tokenizer : BPE fixes (#7530 )

* Random test: add_bos_token, add_eos_token * Random test: add BPE models for testing * Custom regex split fails with codepoint 0 * Fix falcon punctuation regex * Refactor llm_tokenizer_bpe: move code to constructor * Move 'add_special_bos/eos' logic to llm_tokenizer_bpe * Move tokenizer flags to vocab structure. * Default values for special_add_bos/eos * Build vocab.special_tokens_cache using vocab token types * Generalize 'jina-v2' per token attributes * Fix unicode whitespaces (deepseek-coder, deepseek-llm) * Skip missing byte tokens (falcon) * Better unicode data generation * Replace char32_t with uint32_t
Only use FIM middle token if it exists (#7648 )
2026-06-16 10:46:43 +02:00 · 2024-06-18 18:40:52 +02:00 · 2024-06-18 22:19:45 +10:00 · 2024-06-18 22:18:32 +10:00 · 2024-06-18 14:00:14 +02:00 · 2024-06-18 10:11:40 +03:00
18 changed files with 1618 additions and 1106 deletions
@@ -507,7 +507,7 @@ ifdef LLAMA_CUDA
 		CUDA_PATH ?= /usr/local/cuda
 	endif
 	MK_CPPFLAGS  += -DGGML_USE_CUDA -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include -DGGML_CUDA_USE_GRAPHS
-	MK_LDFLAGS   += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib
+	MK_LDFLAGS   += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L$(CUDA_PATH)/lib64/stubs -L/usr/lib/wsl/lib
 	OBJS         += ggml-cuda.o
 	OBJS         += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
 	OBJS         += $(OBJS_CUDA_TEMP_INST)
@@ -209,6 +209,7 @@ Unless otherwise noted these projects are open-source with permissive licensing:
 - [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
 - [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
 - [AIKit](https://github.com/sozercan/aikit) (MIT)
+- [LARS - The LLM & Advanced Referencing Solution](https://github.com/abgulati/LARS) (AGPL)

 *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*

@@ -387,6 +388,30 @@ brew install llama.cpp
 ```
 The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668

+### Nix
+
+On Mac and Linux, the Nix package manager can be used via
+```
+nix profile install nixpkgs#llama-cpp
+```
+For flake enabled installs.
+
+Or
+```
+nix-env --file '<nixpkgs>' --install --attr llama-cpp
+```
+For non-flake enabled installs.
+
+This expression is automatically updated within the [nixpkgs repo](https://github.com/NixOS/nixpkgs/blob/nixos-24.05/pkgs/by-name/ll/llama-cpp/package.nix#L164).
+
+#### Flox
+
+On Mac and Linux, Flox can be used to install llama.cpp within a Flox environment via
+```
+flox install llama-cpp
+```
+Flox follows the nixpkgs build of llama.cpp.
+
 ### Metal Build

 On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
@@ -73,7 +73,6 @@ struct gpt_params {
    int32_t n_gpu_layers_draft    =    -1; // number of layers to store in VRAM for the draft model (-1 - use default)
    int32_t main_gpu              =     0; // the GPU that is used for scratch and small tensors
    float   tensor_split[128]     =   {0}; // how split tensors should be distributed across GPUs
-    int32_t n_beams               =     0; // if non-zero then use beam search of given width.
    int32_t grp_attn_n            =     1; // group-attention factor
    int32_t grp_attn_w            =   512; // group-attention width
    int32_t n_print               =    -1; // print token count every n tokens (-1 = disabled)
@@ -1632,6 +1632,12 @@ class Qwen2MoeModel(Model):
        super().set_gguf_parameters()
        if (n_experts := self.hparams.get("num_experts")) is not None:
            self.gguf_writer.add_expert_count(n_experts)
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
+        if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None:
+            self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size)
+            logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}")

    _experts: list[dict[str, Tensor]] | None = None

@@ -223,7 +223,11 @@ int main(int argc, char ** argv) {
    inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
    embd_inp = inp_pfx;
    embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
-    embd_inp.push_back(llama_token_middle(model));
+
+    const llama_token middle_token = llama_token_middle(model);
+    if (middle_token >= 0) {
+        embd_inp.push_back(middle_token);
+    }

    LOG("prefix: \"%s\"\n", log_tostr(params.input_prefix));
    LOG("suffix: \"%s\"\n", log_tostr(params.input_suffix));
@@ -528,7 +532,12 @@ int main(int argc, char ** argv) {
                inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
                embd_inp = inp_pfx;
                embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
-                embd_inp.push_back(llama_token_middle(model));
+
+                const llama_token middle_token = llama_token_middle(model);
+                if (middle_token >= 0) {
+                    embd_inp.push_back(middle_token);
+                }
+
                embd.clear();
                n_remain = params.n_predict;
                n_past = 0;
@@ -2038,7 +2038,12 @@ struct server_context {
                            prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
                            prefix_tokens.insert(prefix_tokens.end(),   llama_token_suffix(model));
                            prefix_tokens.insert(prefix_tokens.end(),   suffix_tokens.begin(), suffix_tokens.end());
-                            prefix_tokens.push_back(llama_token_middle(model));
+
+                            const llama_token middle_token = llama_token_middle(model);
+                            if (middle_token >= 0) {
+                                prefix_tokens.push_back(middle_token);
+                            }
+
                            prompt_tokens = prefix_tokens;
                        } else {
                            prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
@@ -1172,7 +1172,7 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
            // check if a backend with higher prio wants to offload the op
            if (src_backend_id == sched->n_backends - 1) {
                for (int b = 0; b < src_backend_id; b++) {
-                    if (ggml_backend_offload_op(sched->backends[b], tensor)) {
+                    if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) {
                        SET_CAUSE(tensor, "1.off");
                        return b;
                    }
@@ -1706,14 +1706,16 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
    bool backend_ids_changed = false;
    for (int i = 0; i < sched->graph->n_nodes; i++) {
-        if (sched->node_backend_ids[i] != sched->prev_node_backend_ids[i]) {
+        if (sched->node_backend_ids[i] != sched->prev_node_backend_ids[i] &&
+            sched->bufts[sched->node_backend_ids[i]] != sched->bufts[sched->prev_node_backend_ids[i]]) {
            backend_ids_changed = true;
            break;
        }
    }
    if (!backend_ids_changed) {
        for (int i = 0; i < sched->graph->n_leafs; i++) {
-            if (sched->leaf_backend_ids[i] != sched->prev_leaf_backend_ids[i]) {
+            if (sched->leaf_backend_ids[i] != sched->prev_leaf_backend_ids[i] &&
+                sched->bufts[sched->leaf_backend_ids[i]] != sched->bufts[sched->prev_leaf_backend_ids[i]]) {
                backend_ids_changed = true;
                break;
            }
@@ -1977,6 +1979,15 @@ int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) {
    return sched->n_copies;
 }

+int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched) {
+    return sched->n_backends;
+}
+
+ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i) {
+    GGML_ASSERT(i >= 0 && i < sched->n_backends);
+    return sched->backends[i];
+}
+
 size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
    int backend_index = ggml_backend_sched_backend_id(sched, backend);
    GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
@@ -182,6 +182,9 @@ extern "C" {
    // Initialize backend buffers from a measure graph
    GGML_API bool                 ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);

+    GGML_API int                  ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched);
+    GGML_API ggml_backend_t       ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i);
+
    // Get the number of splits of the last graph
    GGML_API int                  ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
    GGML_API int                  ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
@@ -17,7 +17,7 @@
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
 #define MAX(a, b) ((a) > (b) ? (a) : (b))

-#if defined(_WIN32)
+#if defined(_MSC_VER)

 #define m512bh(p) p
 #define m512i(p) p
@@ -33,21 +33,22 @@ class Keys:
        FILE_TYPE            = "general.file_type"

    class LLM:
-        VOCAB_SIZE                 = "{arch}.vocab_size"
-        CONTEXT_LENGTH             = "{arch}.context_length"
-        EMBEDDING_LENGTH           = "{arch}.embedding_length"
-        BLOCK_COUNT                = "{arch}.block_count"
-        LEADING_DENSE_BLOCK_COUNT  = "{arch}.leading_dense_block_count"
-        FEED_FORWARD_LENGTH        = "{arch}.feed_forward_length"
-        EXPERT_FEED_FORWARD_LENGTH = "{arch}.expert_feed_forward_length"
-        USE_PARALLEL_RESIDUAL      = "{arch}.use_parallel_residual"
-        TENSOR_DATA_LAYOUT         = "{arch}.tensor_data_layout"
-        EXPERT_COUNT               = "{arch}.expert_count"
-        EXPERT_USED_COUNT          = "{arch}.expert_used_count"
-        EXPERT_SHARED_COUNT        = "{arch}.expert_shared_count"
-        EXPERT_WEIGHTS_SCALE       = "{arch}.expert_weights_scale"
-        POOLING_TYPE               = "{arch}.pooling_type"
-        LOGIT_SCALE                = "{arch}.logit_scale"
+        VOCAB_SIZE                        = "{arch}.vocab_size"
+        CONTEXT_LENGTH                    = "{arch}.context_length"
+        EMBEDDING_LENGTH                  = "{arch}.embedding_length"
+        BLOCK_COUNT                       = "{arch}.block_count"
+        LEADING_DENSE_BLOCK_COUNT         = "{arch}.leading_dense_block_count"
+        FEED_FORWARD_LENGTH               = "{arch}.feed_forward_length"
+        EXPERT_FEED_FORWARD_LENGTH        = "{arch}.expert_feed_forward_length"
+        EXPERT_SHARED_FEED_FORWARD_LENGTH = "{arch}.expert_shared_feed_forward_length"
+        USE_PARALLEL_RESIDUAL             = "{arch}.use_parallel_residual"
+        TENSOR_DATA_LAYOUT                = "{arch}.tensor_data_layout"
+        EXPERT_COUNT                      = "{arch}.expert_count"
+        EXPERT_USED_COUNT                 = "{arch}.expert_used_count"
+        EXPERT_SHARED_COUNT               = "{arch}.expert_shared_count"
+        EXPERT_WEIGHTS_SCALE              = "{arch}.expert_weights_scale"
+        POOLING_TYPE                      = "{arch}.pooling_type"
+        LOGIT_SCALE                       = "{arch}.logit_scale"

    class Attention:
        HEAD_COUNT        = "{arch}.attention.head_count"
@@ -394,6 +394,9 @@ class GGUFWriter:
    def add_expert_feed_forward_length(self, length: int) -> None:
        self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length)

+    def add_expert_shared_feed_forward_length(self, length: int) -> None:
+        self.add_uint32(Keys.LLM.EXPERT_SHARED_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
+
    def add_parallel_residual(self, use: bool) -> None:
        self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)

@@ -286,6 +286,7 @@ enum llm_kv {
    LLM_KV_LEADING_DENSE_BLOCK_COUNT,
    LLM_KV_FEED_FORWARD_LENGTH,
    LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
+    LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
    LLM_KV_USE_PARALLEL_RESIDUAL,
    LLM_KV_TENSOR_DATA_LAYOUT,
    LLM_KV_EXPERT_COUNT,
@@ -364,21 +365,22 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
    { LLM_KV_GENERAL_SOURCE_URL,            "general.source.url"                    },
    { LLM_KV_GENERAL_SOURCE_HF_REPO,        "general.source.huggingface.repository" },

-    { LLM_KV_VOCAB_SIZE,                    "%s.vocab_size"                 },
-    { LLM_KV_CONTEXT_LENGTH,                "%s.context_length"             },
-    { LLM_KV_EMBEDDING_LENGTH,              "%s.embedding_length"           },
-    { LLM_KV_BLOCK_COUNT,                   "%s.block_count"                },
-    { LLM_KV_LEADING_DENSE_BLOCK_COUNT,     "%s.leading_dense_block_count"  },
-    { LLM_KV_FEED_FORWARD_LENGTH,           "%s.feed_forward_length"        },
-    { LLM_KV_EXPERT_FEED_FORWARD_LENGTH,    "%s.expert_feed_forward_length" },
-    { LLM_KV_USE_PARALLEL_RESIDUAL,         "%s.use_parallel_residual"      },
-    { LLM_KV_TENSOR_DATA_LAYOUT,            "%s.tensor_data_layout"         },
-    { LLM_KV_EXPERT_COUNT,                  "%s.expert_count"               },
-    { LLM_KV_EXPERT_USED_COUNT,             "%s.expert_used_count"          },
-    { LLM_KV_EXPERT_SHARED_COUNT,           "%s.expert_shared_count"        },
-    { LLM_KV_EXPERT_WEIGHTS_SCALE,          "%s.expert_weights_scale"       },
-    { LLM_KV_POOLING_TYPE ,                 "%s.pooling_type"               },
-    { LLM_KV_LOGIT_SCALE,                   "%s.logit_scale"                },
+    { LLM_KV_VOCAB_SIZE,                        "%s.vocab_size"                        },
+    { LLM_KV_CONTEXT_LENGTH,                    "%s.context_length"                    },
+    { LLM_KV_EMBEDDING_LENGTH,                  "%s.embedding_length"                  },
+    { LLM_KV_BLOCK_COUNT,                       "%s.block_count"                       },
+    { LLM_KV_LEADING_DENSE_BLOCK_COUNT,         "%s.leading_dense_block_count"         },
+    { LLM_KV_FEED_FORWARD_LENGTH,               "%s.feed_forward_length"               },
+    { LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        "%s.expert_feed_forward_length"        },
+    { LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" },
+    { LLM_KV_USE_PARALLEL_RESIDUAL,             "%s.use_parallel_residual"             },
+    { LLM_KV_TENSOR_DATA_LAYOUT,                "%s.tensor_data_layout"                },
+    { LLM_KV_EXPERT_COUNT,                      "%s.expert_count"                      },
+    { LLM_KV_EXPERT_USED_COUNT,                 "%s.expert_used_count"                 },
+    { LLM_KV_EXPERT_SHARED_COUNT,               "%s.expert_shared_count"               },
+    { LLM_KV_EXPERT_WEIGHTS_SCALE,              "%s.expert_weights_scale"              },
+    { LLM_KV_POOLING_TYPE ,                     "%s.pooling_type"                      },
+    { LLM_KV_LOGIT_SCALE,                       "%s.logit_scale"                       },

    { LLM_KV_ATTENTION_HEAD_COUNT,          "%s.attention.head_count"             },
    { LLM_KV_ATTENTION_HEAD_COUNT_KV,       "%s.attention.head_count_kv"          },
@@ -1278,6 +1280,126 @@ struct no_init {
 };

 struct llama_file {
+
+#if defined(_WIN32)
+    // use FILE * so we don't have to re-open the file to mmap
+    FILE * fp;
+    HANDLE fp_win32;
+    size_t size;
+
+private:
+    std::string GetErrorMessageWin32(DWORD error_code) const {
+        std::string ret;
+        LPSTR lpMsgBuf = NULL;
+        DWORD bufLen = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
+                                    NULL, error_code, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&lpMsgBuf, 0, NULL);
+        if (!bufLen) {
+            ret = format("Win32 error code: %s", error_code);
+        } else {
+            ret = lpMsgBuf;
+            LocalFree(lpMsgBuf);
+        }
+
+        return ret;
+    }
+
+public:
+
+    llama_file(const char * fname, const char * mode) {
+        fp = ggml_fopen(fname, mode);
+        if (fp == NULL) {
+            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
+        }
+        fp_win32 = (HANDLE) _get_osfhandle(_fileno(fp));
+        seek(0, SEEK_END);
+        size = tell();
+        seek(0, SEEK_SET);
+    }
+
+    size_t tell() const {
+        // SetFilePointerEx returns the current position when seeking relative 0 bytes
+        LARGE_INTEGER li;
+        li.QuadPart = 0;
+        BOOL ret = SetFilePointerEx(fp_win32, li, &li, FILE_CURRENT);
+        if (!ret) {
+            throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+        }
+
+        return li.QuadPart;
+    }
+
+    void seek(size_t offset, int whence) const {
+        // no need to convert SEEK_* to FILE_*. The enums are the same.
+        // Still, keep static asserts to avoid failures in the future.
+        static_assert(SEEK_SET == FILE_BEGIN, "SEEK_SET != FILE_BEGIN");
+        static_assert(SEEK_CUR == FILE_CURRENT, "SEEK_CUR != FILE_CURRENT");
+        static_assert(SEEK_END == FILE_END, "SEEK_END != FILE_END");
+
+        LARGE_INTEGER li;
+        li.QuadPart = offset;
+        BOOL ret = SetFilePointerEx(fp_win32, li, NULL, whence);
+        if (!ret) {
+            throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+        }
+    }
+
+    void read_raw(void * ptr, size_t len) const {
+        // On Win32 ReadFile is significant faster than fread which is again significant faster than std::fstream. Thus
+        // use the Win32 API to do file io instead of the C/C++ library functions.
+
+        // There are conditions under which ReadFile cannot read chunks >64MB.
+        // Thus split the operation into smaller chunks if len exceeds this limit.
+        size_t bytes_read = 0;
+        while (bytes_read < len) {
+            size_t chunk_size = std::min<size_t>(len - bytes_read, 64*1024*1024);
+            DWORD chunk_read = 0;
+            BOOL result = ReadFile(fp_win32, reinterpret_cast<char*>(ptr) + bytes_read, chunk_size, &chunk_read, NULL);
+            if (!result) {
+                throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+            }
+            if (chunk_read < chunk_size || chunk_read == 0) {
+                throw std::runtime_error("unexpectedly reached end of file");
+            }
+
+            bytes_read += chunk_read;
+        } ;
+    }
+
+    uint32_t read_u32() const {
+        uint32_t val;
+        read_raw(&val, sizeof(val));
+        return val;
+    }
+
+    void write_raw(const void * ptr, size_t len) const {
+        // There are conditions under which WriteFile cannot write chunks >64MB.
+        // Thus split the operation into smaller chunks if len exceeds this limit.
+        size_t bytes_written = 0;
+        while (bytes_written < len) {
+            size_t chunk_size = std::min<size_t>(len - bytes_written, 64*1024*1024);
+            DWORD chunk_written = 0;
+            BOOL result = WriteFile(fp_win32, reinterpret_cast<char const*>(ptr) + bytes_written, chunk_size, &chunk_written, NULL);
+            if (!result) {
+                throw std::runtime_error(format("write error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+            }
+            if (chunk_written < chunk_size || chunk_written == 0) {
+                throw std::runtime_error("unexpectedly failed to write bytes");
+            }
+
+            bytes_written += chunk_written;
+        }
+    }
+
+    void write_u32(std::uint32_t val) const {
+        write_raw(&val, sizeof(val));
+    }
+
+    ~llama_file() {
+        if (fp) {
+            std::fclose(fp);
+        }
+    }
+#else
    // use FILE * so we don't have to re-open the file to mmap
    FILE * fp;
    size_t size;
@@ -1298,7 +1420,10 @@ struct llama_file {
 #else
        long ret = std::ftell(fp);
 #endif
-        GGML_ASSERT(ret != -1); // this really shouldn't fail
+        if (ret == -1) {
+            throw std::runtime_error(format("ftell error: %s", strerror(errno)));
+        }
+
        return (size_t) ret;
    }

@@ -1308,7 +1433,9 @@ struct llama_file {
 #else
        int ret = std::fseek(fp, (long) offset, whence);
 #endif
-        GGML_ASSERT(ret == 0); // same
+        if (ret != 0) {
+            throw std::runtime_error(format("seek error: %s", strerror(errno)));
+        }
    }

    void read_raw(void * ptr, size_t len) const {
@@ -1351,6 +1478,7 @@ struct llama_file {
            std::fclose(fp);
        }
    }
+#endif
 };
 using llama_files = std::vector<std::unique_ptr<llama_file>>;

@@ -1844,6 +1972,7 @@ struct llama_hparams {
    uint32_t n_lora_q = 0;
    uint32_t n_lora_kv = 0;
    uint32_t n_ff_exp = 0;
+    uint32_t n_ff_shexp = 0;
    uint32_t n_expert_shared = 0;
    float    expert_weights_scale = 0.0;

@@ -1892,6 +2021,7 @@ struct llama_hparams {
        if (this->n_lora_q           != other.n_lora_q)           return true;
        if (this->n_lora_kv          != other.n_lora_kv)          return true;
        if (this->n_ff_exp           != other.n_ff_exp)           return true;
+        if (this->n_ff_shexp         != other.n_ff_shexp)         return true;
        if (this->n_expert_shared    != other.n_expert_shared)    return true;

        if (this->rope_finetuned  != other.rope_finetuned)  return true;
@@ -2180,16 +2310,17 @@ struct llama_vocab {
    id special_cls_id  = -1;
    id special_mask_id = -1;

-    int special_add_bos = -1; // -1 unknown, 1 add, 0 don't add.
-    int special_add_eos = -1; // -1 unknown, 1 add, 0 don't add.
-
    id linefeed_id       = 13;
    id special_prefix_id = -1;
    id special_suffix_id = -1;
    id special_middle_id = -1;
    id special_eot_id    = -1; // TODO: move above after "eos_id", and here add "file separator" token

-    bool add_space_prefix = true;
+    // tokenizer flags
+    bool tokenizer_add_space_prefix = true;
+    bool tokenizer_add_bos          = false;
+    bool tokenizer_add_eos          = false;
+    bool tokenizer_ignore_merges    = false;

    int find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
        GGML_ASSERT(token_left.find(' ') == std::string::npos);
@@ -3721,6 +3852,44 @@ struct llama_model_loader {
        std::vector<no_init<uint8_t>> read_buf;
        std::vector<std::future<std::pair<ggml_tensor *, bool>>> validation_result;

+#if defined(GGML_USE_CUDA)
+        // 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives.
+        // NVMe raid configurations might require more / larger buffers.
+        constexpr size_t num_buffers = 4;
+        constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB
+
+        std::vector<ggml_backend_buffer_t> host_buffers;
+        std::vector<void*> host_ptrs;
+        std::vector<ggml_backend_event_t> events;
+        size_t buffer_idx = 0; // buffer to use for async loads
+
+        ggml_backend_t cuda_backend = nullptr;
+        if (!use_mmap && !check_tensors) {
+            // When not using mmaped io use async uploads from pinned memory to GPU memory.
+            // First determine if the CUDA backend is active, and if so, determine the device ID.
+            ggml_backend_buffer_t buf = bufs_mmap.count(0) ? bufs_mmap.at(0) : nullptr;
+            if (buf) {
+                ggml_backend_buffer_type_t buffer_type = ggml_backend_buffer_get_type(buf);
+                for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) {
+                    auto * cuda_buffer_type = ggml_backend_cuda_buffer_type(i);
+                    if (buffer_type == cuda_buffer_type) {
+                        cuda_backend = ggml_backend_cuda_init(i);
+                        break;
+                    }
+                }
+            }
+
+            // If the cuda backend is active create pinned memory buffers and events for synchronisation.
+            if (cuda_backend) {
+                for (size_t idx = 0; idx < num_buffers; ++idx) {
+                    host_buffers.emplace_back(ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), buffer_size));
+                    host_ptrs.emplace_back(ggml_backend_buffer_get_base(host_buffers[idx]));
+                    events.emplace_back(ggml_backend_event_new(cuda_backend));
+                }
+            }
+        }
+#endif
+
        for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
            const auto * weight = get_weight(ggml_get_name(cur));
            if (weight == nullptr) {
@@ -3776,12 +3945,36 @@ struct llama_model_loader {
                        }));
                    }
                } else {
-                    read_buf.resize(n_size);
-                    file->seek(weight->offs, SEEK_SET);
-                    file->read_raw(read_buf.data(), n_size);
-                    ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
-                    if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
-                        throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
+#if defined(GGML_USE_CUDA)
+                    // If cuda_backend is valid load the tensor in chunks to pinned memory and upload the buffers asynchronously to the GPU.
+                    if (cuda_backend) {
+                        file->seek(weight->offs, SEEK_SET);
+
+                        size_t bytes_read = 0;
+
+                        while (bytes_read < n_size) {
+                            size_t read_iteration = std::min<size_t>(buffer_size, n_size - bytes_read);
+
+                            ggml_backend_event_synchronize(events[buffer_idx]);
+                            file->read_raw(host_ptrs[buffer_idx], read_iteration);
+                            ggml_backend_tensor_set_async(cuda_backend, cur, host_ptrs[buffer_idx], bytes_read, read_iteration);
+                            ggml_backend_event_record(events[buffer_idx]);
+
+                            bytes_read += read_iteration;
+                            ++buffer_idx;
+                            buffer_idx %= num_buffers;
+                        }
+                    }
+                    else
+#endif
+                    {
+                        read_buf.resize(n_size);
+                        file->seek(weight->offs, SEEK_SET);
+                        file->read_raw(read_buf.data(), n_size);
+                        ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
+                        if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
+                            throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
+                        }
                    }
                }
            }
@@ -3789,6 +3982,18 @@ struct llama_model_loader {
            size_done += n_size;
        }

+#if defined(GGML_USE_CUDA)
+        // free temporary resources used for async cuda uploads
+        if (cuda_backend) {
+            for (size_t idx = 0; idx < num_buffers;++idx) {
+                ggml_backend_event_synchronize(events[idx]);
+                ggml_backend_event_free(events[idx]);
+                ggml_backend_buffer_free(host_buffers[idx]);
+            }
+            ggml_backend_free(cuda_backend);
+        }
+#endif
+
        // check validation results
        bool validation_failed = false;
        for (auto & future : validation_result) {
@@ -4255,6 +4460,9 @@ static void llm_load_hparams(
            } break;
        case LLM_ARCH_QWEN2MOE:
            {
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                switch (hparams.n_layer) {
                    case 24: model.type = e_model::MODEL_A2_7B; break;
@@ -4563,7 +4771,7 @@ static void llm_load_vocab(

            const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
            if (add_space_prefix_keyidx != -1) {
-                vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
+                vocab.tokenizer_add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
            } // The default value of add_space_prefix is true.
        } else if (tokenizer_model == "bert") {
            vocab.type = LLAMA_VOCAB_TYPE_WPM;
@@ -4576,13 +4784,13 @@ static void llm_load_vocab(
            vocab.special_pad_id  = 0;
            vocab.special_cls_id  = 101;
            vocab.special_mask_id = 103;
-            vocab.add_space_prefix = false;
+            vocab.tokenizer_add_space_prefix = false;
        } else if (tokenizer_model == "gpt2") {
            vocab.type = LLAMA_VOCAB_TYPE_BPE;

            const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
            if (add_space_prefix_keyidx != -1) {
-                vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
+                vocab.tokenizer_add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
            }

            // read bpe merges and populate bpe ranks
@@ -4640,6 +4848,8 @@ static void llm_load_vocab(
                    tokenizer_pre == "llama-v3" ||
                    tokenizer_pre == "llama-bpe") {
                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
+                vocab.tokenizer_ignore_merges = true;
+                vocab.tokenizer_add_bos = true;
            } else if (
                    tokenizer_pre == "deepseek-llm") {
                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM;
@@ -4690,6 +4900,14 @@ static void llm_load_vocab(
            } else {
                throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
            }
+        } else if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
+            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            vocab.tokenizer_add_bos = true;
+            vocab.tokenizer_add_eos = false;
+        } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
+            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            vocab.tokenizer_add_bos = true;
+            vocab.tokenizer_add_eos = false;
        } else {
            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
        }
@@ -4834,10 +5052,10 @@ static void llm_load_vocab(
            bool temp = true;

            if (ml.get_key(LLM_KV_TOKENIZER_ADD_BOS, temp, false)) {
-                vocab.special_add_bos = int(temp);
+                vocab.tokenizer_add_bos = temp;
            }
            if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) {
-                vocab.special_add_eos = int(temp);
+                vocab.tokenizer_add_eos = temp;
            }
        }

@@ -4937,7 +5155,7 @@ static void llm_load_vocab(
        );

        // set attributes by model/tokenizer name
-        if (_contains_any(tokenizer_pre, {"jina-v2-es", "jina-v2-de"})) {
+        if (_contains_any(tokenizer_pre, {"jina-v2-de", "jina-v2-es", "jina-v2-code"})) {
            _set_token_attr("<mask>", LLAMA_TOKEN_ATTR_LSTRIP, true);
        } else if (_contains_any(model_name, {"phi-3", "phi3"})) {
            for (auto id : vocab.cache_special_tokens) {
@@ -5040,6 +5258,11 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
        LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
        LLAMA_LOG_INFO("%s: rope_yarn_log_mul    = %.4f\n",   __func__, hparams.rope_yarn_log_mul);
    }
+
+    if (model.arch == LLM_ARCH_QWEN2MOE) {
+        LLAMA_LOG_INFO("%s: n_ff_exp         = %d\n",     __func__, hparams.n_ff_exp);
+        LLAMA_LOG_INFO("%s: n_ff_shexp       = %d\n",     __func__, hparams.n_ff_shexp);
+    }
 }

 // Returns false if cancelled by progress_callback
@@ -5183,7 +5406,7 @@ static bool llm_load_tensors(
    // create tensors for the weights
    {
        const int64_t n_embd       = hparams.n_embd;
-        const int64_t n_embd_head  = n_embd / hparams.n_head;
+        const int64_t n_embd_head  = (hparams.n_head == 0) ? 0 : n_embd / hparams.n_head;
        const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
        const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
        const int64_t n_embd_gqa   = n_embd_v_gqa;
@@ -5826,16 +6049,17 @@ static bool llm_load_tensors(
                        GGML_ASSERT(hparams.n_expert_used > 0);

                        // MoE branch
-                        auto n_ff_exp = n_ff / hparams.n_expert_used;
+                        auto n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / hparams.n_expert_used;
                        layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert});
                        layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert});
                        layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert});

                        // Shared expert branch
+                        auto n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
                        layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd});
-                        layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd,   n_ff});
-                        layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {  n_ff, n_embd});
-                        layer.ffn_up_shexp   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd,   n_ff});
+                        layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp});
+                        layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd});
+                        layer.ffn_up_shexp   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp});
                    }
                } break;
            case LLM_ARCH_PHI2:
@@ -12945,112 +13169,142 @@ struct llm_bigram_bpe {
 };

 struct llm_tokenizer_bpe {
-    llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {}
+    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:
+                regex_exprs = {
+                    // original regex from tokenizer.json
+                    //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+
+                    // adapted: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2080233989
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_DBRX:
+            case LLAMA_VOCAB_PRE_TYPE_SMAUG:
+                regex_exprs = {
+                    // same as llama3
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM:
+                regex_exprs = {
+                    "[\r\n]",
+                    "\\s?[A-Za-zµÀ-ÖØ-öø-ƺƼ-ƿǄ-ʓʕ-ʯͰ-ͳͶͷͻ-ͽͿΆΈ-ΊΌΎ-ΡΣ-ϵϷ-ҁҊ-ԯԱ-ՖႠ-ჅᎠ-Ᏽᏸ-ᏽᲐ-ᲺᲽ-Ჿᴀ-ᴫᵫ-ᵷᵹ-ᶚḀ-ἕἘ-Ἕἠ-ὅὈ-Ὅὐ-ὗὙὛὝὟ-ώᾀ-ᾴᾶ-ᾼιῂ-ῄῆ-ῌῐ-ΐῖ-Ίῠ-Ῥῲ-ῴῶ-ῼℂℇℊ-ℓℕℙ-ℝℤΩℨK-ℭℯ-ℴℹℼ-ℿⅅ-ⅉⅎↃↄⰀ-ⱻⱾ-ⳤⳫ-ⳮⳲⳳꙀ-ꙭꚀ-ꚛꜢ-ꝯꝱ-ꞇꞋ-ꞎꭰ-ꮿﬀ-ﬆﬓ-ﬗＡ-Ｚａ-ｚ𐐀-𐑏𐒰-𐓓𐓘-𐓻𐲀-𐲲𐳀-𐳲𑢠-𑣟𞤀-𞥃]+",
+                    "\\s?[!-/:-~！-／：-～‘-‟　-。]+",
+                    "\\s+$",
+                    "[一-龥ࠀ-一가-퟿]+",
+                    "\\p{N}+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
+                regex_exprs = {
+                    "[\r\n]",
+                    "\\s?\\p{L}+",
+                    "\\s?\\p{P}+",
+                    "[一-龥ࠀ-一가-퟿]+",
+                    "\\p{N}",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_FALCON:
+                regex_exprs = {
+                    "[\\p{P}\\$\\+<=>\\^~\\|`]+",
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                    "[0-9][0-9][0-9]",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_MPT:
+                // TODO: MPT pre-tokenization regexes are unknown
+                //       the following are close, but not exact. run the following:
+                //       ./bin/test-tokenizer-0 ../models/ggml-vocab-mpt.gguf
+                GGML_ASSERT("MPT pre-tokenization regexes are unknown - fixes needed");
+                regex_exprs = {
+                    "\\s?\\p{L}+",
+                    "\\s?\\p{P}+",
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_STARCODER:
+            case LLAMA_VOCAB_PRE_TYPE_REFACT:
+            case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
+                regex_exprs = {
+                    "\\p{N}",
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_GPT2:
+            case LLAMA_VOCAB_PRE_TYPE_OLMO:
+                regex_exprs = {
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_STABLELM2:
+            case LLAMA_VOCAB_PRE_TYPE_QWEN2:
+                regex_exprs = {
+                    // original regex from tokenizer.json
+                    // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_PORO:
+                regex_exprs = {
+                    " ?[^(\\s|.,!?…。，、।۔،)]+",
+                };
+                break;
+            default:
+                // default regex for BPE tokenization pre-processing
+                regex_exprs = {
+                    "[\\p{P}\\$\\+<=>\\^~\\|]+",
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                    "\\p{N}+",
+                    "[0-9][0-9][0-9]",
+                };
+                break;
+        }
+    }
+
+    void append(const llama_vocab::id token_id, std::vector<llama_vocab::id> & output) const {
+        output.push_back(token_id);
+    }
+
+    bool append_bos(std::vector<llama_vocab::id> & output) const {
+        if (vocab.tokenizer_add_bos) {
+            GGML_ASSERT(vocab.special_bos_id != -1);
+            output.push_back(vocab.special_bos_id);
+            return true;
+        }
+        return false;
+    }
+
+    bool append_eos(std::vector<llama_vocab::id> & output) const {
+        if (vocab.tokenizer_add_eos) {
+            GGML_ASSERT(vocab.special_eos_id != -1);
+            output.push_back(vocab.special_eos_id);
+            return true;
+        }
+        return false;
+    }
+
+    void check_double_bos_eos(const std::vector<llama_vocab::id> & output) const {
+        if (vocab.tokenizer_add_bos && 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 (vocab.tokenizer_add_eos && output.size() >= 2 && *(output.end()-2) == vocab.special_eos_id) {
+            LLAMA_LOG_WARN(
+                "%s: Added a EOS token to the prompt as specified by the model but the prompt "
+                "also ends with a EOS token. So now the final prompt ends with 2 EOS tokens. "
+                "Are you sure this is what you want?\n", __FUNCTION__);
+        }
+    }

    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
        int final_prev_index = -1;
-        bool ignore_merges = false;

-        std::vector<std::string> word_collection;
-        switch (vocab.type) {
-            case LLAMA_VOCAB_TYPE_BPE:
-                switch (vocab.type_pre) {
-                    case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
-                        ignore_merges = true;
-                        word_collection = unicode_regex_split(text, {
-                            // original regex from tokenizer.json
-                            //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-
-                            // adapted: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2080233989
-                            "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_DBRX:
-                    case LLAMA_VOCAB_PRE_TYPE_SMAUG:
-                        word_collection = unicode_regex_split(text, {
-                            // same as llama3
-                            "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM:
-                        word_collection = unicode_regex_split(text, {
-                            "[\r\n]",
-                            "\\s?[A-Za-zµÀ-ÖØ-öø-ƺƼ-ƿǄ-ʓʕ-ʯͰ-ͳͶͷͻ-ͽͿΆΈ-ΊΌΎ-ΡΣ-ϵϷ-ҁҊ-ԯԱ-ՖႠ-ჅᎠ-Ᏽᏸ-ᏽᲐ-ᲺᲽ-Ჿᴀ-ᴫᵫ-ᵷᵹ-ᶚḀ-ἕἘ-Ἕἠ-ὅὈ-Ὅὐ-ὗὙὛὝὟ-ώᾀ-ᾴᾶ-ᾼιῂ-ῄῆ-ῌῐ-ΐῖ-Ίῠ-Ῥῲ-ῴῶ-ῼℂℇℊ-ℓℕℙ-ℝℤΩℨK-ℭℯ-ℴℹℼ-ℿⅅ-ⅉⅎↃↄⰀ-ⱻⱾ-ⳤⳫ-ⳮⳲⳳꙀ-ꙭꚀ-ꚛꜢ-ꝯꝱ-ꞇꞋ-ꞎꭰ-ꮿﬀ-ﬆﬓ-ﬗＡ-Ｚａ-ｚ𐐀-𐑏𐒰-𐓓𐓘-𐓻𐲀-𐲲𐳀-𐳲𑢠-𑣟𞤀-𞥃]+",
-                            "\\s?[!-/:-~！-／：-～‘-‟　-。]+",
-                            "\\s+$",
-                            "[一-龥ࠀ-一가-퟿]+",
-                            "\\p{N}+",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
-                        word_collection = unicode_regex_split(text, {
-                            "[\r\n]",
-                            "\\s?\\p{L}+",
-                            "\\s?\\p{P}+",
-                            "[一-龥ࠀ-一가-퟿]+",
-                            "\\p{N}",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_FALCON:
-                        word_collection = unicode_regex_split(text, {
-                            "[\\p{P}\\$\\+<=>\\^~\\|]+",
-                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                            "[0-9][0-9][0-9]",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_MPT:
-                        // TODO: MPT pre-tokenization regexes are unknown
-                        //       the following are close, but not exact. run the following:
-                        //       ./bin/test-tokenizer-0 ../models/ggml-vocab-mpt.gguf
-                        GGML_ASSERT("MPT pre-tokenization regexes are unknown - fixes needed");
-                        word_collection = unicode_regex_split(text, {
-                            "\\s?\\p{L}+",
-                            "\\s?\\p{P}+",
-                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_STARCODER:
-                    case LLAMA_VOCAB_PRE_TYPE_REFACT:
-                    case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
-                        word_collection = unicode_regex_split(text, {
-                            "\\p{N}",
-                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_GPT2:
-                    case LLAMA_VOCAB_PRE_TYPE_OLMO:
-                        word_collection = unicode_regex_split(text, {
-                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_STABLELM2:
-                    case LLAMA_VOCAB_PRE_TYPE_QWEN2:
-                        word_collection = unicode_regex_split(text, {
-                            // original regex from tokenizer.json
-                            // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
-                            "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_PORO:
-                        word_collection = unicode_regex_split(text, {
-                            " ?[^(\\s|.,!?…。，、।۔،)]+",
-                        });
-                        break;
-                    default:
-                        // default regex for BPE tokenization pre-processing
-                        word_collection = unicode_regex_split(text, {
-                            "[\\p{P}\\$\\+<=>\\^~\\|]+",
-                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                            "\\p{N}+",
-                            "[0-9][0-9][0-9]",
-                        });
-                        break;
-                }
-                break;
-            default:
-                GGML_ASSERT(false);
-                break;
-        }
+        const auto word_collection = unicode_regex_split(text, regex_exprs);

        symbols_final.clear();

@@ -13061,7 +13315,7 @@ struct llm_tokenizer_bpe {
            int index = 0;
            size_t offset = 0;

-            if (ignore_merges && vocab.token_to_id.find(word) != vocab.token_to_id.end()) {
+            if (vocab.tokenizer_ignore_merges && vocab.token_to_id.find(word) != vocab.token_to_id.end()) {
                symbols.emplace_back(llm_symbol{-1, -1, word.c_str(), word.size()});
                offset = word.size();
            }
@@ -13142,10 +13396,9 @@ struct llm_tokenizer_bpe {
                    for (auto j = str.begin(); j != str.end(); ++j) {
                        std::string byte_str(1, *j);
                        auto token_multibyte = vocab.token_to_id.find(byte_str);
-                        if (token_multibyte == vocab.token_to_id.end()) {
-                            throw std::runtime_error("ERROR: byte not found in vocab");
+                        if (token_multibyte != vocab.token_to_id.end()) {
+                            output.push_back(token_multibyte->second);
                        }
-                        output.push_back((*token_multibyte).second);
                    }
                } else {
                    output.push_back((*token).second);
@@ -13184,6 +13437,8 @@ private:

    const llama_vocab & vocab;

+    std::vector<std::string> regex_exprs;
+
    std::vector<llm_symbol> symbols;
    std::vector<llm_symbol> symbols_final;

@@ -13464,7 +13719,7 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &

                bool is_prev_special = false;

-                if (add_special && vocab.special_add_bos != 0) {
+                if (add_special && vocab.tokenizer_add_bos) {
                    GGML_ASSERT(vocab.special_bos_id != -1);
                    output.push_back(vocab.special_bos_id);
                    is_prev_special = true;
@@ -13474,7 +13729,7 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);

-                        if (vocab.add_space_prefix) {
+                        if (vocab.tokenizer_add_space_prefix) {
                            if (!output.size() || is_prev_special) {  // prefix with space if first token
                                raw_text = " " + raw_text;
                            }
@@ -13492,23 +13747,24 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
                    }
                }

-                if (add_special && vocab.special_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) {
+                if (add_special && vocab.tokenizer_add_bos && 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.special_add_eos == 1) {
+                if (add_special && vocab.tokenizer_add_eos) {
                    GGML_ASSERT(vocab.special_eos_id != -1);
                    output.push_back(vocab.special_eos_id);
                }
            } break;
        case LLAMA_VOCAB_TYPE_BPE:
            {
-                if (add_special && vocab.special_add_bos != 0) {
-                    GGML_ASSERT(vocab.special_bos_id != -1);
-                    output.push_back(vocab.special_bos_id);
+                llm_tokenizer_bpe tokenizer(vocab);
+
+                if (add_special) {
+                    tokenizer.append_bos(output);
                }

                for (const auto & fragment : fragment_buffer) {
@@ -13518,23 +13774,15 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
 #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_bpe tokenizer(vocab);
                        tokenizer.tokenize(raw_text, output);
                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        output.push_back(fragment.token);
+                        tokenizer.append(fragment.token, output);
                    }
                }

-                if (add_special && vocab.special_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.special_add_eos == 1) {
-                    GGML_ASSERT(vocab.special_add_eos != -1);
-                    output.push_back(vocab.special_eos_id);
+                if (add_special) {
+                    tokenizer.append_eos(output);
+                    tokenizer.check_double_bos_eos(output);
                }
            } break;
        case LLAMA_VOCAB_TYPE_WPM:
@@ -16060,6 +16308,11 @@ struct llama_context * llama_new_context_with_model(
        params.flash_attn = false;
    }

+    if (params.flash_attn && model->hparams.n_embd_head_k != model->hparams.n_embd_head_v) {
+        LLAMA_LOG_WARN("%s: flash_attn requires n_embd_head_k == n_embd_head_v - forcing off\n", __func__);
+        params.flash_attn = false;
+    }
+
    if (params.type_v != GGML_TYPE_F16 && !params.flash_attn) {
        LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__);
        return nullptr;
@@ -18102,11 +18355,11 @@ llama_token llama_token_nl(const struct llama_model * model) {
 }

 int32_t llama_add_bos_token(const struct llama_model * model) {
-    return model->vocab.special_add_bos;
+    return model->vocab.tokenizer_add_bos;
 }

 int32_t llama_add_eos_token(const struct llama_model * model) {
-    return model->vocab.special_add_eos;
+    return model->vocab.tokenizer_add_eos;
 }

 llama_token llama_token_prefix(const struct llama_model * model) {
@@ -1,83 +1,143 @@
-import regex
-import ctypes
+import array
 import unicodedata
-
-
-class CoodepointFlags (ctypes.Structure):
-    _fields_ = [  # see definition in unicode.h
-        ("is_undefined",   ctypes.c_uint16, 1),
-        ("is_number",      ctypes.c_uint16, 1),  # regex: \p{N}
-        ("is_letter",      ctypes.c_uint16, 1),  # regex: \p{L}
-        ("is_separator",   ctypes.c_uint16, 1),  # regex: \p{Z}
-        ("is_accent_mark", ctypes.c_uint16, 1),  # regex: \p{M}
-        ("is_punctuation", ctypes.c_uint16, 1),  # regex: \p{P}
-        ("is_symbol",      ctypes.c_uint16, 1),  # regex: \p{S}
-        ("is_control",     ctypes.c_uint16, 1),  # regex: \p{C}
-    ]
-
-
-assert (ctypes.sizeof(CoodepointFlags) == 2)
+import requests


 MAX_CODEPOINTS = 0x110000

-regex_number      = regex.compile(r'\p{N}')
-regex_letter      = regex.compile(r'\p{L}')
-regex_separator   = regex.compile(r'\p{Z}')
-regex_accent_mark = regex.compile(r'\p{M}')
-regex_punctuation = regex.compile(r'\p{P}')
-regex_symbol      = regex.compile(r'\p{S}')
-regex_control     = regex.compile(r'\p{C}')
-regex_whitespace  = regex.compile(r'\s')
+UNICODE_DATA_URL = "https://www.unicode.org/Public/UCD/latest/ucd/UnicodeData.txt"

-codepoint_flags = (CoodepointFlags * MAX_CODEPOINTS)()
+
+# see https://www.unicode.org/L2/L1999/UnicodeData.html
+def unicode_data_iter():
+    res = requests.get(UNICODE_DATA_URL)
+    res.raise_for_status()
+    data = res.content.decode()
+
+    prev = []
+
+    for line in data.splitlines():
+        # ej: 0000;<control>;Cc;0;BN;;;;;N;NULL;;;;
+        line = line.split(";")
+
+        cpt = int(line[0], base=16)
+        assert cpt < MAX_CODEPOINTS
+
+        cpt_lower = int(line[-2] or "0", base=16)
+        assert cpt_lower < MAX_CODEPOINTS
+
+        cpt_upper = int(line[-3] or "0", base=16)
+        assert cpt_upper < MAX_CODEPOINTS
+
+        categ = line[2].strip()
+        assert len(categ) == 2
+
+        bidir = line[4].strip()
+        assert len(categ) == 2
+
+        name = line[1]
+        if name.endswith(", First>"):
+            prev = (cpt, cpt_lower, cpt_upper, categ, bidir)
+            continue
+        if name.endswith(", Last>"):
+            assert prev[1:] == (0, 0, categ, bidir)
+            for c in range(prev[0], cpt):
+                yield (c, cpt_lower, cpt_upper, categ, bidir)
+
+        yield (cpt, cpt_lower, cpt_upper, categ, bidir)
+
+
+# see definition in unicode.h
+CODEPOINT_FLAG_UNDEFINED   = 0x0001  #
+CODEPOINT_FLAG_NUMBER      = 0x0002  # \p{N}
+CODEPOINT_FLAG_LETTER      = 0x0004  # \p{L}
+CODEPOINT_FLAG_SEPARATOR   = 0x0008  # \p{Z}
+CODEPOINT_FLAG_MARK        = 0x0010  # \p{M}
+CODEPOINT_FLAG_PUNCTUATION = 0x0020  # \p{P}
+CODEPOINT_FLAG_SYMBOL      = 0x0040  # \p{S}
+CODEPOINT_FLAG_CONTROL     = 0x0080  # \p{C}
+
+UNICODE_CATEGORY_TO_FLAG = {
+    "Cn": CODEPOINT_FLAG_UNDEFINED,    # Undefined
+    "Cc": CODEPOINT_FLAG_CONTROL,      # Control
+    "Cf": CODEPOINT_FLAG_CONTROL,      # Format
+    "Co": CODEPOINT_FLAG_CONTROL,      # Private Use
+    "Cs": CODEPOINT_FLAG_CONTROL,      # Surrrogate
+    "Ll": CODEPOINT_FLAG_LETTER,       # Lowercase Letter
+    "Lm": CODEPOINT_FLAG_LETTER,       # Modifier Letter
+    "Lo": CODEPOINT_FLAG_LETTER,       # Other Letter
+    "Lt": CODEPOINT_FLAG_LETTER,       # Titlecase Letter
+    "Lu": CODEPOINT_FLAG_LETTER,       # Uppercase Letter
+    "L&": CODEPOINT_FLAG_LETTER,       # Cased Letter
+    "Mc": CODEPOINT_FLAG_MARK,         # Spacing Mark
+    "Me": CODEPOINT_FLAG_MARK,         # Enclosing Mark
+    "Mn": CODEPOINT_FLAG_MARK,         # Nonspacing Mark
+    "Nd": CODEPOINT_FLAG_NUMBER,       # Decimal Number
+    "Nl": CODEPOINT_FLAG_NUMBER,       # Letter Number
+    "No": CODEPOINT_FLAG_NUMBER,       # Other Number
+    "Pc": CODEPOINT_FLAG_PUNCTUATION,  # Connector Punctuation
+    "Pd": CODEPOINT_FLAG_PUNCTUATION,  # Dash Punctuation
+    "Pe": CODEPOINT_FLAG_PUNCTUATION,  # Close Punctuation
+    "Pf": CODEPOINT_FLAG_PUNCTUATION,  # Final Punctuation
+    "Pi": CODEPOINT_FLAG_PUNCTUATION,  # Initial Punctuation
+    "Po": CODEPOINT_FLAG_PUNCTUATION,  # Other Punctuation
+    "Ps": CODEPOINT_FLAG_PUNCTUATION,  # Open Punctuation
+    "Sc": CODEPOINT_FLAG_SYMBOL,       # Currency Symbol
+    "Sk": CODEPOINT_FLAG_SYMBOL,       # Modifier Symbol
+    "Sm": CODEPOINT_FLAG_SYMBOL,       # Math Symbol
+    "So": CODEPOINT_FLAG_SYMBOL,       # Other Symbol
+    "Zl": CODEPOINT_FLAG_SEPARATOR,    # Line Separator
+    "Zp": CODEPOINT_FLAG_SEPARATOR,    # Paragraph Separator
+    "Zs": CODEPOINT_FLAG_SEPARATOR,    # Space Separator
+}
+
+
+codepoint_flags = array.array('H', [CODEPOINT_FLAG_UNDEFINED]) * MAX_CODEPOINTS
 table_whitespace = []
 table_lowercase = []
 table_uppercase = []
 table_nfd = []

-for codepoint in range(MAX_CODEPOINTS):
+for (cpt, cpt_lower, cpt_upper, categ, bidir) in unicode_data_iter():
    # convert codepoint to unicode character
-    char = chr(codepoint)
+    char = chr(cpt)

-    # regex categories
-    flags = codepoint_flags[codepoint]
-    flags.is_number      = bool(regex_number.match(char))
-    flags.is_letter      = bool(regex_letter.match(char))
-    flags.is_separator   = bool(regex_separator.match(char))
-    flags.is_accent_mark = bool(regex_accent_mark.match(char))
-    flags.is_punctuation = bool(regex_punctuation.match(char))
-    flags.is_symbol      = bool(regex_symbol.match(char))
-    flags.is_control     = bool(regex_control.match(char))
-    flags.is_undefined   = bytes(flags)[0] == 0
-    assert (not flags.is_undefined)
-
-    # whitespaces
-    if bool(regex_whitespace.match(char)):
-        table_whitespace.append(codepoint)
+    # codepoint category flags
+    codepoint_flags[cpt] = UNICODE_CATEGORY_TO_FLAG[categ]

    # lowercase conversion
-    lower = ord(char.lower()[0])
-    if codepoint != lower:
-        table_lowercase.append((codepoint, lower))
+    if cpt_lower:
+        table_lowercase.append((cpt, cpt_lower))

    # uppercase conversion
-    upper = ord(char.upper()[0])
-    if codepoint != upper:
-        table_uppercase.append((codepoint, upper))
+    if cpt_upper:
+        table_uppercase.append((cpt, cpt_upper))

    # NFD normalization
    norm = ord(unicodedata.normalize('NFD', char)[0])
-    if codepoint != norm:
-        table_nfd.append((codepoint, norm))
+    if cpt != norm:
+        table_nfd.append((cpt, norm))
+
+
+# whitespaces, see "<White_Space>" https://www.unicode.org/Public/UCD/latest/ucd/PropList.txt
+table_whitespace.extend(range(0x0009, 0x000D + 1))
+table_whitespace.extend(range(0x2000, 0x200A + 1))
+table_whitespace.extend([0x0020, 0x0085, 0x00A0, 0x1680, 0x2028, 0x2029, 0x202F, 0x205F, 0x3000])
+
+
+# sort by codepoint
+table_whitespace.sort()
+table_lowercase.sort()
+table_uppercase.sort()
+table_nfd.sort()


 # group ranges with same flags
 ranges_flags = [(0, codepoint_flags[0])]  # start, flags
 for codepoint, flags in enumerate(codepoint_flags):
-    if bytes(flags) != bytes(ranges_flags[-1][1]):
+    if flags != ranges_flags[-1][1]:
        ranges_flags.append((codepoint, flags))
-ranges_flags.append((MAX_CODEPOINTS, CoodepointFlags()))
+ranges_flags.append((MAX_CODEPOINTS, 0x0000))


 # group ranges with same nfd
@@ -90,8 +150,8 @@ for codepoint, norm in table_nfd:
    ranges_nfd[-1] = (start, codepoint, norm)


-# Generate 'unicode-data.cpp'
-
+# Generate 'unicode-data.cpp':
+#   python ./scripts//gen-unicode-data.py > unicode-data.cpp

 def out(line=""):
    print(line, end='\n')  # noqa
@@ -110,12 +170,12 @@ out("""\

 out("const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // start, flags // last=next_start-1")
 for codepoint, flags in ranges_flags:
-    flags = int.from_bytes(bytes(flags), "little")
    out("{0x%06X, 0x%04X}," % (codepoint, flags))
 out("};\n")

 out("const std::unordered_set<uint32_t> unicode_set_whitespace = {")
-out(", ".join("0x%06X" % cpt for cpt in table_whitespace))
+for codepoint in table_whitespace:
+    out("0x%06X," % codepoint)
 out("};\n")

 out("const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase = {")
@@ -1 +1 @@
-2aae01fd9b8f9399f343cf18f46f38996ef52e2c
+5653a195935ea3ac54652644c9daf154dbc1571b
@@ -43,8 +43,10 @@
 // [1] J. Tunney, ‘LLaMA Now Goes Faster on CPUs’, Mar. 2024. [Online].
 //     Available: https://justine.lol/matmul/. [Accessed: 29-Mar-2024].

+#if defined(__GNUC__)
 #pragma GCC diagnostic ignored "-Wpedantic"
 #pragma GCC diagnostic ignored "-Wignored-attributes"
+#endif

 #include "sgemm.h"
 #include "ggml-impl.h"
@@ -11,13 +11,15 @@ import logging
 import argparse
 import subprocess
 import random
+import unicodedata

 from typing import Callable, Iterator

 import cffi
 from transformers import AutoTokenizer

-logger = logging.getLogger("test-tokenizer-random-bpe")
+
+logger = logging.getLogger("test-tokenizer-random")


 class LibLlama:
@@ -155,9 +157,14 @@ def generator_custom_text_edge_cases() -> Iterator[str]:
        'Cửa Việt',   # llama-3, ignore_merges = true
        '<s>a',       # Phi-3 fail
        '<unk><|endoftext|><s>',  # Phi-3 fail
-        'a\na',       # TODO: Bert fail
-        'a </s> b',   # rstrip phi-3
-        'a <mask> b', # lstrip jina-v2
+        'a\na',            # bert fail
+        '"`',              # falcon
+        ' \u2e4e',         # falcon
+        'a\xa0\xa0\x00b',  # jina-v2-es
+        'one <mask>',      # jina-v2-es  <mask> lstrip=true
+        'a </s> b',        # rstrip phi-3
+        'a <mask> b',      # lstrip jina-v2
+        '\xa0aC',          # deepseek
    ]


@@ -189,17 +196,23 @@ def generator_random_added_tokens(tokenizer, iterations=100) -> Iterator[str]:
    for m in range(iterations):
        rand.seed(m)
        words = rand.choices(all_tokens, k=500)
-        if words[0] == tokenizer.bos_token:  # skip spam warning of double BOS
+        if words and words[0] == tokenizer.bos_token:  # skip spam warning of double BOS
            while len(words) > 1 and words[1] == tokenizer.bos_token:  # leave one starting BOS
                words.pop(0)
            if tokenizer.add_bos_token:  # drop all starting BOS
                words.pop(0)
+        if words and words[-1] == tokenizer.eos_token:  # skip spam warning of double EOS
+            while len(words) > 1 and words[-2] == tokenizer.eos_token:  # leave one trailing EOS
+                words.pop(-1)
+            if tokenizer.add_bos_token:  # drop all trailing EOS
+                words.pop(-1)
        yield "".join(words)


 def generator_random_chars(iterations=100) -> Iterator[str]:
    """Brute force random text with simple characters"""

+    NUM_WORDS = 400
    WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
    CHARS = list(sorted(set("""
        ABCDEFGHIJKLMNOPQRSTUVWXYZ
@@ -213,12 +226,50 @@ def generator_random_chars(iterations=100) -> Iterator[str]:
    for m in range(iterations):
        rand.seed(m)
        text = []
-        num_words = rand.randint(300, 400)
-        for i in range(num_words):
+        for _ in range(NUM_WORDS):
            k = rand.randint(1, 7)
            word = rand.choices(CHARS, k=k)
-            space = rand.choice(WHITESPACES)
-            text.append("".join(word) + space)
+            word.append(rand.choice(WHITESPACES))
+            text.append("".join(word))
+        yield "".join(text)
+
+
+def generator_unicodes() -> Iterator[str]:
+    """Iterate unicode characters"""
+
+    MAX_CODEPOINTS = 0x30000  # 0x110000
+
+    def _valid(cpt):
+        if cpt >= 0x30000:  # unassigned and supplementary
+            return False
+        if 0x00D800 <= cpt <= 0x00F8FF:  # Surrogates
+            return False
+        if unicodedata.category(chr(cpt)) == "Cn":
+            return False
+        return True
+
+    characters = [chr(cpt) for cpt in range(1, MAX_CODEPOINTS) if _valid(cpt)]
+
+    yield from characters
+
+
+def generator_random_unicodes(iterations=100) -> Iterator[str]:
+    """Brute force random text with unicode characters"""
+
+    NUM_WORDS = 200
+    WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
+
+    characters = list(generator_unicodes())
+
+    rand = random.Random()
+    for m in range(iterations):
+        rand.seed(m)
+        text = []
+        for _ in range(NUM_WORDS):
+            k = rand.randint(1, 7)
+            word = rand.choices(characters, k=k)
+            word.append(rand.choice(WHITESPACES))
+            text.append("".join(word))
        yield "".join(text)


@@ -256,25 +307,7 @@ def generator_random_vocab_words(vocab: list[str], iterations=100) -> Iterator[s
        yield "".join(text)


-def generator_random_bytes(iterations=100) -> Iterator[str]:
-    """Brute force random bytes"""
-
-    WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
-
-    rand = random.Random()
-    for m in range(iterations):
-        rand.seed(m)
-        text = []
-        num_words = rand.randint(300, 400)
-        for i in range(num_words):
-            k = rand.randint(1, 8)
-            word = [chr(r) for r in rand.randbytes(k) if r]
-            word.append(rand.choice(WHITESPACES))
-            text.append("".join(word))
-        yield "".join(text)
-
-
-def test_compare_tokenizer(func_tokenize1: Callable, func_tokenize2: Callable, generator: Iterator[str]):
+def compare_tokenizers(func_tokenize1: Callable, func_tokenize2: Callable, generator: Iterator[str]):

    def find_first_mismatch(ids1: list[int], ids2: list[int]):
        for i, (a, b) in enumerate(zip(ids1, ids2)):
@@ -284,20 +317,34 @@ def test_compare_tokenizer(func_tokenize1: Callable, func_tokenize2: Callable, g
            return -1
        return min(len(ids1), len(ids2))

-    t0 = time.perf_counter()
+    t_tokenizer1 = 0
+    t_tokenizer2 = 0
+    t_start = time.perf_counter()
+    num_errors = 10
+
    logger.info("%s: %s" % (generator.__name__, "ini"))
    for text in generator:
+        # print(repr(text), hex(ord(text[0])), text.encode())
+        t0 = time.perf_counter()
        ids1 = func_tokenize1(text)
+        t1 = time.perf_counter()
        ids2 = func_tokenize2(text)
+        t2 = time.perf_counter()
+        t_tokenizer1 += t1 - t0
+        t_tokenizer2 += t2 - t1
        if ids1 != ids2:
            i = find_first_mismatch(ids1, ids2)
            ids1 = list(ids1)[max(0, i - 2) : i + 5 + 1]
            ids2 = list(ids2)[max(0, i - 2) : i + 5 + 1]
-            logger.info(" TokenIDs: " + str(ids1))
-            logger.info(" Expected: " + str(ids2))
-            raise Exception()
-    t1 = time.perf_counter()
-    logger.info("%s: end, time: %.3f secs" % (generator.__name__, t1 - t0))
+            logger.error(" TokenIDs: " + str(ids1))
+            logger.error(" Expected: " + str(ids2))
+            # raise Exception()
+            num_errors += 1
+            if num_errors > 10:
+                break
+
+    t_total = time.perf_counter() - t_start
+    logger.info("%s: end,  tok1: %.3f  tok2: %.3f  total: %.3f" % (generator.__name__, t_tokenizer1, t_tokenizer2, t_total))


 def main(argv: list[str] = None):
@@ -307,7 +354,8 @@ def main(argv: list[str] = None):
    parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
    args = parser.parse_args(argv)

-    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+    logging.basicConfig(level = logging.DEBUG if args.verbose else logging.INFO)
+    logger.info(f"VOCABFILE: '{args.vocab_file}'")

    model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096))
    tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer)
@@ -321,18 +369,22 @@ def main(argv: list[str] = None):
    ids = func_tokenize2("a")
    assert 1 <= len(ids) <= 3
    add_bos_token = len(ids) > 1 and tokenizer.bos_token_id == ids[0]
+    add_eos_token = len(ids) > 1 and tokenizer.eos_token_id == ids[-1]
    tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", add_bos_token)
+    tokenizer.add_eos_token = getattr(tokenizer, "add_eos_token", add_eos_token)

    vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True)))
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text())
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases())
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab))
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_added_lr_strip(tokenizer))
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_added_tokens(tokenizer, 10_000))
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000))
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000))
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 5_000))
-    # test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_bytes(10_000)) # FAIL
+
+    compare_tokenizers(func_tokenize1, func_tokenize2, generator_custom_text())
+    compare_tokenizers(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases())
+    compare_tokenizers(func_tokenize1, func_tokenize2, generator_unicodes())
+    compare_tokenizers(func_tokenize1, func_tokenize2, generator_vocab_words(vocab))
+    compare_tokenizers(func_tokenize1, func_tokenize2, generator_added_lr_strip(tokenizer))
+    compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_added_tokens(tokenizer, 10_000))
+    compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_chars(10_000))
+    compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_unicodes(10_000))
+    compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000))
+    compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 5_000))

    model.free()

@@ -340,20 +392,40 @@ def main(argv: list[str] = None):
 if __name__ == "__main__":
    # main()

+    logging.basicConfig(
+        level    = logging.DEBUG,
+        format   = "%(asctime)s.%(msecs)03d %(name)s %(levelname)s %(message)s",
+        datefmt  = "%Y-%m-%d %H:%M:%S",
+        filename = logger.name + ".log",
+        filemode = "a"
+    )
+
    path_tokenizers   = "./models/tokenizers/"
    path_vocab_format = "./models/ggml-vocab-%s.gguf"

    # import os
    # tokenizers = os.listdir(path_tokenizers)
    tokenizers = [
-        "llama-spm",   # SPM
-        "phi-3",       # SPM
-        "jina-v2-en",  # WPM
-        "bert-bge",    # WPM
+        # "llama-spm",   # SPM
+        # "phi-3",       # SPM
+        # "bert-bge",    # WPM
+        # "jina-v2-en",  # WPM
+        "gpt-2",          # BPE
+        "llama-bpe",      # BPE
+        "falcon",         # BPE
+        "starcoder",      # BPE
+        "jina-v2-es",     # BPE
+        "jina-v2-de",     # BPE
+        "jina-v2-code",   # BPE
+        "smaug-bpe",      # BPE
+        "phi-2",          # BPE
+        "deepseek-coder", # BPE
+        "deepseek-llm",   # BPE
    ]

    for tokenizer in tokenizers:
-        print("\n" + "=" * 50 + "\n" + tokenizer + "\n")  # noqa
+        logger.info("=" * 50)
+        logger.info(f"TOKENIZER: '{tokenizer}'")
        vocab_file = path_vocab_format % tokenizer
        dir_tokenizer = path_tokenizers + "/" + tokenizer
        main([vocab_file, dir_tokenizer, "--verbose"])
@@ -226,8 +226,9 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t
        assert(offset_end <= cpts.size());
        start = offset_end;

+        static const uint32_t OUT_OF_RANGE = 0xFFFFFFFF;
        auto _get_cpt = [&] (const size_t pos) -> uint32_t {
-            return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0;
+            return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : OUT_OF_RANGE;
        };

        auto _get_flags = [&] (const size_t pos) -> codepoint_flags {
@@ -309,7 +310,7 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t
            }

            // regex: \s+(?!\S)
-            if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != 0) {
+            if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != OUT_OF_RANGE) {
                pos += num_whitespaces - 1;
                _add_token(pos);
                continue;
@@ -344,8 +345,9 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
        assert(offset_end <= cpts.size());
        start = offset_end;

+        static const uint32_t OUT_OF_RANGE = 0xFFFFFFFF;
        auto _get_cpt = [&] (const size_t pos) -> uint32_t {
-            return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0;
+            return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : OUT_OF_RANGE;
        };

        auto _get_flags = [&] (const size_t pos) -> codepoint_flags {
@@ -450,7 +452,7 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
            }

            // regex: \s+(?!\S)
-            if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != 0) {
+            if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != OUT_OF_RANGE) {
                pos += num_whitespaces - 1;
                _add_token(pos);
                continue;
@@ -679,10 +681,14 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
                continue;
            }

-            const int cpt_flag = unicode_cpt_flags(cpts[i]).category_flag();
+            const auto flags = unicode_cpt_flags(cpts[i]);

-            if (k_ucat_cpt.find(cpt_flag) != k_ucat_cpt.end()) {
-                text_collapsed[i] = k_ucat_cpt.at(cpt_flag);
+            if (flags.is_whitespace) {
+                //NOTE: C++ std::regex \s does not mach 0x85, Rust and Python regex does.
+                //text_collapsed[i] = (char) 0x85;  // <Next Line> as whitespace fallback
+                text_collapsed[i] = (char) 0x0B;    // <vertical tab> as whitespace fallback
+            } else if (k_ucat_cpt.find(flags.category_flag()) != k_ucat_cpt.end()) {
+                text_collapsed[i] = k_ucat_cpt.at(flags.category_flag());
            } else {
                text_collapsed[i] = (char) 0xD0; // fallback
            }
@@ -766,9 +772,16 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
                bpe_offsets = unicode_regex_split_stl(text_collapsed, regex_expr_collapsed, bpe_offsets);
            } else {
                // no unicode category used, we can use std::wregex directly
-                const std::wstring wtext       = unicode_wstring_from_utf8(text);
                const std::wstring wregex_expr = unicode_wstring_from_utf8(regex_expr);

+                // std::wregex \s does not mach non-ASCII whitespaces, using 0x0B as fallback
+                std::wstring wtext(cpts.begin(), cpts.end());
+                for (size_t i = 0; i < wtext.size(); ++i) {
+                    if (wtext[i] > 0x7F && unicode_cpt_flags(wtext[i]).is_whitespace) {
+                        wtext[i] = 0x0B;
+                    }
+                }
+
                //printf("text: %s\n", text.c_str());
                //printf("regex_expr: %s\n", regex_expr.c_str());
                bpe_offsets = unicode_regex_split_stl(wtext, wregex_expr, bpe_offsets);
Author	SHA1	Message	Date
jaime-m-p	37bef89433	tokenizer : BPE fixes (#7530 ) * Random test: add_bos_token, add_eos_token * Random test: add BPE models for testing * Custom regex split fails with codepoint 0 * Fix falcon punctuation regex * Refactor llm_tokenizer_bpe: move code to constructor * Move 'add_special_bos/eos' logic to llm_tokenizer_bpe * Move tokenizer flags to vocab structure. * Default values for special_add_bos/eos * Build vocab.special_tokens_cache using vocab token types * Generalize 'jina-v2' per token attributes * Fix unicode whitespaces (deepseek-coder, deepseek-llm) * Skip missing byte tokens (falcon) * Better unicode data generation * Replace char32_t with uint32_t	2024-06-18 18:40:52 +02:00
Sigbjørn Skjæret	91c188d6c2	Only use FIM middle token if it exists (#7648 ) * Only use FIM middle if it exists * Only use FIM middle if it exists	2024-06-18 22:19:45 +10:00
jojorne	84f6de17f6	Fix no gcc pragma on Windows (#7751 )	2024-06-18 22:18:32 +10:00
Ulrich Drepper	61665277af	Allow compiling with CUDA without CUDA runtime installed (#7989 ) On hosts which are not prepared/dedicated to execute code using CUDA it is still possible to compile llama.cpp with CUDA support by just installing the development packages. Missing are the runtime libraries like /usr/lib64/libcuda.so* and currently the link step will fail. The development environment is prepared for such situations. There are stub libraries for all the CUDA libraries available in the $(CUDA_PATH)/lib64/stubs directory. Adding this directory to the end of the search path will not change anything for environments which currently work fine but will enable compiling llama.cpp also in case the runtime code is not available.	2024-06-18 14:00:14 +02:00
Frank Mai	b96f9afb0d	chore: clean useless beam search param (#7985 ) Signed-off-by: thxCode <thxcode0824@gmail.com>	2024-06-18 10:11:40 +03:00
Abheek Gulati	1193778105	readme : update UI list (#7943 )	2024-06-18 09:57:41 +03:00
Georgi Gerganov	5326bcceeb	ggml : sync	2024-06-18 09:50:45 +03:00
Georgi Gerganov	e6ecc2be47	whisper : use ggml_backend_sched (whisper/2239) * whisper : use ggml_backend_sched (wip) * use sched in whisper_allocr * whisper : single backend in whisper_context * whisper : remove whisper_state->backends_used * whisper : remove whisper_context->backend * whisper : reset scheduler after init * whisper : fix external encoder (e.g. CoreML) * whisper : cleanup * whisper : handle null GPU buffer types + fix sycl --------- Co-authored-by: slaren <slarengh@gmail.com>	2024-06-18 09:50:40 +03:00
Ștefan-Gabriel Muscalu	a94e6ff877	update: support Qwen2-57B-A14B (#7835 ) * update: convert-hf-to-gguf.py to support Qwen2-57B-A14B * fix: QWEN2MOE support for expert_feed_forward_length previously, expert ff was taken from n_ff (intermediate size) but it is now properly taken from LLM_KV_EXPERT_FEED_FORWARD_LENGTH n_ff_exp and n_ff_shared_exp are now properly calculated * update: convert-hf-to-gguf.py cleanup for Qwen2MoeForCausalLM * fix: QWEN2MOE support for expert_feed_forward_length previously, expert ff was taken from n_ff (intermediate size) but it is now properly taken from LLM_KV_EXPERT_FEED_FORWARD_LENGTH n_ff_exp and n_ff_shexp are now properly calculated	2024-06-17 21:08:46 +02:00
Srihari-mcw	5b6da18750	Make updates to type cast based on compiler instead of OS (#7851 )	2024-06-17 20:23:17 +02:00
Georgi Gerganov	7c26775adb	llama : disable FA if KV head size do not match (#7982 )	2024-06-17 19:40:01 +03:00
Bryan Honof	b473e95084	Add Nix and Flox install instructions (#7899 )	2024-06-17 09:37:55 -06:00
slaren	99052cd227	sched : offload_op also requires supports_op (#7977 )	2024-06-17 16:51:42 +02:00
Frank Mai	c637fcd34d	fix: divide 0 exception in mamba (#7932 ) Signed-off-by: thxCode <thxcode0824@gmail.com>	2024-06-17 16:11:08 +02:00
Markus Tavenrath	6a2f0b3474	Implement non-mapped async IO for CUDA on Windows. (#7896 ) * Implement non-mapped async IO for CUDA on Windows. On a fast Gen5 NVMe drive this change improves model load time by >3x while it should be the same (or slightly faster) on any other drive. * Free resources except for backend. * Change assertions to exceptions in llama_file, find correct cuda backend to create CUDA resources and respect the use_mmap flag again for CUDA. * Apply suggestions from code review Co-authored-by: slaren <slarengh@gmail.com> * Fix editorconfig and unused variable * Fix issues with Windows build --------- Co-authored-by: slaren <slarengh@gmail.com>	2024-06-17 16:10:15 +02:00