for invalid values (default: false) |
-| `--override-kv KEY=TYPE:VALUE` | advanced option to override model metadata by key. may be specified multiple times.<br/>types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false |
-| `--lora FNAME` | path to LoRA adapter (can be repeated to use multiple adapters) |
-| `--lora-scaled FNAME SCALE` | path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters) |
-| `--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 |
-| `-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) |
-| `-hff, --hf-file FILE` | Hugging Face model file (default: unused)<br/>(env: LLAMA_ARG_HF_FILE) |
-| `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)<br/>(env: HF_TOKEN) |
-| `-ld, --logdir LOGDIR` | path under which to save YAML logs (no logging if unset) |
-| `--log-disable` | Log disable |
-| `--log-file FNAME` | Log to file |
-| `--log-colors` | Enable colored logging<br/>(env: LLAMA_LOG_COLORS) |
-| `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) |
-| `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored.<br/>(env: LLAMA_LOG_VERBOSITY) |
-| `--log-prefix` | Enable prefx in log messages<br/>(env: LLAMA_LOG_PREFIX) |
-| `--log-timestamps` | Enable timestamps in log messages<br/>(env: LLAMA_LOG_TIMESTAMPS) |
-
-
-**Sampling params**
-
-| Argument | Explanation |
-| -------- | ----------- |
+| `--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;typ_p;top_p;min_p;temperature) |
-| `-s, --seed SEED` | RNG seed (default: 4294967295, use random seed for 4294967295) |
+| `-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) |
@@ -123,29 +71,54 @@ The project is under active development, and we are [looking for feedback and co
 | `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') |
 | `--grammar-file FNAME` | file to read grammar from |
 | `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
-
-
-**Example-specific params**
-
-| Argument | Explanation |
-| -------- | ----------- |
-| `--no-context-shift` | disables context shift on inifinite text generation (default: disabled)<br/>(env: LLAMA_ARG_NO_CONTEXT_SHIFT) |
-| `-sp, --special` | special tokens output enabled (default: false) |
-| `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
-| `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified<br/>(env: LLAMA_ARG_POOLING) |
+| `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model |
+| `--rope-scale N` | RoPE context scaling factor, expands context by a factor of N |
+| `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model) |
+| `--rope-freq-scale N` | RoPE frequency scaling factor, expands context by a factor of 1/N |
+| `--yarn-orig-ctx N` | YaRN: original context size of model (default: 0 = model training context size) |
+| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation) |
+| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: 1.0) |
+| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: 1.0) |
+| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0) |
+| `-gan, --grp-attn-n N` | group-attention factor (default: 1) |
+| `-gaw, --grp-attn-w N` | group-attention width (default: 512.0) |
+| `-dkvc, --dump-kv-cache` | verbose print of the KV cache |
+| `-nkvo, --no-kv-offload` | disable KV offload |
+| `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16) |
+| `-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)<br/>(env:  LLAMA_ARG_N_PARALLEL) |
 | `-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) |
-| `-a, --alias STRING` | set alias for model name (to be used by REST API)<br/>(env: LLAMA_ARG_ALIAS) |
+| `--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-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) |
+| `--check-tensors` | check model tensor data for invalid values (default: false) |
+| `--override-kv KEY=TYPE:VALUE` | advanced option to override model metadata by key. may be specified multiple times.<br/>types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false |
+| `--lora FNAME` | path to LoRA adapter (can be repeated to use multiple adapters) |
+| `--lora-scaled FNAME SCALE` | path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters) |
+| `--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) |
+| `-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) |
+| `-hff, --hf-file FILE` | Hugging Face model file (default: unused)<br/>(env: LLAMA_ARG_HF_FILE) |
+| `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)<br/>(env: HF_TOKEN) |
 | `--host HOST` | ip address to listen (default: 127.0.0.1)<br/>(env: LLAMA_ARG_HOST) |
 | `--port PORT` | port to listen (default: 8080)<br/>(env: LLAMA_ARG_PORT) |
-| `--path PATH` | path to serve static files from (default: )<br/>(env: LLAMA_ARG_STATIC_PATH) |
+| `--path PATH` | path to serve static files from (default: ) |
 | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
-| `--reranking, --rerank` | enable reranking endpoint on server (default: disabled)<br/>(env: LLAMA_ARG_RERANKING) |
 | `--api-key KEY` | API key to use for authentication (default: none)<br/>(env: LLAMA_API_KEY) |
 | `--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<br/>(env: LLAMA_ARG_SSL_KEY_FILE) |
-| `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate<br/>(env: LLAMA_ARG_SSL_CERT_FILE) |
-| `-to, --timeout N` | server read/write timeout in seconds (default: 600)<br/>(env: LLAMA_ARG_TIMEOUT) |
+| `--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 |
+| `-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 |
 | `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_METRICS) |
@@ -154,7 +127,13 @@ The project is under active development, and we are [looking for feedback and co
 | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted:<br/>https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
 | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)<br/> |
 | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
-
+| `-ld, --logdir LOGDIR` | path under which to save YAML logs (no logging if unset) |
+| `--log-test` | Log test |
+| `--log-disable` | Log disable |
+| `--log-enable` | Log enable |
+| `--log-new` | Log new |
+| `--log-append` | Log append |
+| `--log-file FNAME` | Log file |
 
 Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var.
 
@@ -482,39 +461,6 @@ The same as [the embedding example](../embedding) does.
 
     `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
 
-### POST `/reranking`: Rerank documents according to a given query
-
-Similar to https://jina.ai/reranker/ but might change in the future.
-Requires a reranker model (such as [bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3)) and the `--embedding --pooling rank` options.
-
-    *Options:*
-
-    `query`: The query against which the documents will be ranked.
-
-    `documents`: An array strings representing the documents to be ranked.
-
-    *Aliases:*
-      - `/rerank`
-      - `/v1/rerank`
-      - `/v1/reranking`
-
-    *Examples:*
-
-    ```shell
-    curl http://127.0.0.1:8012/v1/rerank \
-        -H "Content-Type: application/json" \
-        -d '{
-            "model": "some-model",
-                "query": "What is panda?",
-                "top_n": 3,
-                "documents": [
-                    "hi",
-                "it is a bear",
-                "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China."
-                ]
-        }' | jq
-    ```
-
 ### POST `/infill`: For code infilling.
 
 Takes a prefix and a suffix and returns the predicted completion as stream.

examples/server/server.cpp

@@ -92,7 +92,6 @@ enum server_task_type {
 enum server_task_cmpl_type {
     SERVER_TASK_CMPL_TYPE_NORMAL,
     SERVER_TASK_CMPL_TYPE_EMBEDDING,
-    SERVER_TASK_CMPL_TYPE_RERANK,
     SERVER_TASK_CMPL_TYPE_INFILL,
 };
 
@@ -173,7 +172,6 @@ struct server_slot {
     std::vector<completion_token_output> generated_token_probs;
 
     server_task_cmpl_type cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL;
-
     bool has_next_token = true;
     bool truncated      = false;
     bool stopped_eos    = false;
@@ -956,17 +954,8 @@ struct server_context {
                 slot.prompt = *prompt;
             } else if (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_array()) {
                 slot.prompt = prompt->at(0);
-            } else if (prompt->is_array() && prompt->size() > 1) {
-                // array of strings
-                for (const auto & el : *prompt) {
-                    if (!el.is_string()) {
-                        send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST);
-                        return false;
-                    }
-                }
-                slot.prompt = *prompt;
             } else {
-                send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST);
+                send_error(task, "\"prompt\" must be a string or an array of integers", ERROR_TYPE_INVALID_REQUEST);
                 return false;
             }
         }
@@ -1191,15 +1180,6 @@ struct server_context {
             SLT_DBG(slot, "stopped by limit, n_decoded = %d, n_predict = %d\n", slot.n_decoded, slot.params.n_predict);
         }
 
-        // if context shift is disabled, we stop when it reaches the context limit
-        if (slot.n_decoded >= slot.n_ctx) {
-            slot.truncated      = true;
-            slot.stopped_limit  = true;
-            slot.has_next_token = false;
-
-            SLT_DBG(slot, "stopped due to running out of context capacity, n_decoded = %d, n_ctx = %d\n", slot.n_decoded, slot.n_ctx);
-        }
-
         if (llama_token_is_eog(model, result.tok)) {
             slot.stopped_eos    = true;
             slot.has_next_token = false;
@@ -1400,7 +1380,6 @@ struct server_context {
 
                 res.data = json {
                     {"embedding", std::vector<float>(n_embd, 0.0f)},
-                    {"index",     slot.index},
                 };
 
                 continue;
@@ -1419,44 +1398,6 @@ struct server_context {
         queue_results.send(res);
     }
 
-    void send_rerank(const server_slot & slot, const llama_batch & batch) {
-        server_task_result res;
-        res.id       = slot.id_task;
-        res.error    = false;
-        res.stop     = true;
-
-        for (int i = 0; i < batch.n_tokens; ++i) {
-            if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) {
-                continue;
-            }
-
-            const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
-            if (embd == NULL) {
-                embd = llama_get_embeddings_ith(ctx, i);
-            }
-
-            if (embd == NULL) {
-                SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]);
-
-                res.data = json {
-                    {"index", slot.index},
-                    {"score", -1e6},
-                };
-
-                continue;
-            }
-
-            res.data = json {
-                {"index", slot.index},
-                {"score", embd[0]},
-            };
-        }
-
-        SLT_DBG(slot, "sending rerank result, res = '%s'\n", res.data.dump().c_str());
-
-        queue_results.send(res);
-    }
-
     //
     // Functions to create new task(s) and receive result(s)
     //
@@ -1492,27 +1433,13 @@ struct server_context {
         // otherwise, it's a multiple-prompt task, we break it into smaller tasks
         else if (prompt.is_array()) {
             std::vector<json> prompts = prompt;
-            if (cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
-                // prompts[0] is the question
-                // the rest are the answers/documents
-                SRV_DBG("creating rerank tasks, n_prompts = %d\n", (int) prompts.size() - 1);
-                for (size_t i = 1; i < prompts.size(); i++) {
-                    json qd;
-                    qd.push_back(prompts[0]);
-                    qd.push_back(prompts[i]);
-                    data["index"] = i - 1;
-                    create_task(data, true, qd);
-                }
-            } else {
-                SRV_DBG("creating multi-prompt tasks, n_prompts = %d\n", (int) prompts.size());
-                for (size_t i = 0; i < prompts.size(); i++) {
-                    const auto & e = prompts[i];
-                    if (e.is_string() || json_is_array_of_numbers(e)) {
-                        data["index"] = i;
-                        create_task(data, true, e);
-                    } else {
-                        throw std::runtime_error(error_msg);
-                    }
+            for (size_t i = 0; i < prompts.size(); i++) {
+                const auto & e = prompts[i];
+                if (e.is_string() || json_is_array_of_numbers(e)) {
+                    data["index"] = i;
+                    create_task(data, true, e);
+                } else {
+                    throw std::runtime_error(error_msg);
                 }
             }
         }
@@ -1553,12 +1480,10 @@ struct server_context {
             if (result.error) {
                 error_handler(result.data);
                 cancel_tasks(id_tasks);
-                return;
+                break;
             }
 
-            const size_t idx = result.data["index"];
-            GGML_ASSERT(idx < results.size() && "index out of range");
-
+            size_t idx = result.data["index"];
             results[idx] = result;
         }
         result_handler(results);
@@ -1902,14 +1827,6 @@ struct server_context {
         for (server_slot & slot : slots) {
             if (slot.ga_n == 1) {
                 if (slot.is_processing() && (int) system_tokens.size() + slot.n_past >= slot.n_ctx - 1) {
-                    if (!params.ctx_shift) {
-                        // this check is redundant (for good)
-                        // we should never get here, because generation should already stopped in process_token()
-                        slot.release();
-                        send_error(slot, "context shift is disabled", ERROR_TYPE_SERVER);
-                        continue;
-                    }
-
                     // Shift context
                     const int n_keep    = slot.params.n_keep + add_bos_token;
                     const int n_left    = (int) system_tokens.size() + slot.n_past - n_keep;
@@ -1969,7 +1886,6 @@ struct server_context {
         // track if this is an embedding or non-embedding batch
         // if we've added sampled tokens above, we are in non-embedding mode
         // -1: none, 0: non-embedding, 1: embedding
-        // TODO: make enum
         int32_t batch_type = batch.n_tokens > 0 ? 0 : -1;
 
         // next, batch any pending prompts without exceeding n_batch
@@ -2018,29 +1934,6 @@ struct server_context {
                             }
 
                             prompt_tokens = embd_inp;
-                        } else if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
-                            // require slot.prompt to be array of 2 strings
-                            if (!slot.prompt.is_array() || slot.prompt.size() != 2) {
-                                SLT_ERR(slot, "%s", "invalid prompt for rerank task\n");
-                                slot.release();
-                                send_error(slot, "invalid prompt for rerank task", ERROR_TYPE_INVALID_REQUEST);
-                                continue;
-                            }
-
-                            // prompt: <s>query</s><s>doc</s>
-                            prompt_tokens.clear();
-                            prompt_tokens.push_back(llama_token_bos(model));
-                            {
-                                const auto part = tokenize(slot.prompt[0], false);
-                                prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
-                            }
-                            prompt_tokens.push_back(llama_token_eos(model));
-                            prompt_tokens.push_back(llama_token_bos(model));
-                            {
-                                const auto part = tokenize(slot.prompt[1], false);
-                                prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
-                            }
-                            prompt_tokens.push_back(llama_token_eos(model));
                         } else {
                             prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
                         }
@@ -2060,7 +1953,7 @@ struct server_context {
                             continue;
                         }
 
-                        if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
+                        if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING) {
                             // this prompt is too large to process - discard it
                             if (slot.n_prompt_tokens > n_ubatch) {
                                 slot.release();
@@ -2068,14 +1961,6 @@ struct server_context {
                                 continue;
                             }
                         } else {
-                            if (!params.ctx_shift) {
-                                // if context shift is disabled, we make sure prompt size is smaller than KV size
-                                if ((int) system_tokens.size() + slot.n_prompt_tokens >= slot.n_ctx) {
-                                    slot.release();
-                                    send_error(slot, "the request exceeds the available context size. try increasing the context size or enable context shift", ERROR_TYPE_INVALID_REQUEST);
-                                    continue;
-                                }
-                            }
                             if (slot.params.n_keep < 0) {
                                 slot.params.n_keep = slot.n_prompt_tokens;
                             }
@@ -2138,8 +2023,7 @@ struct server_context {
                         slot.n_prompt_tokens_processed = 0;
                     }
 
-                    // non-causal tasks require to fit the entire prompt in the physical batch
-                    if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
+                    if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING) {
                         // cannot fit the prompt in the current batch - will try next iter
                         if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
                             continue;
@@ -2147,10 +2031,7 @@ struct server_context {
                     }
 
                     // check that we are in the right batch_type, if not defer the slot
-                    const bool slot_type =
-                        slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING ||
-                        slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK     ? 1 : 0;
-
+                    bool slot_type = slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING ? 1 : 0;
                     if (batch_type == -1) {
                         batch_type = slot_type;
                     } else if (batch_type != slot_type) {
@@ -2323,13 +2204,6 @@ struct server_context {
                         continue; // continue loop of slots
                     }
 
-                    if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
-                        send_rerank(slot, batch_view);
-                        slot.release();
-                        slot.i_batch = -1;
-                        continue; // continue loop of slots
-                    }
-
                     // prompt evaluated for next-token prediction
                     slot.state = SLOT_STATE_GENERATING;
                 } else if (slot.state != SLOT_STATE_GENERATING) {
@@ -2457,10 +2331,6 @@ int main(int argc, char ** argv) {
         svr.reset(new httplib::Server());
     }
 #else
-    if (params.ssl_file_key != "" && params.ssl_file_cert != "") {
-        LOG_ERR("Server is built without SSL support\n");
-        return 1;
-    }
     svr.reset(new httplib::Server());
 #endif
 
@@ -2888,8 +2758,8 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](server_task_cmpl_type cmpl_type, json & data, httplib::Response & res) {
-        if (ctx_server.params.embedding || ctx_server.params.reranking) {
-            res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
+        if (ctx_server.params.embedding) {
+            res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
@@ -2949,8 +2819,8 @@ int main(int argc, char ** argv) {
 
     // TODO: maybe merge this function with "handle_completions_generic"
     const auto handle_chat_completions = [&ctx_server, &params, &res_error, &res_ok, verbose](const httplib::Request & req, httplib::Response & res) {
-        if (ctx_server.params.embedding || ctx_server.params.reranking) {
-            res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
+        if (ctx_server.params.embedding) {
+            res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
@@ -3074,11 +2944,6 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_embeddings = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
-        // TODO: somehow clean up this checks in the future
-        if (!ctx_server.params.embedding || ctx_server.params.reranking) {
-            res_error(res, format_error_response("This server does not support embeddings. Start it with `--embeddings` and without `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
-            return;
-        }
         const json body = json::parse(req.body);
         bool is_openai = false;
 
@@ -3129,79 +2994,6 @@ int main(int argc, char ** argv) {
         res_ok(res, root);
     };
 
-    const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
-        if (!ctx_server.params.reranking) {
-            res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
-            return;
-        }
-        const json body = json::parse(req.body);
-
-        // TODO: implement
-        //int top_n = 1;
-        //if (body.count("top_n") != 1) {
-        //    top_n = body.at("top_n");
-        //} else {
-        //    res_error(res, format_error_response("\"top_n\" must be provided", ERROR_TYPE_INVALID_REQUEST));
-        //    return;
-        //}
-
-        json query;
-        if (body.count("query") == 1) {
-            query = body.at("query");
-            if (!query.is_string()) {
-                res_error(res, format_error_response("\"query\" must be a string", ERROR_TYPE_INVALID_REQUEST));
-                return;
-            }
-        } else {
-            res_error(res, format_error_response("\"query\" must be provided", ERROR_TYPE_INVALID_REQUEST));
-            return;
-        }
-
-        std::vector<std::string> documents = json_value(body, "documents", std::vector<std::string>());
-        if (documents.empty()) {
-            res_error(res, format_error_response("\"documents\" must be a non-empty string array", ERROR_TYPE_INVALID_REQUEST));
-            return;
-        }
-
-        // construct prompt object: array of ["query", "doc0", "doc1", ...]
-        json prompt;
-        prompt.push_back(query);
-        for (const auto & doc : documents) {
-            prompt.push_back(doc);
-        }
-
-        LOG_DBG("rerank prompt: %s\n", prompt.dump().c_str());
-
-        // create and queue the task
-        json responses = json::array();
-        bool error = false;
-        {
-            std::vector<server_task> tasks = ctx_server.create_tasks_cmpl({{"prompt", prompt}}, SERVER_TASK_CMPL_TYPE_RERANK);
-            ctx_server.queue_results.add_waiting_tasks(tasks);
-            ctx_server.queue_tasks.post(tasks);
-
-            // get the result
-            std::unordered_set<int> task_ids = server_task::get_list_id(tasks);
-
-            ctx_server.receive_cmpl_results(task_ids, [&](std::vector<server_task_result> & results) {
-                for (const auto & res : results) {
-                    responses.push_back(res.data);
-                }
-            }, [&](const json & error_data) {
-                res_error(res, error_data);
-                error = true;
-            });
-        }
-
-        if (error) {
-            return;
-        }
-
-        // write JSON response
-        json root = format_response_rerank(body, responses);
-        res_ok(res, root);
-    };
-
     const auto handle_lora_adapters_list = [&](const httplib::Request &, httplib::Response & res) {
         json result = json::array();
         for (size_t i = 0; i < ctx_server.loras.size(); ++i) {
@@ -3298,10 +3090,6 @@ int main(int argc, char ** argv) {
     svr->Post("/embedding",           handle_embeddings); // legacy
     svr->Post("/embeddings",          handle_embeddings);
     svr->Post("/v1/embeddings",       handle_embeddings);
-    svr->Post("/rerank",              handle_rerank);
-    svr->Post("/reranking",           handle_rerank);
-    svr->Post("/v1/rerank",           handle_rerank);
-    svr->Post("/v1/reranking",        handle_rerank);
     svr->Post("/tokenize",            handle_tokenize);
     svr->Post("/detokenize",          handle_detokenize);
     // LoRA adapters hotswap
@@ -3366,7 +3154,7 @@ int main(int argc, char ** argv) {
     }
 
     // print sample chat example to make it clear which template is used
-    LOG_INF("%s: chat template, built_in: %d, chat_example: '%s'\n", __func__, params.chat_template.empty(), llama_chat_format_example(ctx_server.model, params.chat_template).c_str());
+    LOG_INF("%s: chat template, built_in: %d, chat_example: '%s\n'", __func__, params.chat_template.empty(), llama_chat_format_example(ctx_server.model, params.chat_template).c_str());
 
     ctx_server.queue_tasks.on_new_task(std::bind(
                 &server_context::process_single_task, &ctx_server, std::placeholders::_1));

examples/server/tests/features/ctx_shift.feature

@@ -1,62 +0,0 @@
-@llama.cpp
-@ctx_shift
-Feature: llama.cpp server
-
-  Background: Server startup
-    Given a server listening on localhost:8080
-    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
-    And   a model file test-model.gguf
-    And   a model alias tinyllama-2
-    And   BOS token is 1
-    And   42 as server seed
-    And   256 KV cache size
-    And   32 as batch size
-    And   2 slots
-
-  Scenario: Inference with context shift
-    And   64 server max tokens to predict
-    Then  the server is starting
-    Then  the server is healthy
-    Given a prompt:
-    """
-    Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
-    Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
-    Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
-    Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
-    """
-    And   a completion request with no api error
-    Then  64 tokens are predicted matching fun|Annaks|popcorns|pictry|bowl
-    And   the completion is  truncated
-    And   109 prompt tokens are processed
-
-  Scenario Outline: Inference without context shift
-    And   <n_predict> server max tokens to predict
-    And   disable context shifting
-    Then  the server is starting
-    Then  the server is healthy
-    Given a prompt:
-    """
-    Hi how are you
-    """
-    And   a completion request with no api error
-    Then  <n_token_output> tokens are predicted matching twind|Anna
-    And   the completion is <truncated> truncated
-    And   8 prompt tokens are processed
-    Examples:
-      | n_predict | n_token_output | truncated |
-      | 64        | 64             | not       |
-      | -1        | 120            |           |
-
-  Scenario: Inference without context shift (expected error: prompt too long)
-    And   disable context shifting
-    Then  the server is starting
-    Then  the server is healthy
-    Given a prompt:
-    """
-    Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
-    Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
-    Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
-    Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
-    """
-    And   a completion request with 400 api error
-

examples/server/tests/features/embeddings.feature

@@ -10,12 +10,12 @@ Feature: llama.cpp server
     And   42 as server seed
     And   2 slots
     # the bert-bge-small model has context size of 512
-    # since the generated prompts are as big as the batch size, we need to set the batch size to <= 512
+    # since the generated prompts are as big as the batch size, we need to set the batch size to 512
     # ref: https://huggingface.co/BAAI/bge-small-en-v1.5/blob/5c38ec7c405ec4b44b94cc5a9bb96e735b38267a/config.json#L20
-    And   128 as batch size
-    And   128 as ubatch size
-    And   512 KV cache size
-    And   enable embeddings endpoint
+    And   512 as batch size
+    And   512 as ubatch size
+    And   2048 KV cache size
+    And   embeddings extraction
     Then  the server is starting
     Then  the server is healthy
 
@@ -26,20 +26,6 @@ Feature: llama.cpp server
     """
     Then embeddings are generated
 
-  Scenario: Embedding (error: prompt too long)
-    When embeddings are computed for:
-    """
-    Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
-    Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
-    Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
-    Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
-    Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
-    Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
-    Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
-    Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
-    """
-    And  embeddings request with 500 api error
-
   Scenario: OAI Embeddings compatibility
     Given a model bert-bge-small
     When an OAI compatible embeddings computation request for:

examples/server/tests/features/rerank.feature

@@ -1,42 +0,0 @@
-@llama.cpp
-@rerank
-Feature: llama.cpp server
-
-  Background: Server startup
-    Given a server listening on localhost:8080
-    And   a model url https://huggingface.co/ggml-org/models/resolve/main/jina-reranker-v1-tiny-en/ggml-model-f16.gguf
-    And   a model file jina-reranker-v1-tiny-en.gguf
-    And   a model alias jina-reranker-v1-tiny-en
-    And   42 as server seed
-    And   2 slots
-    And   512 as batch size
-    And   512 as ubatch size
-    And   512 KV cache size
-    And   enable reranking endpoint
-    Then  the server is starting
-    Then  the server is healthy
-
-  Scenario: Rerank
-    Given a rerank query:
-      """
-      Machine learning is
-      """
-    And   a rerank document:
-      """
-      A machine is a physical system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines.
-      """
-    And   a rerank document:
-      """
-      Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, non-human animals, and some machines; there is also evidence for some kind of learning in certain plants.
-      """
-    And   a rerank document:
-      """
-      Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.
-      """
-    And   a rerank document:
-      """
-      Paris, capitale de la France, est une grande ville européenne et un centre mondial de l'art, de la mode, de la gastronomie et de la culture. Son paysage urbain du XIXe siècle est traversé par de larges boulevards et la Seine.
-      """
-    When  reranking request
-    Then  reranking results are returned
-    Then  reranking highest score is index 2 and lowest score is index 3

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

@@ -68,7 +68,6 @@ def step_server_config(context, server_fqdn: str, server_port: str):
     context.server_api_key = None
     context.server_continuous_batching = False
     context.server_embeddings = False
-    context.server_reranking = False
     context.server_metrics = False
     context.server_process = None
     context.seed = None
@@ -78,16 +77,11 @@ def step_server_config(context, server_fqdn: str, server_port: str):
     context.response_format = None
     context.temperature = None
     context.lora_file = None
-    context.disable_ctx_shift = False
 
     context.tasks_result = []
     context.concurrent_tasks = []
     context.prompts = []
 
-    context.reranking_query = None
-    context.reranking_documents = []
-    context.reranking_results = None
-
 
 @step('a model file {hf_file} from HF repo {hf_repo}')
 def step_download_hf_model(context, hf_file: str, hf_repo: str):
@@ -154,7 +148,7 @@ def step_n_slots(context, n_slots: int):
 
 @step('{n_predict:d} server max tokens to predict')
 def step_server_n_predict(context, n_predict: int):
-    context.n_server_predict = n_predict if n_predict > 0 else None
+    context.n_server_predict = n_predict
 
 
 @step('{slot_save_path} as slot save path')
@@ -177,21 +171,15 @@ def step_server_continuous_batching(context):
     context.server_continuous_batching = True
 
 
-@step('enable embeddings endpoint')
+@step('embeddings extraction')
 def step_server_embeddings(context):
     context.server_embeddings = True
 
-@step('enable reranking endpoint')
-def step_server_reranking(context):
-    context.server_reranking = True
 
 @step('prometheus compatible metrics exposed')
 def step_server_metrics(context):
     context.server_metrics = True
 
-@step('disable context shifting')
-def step_server_disable_ctx_shift(context):
-    context.disable_ctx_shift = True
 
 @step("the server is starting")
 def step_start_server(context):
@@ -269,7 +257,7 @@ async def step_all_slots_status(context, expected_slot_status_string: Literal['i
 @step('a completion request with {api_error} api error')
 @async_run_until_complete
 async def step_request_completion(context, api_error: Literal['raised'] | str):
-    expect_api_error = api_error == 'raised' or api_error != 'no'
+    expect_api_error = api_error == 'raised'
     seeds = await completions_seed(context, num_seeds=1)
     completion = await request_completion(context.prompts.pop(),
                                           seeds[0] if seeds is not None else seeds,
@@ -284,11 +272,8 @@ async def step_request_completion(context, api_error: Literal['raised'] | str):
     context.tasks_result.append(completion)
     if context.debug:
         print(f"Completion response: {completion}")
-    if api_error == 'raised':
+    if expect_api_error:
         assert completion == 401, f"completion must be an 401 status code: {completion}"
-    elif api_error.isdigit():
-        api_error_code = int(api_error)
-        assert completion == api_error_code, f"completion must be an {api_error_code} status code: {completion}"
 
 
 @step('{predicted_n:d} tokens are predicted matching {re_content}')
@@ -460,14 +445,6 @@ def step_impl(context, n_ga_w):
 def step_prompt_passkey(context):
     context.prompt_passkey = context_text(context)
 
-@step('a rerank query')
-def step_set_rerank_query(context):
-    context.reranking_query = context_text(context)
-    context.reranking_documents = []
-
-@step('a rerank document')
-def step_set_rerank_document(context):
-    context.reranking_documents.append(context_text(context))
 
 @step('{n_prompts:d} fixed prompts')
 def step_fixed_prompts(context, n_prompts):
@@ -635,22 +612,6 @@ async def step_compute_embedding(context):
     context.embeddings = await request_embedding(context_text(context), None, base_url=context.base_url)
 
 
-@step('reranking request')
-@async_run_until_complete
-async def step_compute_reranking(context):
-    async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
-        async with session.post(f'{context.base_url}/reranking',
-                                json={
-                                    "query": context.reranking_query,
-                                    "documents": context.reranking_documents,
-                                }) as response:
-            if response.status == 200:
-                response_json = await response.json()
-                context.reranking_results = response_json['results']
-            else:
-                context.reranking_results = response.status
-
-
 @step('all embeddings are the same')
 @async_run_until_complete
 async def step_all_embeddings_are_the_same(context):
@@ -684,9 +645,6 @@ def step_assert_embeddings(context):
     for embedding in context.embeddings:
         assert_embeddings(embedding)
 
-@step('embeddings request with {api_error_code:d} api error')
-def step_assert_embeddings(context, api_error_code: int):
-    assert context.embeddings == api_error_code, f"embeddings request must return code {api_error_code}, but got {context.embeddings}"
 
 @step('an OAI compatible embeddings computation request for')
 @async_run_until_complete
@@ -736,24 +694,6 @@ async def all_embeddings_are_generated(context):
     for i in range(n_embedding_requests):
         assert_embeddings(context.tasks_result.pop().pop())
 
-@step('reranking results are returned')
-def reranking_results_are_returned(context):
-    assert len(context.reranking_results) == len(context.reranking_documents)
-
-@step('reranking highest score is index {idx_high:d} and lowest score is index {idx_low:d}')
-def reranking_results_are_returned(context, idx_high: int, idx_low: int):
-    max_score, max_idx = 0, 0
-    min_score, min_idx = 0, 0
-    for res in context.reranking_results:
-        if max_score < res['relevance_score']:
-            max_score = res['relevance_score']
-            max_idx   = res['index']
-        if min_score > res['relevance_score']:
-            min_score = res['relevance_score']
-            min_idx   = res['index']
-    print(context.reranking_results)
-    assert max_idx == idx_high
-    assert min_idx == idx_low
 
 @step('adding special tokens')
 def step_tokenize_set_add_special(context):
@@ -1149,17 +1089,15 @@ async def oai_chat_completions(user_prompt,
     return completion_response
 
 
-async def request_embedding(content, seed, base_url=None) -> list[list[float]] | int:
+async def request_embedding(content, seed, base_url=None) -> list[list[float]]:
     async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
         async with session.post(f'{base_url}/embedding',
                                 json={
                                     "content": content,
                                 }) as response:
-            if response.status == 200:
-                response_json = await response.json()
-                return [response_json['embedding']]
-            else:
-                return response.status
+            assert response.status == 200
+            response_json = await response.json()
+            return [response_json['embedding']]
 
 
 async def request_oai_embeddings(input, seed,
@@ -1412,8 +1350,6 @@ def start_server_background(context):
         server_args.append('--cont-batching')
     if context.server_embeddings:
         server_args.append('--embedding')
-    if context.server_reranking:
-        server_args.append('--reranking')
     if context.server_metrics:
         server_args.append('--metrics')
     if context.model_alias:
@@ -1436,8 +1372,6 @@ def start_server_background(context):
         server_args.append('--verbose')
     if context.lora_file:
         server_args.extend(['--lora', context.lora_file])
-    if context.disable_ctx_shift:
-        server_args.extend(['--no-context-shift'])
 
     args = [str(arg) for arg in [context.server_path, *server_args]]
     print(f"bench: starting server with: {' '.join(args)}")

examples/server/utils.hpp

@@ -537,7 +537,7 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
     json res = json {
         {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
         {"object", "list"},
-        {"usage", json { // TODO: fill
+        {"usage", json {
             {"prompt_tokens", 0},
             {"total_tokens", 0}
         }},
@@ -547,29 +547,6 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
     return res;
 }
 
-static json format_response_rerank(const json & request, const json & ranks) {
-    json data = json::array();
-    int i = 0;
-    for (const auto & rank : ranks) {
-        data.push_back(json{
-            {"index",    i++},
-            {"relevance_score", json_value(rank, "score", 0.0)},
-        });
-    }
-
-    json res = json {
-        {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
-        {"object", "list"},
-        {"usage", json { // TODO: fill
-            {"prompt_tokens", 0},
-            {"total_tokens", 0}
-        }},
-        {"results", data}
-    };
-
-    return res;
-}
-
 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();

examples/speculative/speculative.cpp

@@ -32,9 +32,6 @@ struct seq_draft {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    // needed to get candidate probs even for temp <= 0.0
-    params.sparams.n_probs = 128;
-
     if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
         return 1;
     }
@@ -52,7 +49,7 @@ int main(int argc, char ** argv) {
     // probability threshold for splitting a draft branch (only for n_seq_dft > 1)
     const float p_split  = params.p_split;
 
-    std::default_random_engine rng(params.sparams.seed == LLAMA_DEFAULT_SEED ? std::random_device()() : params.sparams.seed);
+    std::default_random_engine rng(params.sparams.seed);
     std::uniform_real_distribution<> u_dist;
 
     // init llama.cpp

flake.lock

@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1726755586,
-        "narHash": "sha256-PmUr/2GQGvFTIJ6/Tvsins7Q43KTMvMFhvG6oaYK+Wk=",
+        "lastModified": 1726062873,
+        "narHash": "sha256-IiA3jfbR7K/B5+9byVi9BZGWTD4VSbWe8VLpp9B/iYk=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "c04d5652cfa9742b1d519688f65d1bbccea9eb7e",
+        "rev": "4f807e8940284ad7925ebd0a0993d2a1791acb2f",
         "type": "github"
       },
       "original": {

ggml/include/ggml.h

@@ -229,16 +229,14 @@
 #define GGML_MAX_PARAMS         2048
 #define GGML_MAX_CONTEXTS       64
 #define GGML_MAX_SRC            10
-#define GGML_MAX_N_THREADS      512
-#define GGML_MAX_OP_PARAMS      64
-
 #ifndef GGML_MAX_NAME
-#   define GGML_MAX_NAME        64
-#endif
+#define GGML_MAX_NAME           64
+#define GGML_MAX_N_THREADS      512
 
+#endif
+#define GGML_MAX_OP_PARAMS      64
 #define GGML_DEFAULT_N_THREADS  4
 #define GGML_DEFAULT_GRAPH_SIZE 2048
-
 #if UINTPTR_MAX == 0xFFFFFFFF
     #define GGML_MEM_ALIGN 4
 #else
@@ -261,21 +259,21 @@
 #define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
 
 #ifndef NDEBUG
-#   define GGML_UNREACHABLE() do { fprintf(stderr, "statement should be unreachable\n"); abort(); } while(0)
+#define GGML_UNREACHABLE() do { fprintf(stderr, "statement should be unreachable\n"); abort(); } while(0)
 #elif defined(__GNUC__)
-#   define GGML_UNREACHABLE() __builtin_unreachable()
+#define GGML_UNREACHABLE() __builtin_unreachable()
 #elif defined(_MSC_VER)
-#   define GGML_UNREACHABLE() __assume(0)
+#define GGML_UNREACHABLE() __assume(0)
 #else
-#   define GGML_UNREACHABLE() ((void) 0)
+#define GGML_UNREACHABLE() ((void) 0)
 #endif
 
 #ifdef __cplusplus
-#   define GGML_NORETURN [[noreturn]]
+#define GGML_NORETURN [[noreturn]]
 #elif defined(_MSC_VER)
-#   define GGML_NORETURN __declspec(noreturn)
+#define GGML_NORETURN __declspec(noreturn)
 #else
-#   define GGML_NORETURN _Noreturn
+#define GGML_NORETURN _Noreturn
 #endif
 
 #define GGML_ABORT(...) ggml_abort(__FILE__, __LINE__, __VA_ARGS__)
@@ -572,7 +570,6 @@ extern "C" {
         GGML_LOG_LEVEL_WARN  = 2,
         GGML_LOG_LEVEL_ERROR = 3,
         GGML_LOG_LEVEL_DEBUG = 4,
-        GGML_LOG_LEVEL_CONT  = 5, // continue previous log
     };
 
     // this tensor...
@@ -1982,9 +1979,6 @@ 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)
-    // n_tasks == GGML_N_TASKS_MAX means to use max number of tasks
-
     GGML_API struct ggml_tensor * ggml_map_custom1(
             struct ggml_context   * ctx,
             struct ggml_tensor    * a,
@@ -2509,9 +2503,6 @@ extern "C" {
     GGML_API int ggml_cpu_has_cann       (void);
     GGML_API int ggml_cpu_has_llamafile  (void);
 
-    // get the sve vector length in bytes
-    GGML_API int ggml_cpu_get_sve_cnt(void);
-
     //
     // Internal types and functions exposed for tests and benchmarks
     //

ggml/src/CMakeLists.txt

@@ -1186,7 +1186,6 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW
         endif()
         if (GGML_AVX512)
             list(APPEND ARCH_FLAGS -mavx512f)
-            list(APPEND ARCH_FLAGS -mavx512dq)
             list(APPEND ARCH_FLAGS -mavx512bw)
         endif()
         if (GGML_AVX512_VBMI)

ggml/src/ggml-cann/common.h

@@ -227,7 +227,6 @@ struct ggml_backend_cann_context {
      * @brief Destructor for cleaning up resources.
      */
     ~ggml_backend_cann_context() {
-        ggml_cann_set_device(device);
         if (copy_event != nullptr) {
             ACL_CHECK(aclrtDestroyEvent(copy_event));
         }

ggml/src/ggml-cuda.cu

@@ -187,7 +187,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
     for (int id = 0; id < info.device_count; ++id) {
         int device_vmm = 0;
 
-#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
+#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA)
         CUdevice device;
         CU_CHECK(cuDeviceGet(&device, id));
         CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device));
@@ -199,7 +199,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
             alloc_prop.location.id = id;
             CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED));
         }
-#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
+#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA)
         info.devices[id].vmm = !!device_vmm;
 
         cudaDeviceProp prop;
@@ -335,7 +335,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
 };
 
 // pool with virtual memory
-#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
+#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA)
 struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
     static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB
 
@@ -429,14 +429,14 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
         GGML_ASSERT(ptr == (void *) (pool_addr + pool_used));
     }
 };
-#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
+#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA)
 
 std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(int device) {
-#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
+#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA)
     if (ggml_cuda_info().devices[device].vmm) {
         return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_vmm(device));
     }
-#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
+#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA)
     return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_leg(device));
 }
 
@@ -2899,9 +2899,6 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
                 if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q8_0) {
                     return true;
                 }
-                if (src0_type == GGML_TYPE_Q8_0 && src1_type == GGML_TYPE_F32) {
-                    return true;
-                }
                 if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_0) {
                     return true;
                 }

ggml/src/ggml-cuda/cpy.cu

@@ -81,17 +81,6 @@ static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
     }
 }
 
-static __device__ void cpy_blck_q8_0_f32(const char * cxi, char * cdsti) {
-    const block_q8_0 * xi = (const block_q8_0 *) cxi;
-    float * dsti = (float *) cdsti;
-
-    const float d = (float)xi->d;
-
-    for (int j = 0; j < QK8_0; j++) {
-       dsti[j] = xi->qs[j] * d;
-    }
-}
-
 static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
     const float * xi = (const float *) cxi;
     block_q4_0 * dsti = (block_q4_0 *) cdsti;
@@ -299,32 +288,6 @@ static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne,
     cpy_blck(cx + x_offset, cdst + dst_offset);
 }
 
-template <cpy_kernel_t cpy_blck, int qk>
-static __global__ void cpy_q_f32(const char * cx, char * cdst, const int ne,
-                                 const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-                                 const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
-                                 const int nb12, const int nb13) {
-    const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk;
-
-    if (i >= ne) {
-        return;
-    }
-
-    const int i03 = i/(ne00 * ne01 * ne02);
-    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
-    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
-    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
-    const int x_offset = (i00/qk)*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
-
-    const int i13 = i/(ne10 * ne11 * ne12);
-    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
-    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
-    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
-    const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
-
-    cpy_blck(cx + x_offset, cdst + dst_offset);
-}
-
 static void ggml_cpy_f16_f32_cuda(
     const char * cx, char * cdst, const int ne,
     const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
@@ -366,16 +329,6 @@ static void ggml_cpy_f32_q8_0_cuda(
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
 
-static void ggml_cpy_q8_0_f32_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
-
-    const int num_blocks = ne;
-    cpy_q_f32<cpy_blck_q8_0_f32, QK8_0><<<num_blocks, 1, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
-}
-
 static void ggml_cpy_f32_q4_0_cuda(
     const char * cx, char * cdst, const int ne,
     const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
@@ -484,8 +437,6 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
         ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
         ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_Q8_0 && src1->type == GGML_TYPE_F32) {
-        ggml_cpy_q8_0_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
         ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
@@ -520,8 +471,6 @@ void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
         return (void*) cpy_f32_f16<cpy_1_f32_f16>;
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
         return (void*) cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>;
-    } else if (src0->type == GGML_TYPE_Q8_0 && src1->type == GGML_TYPE_F32) {
-        return (void*) cpy_q_f32<cpy_blck_q8_0_f32, QK8_0>;
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
         return (void*) cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>;
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {

ggml/src/ggml-cuda/im2col.cu

@@ -69,6 +69,7 @@ void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
 

ggml/src/ggml-quants.c

@@ -4013,7 +4013,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     svfloat32_t sumv0 = svdup_n_f32(0.0f);
     svfloat32_t sumv1 = svdup_n_f32(0.0f);
 
-    const int vector_length = ggml_cpu_get_sve_cnt()*8;
+    const int vector_length = ggml_sve_cnt_b*8;
 
     // VLA Implementation using switch case
     switch (vector_length) {
@@ -5597,7 +5597,7 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     svfloat32_t sumv0 = svdup_n_f32(0.0f);
     svfloat32_t sumv1 = svdup_n_f32(0.0f);
 
-    const int vector_length = ggml_cpu_get_sve_cnt()*8;
+    const int vector_length = ggml_sve_cnt_b*8;
 
     //VLA Implemenation for SVE
     switch (vector_length) {

ggml/src/ggml-quants.h

@@ -142,6 +142,10 @@ void iq2xs_free_impl(enum ggml_type type);
 void iq3xs_init_impl(int grid_size);
 void iq3xs_free_impl(int grid_size);
 
+#if defined(__ARM_FEATURE_SVE)
+extern int ggml_sve_cnt_b;
+#endif
+
 #ifdef __cplusplus
 }
 #endif

ggml/src/ggml-vulkan.cpp

@@ -20,8 +20,6 @@
 #include <unordered_map>
 #include <memory>
 #include <mutex>
-#include <future>
-#include <thread>
 
 #include "ggml-impl.h"
 #include "ggml-backend-impl.h"
@@ -609,16 +607,13 @@ typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context& subctx
 
 GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend);
 
-// variables to track number of compiles in progress
-static uint32_t compile_count = 0;
-static std::mutex compile_count_mutex;
-static std::condition_variable compile_count_cond;
-
-static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, const std::string name, size_t spv_size, const void* spv_data, const std::string entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t> specialization_constants, uint32_t align) {
+static void ggml_vk_create_pipeline(vk_device& device, vk_pipeline& pipeline, const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t>&& specialization_constants, uint32_t align) {
     VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")");
     GGML_ASSERT(parameter_count > 0);
     GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT
 
+    std::lock_guard<std::mutex> guard(device->mutex);
+
     pipeline = std::make_shared<vk_pipeline_struct>();
     pipeline->name = name;
     pipeline->parameter_count = parameter_count;
@@ -686,17 +681,7 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
         pipeline->layout);
     pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
 
-    {
-        std::lock_guard<std::mutex> guard(device->mutex);
-        device->pipelines.insert({ pipeline->name, pipeline });
-    }
-
-    {
-        std::lock_guard<std::mutex> guard(compile_count_mutex);
-        assert(compile_count > 0);
-        compile_count--;
-    }
-    compile_count_cond.notify_all();
+    device->pipelines.insert({ pipeline->name, pipeline });
 }
 
 static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) {
@@ -1094,8 +1079,7 @@ static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) {
             // Fall back to host memory type
             buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
         } else {
-            // use rebar if available, otherwise fallback to device only visible memory
-            buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, vk::MemoryPropertyFlagBits::eDeviceLocal);
+            buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal);
         }
     } catch (const vk::SystemError& e) {
         std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl;
@@ -1209,20 +1193,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
     device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K] = std::make_shared<vk_matmul_pipeline_struct>();
     device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL] = std::make_shared<vk_matmul_pipeline_struct>();
 
-    std::vector<std::future<void>> compiles;
-    auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t>&& specialization_constants, uint32_t align) {
-        {
-            // wait until fewer than N compiles are in progress
-            uint32_t N = std::max(1u, std::thread::hardware_concurrency());
-            std::unique_lock<std::mutex> guard(compile_count_mutex);
-            while (compile_count >= N) {
-                compile_count_cond.wait(guard);
-            }
-            compile_count++;
-        }
-        compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint, parameter_count, push_constant_size, wg_denoms, specialization_constants, align));
-    };
-
     if (device->fp16) {
         ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
         ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
@@ -1772,10 +1742,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16_len, im2col_f32_f16_data, "main", 2, sizeof(vk_op_im2col_push_constants), {256, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_timestep_embedding_f32, "timestep_embedding_f32", timestep_embedding_f32_len, timestep_embedding_f32_data, "main", 2, sizeof(vk_op_timestep_embedding_push_constants), {256, 1, 1}, {}, 1);
-
-    for (auto &c : compiles) {
-        c.wait();
-    }
 }
 
 static vk_device ggml_vk_get_device(size_t idx) {
@@ -2840,11 +2806,7 @@ static void ggml_vk_buffer_read_async(vk_context subctx, vk_buffer& src, size_t
 
 static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_t size) {
     VK_LOG_DEBUG("ggml_vk_buffer_read(" << src->buffer << ", " << offset << ", " << size << ")");
-
-    // If the device is not an UMA device the memory is host-accessible through rebar. While writing
-    // through PCIe is sufficient fast reading back data from PCIe is slower than going through
-    // the HW device to host copy path.
-    if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible && src->device->uma) {
+    if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
         GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
 
         memcpy(dst, (uint8_t *) src->ptr + offset, size);
@@ -5046,8 +5008,6 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
         }
     }
 
-    ggml_pipeline_allocate_descriptor_sets(ctx->device);
-
     vk_buffer d_X = ggml_vk_create_buffer_check(ctx->device, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
     vk_buffer d_Y = ggml_vk_create_buffer_check(ctx->device, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
     vk_buffer d_D = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
@@ -5164,9 +5124,7 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
 
     avg_err /= m * n;
 
-    double tflops = 2.0*m*n*k*batch*num_it / (time / 1000.0) / (1000.0*1000.0*1000.0*1000.0);
-
-    std::cerr << "TEST " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time / num_it << "ms " << tflops << " TFLOPS avg_err=" << avg_err << std::endl;
+    std::cerr << "TEST " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time / num_it << "ms avg_err=" << avg_err << std::endl;
 
     if (avg_err > 0.1) {
         std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
@@ -5288,14 +5246,12 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
 
     ggml_pipeline_request_descriptor_sets(ctx->device, p, 1);
 
-    ggml_pipeline_allocate_descriptor_sets(ctx->device);
-
     ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz);
 
     vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
     ggml_vk_ctx_begin(ctx->device, subctx);
     const std::vector<uint32_t> pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne };
-    ggml_vk_dispatch_pipeline(ctx, subctx, p, { vk_subbuffer{ qx_buf, 0, qx_sz }, vk_subbuffer{ x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1});
+    ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1});
     ggml_vk_ctx_end(subctx);
 
     auto begin = std::chrono::high_resolution_clock::now();
@@ -5422,8 +5378,6 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
         }
     }
 
-    ggml_pipeline_allocate_descriptor_sets(ctx->device);
-
     ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz);
     ggml_vk_buffer_write(y_buf, 0, y, y_sz);
 
@@ -5491,9 +5445,7 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
 
     avg_err /= m * n;
 
-    double tflops = 2.0*m*n*k*batch*num_it / (time_ms / 1000.0) / (1000.0*1000.0*1000.0*1000.0);
-
-    std::cerr << "TEST MMQ " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms " << tflops << " TFLOPS avg_err=" << avg_err << std::endl;
+    std::cerr << "TEST MMQ " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms avg_err=" << avg_err << std::endl;
 
     if (avg_err > 0.01 || std::isnan(avg_err)) {
         std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
@@ -5545,6 +5497,9 @@ static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor)
 
 static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
 #if defined(GGML_VULKAN_RUN_TESTS)
+    ctx->staging = ggml_vk_create_buffer_check(ctx->device, 100ul * 1024ul * 1024ul,
+        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
+        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
     ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_F32);
     ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_0);
     ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_1);

ggml/src/ggml.c

@@ -39,6 +39,9 @@
 #include <unistd.h>
 #endif
 
+#if defined(__ARM_FEATURE_SVE)
+int ggml_sve_cnt_b = 0;
+#endif
 #if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8)
 #undef GGML_USE_LLAMAFILE
 #endif
@@ -60,25 +63,6 @@
 #pragma warning(disable: 4702)
 #endif
 
-// Note: once we move threading into a separate C++ file
-// will use std::hardware_destructive_interference_size instead of hardcoding it here
-// and we'll use C++ attribute syntax.
-#define GGML_CACHE_LINE  64
-
-#if defined(__clang__) || defined(__GNUC__)
-#define GGML_CACHE_ALIGN __attribute__((aligned(GGML_CACHE_LINE)))
-#endif
-
-#if defined(__has_feature)
-#if __has_feature(thread_sanitizer)
-#define GGML_TSAN_ENABLED 1
-#endif
-#else  // __has_feature
-#if defined(__SANITIZE_THREAD__)
-#define GGML_TSAN_ENABLED 1
-#endif
-#endif // __has_feature
-
 #if defined(_WIN32)
 
 #define WIN32_LEAN_AND_MEAN
@@ -88,8 +72,6 @@
 #include <windows.h>
 
 #if !defined(__clang__)
-#define GGML_CACHE_ALIGN __declspec(align(GGML_CACHE_LINE))
-
 typedef volatile LONG atomic_int;
 typedef atomic_int atomic_bool;
 typedef atomic_int atomic_flag;
@@ -132,9 +114,6 @@ static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) {
 static void atomic_flag_clear(atomic_flag * ptr) {
     InterlockedExchange(ptr, 0);
 }
-static void atomic_thread_fence(memory_order mo) {
-    MemoryBarrier();
-}
 #else // clang
 #include <stdatomic.h>
 #endif
@@ -310,6 +289,7 @@ 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
@@ -452,15 +432,6 @@ static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16];
 // precomputed f32 table for f16 (256 KB) (ggml-impl.h)
 float ggml_table_f32_f16[1 << 16];
 
-#if defined(__ARM_ARCH)
-struct ggml_arm_arch_features_type {
-    int has_neon;
-    int has_i8mm;
-    int has_sve;
-    int sve_cnt;
-} ggml_arm_arch_features = {-1, -1, -1, 0};
-#endif
-
 GGML_CALL const char * ggml_status_to_string(enum ggml_status status) {
     switch (status) {
         case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)";
@@ -2036,8 +2007,8 @@ struct ggml_threadpool {
 
     // synchronization primitives
     atomic_int n_graph;       // incremented when there is work to be done (i.e each graph)
-    atomic_int GGML_CACHE_ALIGN n_barrier;
-    atomic_int GGML_CACHE_ALIGN n_barrier_passed;
+    atomic_int n_barrier;
+    atomic_int n_barrier_passed;
     atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads.
 
     // these are atomic as an annotation for thread-sanitizer
@@ -3225,27 +3196,20 @@ static void ggml_barrier(struct ggml_threadpool * tp) {
     // enter barrier (full seq-cst fence)
     int n_barrier = atomic_fetch_add_explicit(&tp->n_barrier, 1, memory_order_seq_cst);
 
+    int last = 0;
     if (n_barrier == (n_threads - 1)) {
         // last thread
         atomic_store_explicit(&tp->n_barrier, 0, memory_order_relaxed);
-
-        // exit barrier (fill seq-cst fence)
-        atomic_fetch_add_explicit(&tp->n_barrier_passed, 1, memory_order_seq_cst);
-        return;
-    }
-
-    // wait for other threads
-    while (atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed) == n_passed) {
-        ggml_thread_cpu_relax();
+        last = 1;
+    } else {
+        // wait for other threads
+        while (atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed) == n_passed) {
+            ggml_thread_cpu_relax();
+        }
     }
 
     // exit barrier (full seq-cst fence)
-    // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead
-    #ifdef GGML_TSAN_ENABLED
-    atomic_fetch_add_explicit(&tp->n_barrier_passed, 0, memory_order_seq_cst);
-    #else
-    atomic_thread_fence(memory_order_seq_cst);
-    #endif
+    atomic_fetch_add_explicit(&tp->n_barrier_passed, last, memory_order_seq_cst);
 #endif
 }
 
@@ -3679,66 +3643,6 @@ static inline int ggml_up(int n, int m) {
 
 ////////////////////////////////////////////////////////////////////////////////
 
-#if defined(__ARM_ARCH)
-
-#if defined(__linux__) && defined(__aarch64__)
-#include <sys/auxv.h>
-#elif defined(__APPLE__)
-#include <sys/sysctl.h>
-#endif
-
-static void ggml_init_arm_arch_features(void) {
-#if defined(__linux__) && defined(__aarch64__)
-    uint32_t hwcap = getauxval(AT_HWCAP);
-    uint32_t hwcap2 = getauxval(AT_HWCAP2);
-
-    ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD);
-    ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM);
-    ggml_arm_arch_features.has_sve  = !!(hwcap & HWCAP_SVE);
-
-#if defined(__ARM_FEATURE_SVE)
-    ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL);
-#endif
-#elif defined(__APPLE__)
-    int oldp = 0;
-    size_t size = sizeof(oldp);
-    if (sysctlbyname("hw.optional.AdvSIMD", &oldp, &size, NULL, 0) != 0) {
-        oldp = 0;
-    }
-    ggml_arm_arch_features.has_neon = oldp;
-
-    if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) != 0) {
-        oldp = 0;
-    }
-    ggml_arm_arch_features.has_i8mm = oldp;
-
-    ggml_arm_arch_features.has_sve = 0;
-    ggml_arm_arch_features.sve_cnt = 0;
-#else
-// Run-time CPU feature detection not implemented for this platform, fallback to compile time
-#if defined(__ARM_NEON)
-    ggml_arm_arch_features.has_neon = 1;
-#else
-    ggml_arm_arch_features.has_neon = 0;
-#endif
-
-#if defined(__ARM_FEATURE_MATMUL_INT8)
-    ggml_arm_arch_features.has_i8mm = 1;
-#else
-    ggml_arm_arch_features.has_i8mm = 0;
-#endif
-
-#if defined(__ARM_FEATURE_SVE)
-    ggml_arm_arch_features.has_sve = 1;
-    ggml_arm_arch_features.sve_cnt = 16;
-#else
-    ggml_arm_arch_features.has_sve = 0;
-    ggml_arm_arch_features.sve_cnt = 0;
-#endif
-#endif
-}
-#endif
-
 struct ggml_context * ggml_init(struct ggml_init_params params) {
     // make this function thread safe
     ggml_critical_section_start();
@@ -3789,10 +3693,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
             GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
         }
 
-#if defined(__ARM_ARCH)
-        ggml_init_arm_arch_features();
-#endif
-
         is_first_call = false;
     }
 
@@ -3841,6 +3741,12 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
 
     GGML_ASSERT_ALIGNED(ctx->mem_buffer);
 
+#if defined(__ARM_FEATURE_SVE)
+    if (!ggml_sve_cnt_b) {
+        ggml_sve_cnt_b = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL);
+    }
+#endif
+
     GGML_PRINT_DEBUG("%s: context initialized\n", __func__);
 
     ggml_critical_section_end();
@@ -20334,13 +20240,10 @@ static inline bool ggml_graph_compute_thread_ready(struct ggml_compute_state * s
 
 // sync thread state after polling
 static inline void ggml_graph_compute_thread_sync(struct ggml_compute_state * state) {
-    // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead
-    #ifdef GGML_TSAN_ENABLED
-    atomic_fetch_add_explicit(&state->threadpool->n_graph, 0, memory_order_seq_cst);
-    #else
-    atomic_thread_fence(memory_order_seq_cst);
-    #endif
-    UNUSED(state);
+    struct ggml_threadpool * threadpool = state->threadpool;
+    // this should just be atomic_thread_fence(seq_cst) but it confuses thread-sanitizer
+    // so instead we just use a dummy read-modify-write
+    atomic_fetch_add_explicit(&threadpool->n_graph, 0, memory_order_seq_cst);
 }
 
 static inline bool ggml_graph_compute_poll_for_work(struct ggml_compute_state * state) {
@@ -23642,16 +23545,16 @@ int ggml_cpu_has_fma(void) {
 }
 
 int ggml_cpu_has_neon(void) {
-#if defined(__ARM_ARCH)
-    return ggml_arm_arch_features.has_neon;
+#if defined(__ARM_NEON)
+    return 1;
 #else
     return 0;
 #endif
 }
 
 int ggml_cpu_has_sve(void) {
-#if defined(__ARM_ARCH)
-    return ggml_arm_arch_features.has_sve;
+#if defined(__ARM_FEATURE_SVE)
+    return 1;
 #else
     return 0;
 #endif
@@ -23798,18 +23701,11 @@ int ggml_cpu_has_vsx(void) {
 }
 
 int ggml_cpu_has_matmul_int8(void) {
-#if defined(__ARM_ARCH)
-    return ggml_arm_arch_features.has_i8mm;
+#if defined(__ARM_FEATURE_MATMUL_INT8)
+    return 1;
 #else
     return 0;
 #endif
 }
 
-int ggml_cpu_get_sve_cnt(void) {
-#if defined(__ARM_ARCH)
-    return ggml_arm_arch_features.sve_cnt;
-#else
-    return 0;
-#endif
-}
 ////////////////////////////////////////////////////////////////////////////////

ggml/src/vulkan-shaders/argsort.comp

@@ -29,18 +29,20 @@ void main() {
     const int col = int(gl_LocalInvocationID.x);
     const uint row = gl_WorkGroupID.y;
 
+    if (col >= p.ncols_pad) {
+        return;
+    }
+
     const uint row_offset = row * p.ncols;
 
     // initialize indices
-    if (col < p.ncols_pad) {
-        dst_row[col] = col;
-    }
+    dst_row[col] = col;
     barrier();
 
     for (uint k = 2; k <= p.ncols_pad; k *= 2) {
         for (uint j = k / 2; j > 0; j /= 2) {
             const uint ixj = col ^ j;
-            if (col < p.ncols_pad && ixj > col) {
+            if (ixj > col) {
                 if ((col & k) == 0) {
                     if (dst_row[col] >= p.ncols ||
                         (dst_row[ixj] < p.ncols && (p.order == ASC ?

gguf-py/gguf/constants.py

@@ -94,7 +94,6 @@ class Keys:
         DECODER_START_TOKEN_ID            = "{arch}.decoder_start_token_id"
         ATTN_LOGIT_SOFTCAPPING            = "{arch}.attn_logit_softcapping"
         FINAL_LOGIT_SOFTCAPPING           = "{arch}.final_logit_softcapping"
-        SWIN_NORM                         = "{arch}.swin_norm"
         RESCALE_EVERY_N_LAYERS            = "{arch}.rescale_every_n_layers"
         TIME_MIX_EXTRA_DIM                = "{arch}.time_mix_extra_dim"
         TIME_DECAY_EXTRA_DIM              = "{arch}.time_decay_extra_dim"
@@ -236,8 +235,6 @@ class MODEL_ARCH(IntEnum):
     NEMOTRON     = auto()
     EXAONE       = auto()
     GRANITE      = auto()
-    GRANITE_MOE  = auto()
-    CHAMELEON    = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -345,8 +342,6 @@ class MODEL_TENSOR(IntEnum):
     ENC_FFN_DOWN         = auto()
     ENC_FFN_UP           = auto()
     ENC_OUTPUT_NORM      = auto()
-    CLS                  = auto() # classifier
-    CLS_OUT              = auto() # classifier output projection
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -397,8 +392,6 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.NEMOTRON:       "nemotron",
     MODEL_ARCH.EXAONE:         "exaone",
     MODEL_ARCH.GRANITE:        "granite",
-    MODEL_ARCH.GRANITE_MOE:    "granitemoe",
-    MODEL_ARCH.CHAMELEON:      "chameleon",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -506,8 +499,6 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ENC_FFN_DOWN:              "enc.blk.{bid}.ffn_down",
     MODEL_TENSOR.ENC_FFN_UP:                "enc.blk.{bid}.ffn_up",
     MODEL_TENSOR.ENC_OUTPUT_NORM:           "enc.output_norm",
-    MODEL_TENSOR.CLS:                       "cls",
-    MODEL_TENSOR.CLS_OUT:                   "cls.output",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -617,8 +608,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
         MODEL_TENSOR.LAYER_OUT_NORM,
-        MODEL_TENSOR.CLS,
-        MODEL_TENSOR.CLS_OUT,
     ],
     MODEL_ARCH.NOMIC_BERT: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -650,7 +639,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_GATE,
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.LAYER_OUT_NORM,
-        MODEL_TENSOR.CLS,
     ],
     MODEL_ARCH.MPT: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -1244,7 +1232,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
     MODEL_ARCH.GRANITE: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
         MODEL_TENSOR.ATTN_NORM,
         MODEL_TENSOR.ATTN_Q,
         MODEL_TENSOR.ATTN_K,
@@ -1255,37 +1242,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
-    MODEL_ARCH.GRANITE_MOE: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_Q,
-        MODEL_TENSOR.ATTN_K,
-        MODEL_TENSOR.ATTN_V,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_NORM,
-        MODEL_TENSOR.FFN_GATE_INP,
-        MODEL_TENSOR.FFN_GATE_EXP,
-        MODEL_TENSOR.FFN_DOWN_EXP,
-        MODEL_TENSOR.FFN_UP_EXP,
-    ],
-    MODEL_ARCH.CHAMELEON: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_Q,
-        MODEL_TENSOR.ATTN_Q_NORM,
-        MODEL_TENSOR.ATTN_K,
-        MODEL_TENSOR.ATTN_K_NORM,
-        MODEL_TENSOR.ATTN_V,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_NORM,
-        MODEL_TENSOR.FFN_GATE,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-    ],
     # TODO
 }
 

gguf-py/gguf/gguf_writer.py

@@ -670,9 +670,6 @@ class GGUFWriter:
     def add_expert_weights_scale(self, value: float) -> None:
         self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value)
 
-    def add_swin_norm(self, value: bool) -> None:
-        self.add_bool(Keys.LLM.SWIN_NORM.format(arch=self.arch), value)
-
     def add_rescale_every_n_layers(self, count: int) -> None:
         self.add_uint32(Keys.LLM.RESCALE_EVERY_N_LAYERS.format(arch=self.arch), count)
 

gguf-py/gguf/tensor_mapping.py

@@ -251,12 +251,11 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.FFN_GATE_INP: (
-            "layers.{bid}.feed_forward.gate",                   # mixtral
-            "model.layers.{bid}.block_sparse_moe.gate",         # mixtral
-            "model.layers.{bid}.mlp.gate",                      # qwen2moe olmoe
-            "transformer.decoder_layer.{bid}.router",           # Grok
-            "transformer.blocks.{bid}.ffn.router.layer",        # dbrx
-            "model.layers.{bid}.block_sparse_moe.router.layer", # granitemoe
+            "layers.{bid}.feed_forward.gate",             # mixtral
+            "model.layers.{bid}.block_sparse_moe.gate",   # mixtral
+            "model.layers.{bid}.mlp.gate",                # qwen2moe olmoe
+            "transformer.decoder_layer.{bid}.router",     # Grok
+            "transformer.blocks.{bid}.ffn.router.layer",  # dbrx
         ),
 
         MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
@@ -365,11 +364,10 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.FFN_DOWN_EXP: (
-            "layers.{bid}.feed_forward.experts.w2",              # mixtral (merged)
-            "transformer.decoder_layer.{bid}.moe.linear_1",      # Grok (merged)
-            "transformer.blocks.{bid}.ffn.experts.mlp.w2",       # dbrx
-            "model.layers.{bid}.mlp.experts.down_proj",          # qwen2moe olmoe (merged)
-            "model.layers.{bid}.block_sparse_moe.output_linear", # granitemoe
+            "layers.{bid}.feed_forward.experts.w2",          # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear_1",  # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.w2",   # dbrx
+            "model.layers.{bid}.mlp.experts.down_proj",      # qwen2moe olmoe (merged)
         ),
 
         MODEL_TENSOR.FFN_DOWN_SHEXP: (
@@ -380,7 +378,7 @@ class TensorNameMap:
         MODEL_TENSOR.ATTN_Q_NORM: (
             "language_model.encoder.layers.{bid}.self_attention.q_layernorm",
             "model.layers.{bid}.self_attn.q_layernorm",                       # persimmon
-            "model.layers.{bid}.self_attn.q_norm",                            # cohere olmoe chameleon
+            "model.layers.{bid}.self_attn.q_norm",                            # cohere olmoe
             "transformer.blocks.{bid}.attn.q_ln",                             # sea-lion
             "encoder.layer.{bid}.attention.self.layer_norm_q",                # jina-bert-v2
             "transformer.layers.{bid}.attn.q_norm",                           # openelm
@@ -389,7 +387,7 @@ class TensorNameMap:
         MODEL_TENSOR.ATTN_K_NORM: (
             "language_model.encoder.layers.{bid}.self_attention.k_layernorm",
             "model.layers.{bid}.self_attn.k_layernorm",                       # persimmon
-            "model.layers.{bid}.self_attn.k_norm",                            # cohere olmoe chameleon
+            "model.layers.{bid}.self_attn.k_norm",                            # cohere olmoe
             "transformer.blocks.{bid}.attn.k_ln",                             # sea-lion
             "encoder.layer.{bid}.attention.self.layer_norm_k",                # jina-bert-v2
             "transformer.layers.{bid}.attn.k_norm",                           # openelm
@@ -679,15 +677,6 @@ class TensorNameMap:
         MODEL_TENSOR.ENC_OUTPUT_NORM: (
             "encoder.final_layer_norm", # t5
         ),
-
-        MODEL_TENSOR.CLS: (
-            "classifier",       # jina
-            "classifier.dense", # roberta
-        ),
-
-        MODEL_TENSOR.CLS_OUT: (
-            "classifier.out_proj", # roberta
-        ),
     }
 
     # architecture-specific block mappings

include/llama.h

@@ -102,7 +102,6 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_BLOOM          = 23,
         LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH   = 24,
         LLAMA_VOCAB_PRE_TYPE_EXAONE         = 25,
-        LLAMA_VOCAB_PRE_TYPE_CHAMELEON      = 26,
     };
 
     enum llama_rope_type {
@@ -193,7 +192,6 @@ extern "C" {
         LLAMA_POOLING_TYPE_MEAN = 1,
         LLAMA_POOLING_TYPE_CLS  = 2,
         LLAMA_POOLING_TYPE_LAST = 3,
-        LLAMA_POOLING_TYPE_RANK = 4, // used by reranking models to attach the classification head to the graph
     };
 
     enum llama_attention_type {
@@ -203,9 +201,9 @@ extern "C" {
     };
 
     enum llama_split_mode {
-        LLAMA_SPLIT_MODE_NONE  = 0, // single GPU
-        LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
-        LLAMA_SPLIT_MODE_ROW   = 2, // split rows across GPUs
+        LLAMA_SPLIT_MODE_NONE    = 0, // single GPU
+        LLAMA_SPLIT_MODE_LAYER   = 1, // split layers and KV across GPUs
+        LLAMA_SPLIT_MODE_ROW     = 2, // split rows across GPUs
     };
 
     // TODO: simplify (https://github.com/ggerganov/llama.cpp/pull/9294#pullrequestreview-2286561979)
@@ -873,8 +871,7 @@ extern "C" {
 
     // Get the embeddings for a sequence id
     // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE
-    // when pooling_type == LLAMA_POOLING_TYPE_RANK, returns float[1] with the rank of the sequence
-    // otherwise: float[n_embd] (1-dimensional)
+    // shape: [n_embd] (1-dimensional)
     LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id);
 
     //
@@ -913,8 +910,6 @@ extern "C" {
     //
     // Tokenization
     //
-    // The API is thread-safe.
-    //
 
     /// @details Convert the provided text into tokens.
     /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
@@ -1071,7 +1066,6 @@ extern "C" {
     LLAMA_API struct llama_sampler * llama_sampler_init_dist       (uint32_t seed);
 
     /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
-    /// NOTE: Avoid using on the full vocabulary as the sorting can become slow. For example, apply top-k or top-p sampling first.
     LLAMA_API struct llama_sampler * llama_sampler_init_softmax    (void);
 
     /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751

models/ggml-vocab-chameleon.gguf.inp

@@ -1,112 +0,0 @@
-ied 4 ½ months
-__ggml_vocab_test__
-Führer
-__ggml_vocab_test__
-
-__ggml_vocab_test__
- 
-__ggml_vocab_test__
-  
-__ggml_vocab_test__
-   
-__ggml_vocab_test__
-	
-__ggml_vocab_test__
-
-
-__ggml_vocab_test__
-
-
-
-__ggml_vocab_test__
-
-
-
-
-__ggml_vocab_test__
-	
-
-__ggml_vocab_test__
-Hello world
-__ggml_vocab_test__
- Hello world
-__ggml_vocab_test__
-Hello World
-__ggml_vocab_test__
- Hello World
-__ggml_vocab_test__
- Hello World!
-__ggml_vocab_test__
-Hello, world!
-__ggml_vocab_test__
- Hello, world!
-__ggml_vocab_test__
- this is 🦙.cpp
-__ggml_vocab_test__
-w048 7tuijk dsdfhu
-__ggml_vocab_test__
-нещо на Български
-__ggml_vocab_test__
-កាន់តែពិសេសអាចខលចេញ
-__ggml_vocab_test__
-🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
-__ggml_vocab_test__
-Hello
-__ggml_vocab_test__
- Hello
-__ggml_vocab_test__
-  Hello
-__ggml_vocab_test__
-   Hello
-__ggml_vocab_test__
-    Hello
-__ggml_vocab_test__
-    Hello
-    Hello
-__ggml_vocab_test__
- (
-__ggml_vocab_test__
-
- =
-__ggml_vocab_test__
-' era
-__ggml_vocab_test__
-Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
-__ggml_vocab_test__
-!!!!!!
-__ggml_vocab_test__
-3
-__ggml_vocab_test__
-33
-__ggml_vocab_test__
-333
-__ggml_vocab_test__
-3333
-__ggml_vocab_test__
-33333
-__ggml_vocab_test__
-333333
-__ggml_vocab_test__
-3333333
-__ggml_vocab_test__
-33333333
-__ggml_vocab_test__
-333333333
-__ggml_vocab_test__
-Cửa Việt
-__ggml_vocab_test__
- discards
-__ggml_vocab_test__
-
- 
-
- 
-
-
- 	 		 	
-  
-   
-    
-     
-🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
-__ggml_vocab_test__

models/ggml-vocab-chameleon.gguf.out

@@ -1,46 +0,0 @@
- 17245 16604 16403 16604 33583 18355
- 16421 51153
-
- 16604
- 16650
- 16650 16604
- 16581
- 16582
- 16582 16582
- 16582 16582 16582
- 16581 16582
- 31596 17394
- 34926 17394
- 31596 18671
- 34926 18671
- 34926 18671 16384
- 31596 16395 17394 16384
- 34926 16395 17394 16384
- 16811 16704 20410 16483 16631 16397 52854
- 16470 16399 16403 16407 16604 16406 35764 38185 51595 22592 26639
- 29479 23955 17012 20103 25527 27670 17408 19005 21473 24774
- 54254 42231 48084 29409 16617 61889 29409 16608 21954 16628 21954 16499 58445 29409 16607 58445 21954 16479 42231 21954 16611 21954 16607 21954 16633 21954 16611 29409 16607 21954 16615
- 52351 16604 16391 25825 16392 23686 16498 39161 18885 16618 16488 30853 16604 16391 54124 17153 25134 16656 18476 26169 16895 16392 62193 16611 16604 16391 24664 17153 57169 16721 16872 17073 17304 28729 16392
- 31596
- 34926
- 16650 31596
- 16650 34926
- 16696 31596
- 16696 31596 16582 16696 31596
- 16604 16391
- 16582 16604 16412
- 16390 22623
- 31596 16395 16712 16390 16828 16384 17674 16769 16732 23686 16607 16604 16414 24427 16623 41809 16495 28999 36469 45292 30197 16400 16402 16400 16403 16400 16404 16400 43969 65211 16636
- 16384 16384 16384 16384 16384 16384
- 16402
- 16402 16402
- 16402 16402 16402
- 16402 16402 16402 16402
- 16402 16402 16402 16402 16402
- 16402 16402 16402 16402 16402 16402
- 16402 16402 16402 16402 16402 16402 16402
- 16402 16402 16402 16402 16402 16402 16402 16402
- 16402 16402 16402 16402 16402 16402 16402 16402 16402
- 16418 19038 16639 16448 24315 33727 16467
- 18765 17981
- 16582 16604 16582 16582 16604 16582 16582 16582 16604 16581 16604 16581 16581 16604 16581 16582 16650 16582 16650 16604 16582 16696 16582 16696 16604 16582 52351 16604 16391 25825 16392 23686 16498 39161 18885 16618 16488 30853 16604 16391 54124 17153 25134 16656 18476 26169 16895 16392 62193 16611 20410 16483 16631 18885 16483 16631 16604 16402 16604 16402 16402 16604 16402 16402 16402 16604 16402 16402 16402 16402 16604 16402 16402 16402 16402 16402 16604 16402 16402 16402 16402 16402 16402 16604 16402 16402 16402 16402 16402 16402 16402 16604 16402 16402 16402 16402 16402 16402 16402 16402 16604 16402 16397 16402 16604 16402 16397 16397 16402 16604 16402 16397 16397 16397 16402 16604 54254 42231 48084 29409 16617 61889 29409 16608 21954 16628 21954 16499 58445 29409 16607 58445 21954 16479 42231 21954 16611 27683 16607 16604 16414 24427 16623 41809 16495 28999 36469 45292 30197 16400 16402 16400 16403 16400 16404 16400 43969 65211 16636 16604 16396 16396 16396 16396 16396 16396 16412 16412 16412 16412 16412 16412 16412 27268 23955 17012 20103 25527 27670 17408 19005 21473 24774 16604 16390 16390 16390 16390 16390 16390 16447 16447 16447 16447 16447 16447 16447 16385 16385 16385 16385 16397 16397 16397 16397 16397 16397 16384 16384 16384 16384 16384 16384 16414 16414 16414 16414 16414 16414 16687 16390 16690 16992 16604 16390 61797 16733 16390 16466 16986 16395 16604 16390 17879 16732 17811 16414 16604 16390 16428 16804 17811 16687 16390 16683 17190 16728 16395 16604 16390 16419 16732 16945 16991 25251 16414 17119 16390 38127 16641 16390 16459 16427

scripts/sync-ggml.last

@@ -1 +1 @@
-9a24b8c8c40eab7262d067e91d08df160678df8d
+e7b23907cb2816e9951fe9b524d7127ab777297a

src/llama-impl.h

@@ -28,8 +28,6 @@ void llama_log_callback_default(ggml_log_level level, const char * text, void *
 #define LLAMA_LOG_INFO(...)  llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
 #define LLAMA_LOG_WARN(...)  llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
 #define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
-#define LLAMA_LOG_DEBUG(...) llama_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
-#define LLAMA_LOG_CONT(...)  llama_log_internal(GGML_LOG_LEVEL_CONT , __VA_ARGS__)
 
 //
 // helpers

src/llama-sampling.cpp

@@ -3,14 +3,13 @@
 #include "llama-vocab.h"
 #include "llama-grammar.h"
 
-#include <algorithm>
 #include <cassert>
+#include <algorithm>
+#include <cstring>
+#include <ctime>
 #include <cfloat>
 #include <chrono>
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
-#include <ctime>
 #include <numeric>
 #include <random>
 #include <unordered_map>
@@ -757,20 +756,22 @@ static void llama_sampler_tail_free_apply(struct llama_sampler * smpl, llama_tok
         }
     }
 
+    assert(cur_p->size > 0); // guaranteed earlier
+    size_t last_idx = cur_p->size - 1;
+
     float cum_sum = 0.0f;
-    size_t last_idx = cur_p->size;
     for (size_t i = 0; i < second_derivatives.size(); ++i) {
         cum_sum += second_derivatives[i];
 
         // Check if the running sum is greater than z or if we have kept at least min_keep tokens
-        if (cum_sum > ctx->z && i >= ctx->min_keep) {
+        if (cum_sum > ctx->z && (i + 1) >= ctx->min_keep) {
             last_idx = i;
             break;
         }
     }
 
     // Resize the output vector to keep only the tokens above the tail location
-    cur_p->size = last_idx;
+    cur_p->size = last_idx + 1;
 }
 
 static struct llama_sampler * llama_sampler_tail_free_clone(const struct llama_sampler * smpl) {

src/llama-vocab.cpp

@@ -50,7 +50,7 @@ struct naive_trie {
             res.first->second.insert(key + 1, len - 1, value);
         }
     }
-    std::pair<const char *, size_t> get_longest_prefix(const char * key, size_t len, size_t offset = 0) const {
+    std::pair<const char *, size_t> get_longest_prefix(const char * key, size_t len, size_t offset = 0) {
         if (len == 0 || offset == len) {
             return std::make_pair(key, offset);
         }
@@ -79,15 +79,6 @@ struct naive_trie {
 // impl
 //
 
-struct llm_tokenizer {
-   llm_tokenizer() {}
-   virtual ~llm_tokenizer() = default;
-};
-
-llama_vocab::~llama_vocab() {
-    delete tokenizer;
-}
-
 int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
     GGML_ASSERT(token_left.find(' ')   == std::string::npos);
     GGML_ASSERT(token_left.find('\n')  == std::string::npos);
@@ -196,15 +187,10 @@ struct llm_bigram_spm {
     size_t size;
 };
 
-struct llm_tokenizer_spm : llm_tokenizer {
-    llm_tokenizer_spm(const llama_vocab & /*vocab*/) : llm_tokenizer() {}
-};
-
-struct llm_tokenizer_spm_session {
-    llm_tokenizer_spm_session(const llama_vocab & vocab) : vocab(vocab) {}
+struct llm_tokenizer_spm {
+    llm_tokenizer_spm(const llama_vocab & vocab) : vocab(vocab) {}
 
     void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
-
         // split string into utf8 chars
         int index = 0;
         size_t offs = 0;
@@ -285,7 +271,7 @@ private:
             return;
         }
 
-        resegment(symbols[p->second.first], output);
+        resegment(symbols[p->second.first],  output);
         resegment(symbols[p->second.second], output);
     }
 
@@ -293,6 +279,7 @@ private:
         if (left == -1 || right == -1) {
             return;
         }
+
         const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
         auto token = vocab.token_to_id.find(text);
 
@@ -319,11 +306,10 @@ private:
     }
 
     const llama_vocab & vocab;
-    // currently unused
-    // const llm_tokenizer_spm * spm_tokenizer;
 
     std::vector<llm_symbol> symbols;
     llm_bigram_spm::queue work_queue;
+
     std::map<std::string, std::pair<int, int>> rev_merge;
 };
 
@@ -366,8 +352,8 @@ struct llm_bigram_bpe {
     size_t size;
 };
 
-struct llm_tokenizer_bpe : llm_tokenizer {
-    llm_tokenizer_bpe(const llama_vocab & vocab) : llm_tokenizer() {
+struct llm_tokenizer_bpe {
+    llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {
         GGML_ASSERT(vocab.type == LLAMA_VOCAB_TYPE_BPE);
         switch (vocab.type_pre) {
             case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
@@ -464,20 +450,6 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
                 };
                 break;
-            case LLAMA_VOCAB_PRE_TYPE_CHAMELEON:
-                // Note: in theory, the special token (sentinel and image token) regex_exprs below
-                // are unnecessary, as they are split in `tokenizer_st_partition` anyway.
-                // However, since the upstream pre-tokenizer uses them, they are also
-                // included here (see https://huggingface.co/facebook/chameleon-7b).
-                regex_exprs = {
-                    "<sentinel:[0-9]+>",  // Sentinel tokens
-                    "(IMGIMG)((A|B|C|D|E|F|G|H|I){1,4})Z",  // Image tokens
-                    "([\\t\\n]|    |  )",  // directly from tokenizer.json
-                    "\\p{N}", // Individual digits
-                    "[\\p{P}!-/:-@\\[-`{-~]",  // Punctuation, Isolated
-                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                };
-                break;
             default:
                 // default regex for BPE tokenization pre-processing
                 regex_exprs = {
@@ -490,14 +462,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
         }
     }
 
-    std::vector<std::string> regex_exprs;
-};
-
-struct llm_tokenizer_bpe_session {
-    llm_tokenizer_bpe_session(const llama_vocab & vocab) : vocab(vocab),
-        bpe_tokenizer(static_cast<const llm_tokenizer_bpe *>(vocab.tokenizer)) {}
-
-    static void append(const llama_vocab::id token_id, std::vector<llama_vocab::id> & output)  {
+    void append(const llama_vocab::id token_id, std::vector<llama_vocab::id> & output) const {
         output.push_back(token_id);
     }
 
@@ -536,11 +501,12 @@ struct llm_tokenizer_bpe_session {
 
     void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
         int final_prev_index = -1;
-        const auto word_collection = unicode_regex_split(text, bpe_tokenizer->regex_exprs);
+
+        const auto word_collection = unicode_regex_split(text, regex_exprs);
 
         symbols_final.clear();
 
-        for (const auto & word : word_collection) {
+        for (auto & word : word_collection) {
             work_queue = llm_bigram_bpe::queue();
             symbols.clear();
 
@@ -643,6 +609,7 @@ private:
         if (left == -1 || right == -1) {
             return;
         }
+
         std::string left_token  = std::string(symbols[left].text,  symbols[left].n);
         std::string right_token = std::string(symbols[right].text, symbols[right].n);
 
@@ -666,10 +633,12 @@ private:
     }
 
     const llama_vocab & vocab;
-    const llm_tokenizer_bpe * bpe_tokenizer;
+
+    std::vector<std::string> regex_exprs;
 
     std::vector<llm_symbol> symbols;
     std::vector<llm_symbol> symbols_final;
+
     llm_bigram_bpe::queue work_queue;
 };
 
@@ -677,17 +646,15 @@ private:
 // WPM tokenizer
 //
 
-struct llm_tokenizer_wpm : llm_tokenizer {
-    llm_tokenizer_wpm(const llama_vocab & /*vocab*/) : llm_tokenizer() {}
-};
+struct llm_tokenizer_wpm {
+    llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {}
 
-struct llm_tokenizer_wpm_session {
-    llm_tokenizer_wpm_session(const llama_vocab & vocab) : vocab(vocab) {}
-
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) const {
         const auto & token_map = vocab.token_to_id;
+
         // normalize and split by whitespace
         std::vector<std::string> words = preprocess(text);
+
         // bos token prepended already
 
         // find the longest tokens that form the words
@@ -732,7 +699,7 @@ struct llm_tokenizer_wpm_session {
     }
 
     // TODO: reduce string copies by using cpts_offs array
-    static std::vector<std::string> preprocess(const std::string & text)  {
+    std::vector<std::string> preprocess(const std::string & text) const {
         const std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
         std::vector<std::string> words(1, "");
 
@@ -784,18 +751,15 @@ struct llm_tokenizer_wpm_session {
             //(cpt >= 0xFF00  && cpt <= 0xFFEF);
     }
 
-private:
     const llama_vocab & vocab;
-    // currently unused
-    // const llm_tokenizer_wpm * wpm_tokenizer;
 };
 
 //
 // UGM tokenizer
 //
 
-struct llm_tokenizer_ugm : llm_tokenizer {
-    llm_tokenizer_ugm(const llama_vocab & vocab) : llm_tokenizer() {
+struct llm_tokenizer_ugm {
+    llm_tokenizer_ugm(const llama_vocab & vocab) : vocab(vocab) {
         if (vocab.precompiled_charsmap.size() > 0) {
             size_t charsmap_offset = 0;
 
@@ -841,30 +805,6 @@ struct llm_tokenizer_ugm : llm_tokenizer {
         unknown_token_score = min_score - unknown_token_score_penalty;
     }
 
-    // escaped space symbol - U+2581 (Lower One Eighth Block)
-    const std::string escaped_space = "\xE2\x96\x81";
-
-    const char * prefix_replacements = NULL;
-    size_t prefix_replacements_size = 0;
-
-    const uint32_t * xcda_array = NULL;
-    size_t xcda_array_size = 0;
-
-    struct naive_trie user_defined_token_matcher;
-
-    float min_score = FLT_MAX;
-    float max_score = -FLT_MAX;
-
-    float unknown_token_score_penalty = 10.0;
-    float unknown_token_score;
-
-    struct naive_trie token_matcher;
-};
-
-struct llm_tokenizer_ugm_session {
-    llm_tokenizer_ugm_session(const llama_vocab & vocab) : vocab(vocab),
-        ugm_tokenizer(static_cast<const llm_tokenizer_ugm *>(vocab.tokenizer)) {}
-
     /* This implementation is based on SentencePiece optimized Viterbi algorithm for
      * unigram language models. The general idea is to:
      * - move along the input sequence in steps of one UTF code point,
@@ -903,7 +843,7 @@ struct llm_tokenizer_ugm_session {
             // traverse the token matcher trie to find a matching token
             bool single_codepoint_token_found = false;
             const struct best_tokenization & current_best = tokenization_results[input_offset];
-            const struct naive_trie * node = ugm_tokenizer->token_matcher.traverse(normalized[prefix_offset++]);
+            const struct naive_trie * node = token_matcher.traverse(normalized[prefix_offset++]);
 
             while (prefix_offset <= input_len && node != NULL) {
                 // check if we found valid token in prefix
@@ -933,7 +873,7 @@ struct llm_tokenizer_ugm_session {
             // if we didn't find a valid token corresponding to the whole UTF code point
             // then use unknown token as the tokenization of this UTF code point
             if (!single_codepoint_token_found) {
-                const double challenger_score = current_best.score_sum + ugm_tokenizer->unknown_token_score;
+                const double challenger_score = current_best.score_sum + unknown_token_score;
                 prefix_offset = input_offset + n_utf8_code_units;
                 struct best_tokenization & current_champ = tokenization_results[prefix_offset];
                 if (challenger_score > current_champ.score_sum) {
@@ -965,6 +905,7 @@ struct llm_tokenizer_ugm_session {
     }
 
 private:
+    const llama_vocab & vocab;
 
     // helper structure for returning normalization results
     struct normalization_result {
@@ -977,7 +918,7 @@ private:
         normalized->clear();
         normalized->reserve(input.size() * 3);
 
-        const std::string space = vocab.tokenizer_escape_whitespaces ? ugm_tokenizer->escaped_space : " ";
+        const std::string space = vocab.tokenizer_escape_whitespaces ? escaped_space : " ";
 
         bool shall_prepend_space = !vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
         bool shall_append_space = vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
@@ -1059,21 +1000,13 @@ private:
         size_t xcda_array_size;
     };
 
-    // this structure stores the best tokenization so far at input_offset
-    struct best_tokenization {
-        llama_token token_id;
-        size_t input_offset;
-        float score_sum;
-    };
-
     struct normalization_result normalize_prefix(const std::string & input, size_t input_offset) {
         if (input_offset == input.size()) {
             return { &input[input_offset], 0, 0 };
         }
 
         // if input prefix matches some user-defined token return this token as normalization result
-        auto user_defined_token_match =
-           ugm_tokenizer->user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset);
+        auto user_defined_token_match = user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset);
         if (user_defined_token_match.second > 0) {
             return { &input[input_offset], user_defined_token_match.second, user_defined_token_match.second };
         }
@@ -1081,8 +1014,8 @@ private:
         size_t longest_prefix_length = 0;
         size_t longest_prefix_offset = 0;
 
-        if (ugm_tokenizer->xcda_array_size > 0) {
-            struct xcda_array_view xcda_view(ugm_tokenizer->xcda_array, ugm_tokenizer->xcda_array_size);
+        if (xcda_array_size > 0) {
+            struct xcda_array_view xcda_view(xcda_array, xcda_array_size);
 
             // Find the longest normalized sequence matching the input prefix by walking
             // the XOR-compressed compact double array (XCDA) starting from the root node
@@ -1118,27 +1051,50 @@ private:
 
         if (longest_prefix_length > 0) {
             // we have a match, so return the replacement sequence
-            if (longest_prefix_offset >= ugm_tokenizer->prefix_replacements_size) {
+            if (longest_prefix_offset >= prefix_replacements_size) {
                 throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
             }
-            const char * prefix_replacement = &(ugm_tokenizer->prefix_replacements)[longest_prefix_offset];
+            const char * prefix_replacement = &prefix_replacements[longest_prefix_offset];
             return { prefix_replacement, strlen(prefix_replacement), longest_prefix_length };
-        }
-
-        // check if the input prefix contains a valid sequence of UTF-8 code units
-        try {
-            // if yes, return this sequence unmodified
-            size_t prefix_offset = input_offset;
-            unicode_cpt_from_utf8(input, prefix_offset);
-            return { &input[input_offset], prefix_offset - input_offset, prefix_offset - input_offset };
-        } catch (std::invalid_argument & /*ex*/) {
-            // if no, consume 1 byte and return U+FFFD - REPLACEMENT CHARACTER
-            return { "\xEF\xBF\xBD", 3, 1 };
+        } else {
+            // check if the input prefix contains a valid sequence of UTF-8 code units
+            try {
+                // if yes, return this sequence unmodified
+                size_t prefix_offset = input_offset;
+                unicode_cpt_from_utf8(input, prefix_offset);
+                return { &input[input_offset], prefix_offset - input_offset, prefix_offset - input_offset };
+            } catch (std::invalid_argument & /*ex*/) {
+                // if no, consume 1 byte and return U+FFFD - REPLACEMENT CHARACTER
+                return { "\xEF\xBF\xBD", 3, 1 };
+            }
         }
     }
 
-    const llama_vocab & vocab;
-    const llm_tokenizer_ugm * ugm_tokenizer;
+    // escaped space symbol - U+2581 (Lower One Eighth Block)
+    const std::string escaped_space = "\xE2\x96\x81";
+
+    const char * prefix_replacements = NULL;
+    size_t prefix_replacements_size = 0;
+
+    const uint32_t * xcda_array = NULL;
+    size_t xcda_array_size = 0;
+
+    struct naive_trie user_defined_token_matcher;
+
+    // this structure stores the best tokenization so far at input_offset
+    struct best_tokenization {
+        llama_token token_id;
+        size_t input_offset;
+        float score_sum;
+    };
+
+    float min_score = FLT_MAX;
+    float max_score = -FLT_MAX;
+
+    float unknown_token_score_penalty = 10.0;
+    float unknown_token_score;
+
+    struct naive_trie token_matcher;
 };
 
 //
@@ -1199,8 +1155,8 @@ static std::vector<uint8_t> llama_unescape_rwkv_token(const std::string & escape
     return output;
 }
 
-struct llm_tokenizer_rwkv : llm_tokenizer {
-    llm_tokenizer_rwkv(const llama_vocab & vocab) : llm_tokenizer() {
+struct llm_tokenizer_rwkv {
+    llm_tokenizer_rwkv(const llama_vocab & vocab): vocab(vocab) {
         // RWKV supports arbitrary byte tokens, but the vocab struct only supports string tokens.
         // For now, we decode the vocab here into the lookup we'll use for tokenization.
 
@@ -1212,17 +1168,11 @@ struct llm_tokenizer_rwkv : llm_tokenizer {
         }
     }
 
-    struct naive_trie token_matcher;
-};
-
-struct llm_tokenizer_rwkv_session {
-    llm_tokenizer_rwkv_session(const llama_vocab & vocab) : vocab(vocab),
-        rwkv_tokenizer(static_cast<const llm_tokenizer_rwkv &>(*vocab.tokenizer)) {}
-
     void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
         uint32_t position = 0;
+
         while (position < text.size()) {
-            const struct naive_trie * node = rwkv_tokenizer.token_matcher.traverse(text[position]);
+            const struct naive_trie * node = token_matcher.traverse(text[position]);
             if (node == NULL) {
                 // no matching token found, add unknown token
                 output.push_back(vocab.special_unk_id);
@@ -1247,32 +1197,10 @@ struct llm_tokenizer_rwkv_session {
         }
     }
 
-private:
     const llama_vocab & vocab;
-    const llm_tokenizer_rwkv & rwkv_tokenizer;
-};
 
-void llama_vocab::init_tokenizer() {
-    switch (type) {
-        case LLAMA_VOCAB_TYPE_SPM:
-            tokenizer = new llm_tokenizer_spm(*this);
-            break;
-        case LLAMA_VOCAB_TYPE_BPE:
-            tokenizer = new llm_tokenizer_bpe(*this);
-            break;
-        case LLAMA_VOCAB_TYPE_WPM:
-            tokenizer = new llm_tokenizer_wpm(*this);
-            break;
-        case LLAMA_VOCAB_TYPE_UGM:
-            tokenizer = new llm_tokenizer_ugm(*this);
-            break;
-        case LLAMA_VOCAB_TYPE_RWKV:
-            tokenizer = new llm_tokenizer_rwkv(*this);
-            break;
-        default:
-            GGML_ABORT("unsupported vocab type");
-    }
-}
+    struct naive_trie token_matcher;
+};
 
 //
 // (de-) tokenize
@@ -1335,7 +1263,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
 
             // if a fragment is text ( not yet processed )
             if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                const auto & raw_text = fragment.raw_text;
+                auto & raw_text = fragment.raw_text;
 
                 auto raw_text_base_offset = fragment.offset;
                 auto raw_text_base_length = fragment.length;
@@ -1434,13 +1362,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
     }
 }
 
-std::vector<llama_vocab::id> llama_tokenize_internal(
-        const llama_vocab & vocab,
-        std::string raw_text,
-        bool add_special,
-        bool parse_special) {
-    GGML_ASSERT(vocab.tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
-
+std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special) {
     std::vector<llama_vocab::id> output;
     std::forward_list<fragment_buffer_variant> fragment_buffer;
 
@@ -1477,9 +1399,9 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
 #ifdef PRETOKENIZERDEBUG
                         LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
 #endif
+                        llm_tokenizer_spm tokenizer(vocab);
                         llama_escape_whitespace(raw_text);
-                        llm_tokenizer_spm_session session(vocab);
-                        session.tokenize(raw_text, output);
+                        tokenizer.tokenize(raw_text, output);
                         is_prev_special = false;
                     } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                         output.push_back(fragment.token);
@@ -1501,11 +1423,10 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
             } break;
         case LLAMA_VOCAB_TYPE_BPE:
             {
-                llm_tokenizer_bpe_session session(vocab);
-                // it calls some other methods that are not exist in llm_tokenizer,
-                // here just cast it to bpe tokenizer object
+                llm_tokenizer_bpe tokenizer(vocab);
+
                 if (add_special) {
-                    session.append_bos(output);
+                    tokenizer.append_bos(output);
                 }
                 for (const auto & fragment : fragment_buffer) {
                     if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
@@ -1514,15 +1435,15 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
 #ifdef PRETOKENIZERDEBUG
                         LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
 #endif
-                        session.tokenize(raw_text, output);
+                        tokenizer.tokenize(raw_text, output);
                     } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        session.append(fragment.token, output);
+                        tokenizer.append(fragment.token, output);
                     }
                 }
 
                 if (add_special) {
-                    session.append_eos(output);
-                    session.check_double_bos_eos(output);
+                    tokenizer.append_eos(output);
+                    tokenizer.check_double_bos_eos(output);
                 }
             } break;
         case LLAMA_VOCAB_TYPE_WPM:
@@ -1532,7 +1453,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
                     output.push_back(vocab.special_cls_id);
                 }
 
-                llm_tokenizer_wpm_session session(vocab);
+                llm_tokenizer_wpm tokenizer(vocab);
 
                 for (const auto & fragment : fragment_buffer) {
                     if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
@@ -1541,7 +1462,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
 #ifdef PRETOKENIZERDEBUG
                         LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
 #endif
-                        session.tokenize(raw_text, output);
+                        tokenizer.tokenize(raw_text, output);
                     } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                         output.push_back(fragment.token);
                     }
@@ -1554,11 +1475,12 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
             } break;
         case LLAMA_VOCAB_TYPE_UGM:
             {
-                if (add_special && vocab.tokenizer_add_bos) {
+                llm_tokenizer_ugm tokenizer(vocab);
+
+                if (add_special && vocab.tokenizer_add_bos != 0) {
                     GGML_ASSERT(vocab.special_bos_id != -1);
                     output.push_back(vocab.special_bos_id);
                 }
-                llm_tokenizer_ugm_session session(vocab);
 
                 for (const auto & fragment : fragment_buffer) {
                     if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
@@ -1566,27 +1488,26 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
 #ifdef PRETOKENIZERDEBUG
                         LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
 #endif
-                        session.tokenize(raw_text, output);
+                        tokenizer.tokenize(raw_text, output);
                     } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                         output.push_back(fragment.token);
                     }
                 }
 
-                if (add_special && vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.special_bos_id) {
+                if (add_special && vocab.tokenizer_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) {
                     LLAMA_LOG_WARN(
                         "%s: Added a BOS token to the prompt as specified by the model but the prompt "
                         "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
                         "Are you sure this is what you want?\n", __FUNCTION__);
                 }
 
-                if (add_special && vocab.tokenizer_add_eos) {
+                if (add_special && vocab.tokenizer_add_eos == 1) {
                     GGML_ASSERT(vocab.special_eos_id != -1);
                     output.push_back(vocab.special_eos_id);
                 }
             } break;
         case LLAMA_VOCAB_TYPE_RWKV:
             {
-                llm_tokenizer_rwkv_session session(vocab);
                 for (const auto & fragment : fragment_buffer) {
                     if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                         auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
@@ -1595,7 +1516,8 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
                         LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
 #endif
 
-                        session.tokenize(raw_text, output);
+                        llm_tokenizer_rwkv tokenizer(vocab);
+                        tokenizer.tokenize(raw_text, output);
                     } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                         output.push_back(fragment.token);
                     }
@@ -1648,7 +1570,11 @@ llama_token_attr llama_token_get_attr_impl(const struct llama_vocab & vocab, lla
 }
 
 bool llama_token_is_eog_impl(const struct llama_vocab & vocab, llama_token token) {
-    return token != -1 && vocab.special_eog_ids.count(token) > 0;
+    return token != -1 && (
+        token == llama_token_eos_impl(vocab) ||
+        token == llama_token_eot_impl(vocab) ||
+        token == llama_token_eom_impl(vocab)
+    );
 }
 
 bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token token) {
@@ -1708,13 +1634,13 @@ llama_token llama_token_eom_impl(const struct llama_vocab & vocab) {
 }
 
 int32_t llama_tokenize_impl(
-        const struct llama_vocab & vocab,
-                      const char * text,
-                         int32_t   text_len,
-                     llama_token * tokens,
-                         int32_t   n_tokens_max,
-                            bool   add_special,
-                            bool   parse_special) {
+    const struct llama_vocab & vocab,
+                  const char * text,
+                     int32_t   text_len,
+                 llama_token * tokens,
+                     int32_t   n_tokens_max,
+                        bool   add_special,
+                        bool   parse_special) {
     auto res = llama_tokenize_internal(vocab, std::string(text, text_len), add_special, parse_special);
     if (n_tokens_max < (int) res.size()) {
         // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
@@ -1791,13 +1717,11 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token
                 // suppressing them like CONTROL tokens.
                 if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) {
                     return _try_copy(token_text.data(), token_text.size());
-                }
-                if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
+                } else if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
                     std::string result = token_text;
                     llama_unescape_whitespace(result);
                     return _try_copy(result.data(), result.size());
-                }
-                if (attr & LLAMA_TOKEN_ATTR_BYTE) {
+                } else if (attr & LLAMA_TOKEN_ATTR_BYTE) {
                     char byte = (char) llama_token_to_byte(vocab, token);
                     return _try_copy((char*) &byte, 1);
                 }
@@ -1808,8 +1732,7 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token
                 // suppressing them like CONTROL tokens.
                 if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) {
                     return _try_copy(token_text.data(), token_text.size());
-                }
-                if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
+                } else if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
                     std::string result = llama_decode_text(token_text);
                     return _try_copy(result.data(), result.size());
                 }
@@ -1842,8 +1765,6 @@ int32_t llama_detokenize_impl(
                          int32_t   text_len_max,
                             bool   remove_special,
                             bool   unparse_special) {
-    GGML_ASSERT(vocab.tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
-
     int32_t avail = text_len_max;
     int32_t total = 0;
 

src/llama-vocab.h

@@ -6,9 +6,6 @@
 #include <vector>
 #include <unordered_map>
 #include <map>
-#include <set>
-
-struct llm_tokenizer;
 
 struct llama_vocab {
     using id    = llama_token;
@@ -52,29 +49,19 @@ struct llama_vocab {
     id special_eot_id    = -1; // TODO: move above after "eos_id", and here add "file separator" token
     id special_eom_id    = -1;
 
-    // set of all tokens that cause "end of generation"
-    std::set<id> special_eog_ids;
-
     // tokenizer flags
-    bool tokenizer_add_space_prefix           = false;
-    bool tokenizer_add_bos                    = false;
-    bool tokenizer_add_eos                    = false;
-    bool tokenizer_ignore_merges              = false;
-    bool tokenizer_clean_spaces               = false;  // clean_up_tokenization_spaces
+    bool tokenizer_add_space_prefix = false;
+    bool tokenizer_add_bos          = false;
+    bool tokenizer_add_eos          = false;
+    bool tokenizer_ignore_merges    = false;
+    bool tokenizer_clean_spaces     = false;  // clean_up_tokenization_spaces
     bool tokenizer_remove_extra_whitespaces   = false;
     bool tokenizer_escape_whitespaces         = true;
     bool tokenizer_treat_whitespace_as_suffix = false;
 
     std::vector<char> precompiled_charsmap;
 
-    llm_tokenizer * tokenizer = nullptr;
-
-    llama_vocab() = default;
-    ~llama_vocab();
-
     int find_bpe_rank(const std::string & token_left, const std::string & token_right) const;
-
-    void init_tokenizer();
 };
 
 //

src/llama.cpp

@@ -215,8 +215,6 @@ enum llm_arch {
     LLM_ARCH_EXAONE,
     LLM_ARCH_RWKV6,
     LLM_ARCH_GRANITE,
-    LLM_ARCH_GRANITE_MOE,
-    LLM_ARCH_CHAMELEON,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -268,8 +266,6 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_EXAONE,          "exaone"       },
     { LLM_ARCH_RWKV6,           "rwkv6"        },
     { LLM_ARCH_GRANITE,         "granite"      },
-    { LLM_ARCH_GRANITE_MOE,     "granitemoe"   },
-    { LLM_ARCH_CHAMELEON,       "chameleon"    },
     { LLM_ARCH_UNKNOWN,         "(unknown)"    },
 };
 
@@ -306,7 +302,6 @@ enum llm_kv {
     LLM_KV_DECODER_START_TOKEN_ID,
     LLM_KV_ATTN_LOGIT_SOFTCAPPING,
     LLM_KV_FINAL_LOGIT_SOFTCAPPING,
-    LLM_KV_SWIN_NORM,
     LLM_KV_RESCALE_EVERY_N_LAYERS,
     LLM_KV_TIME_MIX_EXTRA_DIM,
     LLM_KV_TIME_DECAY_EXTRA_DIM,
@@ -414,7 +409,6 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_DECODER_START_TOKEN_ID,            "%s.decoder_start_token_id"            },
     { LLM_KV_ATTN_LOGIT_SOFTCAPPING,            "%s.attn_logit_softcapping"            },
     { LLM_KV_FINAL_LOGIT_SOFTCAPPING,           "%s.final_logit_softcapping"           },
-    { LLM_KV_SWIN_NORM,                         "%s.swin_norm"                         },
     { LLM_KV_RESCALE_EVERY_N_LAYERS,            "%s.rescale_every_n_layers"            },
     { LLM_KV_TIME_MIX_EXTRA_DIM,                "%s.time_mix_extra_dim"                },
     { LLM_KV_TIME_DECAY_EXTRA_DIM,              "%s.time_decay_extra_dim"              },
@@ -606,8 +600,6 @@ enum llm_tensor {
     LLM_TENSOR_ENC_FFN_DOWN,
     LLM_TENSOR_ENC_FFN_UP,
     LLM_TENSOR_ENC_OUTPUT_NORM,
-    LLM_TENSOR_CLS,
-    LLM_TENSOR_CLS_OUT,
 };
 
 static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@@ -795,8 +787,6 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
             { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_CLS,             "cls" },
-            { LLM_TENSOR_CLS_OUT,         "cls.output" },
         },
     },
     {
@@ -832,7 +822,6 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
             { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_CLS,             "cls" },
         },
     },
     {
@@ -1478,7 +1467,6 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
         {
             { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
             { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
             { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
             { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
             { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
@@ -1490,43 +1478,6 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
-    {
-        LLM_ARCH_GRANITE_MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_CHAMELEON,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-        },
-    },
     {
         LLM_ARCH_UNKNOWN,
         {
@@ -2390,7 +2341,6 @@ struct llama_hparams {
     bool vocab_only;
     bool rope_finetuned;
     bool use_par_res;
-    bool swin_norm;
 
     uint32_t n_vocab;
     uint32_t n_ctx_train; // context size the model was trained on
@@ -2446,7 +2396,7 @@ struct llama_hparams {
     float f_max_alibi_bias = 0.0f;
     float f_logit_scale    = 0.0f;
 
-    // Additional scale factors (Granite/Granite MoE)
+    // Additional scale factors (Granite)
     float f_residual_scale  = 0.0f;
     float f_embedding_scale = 0.0f;
     float f_attention_scale = 0.0f;
@@ -2899,7 +2849,6 @@ struct llama_model {
     llama_hparams hparams = {};
     llama_vocab   vocab;
 
-    // TODO: should init all tensors to nullptr
     struct ggml_tensor * tok_embd;
     struct ggml_tensor * type_embd;
     struct ggml_tensor * pos_embd;
@@ -2912,12 +2861,6 @@ struct llama_model {
     struct ggml_tensor * output_b;
     struct ggml_tensor * output_norm_enc;
 
-    // classifier
-    struct ggml_tensor * cls;
-    struct ggml_tensor * cls_b;
-    struct ggml_tensor * cls_out   = nullptr;
-    struct ggml_tensor * cls_out_b = nullptr;
-
     std::vector<llama_layer> layers;
 
     llama_split_mode split_mode;
@@ -5616,11 +5559,11 @@ static void llm_load_hparams(
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
                 ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
                 ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
-                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
+                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type);
                 hparams.f_max_alibi_bias = 8.0f;
 
                 switch (hparams.n_layer) {
-                    case 4:  model.type = e_model::MODEL_33M;  break; // jina-embeddings-small
+                    case 4: model.type = e_model::MODEL_33M; break; // jina-embeddings-small
                     case 12: model.type = e_model::MODEL_137M; break; // jina-embeddings-base
                 }
             } break;
@@ -6105,7 +6048,6 @@ static void llm_load_hparams(
                 }
             } break;
         case LLM_ARCH_GRANITE:
-        case LLM_ARCH_GRANITE_MOE:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
@@ -6114,24 +6056,11 @@ static void llm_load_hparams(
                 ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale);
 
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_3B; break;
                     case 40: model.type = e_model::MODEL_3B; break;
                     // Add additional layer/vocab/etc checks here for other model sizes
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
-        case LLM_ARCH_CHAMELEON:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                hparams.f_norm_eps = 1e-5;  // eps for qk-norm, torch default
-                ml.get_key(LLM_KV_SWIN_NORM, hparams.swin_norm);
-
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 48: model.type = e_model::MODEL_34B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-               }
-            } break;
         default: (void)0;
     }
 
@@ -6325,7 +6254,6 @@ static void llm_load_vocab(
                     tokenizer_pre == "phi-2"   ||
                     tokenizer_pre == "jina-es" ||
                     tokenizer_pre == "jina-de" ||
-                    tokenizer_pre == "jina-v1-en" ||
                     tokenizer_pre == "jina-v2-es" ||
                     tokenizer_pre == "jina-v2-de" ||
                     tokenizer_pre == "jina-v2-code") {
@@ -6390,11 +6318,6 @@ static void llm_load_vocab(
             } else if (
                 tokenizer_pre == "exaone") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_EXAONE;
-            } else if (
-                tokenizer_pre == "chameleon") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHAMELEON;
-                vocab.tokenizer_add_bos = true;
-                vocab.tokenizer_clean_spaces = false;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
@@ -6452,12 +6375,7 @@ static void llm_load_vocab(
 
     for (uint32_t i = 0; i < n_vocab; i++) {
         std::string word = gguf_get_arr_str(ctx, token_idx, i);
-
-        //GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
-        if (word.empty()) {
-            LLAMA_LOG_WARN("%s: empty token at index %u\n", __func__, i);
-            word = "[EMPTY_" + std::to_string(i) + "]";
-        }
+        GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
 
         vocab.token_to_id[word] = i;
         vocab.max_token_len = std::max(vocab.max_token_len, (int) word.size());
@@ -6482,8 +6400,6 @@ static void llm_load_vocab(
     }
     GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());
 
-    vocab.init_tokenizer();
-
     // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
     if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
         // For Fill-In-the-Middle (FIM)/infill models which where converted
@@ -6538,14 +6454,8 @@ static void llm_load_vocab(
         vocab.linefeed_id = ids[0];
     } else {
         const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A
-
-        //GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
-        if (ids.empty()) {
-            LLAMA_LOG_WARN("%s: model vocab missing newline token, using special_pad_id instead\n", __func__);
-            vocab.linefeed_id = vocab.special_pad_id;
-        } else {
-            vocab.linefeed_id = ids[0];
-        }
+        GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
+        vocab.linefeed_id = ids[0];
     }
 
     // special tokens
@@ -6599,21 +6509,21 @@ static void llm_load_vocab(
         //       for now, we apply this workaround to find the EOT token based on its text
         if (vocab.special_eot_id == -1) {
             for (const auto & t : vocab.token_to_id) {
-                if (false
+                if (
                         // TODO: gemma "<end_of_turn>" is exported as a normal token, so the following check does not work
                         //       need to fix convert script
                         //vocab.id_to_token[t.second].type == LLAMA_TOKEN_TYPE_CONTROL &&
-                        || t.first == "<|eot_id|>"
-                        || t.first == "<|im_end|>"
-                        || t.first == "<|end|>"
-                        || t.first == "<end_of_turn>"
-                        || t.first == "<|endoftext|>"
-                        || t.first == "<EOT>"
+                        (t.first == "<|eot_id|>" ||
+                         t.first == "<|im_end|>" ||
+                         t.first == "<|end|>" ||
+                         t.first == "<end_of_turn>" ||
+                         t.first == "<|endoftext|>"
+                        )
                    ) {
                     vocab.special_eot_id = t.second;
                     if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
                         LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.first.c_str());
+                            __func__, t.first.c_str());
                         vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
                     }
                     break;
@@ -6636,44 +6546,6 @@ static void llm_load_vocab(
                 }
             }
         }
-
-        // maintain a list of tokens that cause end-of-generation
-        // this is currently determined based on the token text, which is obviously not ideal
-        // ref: https://github.com/ggerganov/llama.cpp/issues/9606
-        vocab.special_eog_ids.clear();
-        for (const auto & t : vocab.token_to_id) {
-            if (false
-                    || t.first == "<|eot_id|>"
-                    || t.first == "<|im_end|>"
-                    || t.first == "<|end|>"
-                    || t.first == "<end_of_turn>"
-                    || t.first == "<|endoftext|>"
-                    || t.first == "<|eom_id|>"
-                    || t.first == "<EOT>"
-               ) {
-                vocab.special_eog_ids.insert(t.second);
-                if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                    LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                            __func__, t.first.c_str());
-                    vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                }
-            }
-        }
-
-        if (vocab.special_eos_id != -1 && vocab.special_eog_ids.count(vocab.special_eos_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_eos_id);
-            LLAMA_LOG_WARN("%s: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
-        }
-
-        if (vocab.special_eot_id != -1 && vocab.special_eog_ids.count(vocab.special_eot_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_eot_id);
-            LLAMA_LOG_WARN("%s: special_eot_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
-        }
-
-        if (vocab.special_eom_id != -1 && vocab.special_eog_ids.count(vocab.special_eom_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_eom_id);
-            LLAMA_LOG_WARN("%s: special_eom_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
-        }
     }
 
     // build special tokens cache
@@ -6877,11 +6749,6 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token        = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); }
     if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token        = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
     if (vocab.special_eot_id    != -1) { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,    vocab.id_to_token[vocab.special_eot_id].text.c_str() );    }
-    if (vocab.special_eom_id    != -1) { LLAMA_LOG_INFO( "%s: EOM token        = %d '%s'\n", __func__, vocab.special_eom_id,    vocab.id_to_token[vocab.special_eom_id].text.c_str() );    }
-
-    for (const auto & id : vocab.special_eog_ids) {
-        LLAMA_LOG_INFO( "%s: EOG token        = %d '%s'\n", __func__, id, vocab.id_to_token[id].text.c_str() );
-    }
 
     LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, vocab.max_token_len);
 
@@ -6900,7 +6767,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
         LLAMA_LOG_INFO("%s: n_ff_shexp       = %d\n",     __func__, hparams.n_ff_shexp);
     }
 
-    if (model.arch == LLM_ARCH_GRANITE || model.arch == LLM_ARCH_GRANITE_MOE) {
+    if (model.arch == LLM_ARCH_GRANITE) {
         LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
         LLAMA_LOG_INFO("%s: f_residual_scale  = %f\n", __func__, hparams.f_residual_scale);
         LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
@@ -7074,7 +6941,6 @@ static bool llm_load_tensors(
             case LLM_ARCH_REFACT:
             case LLM_ARCH_MINICPM:
             case LLM_ARCH_GRANITE:
-            case LLM_ARCH_GRANITE_MOE:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
@@ -7418,12 +7284,6 @@ static bool llm_load_tensors(
 
                     if (model.arch == LLM_ARCH_BERT) {
                         model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, n_ctx_train});
-
-                        model.cls   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        model.cls_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        model.cls_out   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        model.cls_out_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "bias"),   {1},         llama_model_loader::TENSOR_NOT_REQUIRED);
                     }
 
                     model.tok_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
@@ -7476,8 +7336,6 @@ static bool llm_load_tensors(
                     model.tok_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm
                     model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}); //LayerNorm bias
 
-                    model.cls   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    model.cls_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "bias"),   {1},         llama_model_loader::TENSOR_NOT_REQUIRED);
                     for (int i = 0; i < n_layer; ++i) {
                         ggml_context * ctx_layer = ctx_for_layer(i);
                         ggml_context * ctx_split = ctx_for_layer_split(i);
@@ -8803,45 +8661,6 @@ static bool llm_load_tensors(
                     }
 
                 } break;
-            case LLM_ARCH_CHAMELEON:
-                {
-                 model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-
-                 // output
-                    {
-                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        // if output is NULL, init from the input tok embed
-                        if (model.output == NULL) {
-                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                        }
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        ggml_context * ctx_layer = ctx_for_layer(i);
-                        ggml_context * ctx_split = ctx_for_layer_split(i);
-
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
-                        layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head});
-                        layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv});
-                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i),  {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i),  {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
-                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
-                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
-                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
-
-                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
-
-                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
-                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
-                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
-                    }
-                } break;
             default:
                 throw std::runtime_error("unknown architecture");
         }
@@ -10111,36 +9930,17 @@ struct llm_build_context {
             const int64_t n_head_kv = hparams.n_head_kv(il);
             const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
             struct ggml_tensor * rope_factors = build_rope_factors(il);
-            struct ggml_tensor * k =
-                ggml_view_3d(ctx0, kv_self.k_l[il],
-                    n_embd_head_k, n_head_kv, n_ctx,
-                    ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
-                    ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
-                    0);
-
-            struct ggml_tensor * tmp;
-            if (ggml_is_quantized(k->type)) {
-                // dequantize to f32 -> RoPE -> quantize back
-                tmp = ggml_cast(ctx0, k, GGML_TYPE_F32);
-                cb(tmp, "K_f32", il);
-                for (auto * backend : lctx.backends) {
-                    // Figure out which backend KV cache belongs to
-                    if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft)) {
-                        ggml_backend_sched_set_tensor_backend(lctx.sched, tmp, backend);
-                        break;
-                    }
-                }
-                tmp = ggml_rope_ext_inplace(ctx0, tmp,
-                        lctx.inp_K_shift, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                        ext_factor, attn_factor, beta_fast, beta_slow);
-                cb(tmp, "K_shifted_f32", il);
-                tmp = ggml_cpy(ctx0, tmp, k);
-            } else {
+            struct ggml_tensor * tmp =
                 // we rotate only the first n_rot dimensions
-                tmp = ggml_rope_ext_inplace(ctx0, k,
+                ggml_rope_ext_inplace(ctx0,
+                        ggml_view_3d(ctx0, kv_self.k_l[il],
+                            n_embd_head_k, n_head_kv, n_ctx,
+                            ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
+                            ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+                            0),
                         lctx.inp_K_shift, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
-            }
+
             cb(tmp, "K_shifted", il);
             ggml_build_forward_expand(gf, tmp);
         }
@@ -10311,10 +10111,6 @@ struct llm_build_context {
         struct ggml_tensor * cur;
 
         switch (pooling_type) {
-            case LLAMA_POOLING_TYPE_NONE:
-                {
-                    cur = inp;
-                } break;
             case LLAMA_POOLING_TYPE_MEAN:
                 {
                     struct ggml_tensor * inp_mean = build_inp_mean();
@@ -10326,26 +10122,9 @@ struct llm_build_context {
                     struct ggml_tensor * inp_cls = build_inp_cls();
                     cur = ggml_get_rows(ctx0, inp, inp_cls);
                 } break;
-            case LLAMA_POOLING_TYPE_RANK:
+            case LLAMA_POOLING_TYPE_NONE:
                 {
-                    struct ggml_tensor * inp_cls = build_inp_cls();
-                    inp = ggml_get_rows(ctx0, inp, inp_cls);
-
-                    // classification head
-                    // https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
-                    GGML_ASSERT(model.cls       != nullptr);
-                    GGML_ASSERT(model.cls_b     != nullptr);
-
-                    cur = ggml_add (ctx0, ggml_mul_mat(ctx0, model.cls, inp), model.cls_b);
-                    cur = ggml_tanh(ctx0, cur);
-
-                    // some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
-                    // https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896
-                    if (model.cls_out) {
-                        GGML_ASSERT(model.cls_out_b != nullptr);
-
-                        cur = ggml_add (ctx0, ggml_mul_mat(ctx0, model.cls_out, cur), model.cls_out_b);
-                    }
+                    cur = inp;
                 } break;
             default:
                 {
@@ -11574,8 +11353,8 @@ struct llm_build_context {
             inpL = cur;
         }
 
+        // final output
         cur = inpL;
-
         cb(cur, "result_embd", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -16007,184 +15786,6 @@ struct llm_build_context {
 
         return gf;
     }
-
-    // ref: https://github.com/facebookresearch/chameleon
-    // based on the original build_llama() function, changes:
-    //   * qk-norm
-    //   * swin-norm
-    //   * removed bias
-    //   * removed MoE
-    struct ggml_cgraph * build_chameleon() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
-
-        // mutable variable, needed during the last layer of the computation to skip unused tokens
-        int32_t n_tokens = this->n_tokens;
-
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        struct ggml_tensor * cur;
-        struct ggml_tensor * inpL;
-
-        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
-
-        // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = build_inp_pos();
-
-        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
-
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * inpSA = inpL;
-
-            // norm
-            if (hparams.swin_norm) {
-                cur = inpL;
-            } else {
-                cur = llm_build_norm(ctx0, inpL, hparams,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, cb, il);
-                cb(cur, "attn_norm", il);
-            }
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
-                cb(Vcur, "Vcur", il);
-
-                if (model.layers[il].attn_q_norm) {
-                    Qcur = ggml_view_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens,
-                                ggml_element_size(Qcur) * n_embd_head,
-                                ggml_element_size(Qcur) * n_embd_head * n_head,
-                                0);
-                    cb(Qcur, "Qcur", il);
-
-                    Qcur = llm_build_norm(ctx0, Qcur, hparams,
-                                model.layers[il].attn_q_norm,
-                                model.layers[il].attn_q_norm_b,
-                                LLM_NORM, cb, il);
-                    cb(Qcur, "Qcur", il);
-                }
-
-                if (model.layers[il].attn_k_norm) {
-                    Kcur = ggml_view_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens,
-                                ggml_element_size(Kcur) * n_embd_head,
-                                ggml_element_size(Kcur) * n_embd_head * n_head_kv,
-                                0);
-                    cb(Kcur, "Kcur", il);
-
-                    Kcur = llm_build_norm(ctx0, Kcur, hparams,
-                               model.layers[il].attn_k_norm,
-                               model.layers[il].attn_k_norm_b,
-                               LLM_NORM, cb, il);
-                    cb(Kcur, "Kcur", il);
-                }
-
-                Qcur = ggml_rope_ext(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(Qcur, "Qcur", il);
-
-                Kcur = ggml_rope_ext(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(Kcur, "Kcur", il);
-
-                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
-                        model.layers[il].wo, nullptr,
-                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-
-                if (hparams.swin_norm) {
-                    cur = llm_build_norm(ctx0, cur, hparams,
-                        model.layers[il].attn_norm, NULL,
-                        LLM_NORM_RMS, cb, il);
-                }
-            }
-
-            if (il == n_layer - 1) {
-                // skip computing output for unused tokens
-                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-                n_tokens = n_outputs;
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            if (!hparams.swin_norm) {
-                cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, cb, il);
-                cb(cur, "ffn_norm", il);
-            }
-
-            cur = llm_build_ffn(ctx0, lctx, cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
-            cb(cur, "ffn_out", il);
-
-            if (hparams.swin_norm) {
-                cur = llm_build_norm(ctx0, cur, hparams,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, cb, il);
-                cb(cur, "ffn_norm", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = lctx.cvec.apply_to(ctx0, cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = llm_build_norm(ctx0, cur, hparams,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, cb, -1);
-        cb(cur, "result_norm", -1);
-
-        // lm_head
-        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
-        cb(cur, "result_output_with_img_logits", -1);
-
-        // TODO: this suppresses the output of image tokens, which is required to enable text-only outputs.
-        // Needs to be removed once image outputs are supported.
-        int img_token_end_idx = 8196;
-        int img_token_start_idx = 4;
-        int num_img_tokens = img_token_end_idx - img_token_start_idx;
-        // creates 1d tensor of size num_img_tokens and values -FLT_MAX,
-        // which ensures that text token values are always at least larger than image token values
-        struct ggml_tensor * img_logits = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, num_img_tokens);
-        img_logits = ggml_clamp(ctx0, img_logits, -FLT_MAX, -FLT_MAX);
-        cb(img_logits, "img_logits", -1);
-        cur = ggml_set_1d(ctx0, cur, img_logits, ggml_element_size(cur) * img_token_start_idx);
-        cb(cur, "result_output", -1);
-
-        ggml_build_forward_expand(gf, cur);
-
-        return gf;
-    }
 };
 
 static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
@@ -16267,7 +15868,6 @@ static struct ggml_cgraph * llama_build_graph(
     switch (model.arch) {
         case LLM_ARCH_LLAMA:
         case LLM_ARCH_GRANITE:
-        case LLM_ARCH_GRANITE_MOE:
             {
                 result = llm.build_llama();
             } break;
@@ -16445,10 +16045,6 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_rwkv6();
             } break;
-        case LLM_ARCH_CHAMELEON:
-            {
-                result = llm.build_chameleon();
-            } break;
         default:
             GGML_ABORT("fatal error");
     }
@@ -16735,9 +16331,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
         }
     }
 
-    if (cparams.embeddings && (
-                cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
-                cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) {
+    if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
         const int64_t n_tokens     = batch.n_tokens;
         const int64_t n_seq_tokens = batch.n_seq_tokens;
         const int64_t n_seqs       = batch.n_seqs;
@@ -16752,7 +16346,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
             const llama_seq_id seq_id = batch.seq_id[s][0];
 
             // TODO: adapt limits to n_seqs when batch.equal_seqs is true
-            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK");
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
 
             for (int i = 0; i < n_seq_tokens; ++i) {
                 const llama_pos pos = batch.pos[s*n_seq_tokens + i];
@@ -17292,20 +16886,6 @@ static int llama_decode_internal(
                             ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
                         }
                     } break;
-                case LLAMA_POOLING_TYPE_RANK:
-                    {
-                        // extract the rerank score - a single float per sequence
-                        auto & embd_seq_out = lctx.embd_seq;
-
-                        for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
-                            const llama_seq_id seq_id = ubatch.seq_id[s][0];
-                            if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
-                                continue;
-                            }
-                            embd_seq_out[seq_id].resize(1);
-                            ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (seq_id)*sizeof(float), sizeof(float));
-                        }
-                    } break;
                 case LLAMA_POOLING_TYPE_UNSPECIFIED:
                     {
                         GGML_ABORT("unknown pooling type");
@@ -17512,13 +17092,6 @@ static int llama_encode_internal(
                             ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
                         }
                     } break;
-                case LLAMA_POOLING_TYPE_RANK:
-                    {
-                        // TODO: this likely should be the same logic as in llama_decoder_internal, but better to
-                        //       wait for an encoder model that requires this pooling type in order to test it
-                        //       https://github.com/ggerganov/llama.cpp/pull/9510
-                        GGML_ABORT("RANK pooling not implemented yet");
-                    }
                 case LLAMA_POOLING_TYPE_UNSPECIFIED:
                     {
                         GGML_ABORT("unknown pooling type");
@@ -19079,9 +18652,9 @@ struct llama_model * llama_load_model_from_file(
             unsigned percentage = (unsigned) (100 * progress);
             while (percentage > *cur_percentage_p) {
                 *cur_percentage_p = percentage;
-                LLAMA_LOG_CONT(".");
+                LLAMA_LOG(".");
                 if (percentage >= 100) {
-                    LLAMA_LOG_CONT("\n");
+                    LLAMA_LOG("\n");
                 }
             }
             return true;
@@ -19596,8 +19169,6 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_DEEPSEEK2:
         case LLM_ARCH_CHATGLM:
         case LLM_ARCH_GRANITE:
-        case LLM_ARCH_GRANITE_MOE:
-        case LLM_ARCH_CHAMELEON:
             return LLAMA_ROPE_TYPE_NORM;
 
         // the pairs of head values are offset by n_rot/2

tests/test-backend-ops.cpp

@@ -32,52 +32,63 @@
 #include <stdlib.h>
 #include <string>
 #include <thread>
-#include <future>
 #include <vector>
 
 static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) {
-    size_t nels = ggml_nelements(tensor);
-    std::vector<float> data(nels);
-    {
-        // parallel initialization
-        static const size_t n_threads = std::thread::hardware_concurrency();
-        // static RNG initialization (revisit if n_threads stops being constant)
-        static std::vector<std::default_random_engine> generators = []() {
-            std::random_device rd;
-            std::vector<std::default_random_engine> vec;
-            vec.reserve(n_threads);
-            //for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(1234 + i); } // fixed seed
-            for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(rd()); }
-            return vec;
-        }();
+    // static RNG initialization (revisit if n_threads stops being constant)
+    static const size_t n_threads = std::thread::hardware_concurrency();
+    static std::vector<std::default_random_engine> generators = []() {
+        std::random_device rd;
+        std::vector<std::default_random_engine> vec;
+        vec.reserve(n_threads);
+        //for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(1234 + i); } // fixed seed
+        for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(rd()); }
+        return vec;
+    }();
 
-        auto init_thread = [&](size_t ith, size_t start, size_t end) {
-            std::uniform_real_distribution<float> distribution(min, max);
-            auto & gen = generators[ith];
-            for (size_t i = start; i < end; i++) {
-                data[i] = distribution(gen);
-            }
-        };
+    size_t size = ggml_nelements(tensor);
+    std::vector<float> data(size);
 
-        std::vector<std::future<void>> tasks;
-        tasks.reserve(n_threads);
-        for (size_t i = 0; i < n_threads; i++) {
-            size_t start =     i*nels/n_threads;
-            size_t end   = (i+1)*nels/n_threads;
-            tasks.push_back(std::async(std::launch::async, init_thread, i, start, end));
-        }
-        for (auto & t : tasks) {
-            t.get();
+    auto init_thread = [&](size_t ith, size_t start, size_t end) {
+        std::uniform_real_distribution<float> distribution(min, max);
+        for (size_t i = start; i < end; i++) {
+            data[i] = distribution(generators[ith]);
         }
+    };
+
+    std::vector<std::thread> threads;
+    threads.reserve(n_threads);
+    for (size_t i = 0; i < n_threads; i++) {
+        size_t start =     i*size/n_threads;
+        size_t end   = (i+1)*size/n_threads;
+        threads.emplace_back(init_thread, i, start, end);
+    }
+    for (auto & t : threads) {
+        t.join();
     }
 
-    if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
-        ggml_backend_tensor_set(tensor, data.data(), 0, nels * sizeof(float));
-    } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16 || tensor->type == GGML_TYPE_BF16) {
-        GGML_ASSERT(nels % ggml_blck_size(tensor->type) == 0);
+#if 0
+    const char * val_str = getenv("GGML_TEST_EPS");
+    float val = 1e-9f;
+    if (val_str != nullptr) {
+        val = std::stof(val_str);
+        printf("GGML_TEST_EPS=%e\n", val);
+    }
 
-         // dummy importance matrix
-        std::vector<float> imatrix(tensor->ne[0], 1.0f);
+    // test quantization with very small values that may result in nan scales due to division by zero
+    if (ggml_is_quantized(tensor->type)) {
+        for (int i = 0; i < 256; i++) {
+            data[i] = val;
+        }
+    }
+#endif
+
+    if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
+        ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float));
+    } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16 || tensor->type == GGML_TYPE_BF16) {
+        GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0);
+        std::vector<uint8_t> dataq(ggml_row_size(tensor->type, size));
+        std::vector<float> imatrix(tensor->ne[0], 1.0f); // dummy importance matrix
         const float * im = imatrix.data();
         if (!ggml_quantize_requires_imatrix(tensor->type)) {
             // when the imatrix is optional, we want to test both quantization with and without imatrix
@@ -87,31 +98,15 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
             }
         }
 
-        std::vector<uint8_t> dataq(ggml_row_size(tensor->type, nels));
-        {
-            // parallel quantization by block
-            size_t blck_size = ggml_blck_size(tensor->type);
-            size_t n_blocks = nels / blck_size;
+        ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im);
+        GGML_ASSERT(ggml_validate_row_data(tensor->type, dataq.data(), dataq.size()));
+        // TODO: other cases
+        //#pragma omp parallel for
+        //for (int i = 0; i < tensor->ne[1]; i++) {
+        //    ggml_quantize_chunk(tensor->type, data.data(), dataq.data(),
+        //        i * tensor->ne[0], 1, tensor->ne[0], im);
+        //}
 
-            auto quantize_thread = [&](size_t start, size_t end) {
-                ggml_quantize_chunk(tensor->type, data.data(), dataq.data(),
-                    start * blck_size, end - start, blck_size, im);
-            };
-
-            const size_t min_blocks_per_thread = 1;
-            const size_t n_threads = std::min<size_t>(std::thread::hardware_concurrency()/2,
-                                                      std::max<size_t>(1, n_blocks / min_blocks_per_thread));
-            std::vector<std::future<void>> tasks;
-            tasks.reserve(n_threads);
-            for (size_t i = 0; i < n_threads; i++) {
-                size_t start =     i*n_blocks/n_threads;
-                size_t end   = (i+1)*n_blocks/n_threads;
-                tasks.push_back(std::async(std::launch::async, quantize_thread, start, end));
-            }
-            for (auto & t : tasks) {
-                t.get();
-            }
-        }
         ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
     } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
         // This is going to create some weird integers though.
@@ -165,6 +160,60 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) {
     return tv;
 }
 
+/*
+static double cosine_similarity(const float * v1, const float * v2, size_t n) {
+    double dot = 0.0;
+    double mag1 = 0.0;
+    double mag2 = 0.0;
+
+    for (size_t i = 0; i < n; i++) {
+        if (std::isnan(v1[i]) || std::isnan(v2[i])) {
+            return -1.0f;
+        }
+        if (std::isinf(v1[i]) && std::isinf(v2[i])) {
+            continue;
+        }
+        dot  += v1[i]*v2[i];
+        mag1 += v1[i]*v1[i];
+        mag2 += v2[i]*v2[i];
+    }
+
+    return dot/sqrt(mag1*mag2);
+}
+
+static float distance(const float * v1, const float * v2, size_t n) {
+    double d = 0.0;
+
+    for (size_t i = 0; i < n; i++) {
+        if (std::isnan(v1[i]) || std::isnan(v2[i])) {
+            return INFINITY;
+        }
+        if (std::isinf(v1[i]) && std::isinf(v2[i])) {
+            continue;
+        }
+        d += (v1[i] - v2[i])*(v1[i] - v2[i]);
+    }
+
+    return sqrt(d);
+}
+
+static float vec_len(const float * v, size_t n) {
+    double d = 0.0;
+
+    for (size_t i = 0; i < n; i++) {
+        if (std::isnan(v[i])) {
+            return INFINITY;
+        }
+        if (std::isinf(v[i])) {
+            continue;
+        }
+        d += v[i]*v[i];
+    }
+
+    return sqrt(d);
+}
+*/
+
 // normalized mean squared error = mse(a, b) / mse(a, 0)
 static double nmse(const float * a, const float * b, size_t n) {
     double mse_a_b = 0.0;
@@ -215,6 +264,7 @@ static double mean_abs_asymm(const float * a, const float * b, const size_t n, c
 }
 
 // utils for printing the variables of the test cases
+#define VAR_TO_STR(x) (#x "=" + var_to_str(x))
 
 template<typename T>
 static std::string var_to_str(const T & x) {
@@ -247,6 +297,10 @@ static std::string var_to_str(const std::array<T, N> & x) {
     return s;
 }
 
+//static std::string var_to_str(ggml_unary_op unary_op) {
+//    return ggml_unary_op_name(unary_op);
+//}
+
 static std::string var_to_str(ggml_type type) {
     return ggml_type_name(type);
 }
@@ -259,8 +313,6 @@ static std::string var_to_str(ggml_op_pool pool) {
     }
 }
 
-#define VAR_TO_STR(x) (#x "=" + var_to_str(x))
-
 #define VARS_TO_STR1(a) VAR_TO_STR(a)
 #define VARS_TO_STR2(a, b) VAR_TO_STR(a) + "," + VAR_TO_STR(b)
 #define VARS_TO_STR3(a, b, c) VAR_TO_STR(a) + "," + VARS_TO_STR2(b, c)
@@ -318,13 +370,13 @@ struct test_case {
         return 1e-4;
     }
 
-    virtual float grad_eps() {
+    virtual float grad_eps(){
         return 1e-1f;
     }
 
     // If false, estimate gradient with 2 points, neglects 3rd order derivative and higher.
     // If true,  estimate gradient with 4 points, neglects 5th order derivative and higher.
-    virtual bool grad_precise() {
+    virtual bool grad_precise(){
         return false;
     }
 
@@ -357,11 +409,6 @@ struct test_case {
         return size;
     }
 
-    virtual uint64_t op_flops(ggml_tensor * t) {
-        GGML_UNUSED(t);
-        return 0;
-    }
-
     ggml_cgraph * gf = nullptr;
     ggml_cgraph * gb = nullptr;
 
@@ -604,11 +651,12 @@ struct test_case {
         }
 
         // align while also leaving some margin for variations in parameters
-        int align = 8;
+        int align = 20;
         int last = (len + align - 1) / align * align;
         if (last - len < 5) {
             last += align;
         }
+        last = std::max(last, 60);
         printf("%*s", last - len, "");
 
         // allocate
@@ -629,25 +677,9 @@ struct test_case {
         // warmup run
         ggml_backend_graph_compute(backend, gf);
 
-        // determine number of runs
-        int n_runs;
-        if (op_flops(out) > 0) {
-            // based on flops
-            const uint64_t GFLOP = 1000 * 1000 * 1000;
-            const uint64_t target_flops_cpu =   8ULL * GFLOP;
-            const uint64_t target_flops_gpu = 100ULL * GFLOP;
-            uint64_t target_flops = ggml_backend_is_cpu(backend) ? target_flops_cpu : target_flops_gpu;
-            n_runs = std::min<int>(ggml_graph_size(gf) - ggml_graph_n_nodes(gf), target_flops / op_flops(out)) + 1;
-        } else {
-            // based on memory size
-            const size_t GB = 1ULL << 30;
-            const size_t target_size_cpu =  8 * GB;
-            const size_t target_size_gpu = 32 * GB;
-            size_t target_size = ggml_backend_is_cpu(backend) ? target_size_cpu : target_size_gpu;
-            n_runs = std::min<int>(ggml_graph_size(gf) - ggml_graph_n_nodes(gf), target_size / op_size(out)) + 1;
-        }
-
         // 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) ggml_graph_size(gf) - ggml_graph_n_nodes(gf), target_size / op_size(out)) + 1;
         for (int i = 1; i < n_runs; i++) {
             ggml_graph_add_node(gf, out);
         }
@@ -674,47 +706,17 @@ struct test_case {
         // run
         ggml_backend_synchronize(backend);
 
-        int64_t total_time_us = 0;
-        int total_runs = 0;
-        do {
-            int64_t start_time = ggml_time_us();
-            ggml_backend_graph_compute(backend, gf);
-            ggml_backend_synchronize(backend);
-            int64_t end_time = ggml_time_us();
+        int64_t start_time = ggml_time_us();
+        ggml_backend_graph_compute(backend, gf);
+        ggml_backend_synchronize(backend);
+        int64_t end_time = ggml_time_us();
+        double time_us = end_time - start_time;
 
-            total_time_us += end_time - start_time;
-            total_runs += n_runs;
-        } while (total_time_us < 1000*1000); // run for at least 1 second
-
-        printf("    %8d runs - %8.2f us/run - ",
-            total_runs,
-            (double)total_time_us / total_runs);
-
-        if (op_flops(out) > 0) {
-            double flops_per_sec = (op_flops(out) * total_runs) / (total_time_us / 1e6);
-            auto format_flops = [](double flops) -> std::string {
-                char buf[256];
-                if (flops >= 1e12) {
-                    snprintf(buf, sizeof(buf), "%6.2f TFLOP", flops / 1e12);
-                } else if (flops >= 1e9) {
-                    snprintf(buf, sizeof(buf), "%6.2f GFLOP", flops / 1e9);
-                } else if (flops >= 1e6) {
-                    snprintf(buf, sizeof(buf), "%6.2f MFLOP", flops / 1e6);
-                } else {
-                    snprintf(buf, sizeof(buf), "%6.2f KFLOP", flops / 1e3);
-                }
-                return buf;
-            };
-            printf("%s/run - \033[1;34m%sS\033[0m",
-                format_flops(op_flops(out)).c_str(),
-                format_flops(flops_per_sec).c_str());
-
-        } else {
-            printf("%8zu kB/run - \033[1;34m%7.2f GB/s\033[0m",
-                op_size(out) / 1024,
-                mem / (total_time_us / 1e6) / 1024.0 / 1024.0 / 1024.0);
-        }
-        printf("\n");
+        printf("    %5d runs - %8.2f us/run - %8zu kB/run - \033[1;34m%7.2f GB/s\033[0m\n",
+            n_runs,
+            time_us / n_runs,
+            op_size(out) / 1024,
+            mem / (time_us/1e6) / 1024.0 / 1024.0 / 1024.0);
 
         ggml_backend_buffer_free(buf);
 
@@ -1589,9 +1591,13 @@ struct test_mul_mat : public test_case {
         return 5e-4;
     }
 
-    uint64_t op_flops(ggml_tensor * t) override {
+    size_t op_size(ggml_tensor * t) override {
+        size_t a = ggml_nbytes(t->src[0]) * n * nr[0] * nr[1];
+        size_t b = ggml_nbytes(t->src[1]) * m;
+        size_t c  = ggml_nbytes(t);
+        return a + b + c;
+
         GGML_UNUSED(t);
-        return 2 * m * n * k * bs[0] * nr[0] * bs[1] * nr[1];
     }
 
     test_mul_mat(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
@@ -1635,9 +1641,13 @@ struct test_mul_mat_id : public test_case {
         return 5e-4;
     }
 
-    uint64_t op_flops(ggml_tensor * t) override {
+    size_t op_size(ggml_tensor * t) override {
+        size_t a = ggml_nbytes(t->src[2]) * n;
+        size_t b = ggml_nbytes(t->src[1]) * m;
+        size_t c  = ggml_nbytes(t);
+        return a + b + c;
+
         GGML_UNUSED(t);
-        return 2 * m * k * n * n_used;
     }
 
     test_mul_mat_id(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
@@ -3153,46 +3163,47 @@ struct test_falcon : public test_llm {
 // ###########################################
 // ## Section 3: GGML Op Test Instantiation ##
 // ###########################################
-static const ggml_type all_types[] = {
-    GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16,
-    GGML_TYPE_Q4_0, GGML_TYPE_Q4_1,
-    GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
-    GGML_TYPE_Q8_0,
-    GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
-    GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
-    GGML_TYPE_Q6_K,
-    // GGML_TYPE_TQ1_0, GGML_TYPE_TQ2_0, // TODO: implement for all backends
-    GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
-    GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
-    GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
-};
 
-static const ggml_type base_types[] = {
-    GGML_TYPE_F32, GGML_TYPE_F16,
-    GGML_TYPE_Q4_0,
-    GGML_TYPE_Q4_K,
-    GGML_TYPE_IQ2_XXS
-};
 
-static const ggml_type other_types[] = {
-    GGML_TYPE_Q4_1,
-    GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
-    GGML_TYPE_Q8_0,
-    GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
-    GGML_TYPE_Q5_K,
-    GGML_TYPE_Q6_K,
-    // GGML_TYPE_TQ1_0, GGML_TYPE_TQ2_0, // TODO: implement for all backends
-    GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
-    GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
-    GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
-    GGML_TYPE_BF16,
-};
-
-// Test cases for evaluation: should try to cover edge cases while using small input sizes to keep the runtime low
-static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
+static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op_name) {
     std::vector<std::unique_ptr<test_case>> test_cases;
     std::default_random_engine rng(0);
 
+    const ggml_type all_types[] = {
+        GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16,
+        GGML_TYPE_Q4_0, GGML_TYPE_Q4_1,
+        GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
+        GGML_TYPE_Q8_0,
+        GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
+        GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
+        GGML_TYPE_Q6_K,
+        // GGML_TYPE_TQ1_0, GGML_TYPE_TQ2_0, // TODO: implement for all backends
+        GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
+        GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
+        GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
+    };
+
+    const ggml_type base_types[] = {
+        GGML_TYPE_F32, GGML_TYPE_F16,
+        GGML_TYPE_Q4_0,
+        GGML_TYPE_Q4_K,
+        GGML_TYPE_IQ2_XXS
+    };
+
+    const ggml_type other_types[] = {
+        GGML_TYPE_Q4_1,
+        GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
+        GGML_TYPE_Q8_0,
+        GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
+        GGML_TYPE_Q5_K,
+        GGML_TYPE_Q6_K,
+        // GGML_TYPE_TQ1_0, GGML_TYPE_TQ2_0, // TODO: implement for all backends
+        GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
+        GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
+        GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
+        GGML_TYPE_BF16,
+    };
+
     // unary ops
     for (int v : {0, 1}) {
         for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
@@ -3381,14 +3392,6 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
             test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 2}));
         }
     }
-    for (ggml_type type_a : other_types) {
-        for (ggml_type type_b : {GGML_TYPE_F32}) {
-            if (ggml_blck_size(type_a) != 256) {
-                test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, ggml_blck_size(type_a), {1,  1}, {1, 1}));
-            }
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {1,  1}, {1, 1}));
-        }
-    }
 #else
     // m = a rows
     // n = b rows
@@ -3408,6 +3411,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     }
 #endif
 
+    for (ggml_type type_a : other_types) {
+        for (ggml_type type_b : {GGML_TYPE_F32}) {
+            if (ggml_blck_size(type_a) != 256) {
+                test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, ggml_blck_size(type_a), {1,  1}, {1, 1}));
+            }
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {1,  1}, {1, 1}));
+        }
+    }
+
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  64, 2,  128, { 8,  1}, {1, 1}));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  83, 2,  128, { 8,  1}, {4, 1}));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  64, 2,   64, { 8,  1}, {4, 1}));
@@ -3612,30 +3624,20 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_falcon(2));
 #endif
 
-    return test_cases;
-}
-
-// Test cases for performance evaluation: should be representative of real-world use cases
-static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
-    std::vector<std::unique_ptr<test_case>> test_cases;
-
-    test_cases.emplace_back(new test_bin_bcast(ggml_add, GGML_TYPE_F32, {4096, 1, 1, 1}, {1,   1, 1, 1}));
-    test_cases.emplace_back(new test_bin_bcast(ggml_add, GGML_TYPE_F32, {4096, 1, 1, 1}, {1, 512, 1, 1}));
-
-    for (int bs : {1, 512}) {
-        for (ggml_type type_a : all_types) {
-            for (ggml_type type_b : {GGML_TYPE_F32}) {
-                test_cases.emplace_back(new test_mul_mat(type_a, type_b, 4096, bs, 14336, {1,  1}, {1, 1}));
+    // run tests
+    if (mode == MODE_GRAD) {
+        size_t n_ok = 0;
+        for (auto & test : test_cases) {
+            if (test->eval_grad(backend, op_name)) {
+                n_ok++;
             }
         }
+        printf("  %zu/%zu tests passed\n", n_ok, test_cases.size());
+
+        return n_ok == test_cases.size();
     }
 
-    return test_cases;
-}
-
-static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op_name) {
     if (mode == MODE_TEST) {
-        auto test_cases = make_test_cases_eval();
         ggml_backend_t backend_cpu = ggml_backend_cpu_init();
 
         size_t n_ok = 0;
@@ -3651,21 +3653,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         return n_ok == test_cases.size();
     }
 
-    if (mode == MODE_GRAD) {
-        auto test_cases = make_test_cases_eval();
-        size_t n_ok = 0;
-        for (auto & test : test_cases) {
-            if (test->eval_grad(backend, op_name)) {
-                n_ok++;
-            }
-        }
-        printf("  %zu/%zu tests passed\n", n_ok, test_cases.size());
-
-        return n_ok == test_cases.size();
-    }
-
     if (mode == MODE_PERF) {
-        auto test_cases = make_test_cases_perf();
         for (auto & test : test_cases) {
             test->eval_perf(backend, op_name);
         }
@@ -3679,9 +3667,9 @@ static void usage(char ** argv) {
     printf("Usage: %s [mode] [-o op] [-b backend]\n", argv[0]);
     printf("    valid modes:\n");
     printf("      - test (default, compare with CPU backend for correctness)\n");
-    printf("      - grad (compare gradients from backpropagation with method of finite differences)\n");
     printf("      - perf (performance evaluation)\n");
-    printf("    op names for -o are as given by ggml_op_desc() (e.g. ADD, MUL_MAT, etc)\n");
+    printf("      - grad (compare gradients from backpropagation with method of finite differences)\n");
+    printf("    op names are as given by ggml_op_desc() (e.g. GGML_ADD)\n");
 }
 
 int main(int argc, char ** argv) {
@@ -3740,11 +3728,6 @@ int main(int argc, char ** argv) {
             continue;
         }
 
-        if (ggml_backend_is_cpu(backend)) {
-            // TODO: better value for n_threads
-            ggml_backend_cpu_set_n_threads(backend, std::thread::hardware_concurrency() / 2);
-        }
-
         printf("  Backend name: %s\n", ggml_backend_name(backend));
 
         bool ok = test_backend(backend, mode, op_name_filter);

tests/test-sampling.cpp

@@ -1,5 +1,6 @@
 #include "ggml.h"
 #include "llama.h"
+#include "llama-sampling.h"
 
 #ifdef NDEBUG
 #undef NDEBUG
@@ -248,45 +249,6 @@ static void test_sampler_queue(const size_t n_vocab, const std::string & sampler
            samplers_sequence.c_str(), n_vocab, top_k, top_p, min_p);
 }
 
-static void bench(llama_sampler * cnstr, const char * cnstr_name, const std::vector<llama_token_data> & data, int n_iter) {
-    std::vector<llama_token_data> cur(data.size());
-    std::copy(data.begin(), data.end(), cur.begin());
-    llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
-    llama_sampler_apply(cnstr, &cur_p);
-    llama_sampler_reset(cnstr);
-    const int64_t t_start = ggml_time_us();
-    for (int i = 0; i < n_iter; i++) {
-        std::copy(data.begin(), data.end(), cur.begin());
-        llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
-        llama_sampler_apply(cnstr, &cur_p);
-        llama_sampler_reset(cnstr);
-    }
-    const int64_t t_end = ggml_time_us();
-    llama_sampler_free(cnstr);
-    printf("%-42s: %8.3f us/iter\n", cnstr_name, (t_end - t_start) / (float)n_iter);
-}
-
-#define BENCH(__cnstr, __data, __n_iter) bench((__cnstr), #__cnstr, (__data), (__n_iter))
-
-static void test_perf() {
-    const int n_vocab = 1 << 17;
-
-    std::vector<llama_token_data> data;
-
-    data.reserve(n_vocab);
-    for (int i = 0; i < n_vocab; i++) {
-        const float logit = 2.0f*((float)(rand())/RAND_MAX - 0.5f);
-        data.emplace_back(llama_token_data{i, logit, 0.0f});
-    }
-
-    BENCH(llama_sampler_init_top_k    (40),      data, 32);
-    BENCH(llama_sampler_init_top_p    (0.8f, 1), data, 32);
-    BENCH(llama_sampler_init_min_p    (0.2f, 1), data, 32);
-    BENCH(llama_sampler_init_tail_free(0.5f, 1), data, 32);
-    BENCH(llama_sampler_init_typical  (0.5f, 1), data, 32);
-    BENCH(llama_sampler_init_softmax  (),        data, 32);
-}
-
 int main(void) {
     ggml_time_init();
 
@@ -309,9 +271,9 @@ int main(void) {
     test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f},                                  0.76f);
     test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f},                                  1.00f);
 
-    test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f);
-    test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f);
-    test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f);
+    test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f},               0.25f);
+    test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f},        0.50f);
+    test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f, 0.20f}, 0.80f);
 
     test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
     test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
@@ -354,7 +316,5 @@ int main(void) {
 
     printf("OK\n");
 
-    test_perf();
-
     return 0;
 }

tests/test-tokenizer-0.cpp

@@ -7,7 +7,6 @@
 #include <map>
 #include <vector>
 #include <fstream>
-#include <thread>
 
 //static const std::map<std::string, std::vector<llama_token>> & k_tests() {
 //    static std::map<std::string, std::vector<llama_token>> _k_tests = {
@@ -195,64 +194,45 @@ int main(int argc, char **argv) {
 
     const bool add_special = false;
 
-    // multi-threaded tokenization
-    const int nthread = std::thread::hardware_concurrency();
-    std::vector<std::thread> threads(nthread);
+    for (const auto & test_kv : k_tests) {
+        const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, false);
 
-    for (int i = 0; i < nthread; i++) {
-        threads[i] = std::thread([&, i]() {
-            for (const auto & test_kv : k_tests) {
-                const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, false);
+        printf("\n");
+        printf("src: '%s'\n", test_kv.first.c_str());
+        printf("res: '%s'\n", llama_detokenize(ctx, res).c_str());
+        printf("tok: ");
+        for (const auto & tok : res) {
+            printf("%d ", tok);
+        }
+        printf("\n");
 
-                // here only print the result of the first thread
-                // because the other threads are running the same tests
-                if (i != 0) {
-                    continue;
-                }
-
-                printf("\n");
-                printf("src: '%s'\n", test_kv.first.c_str());
-                printf("res: '%s'\n", llama_detokenize(ctx, res).c_str());
-                printf("tok: ");
-                for (const auto & tok : res) {
-                    printf("%d ", tok);
-                }
-                printf("\n");
-
-                bool correct = res.size() == test_kv.second.size();
-                for (int i = 0; i < (int) res.size() && correct; ++i) {
-                    if (test_kv.second[i] != res[i]) {
-                        correct = false;
-                    }
-                }
-
-                if (!correct) {
-                    fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
-                    fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
-                        llama_detokenize(ctx, res).c_str(),
-                        llama_detokenize(ctx, test_kv.second).c_str());
-                    fprintf(stderr, "%s : expected tokens: ", __func__);
-                    for (const auto & t : test_kv.second) {
-                        fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str());
-                    }
-                    fprintf(stderr, "\n");
-                    fprintf(stderr, "%s : got tokens:      ", __func__);
-                    for (const auto & t : res) {
-                        fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str());
-                    }
-                    fprintf(stderr, "\n");
-
-                    success = false;
-                }
+        bool correct = res.size() == test_kv.second.size();
+        for (int i = 0; i < (int) res.size() && correct; ++i) {
+            if (test_kv.second[i] != res[i]) {
+                correct = false;
             }
-        });
+        }
+
+        if (!correct) {
+            fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
+            fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
+                llama_detokenize(ctx, res).c_str(),
+                llama_detokenize(ctx, test_kv.second).c_str());
+            fprintf(stderr, "%s : expected tokens: ", __func__);
+            for (const auto & t : test_kv.second) {
+                fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str());
+            }
+            fprintf(stderr, "\n");
+            fprintf(stderr, "%s : got tokens:      ", __func__);
+            for (const auto & t : res) {
+                fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str());
+            }
+            fprintf(stderr, "\n");
+
+            success = false;
+        }
     }
 
-    for (int i = 0; i < nthread; i++) {
-        threads[i].join();
-    }
-
-    // single threaded tokenization
     if (!fname_text.empty()) {
         fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());