diff --git a/CMakeLists.txt b/CMakeLists.txt index dee3534af..0ca43bddc 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -355,6 +355,11 @@ else() message(STATUS "Unknown architecture") endif() +if (MINGW) + # Target Windows 8 for PrefetchVirtualMemory + add_compile_definitions(_WIN32_WINNT=0x602) +endif() + # # Build libraries # diff --git a/common/log.h b/common/log.h index c0e814861..e4e1b9f4f 100644 --- a/common/log.h +++ b/common/log.h @@ -61,13 +61,13 @@ // #define LOG_TARGET stderr // #include "log.h" // -// The log target can also be redirected to a diffrent function +// The log target can also be redirected to a different function // like so: // -// #define LOG_TARGET log_handler_diffrent() +// #define LOG_TARGET log_handler_different() // #include "log.h" // -// FILE* log_handler_diffrent() +// FILE* log_handler_different() // { // return stderr; // } @@ -421,7 +421,7 @@ inline FILE *log_handler2_impl(bool change = false, LogTriState append = LogTriS // Disables logs entirely at runtime. // Makes LOG() and LOG_TEE() produce no output, -// untill enabled back. +// until enabled back. #define log_disable() log_disable_impl() // INTERNAL, DO NOT USE diff --git a/convert.py b/convert.py index 6e95d6cb3..a6fc6b8ea 100755 --- a/convert.py +++ b/convert.py @@ -585,7 +585,7 @@ def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus: if any("model.embed_tokens.weight" in mp.model for mp in models_plus): # Transformers models put different tensors in different files, but - # don't split indivdual tensors between files. + # don't split individual tensors between files. model: LazyModel = {} for mp in models_plus: model.update(mp.model) @@ -678,7 +678,7 @@ class LazyUnpickler(pickle.Unpickler): return func(*args) CLASSES: dict[tuple[str, str], Any] = { - # getattr used here as a workaround for mypy not being smart enough to detrmine + # getattr used here as a workaround for mypy not being smart enough to determine # the staticmethods have a __func__ attribute. ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'), ('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'), diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index fc0656c23..4bb7b93b6 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -739,7 +739,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip temp->ny = longer_side; temp->size = 3 * longer_side * longer_side; temp->data = new uint8_t[temp->size](); - uint8_t bc[3] = {122, 116, 104}; // bakground color in RGB from LLaVA + uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA // fill with background color for (size_t i = 0; i < temp->size; i++) { diff --git a/examples/llava/convert-image-encoder-to-gguf.py b/examples/llava/convert-image-encoder-to-gguf.py index 729aaef8f..03688e0ea 100644 --- a/examples/llava/convert-image-encoder-to-gguf.py +++ b/examples/llava/convert-image-encoder-to-gguf.py @@ -51,7 +51,7 @@ def bytes_to_unicode(): The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. - This is a signficant percentage of your normal, say, 32K bpe vocab. + This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on. """ diff --git a/examples/lookahead/README.md b/examples/lookahead/README.md index 252a6689e..a69a471b4 100644 --- a/examples/lookahead/README.md +++ b/examples/lookahead/README.md @@ -1,6 +1,6 @@ # llama.cpp/examples/lookahead -Demonstartion of lookahead decoding technique: +Demonstration of lookahead decoding technique: https://lmsys.org/blog/2023-11-21-lookahead-decoding/ diff --git a/examples/server/README.md b/examples/server/README.md index cfc220f58..0751b9612 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -222,7 +222,7 @@ node index.js `content`: Set the text to process. - **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream. +- **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream. *Options:* diff --git a/examples/server/json.hpp b/examples/server/json.hpp index 4d1a37ad7..ea945f346 100644 --- a/examples/server/json.hpp +++ b/examples/server/json.hpp @@ -11227,7 +11227,7 @@ class binary_reader } if (is_ndarray) // ndarray dimensional vector can only contain integers, and can not embed another array { - return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read, exception_message(input_format, "ndarray dimentional vector is not allowed", "size"), nullptr)); + return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read, exception_message(input_format, "ndarray dimensional vector is not allowed", "size"), nullptr)); } std::vector dim; if (JSON_HEDLEY_UNLIKELY(!get_ubjson_ndarray_size(dim))) diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js index b9c442509..c281f0fbd 100644 --- a/examples/server/public/completion.js +++ b/examples/server/public/completion.js @@ -114,7 +114,7 @@ export async function* llama(prompt, params = {}, config = {}) { return content; } -// Call llama, return an event target that you can subcribe to +// Call llama, return an event target that you can subscribe to // // Example: // diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 175c52478..451fd4a3b 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -223,7 +223,7 @@ repeat_last_n: 256, // 0 = disable penalty, -1 = context size repeat_penalty: 1.18, // 1.0 = disabled top_k: 40, // <= 0 to use vocab size - top_p: 0.5, // 1.0 = disabled + top_p: 0.95, // 1.0 = disabled min_p: 0.05, // 0 = disabled tfs_z: 1.0, // 1.0 = disabled typical_p: 1.0, // 1.0 = disabled @@ -238,7 +238,7 @@ cache_prompt: true }) - /* START: Support for storing prompt templates and parameters in borwser LocalStorage */ + /* START: Support for storing prompt templates and parameters in browsers LocalStorage */ const local_storage_storageKey = "llamacpp_server_local_storage"; @@ -282,7 +282,7 @@ let importedTemplates = local_storage_getDataAsObject('user_templates') if (importedTemplates) { - // saved templates were successfuly imported. + // saved templates were successfully imported. console.log('Processing saved templates and updating default template') params.value = { ...params.value, image_data: [] }; @@ -303,7 +303,7 @@ } function userTemplateResetToDefault() { - console.log('Reseting themplate to default') + console.log('Resetting template to default') selectedUserTemplate.value.name = 'default'; selectedUserTemplate.value.data = savedUserTemplates.value['default']; } @@ -762,7 +762,7 @@
${IntField({ label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict })} - ${FloatField({ label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })} + ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })} ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })} ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })} ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })} diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 9f464a4ea..b13951432 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2383,6 +2383,7 @@ json oaicompat_completion_params_parse( llama_params["__oaicompat"] = true; // Map OpenAI parameters to llama.cpp parameters + llama_params["model"] = json_value(body, "model", std::string("uknown")); llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt' llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); llama_params["temperature"] = json_value(body, "temperature", 0.8); diff --git a/examples/speculative/README.md b/examples/speculative/README.md index d88fd3790..814efa592 100644 --- a/examples/speculative/README.md +++ b/examples/speculative/README.md @@ -1,6 +1,6 @@ # llama.cpp/examples/speculative -Demonstartion of speculative decoding and tree-based speculative decoding techniques +Demonstration of speculative decoding and tree-based speculative decoding techniques More info: diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index e4d7f64d8..d2174eb51 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -430,7 +430,7 @@ int main(int argc, char ** argv) { ++n_past_tgt; } - // the first token is always proposed by the traget model before the speculation loop so we erase it here + // the first token is always proposed by the target model before the speculation loop so we erase it here for (int s = 0; s < n_seq_dft; ++s) { if (!drafts[s].active) { continue; diff --git a/ggml-alloc.h b/ggml-alloc.h index ad87cebc8..64a412468 100644 --- a/ggml-alloc.h +++ b/ggml-alloc.h @@ -43,7 +43,7 @@ GGML_API size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph // ggml-backend v2 API // -// Seperate tensor and graph allocator objects +// Separate tensor and graph allocator objects // This is necessary for multi-backend allocation because the graph allocator needs to use multiple tensor allocators // The original API is kept as a wrapper around the new API diff --git a/ggml-quants.c b/ggml-quants.c index 2879ab946..ca3a4980a 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3116,7 +3116,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri size_t vl = __riscv_vsetvl_e8m1(qk/2); - // These tempory registers are for masking and shift operations + // These temporary registers are for masking and shift operations vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); vuint32m2_t vt_2 = __riscv_vsll_vv_u32m2(__riscv_vmv_v_x_u32m2(1, vl), vt_1, vl); @@ -4759,7 +4759,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri vl = 16; - // retreive lane to multiply with scale + // retrieve lane to multiply with scale vint32m2_t aux0_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 0), (scale[0]), vl); vint32m2_t aux0_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 1), (scale[1]), vl); vint32m2_t aux1_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 0), (scale[2]), vl); diff --git a/ggml.c b/ggml.c index 74cd646d4..c1bb6a448 100644 --- a/ggml.c +++ b/ggml.c @@ -1,4 +1,4 @@ -#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows +#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnings on Windows #define _USE_MATH_DEFINES // For M_PI on MSVC #include "ggml-impl.h" @@ -33,7 +33,7 @@ // we should just be careful :) #pragma warning(disable: 4244 4267) -// disable POSIX deprecation warnigns +// disable POSIX deprecation warnings // these functions are never going away, anyway #pragma warning(disable: 4996) #endif @@ -1760,7 +1760,7 @@ static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); // WARN: -// Mis-confguration can lead to problem that's hard to reason about: +// Mis-configuration can lead to problem that's hard to reason about: // * At best it crash or talks nosense. // * At worst it talks slightly difference but hard to perceive. // @@ -7520,7 +7520,7 @@ static void ggml_compute_forward_acc_f32( GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); // view src0 and dst with these strides and data offset inbytes during acc - // nb0 is implicitely element_size because src0 and dst are contiguous + // nb0 is implicitly element_size because src0 and dst are contiguous size_t nb1 = ((int32_t *) dst->op_params)[0]; size_t nb2 = ((int32_t *) dst->op_params)[1]; size_t nb3 = ((int32_t *) dst->op_params)[2]; @@ -10161,7 +10161,7 @@ static void ggml_compute_forward_set_f32( GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); // view src0 and dst with these strides and data offset inbytes during set - // nb0 is implicitely element_size because src0 and dst are contiguous + // nb0 is implicitly element_size because src0 and dst are contiguous size_t nb1 = ((int32_t *) dst->op_params)[0]; size_t nb2 = ((int32_t *) dst->op_params)[1]; size_t nb3 = ((int32_t *) dst->op_params)[2]; @@ -14475,7 +14475,7 @@ void ggml_build_backward_gradient_checkpointing( // insert new tensors recomputing src, reusing already made replacements, // remember replacements: remember new tensors with mapping from corresponding gf nodes // recurse for input tensors, - // unless (i.e. terminating when) input tensors are replacments (like checkpoints) + // unless (i.e. terminating when) input tensors are replacements (like checkpoints) node->src[k] = ggml_recompute_graph_node(ctx, gf, replacements, node->src[k]); } // insert rewritten backward node with replacements made into resulting backward graph gb diff --git a/ggml.h b/ggml.h index 84b53946e..47eb5fab8 100644 --- a/ggml.h +++ b/ggml.h @@ -215,7 +215,7 @@ #define GGML_QNT_VERSION_FACTOR 1000 // do not change this #define GGML_MAX_DIMS 4 -#define GGML_MAX_PARAMS 1024 +#define GGML_MAX_PARAMS 2048 #define GGML_MAX_CONTEXTS 64 #define GGML_MAX_SRC 6 #define GGML_MAX_NAME 64 diff --git a/gguf-py/README.md b/gguf-py/README.md index 502b6a510..a27d2fc0e 100644 --- a/gguf-py/README.md +++ b/gguf-py/README.md @@ -61,7 +61,7 @@ If you want to publish the package manually for any reason, you need to have `tw pip install build twine ``` -Then, folow these steps to release a new version: +Then, follow these steps to release a new version: 1. Bump the version in `pyproject.toml`. 2. Build the package: diff --git a/llama.cpp b/llama.cpp index 8bce9ef6b..4f880889a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2788,7 +2788,7 @@ static void llm_load_vocab( // The assumption is, since special tokens aren't meant to be exposed to end user, they are designed // to be unmatchable by the tokenizer, therefore tokens from the vocab, which are unmatchable by the tokenizer // are special tokens. - // From testing, this appears to corelate 1:1 with special tokens. + // From testing, this appears to correlate 1:1 with special tokens. // // Counting special tokens and verifying in only one direction @@ -5876,7 +5876,7 @@ static int llama_decode_internal( const int64_t n_embd = hparams.n_embd; const int64_t n_vocab = hparams.n_vocab; - // helpers for smoother batch API transistion + // helpers for smoother batch API transition // after deprecating the llama_eval calls, these will be removed std::vector pos; @@ -6876,12 +6876,12 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< // loop over the text while (true) { - // find the first occurence of a given special token in this fragment + // find the first occurrence of a given special token in this fragment // passing offset argument only limit the "search area" but match coordinates // are still relative to the source full raw_text auto match = raw_text->find(special_token, raw_text_base_offset); - // no occurences found, stop processing this fragment for a given special token + // no occurrences found, stop processing this fragment for a given special token if (match == std::string::npos) break; // check if match is within bounds of offset <-> length @@ -7766,7 +7766,7 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c for (size_t i = 0; i < candidates->size; ++i) { const llama_token id = candidates->data[i].id; - const std::string & piece = ctx->model.vocab.id_to_token[id].text; + const std::string piece = llama_token_to_piece(ctx, id); if (id == eos) { if (!allow_eos) { candidates->data[i].logit = -INFINITY; @@ -7978,7 +7978,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar GGML_ASSERT(false); } - const std::string & piece = ctx->model.vocab.id_to_token[token].text; + const std::string piece = llama_token_to_piece(ctx, token); // Note terminating 0 in decoded string const auto decoded = decode_utf8(piece, grammar->partial_utf8); @@ -8092,7 +8092,7 @@ struct llama_beam_search_data { } // Min-heaps are used to efficiently collect the top-k elements (k=n_beams). - // The repetative patterns below reflect the 2 stages of heaps: + // The repetitive patterns below reflect the 2 stages of heaps: // * Gather elements until the vector is full, then call std::make_heap() on it. // * If the heap is full and a new element is found that should be included, pop the // least element to the back(), replace it with the new, then push it into the heap. diff --git a/llama.h b/llama.h index 5592166e3..d713821ce 100644 --- a/llama.h +++ b/llama.h @@ -216,7 +216,7 @@ extern "C" { // Keep the booleans together to avoid misalignment during copy-by-value. bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true) - bool logits_all; // the llama_eval() call computes all logits, not just the last one + bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embedding; // embedding mode only bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU };