vbatts/llama.cpp
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Merge branch 'master' into gg/rerank

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Georgi Gerganov 2024-09-28 15:15:29 +03:00 • committed by GitHub
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2
.dockerignore
View file

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

1
.github/workflows/build.yml vendored
View file

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

58
.github/workflows/docker.yml vendored
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@ -15,11 +15,17 @@ on:
branches: branches:
- master - master
paths: ['.github/workflows/docker.yml', '.devops/*.Dockerfile', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal'] 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: concurrency:
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
cancel-in-progress: true 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: jobs:
push_to_registry: push_to_registry:
name: Push Docker image to Docker Hub name: Push Docker image to Docker Hub
@ -46,6 +52,8 @@ jobs:
steps: steps:
- name: Check out the repo - name: Check out the repo
uses: actions/checkout@v4 uses: actions/checkout@v4
with:
fetch-depth: 0 # preserve git history, so we can determine the build number
- name: Set up QEMU - name: Set up QEMU
uses: docker/setup-qemu-action@v2 uses: docker/setup-qemu-action@v2
@ -60,6 +68,34 @@ jobs:
username: ${{ github.repository_owner }} username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }} 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 # https://github.com/jlumbroso/free-disk-space/tree/54081f138730dfa15788a46383842cd2f914a1be#example
- name: Free Disk Space (Ubuntu) - name: Free Disk Space (Ubuntu)
uses: jlumbroso/free-disk-space@main uses: jlumbroso/free-disk-space@main
@ -77,25 +113,6 @@ jobs:
docker-images: true docker-images: true
swap-storage: 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) - name: Build and push Docker image (tagged + versioned)
if: github.event_name == 'push' if: github.event_name == 'push'
uses: docker/build-push-action@v6 uses: docker/build-push-action@v6
@ -103,5 +120,6 @@ jobs:
context: . context: .
push: true push: true
platforms: ${{ matrix.config.platforms }} platforms: ${{ matrix.config.platforms }}
tags: "ghcr.io/${{ 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 }}" # tag list is generated from step above
tags: ${{ steps.tag.outputs.output_tags }}
file: ${{ matrix.config.dockerfile }} file: ${{ matrix.config.dockerfile }}

11
CMakeLists.txt
View file

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

4
README.md
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@ -17,7 +17,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
## Hot topics ## Hot topics
- Huggingface GGUF editor: [discussion](https://github.com/ggerganov/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor) - **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)
---- ----
@ -173,6 +174,7 @@ Unless otherwise noted these projects are open-source with permissive licensing:
**Tools:** **Tools:**
- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML - [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 - [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 - [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) - [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)

44
convert_hf_to_gguf.py
View file

@ -648,6 +648,9 @@ class Model:
if chkhsh == "fcace8b9cac38ce847670c970cd5892031a753a1ef381abd1d9af00f713da085": if chkhsh == "fcace8b9cac38ce847670c970cd5892031a753a1ef381abd1d9af00f713da085":
# ref: https://huggingface.co/microsoft/phi-2 # ref: https://huggingface.co/microsoft/phi-2
res = "phi-2" res = "phi-2"
if chkhsh == "60824e3c0d9401f89943cbb2fff727f0e2d4c545ba4df2d6e4f09a6db0f5b450":
# ref: https://huggingface.co/facebook/chameleon-7b
res = "chameleon"
if res is None: if res is None:
logger.warning("\n") logger.warning("\n")
@ -4159,6 +4162,47 @@ class GraniteMoeModel(GraniteModel):
return super().modify_tensors(data_torch, name, bid) return super().modify_tensors(data_torch, name, bid)
@Model.register("ChameleonForCausalLM")
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
###### CONVERSION LOGIC ###### ###### CONVERSION LOGIC ######

1
convert_hf_to_gguf_update.py
View file

@ -100,6 +100,7 @@ models = [
{'name': "gpt3-finnish", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", }, {'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": "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": "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", },
] ]

14
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View file

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

3
ggml/include/ggml.h
View file

@ -2507,6 +2507,9 @@ extern "C" {
GGML_API int ggml_cpu_has_cann (void); GGML_API int ggml_cpu_has_cann (void);
GGML_API int ggml_cpu_has_llamafile (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 // Internal types and functions exposed for tests and benchmarks
// //

13
ggml/src/ggml-aarch64.c
View file

@ -598,15 +598,6 @@ size_t quantize_q4_0_8x8(const float * restrict src, void * restrict dst, int64_
return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 8, 8); return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 8, 8);
} }
// Return the number of byte lanes in the SVE vector if SVE is supported; otherwise, returns 0 if SVE is not supported.
static int sve_lane_count(void) {
#if defined(__ARM_FEATURE_SVE)
return ggml_sve_cnt_b;
#else
return 0;
#endif
}
void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
const int qk = QK8_0; const int qk = QK8_0;
const int nb = n / qk; const int nb = n / qk;
@ -843,7 +834,7 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)
#if defined(__ARM_FEATURE_SVE) #if defined(__ARM_FEATURE_SVE)
if (ggml_cpu_has_sve() && sve_lane_count() == QK8_0) { if (ggml_cpu_has_sve() && ggml_cpu_get_sve_cnt() == QK8_0) {
const void * b_ptr = vx; const void * b_ptr = vx;
const void * a_ptr = vy; const void * a_ptr = vy;
float * res_ptr = s; float * res_ptr = s;
@ -2020,7 +2011,7 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)
#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) #if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
if (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && sve_lane_count() == QK8_0) { if (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0) {
const void * b_ptr = vx; const void * b_ptr = vx;
const void * a_ptr = vy; const void * a_ptr = vy;
float * res_ptr = s; float * res_ptr = s;

12
ggml/src/ggml-cuda.cu
View file

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

4
ggml/src/ggml-quants.c
View file

@ -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 sumv0 = svdup_n_f32(0.0f);
svfloat32_t sumv1 = svdup_n_f32(0.0f); svfloat32_t sumv1 = svdup_n_f32(0.0f);
const int vector_length = ggml_sve_cnt_b*8; const int vector_length = ggml_cpu_get_sve_cnt()*8;
// VLA Implementation using switch case // VLA Implementation using switch case
switch (vector_length) { 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 sumv0 = svdup_n_f32(0.0f);
svfloat32_t sumv1 = svdup_n_f32(0.0f); svfloat32_t sumv1 = svdup_n_f32(0.0f);
const int vector_length = ggml_sve_cnt_b*8; const int vector_length = ggml_cpu_get_sve_cnt()*8;
//VLA Implemenation for SVE //VLA Implemenation for SVE
switch (vector_length) { switch (vector_length) {

4
ggml/src/ggml-quants.h
View file

@ -142,10 +142,6 @@ void iq2xs_free_impl(enum ggml_type type);
void iq3xs_init_impl(int grid_size); void iq3xs_init_impl(int grid_size);
void iq3xs_free_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 #ifdef __cplusplus
} }
#endif #endif

9
ggml/src/ggml-vulkan.cpp
View file

@ -1079,7 +1079,8 @@ static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) {
// Fall back to host memory type // Fall back to host memory type
buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
} else { } else {
buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal); // 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);
} }
} catch (const vk::SystemError& e) { } catch (const vk::SystemError& e) {
std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl; std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl;
@ -2806,7 +2807,11 @@ 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) { 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 << ")"); VK_LOG_DEBUG("ggml_vk_buffer_read(" << src->buffer << ", " << offset << ", " << size << ")");
if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
// 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) {
GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent); GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
memcpy(dst, (uint8_t *) src->ptr + offset, size); memcpy(dst, (uint8_t *) src->ptr + offset, size);

101
ggml/src/ggml.c
View file

@ -39,9 +39,6 @@
#include <unistd.h> #include <unistd.h>
#endif #endif
#if defined(__ARM_FEATURE_SVE)
int ggml_sve_cnt_b = 0;
#endif
#if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8) #if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8)
#undef GGML_USE_LLAMAFILE #undef GGML_USE_LLAMAFILE
#endif #endif
@ -455,6 +452,15 @@ static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16];
// precomputed f32 table for f16 (256 KB) (ggml-impl.h) // precomputed f32 table for f16 (256 KB) (ggml-impl.h)
float ggml_table_f32_f16[1 << 16]; 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) { GGML_CALL const char * ggml_status_to_string(enum ggml_status status) {
switch (status) { switch (status) {
case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)"; case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)";
@ -3673,6 +3679,66 @@ 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) { struct ggml_context * ggml_init(struct ggml_init_params params) {
// make this function thread safe // make this function thread safe
ggml_critical_section_start(); ggml_critical_section_start();
@ -3723,6 +3789,10 @@ 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); 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; is_first_call = false;
} }
@ -3771,12 +3841,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
GGML_ASSERT_ALIGNED(ctx->mem_buffer); 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_PRINT_DEBUG("%s: context initialized\n", __func__);
ggml_critical_section_end(); ggml_critical_section_end();
@ -23578,16 +23642,16 @@ int ggml_cpu_has_fma(void) {
} }
int ggml_cpu_has_neon(void) { int ggml_cpu_has_neon(void) {
#if defined(__ARM_NEON) #if defined(__ARM_ARCH)
return 1; return ggml_arm_arch_features.has_neon;
#else #else
return 0; return 0;
#endif #endif
} }
int ggml_cpu_has_sve(void) { int ggml_cpu_has_sve(void) {
#if defined(__ARM_FEATURE_SVE) #if defined(__ARM_ARCH)
return 1; return ggml_arm_arch_features.has_sve;
#else #else
return 0; return 0;
#endif #endif
@ -23734,11 +23798,18 @@ int ggml_cpu_has_vsx(void) {
} }
int ggml_cpu_has_matmul_int8(void) { int ggml_cpu_has_matmul_int8(void) {
#if defined(__ARM_FEATURE_MATMUL_INT8) #if defined(__ARM_ARCH)
return 1; return ggml_arm_arch_features.has_i8mm;
#else #else
return 0; return 0;
#endif #endif
} }
int ggml_cpu_get_sve_cnt(void) {
#if defined(__ARM_ARCH)
return ggml_arm_arch_features.sve_cnt;
#else
return 0;
#endif
}
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////

19
gguf-py/gguf/constants.py
View file

@ -94,6 +94,7 @@ class Keys:
DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id" DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id"
ATTN_LOGIT_SOFTCAPPING = "{arch}.attn_logit_softcapping" ATTN_LOGIT_SOFTCAPPING = "{arch}.attn_logit_softcapping"
FINAL_LOGIT_SOFTCAPPING = "{arch}.final_logit_softcapping" FINAL_LOGIT_SOFTCAPPING = "{arch}.final_logit_softcapping"
SWIN_NORM = "{arch}.swin_norm"
RESCALE_EVERY_N_LAYERS = "{arch}.rescale_every_n_layers" RESCALE_EVERY_N_LAYERS = "{arch}.rescale_every_n_layers"
TIME_MIX_EXTRA_DIM = "{arch}.time_mix_extra_dim" TIME_MIX_EXTRA_DIM = "{arch}.time_mix_extra_dim"
TIME_DECAY_EXTRA_DIM = "{arch}.time_decay_extra_dim" TIME_DECAY_EXTRA_DIM = "{arch}.time_decay_extra_dim"
@ -236,6 +237,7 @@ class MODEL_ARCH(IntEnum):
EXAONE = auto() EXAONE = auto()
GRANITE = auto() GRANITE = auto()
GRANITE_MOE = auto() GRANITE_MOE = auto()
CHAMELEON = auto()
class MODEL_TENSOR(IntEnum): class MODEL_TENSOR(IntEnum):
@ -396,6 +398,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.EXAONE: "exaone", MODEL_ARCH.EXAONE: "exaone",
MODEL_ARCH.GRANITE: "granite", MODEL_ARCH.GRANITE: "granite",
MODEL_ARCH.GRANITE_MOE: "granitemoe", MODEL_ARCH.GRANITE_MOE: "granitemoe",
MODEL_ARCH.CHAMELEON: "chameleon",
} }
TENSOR_NAMES: dict[MODEL_TENSOR, str] = { TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -1267,6 +1270,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_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 # TODO
} }

3
gguf-py/gguf/gguf_writer.py
View file

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

4
gguf-py/gguf/tensor_mapping.py
View file

@ -380,7 +380,7 @@ class TensorNameMap:
MODEL_TENSOR.ATTN_Q_NORM: ( MODEL_TENSOR.ATTN_Q_NORM: (
"language_model.encoder.layers.{bid}.self_attention.q_layernorm", "language_model.encoder.layers.{bid}.self_attention.q_layernorm",
"model.layers.{bid}.self_attn.q_layernorm", # persimmon "model.layers.{bid}.self_attn.q_layernorm", # persimmon
"model.layers.{bid}.self_attn.q_norm", # cohere olmoe "model.layers.{bid}.self_attn.q_norm", # cohere olmoe chameleon
"transformer.blocks.{bid}.attn.q_ln", # sea-lion "transformer.blocks.{bid}.attn.q_ln", # sea-lion
"encoder.layer.{bid}.attention.self.layer_norm_q", # jina-bert-v2 "encoder.layer.{bid}.attention.self.layer_norm_q", # jina-bert-v2
"transformer.layers.{bid}.attn.q_norm", # openelm "transformer.layers.{bid}.attn.q_norm", # openelm
@ -389,7 +389,7 @@ class TensorNameMap:
MODEL_TENSOR.ATTN_K_NORM: ( MODEL_TENSOR.ATTN_K_NORM: (
"language_model.encoder.layers.{bid}.self_attention.k_layernorm", "language_model.encoder.layers.{bid}.self_attention.k_layernorm",
"model.layers.{bid}.self_attn.k_layernorm", # persimmon "model.layers.{bid}.self_attn.k_layernorm", # persimmon
"model.layers.{bid}.self_attn.k_norm", # cohere olmoe "model.layers.{bid}.self_attn.k_norm", # cohere olmoe chameleon
"transformer.blocks.{bid}.attn.k_ln", # sea-lion "transformer.blocks.{bid}.attn.k_ln", # sea-lion
"encoder.layer.{bid}.attention.self.layer_norm_k", # jina-bert-v2 "encoder.layer.{bid}.attention.self.layer_norm_k", # jina-bert-v2
"transformer.layers.{bid}.attn.k_norm", # openelm "transformer.layers.{bid}.attn.k_norm", # openelm

3
include/llama.h
View file

@ -102,6 +102,7 @@ extern "C" {
LLAMA_VOCAB_PRE_TYPE_BLOOM = 23, LLAMA_VOCAB_PRE_TYPE_BLOOM = 23,
LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24, LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24,
LLAMA_VOCAB_PRE_TYPE_EXAONE = 25, LLAMA_VOCAB_PRE_TYPE_EXAONE = 25,
LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26,
}; };
enum llama_rope_type { enum llama_rope_type {
@ -912,6 +913,8 @@ extern "C" {
// //
// Tokenization // Tokenization
// //
// The API is thread-safe.
//
/// @details Convert the provided text into tokens. /// @details Convert the provided text into tokens.
/// @param tokens The tokens pointer must be large enough to hold the resulting tokens. /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.

112
models/ggml-vocab-chameleon.gguf.inp Normal file
View file

@ -0,0 +1,112 @@
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__ggml_vocab_test__
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нещо на Български
__ggml_vocab_test__
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🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
__ggml_vocab_test__
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46
models/ggml-vocab-chameleon.gguf.out Normal file
View file

@ -0,0 +1,46 @@
17245 16604 16403 16604 33583 18355
16421 51153
16604
16650
16650 16604
16581
16582
16582 16582
16582 16582 16582
16581 16582
31596 17394
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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

246
src/llama-vocab.cpp
View file

@ -50,7 +50,7 @@ struct naive_trie {
res.first->second.insert(key + 1, len - 1, value); 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) { std::pair<const char *, size_t> get_longest_prefix(const char * key, size_t len, size_t offset = 0) const {
if (len == 0 || offset == len) { if (len == 0 || offset == len) {
return std::make_pair(key, offset); return std::make_pair(key, offset);
} }
@ -79,6 +79,15 @@ struct naive_trie {
// impl // 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 { 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(' ') == std::string::npos);
GGML_ASSERT(token_left.find('\n') == std::string::npos); GGML_ASSERT(token_left.find('\n') == std::string::npos);
@ -187,10 +196,15 @@ struct llm_bigram_spm {
size_t size; size_t size;
}; };
struct llm_tokenizer_spm { struct llm_tokenizer_spm : llm_tokenizer {
llm_tokenizer_spm(const llama_vocab & vocab) : vocab(vocab) {} llm_tokenizer_spm(const llama_vocab & /*vocab*/) : llm_tokenizer() {}
};
struct llm_tokenizer_spm_session {
llm_tokenizer_spm_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) {
// split string into utf8 chars // split string into utf8 chars
int index = 0; int index = 0;
size_t offs = 0; size_t offs = 0;
@ -279,7 +293,6 @@ private:
if (left == -1 || right == -1) { if (left == -1 || right == -1) {
return; return;
} }
const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n); const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
auto token = vocab.token_to_id.find(text); auto token = vocab.token_to_id.find(text);
@ -306,10 +319,11 @@ private:
} }
const llama_vocab & vocab; const llama_vocab & vocab;
// currently unused
// const llm_tokenizer_spm * spm_tokenizer;
std::vector<llm_symbol> symbols; std::vector<llm_symbol> symbols;
llm_bigram_spm::queue work_queue; llm_bigram_spm::queue work_queue;
std::map<std::string, std::pair<int, int>> rev_merge; std::map<std::string, std::pair<int, int>> rev_merge;
}; };
@ -352,8 +366,8 @@ struct llm_bigram_bpe {
size_t size; size_t size;
}; };
struct llm_tokenizer_bpe { struct llm_tokenizer_bpe : llm_tokenizer {
llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) { llm_tokenizer_bpe(const llama_vocab & vocab) : llm_tokenizer() {
GGML_ASSERT(vocab.type == LLAMA_VOCAB_TYPE_BPE); GGML_ASSERT(vocab.type == LLAMA_VOCAB_TYPE_BPE);
switch (vocab.type_pre) { switch (vocab.type_pre) {
case LLAMA_VOCAB_PRE_TYPE_LLAMA3: case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
@ -450,6 +464,20 @@ struct llm_tokenizer_bpe {
"[^\\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+", "[^\\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; 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:
// default regex for BPE tokenization pre-processing // default regex for BPE tokenization pre-processing
regex_exprs = { regex_exprs = {
@ -462,7 +490,14 @@ struct llm_tokenizer_bpe {
} }
} }
void append(const llama_vocab::id token_id, std::vector<llama_vocab::id> & output) const { 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) {
output.push_back(token_id); output.push_back(token_id);
} }
@ -501,12 +536,11 @@ struct llm_tokenizer_bpe {
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) { void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
int final_prev_index = -1; 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(); symbols_final.clear();
for (auto & word : word_collection) { for (const auto & word : word_collection) {
work_queue = llm_bigram_bpe::queue(); work_queue = llm_bigram_bpe::queue();
symbols.clear(); symbols.clear();
@ -609,7 +643,6 @@ private:
if (left == -1 || right == -1) { if (left == -1 || right == -1) {
return; return;
} }
std::string left_token = std::string(symbols[left].text, symbols[left].n); std::string left_token = std::string(symbols[left].text, symbols[left].n);
std::string right_token = std::string(symbols[right].text, symbols[right].n); std::string right_token = std::string(symbols[right].text, symbols[right].n);
@ -633,12 +666,10 @@ private:
} }
const llama_vocab & vocab; 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;
std::vector<llm_symbol> symbols_final; std::vector<llm_symbol> symbols_final;
llm_bigram_bpe::queue work_queue; llm_bigram_bpe::queue work_queue;
}; };
@ -646,15 +677,17 @@ private:
// WPM tokenizer // WPM tokenizer
// //
struct llm_tokenizer_wpm { struct llm_tokenizer_wpm : llm_tokenizer {
llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {} llm_tokenizer_wpm(const llama_vocab & /*vocab*/) : llm_tokenizer() {}
};
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) const { 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) {
const auto & token_map = vocab.token_to_id; const auto & token_map = vocab.token_to_id;
// normalize and split by whitespace // normalize and split by whitespace
std::vector<std::string> words = preprocess(text); std::vector<std::string> words = preprocess(text);
// bos token prepended already // bos token prepended already
// find the longest tokens that form the words // find the longest tokens that form the words
@ -699,7 +732,7 @@ struct llm_tokenizer_wpm {
} }
// TODO: reduce string copies by using cpts_offs array // TODO: reduce string copies by using cpts_offs array
std::vector<std::string> preprocess(const std::string & text) const { static std::vector<std::string> preprocess(const std::string & text) {
const std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text)); const std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
std::vector<std::string> words(1, ""); std::vector<std::string> words(1, "");
@ -751,15 +784,18 @@ struct llm_tokenizer_wpm {
//(cpt >= 0xFF00 && cpt <= 0xFFEF); //(cpt >= 0xFF00 && cpt <= 0xFFEF);
} }
private:
const llama_vocab & vocab; const llama_vocab & vocab;
// currently unused
// const llm_tokenizer_wpm * wpm_tokenizer;
}; };
// //
// UGM tokenizer // UGM tokenizer
// //
struct llm_tokenizer_ugm { struct llm_tokenizer_ugm : llm_tokenizer {
llm_tokenizer_ugm(const llama_vocab & vocab) : vocab(vocab) { llm_tokenizer_ugm(const llama_vocab & vocab) : llm_tokenizer() {
if (vocab.precompiled_charsmap.size() > 0) { if (vocab.precompiled_charsmap.size() > 0) {
size_t charsmap_offset = 0; size_t charsmap_offset = 0;
@ -805,6 +841,30 @@ struct llm_tokenizer_ugm {
unknown_token_score = min_score - unknown_token_score_penalty; 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 /* This implementation is based on SentencePiece optimized Viterbi algorithm for
* unigram language models. The general idea is to: * unigram language models. The general idea is to:
* - move along the input sequence in steps of one UTF code point, * - move along the input sequence in steps of one UTF code point,
@ -843,7 +903,7 @@ struct llm_tokenizer_ugm {
// traverse the token matcher trie to find a matching token // traverse the token matcher trie to find a matching token
bool single_codepoint_token_found = false; bool single_codepoint_token_found = false;
const struct best_tokenization & current_best = tokenization_results[input_offset]; const struct best_tokenization & current_best = tokenization_results[input_offset];
const struct naive_trie * node = token_matcher.traverse(normalized[prefix_offset++]); const struct naive_trie * node = ugm_tokenizer->token_matcher.traverse(normalized[prefix_offset++]);
while (prefix_offset <= input_len && node != NULL) { while (prefix_offset <= input_len && node != NULL) {
// check if we found valid token in prefix // check if we found valid token in prefix
@ -873,7 +933,7 @@ struct llm_tokenizer_ugm {
// if we didn't find a valid token corresponding to the whole UTF code point // 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 // then use unknown token as the tokenization of this UTF code point
if (!single_codepoint_token_found) { if (!single_codepoint_token_found) {
const double challenger_score = current_best.score_sum + unknown_token_score; const double challenger_score = current_best.score_sum + ugm_tokenizer->unknown_token_score;
prefix_offset = input_offset + n_utf8_code_units; prefix_offset = input_offset + n_utf8_code_units;
struct best_tokenization & current_champ = tokenization_results[prefix_offset]; struct best_tokenization & current_champ = tokenization_results[prefix_offset];
if (challenger_score > current_champ.score_sum) { if (challenger_score > current_champ.score_sum) {
@ -905,7 +965,6 @@ struct llm_tokenizer_ugm {
} }
private: private:
const llama_vocab & vocab;
// helper structure for returning normalization results // helper structure for returning normalization results
struct normalization_result { struct normalization_result {
@ -918,7 +977,7 @@ private:
normalized->clear(); normalized->clear();
normalized->reserve(input.size() * 3); normalized->reserve(input.size() * 3);
const std::string space = vocab.tokenizer_escape_whitespaces ? escaped_space : " "; const std::string space = vocab.tokenizer_escape_whitespaces ? ugm_tokenizer->escaped_space : " ";
bool shall_prepend_space = !vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix; 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; bool shall_append_space = vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
@ -1000,13 +1059,21 @@ private:
size_t xcda_array_size; 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) { struct normalization_result normalize_prefix(const std::string & input, size_t input_offset) {
if (input_offset == input.size()) { if (input_offset == input.size()) {
return { &input[input_offset], 0, 0 }; return { &input[input_offset], 0, 0 };
} }
// if input prefix matches some user-defined token return this token as normalization result // if input prefix matches some user-defined token return this token as normalization result
auto user_defined_token_match = user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset); auto user_defined_token_match =
ugm_tokenizer->user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset);
if (user_defined_token_match.second > 0) { if (user_defined_token_match.second > 0) {
return { &input[input_offset], user_defined_token_match.second, user_defined_token_match.second }; return { &input[input_offset], user_defined_token_match.second, user_defined_token_match.second };
} }
@ -1014,8 +1081,8 @@ private:
size_t longest_prefix_length = 0; size_t longest_prefix_length = 0;
size_t longest_prefix_offset = 0; size_t longest_prefix_offset = 0;
if (xcda_array_size > 0) { if (ugm_tokenizer->xcda_array_size > 0) {
struct xcda_array_view xcda_view(xcda_array, xcda_array_size); struct xcda_array_view xcda_view(ugm_tokenizer->xcda_array, ugm_tokenizer->xcda_array_size);
// Find the longest normalized sequence matching the input prefix by walking // Find the longest normalized sequence matching the input prefix by walking
// the XOR-compressed compact double array (XCDA) starting from the root node // the XOR-compressed compact double array (XCDA) starting from the root node
@ -1051,12 +1118,13 @@ private:
if (longest_prefix_length > 0) { if (longest_prefix_length > 0) {
// we have a match, so return the replacement sequence // we have a match, so return the replacement sequence
if (longest_prefix_offset >= prefix_replacements_size) { if (longest_prefix_offset >= ugm_tokenizer->prefix_replacements_size) {
throw std::runtime_error("Index out of array bounds in precompiled charsmap!"); throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
} }
const char * prefix_replacement = &prefix_replacements[longest_prefix_offset]; const char * prefix_replacement = &(ugm_tokenizer->prefix_replacements)[longest_prefix_offset];
return { prefix_replacement, strlen(prefix_replacement), longest_prefix_length }; return { prefix_replacement, strlen(prefix_replacement), longest_prefix_length };
} else { }
// check if the input prefix contains a valid sequence of UTF-8 code units // check if the input prefix contains a valid sequence of UTF-8 code units
try { try {
// if yes, return this sequence unmodified // if yes, return this sequence unmodified
@ -1068,33 +1136,9 @@ private:
return { "\xEF\xBF\xBD", 3, 1 }; return { "\xEF\xBF\xBD", 3, 1 };
} }
} }
}
// escaped space symbol - U+2581 (Lower One Eighth Block) const llama_vocab & vocab;
const std::string escaped_space = "\xE2\x96\x81"; const llm_tokenizer_ugm * ugm_tokenizer;
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;
}; };
// //
@ -1155,8 +1199,8 @@ static std::vector<uint8_t> llama_unescape_rwkv_token(const std::string & escape
return output; return output;
} }
struct llm_tokenizer_rwkv { struct llm_tokenizer_rwkv : llm_tokenizer {
llm_tokenizer_rwkv(const llama_vocab & vocab): vocab(vocab) { llm_tokenizer_rwkv(const llama_vocab & vocab) : llm_tokenizer() {
// RWKV supports arbitrary byte tokens, but the vocab struct only supports string tokens. // 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. // For now, we decode the vocab here into the lookup we'll use for tokenization.
@ -1168,11 +1212,17 @@ struct llm_tokenizer_rwkv {
} }
} }
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) { void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
uint32_t position = 0; uint32_t position = 0;
while (position < text.size()) { while (position < text.size()) {
const struct naive_trie * node = token_matcher.traverse(text[position]); const struct naive_trie * node = rwkv_tokenizer.token_matcher.traverse(text[position]);
if (node == NULL) { if (node == NULL) {
// no matching token found, add unknown token // no matching token found, add unknown token
output.push_back(vocab.special_unk_id); output.push_back(vocab.special_unk_id);
@ -1197,11 +1247,33 @@ struct llm_tokenizer_rwkv {
} }
} }
private:
const llama_vocab & vocab; const llama_vocab & vocab;
const llm_tokenizer_rwkv & rwkv_tokenizer;
struct naive_trie token_matcher;
}; };
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");
}
}
// //
// (de-) tokenize // (de-) tokenize
// //
@ -1263,7 +1335,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
// if a fragment is text ( not yet processed ) // if a fragment is text ( not yet processed )
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
auto & raw_text = fragment.raw_text; const auto & raw_text = fragment.raw_text;
auto raw_text_base_offset = fragment.offset; auto raw_text_base_offset = fragment.offset;
auto raw_text_base_length = fragment.length; auto raw_text_base_length = fragment.length;
@ -1362,7 +1434,13 @@ 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) { 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> output; std::vector<llama_vocab::id> output;
std::forward_list<fragment_buffer_variant> fragment_buffer; std::forward_list<fragment_buffer_variant> fragment_buffer;
@ -1399,9 +1477,9 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
#ifdef PRETOKENIZERDEBUG #ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif #endif
llm_tokenizer_spm tokenizer(vocab);
llama_escape_whitespace(raw_text); llama_escape_whitespace(raw_text);
tokenizer.tokenize(raw_text, output); llm_tokenizer_spm_session session(vocab);
session.tokenize(raw_text, output);
is_prev_special = false; is_prev_special = false;
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token); output.push_back(fragment.token);
@ -1423,10 +1501,11 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
} break; } break;
case LLAMA_VOCAB_TYPE_BPE: case LLAMA_VOCAB_TYPE_BPE:
{ {
llm_tokenizer_bpe tokenizer(vocab); 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
if (add_special) { if (add_special) {
tokenizer.append_bos(output); session.append_bos(output);
} }
for (const auto & fragment : fragment_buffer) { for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
@ -1435,15 +1514,15 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
#ifdef PRETOKENIZERDEBUG #ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif #endif
tokenizer.tokenize(raw_text, output); session.tokenize(raw_text, output);
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
tokenizer.append(fragment.token, output); session.append(fragment.token, output);
} }
} }
if (add_special) { if (add_special) {
tokenizer.append_eos(output); session.append_eos(output);
tokenizer.check_double_bos_eos(output); session.check_double_bos_eos(output);
} }
} break; } break;
case LLAMA_VOCAB_TYPE_WPM: case LLAMA_VOCAB_TYPE_WPM:
@ -1453,7 +1532,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
output.push_back(vocab.special_cls_id); output.push_back(vocab.special_cls_id);
} }
llm_tokenizer_wpm tokenizer(vocab); llm_tokenizer_wpm_session session(vocab);
for (const auto & fragment : fragment_buffer) { for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
@ -1462,7 +1541,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
#ifdef PRETOKENIZERDEBUG #ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif #endif
tokenizer.tokenize(raw_text, output); session.tokenize(raw_text, output);
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token); output.push_back(fragment.token);
} }
@ -1475,12 +1554,11 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
} break; } break;
case LLAMA_VOCAB_TYPE_UGM: case LLAMA_VOCAB_TYPE_UGM:
{ {
llm_tokenizer_ugm tokenizer(vocab);
if (add_special && vocab.tokenizer_add_bos) { if (add_special && vocab.tokenizer_add_bos) {
GGML_ASSERT(vocab.special_bos_id != -1); GGML_ASSERT(vocab.special_bos_id != -1);
output.push_back(vocab.special_bos_id); output.push_back(vocab.special_bos_id);
} }
llm_tokenizer_ugm_session session(vocab);
for (const auto & fragment : fragment_buffer) { for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
@ -1488,7 +1566,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
#ifdef PRETOKENIZERDEBUG #ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif #endif
tokenizer.tokenize(raw_text, output); session.tokenize(raw_text, output);
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token); output.push_back(fragment.token);
} }
@ -1508,6 +1586,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
} break; } break;
case LLAMA_VOCAB_TYPE_RWKV: case LLAMA_VOCAB_TYPE_RWKV:
{ {
llm_tokenizer_rwkv_session session(vocab);
for (const auto & fragment : fragment_buffer) { for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
@ -1516,8 +1595,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif #endif
llm_tokenizer_rwkv tokenizer(vocab); session.tokenize(raw_text, output);
tokenizer.tokenize(raw_text, output);
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token); output.push_back(fragment.token);
} }
@ -1764,6 +1842,8 @@ int32_t llama_detokenize_impl(
int32_t text_len_max, int32_t text_len_max,
bool remove_special, bool remove_special,
bool unparse_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 avail = text_len_max;
int32_t total = 0; int32_t total = 0;

9
src/llama-vocab.h
View file

@ -8,6 +8,8 @@
#include <map> #include <map>
#include <set> #include <set>
struct llm_tokenizer;
struct llama_vocab { struct llama_vocab {
using id = llama_token; using id = llama_token;
using token = std::string; using token = std::string;
@ -65,7 +67,14 @@ struct llama_vocab {
std::vector<char> precompiled_charsmap; 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; int find_bpe_rank(const std::string & token_left, const std::string & token_right) const;
void init_tokenizer();
}; };
// //

265
src/llama.cpp
View file

@ -216,6 +216,7 @@ enum llm_arch {
LLM_ARCH_RWKV6, LLM_ARCH_RWKV6,
LLM_ARCH_GRANITE, LLM_ARCH_GRANITE,
LLM_ARCH_GRANITE_MOE, LLM_ARCH_GRANITE_MOE,
LLM_ARCH_CHAMELEON,
LLM_ARCH_UNKNOWN, LLM_ARCH_UNKNOWN,
}; };
@ -268,6 +269,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_RWKV6, "rwkv6" }, { LLM_ARCH_RWKV6, "rwkv6" },
{ LLM_ARCH_GRANITE, "granite" }, { LLM_ARCH_GRANITE, "granite" },
{ LLM_ARCH_GRANITE_MOE, "granitemoe" }, { LLM_ARCH_GRANITE_MOE, "granitemoe" },
{ LLM_ARCH_CHAMELEON, "chameleon" },
{ LLM_ARCH_UNKNOWN, "(unknown)" }, { LLM_ARCH_UNKNOWN, "(unknown)" },
}; };
@ -304,6 +306,7 @@ enum llm_kv {
LLM_KV_DECODER_START_TOKEN_ID, LLM_KV_DECODER_START_TOKEN_ID,
LLM_KV_ATTN_LOGIT_SOFTCAPPING, LLM_KV_ATTN_LOGIT_SOFTCAPPING,
LLM_KV_FINAL_LOGIT_SOFTCAPPING, LLM_KV_FINAL_LOGIT_SOFTCAPPING,
LLM_KV_SWIN_NORM,
LLM_KV_RESCALE_EVERY_N_LAYERS, LLM_KV_RESCALE_EVERY_N_LAYERS,
LLM_KV_TIME_MIX_EXTRA_DIM, LLM_KV_TIME_MIX_EXTRA_DIM,
LLM_KV_TIME_DECAY_EXTRA_DIM, LLM_KV_TIME_DECAY_EXTRA_DIM,
@ -411,6 +414,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" }, { LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" },
{ LLM_KV_ATTN_LOGIT_SOFTCAPPING, "%s.attn_logit_softcapping" }, { LLM_KV_ATTN_LOGIT_SOFTCAPPING, "%s.attn_logit_softcapping" },
{ LLM_KV_FINAL_LOGIT_SOFTCAPPING, "%s.final_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_RESCALE_EVERY_N_LAYERS, "%s.rescale_every_n_layers" },
{ LLM_KV_TIME_MIX_EXTRA_DIM, "%s.time_mix_extra_dim" }, { LLM_KV_TIME_MIX_EXTRA_DIM, "%s.time_mix_extra_dim" },
{ LLM_KV_TIME_DECAY_EXTRA_DIM, "%s.time_decay_extra_dim" }, { LLM_KV_TIME_DECAY_EXTRA_DIM, "%s.time_decay_extra_dim" },
@ -1504,6 +1508,25 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_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, LLM_ARCH_UNKNOWN,
{ {
@ -2367,6 +2390,7 @@ struct llama_hparams {
bool vocab_only; bool vocab_only;
bool rope_finetuned; bool rope_finetuned;
bool use_par_res; bool use_par_res;
bool swin_norm;
uint32_t n_vocab; uint32_t n_vocab;
uint32_t n_ctx_train; // context size the model was trained on uint32_t n_ctx_train; // context size the model was trained on
@ -6096,6 +6120,18 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN; default: model.type = e_model::MODEL_UNKNOWN;
} }
} break; } 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; default: (void)0;
} }
@ -6354,6 +6390,11 @@ static void llm_load_vocab(
} else if ( } else if (
tokenizer_pre == "exaone") { tokenizer_pre == "exaone") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_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 { } else {
throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
} }
@ -6441,6 +6482,8 @@ static void llm_load_vocab(
} }
GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size()); 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' // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
if (vocab.type == LLAMA_VOCAB_TYPE_SPM) { if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
// For Fill-In-the-Middle (FIM)/infill models which where converted // For Fill-In-the-Middle (FIM)/infill models which where converted
@ -8760,6 +8803,45 @@ static bool llm_load_tensors(
} }
} break; } 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: default:
throw std::runtime_error("unknown architecture"); throw std::runtime_error("unknown architecture");
} }
@ -15925,6 +16007,184 @@ struct llm_build_context {
return gf; 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) { static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
@ -16185,6 +16445,10 @@ static struct ggml_cgraph * llama_build_graph(
{ {
result = llm.build_rwkv6(); result = llm.build_rwkv6();
} break; } break;
case LLM_ARCH_CHAMELEON:
{
result = llm.build_chameleon();
} break;
default: default:
GGML_ABORT("fatal error"); GGML_ABORT("fatal error");
} }
@ -19333,6 +19597,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_CHATGLM: case LLM_ARCH_CHATGLM:
case LLM_ARCH_GRANITE: case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE: case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_CHAMELEON:
return LLAMA_ROPE_TYPE_NORM; return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2 // the pairs of head values are offset by n_rot/2

20
tests/test-tokenizer-0.cpp
View file

@ -7,6 +7,7 @@
#include <map> #include <map>
#include <vector> #include <vector>
#include <fstream> #include <fstream>
#include <thread>
//static const std::map<std::string, std::vector<llama_token>> & k_tests() { //static const std::map<std::string, std::vector<llama_token>> & k_tests() {
// static std::map<std::string, std::vector<llama_token>> _k_tests = { // static std::map<std::string, std::vector<llama_token>> _k_tests = {
@ -194,9 +195,21 @@ int main(int argc, char **argv) {
const bool add_special = false; const bool add_special = false;
// multi-threaded tokenization
const int nthread = std::thread::hardware_concurrency();
std::vector<std::thread> threads(nthread);
for (int i = 0; i < nthread; i++) {
threads[i] = std::thread([&, i]() {
for (const auto & test_kv : k_tests) { for (const auto & test_kv : k_tests) {
const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, false); const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, false);
// 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("\n");
printf("src: '%s'\n", test_kv.first.c_str()); printf("src: '%s'\n", test_kv.first.c_str());
printf("res: '%s'\n", llama_detokenize(ctx, res).c_str()); printf("res: '%s'\n", llama_detokenize(ctx, res).c_str());
@ -232,7 +245,14 @@ int main(int argc, char **argv) {
success = false; success = false;
} }
} }
});
}
for (int i = 0; i < nthread; i++) {
threads[i].join();
}
// single threaded tokenization
if (!fname_text.empty()) { if (!fname_text.empty()) {
fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str()); fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());