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b/LICENSE index 76f67efdc..acb96ce78 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2023 Georgi Gerganov +Copyright (c) 2023-2024 The ggml authors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.md b/README.md index 9e3cf030c..e5dc937c8 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Recent API changes +- [2024 Apr 4] State and session file functions reorganized under `llama_state_*` https://github.com/ggerganov/llama.cpp/pull/6341 - [2024 Mar 26] Logits and embeddings API updated for compactness https://github.com/ggerganov/llama.cpp/pull/6122 - [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017 - [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328 @@ -120,6 +121,9 @@ Typically finetunes of the base models below are supported as well. - [x] [Xverse](https://huggingface.co/models?search=xverse) - [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01) - [x] [SEA-LION](https://huggingface.co/models?search=sea-lion) +- [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B) + +(instructions for supporting more models: [HOWTO-add-model.md](./docs/HOWTO-add-model.md)) **Multimodal models:** @@ -183,7 +187,8 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file)(Apachev2.0 or later) - [Dot](https://github.com/alexpinel/Dot) (GPL) - [MindMac](https://mindmac.app) (proprietary) - +- [KodiBot](https://github.com/firatkiral/kodibot) (GPL) +- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* --- @@ -494,7 +499,7 @@ Building the program with BLAS support may lead to some performance improvements This provides BLAS acceleration on HIP-supported AMD GPUs. Make sure to have ROCm installed. - You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html). + You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick). - Using `make`: ```bash @@ -511,7 +516,7 @@ Building the program with BLAS support may lead to some performance improvements - Using `make` (example for target gfx1030, build with 16 CPU threads): ```bash - make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gxf1030 + make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gfx1030 ``` - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU): @@ -519,7 +524,7 @@ Building the program with BLAS support may lead to some performance improvements set PATH=%HIP_PATH%\bin;%PATH% mkdir build cd build - cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ .. + cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release .. cmake --build . ``` Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors) diff --git a/common/common.cpp b/common/common.cpp index 3e2df6e34..98fc8388c 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -16,6 +16,7 @@ #include #include #include +#include #if defined(__APPLE__) && defined(__MACH__) #include @@ -27,7 +28,6 @@ #ifndef NOMINMAX # define NOMINMAX #endif -#include #include #include #include @@ -1500,6 +1500,77 @@ std::string gpt_random_prompt(std::mt19937 & rng) { GGML_UNREACHABLE(); } +// Validate if a filename is safe to use +// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function +bool validate_file_name(const std::string & filename) { + if (!filename.length()) { + // Empty filename invalid + return false; + } + if (filename.length() > 255) { + // Limit at common largest possible filename on Linux filesystems + // to avoid unnecessary further validation + // (On systems with smaller limits it will be caught by the OS) + return false; + } + + std::u32string filename_utf32; + try { + std::wstring_convert, char32_t> converter; + filename_utf32 = converter.from_bytes(filename); + + // If the reverse conversion mismatches, it means overlong UTF-8 sequences were used, + // or invalid encodings were encountered. Reject such attempts + std::string filename_reencoded = converter.to_bytes(filename_utf32); + if (filename_reencoded != filename) { + return false; + } + } catch (const std::exception &) { + return false; + } + + // Check for forbidden codepoints: + // - Control characters + // - Unicode equivalents of illegal characters + // - UTF-16 surrogate pairs + // - UTF-8 replacement character + // - Byte order mark (BOM) + // - Illegal characters: / \ : * ? " < > | + for (char32_t c : filename_utf32) { + if (c <= 0x1F // Control characters (C0) + || c == 0x7F // Control characters (DEL) + || (c >= 0x80 && c <= 0x9F) // Control characters (C1) + || c == 0xFF0E // Fullwidth Full Stop (period equivalent) + || c == 0x2215 // Division Slash (forward slash equivalent) + || c == 0x2216 // Set Minus (backslash equivalent) + || (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs + || c == 0xFFFD // Replacement Character (UTF-8) + || c == 0xFEFF // Byte Order Mark (BOM) + || c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters + || c == '?' || c == '"' || c == '<' || c == '>' || c == '|') { + return false; + } + } + + // Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename + // Unicode and other whitespace is not affected, only 0x20 space + if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') { + return false; + } + + // Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead) + if (filename.find("..") != std::string::npos) { + return false; + } + + // Reject "." + if (filename == ".") { + return false; + } + + return true; +} + // // String utils // @@ -2141,23 +2212,23 @@ std::tuple llama_init_from_gpt_par std::vector llama_tokenize( const struct llama_context * ctx, const std::string & text, - bool add_bos, - bool special) { - return llama_tokenize(llama_get_model(ctx), text, add_bos, special); + bool add_special, + bool parse_special) { + return llama_tokenize(llama_get_model(ctx), text, add_special, parse_special); } std::vector llama_tokenize( const struct llama_model * model, const std::string & text, - bool add_bos, - bool special) { + bool add_special, + bool parse_special) { // upper limit for the number of tokens - int n_tokens = text.length() + add_bos; + int n_tokens = text.length() + 2 * add_special; std::vector result(n_tokens); - n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special); + n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); if (n_tokens < 0) { result.resize(-n_tokens); - int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special); + int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); GGML_ASSERT(check == -n_tokens); } else { result.resize(n_tokens); diff --git a/common/common.h b/common/common.h index 99ee90bc3..a7f476c1b 100644 --- a/common/common.h +++ b/common/common.h @@ -179,6 +179,8 @@ std::string gpt_random_prompt(std::mt19937 & rng); void process_escapes(std::string& input); +bool validate_file_name(const std::string & filename); + // // String utils // @@ -221,14 +223,14 @@ void llama_batch_add( std::vector llama_tokenize( const struct llama_context * ctx, const std::string & text, - bool add_bos, - bool special = false); + bool add_special, + bool parse_special = false); std::vector llama_tokenize( const struct llama_model * model, const std::string & text, - bool add_bos, - bool special = false); + bool add_special, + bool parse_special = false); // tokenizes a token into a piece // should work similar to Python's `tokenizer.id_to_piece` diff --git a/common/sampling.h b/common/sampling.h index 56ed991b8..639b819ab 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -129,7 +129,7 @@ llama_token llama_sampling_sample( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, struct llama_context * ctx_cfg, - int idx = 0); + int idx = -1); // Prepares and adjusts the set of token candidates for sampling based on penalties, biases, and sampling parameters. llama_token_data_array llama_sampling_prepare( diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 228b68e14..291a78e34 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -160,7 +160,7 @@ class Model(ABC): data = data.astype(np.float32) # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")): data = data.astype(np.float32) # if f16 desired, convert any float32 2-dim weight tensors to float16 @@ -227,15 +227,14 @@ class Model(ABC): return ("pytorch_model.bin",) return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1)) - def _set_vocab_gpt2(self): - dir_model = self.dir_model - hparams = self.hparams + # used for GPT-2 BPE and WordPiece vocabs + def get_basic_vocab(self) -> tuple[list[str], list[int]]: tokens: list[str] = [] toktypes: list[int] = [] from transformers import AutoTokenizer - tokenizer = AutoTokenizer.from_pretrained(dir_model) - vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) + tokenizer = AutoTokenizer.from_pretrained(self.dir_model) + vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab)) assert max(tokenizer.vocab.values()) < vocab_size reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} @@ -255,11 +254,15 @@ class Model(ABC): tokens.append(reverse_vocab[i]) toktypes.append(gguf.TokenType.NORMAL) + return tokens, toktypes + + def _set_vocab_gpt2(self) -> None: + tokens, toktypes = self.get_basic_vocab() self.gguf_writer.add_tokenizer_model("gpt2") self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) - special_vocab = gguf.SpecialVocab(dir_model, load_merges=True) + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) special_vocab.add_to_gguf(self.gguf_writer) def _set_vocab_qwen(self): @@ -2202,34 +2205,25 @@ class BertModel(Model): self.gguf_writer.add_pooling_type(pooling_type) def set_vocab(self): - # use huggingface vocab to get all tokens - vocab = LlamaHfVocab(self.dir_model, ignore_nonllama=True) - tokens, scores, toktypes = zip(*vocab.all_tokens()) - assert len(tokens) == vocab.vocab_size - self.vocab_size = vocab.vocab_size + tokens, toktypes = self.get_basic_vocab() + self.vocab_size = len(tokens) # we need this to validate the size of the token_type embeddings # though currently we are passing all zeros to the token_type embeddings - n_token_types = len(set(toktypes)) - self.gguf_writer.add_token_type_count(n_token_types) + self.gguf_writer.add_token_type_count(2) # "Sequence A" or "Sequence B" # convert to phantom space vocab - def phantom(tok, typ): - if tok.startswith(b"[") and tok.endswith(b"]"): + def phantom(tok): + if tok.startswith("[") and tok.endswith("]"): return tok - if tok.startswith(b"##"): + if tok.startswith("##"): return tok[2:] - return b"\xe2\x96\x81" + tok - tokens = tuple(phantom(t, y) for t, y in zip(tokens, toktypes)) - - # set up bos and eos tokens (cls and sep) - self.gguf_writer.add_bos_token_id(vocab.tokenizer.cls_token_id) - self.gguf_writer.add_eos_token_id(vocab.tokenizer.sep_token_id) + return "\u2581" + tok + tokens = list(map(phantom, tokens)) # add vocab to gguf self.gguf_writer.add_tokenizer_model("bert") self.gguf_writer.add_token_list(tokens) - self.gguf_writer.add_token_scores(scores) self.gguf_writer.add_token_types(toktypes) # handle special tokens @@ -2301,16 +2295,6 @@ class NomicBertModel(BertModel): super().set_gguf_parameters() self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) - def get_tensors(self): - assert self.vocab_size is not None - for name, data in super().get_tensors(): - # Nomic Embed's token embeddings tensor is padded, but llama.cpp wants tensor sizes to match exactly. - if name == 'embeddings.word_embeddings.weight' and data.shape[1] != self.vocab_size: - rounded_vocab_size = (self.vocab_size + 63) // 64 * 64 - assert data.shape == (rounded_vocab_size, self.hparams["n_embd"]) - data = data[:self.vocab_size, :] - yield name, data - @Model.register("GemmaForCausalLM") class GemmaModel(Model): @@ -2486,7 +2470,8 @@ class MambaModel(Model): data = data.astype(np.float32) # if f16 desired, convert big float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and new_name.removesuffix(".weight").endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2: + new_weight_name = new_name[:-len(".weight")] if new_name.endswith(".weight") else "" + if self.ftype == 1 and data_dtype == np.float32 and new_weight_name.endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2: data = data.astype(np.float16) print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py index ccb99279e..69be17f94 100755 --- a/convert-persimmon-to-gguf.py +++ b/convert-persimmon-to-gguf.py @@ -1,4 +1,6 @@ #!/usr/bin/env python3 +from __future__ import annotations + import argparse import os import sys diff --git a/convert.py b/convert.py index 244eb7582..24df0a4d8 100755 --- a/convert.py +++ b/convert.py @@ -33,7 +33,7 @@ if 'NO_LOCAL_GGUF' not in os.environ: import gguf if TYPE_CHECKING: - from typing import TypeAlias + from typing_extensions import Self, TypeAlias if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'): faulthandler.register(signal.SIGUSR1) @@ -139,7 +139,8 @@ class GGMLFileType(enum.IntEnum): dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self) if dt is None: raise ValueError(self) - # 1D tensors are always F32. + # Convert all 1D tensors to F32. Most of the codebase that takes in 1D tensors only handles F32 tensors, and most of the outputs tensors are F32. + # Also The 1d tensors aren't much of a performance/size issue. So instead of having to have separate F32 and F16 implementations of both, just convert everything to F32 for now. return dt if len(tensor.shape) > 1 else DT_F32 @@ -516,7 +517,7 @@ class LlamaHfVocab(Vocab): tokenizer_model = "llama" name = "hfft" - def __init__(self, base_path: Path, ignore_nonllama: bool = False): + def __init__(self, base_path: Path): fname_tokenizer = base_path / FAST_TOKENIZER_FILE # if this fails, FileNotFoundError propagates to caller with open(fname_tokenizer, encoding='utf-8') as f: @@ -524,9 +525,7 @@ class LlamaHfVocab(Vocab): # pre-check so we know if we need transformers tokenizer_model: dict[str, Any] = tokenizer_json['model'] - if ignore_nonllama: - pass # workaround incorrect use of this class for WordPiece - elif ( + if ( tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False) or tokenizer_json['decoder']['type'] != 'Sequence' ): @@ -646,16 +645,17 @@ def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray: class Tensor(ABC): + ndarray: NDArray data_type: DataType @abstractmethod - def astype(self, data_type: DataType) -> Tensor: ... + def astype(self, data_type: DataType) -> Self: ... @abstractmethod - def permute(self, n_head: int, n_head_kv: int) -> Tensor: ... + def permute(self, n_head: int, n_head_kv: int) -> Self: ... @abstractmethod - def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: ... + def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> Self: ... @abstractmethod - def part(self, n_part: int) -> UnquantizedTensor: ... + def part(self, n_part: int) -> Self: ... @abstractmethod def to_ggml(self) -> GGMLCompatibleTensor: ... @@ -672,13 +672,13 @@ class UnquantizedTensor(Tensor): self.ndarray = ndarray self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype] - def astype(self, data_type: DataType) -> Tensor: + def astype(self, data_type: DataType) -> UnquantizedTensor: dtype = data_type.dtype if self.data_type == DT_BF16: self.ndarray = bf16_to_fp32(self.ndarray) return UnquantizedTensor(self.ndarray.astype(dtype)) - def to_ggml(self) -> UnquantizedTensor: + def to_ggml(self) -> Self: return self def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: @@ -1350,7 +1350,7 @@ def load_some_model(path: Path) -> ModelPlus: # Be extra-friendly and accept either a file or a directory: if path.is_dir(): # Check if it's a set of safetensors files first - globs = ["model-00001-of-*.safetensors", "model.safetensors"] + globs = ["model-00001-of-*.safetensors", "model.safetensors", "consolidated.safetensors"] files = [file for glob in globs for file in path.glob(glob)] if not files: # Try the PyTorch patterns too, with lower priority diff --git a/docs/HOWTO-add-model.md b/docs/HOWTO-add-model.md new file mode 100644 index 000000000..3581f3e65 --- /dev/null +++ b/docs/HOWTO-add-model.md @@ -0,0 +1,117 @@ +## Add a new model architecture to `llama.cpp` + +Adding a model requires few steps: + +1. Convert the model to GGUF +2. Define the model architecture in `llama.cpp` +3. Build the GGML graph implementation + +After following these steps, you can open PR. + +Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially: +- [main](../examples/main) +- [imatrix](../examples/imatrix) +- [quantize](../examples/quantize) +- [server](../examples/server) + +### 1. Convert the model to GGUF + +This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library. +Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py). + +The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors. + +The required steps to implement for an HF model are: + +1. Define the model `Model.register` annotation in a new `Model` subclass, example: + +```python +@Model.register("MyModelForCausalLM") +class MyModel(Model): + model_arch = gguf.MODEL_ARCH.GROK +``` + +2. Define the layout of the GGUF tensors in [constants.py](../gguf-py/gguf/constants.py) + +Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`. + +Example for `falcon` model: +```python + MODEL_ARCH.FALCON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ] +``` + +3. Map the original tensor names to the standardize equivalent in GGUF + +As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist. + +Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](../gguf-py/gguf/tensor_mapping.py) file. + +If the tensor name is part of a repetitive layer/block, the key word `bid` substitutes it. + +Example for the normalization tensor in attention layers: + +```python +block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { + # Attention norm + MODEL_TENSOR.ATTN_NORM: ( + "gpt_neox.layers.{bid}.input_layernorm", # gptneox + "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen + "transformer.blocks.{bid}.norm_1", # mpt + ... + ) +} +``` + +`transformer.blocks.{bid}.norm_1` will be mapped to `blk.{bid}.attn_norm` in GGUF. + +Depending on the model configuration, tokenizer, code and tensors layout, you will have to override: +- `Model#set_gguf_parameters` +- `Model#set_vocab` +- `Model#write_tensors` + +NOTE: Tensor names must end with `.weight` suffix, that is the convention and several tools like `quantize` expect this to proceed the weights. + +### 2. Define the model architecture in `llama.cpp` + +The model params and tensors layout must be defined in `llama.cpp`: +1. Define a new `llm_arch` +2. Define the tensors layout in `LLM_TENSOR_NAMES` +3. Add any non standard metadata in `llm_load_hparams` +4. Create the tensors for inference in `llm_load_tensors` +5. If the model has a RoPE operation, add the rope type in `llama_rope_type` + +NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions. + +### 3. Build the GGML graph implementation + +This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`. + +Have a look to existing implementation like `build_llama`, `build_dbrx` or `build_bert`. + +When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support of missing backend operations can be added in another PR. + +## GGUF specification + +https://github.com/ggerganov/ggml/blob/master/docs/gguf.md + +## Resources + +- YaRN RoPE scaling https://github.com/ggerganov/llama.cpp/pull/2268 +- support Baichuan serial models https://github.com/ggerganov/llama.cpp/pull/3009 +- support attention bias https://github.com/ggerganov/llama.cpp/pull/4283 +- Mixtral support https://github.com/ggerganov/llama.cpp/pull/4406 +- BERT embeddings https://github.com/ggerganov/llama.cpp/pull/5423 +- Grok-1 support https://github.com/ggerganov/llama.cpp/pull/6204 +- Command R Plus support https://github.com/ggerganov/llama.cpp/pull/6491 +- support arch DBRX https://github.com/ggerganov/llama.cpp/pull/6515 +- How to convert HuggingFace model to GGUF format https://github.com/ggerganov/llama.cpp/discussions/2948 diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 536657526..6a93147d7 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -123,10 +123,10 @@ int main(int argc, char ** argv) { inputs.push_back(inp); } - // add eos if not present + // add SEP if not present for (auto & inp : inputs) { - if (inp.empty() || inp.back() != llama_token_eos(model)) { - inp.push_back(llama_token_eos(model)); + if (inp.empty() || inp.back() != llama_token_sep(model)) { + inp.push_back(llama_token_sep(model)); } } diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index d8cb0a642..1bf55f90c 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -349,12 +349,13 @@ static void process_logits( static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) { const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1); const int n_ctx = llama_n_ctx(ctx); auto tim1 = std::chrono::high_resolution_clock::now(); fprintf(stderr, "%s: tokenizing the input ..\n", __func__); - std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + std::vector tokens = ::llama_tokenize(ctx, params.prompt, true); auto tim2 = std::chrono::high_resolution_clock::now(); fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 91c39c5ae..c69dcd06e 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -239,6 +239,7 @@ int main(int argc, char ** argv) { LOG_TEE("%s\n", get_system_info(params).c_str()); } const bool add_bos = llama_should_add_bos_token(model); + GGML_ASSERT(llama_add_eos_token(model) != 1); LOG("add_bos: %d\n", add_bos); bool suff_rm_leading_spc = params.escape; @@ -279,10 +280,10 @@ int main(int argc, char ** argv) { if (ctx_guidance) { LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt)); - guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos); + guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true); LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str()); - std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos); + std::vector original_inp = ::llama_tokenize(ctx, params.prompt, true); LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str()); original_prompt_len = original_inp.size(); diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index e29da6cb2..75948806e 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -146,7 +146,6 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ int n_past = 0; const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict; - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx_llava->ctx_llama)); std::string system_prompt, user_prompt; size_t image_pos = prompt.find(""); @@ -180,7 +179,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ } } - eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, add_bos); + eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, true); llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past); eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false); diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index e2551e7a4..5af6a8ab6 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -64,13 +64,10 @@ int main(int argc, char ** argv) { std::tie(model, ctx) = llama_init_from_gpt_params(params); // Tokenize the prompt - const bool add_bos = llama_should_add_bos_token(model); - LOG("add_bos tgt: %d\n", add_bos); - std::vector inp; std::vector all; - inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); + inp = ::llama_tokenize(ctx, params.prompt, true, true); all = inp; const int max_context_size = llama_n_ctx(ctx); diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp index 46a6bed07..1c230c966 100644 --- a/examples/lookup/lookup-create.cpp +++ b/examples/lookup/lookup-create.cpp @@ -28,10 +28,8 @@ int main(int argc, char ** argv){ GGML_ASSERT(model != nullptr); // tokenize the prompt - const bool add_bos = llama_should_add_bos_token(model); - std::vector inp; - inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); + inp = ::llama_tokenize(ctx, params.prompt, true, true); fprintf(stderr, "%s: tokenization done\n", __func__); diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp index 31f227773..41b62c2fe 100644 --- a/examples/lookup/lookup-stats.cpp +++ b/examples/lookup/lookup-stats.cpp @@ -34,11 +34,8 @@ int main(int argc, char ** argv){ GGML_ASSERT(llama_n_vocab(model) < (1 << 16)); // tokenize the prompt - const bool add_bos = llama_should_add_bos_token(model); - LOG("add_bos tgt: %d\n", add_bos); - std::vector inp; - inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); + inp = ::llama_tokenize(ctx, params.prompt, true, true); llama_ngram_cache ngram_cache_context; llama_ngram_cache ngram_cache_dynamic; diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index 2e8c35de3..65ed408a2 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -42,11 +42,8 @@ int main(int argc, char ** argv){ GGML_ASSERT(llama_n_vocab(model) < (1 << 16)); // tokenize the prompt - const bool add_bos = llama_should_add_bos_token(model); - LOG("add_bos tgt: %d\n", add_bos); - std::vector inp; - inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); + inp = ::llama_tokenize(ctx, params.prompt, true, true); llama_ngram_cache ngram_cache_context; llama_ngram_cache ngram_cache_dynamic; diff --git a/examples/main/main.cpp b/examples/main/main.cpp index e2d07a631..249fc2bb6 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -235,7 +235,7 @@ int main(int argc, char ** argv) { // The file exists and is not empty session_tokens.resize(n_ctx); size_t n_token_count_out = 0; - if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) { + if (!llama_state_load_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) { LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str()); return 1; } @@ -246,6 +246,7 @@ int main(int argc, char ** argv) { } const bool add_bos = llama_should_add_bos_token(model); + GGML_ASSERT(llama_add_eos_token(model) != 1); LOG("add_bos: %d\n", add_bos); std::vector embd_inp; @@ -255,7 +256,7 @@ int main(int argc, char ** argv) { if (params.chatml) { params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>"; } - embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); + embd_inp = ::llama_tokenize(ctx, params.prompt, true, true); } else { LOG("use session tokens\n"); embd_inp = session_tokens; @@ -277,10 +278,10 @@ int main(int argc, char ** argv) { if (ctx_guidance) { LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt)); - guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true); + guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true, true); LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str()); - std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); + std::vector original_inp = ::llama_tokenize(ctx, params.prompt, true, true); LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str()); original_prompt_len = original_inp.size(); @@ -339,14 +340,14 @@ int main(int argc, char ** argv) { } // prefix & suffix for instruct mode - const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true); - const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false, true); + const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true, true); + const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false, true); LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str()); LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str()); // chatml prefix & suffix - const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", add_bos, true); + const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", true, true); const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true); LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str()); @@ -693,7 +694,7 @@ int main(int argc, char ** argv) { // optionally save the session on first sample (for faster prompt loading next time) if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) { need_to_save_session = false; - llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); + llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); LOG("saved session to %s\n", path_session.c_str()); } @@ -935,7 +936,7 @@ int main(int argc, char ** argv) { if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) { LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str()); - llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); + llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); } llama_print_timings(ctx); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index c70385c62..bab79aaea 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -315,10 +315,11 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & // BOS tokens will be added for each chunk before eval const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1); fprintf(stderr, "%s: tokenizing the input ..\n", __func__); - std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + std::vector tokens = ::llama_tokenize(ctx, params.prompt, true); const int n_ctx = llama_n_ctx(ctx); @@ -454,6 +455,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par // BOS tokens will be added for each chunk before eval const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1); std::ofstream logits_stream; if (!params.logits_file.empty()) { @@ -470,7 +472,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par auto tim1 = std::chrono::high_resolution_clock::now(); fprintf(stderr, "%s: tokenizing the input ..\n", __func__); - std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + std::vector tokens = ::llama_tokenize(ctx, params.prompt, true); auto tim2 = std::chrono::high_resolution_clock::now(); fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); @@ -771,9 +773,6 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM; fprintf(stderr, "================================= is_spm = %d\n", is_spm); - // This is needed as usual for LLaMA models - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); - // The tasks should be randomized so the score stabilizes quickly. bool randomize_tasks = true; @@ -818,7 +817,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] ); for (size_t j = 0; j < 4; j++) { hs_cur.ending[j] = prompt_lines[idx*6+2+j]; - hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], add_bos); + hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true); } // determine the common prefix of the endings @@ -837,7 +836,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { hs_cur.seq_tokens[2].size() - hs_cur.common_prefix + hs_cur.seq_tokens[3].size() - hs_cur.common_prefix; - //GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, add_bos).size()); + //GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, true).size()); // Delete the selected random example from the prompt if (randomize_tasks) { @@ -1110,12 +1109,9 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { fprintf(stderr, "%s : tokenizing selected tasks\n", __func__); - // This is needed as usual for LLaMA models - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); - for (auto & task : data) { - task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, add_bos); - task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, add_bos); + task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, true); + task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, true); task.common_prefix = 0; for (size_t k = 0; k < task.seq_tokens[0].size(); k++) { @@ -1130,8 +1126,8 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { task.seq_tokens[0].size() - task.common_prefix + task.seq_tokens[1].size() - task.common_prefix; - task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], add_bos).size(); - task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], add_bos).size(); + task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], true).size(); + task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], true).size(); } fprintf(stderr, "%s : calculating winogrande score over selected tasks.\n", __func__); @@ -1322,7 +1318,7 @@ struct multiple_choice_task { std::vector log_probs; }; -static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos, multiple_choice_task& task, bool log_error) { +static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choice_task& task, bool log_error) { if (task.question.empty() || task.mc1.answers.empty()) { if (log_error) { printf("%s: found bad task with empty question and/or answers\n", __func__); @@ -1337,7 +1333,7 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos, } return false; } - task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, add_bos)); + task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, true)); } auto min_len = task.seq_tokens.front().size(); for (auto& seq : task.seq_tokens) { @@ -1436,9 +1432,6 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params n_task = params.multiple_choice_tasks; } - // This is needed as usual for LLaMA models - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); - printf("%s: preparing task data", __func__); fflush(stdout); if (n_task > 500) { @@ -1446,7 +1439,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params fflush(stdout); std::atomic counter(0); std::atomic n_bad(0); - auto prepare = [&counter, &n_bad, &tasks, ctx, add_bos] () { + auto prepare = [&counter, &n_bad, &tasks, ctx] () { int num_tasks = tasks.size(); int n_bad_local = 0; while (true) { @@ -1457,7 +1450,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params } int last = std::min(first + K_TOKEN_CHUNK, num_tasks); for (int i = first; i < last; ++i) { - if (!multiple_choice_prepare_one_task(ctx, add_bos, tasks[i], false)) ++n_bad_local; + if (!multiple_choice_prepare_one_task(ctx, tasks[i], false)) ++n_bad_local; } } }; @@ -1479,7 +1472,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params int i_task = 0; for (auto& task : tasks) { ++i_task; - if (!multiple_choice_prepare_one_task(ctx, add_bos, task, true)) { + if (!multiple_choice_prepare_one_task(ctx, task, true)) { return; } if (i_task%n_dot == 0) { @@ -1715,6 +1708,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { const int num_batches = (n_ctx + n_batch - 1)/n_batch; const int nv = 2*((n_vocab + 1)/2) + 4; const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1); std::vector log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv); std::vector kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index ef952e2bd..c3b766882 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -24,6 +24,7 @@ int main(int argc, char ** argv) { std::string result0; std::string result1; + std::string result2; // init llama_model * model; @@ -44,8 +45,8 @@ int main(int argc, char ** argv) { // save state (rng, logits, embedding and kv_cache) to file { - std::vector state_mem(llama_get_state_size(ctx)); - const size_t written = llama_copy_state_data(ctx, state_mem.data()); + std::vector state_mem(llama_state_get_size(ctx)); + const size_t written = llama_state_get_data(ctx, state_mem.data()); FILE *fp_write = fopen("dump_state.bin", "wb"); fwrite(state_mem.data(), 1, written, fp_write); @@ -97,13 +98,13 @@ int main(int argc, char ** argv) { // load state (rng, logits, embedding and kv_cache) from file { - std::vector state_mem(llama_get_state_size(ctx2)); + std::vector state_mem(llama_state_get_size(ctx2)); FILE * fp_read = fopen("dump_state.bin", "rb"); const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read); fclose(fp_read); - if (read != llama_set_state_data(ctx2, state_mem.data())) { + if (read != llama_state_set_data(ctx2, state_mem.data())) { fprintf(stderr, "\n%s : failed to read state\n", __func__); llama_free(ctx2); llama_free_model(model); @@ -141,16 +142,104 @@ int main(int argc, char ** argv) { n_past += 1; } - printf("\n"); + printf("\n\n"); llama_free(ctx2); - llama_free_model(model); if (result0 != result1) { fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__); return 1; } + // make new context + auto* ctx3 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params)); + + printf("\nsingle seq run: %s", params.prompt.c_str()); + + // load state (rng, logits, embedding and kv_cache) from file + { + std::vector state_mem(llama_state_get_size(ctx3)); + + FILE * fp_read = fopen("dump_state.bin", "rb"); + const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read); + fclose(fp_read); + + if (read != llama_state_set_data(ctx3, state_mem.data())) { + fprintf(stderr, "\n%s : failed to read state\n", __func__); + llama_free(ctx3); + llama_free_model(model); + return 1; + } + + fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size()); + } + + // restore state (last tokens) + n_past = n_past_saved; + + // save seq 0 and load into seq 1 + { + // save kv of seq 0 + std::vector seq_store(llama_state_seq_get_size(ctx3, 0)); + const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), 0); + if (ncopy != seq_store.size()) { + fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size()); + llama_free(ctx3); + llama_free_model(model); + return 1; + } + fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy); + + // erase whole kv + llama_kv_cache_clear(ctx3); + fprintf(stderr, "%s : kv cache cleared\n", __func__); + + // restore kv into seq 1 + const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), 1); + if (nset != seq_store.size()) { + fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size()); + llama_free(ctx3); + llama_free_model(model); + return 1; + } + fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset); + } + + // third run with seq 1 instead of 0 + for (auto i = 0; i < params.n_predict; i++) { + auto * logits = llama_get_logits(ctx3); + auto n_vocab = llama_n_vocab(model); + std::vector candidates; + candidates.reserve(n_vocab); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + auto next_token = llama_sample_token(ctx3, &candidates_p); + auto next_token_str = llama_token_to_piece(ctx3, next_token); + + printf("%s", next_token_str.c_str()); + result2 += next_token_str; + + if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1, n_past, 1))) { + fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_free(ctx3); + llama_free_model(model); + return 1; + } + n_past += 1; + } + + printf("\n"); + + llama_free(ctx3); + llama_free_model(model); + + if (result0 != result2) { + fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__); + return 1; + } + fprintf(stderr, "\n%s : success\n", __func__); return 0; diff --git a/examples/server/README.md b/examples/server/README.md index 0d8564a1f..a6fc92eaa 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -57,6 +57,7 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/ - `-n N, --n-predict N`: Set the maximum tokens to predict. Default: `-1` - `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included. - `--metrics`: enable prometheus `/metrics` compatible endpoint. Default: disabled +- `--slot-save-path PATH`: Specifies the path where the state of slots (the prompt cache) can be stored. If not provided, the slot management endpoints will be disabled. - `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name. Default: template taken from model's metadata. We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) - `--log-disable`: Output logs to stdout only, not to `llama.log`. Default: enabled - `--log-format FORMAT`: Define the log output to FORMAT: json or text Default: `json` @@ -517,6 +518,57 @@ Available metrics: - `llamacpp:requests_processing`: Number of requests processing. - `llamacpp:requests_deferred`: Number of requests deferred. +- **POST** `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file. + + *Options:* + + `filename`: Name of the file to save the slot's prompt cache. The file will be saved in the directory specified by the `--slot-save-path` server parameter. + +### Result JSON + +```json +{ + "id_slot": 0, + "filename": "slot_save_file.bin", + "n_saved": 1745, + "n_written": 14309796, + "timings": { + "save_ms": 49.865 + } +} +``` + +- **POST** `/slots/{id_slot}?action=restore`: Restore the prompt cache of the specified slot from a file. + + *Options:* + + `filename`: Name of the file to restore the slot's prompt cache from. The file should be located in the directory specified by the `--slot-save-path` server parameter. + +### Result JSON + +```json +{ + "id_slot": 0, + "filename": "slot_save_file.bin", + "n_restored": 1745, + "n_read": 14309796, + "timings": { + "restore_ms": 42.937 + } +} +``` + +- **POST** `/slots/{id_slot}?action=erase`: Erase the prompt cache of the specified slot. + +### Result JSON + +```json +{ + "id_slot": 0, + "n_erased": 1745 +} +``` + ## More examples ### Change system prompt on runtime diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 2de8a5fd2..14f0e8f19 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -82,1227 +82,1299 @@ unsigned char index_html[] = { 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x2d, 0x62, 0x6f, 0x74, 0x74, 0x6f, 0x6d, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 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0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c, 0x3e, + 0x0a, 0x0a }; -unsigned int index_html_len = 34412; +unsigned int index_html_len = 34994; diff --git a/examples/server/public/index.html b/examples/server/public/index.html index c91a357c1..68661ee4d 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -51,6 +51,26 @@ margin-bottom: 0.5em; } + button, input, textarea, .button, a.button, select { + color: #666; + border: 1px solid #ddd; + border-radius: 4px; + line-height: 1.5em; + padding: 0.25em 0.25em; + text-decoration: none; + font-size: 1.1rem; + } + + button { + border: 1px solid #2a8aad; + background: #3584e4; + font-weight: normal; + color: #fff; + } + button:disabled { + background: #9cbce5; + } + #write form { margin: 1em 0 0 0; display: flex; @@ -406,7 +426,7 @@ throw new Error("already running"); } controller.value = new AbortController(); - for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: document.baseURI.replace(/\/+$/, '') })) { + for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: location.pathname.replace(/\/+$/, '') })) { const data = chunk.data; if (data.stop) { @@ -567,7 +587,7 @@ runCompletion(); } return html` -
+
@@ -1015,6 +1035,10 @@ } function App(props) { + useEffect(() => { + const query = new URLSearchParams(location.search).get("q"); + if (query) chat(query); + }, []); return html`
diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 183f0d3c8..2e791190b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -61,7 +61,10 @@ enum server_task_type { SERVER_TASK_TYPE_COMPLETION, SERVER_TASK_TYPE_CANCEL, SERVER_TASK_TYPE_NEXT_RESPONSE, - SERVER_TASK_TYPE_METRICS + SERVER_TASK_TYPE_METRICS, + SERVER_TASK_TYPE_SLOT_SAVE, + SERVER_TASK_TYPE_SLOT_RESTORE, + SERVER_TASK_TYPE_SLOT_ERASE, }; struct server_task { @@ -128,6 +131,7 @@ struct server_params { bool slots_endpoint = true; bool metrics_endpoint = false; + std::string slot_save_path; }; struct server_slot { @@ -685,6 +689,7 @@ struct server_context { n_ctx = llama_n_ctx(ctx); add_bos_token = llama_should_add_bos_token(model); + GGML_ASSERT(llama_add_eos_token(model) != 1); return true; } @@ -754,7 +759,7 @@ struct server_context { metrics.init(); } - std::vector tokenize(const json & json_prompt, bool add_bos) const { + std::vector tokenize(const json & json_prompt, bool add_special) const { // TODO: currently, we tokenize using special tokens by default // this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216) // but it's better compared to completely ignoring ChatML and other chat templates @@ -772,7 +777,7 @@ struct server_context { std::vector p; if (first) { - p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL); + p = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL); first = false; } else { p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL); @@ -789,7 +794,7 @@ struct server_context { } } else { auto s = json_prompt.template get(); - prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL); + prompt_tokens = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL); } return prompt_tokens; @@ -1054,7 +1059,7 @@ struct server_context { system_tokens.clear(); if (!system_prompt.empty()) { - system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token); + system_tokens = ::llama_tokenize(ctx, system_prompt, true); llama_batch_clear(batch); @@ -1612,6 +1617,107 @@ struct server_context { } queue_results.send(res); } break; + case SERVER_TASK_TYPE_SLOT_SAVE: + { + int id_slot = task.data["id_slot"]; + server_slot * slot = get_slot(id_slot); + if (slot == nullptr) { + send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); + break; + } + + const size_t token_count = slot->cache_tokens.size(); + const int64_t t_start = ggml_time_us(); + + std::string filename = task.data["filename"]; + std::string filepath = task.data["filepath"]; + + const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count); + + const int64_t t_end = ggml_time_us(); + const double t_save_ms = (t_end - t_start) / 1000.0; + + server_task_result result; + result.id = task.id; + result.stop = true; + result.error = false; + result.data = json { + { "id_slot", id_slot }, + { "filename", filename }, + { "n_saved", token_count }, // tokens saved + { "n_written", nwrite }, // bytes written + { "timings", { + { "save_ms", t_save_ms } + } } + }; + queue_results.send(result); + } break; + case SERVER_TASK_TYPE_SLOT_RESTORE: + { + int id_slot = task.data["id_slot"]; + server_slot * slot = get_slot(id_slot); + if (slot == nullptr) { + send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); + break; + } + + const int64_t t_start = ggml_time_us(); + + std::string filename = task.data["filename"]; + std::string filepath = task.data["filepath"]; + + slot->cache_tokens.resize(slot->n_ctx); + size_t token_count = 0; + size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count); + if (nread == 0) { + slot->cache_tokens.resize(0); + send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST); + break; + } + slot->cache_tokens.resize(token_count); + + const int64_t t_end = ggml_time_us(); + const double t_restore_ms = (t_end - t_start) / 1000.0; + + server_task_result result; + result.id = task.id; + result.stop = true; + result.error = false; + result.data = json { + { "id_slot", id_slot }, + { "filename", filename }, + { "n_restored", token_count }, // tokens restored + { "n_read", nread }, // bytes read + { "timings", { + { "restore_ms", t_restore_ms } + } } + }; + queue_results.send(result); + } break; + case SERVER_TASK_TYPE_SLOT_ERASE: + { + int id_slot = task.data["id_slot"]; + server_slot * slot = get_slot(id_slot); + if (slot == nullptr) { + send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); + break; + } + + // Erase token cache + const size_t n_erased = slot->cache_tokens.size(); + llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1); + slot->cache_tokens.clear(); + + server_task_result result; + result.id = task.id; + result.stop = true; + result.error = false; + result.data = json { + { "id_slot", id_slot }, + { "n_erased", n_erased } + }; + queue_results.send(result); + } break; } } @@ -1809,7 +1915,7 @@ struct server_context { prefix_tokens.push_back(llama_token_middle(model)); prompt_tokens = prefix_tokens; } else { - prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt + prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt } slot.n_past = 0; @@ -2249,6 +2355,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" --log-disable disables logging to a file.\n"); printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n"); printf(" --metrics enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled"); + printf(" --slot-save-path PATH path to save slot kv cache (default: disabled)\n"); printf("\n"); printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict); printf(" --override-kv KEY=TYPE:VALUE\n"); @@ -2657,6 +2764,16 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, sparams.slots_endpoint = false; } else if (arg == "--metrics") { sparams.metrics_endpoint = true; + } else if (arg == "--slot-save-path") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.slot_save_path = argv[i]; + // if doesn't end with DIRECTORY_SEPARATOR, add it + if (!sparams.slot_save_path.empty() && sparams.slot_save_path[sparams.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) { + sparams.slot_save_path += DIRECTORY_SEPARATOR; + } } else if (arg == "--chat-template") { if (++i >= argc) { invalid_param = true; @@ -3159,6 +3276,112 @@ int main(int argc, char ** argv) { res.status = 200; // HTTP OK }; + const auto handle_slots_save = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { + json request_data = json::parse(req.body); + std::string filename = request_data["filename"]; + if (!validate_file_name(filename)) { + res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); + return; + } + std::string filepath = sparams.slot_save_path + filename; + + server_task task; + task.type = SERVER_TASK_TYPE_SLOT_SAVE; + task.data = { + { "id_slot", id_slot }, + { "filename", filename }, + { "filepath", filepath } + }; + + const int id_task = ctx_server.queue_tasks.post(task); + ctx_server.queue_results.add_waiting_task_id(id_task); + + server_task_result result = ctx_server.queue_results.recv(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + + if (result.error) { + res_error(res, result.data); + } else { + res.set_content(result.data.dump(), "application/json"); + } + }; + + const auto handle_slots_restore = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { + json request_data = json::parse(req.body); + std::string filename = request_data["filename"]; + if (!validate_file_name(filename)) { + res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); + return; + } + std::string filepath = sparams.slot_save_path + filename; + + server_task task; + task.type = SERVER_TASK_TYPE_SLOT_RESTORE; + task.data = { + { "id_slot", id_slot }, + { "filename", filename }, + { "filepath", filepath } + }; + + const int id_task = ctx_server.queue_tasks.post(task); + ctx_server.queue_results.add_waiting_task_id(id_task); + + server_task_result result = ctx_server.queue_results.recv(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + + if (result.error) { + res_error(res, result.data); + } else { + res.set_content(result.data.dump(), "application/json"); + } + }; + + const auto handle_slots_erase = [&ctx_server, &res_error](const httplib::Request & /* req */, httplib::Response & res, int id_slot) { + server_task task; + task.type = SERVER_TASK_TYPE_SLOT_ERASE; + task.data = { + { "id_slot", id_slot }, + }; + + const int id_task = ctx_server.queue_tasks.post(task); + ctx_server.queue_results.add_waiting_task_id(id_task); + + server_task_result result = ctx_server.queue_results.recv(id_task); + ctx_server.queue_results.remove_waiting_task_id(id_task); + + if (result.error) { + res_error(res, result.data); + } else { + res.set_content(result.data.dump(), "application/json"); + } + }; + + const auto handle_slots_action = [&res_error, &handle_slots_save, &handle_slots_restore, &handle_slots_erase](const httplib::Request & req, httplib::Response & res) { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + + std::string id_slot_str = req.path_params.at("id_slot"); + int id_slot; + + try { + id_slot = std::stoi(id_slot_str); + } catch (const std::exception &) { + res_error(res, format_error_response("Invalid slot ID", ERROR_TYPE_INVALID_REQUEST)); + return; + } + + std::string action = req.get_param_value("action"); + + if (action == "save") { + handle_slots_save(req, res, id_slot); + } else if (action == "restore") { + handle_slots_restore(req, res, id_slot); + } else if (action == "erase") { + handle_slots_erase(req, res, id_slot); + } else { + res_error(res, format_error_response("Invalid action", ERROR_TYPE_INVALID_REQUEST)); + } + }; + const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { @@ -3521,6 +3744,10 @@ int main(int argc, char ** argv) { svr->Post("/v1/embeddings", handle_embeddings); svr->Post("/tokenize", handle_tokenize); svr->Post("/detokenize", handle_detokenize); + if (!sparams.slot_save_path.empty()) { + // only enable slot endpoints if slot_save_path is set + svr->Post("/slots/:id_slot", handle_slots_action); + } // // Start the server diff --git a/examples/server/tests/features/slotsave.feature b/examples/server/tests/features/slotsave.feature new file mode 100644 index 000000000..1c281c074 --- /dev/null +++ b/examples/server/tests/features/slotsave.feature @@ -0,0 +1,58 @@ +@llama.cpp +@slotsave +Feature: llama.cpp server slot management + + Background: Server startup + Given a server listening on localhost:8080 + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models + And prompt caching is enabled + And 2 slots + And . as slot save path + And 2048 KV cache size + And 42 as server seed + And 24 max tokens to predict + Then the server is starting + Then the server is healthy + + Scenario: Save and Restore Slot + # First prompt in slot 1 should be fully processed + Given a user prompt "What is the capital of France?" + And using slot id 1 + And a completion request with no api error + Then 24 tokens are predicted matching (Lily|cake) + And 22 prompt tokens are processed + When the slot 1 is saved with filename "slot1.bin" + Then the server responds with status code 200 + # Since we have cache, this should only process the last tokens + Given a user prompt "What is the capital of Germany?" + And a completion request with no api error + Then 24 tokens are predicted matching (Thank|special) + And 7 prompt tokens are processed + # Loading the original cache into slot 0, + # we should only be processing 1 prompt token and get the same output + When the slot 0 is restored with filename "slot1.bin" + Then the server responds with status code 200 + Given a user prompt "What is the capital of France?" + And using slot id 0 + And a completion request with no api error + Then 24 tokens are predicted matching (Lily|cake) + And 1 prompt tokens are processed + # For verification that slot 1 was not corrupted during slot 0 load, same thing + Given a user prompt "What is the capital of Germany?" + And using slot id 1 + And a completion request with no api error + Then 24 tokens are predicted matching (Thank|special) + And 1 prompt tokens are processed + + Scenario: Erase Slot + Given a user prompt "What is the capital of France?" + And using slot id 1 + And a completion request with no api error + Then 24 tokens are predicted matching (Lily|cake) + And 22 prompt tokens are processed + When the slot 1 is erased + Then the server responds with status code 200 + Given a user prompt "What is the capital of France?" + And a completion request with no api error + Then 24 tokens are predicted matching (Lily|cake) + And 22 prompt tokens are processed diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 9a6cf7d6a..ca400efa4 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -49,6 +49,9 @@ def step_server_config(context, server_fqdn, server_port): context.n_predict = None context.n_prompts = 0 context.n_server_predict = None + context.slot_save_path = None + context.id_slot = None + context.cache_prompt = None context.n_slots = None context.prompt_prefix = None context.prompt_suffix = None @@ -119,6 +122,21 @@ def step_server_n_predict(context, n_predict): context.n_server_predict = n_predict +@step('{slot_save_path} as slot save path') +def step_slot_save_path(context, slot_save_path): + context.slot_save_path = slot_save_path + + +@step('using slot id {id_slot:d}') +def step_id_slot(context, id_slot): + context.id_slot = id_slot + + +@step('prompt caching is enabled') +def step_enable_prompt_cache(context): + context.cache_prompt = True + + @step('continuous batching') def step_server_continuous_batching(context): context.server_continuous_batching = True @@ -212,6 +230,8 @@ async def step_request_completion(context, api_error): context.base_url, debug=context.debug, n_predict=context.n_predict, + cache_prompt=context.cache_prompt, + id_slot=context.id_slot, seed=await completions_seed(context), expect_api_error=expect_api_error, user_api_key=context.user_api_key) @@ -711,12 +731,48 @@ async def concurrent_requests(context, f_completion, *args, **kwargs): await asyncio.sleep(0.1) +@step('the slot {slot_id:d} is saved with filename "{filename}"') +@async_run_until_complete +async def step_save_slot(context, slot_id, filename): + async with aiohttp.ClientSession() as session: + async with session.post(f'{context.base_url}/slots/{slot_id}?action=save', + json={"filename": filename}, + headers={"Content-Type": "application/json"}) as response: + context.response = response + + +@step('the slot {slot_id:d} is restored with filename "{filename}"') +@async_run_until_complete +async def step_restore_slot(context, slot_id, filename): + async with aiohttp.ClientSession() as session: + async with session.post(f'{context.base_url}/slots/{slot_id}?action=restore', + json={"filename": filename}, + headers={"Content-Type": "application/json"}) as response: + context.response = response + + +@step('the slot {slot_id:d} is erased') +@async_run_until_complete +async def step_erase_slot(context, slot_id): + async with aiohttp.ClientSession() as session: + async with session.post(f'{context.base_url}/slots/{slot_id}?action=erase', + headers={"Content-Type": "application/json"}) as response: + context.response = response + + +@step('the server responds with status code {status_code:d}') +def step_server_responds_with_status_code(context, status_code): + assert context.response.status == status_code + + async def request_completion(prompt, base_url, debug=False, prompt_prefix=None, prompt_suffix=None, n_predict=None, + cache_prompt=False, + id_slot=None, seed=None, expect_api_error=None, user_api_key=None): @@ -738,6 +794,8 @@ async def request_completion(prompt, "prompt": prompt, "input_suffix": prompt_suffix, "n_predict": n_predict if n_predict is not None else -1, + "cache_prompt": cache_prompt, + "id_slot": id_slot, "seed": seed if seed is not None else 42 }, headers=headers, @@ -1104,6 +1162,8 @@ def start_server_background(context): server_args.extend(['--parallel', context.n_slots]) if context.n_server_predict: server_args.extend(['--n-predict', context.n_server_predict]) + if context.slot_save_path: + server_args.extend(['--slot-save-path', context.slot_save_path]) if context.server_api_key: server_args.extend(['--api-key', context.server_api_key]) if context.n_ga: diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 6e0815b36..6a7367b0c 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -76,6 +76,28 @@ int main(int argc, char ** argv) { params.n_threads_batch = params.n_threads_batch_draft; std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params); + const bool vocab_type_tgt = llama_vocab_type(model_tgt); + LOG("vocab_type tgt: %d\n", vocab_type_tgt); + + const bool vocab_type_dft = llama_vocab_type(model_dft); + LOG("vocab_type dft: %d\n", vocab_type_dft); + + if (vocab_type_tgt != vocab_type_dft) { + fprintf(stderr, "%s: error: draft model vocab type must match target model to use speculation but ", __func__); + fprintf(stderr, "vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt); + return 1; + } + + if ( + llama_add_bos_token(model_tgt) != llama_add_bos_token(model_dft) || + llama_add_eos_token(model_tgt) != llama_add_eos_token(model_dft) || + llama_token_bos(model_tgt) != llama_token_bos(model_dft) || + llama_token_eos(model_tgt) != llama_token_eos(model_dft) + ) { + fprintf(stderr, "%s: error: draft model special tokens must match target model to use speculation\n", __func__); + return 1; + } + { const int n_vocab_tgt = llama_n_vocab(model_tgt); const int n_vocab_dft = llama_n_vocab(model_dft); @@ -105,20 +127,8 @@ int main(int argc, char ** argv) { // Tokenize the prompt - const bool add_bos_tgt = llama_should_add_bos_token(model_tgt); - LOG("add_bos tgt: %d\n", add_bos_tgt); - - const bool add_bos_dft = llama_should_add_bos_token(model_dft); - LOG("add_bos dft: %d\n", add_bos_dft); - - if (add_bos_tgt != add_bos_dft) { - fprintf(stderr, "%s: error: draft model add_bos must match target model to use speculation but ", __func__); - fprintf(stderr, "add_bos_dft = %d while add_bos_tgt = %d\n", add_bos_dft, add_bos_tgt); - return 1; - } - std::vector inp; - inp = ::llama_tokenize(ctx_tgt, params.prompt, add_bos_tgt, true); + inp = ::llama_tokenize(ctx_tgt, params.prompt, true, true); const int max_context_size = llama_n_ctx(ctx_tgt); const int max_tokens_list_size = max_context_size - 4; diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp index d95a92475..8b1baea80 100644 --- a/examples/tokenize/tokenize.cpp +++ b/examples/tokenize/tokenize.cpp @@ -26,11 +26,9 @@ int main(int argc, char ** argv) { llama_context_params ctx_params = llama_context_default_params(); llama_context * ctx = llama_new_context_with_model(model, ctx_params); - const bool add_bos = llama_should_add_bos_token(model); - std::vector tokens; - tokens = ::llama_tokenize(model, prompt, add_bos, true); + tokens = ::llama_tokenize(model, prompt, true, true); for (int i = 0; i < (int) tokens.size(); i++) { if (printing_ids) { diff --git a/flake.lock b/flake.lock index c32725112..ed48dd8da 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1709336216, - "narHash": "sha256-Dt/wOWeW6Sqm11Yh+2+t0dfEWxoMxGBvv3JpIocFl9E=", + "lastModified": 1712014858, + "narHash": "sha256-sB4SWl2lX95bExY2gMFG5HIzvva5AVMJd4Igm+GpZNw=", "owner": "hercules-ci", "repo": "flake-parts", - "rev": "f7b3c975cf067e56e7cda6cb098ebe3fb4d74ca2", + "rev": "9126214d0a59633752a136528f5f3b9aa8565b7d", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1711703276, - "narHash": "sha256-iMUFArF0WCatKK6RzfUJknjem0H9m4KgorO/p3Dopkk=", + "lastModified": 1712163089, + "narHash": "sha256-Um+8kTIrC19vD4/lUCN9/cU9kcOsD1O1m+axJqQPyMM=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "d8fe5e6c92d0d190646fb9f1056741a229980089", + "rev": "fd281bd6b7d3e32ddfa399853946f782553163b5", "type": "github" }, "original": { @@ -37,11 +37,11 @@ "nixpkgs-lib": { "locked": { "dir": "lib", - "lastModified": 1709237383, - "narHash": "sha256-cy6ArO4k5qTx+l5o+0mL9f5fa86tYUX3ozE1S+Txlds=", + "lastModified": 1711703276, + "narHash": "sha256-iMUFArF0WCatKK6RzfUJknjem0H9m4KgorO/p3Dopkk=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "1536926ef5621b09bba54035ae2bb6d806d72ac8", + "rev": "d8fe5e6c92d0d190646fb9f1056741a229980089", "type": "github" }, "original": { diff --git a/ggml-cuda.cu b/ggml-cuda.cu index ce28cb55d..bff8ad9d9 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1225,7 +1225,7 @@ static void ggml_cuda_op_mul_mat_cublas( // the main device has a larger memory buffer to hold the results from all GPUs // ldc == nrows of the matrix that cuBLAS writes into - int ldc = id == ctx.device ? ne0 : row_diff; + int64_t ldc = id == ctx.device ? ne0 : row_diff; const int compute_capability = ggml_cuda_info().devices[id].cc; @@ -1377,8 +1377,8 @@ static void ggml_cuda_op_mul_mat( const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + const int64_t nb2 = dst->nb[2]; + const int64_t nb3 = dst->nb[3]; GGML_ASSERT(ggml_backend_buffer_is_cuda(dst->buffer)); GGML_ASSERT(ggml_backend_buffer_is_cuda(src1->buffer)); diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index b98d7cbd0..481065b2a 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -394,7 +394,7 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { // TODO: move to ggml-common.h static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; -typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v); +typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v); ////////////////////// diff --git a/ggml-cuda/convert.cu b/ggml-cuda/convert.cu index 18a31edc3..ed4fa2748 100644 --- a/ggml-cuda/convert.cu +++ b/ggml-cuda/convert.cu @@ -4,14 +4,14 @@ #define CUDA_Q8_0_NE_ALIGN 2048 template -static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) { - const int i = 2*(blockDim.x*blockIdx.x + threadIdx.x); +static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k) { + const int64_t i = 2*(blockDim.x*blockIdx.x + threadIdx.x); if (i >= k) { return; } - const int ib = i/qk; // block index + const int64_t ib = i/qk; // block index const int iqs = (i%qk)/qr; // quant index const int iybs = i - i%qk; // y block start index const int y_offset = qr == 1 ? 1 : qk/2; @@ -25,7 +25,7 @@ static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __ } template -static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, half * __restrict__ y, const int k) { +static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, half * __restrict__ y, const int64_t k) { #if __CUDA_ARCH__ >= CC_PASCAL constexpr int nint = CUDA_Q8_0_NE_ALIGN/sizeof(int) + WARP_SIZE; @@ -68,13 +68,13 @@ static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, h template static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; // assume 32 threads const int tid = threadIdx.x; const int il = tid/8; const int ir = tid%8; - const int ib = 8*i + ir; + const int64_t ib = 8*i + ir; if (ib >= nb32) { return; } @@ -96,13 +96,13 @@ static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t template static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; // assume 32 threads const int tid = threadIdx.x; const int il = tid/8; const int ir = tid%8; - const int ib = 8*i + ir; + const int64_t ib = 8*i + ir; if (ib >= nb32) { return; } @@ -313,14 +313,14 @@ template static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { const block_q6_K * x = (const block_q6_K *) vx; - const int i = blockIdx.x; + const int64_t i = blockIdx.x; #if QK_K == 256 // assume 64 threads - this is very slightly better than the one below - const int tid = threadIdx.x; - const int ip = tid/32; // ip is 0 or 1 - const int il = tid - 32*ip; // 0...32 - const int is = 8*ip + il/16; + const int64_t tid = threadIdx.x; + const int64_t ip = tid/32; // ip is 0 or 1 + const int64_t il = tid - 32*ip; // 0...32 + const int64_t is = 8*ip + il/16; dst_t * y = yy + i*QK_K + 128*ip + il; @@ -337,9 +337,9 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t #else // assume 32 threads - const int tid = threadIdx.x; - const int ip = tid/16; // 0 or 1 - const int il = tid - 16*ip; // 0...15 + const int64_t tid = threadIdx.x; + const int64_t ip = tid/16; // 0 or 1 + const int64_t il = tid - 16*ip; // 0...15 dst_t * y = yy + i*QK_K + 16*ip + il; @@ -571,12 +571,12 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst #endif template -static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { +static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) { const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE); dequantize_block<<>>(vx, y, k); } -static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * __restrict__ y, const int k, cudaStream_t stream) { +static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * __restrict__ y, const int64_t k, cudaStream_t stream) { const int num_blocks = (k + CUDA_Q8_0_NE_ALIGN - 1) / CUDA_Q8_0_NE_ALIGN; if (k % CUDA_Q8_0_NE_ALIGN == 0) { const bool need_check = false; @@ -588,7 +588,7 @@ static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * } template -static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; #if QK_K == 256 dequantize_block_q2_K<<>>(vx, y); @@ -598,7 +598,7 @@ static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int k, cu } template -static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; #if QK_K == 256 dequantize_block_q3_K<<>>(vx, y); @@ -608,27 +608,27 @@ static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cu } template -static void dequantize_row_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_q4_0_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb32 = k / 32; const int nb = (k + 255) / 256; dequantize_block_q4_0<<>>(vx, y, nb32); } template -static void dequantize_row_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_q4_1_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb32 = k / 32; const int nb = (k + 255) / 256; dequantize_block_q4_1<<>>(vx, y, nb32); } template -static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; dequantize_block_q4_K<<>>(vx, y); } template -static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; #if QK_K == 256 dequantize_block_q5_K<<>>(vx, y); @@ -638,7 +638,7 @@ static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int k, cu } template -static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; #if QK_K == 256 dequantize_block_q6_K<<>>(vx, y); @@ -648,55 +648,55 @@ static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cu } template -static void dequantize_row_iq2_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_iq2_xxs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; dequantize_block_iq2_xxs<<>>(vx, y); } template -static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; dequantize_block_iq2_xs<<>>(vx, y); } template -static void dequantize_row_iq2_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_iq2_s_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; dequantize_block_iq2_s<<>>(vx, y); } template -static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; dequantize_block_iq3_xxs<<>>(vx, y); } template -static void dequantize_row_iq3_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_iq3_s_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; dequantize_block_iq3_s<<>>(vx, y); } template -static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; dequantize_block_iq1_s<<>>(vx, y); } template -static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = (k + QK_K - 1) / QK_K; dequantize_block_iq4_nl<<>>(vx, y); } template -static void dequantize_row_iq1_m_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_iq1_m_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; dequantize_block_iq1_m<<>>(vx, y); } template -static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = (k + QK_K - 1) / QK_K; #if QK_K == 64 dequantize_block_iq4_nl<<>>(vx, y); @@ -706,8 +706,8 @@ static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, } template -static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; +static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k) { + const int64_t i = (int64_t)blockDim.x*blockIdx.x + threadIdx.x; if (i >= k) { return; @@ -719,7 +719,7 @@ static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __res } template -static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { +static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; convert_unary<<>>(vx, y, k); } diff --git a/ggml-cuda/convert.cuh b/ggml-cuda/convert.cuh index db34c0be9..5394be9f1 100644 --- a/ggml-cuda/convert.cuh +++ b/ggml-cuda/convert.cuh @@ -3,7 +3,7 @@ #define CUDA_DEQUANTIZE_BLOCK_SIZE 256 template -using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int k, cudaStream_t stream); +using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int64_t k, cudaStream_t stream); typedef to_t_cuda_t to_fp32_cuda_t; typedef to_t_cuda_t to_fp16_cuda_t; diff --git a/ggml-cuda/dequantize.cuh b/ggml-cuda/dequantize.cuh index b54400632..bd3c2d9db 100644 --- a/ggml-cuda/dequantize.cuh +++ b/ggml-cuda/dequantize.cuh @@ -1,6 +1,6 @@ #include "common.cuh" -static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, dfloat2 & v){ +static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){ const block_q4_0 * x = (const block_q4_0 *) vx; const dfloat d = x[ib].d; @@ -19,7 +19,7 @@ static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const in #endif // GGML_CUDA_F16 } -static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){ +static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){ const block_q4_1 * x = (const block_q4_1 *) vx; const dfloat d = __low2half(x[ib].dm); @@ -39,7 +39,7 @@ static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const in #endif // GGML_CUDA_F16 } -static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, dfloat2 & v){ +static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){ const block_q5_0 * x = (const block_q5_0 *) vx; const dfloat d = x[ib].d; @@ -62,7 +62,7 @@ static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const in #endif // GGML_CUDA_F16 } -static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){ +static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){ const block_q5_1 * x = (const block_q5_1 *) vx; const dfloat d = __low2half(x[ib].dm); @@ -86,7 +86,7 @@ static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const in #endif // GGML_CUDA_F16 } -static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, dfloat2 & v){ +static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){ const block_q8_0 * x = (const block_q8_0 *) vx; const dfloat d = x[ib].d; diff --git a/ggml-cuda/dmmv.cu b/ggml-cuda/dmmv.cu index 0b17e3cb9..7313e3e17 100644 --- a/ggml-cuda/dmmv.cu +++ b/ggml-cuda/dmmv.cu @@ -565,7 +565,7 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, } } -static __device__ void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){ +static __device__ void convert_f16(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){ const half * x = (const half *) vx; // automatic half -> float type cast if dfloat == float @@ -577,7 +577,7 @@ template static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) { // qk = quantized weights per x block // qr = number of quantized weights per data value in x block - const int row = blockIdx.x*blockDim.y + threadIdx.y; + const int64_t row = (int64_t)blockIdx.x*blockDim.y + threadIdx.y; if (row >= nrows) { return; @@ -598,7 +598,7 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons for (int i = 0; i < ncols; i += iter_stride) { const int col = i + vals_per_iter*tid; - const int ib = (row*ncols + col)/qk; // x block index + const int64_t ib = ((int64_t)row*ncols + col)/qk; // x block index const int iqs = (col%qk)/qr; // x quant index const int iybs = col - col%qk; // y block start index diff --git a/ggml-cuda/quantize.cu b/ggml-cuda/quantize.cu index a1fbc9932..7578c4b6c 100644 --- a/ggml-cuda/quantize.cu +++ b/ggml-cuda/quantize.cu @@ -1,20 +1,20 @@ #include "quantize.cuh" -static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) { - const int ix = blockDim.x*blockIdx.x + threadIdx.x; +static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int64_t kx, const int64_t kx_padded) { + const int64_t ix = (int64_t)blockDim.x*blockIdx.x + threadIdx.x; if (ix >= kx_padded) { return; } - const int iy = blockDim.y*blockIdx.y + threadIdx.y; + const int64_t iy = (int64_t)blockDim.y*blockIdx.y + threadIdx.y; - const int i_padded = iy*kx_padded + ix; + const int64_t i_padded = (int64_t)iy*kx_padded + ix; block_q8_1 * y = (block_q8_1 *) vy; - const int ib = i_padded / QK8_1; // block index - const int iqs = i_padded % QK8_1; // quant index + const int64_t ib = i_padded / QK8_1; // block index + const int64_t iqs = i_padded % QK8_1; // quant index const float xi = ix < kx ? x[iy*kx + ix] : 0.0f; float amax = fabsf(xi); @@ -36,8 +36,8 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest reinterpret_cast(y[ib].ds.y) = sum; } -void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) { - const int block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE; +void quantize_row_q8_1_cuda(const float * x, void * vy, const int64_t kx, const int64_t ky, const int64_t kx_padded, cudaStream_t stream) { + const int64_t block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE; const dim3 num_blocks(block_num_x, ky, 1); const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1); quantize_q8_1<<>>(x, vy, kx, kx_padded); diff --git a/ggml-cuda/quantize.cuh b/ggml-cuda/quantize.cuh index adb89c83a..b37a4752f 100644 --- a/ggml-cuda/quantize.cuh +++ b/ggml-cuda/quantize.cuh @@ -2,4 +2,4 @@ #define CUDA_QUANTIZE_BLOCK_SIZE 256 -void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream); +void quantize_row_q8_1_cuda(const float * x, void * vy, const int64_t kx, const int64_t ky, const int64_t kx_padded, cudaStream_t stream); diff --git a/ggml-quants.c b/ggml-quants.c index f2e6c4bd1..32e84434a 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -544,7 +544,7 @@ static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4 #endif // reference implementation for deterministic creation of model files -void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k) { +void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int64_t k) { static const int qk = QK4_0; assert(k % qk == 0); @@ -581,12 +581,12 @@ void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict } } -void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) { +void quantize_row_q4_0(const float * restrict x, void * restrict y, int64_t k) { quantize_row_q4_0_reference(x, y, k); } -void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k) { +void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int64_t k) { const int qk = QK4_1; assert(k % qk == 0); @@ -623,11 +623,11 @@ void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict } } -void quantize_row_q4_1(const float * restrict x, void * restrict y, int k) { +void quantize_row_q4_1(const float * restrict x, void * restrict y, int64_t k) { quantize_row_q4_1_reference(x, y, k); } -void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k) { +void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int64_t k) { static const int qk = QK5_0; assert(k % qk == 0); @@ -671,11 +671,11 @@ void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict } } -void quantize_row_q5_0(const float * restrict x, void * restrict y, int k) { +void quantize_row_q5_0(const float * restrict x, void * restrict y, int64_t k) { quantize_row_q5_0_reference(x, y, k); } -void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k) { +void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int64_t k) { const int qk = QK5_1; assert(k % qk == 0); @@ -719,12 +719,12 @@ void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict } } -void quantize_row_q5_1(const float * restrict x, void * restrict y, int k) { +void quantize_row_q5_1(const float * restrict x, void * restrict y, int64_t k) { quantize_row_q5_1_reference(x, y, k); } // reference implementation for deterministic creation of model files -void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) { +void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int64_t k) { assert(k % QK8_0 == 0); const int nb = k / QK8_0; @@ -749,7 +749,7 @@ void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict } } -void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { +void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k) { assert(QK8_0 == 32); assert(k % QK8_0 == 0); const int nb = k / QK8_0; @@ -938,7 +938,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { } // reference implementation for deterministic creation of model files -void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k) { +void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int64_t k) { assert(QK8_1 == 32); assert(k % QK8_1 == 0); const int nb = k / QK8_1; @@ -973,7 +973,7 @@ void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict } } -void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { +void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) { assert(k % QK8_1 == 0); const int nb = k / QK8_1; @@ -1192,7 +1192,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { #endif } -void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k) { +void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int64_t k) { static const int qk = QK4_0; assert(k % qk == 0); @@ -1212,7 +1212,7 @@ void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int } } -void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k) { +void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int64_t k) { static const int qk = QK4_1; assert(k % qk == 0); @@ -1233,7 +1233,7 @@ void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int } } -void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k) { +void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int64_t k) { static const int qk = QK5_0; assert(k % qk == 0); @@ -1259,7 +1259,7 @@ void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int } } -void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k) { +void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int64_t k) { static const int qk = QK5_1; assert(k % qk == 0); @@ -1286,7 +1286,7 @@ void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int } } -void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k) { +void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int64_t k) { static const int qk = QK8_0; assert(k % qk == 0); @@ -1581,7 +1581,7 @@ static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * //========================- 2-bit (de)-quantization -void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k) { +void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int64_t k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -1658,7 +1658,7 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict } } -void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k) { +void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -1704,7 +1704,7 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int } } -void quantize_row_q2_K(const float * restrict x, void * restrict vy, int k) { +void quantize_row_q2_K(const float * restrict x, void * restrict vy, int64_t k) { quantize_row_q2_K_reference(x, vy, k); } @@ -1960,14 +1960,14 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri } } -size_t quantize_q2_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_q2_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q2_K, n_per_row); if (!quant_weights) { - quantize_row_q2_K_reference(src, dst, nrow*n_per_row); + quantize_row_q2_K_reference(src, dst, (int64_t)nrow*n_per_row); } else { char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_q2_K_impl(src, (block_q2_K*)qrow, n_per_row, quant_weights); src += n_per_row; qrow += row_size; @@ -1978,7 +1978,7 @@ size_t quantize_q2_K(const float * restrict src, void * restrict dst, int nrow, //========================= 3-bit (de)-quantization -void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k) { +void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int64_t k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -2092,7 +2092,7 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict } #if QK_K == 256 -void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k) { +void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -2142,7 +2142,7 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int } } #else -void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k) { +void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); assert(QK_K == 64); const int nb = k / QK_K; @@ -2175,11 +2175,11 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int } #endif -void quantize_row_q3_K(const float * restrict x, void * restrict vy, int k) { +void quantize_row_q3_K(const float * restrict x, void * restrict vy, int64_t k) { quantize_row_q3_K_reference(x, vy, k); } -static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int n_per_row, const float * restrict quant_weights) { +static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int64_t n_per_row, const float * restrict quant_weights) { #if QK_K != 256 (void)quant_weights; quantize_row_q3_K_reference(x, y, n_per_row); @@ -2268,14 +2268,14 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri #endif } -size_t quantize_q3_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_q3_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q3_K, n_per_row); if (!quant_weights) { - quantize_row_q3_K_reference(src, dst, nrow*n_per_row); + quantize_row_q3_K_reference(src, dst, (int64_t)nrow*n_per_row); } else { char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_q3_K_impl(src, (block_q3_K*)qrow, n_per_row, quant_weights); src += n_per_row; qrow += row_size; @@ -2286,7 +2286,7 @@ size_t quantize_q3_K(const float * restrict src, void * restrict dst, int nrow, // ====================== 4-bit (de)-quantization -void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k) { +void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int64_t k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -2393,7 +2393,7 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict } } -void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k) { +void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -2432,19 +2432,19 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int } } -void quantize_row_q4_K(const float * restrict x, void * restrict vy, int k) { +void quantize_row_q4_K(const float * restrict x, void * restrict vy, int64_t k) { assert(k % QK_K == 0); block_q4_K * restrict y = vy; quantize_row_q4_K_reference(x, y, k); } -static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int n_per_row, const float * quant_weights) { +static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int64_t n_per_row, const float * quant_weights) { #if QK_K != 256 (void)quant_weights; quantize_row_q4_K_reference(x, y, n_per_row); #else assert(n_per_row % QK_K == 0); - const int nb = n_per_row / QK_K; + const int64_t nb = n_per_row / QK_K; uint8_t L[QK_K]; uint8_t Laux[32]; @@ -2516,14 +2516,14 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri #endif } -size_t quantize_q4_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_q4_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q4_K, n_per_row); if (!quant_weights) { - quantize_row_q4_K_reference(src, dst, nrow*n_per_row); + quantize_row_q4_K_reference(src, dst, (int64_t)nrow*n_per_row); } else { char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_q4_K_impl(src, (block_q4_K*)qrow, n_per_row, quant_weights); src += n_per_row; qrow += row_size; @@ -2534,9 +2534,9 @@ size_t quantize_q4_K(const float * restrict src, void * restrict dst, int nrow, // ====================== 5-bit (de)-quantization -void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k) { +void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; #if QK_K == 256 uint8_t L[QK_K]; @@ -2676,9 +2676,9 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict } } -void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k) { +void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { @@ -2721,19 +2721,19 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int } } -void quantize_row_q5_K(const float * restrict x, void * restrict vy, int k) { +void quantize_row_q5_K(const float * restrict x, void * restrict vy, int64_t k) { assert(k % QK_K == 0); block_q5_K * restrict y = vy; quantize_row_q5_K_reference(x, y, k); } -static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int n_per_row, const float * quant_weights) { +static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int64_t n_per_row, const float * quant_weights) { #if QK_K != 256 (void)quant_weights; quantize_row_q5_K_reference(x, y, n_per_row); #else assert(n_per_row % QK_K == 0); - const int nb = n_per_row / QK_K; + const int64_t nb = n_per_row / QK_K; uint8_t L[QK_K]; uint8_t Laux[32]; @@ -2825,14 +2825,14 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri #endif } -size_t quantize_q5_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_q5_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q5_K, n_per_row); if (!quant_weights) { - quantize_row_q5_K_reference(src, dst, nrow*n_per_row); + quantize_row_q5_K_reference(src, dst, (int64_t)nrow*n_per_row); } else { char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_q5_K_impl(src, (block_q5_K*)qrow, n_per_row, quant_weights); src += n_per_row; qrow += row_size; @@ -2843,9 +2843,9 @@ size_t quantize_q5_K(const float * restrict src, void * restrict dst, int nrow, // ====================== 6-bit (de)-quantization -void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k) { +void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; int8_t L[QK_K]; float scales[QK_K/16]; @@ -2925,9 +2925,9 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict } } -void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k) { +void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { @@ -2972,19 +2972,19 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int } } -void quantize_row_q6_K(const float * restrict x, void * restrict vy, int k) { +void quantize_row_q6_K(const float * restrict x, void * restrict vy, int64_t k) { assert(k % QK_K == 0); block_q6_K * restrict y = vy; quantize_row_q6_K_reference(x, y, k); } -static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int n_per_row, const float * quant_weights) { +static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int64_t n_per_row, const float * quant_weights) { #if QK_K != 256 (void)quant_weights; quantize_row_q6_K_reference(x, y, n_per_row); #else assert(n_per_row % QK_K == 0); - const int nb = n_per_row / QK_K; + const int64_t nb = n_per_row / QK_K; int8_t L[QK_K]; float scales[QK_K/16]; @@ -3067,14 +3067,14 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri #endif } -size_t quantize_q6_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_q6_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { size_t row_size = ggml_row_size(GGML_TYPE_Q6_K, n_per_row); if (!quant_weights) { - quantize_row_q6_K_reference(src, dst, nrow*n_per_row); + quantize_row_q6_K_reference(src, dst, (int64_t)nrow*n_per_row); } else { char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_q6_K_impl(src, (block_q6_K*)qrow, n_per_row, quant_weights); src += n_per_row; qrow += row_size; @@ -3083,7 +3083,7 @@ size_t quantize_q6_K(const float * restrict src, void * restrict dst, int nrow, return nrow * row_size; } -static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restrict y, int n_per_row, const float * quant_weights) { +static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restrict y, int64_t n_per_row, const float * quant_weights) { static_assert(QK4_0 == 32, "QK4_0 must be 32"); if (!quant_weights) { @@ -3098,7 +3098,7 @@ static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restri for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j]; float sigma2 = sum_x2/n_per_row; - const int nb = n_per_row/QK4_0; + const int64_t nb = n_per_row/QK4_0; for (int ib = 0; ib < nb; ++ib) { const float * xb = x + QK4_0 * ib; const float * qw = quant_weights + QK4_0 * ib; @@ -3111,14 +3111,14 @@ static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restri } } -size_t quantize_q4_0(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_q4_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { if (!quant_weights) { - quantize_row_q4_0_reference(src, dst, nrow*n_per_row); + quantize_row_q4_0_reference(src, dst, (int64_t)nrow*n_per_row); return nrow * ggml_row_size(GGML_TYPE_Q4_0, n_per_row); } size_t row_size = ggml_row_size(GGML_TYPE_Q4_0, n_per_row); char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_q4_0_impl(src, (block_q4_0*)qrow, n_per_row, quant_weights); src += n_per_row; qrow += row_size; @@ -3126,7 +3126,7 @@ size_t quantize_q4_0(const float * restrict src, void * restrict dst, int nrow, return nrow * row_size; } -static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restrict y, int n_per_row, const float * quant_weights) { +static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restrict y, int64_t n_per_row, const float * quant_weights) { static_assert(QK4_1 == 32, "QK4_1 must be 32"); if (!quant_weights) { @@ -3141,7 +3141,7 @@ static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restri for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j]; float sigma2 = sum_x2/n_per_row; - const int nb = n_per_row/QK4_1; + const int64_t nb = n_per_row/QK4_1; for (int ib = 0; ib < nb; ++ib) { const float * xb = x + QK4_1 * ib; const float * qw = quant_weights + QK4_1 * ib; @@ -3156,14 +3156,14 @@ static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restri } } -size_t quantize_q4_1(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_q4_1(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { if (!quant_weights) { - quantize_row_q4_1_reference(src, dst, nrow*n_per_row); + quantize_row_q4_1_reference(src, dst, (int64_t)nrow*n_per_row); return nrow * ggml_row_size(GGML_TYPE_Q4_1, n_per_row); } size_t row_size = ggml_row_size(GGML_TYPE_Q4_1, n_per_row); char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_q4_1_impl(src, (block_q4_1*)qrow, n_per_row, quant_weights); src += n_per_row; qrow += row_size; @@ -3171,7 +3171,7 @@ size_t quantize_q4_1(const float * restrict src, void * restrict dst, int nrow, return nrow * row_size; } -static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restrict y, int n_per_row, const float * quant_weights) { +static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restrict y, int64_t n_per_row, const float * quant_weights) { static_assert(QK5_0 == 32, "QK5_0 must be 32"); if (!quant_weights) { @@ -3186,7 +3186,7 @@ static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restri for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j]; float sigma2 = sum_x2/n_per_row; - const int nb = n_per_row/QK5_0; + const int64_t nb = n_per_row/QK5_0; for (int ib = 0; ib < nb; ++ib) { const float * xb = x + QK5_0 * ib; const float * qw = quant_weights + QK5_0 * ib; @@ -3210,14 +3210,14 @@ static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restri } } -size_t quantize_q5_0(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_q5_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { if (!quant_weights) { - quantize_row_q5_0_reference(src, dst, nrow*n_per_row); + quantize_row_q5_0_reference(src, dst, (int64_t)nrow*n_per_row); return nrow * ggml_row_size(GGML_TYPE_Q5_0, n_per_row); } size_t row_size = ggml_row_size(GGML_TYPE_Q5_0, n_per_row); char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_q5_0_impl(src, (block_q5_0*)qrow, n_per_row, quant_weights); src += n_per_row; qrow += row_size; @@ -3225,7 +3225,7 @@ size_t quantize_q5_0(const float * restrict src, void * restrict dst, int nrow, return nrow * row_size; } -static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restrict y, int n_per_row, const float * quant_weights) { +static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restrict y, int64_t n_per_row, const float * quant_weights) { static_assert(QK5_1 == 32, "QK5_1 must be 32"); if (!quant_weights) { @@ -3240,7 +3240,7 @@ static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restri for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j]; float sigma2 = sum_x2/n_per_row; - const int nb = n_per_row/QK5_1; + const int64_t nb = n_per_row/QK5_1; for (int ib = 0; ib < nb; ++ib) { const float * xb = x + QK5_1 * ib; const float * qw = quant_weights + QK5_1 * ib; @@ -3263,14 +3263,14 @@ static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restri } } -size_t quantize_q5_1(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_q5_1(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { if (!quant_weights) { - quantize_row_q5_1_reference(src, dst, nrow*n_per_row); + quantize_row_q5_1_reference(src, dst, (int64_t)nrow*n_per_row); return nrow * ggml_row_size(GGML_TYPE_Q5_1, n_per_row); } size_t row_size = ggml_row_size(GGML_TYPE_Q5_1, n_per_row); char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_q5_1_impl(src, (block_q5_1*)qrow, n_per_row, quant_weights); src += n_per_row; qrow += row_size; @@ -3278,18 +3278,18 @@ size_t quantize_q5_1(const float * restrict src, void * restrict dst, int nrow, return nrow * row_size; } -size_t quantize_q8_0(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_q8_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { (void)quant_weights; // not used const size_t row_size = ggml_row_size(GGML_TYPE_Q8_0, n_per_row); - quantize_row_q8_0_reference(src, dst, nrow*n_per_row); + quantize_row_q8_0_reference(src, dst, (int64_t)nrow*n_per_row); return nrow * row_size; } // ====================== "True" 2-bit (de)-quantization -void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k) { +void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; uint32_t aux32[2]; const uint8_t * aux8 = (const uint8_t *)aux32; @@ -3315,9 +3315,9 @@ void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y // ====================== 2.3125 bpw (de)-quantization -void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, int k) { +void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; float db[2]; @@ -3342,9 +3342,9 @@ void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, // ====================== 2.5625 bpw (de)-quantization -void dequantize_row_iq2_s(const block_iq2_s * restrict x, float * restrict y, int k) { +void dequantize_row_iq2_s(const block_iq2_s * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; float db[2]; @@ -3374,9 +3374,9 @@ void dequantize_row_iq2_s(const block_iq2_s * restrict x, float * restrict y, in // ====================== 3.0625 bpw (de)-quantization -void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k) { +void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; uint32_t aux32; @@ -3406,9 +3406,9 @@ void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y // ====================== 3.3125 bpw (de)-quantization -void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, int k) { +void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { @@ -3449,9 +3449,9 @@ void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, in // ====================== 1.5625 bpw (de)-quantization -void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, int k) { +void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { @@ -3474,9 +3474,9 @@ void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, in } } -void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, int k) { +void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; float delta[4]; uint16_t idx[4]; @@ -3535,9 +3535,9 @@ void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, in static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; -void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, int k) { +void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, int64_t k) { assert(k % QK4_NL == 0); - const int nb = k / QK4_NL; + const int64_t nb = k / QK4_NL; for (int i = 0; i < nb; i++) { @@ -3553,12 +3553,12 @@ void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, } } -void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, int k) { +void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); #if QK_K == 64 dequantize_row_iq4_nl((const block_iq4_nl *)x, y, k); #else - const int nb = k / QK_K; + const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { @@ -3582,9 +3582,9 @@ void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, //===================================== Q8_K ============================================== -void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) { +void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { @@ -3621,9 +3621,9 @@ void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict } } -void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k) { +void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); - const int nb = k / QK_K; + const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { for (int j = 0; j < QK_K; ++j) { @@ -3632,7 +3632,7 @@ void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int } } -void quantize_row_q8_K(const float * restrict x, void * restrict y, int k) { +void quantize_row_q8_K(const float * restrict x, void * restrict y, int64_t k) { quantize_row_q8_K_reference(x, y, k); } @@ -10648,7 +10648,7 @@ static int iq2_find_best_neighbour(const uint16_t * restrict neighbours, const u return grid_index; } -static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { +static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict vy, int64_t n, const float * restrict quant_weights) { const int gindex = iq2_data_index(GGML_TYPE_IQ2_XXS); @@ -10664,7 +10664,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict const int kMaxQ = 3; - const int nbl = n/QK_K; + const int64_t nbl = n/QK_K; block_iq2_xxs * y = vy; @@ -10821,7 +10821,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict } } -static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { +static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict vy, int64_t n, const float * restrict quant_weights) { const int gindex = iq2_data_index(GGML_TYPE_IQ2_XS); @@ -10837,7 +10837,7 @@ static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict v const int kMaxQ = 3; - const int nbl = n/QK_K; + const int64_t nbl = n/QK_K; block_iq2_xs * y = vy; @@ -11001,11 +11001,11 @@ static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict v } } -size_t quantize_iq2_xxs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_iq2_xxs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); - int nblock = n_per_row/QK_K; + int64_t nblock = n_per_row/QK_K; char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_iq2_xxs_impl(src, qrow, n_per_row, quant_weights); src += n_per_row; qrow += nblock*sizeof(block_iq2_xxs); @@ -11013,11 +11013,11 @@ size_t quantize_iq2_xxs(const float * restrict src, void * restrict dst, int nro return nrow * nblock * sizeof(block_iq2_xxs); } -size_t quantize_iq2_xs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_iq2_xs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); - int nblock = n_per_row/QK_K; + int64_t nblock = n_per_row/QK_K; char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_iq2_xs_impl(src, qrow, n_per_row, quant_weights); src += n_per_row; qrow += nblock*sizeof(block_iq2_xs); @@ -11242,7 +11242,7 @@ static int iq3_find_best_neighbour(const uint16_t * restrict neighbours, const u return grid_index; } -static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, void * restrict vy, int n, +static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, void * restrict vy, int64_t n, const float * restrict quant_weights) { const int gindex = iq3_data_index(grid_size); @@ -11259,7 +11259,7 @@ static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, v const int kMaxQ = 8; - const int nbl = n/QK_K; + const int64_t nbl = n/QK_K; ggml_fp16_t * dh; uint8_t * qs; @@ -11455,11 +11455,11 @@ static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, v } } -size_t quantize_iq3_xxs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_iq3_xxs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); - int nblock = n_per_row/QK_K; + int64_t nblock = n_per_row/QK_K; char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_iq3_xxs_impl(256, src, qrow, n_per_row, quant_weights); src += n_per_row; qrow += nblock*sizeof(block_iq3_xxs); @@ -11467,13 +11467,13 @@ size_t quantize_iq3_xxs(const float * restrict src, void * restrict dst, int nro return nrow * nblock * sizeof(block_iq3_xxs); } -void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int k) { +void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int64_t k) { assert(k % QK_K == 0); block_iq3_xxs * restrict y = vy; quantize_row_iq3_xxs_reference(x, y, k); } -void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k) { +void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int64_t k) { assert(k % QK_K == 0); quantize_row_iq3_xxs_impl(256, x, y, k, NULL); } @@ -11504,7 +11504,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo const int kMaxQ = 8; - const int nbl = n/QK_K; + const int64_t nbl = n/QK_K; block_iq3_s * y = vy; @@ -11661,9 +11661,9 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo } #define IQ3S_BLOCK_SIZE 32 -size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); - int nblock = n_per_row/QK_K; + int64_t nblock = n_per_row/QK_K; float scales[QK_K/IQ3S_BLOCK_SIZE]; float weight[IQ3S_BLOCK_SIZE]; float xval[IQ3S_BLOCK_SIZE]; @@ -11674,7 +11674,7 @@ size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int nrow, bool is_on_grid_aux[IQ3S_BLOCK_SIZE/4]; uint8_t block_signs[IQ3S_BLOCK_SIZE/8]; char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_iq3_s_impl(IQ3S_BLOCK_SIZE, src, qrow, n_per_row, quant_weights, scales, weight, xval, L, Laux, waux, is_on_grid, is_on_grid_aux, block_signs); src += n_per_row; @@ -11683,13 +11683,13 @@ size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int nrow, return nrow * nblock * sizeof(block_iq3_s); } -void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int k) { +void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int64_t k) { assert(k % QK_K == 0); block_iq3_s * restrict y = vy; quantize_row_iq3_s_reference(x, y, k); } -void quantize_row_iq3_s_reference(const float * restrict x, block_iq3_s * restrict y, int k) { +void quantize_row_iq3_s_reference(const float * restrict x, block_iq3_s * restrict y, int64_t k) { assert(k % QK_K == 0); quantize_iq3_s(x, y, 1, k, NULL); } @@ -11822,7 +11822,7 @@ static int iq1_sort_helper(const void * left, const void * right) { #define IQ1S_BLOCK_SIZE 32 #define IQ1M_BLOCK_SIZE 16 -static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights, +static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy, int64_t n, const float * restrict quant_weights, float * scales, float * weight, float * sumx, @@ -11846,7 +11846,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy block_iq1_s * y = vy; - const int nbl = n/QK_K; + const int64_t nbl = n/QK_K; const int block_size = IQ1S_BLOCK_SIZE; @@ -11980,7 +11980,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy } } -size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); float scales[QK_K/IQ1S_BLOCK_SIZE]; float weight[IQ1S_BLOCK_SIZE]; @@ -11990,9 +11990,9 @@ size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int nrow, float pairs[2*IQ1S_BLOCK_SIZE]; uint16_t index[IQ1S_BLOCK_SIZE/8]; int8_t shifts[QK_K/IQ1S_BLOCK_SIZE]; - int nblock = n_per_row/QK_K; + int64_t nblock = n_per_row/QK_K; char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_iq1_s_impl(src, qrow, n_per_row, quant_weights, scales, weight, sumx, sumw, pairs, L, index, shifts); src += n_per_row; qrow += nblock*sizeof(block_iq1_s); @@ -12000,7 +12000,7 @@ size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int nrow, return nrow * nblock * sizeof(block_iq1_s); } -static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights, +static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy, int64_t n, const float * restrict quant_weights, float * scales, float * weight, float * pairs, @@ -12022,7 +12022,7 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy block_iq1_m * y = vy; - const int nbl = n/QK_K; + const int64_t nbl = n/QK_K; const int block_size = IQ1M_BLOCK_SIZE; @@ -12265,7 +12265,7 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy } } -size_t quantize_iq1_m(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_iq1_m(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); float scales[QK_K/IQ1M_BLOCK_SIZE]; float weight[IQ1M_BLOCK_SIZE]; @@ -12273,9 +12273,9 @@ size_t quantize_iq1_m(const float * restrict src, void * restrict dst, int nrow, float pairs[2*IQ1M_BLOCK_SIZE]; uint16_t index[IQ1M_BLOCK_SIZE/8]; int8_t shifts[QK_K/IQ1M_BLOCK_SIZE]; - int nblock = n_per_row/QK_K; + int64_t nblock = n_per_row/QK_K; char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_iq1_m_impl(src, qrow, n_per_row, quant_weights, scales, weight, pairs, L, index, shifts); src += n_per_row; qrow += nblock*sizeof(block_iq1_m); @@ -12407,16 +12407,16 @@ static void quantize_row_iq4_nl_impl(const int super_block_size, const int block } } -size_t quantize_iq4_nl(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_iq4_nl(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK4_NL == 0); - int nblock = n_per_row/QK4_NL; + int64_t nblock = n_per_row/QK4_NL; char * qrow = (char *)dst; uint8_t L[QK4_NL]; float weight[QK4_NL]; uint16_t unused_h; uint8_t * unused_l = NULL; float scale; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { block_iq4_nl * iq4 = (block_iq4_nl *)qrow; for (int ibl = 0; ibl < nblock; ++ibl) { const float * qw = quant_weights ? quant_weights + QK4_NL*ibl : NULL; @@ -12429,9 +12429,9 @@ size_t quantize_iq4_nl(const float * restrict src, void * restrict dst, int nrow return nrow * nblock * sizeof(block_iq4_nl); } -void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int k) { +void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int64_t k) { GGML_ASSERT(k%QK4_NL == 0); - int nblock = k/QK4_NL; + int64_t nblock = k/QK4_NL; uint8_t L[QK4_NL]; float weight[QK4_NL]; uint16_t unused_h; @@ -12444,22 +12444,22 @@ void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int k) { } } -void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * restrict y, int k) { +void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * restrict y, int64_t k) { assert(k % QK4_NL == 0); quantize_row_iq4_nl(x, y, k); } -size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { #if QK_K == 64 return quantize_iq4_nl(src, dst, nrow, n_per_row, quant_weights); #else GGML_ASSERT(n_per_row%QK_K == 0); - int nblock = n_per_row/QK_K; + int64_t nblock = n_per_row/QK_K; char * qrow = (char *)dst; uint8_t L[QK_K]; float weight[32]; float scales[QK_K/32]; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { block_iq4_xs * iq4 = (block_iq4_xs *)qrow; for (int ibl = 0; ibl < nblock; ++ibl) { const float * qw = quant_weights ? quant_weights + QK_K*ibl : NULL; @@ -12473,20 +12473,20 @@ size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int nrow #endif } -void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int k) { +void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int64_t k) { assert(k % QK_K == 0); block_iq4_xs * restrict y = vy; quantize_row_iq4_xs_reference(x, y, k); } -void quantize_row_iq4_xs_reference(const float * restrict x, block_iq4_xs * restrict y, int k) { +void quantize_row_iq4_xs_reference(const float * restrict x, block_iq4_xs * restrict y, int64_t k) { assert(k % QK_K == 0); quantize_iq4_xs(x, y, 1, k, NULL); } // =============================== 2.5625 bpw -static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { +static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy, int64_t n, const float * restrict quant_weights) { const int gindex = iq2_data_index(GGML_TYPE_IQ2_S); @@ -12501,7 +12501,7 @@ static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy const int kMaxQ = 3; - const int nbl = n/QK_K; + const int64_t nbl = n/QK_K; block_iq2_s * y = vy; @@ -12654,11 +12654,11 @@ static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy } } -size_t quantize_iq2_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { +size_t quantize_iq2_s(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); - int nblock = n_per_row/QK_K; + int64_t nblock = n_per_row/QK_K; char * qrow = (char *)dst; - for (int row = 0; row < nrow; ++row) { + for (int64_t row = 0; row < nrow; ++row) { quantize_row_iq2_s_impl(src, qrow, n_per_row, quant_weights); src += n_per_row; qrow += nblock*sizeof(block_iq2_s); @@ -12666,12 +12666,12 @@ size_t quantize_iq2_s(const float * restrict src, void * restrict dst, int nrow, return nrow * nblock * sizeof(block_iq2_s); } -void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restrict y, int k) { +void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restrict y, int64_t k) { assert(k % QK_K == 0); quantize_iq2_s(x, y, 1, k, NULL); } -void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int k) { +void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k) { assert(k % QK_K == 0); block_iq2_s * restrict y = vy; quantize_row_iq2_s_reference(x, y, k); diff --git a/ggml-quants.h b/ggml-quants.h index ac1091c3d..4d436a8f0 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -12,70 +12,70 @@ extern "C" { #endif // Quantization -void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int k); -void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int k); -void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int k); -void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int k); -void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int k); -void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int k); +void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k); +void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k); +void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k); +void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k); +void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k); +void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k); -void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int k); -void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int k); -void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int k); -void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int k); -void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k); -void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k); +void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k); +void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k); +void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k); +void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k); +void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k); +void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k); -void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k); -void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int k); -void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int k); -void quantize_row_iq3_s_reference (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int k); -void quantize_row_iq2_s_reference (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int k); +void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k); +void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int64_t k); +void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int64_t k); +void quantize_row_iq3_s_reference (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int64_t k); +void quantize_row_iq2_s_reference (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int64_t k); -void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_iq3_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_iq2_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_iq3_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_iq2_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); // Dequantization -void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_iq1_m (const block_iq1_m * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_iq1_m (const block_iq1_m * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); // Dot product void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); @@ -101,26 +101,26 @@ void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") -size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_iq1_m (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_iq1_m (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); -size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); +size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); void iq2xs_init_impl(enum ggml_type type); void iq2xs_free_impl(enum ggml_type type); diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index b83881496..55a1eedb5 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -15776,7 +15776,7 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 #ifdef GGML_SYCL_FORCE_DMMV const bool use_mul_mat_vec_q = false; #else - bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1; + bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type); use_mul_mat_vec_q = use_mul_mat_vec_q || (src0->type == GGML_TYPE_IQ2_XXS) || (src0->type == GGML_TYPE_IQ2_XS) || (src0->type == GGML_TYPE_IQ2_S) || (src0->type == GGML_TYPE_IQ3_XXS) || (src0->type == GGML_TYPE_IQ3_S) || @@ -15787,7 +15787,6 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 #endif // GGML_SYCL_FORCE_DMMV if (use_mul_mat_vec_q) { - // NOTE: this kernel does not support ggml_nrows(src1) > 1 // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_vec_q path\n"); ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true); } else { diff --git a/ggml.c b/ggml.c index c9b0a6a0e..793b67f4c 100644 --- a/ggml.c +++ b/ggml.c @@ -338,14 +338,14 @@ ggml_fp16_t ggml_fp32_to_fp16(float x) { return GGML_FP32_TO_FP16(x); } -void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n) { - for (int i = 0; i < n; i++) { +void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int64_t n) { + for (int64_t i = 0; i < n; i++) { y[i] = GGML_FP16_TO_FP32(x[i]); } } -void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) { - int i = 0; +void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int64_t n) { + int64_t i = 0; #if defined(__F16C__) for (; i + 7 < n; i += 8) { __m256 x_vec = _mm256_loadu_ps(x + i); @@ -20331,11 +20331,11 @@ size_t ggml_quantize_chunk( enum ggml_type type, const float * src, void * dst, - int start, - int nrows, - int n_per_row, + int64_t start, + int64_t nrows, + int64_t n_per_row, const float * imatrix) { - const int n = nrows * n_per_row; + const int64_t n = (int64_t) nrows * n_per_row; if (ggml_quantize_requires_imatrix(type)) { GGML_ASSERT(imatrix != NULL); diff --git a/ggml.h b/ggml.h index 5cef45c0b..abe3767f2 100644 --- a/ggml.h +++ b/ggml.h @@ -332,8 +332,8 @@ extern "C" { GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x); GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x); - GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n); - GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n); + GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int64_t n); + GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int64_t n); struct ggml_object; struct ggml_context; @@ -2210,9 +2210,9 @@ extern "C" { enum ggml_type type, const float * src, void * dst, - int start, - int nrows, - int n_per_row, + int64_t start, + int64_t nrows, + int64_t n_per_row, const float * imatrix); // @@ -2377,8 +2377,8 @@ extern "C" { #else #define GGML_RESTRICT restrict #endif - typedef void (*ggml_to_float_t) (const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); - typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); + typedef void (*ggml_to_float_t) (const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); + typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx, const void * GGML_RESTRICT y, size_t by, int nrc); diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index a610acc58..2566b2fb8 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -641,6 +641,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_Q_NORM, ], MODEL_ARCH.DBRX: [ MODEL_TENSOR.TOKEN_EMBD, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 9d34ce5c1..da900f103 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -293,12 +293,14 @@ class TensorNameMap: MODEL_TENSOR.ATTN_Q_NORM: ( "language_model.encoder.layers.{bid}.self_attention.q_layernorm", "model.layers.{bid}.self_attn.q_layernorm", # persimmon + "model.layers.{bid}.self_attn.q_norm", # cohere "transformer.blocks.{bid}.attn.q_ln", # sea-lion ), MODEL_TENSOR.ATTN_K_NORM: ( "language_model.encoder.layers.{bid}.self_attention.k_layernorm", "model.layers.{bid}.self_attn.k_layernorm", # persimmon + "model.layers.{bid}.self_attn.k_norm", # cohere "transformer.blocks.{bid}.attn.k_ln", # sea-lion ), diff --git a/llama.cpp b/llama.cpp index a67149c5f..2841bc271 100644 --- a/llama.cpp +++ b/llama.cpp @@ -320,6 +320,8 @@ enum llm_kv { LLM_KV_TOKENIZER_UNK_ID, LLM_KV_TOKENIZER_SEP_ID, LLM_KV_TOKENIZER_PAD_ID, + LLM_KV_TOKENIZER_CLS_ID, + LLM_KV_TOKENIZER_MASK_ID, LLM_KV_TOKENIZER_ADD_BOS, LLM_KV_TOKENIZER_ADD_EOS, LLM_KV_TOKENIZER_ADD_PREFIX, @@ -390,6 +392,8 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_TOKENIZER_UNK_ID, "tokenizer.ggml.unknown_token_id" }, { LLM_KV_TOKENIZER_SEP_ID, "tokenizer.ggml.seperator_token_id" }, { LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" }, + { LLM_KV_TOKENIZER_CLS_ID, "tokenizer.ggml.cls_token_id" }, + { LLM_KV_TOKENIZER_MASK_ID, "tokenizer.ggml.mask_token_id" }, { LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" }, { LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" }, { LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" }, @@ -928,6 +932,8 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, }, }, { @@ -1718,6 +1724,8 @@ enum e_model { MODEL_MEDIUM, MODEL_LARGE, MODEL_XL, + MODEL_8x7B, + MODEL_8x22B, }; static const size_t kiB = 1024; @@ -2035,11 +2043,13 @@ struct llama_vocab { std::map, int> bpe_ranks; // default LLaMA special tokens - id special_bos_id = 1; - id special_eos_id = 2; - id special_unk_id = 0; - id special_sep_id = -1; - id special_pad_id = -1; + id special_bos_id = 1; + id special_eos_id = 2; + id special_unk_id = 0; + id special_sep_id = -1; + id special_pad_id = -1; + 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. @@ -2196,7 +2206,7 @@ struct llama_context { std::vector output_ids; // map batch token positions to ids of the logits and embd buffers size_t output_size = 0; // capacity (of tokens positions) for the output buffers - int32_t n_outputs = 0; // number of actually-used outputs in the current ubatch + int32_t n_outputs = 0; // number of actually-used outputs in the current ubatch or last logical batch bool logits_all = false; @@ -3570,6 +3580,8 @@ static const char * llama_model_type_name(e_model type) { case MODEL_MEDIUM: return "0.4B"; case MODEL_LARGE: return "0.8B"; case MODEL_XL: return "1.5B"; + case MODEL_8x7B: return "8x7B"; + case MODEL_8x22B: return "8x22B"; default: return "?B"; } } @@ -3684,15 +3696,23 @@ static void llm_load_hparams( { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - switch (hparams.n_layer) { - case 22: model.type = e_model::MODEL_1B; break; - case 26: model.type = e_model::MODEL_3B; break; - case 32: model.type = e_model::MODEL_7B; break; - case 40: model.type = e_model::MODEL_13B; break; - case 48: model.type = e_model::MODEL_34B; break; - case 60: model.type = e_model::MODEL_30B; break; - case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break; - default: model.type = e_model::MODEL_UNKNOWN; + if (hparams.n_expert == 8) { + switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_8x7B; break; + case 56: model.type = e_model::MODEL_8x22B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } else { + switch (hparams.n_layer) { + case 22: model.type = e_model::MODEL_1B; break; + case 26: model.type = e_model::MODEL_3B; break; + case 32: model.type = e_model::MODEL_7B; break; + case 40: model.type = e_model::MODEL_13B; break; + case 48: model.type = e_model::MODEL_34B; break; + case 60: model.type = e_model::MODEL_30B; break; + case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } } } break; case LLM_ARCH_MINICPM: @@ -4006,7 +4026,9 @@ static void llm_load_hparams( } // TODO: This should probably be in llama.h -static std::vector llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special = false); +static std::vector llama_tokenize_internal( + const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special = false +); static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch); static void llm_load_vocab( @@ -4028,23 +4050,27 @@ static void llm_load_vocab( vocab.type = LLAMA_VOCAB_TYPE_NONE; // default special tokens - vocab.special_bos_id = -1; - vocab.special_eos_id = -1; - vocab.special_unk_id = -1; - vocab.special_sep_id = -1; - vocab.special_pad_id = -1; - vocab.linefeed_id = -1; + vocab.special_bos_id = -1; + vocab.special_eos_id = -1; + vocab.special_unk_id = -1; + vocab.special_sep_id = -1; + vocab.special_pad_id = -1; + vocab.special_cls_id = -1; + vocab.special_mask_id = -1; + vocab.linefeed_id = -1; return; } else if (tokenizer_name == "llama") { vocab.type = LLAMA_VOCAB_TYPE_SPM; // default special tokens - vocab.special_bos_id = 1; - vocab.special_eos_id = 2; - vocab.special_unk_id = 0; - vocab.special_sep_id = -1; - vocab.special_pad_id = -1; + vocab.special_bos_id = 1; + vocab.special_eos_id = 2; + vocab.special_unk_id = 0; + vocab.special_sep_id = -1; + vocab.special_pad_id = -1; + vocab.special_cls_id = -1; + vocab.special_mask_id = -1; const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str()); if (add_space_prefix_keyidx != -1) { @@ -4079,20 +4105,24 @@ static void llm_load_vocab( } // default special tokens - vocab.special_bos_id = 11; - vocab.special_eos_id = 11; - vocab.special_unk_id = -1; - vocab.special_sep_id = -1; - vocab.special_pad_id = -1; + vocab.special_bos_id = 11; + vocab.special_eos_id = 11; + vocab.special_unk_id = -1; + vocab.special_sep_id = -1; + vocab.special_pad_id = -1; + vocab.special_cls_id = -1; + vocab.special_mask_id = -1; } else if (tokenizer_name == "bert") { vocab.type = LLAMA_VOCAB_TYPE_WPM; // default special tokens - vocab.special_bos_id = 101; - vocab.special_eos_id = 102; - vocab.special_unk_id = 100; - vocab.special_sep_id = -1; - vocab.special_pad_id = -1; + vocab.special_bos_id = -1; + vocab.special_eos_id = -1; + vocab.special_unk_id = 100; + vocab.special_sep_id = 102; + vocab.special_pad_id = 0; + vocab.special_cls_id = 101; + vocab.special_mask_id = 103; vocab.add_space_prefix = false; } else { LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str()); @@ -4155,11 +4185,13 @@ static void llm_load_vocab( // special tokens { const std::vector> special_token_types = { - { LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id }, - { LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id }, - { LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id }, - { LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id }, - { LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id }, + { LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id }, + { LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id }, + { LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id }, + { LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id }, + { LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id }, + { LLM_KV_TOKENIZER_CLS_ID, vocab.special_cls_id }, + { LLM_KV_TOKENIZER_MASK_ID, vocab.special_mask_id }, }; for (const auto & it : special_token_types) { const std::string & key = kv(std::get<0>(it)); @@ -4351,12 +4383,14 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, model.name.c_str()); // special tokens - if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); } - if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); } - if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); } - if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); } - if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); } - if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); } + if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); } + if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); } + if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); } + if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); } + if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); } + if (vocab.special_cls_id != -1) { LLAMA_LOG_INFO( "%s: CLS token = %d '%s'\n", __func__, vocab.special_cls_id, vocab.id_to_token[vocab.special_cls_id].text.c_str() ); } + if (vocab.special_mask_id != -1) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); } + if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); } } // Returns false if cancelled by progress_callback @@ -5469,6 +5503,11 @@ static bool llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + if (n_layer >= 64){ + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}); + } + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); @@ -9585,6 +9624,31 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } + if (model.layers[il].attn_q_norm) { + Qcur = ggml_view_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens, + ggml_element_size(Qcur) * n_embd_head, + ggml_element_size(Qcur) * n_embd_head * n_head, + 0); + cb(Qcur, "Qcur", il); + Kcur = ggml_view_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens, + ggml_element_size(Kcur) * n_embd_head, + ggml_element_size(Kcur) * n_embd_head * n_head_kv, + 0); + cb(Kcur, "Kcur", il); + + Qcur = llm_build_norm(ctx0, Qcur, hparams, + model.layers[il].attn_q_norm, + NULL, + LLM_NORM, cb, il); + cb(Qcur, "Qcur", il); + + Kcur = llm_build_norm(ctx0, Kcur, hparams, + model.layers[il].attn_k_norm, + NULL, + LLM_NORM, cb, il); + cb(Kcur, "Kcur", il); + } + Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, @@ -10546,6 +10610,9 @@ static int llama_decode_internal( n_outputs_prev += lctx.n_outputs; } + // set to total number of outputs in the batch, for use in llama_get_logits_ith + lctx.n_outputs = n_outputs; + // wait for the computation to finish (automatically done when obtaining the model output) //llama_synchronize(&lctx); @@ -11458,9 +11525,6 @@ struct llm_tokenizer_wpm { output.push_back(vocab.special_unk_id); } } - - // append eos token - output.push_back(vocab.special_eos_id); } std::vector preprocess(const std::string & text) { @@ -11665,30 +11729,28 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< } } -static std::vector llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special) { +static std::vector llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special) { std::vector output; - - // OG tokenizer behavior: - // - // tokenizer.encode('', add_bos=True) returns [1] - // tokenizer.encode('', add_bos=False) returns [] - - if (bos && vocab.special_bos_id != -1) { - output.push_back(vocab.special_bos_id); - } - - if (raw_text.empty()) { - return output; - } - std::forward_list fragment_buffer; - fragment_buffer.emplace_front(raw_text, 0, raw_text.length()); - if (special) tokenizer_st_partition(vocab, fragment_buffer); + if (!raw_text.empty()) { + fragment_buffer.emplace_front(raw_text, 0, raw_text.length()); + if (parse_special) tokenizer_st_partition(vocab, fragment_buffer); + } switch (vocab.type) { case LLAMA_VOCAB_TYPE_SPM: { + // OG tokenizer behavior: + // + // tokenizer.encode('', add_special_tokens=True) returns [1] + // tokenizer.encode('', add_special_tokens=False) returns [] + + if (add_special && vocab.special_add_bos != 0) { + GGML_ASSERT(vocab.special_bos_id != -1); + output.push_back(vocab.special_bos_id); + } + for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { // without adding this leading whitespace, we do not get the same results as the original tokenizer @@ -11714,9 +11776,19 @@ static std::vector llama_tokenize_internal(const llama_vocab & output.push_back(fragment.token); } } + + if (add_special && vocab.special_add_eos == 1) { + 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 == 1) { + GGML_ASSERT(vocab.special_bos_id != -1); + output.push_back(vocab.special_bos_id); + } + for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); @@ -11730,9 +11802,16 @@ static std::vector llama_tokenize_internal(const llama_vocab & output.push_back(fragment.token); } } + + GGML_ASSERT(vocab.special_add_eos != 1); } break; case LLAMA_VOCAB_TYPE_WPM: { + if (add_special) { + GGML_ASSERT(vocab.special_cls_id != -1); + output.push_back(vocab.special_cls_id); + } + for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); @@ -11746,6 +11825,11 @@ static std::vector llama_tokenize_internal(const llama_vocab & output.push_back(fragment.token); } } + + if (add_special) { + GGML_ASSERT(vocab.special_sep_id != -1); + output.push_back(vocab.special_sep_id); + } } break; case LLAMA_VOCAB_TYPE_NONE: GGML_ASSERT(false); @@ -13455,9 +13539,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n return new_type; } -static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, const float * imatrix, std::vector & workers, const int nthread) { +static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector & workers, const int nthread) { std::mutex mutex; - int counter = 0; + int64_t counter = 0; size_t new_size = 0; if (nthread < 2) { // single-thread @@ -13465,11 +13549,11 @@ static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const floa } auto compute = [&mutex, &counter, &new_size, new_type, f32_data, new_data, chunk_size, nrows, n_per_row, imatrix]() { - const int nrows_per_chunk = chunk_size / n_per_row; + const int64_t nrows_per_chunk = chunk_size / n_per_row; size_t local_size = 0; while (true) { std::unique_lock lock(mutex); - int first_row = counter; counter += nrows_per_chunk; + int64_t first_row = counter; counter += nrows_per_chunk; if (first_row >= nrows) { if (local_size > 0) { new_size += local_size; @@ -13477,7 +13561,7 @@ static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const floa break; } lock.unlock(); - const int this_nrow = std::min(nrows - first_row, nrows_per_chunk); + const int64_t this_nrow = std::min(nrows - first_row, nrows_per_chunk); local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix); } }; @@ -13600,7 +13684,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s const std::string name = ggml_get_name(meta); // TODO: avoid hardcoded tensor names - use the TN_* constants - if (name.find("attn_v.weight") != std::string::npos || name.find("attn_qkv.weight") != std::string::npos) { + if (name.find("attn_v.weight") != std::string::npos || + name.find("attn_qkv.weight") != std::string::npos) { ++qs.n_attention_wv; } else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) { qs.has_output = true; @@ -13610,7 +13695,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer; // sanity checks - GGML_ASSERT(qs.n_attention_wv == (int)model.hparams.n_layer && "n_attention_wv != n_layer is unexpected"); + // + // - qs.n_attention_wv == 0 for Mamba models + // - qs.n_attention_wv == model.hparams.n_layer for Transformer models + // + GGML_ASSERT((qs.n_attention_wv == 0 || qs.n_attention_wv == (int)model.hparams.n_layer) && "n_attention_wv is unexpected"); size_t total_size_org = 0; size_t total_size_new = 0; @@ -13666,6 +13755,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // quantize only 2D and 3D tensors (experts) quantize &= (ggml_n_dims(tensor) >= 2); + + // do not quantize norm tensors + quantize &= name.find("_norm.weight") == std::string::npos; + quantize &= params->quantize_output_tensor || name != "output.weight"; quantize &= !params->only_copy; @@ -13694,10 +13787,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (!params->pure && ggml_is_quantized(default_type)) { new_type = llama_tensor_get_type(qs, new_type, tensor, ftype); } - else if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) { + if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) { new_type = params->token_embedding_type; } - else if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) { + if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) { new_type = params->output_tensor_type; } @@ -13712,7 +13805,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s new_size = ggml_nbytes(tensor); LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0); } else { - const size_t nelements = ggml_nelements(tensor); + const int64_t nelements = ggml_nelements(tensor); const float * imatrix = nullptr; if (imatrix_data) { @@ -13764,20 +13857,20 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type)); fflush(stdout); - if (work.size() < nelements * 4) { + if (work.size() < (size_t)nelements * 4) { work.resize(nelements * 4); // upper bound on size } new_data = work.data(); - const int n_per_row = tensor->ne[0]; - const int nrows = tensor->ne[1]; + const int64_t n_per_row = tensor->ne[0]; + const int64_t nrows = tensor->ne[1]; - static const int min_chunk_size = 32 * 512; - const int chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row); + static const int64_t min_chunk_size = 32 * 512; + const int64_t chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row); - const int nelements_matrix = tensor->ne[0] * tensor->ne[1]; - const int nchunk = (nelements_matrix + chunk_size - 1)/chunk_size; - const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1; + const int64_t nelements_matrix = tensor->ne[0] * tensor->ne[1]; + const int64_t nchunk = (nelements_matrix + chunk_size - 1)/chunk_size; + const int64_t nthread_use = nthread > 1 ? std::max((int64_t)1, std::min((int64_t)nthread, nchunk)) : 1; // quantize each expert separately since they have different importance matrices new_size = 0; @@ -15043,9 +15136,33 @@ void llama_kv_cache_update(struct llama_context * ctx) { llama_kv_cache_update_internal(*ctx); } +// deprecated +size_t llama_get_state_size(const struct llama_context * ctx) { + return llama_state_get_size(ctx); +} + +// deprecated +size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) { + return llama_state_get_data(ctx, dst); +} + +// deprecated +size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { + return llama_state_set_data(ctx, src); +} + +// deprecated +bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { + return llama_state_load_file(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out); +} + +// deprecated +bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) { + return llama_state_save_file(ctx, path_session, tokens, n_token_count); +} // Returns the *maximum* size of the state -size_t llama_get_state_size(const struct llama_context * ctx) { +size_t llama_state_get_size(const struct llama_context * ctx) { const auto & cparams = ctx->cparams; const auto & hparams = ctx->model.hparams; @@ -15133,15 +15250,15 @@ struct llama_data_file_context : llama_data_context { * file context: * llama_file file("/path", "wb"); * llama_data_file_context data_ctx(&file); - * llama_copy_state_data(ctx, &data_ctx); + * llama_state_get_data(ctx, &data_ctx); * * buffer context: * std::vector buf(max_size, 0); * llama_data_buffer_context data_ctx(&buf.data()); - * llama_copy_state_data(ctx, &data_ctx); + * llama_state_get_data(ctx, &data_ctx); * */ -static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) { +static void llama_state_get_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) { // copy rng { std::ostringstream rng_ss; @@ -15285,15 +15402,15 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat } } -size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) { +size_t llama_state_get_data(struct llama_context * ctx, uint8_t * dst) { llama_data_buffer_context data_ctx(dst); - llama_copy_state_data_internal(ctx, &data_ctx); + llama_state_get_data_internal(ctx, &data_ctx); return data_ctx.get_size_written(); } // Sets the state reading from the specified source address -size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { +size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) { const uint8_t * inp = src; // set rng @@ -15445,14 +15562,14 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) { } const size_t nread = inp - src; - const size_t max_size = llama_get_state_size(ctx); + const size_t max_size = llama_state_get_size(ctx); GGML_ASSERT(nread <= max_size); return nread; } -static bool llama_load_session_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { +static bool llama_state_load_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { llama_file file(path_session, "rb"); // sanity checks @@ -15490,7 +15607,7 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c // restore the context state { const size_t n_state_size_cur = file.size - file.tell(); - const size_t n_state_size_max = llama_get_state_size(ctx); + const size_t n_state_size_max = llama_state_get_size(ctx); if (n_state_size_cur > n_state_size_max) { LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur); @@ -15500,22 +15617,22 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c std::vector state_data(n_state_size_max); file.read_raw(state_data.data(), n_state_size_cur); - llama_set_state_data(ctx, state_data.data()); + llama_state_set_data(ctx, state_data.data()); } return true; } -bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { +bool llama_state_load_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { try { - return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out); + return llama_state_load_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out); } catch (const std::exception & err) { LLAMA_LOG_ERROR("error loading session file: %s\n", err.what()); return false; } } -bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) { +static bool llama_state_save_file_internal(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) { llama_file file(path_session, "wb"); file.write_u32(LLAMA_SESSION_MAGIC); @@ -15529,11 +15646,420 @@ bool llama_save_session_file(struct llama_context * ctx, const char * path_sessi // save the context state using stream saving llama_data_file_context data_ctx(&file); - llama_copy_state_data_internal(ctx, &data_ctx); + llama_state_get_data_internal(ctx, &data_ctx); return true; } +bool llama_state_save_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) { + try { + return llama_state_save_file_internal(ctx, path_session, tokens, n_token_count); + } catch (const std::exception & err) { + LLAMA_LOG_ERROR("error saving session file: %s\n", err.what()); + return false; + } +} + +size_t llama_state_seq_get_size(struct llama_context* ctx, llama_seq_id seq_id) { + // save the size of size_t as a uint32_t for safety check + const size_t size_t_size_size = sizeof(uint32_t); + + // other values + const size_t s_cell_count_size = sizeof(uint32_t); + const size_t s_layer_count_size = sizeof(uint32_t); + const size_t n_embd_v_gqa_size = sizeof(uint32_t); + + size_t s_cell_count = 0; + size_t s_cell_data_size = 0; + const auto & kv_self = ctx->kv_self; + const auto & hparams = ctx->model.hparams; + + const uint32_t n_layer = hparams.n_layer; + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); + + for (uint32_t i = 0; i < kv_self.size; ++i) { + const auto & cell = kv_self.cells[i]; + if (cell.seq_id.count(seq_id) > 0) { + ++s_cell_count; + s_cell_data_size += sizeof(llama_pos); + } + } + + for (int il = 0; il < (int)n_layer; ++il) { + // types of keys and values + s_cell_data_size += sizeof(int32_t) * 2; + // k_size_row and v_size_el values of layer + s_cell_data_size += sizeof(size_t) * 2; + + // keys + const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa); + s_cell_data_size += k_size_row * s_cell_count; + + // values (transposed) + const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type); + s_cell_data_size += v_size_el * s_cell_count * n_embd_v_gqa; + } + + const size_t s_total = ( + size_t_size_size + + s_cell_count_size + + s_layer_count_size + + n_embd_v_gqa_size + + s_cell_data_size + ); + + return s_total; +} + +static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llama_data_context & data_ctx, llama_seq_id seq_id) { + const auto & kv_self = ctx->kv_self; + GGML_ASSERT(!kv_self.recurrent); // not implemented + + // Save the size of size_t as a uint32_t for safety check + const uint32_t size_t_size = sizeof(size_t); + data_ctx.write(&size_t_size, sizeof(size_t_size)); + + std::vector> cell_ranges; // ranges, from inclusive, to exclusive + uint32_t cell_count = 0; + + // Count the number of cells with the specified seq_id + // Find all the ranges of cells with this seq id + { + uint32_t cell_range_begin = kv_self.size; + for (uint32_t i = 0; i < kv_self.size; ++i) { + const auto & cell = kv_self.cells[i]; + if (cell.has_seq_id(seq_id)) { + ++cell_count; + if (cell_range_begin == kv_self.size) { + cell_range_begin = i; + } + } + else { + if (cell_range_begin != kv_self.size) { + cell_ranges.push_back({ cell_range_begin, i }); + cell_range_begin = kv_self.size; + } + } + } + if (cell_range_begin != kv_self.size) { + cell_ranges.push_back({ cell_range_begin, kv_self.size }); + } + + // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count + uint32_t cell_count_check = 0; + for (const auto & range : cell_ranges) { + cell_count_check += range.second - range.first; + } + GGML_ASSERT(cell_count == cell_count_check); + } + + // Write the cell count + data_ctx.write(&cell_count, sizeof(cell_count)); + + const auto & hparams = ctx->model.hparams; + const uint32_t n_layer = hparams.n_layer; + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); + + // Write the layer count + data_ctx.write(&n_layer, sizeof(n_layer)); + + // Write n_embd_v_gqa + data_ctx.write(&n_embd_v_gqa, sizeof(n_embd_v_gqa)); + + // Iterate the ranges and write all the pos (this is the token position in the prompt) + for (const auto & range : cell_ranges) { + for (uint32_t i = range.first; i < range.second; ++i) { + const auto & cell = kv_self.cells[i]; + data_ctx.write(&cell.pos, sizeof(cell.pos)); + } + } + + // Iterate and write all the keys first, each row is a cell + // Get whole range at a time + std::vector tmp_buf; + for (int il = 0; il < (int)n_layer; ++il) { + // Write key type + const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type; + data_ctx.write(&k_type_i, sizeof(k_type_i)); + + // Write row size of key + const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa); + data_ctx.write(&k_size_row, sizeof(k_size_row)); + + // Read each range of cells of k_size length each into tmp_buf and write out + for (const auto & range : cell_ranges) { + const size_t range_size = range.second - range.first; + tmp_buf.resize(range_size * k_size_row); + ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), range.first * k_size_row, range_size * k_size_row); + data_ctx.write(tmp_buf.data(), tmp_buf.size()); + } + } + + // For the values, they are transposed, so we also need the element size and get the element ranges from each row + const uint32_t kv_size = kv_self.size; + for (int il = 0; il < (int)n_layer; ++il) { + // Write value type + const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type; + data_ctx.write(&v_type_i, sizeof(v_type_i)); + + // Write element size + const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type); + data_ctx.write(&v_size_el, sizeof(v_size_el)); + + // For each row, we get the element values of each cell + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + // Read each range of cells of v_size_el length each into tmp_buf and write out + for (const auto & range : cell_ranges) { + const size_t range_size = range.second - range.first; + const size_t src_offset = (range.first + j * kv_size) * v_size_el; + tmp_buf.resize(range_size * v_size_el); + ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), src_offset, tmp_buf.size()); + data_ctx.write(tmp_buf.data(), tmp_buf.size()); + } + } + } + + return data_ctx.get_size_written(); +} + +size_t llama_state_seq_get_data(struct llama_context* ctx, uint8_t* dst, llama_seq_id seq_id) { + llama_data_buffer_context data_ctx(dst); + return llama_state_seq_get_data_internal(ctx, data_ctx, seq_id); +} + +size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, llama_seq_id dest_seq_id) { + auto & kv_self = ctx->kv_self; + GGML_ASSERT(!kv_self.recurrent); // not implemented + + // Wipe the slot + llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1); + + const uint8_t * inp = src; + + // Read size of size_t + uint32_t size_t_size; + memcpy(&size_t_size, inp, sizeof(size_t_size)); + inp += sizeof(size_t_size); + if (size_t_size != sizeof(size_t)) { + LLAMA_LOG_ERROR("%s: size_t size mismatch\n", __func__); + return 0; + } + + // Read the cell count + uint32_t cell_count; + memcpy(&cell_count, inp, sizeof(cell_count)); + inp += sizeof(cell_count); + + // Read the layer count + uint32_t n_layer_ref; + memcpy(&n_layer_ref, inp, sizeof(n_layer_ref)); + inp += sizeof(n_layer_ref); + + // Read n_embd_v_gqa + uint32_t n_embd_v_gqa_ref; + memcpy(&n_embd_v_gqa_ref, inp, sizeof(n_embd_v_gqa_ref)); + inp += sizeof(n_embd_v_gqa_ref); + + // Sanity check model compatibility + const auto & hparams = ctx->model.hparams; + const uint32_t n_layer = hparams.n_layer; + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); + if (n_layer != n_layer_ref) { + LLAMA_LOG_ERROR("%s: mismatched n_layer (%d != %d)\n", __func__, n_layer, n_layer_ref); + return 0; + } + if (n_embd_v_gqa != n_embd_v_gqa_ref) { + LLAMA_LOG_ERROR("%s: mismatched n_embd_v_gqa (%d != %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref); + return 0; + } + + // Allocate the new cells for the slot + if (cell_count) { + llama_batch batch = llama_batch_init(cell_count, 0, 1); + batch.n_tokens = cell_count; + for (uint32_t i = 0; i < cell_count; ++i) { + llama_pos pos; + memcpy(&pos, inp, sizeof(pos)); + inp += sizeof(pos); + + batch.pos[i] = pos; + batch.n_seq_id[i] = 1; + batch.seq_id[i][0] = dest_seq_id; + } + if (!llama_kv_cache_find_slot(kv_self, batch)) { + llama_batch_free(batch); + LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__); + return 0; + } + + // DEBUG CHECK: kv_self.head should be our first cell, kv_self.head + cell_count - 1 should be our last cell (verify seq_id and pos values) + // Assume that this is one contiguous block of cells + GGML_ASSERT(kv_self.head + cell_count <= kv_self.size); + GGML_ASSERT(kv_self.cells[kv_self.head].pos == batch.pos[0]); + GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].pos == batch.pos[cell_count - 1]); + GGML_ASSERT(kv_self.cells[kv_self.head].has_seq_id(dest_seq_id)); + GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].has_seq_id(dest_seq_id)); + + // Cleanup + llama_batch_free(batch); + } + + const uint32_t kv_size = kv_self.size; + const uint32_t kv_head = kv_self.head; + + // For each layer, read the keys for each cell, one row is one cell, read as one contiguous blo + for (int il = 0; il < (int)n_layer; ++il) { + // Read type of key + int32_t k_type_i_ref; + memcpy(&k_type_i_ref, inp, sizeof(k_type_i_ref)); + inp += sizeof(k_type_i_ref); + const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type; + if (k_type_i != k_type_i_ref) { + llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1); + LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il); + return 0; + } + + // Read row size of key + size_t k_size_row_ref; + memcpy(&k_size_row_ref, inp, sizeof(k_size_row_ref)); + inp += sizeof(k_size_row_ref); + const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa); + if (k_size_row != k_size_row_ref) { + llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1); + LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, k_size_row_ref, il); + return 0; + } + + if (cell_count) { + // Read and set the keys for the whole cell range + ggml_backend_tensor_set(kv_self.k_l[il], inp, kv_head * k_size_row, cell_count * k_size_row); + inp += cell_count * k_size_row; + } + } + + // For each layer, read the values for each cell (transposed) + for (int il = 0; il < (int)n_layer; ++il) { + // Read type of value + int32_t v_type_i_ref; + memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref)); + inp += sizeof(v_type_i_ref); + const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type; + if (v_type_i != v_type_i_ref) { + llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1); + LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il); + return 0; + } + + // Read element size of value + size_t v_size_el_ref; + memcpy(&v_size_el_ref, inp, sizeof(v_size_el_ref)); + inp += sizeof(v_size_el_ref); + const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type); + if (v_size_el != v_size_el_ref) { + llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1); + LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, v_size_el_ref, il); + return 0; + } + + if (cell_count) { + // For each row in the transposed matrix, read the values for the whole cell range + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + const size_t dst_offset = (kv_head + j * kv_size) * v_size_el; + ggml_backend_tensor_set(kv_self.v_l[il], inp, dst_offset, cell_count * v_size_el); + inp += cell_count * v_size_el; + } + } + } + + const size_t nread = inp - src; + return nread; +} + +static size_t llama_state_seq_save_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) { + llama_file file(filepath, "wb"); + + file.write_u32(LLAMA_STATE_SEQ_MAGIC); + file.write_u32(LLAMA_STATE_SEQ_VERSION); + + // save the prompt + file.write_u32((uint32_t)n_token_count); + file.write_raw(tokens, sizeof(llama_token) * n_token_count); + + // save the context state using stream saving + llama_data_file_context data_ctx(&file); + llama_state_seq_get_data_internal(ctx, data_ctx, seq_id); + + const size_t res = file.tell(); + GGML_ASSERT(res == sizeof(uint32_t) * 3 + sizeof(llama_token) * n_token_count + data_ctx.get_size_written()); + return res; +} + +static size_t llama_state_seq_load_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { + llama_file file(filepath, "rb"); + + // version checks + { + const uint32_t magic = file.read_u32(); + const uint32_t version = file.read_u32(); + + if (magic != LLAMA_STATE_SEQ_MAGIC || version != LLAMA_STATE_SEQ_VERSION) { + LLAMA_LOG_ERROR("%s: unknown (magic, version) for sequence state file: %08x, %08x\n", __func__, magic, version); + return 0; + } + } + + // load the prompt + { + const uint32_t n_token_count = file.read_u32(); + + if (n_token_count > n_token_capacity) { + LLAMA_LOG_ERROR("%s: token count in sequence state file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity); + return 0; + } + + file.read_raw(tokens_out, sizeof(llama_token) * n_token_count); + *n_token_count_out = n_token_count; + } + + // restore the context state + { + const size_t state_size = file.size - file.tell(); + std::vector state_data(state_size); + file.read_raw(state_data.data(), state_size); + const size_t nread = llama_state_seq_set_data(ctx, state_data.data(), dest_seq_id); + if (!nread) { + LLAMA_LOG_ERROR("%s: failed to restore sequence state\n", __func__); + return 0; + } + GGML_ASSERT(nread <= state_size); + GGML_ASSERT(nread + sizeof(uint32_t) * 3 + sizeof(llama_token) * *n_token_count_out == file.tell()); + } + + return file.tell(); +} + +size_t llama_state_seq_save_file(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) { + try { + return llama_state_seq_save_file_internal(ctx, filepath, seq_id, tokens, n_token_count); + } catch (const std::exception & err) { + LLAMA_LOG_ERROR("error saving sequence state file: %s\n", err.what()); + return 0; + } +} + +size_t llama_state_seq_load_file(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { + try { + return llama_state_seq_load_file_internal(ctx, filepath, dest_seq_id, tokens_out, n_token_capacity, n_token_count_out); + } catch (const std::exception & err) { + LLAMA_LOG_ERROR("error loading sequence state file: %s\n", err.what()); + return 0; + } +} + void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch) { ctx->cparams.n_threads = n_threads; ctx->cparams.n_threads_batch = n_threads_batch; @@ -15647,23 +16173,31 @@ float * llama_get_logits(struct llama_context * ctx) { } float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) { + int32_t j = -1; llama_synchronize(ctx); try { if (ctx->logits == nullptr) { throw std::runtime_error("no logits"); } - if ((size_t) i >= ctx->output_ids.size()) { + + if (i < 0) { + j = ctx->n_outputs + i; + if (j < 0) { + throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs)); + } + } else if ((size_t) i >= ctx->output_ids.size()) { throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size())); + } else { + j = ctx->output_ids[i]; } - const int32_t j = ctx->output_ids[i]; if (j < 0) { throw std::runtime_error(format("batch.logits[%d] != true", i)); } - if ((size_t) j >= ctx->output_size) { + if (j >= ctx->n_outputs) { // This should not happen - throw std::runtime_error(format("corrupt output buffer (j=%d, output_size=%lu)", j, ctx->output_size)); + throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs)); } return ctx->logits + j*ctx->model.hparams.n_vocab; @@ -15683,23 +16217,32 @@ float * llama_get_embeddings(struct llama_context * ctx) { } float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) { + int32_t j = -1; + llama_synchronize(ctx); try { if (ctx->embd == nullptr) { throw std::runtime_error("no embeddings"); } - if ((size_t) i >= ctx->output_ids.size()) { + + if (i < 0) { + j = ctx->n_outputs + i; + if (j < 0) { + throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs)); + } + } else if ((size_t) i >= ctx->output_ids.size()) { throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size())); + } else { + j = ctx->output_ids[i]; } - const int32_t j = ctx->output_ids[i]; if (j < 0) { throw std::runtime_error(format("batch.logits[%d] != true", i)); } - if ((size_t) j >= ctx->output_size) { + if (j >= ctx->n_outputs) { // This should not happen - throw std::runtime_error(format("corrupt output buffer (j=%d, output_size=%lu)", j, ctx->output_size)); + throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs)); } return ctx->embd + j*ctx->model.hparams.n_embd; @@ -15746,6 +16289,14 @@ llama_token llama_token_eos(const struct llama_model * model) { return model->vocab.special_eos_id; } +llama_token llama_token_cls(const struct llama_model * model) { + return model->vocab.special_cls_id; +} + +llama_token llama_token_sep(const struct llama_model * model) { + return model->vocab.special_sep_id; +} + llama_token llama_token_nl(const struct llama_model * model) { return model->vocab.linefeed_id; } @@ -15780,9 +16331,9 @@ int32_t llama_tokenize( int32_t text_len, llama_token * tokens, int32_t n_tokens_max, - bool add_bos, - bool special) { - auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special); + bool add_special, + bool parse_special) { + auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_special, parse_special); if (n_tokens_max < (int) res.size()) { // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__); diff --git a/llama.h b/llama.h index 036b32685..b770a275f 100644 --- a/llama.h +++ b/llama.h @@ -37,10 +37,14 @@ #define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla' #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn' +#define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq' #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN #define LLAMA_SESSION_VERSION 5 +#define LLAMA_STATE_SEQ_MAGIC LLAMA_FILE_MAGIC_GGSQ +#define LLAMA_STATE_SEQ_VERSION 1 + #ifdef __cplusplus extern "C" { #endif @@ -523,6 +527,7 @@ extern "C" { struct llama_context * ctx); // Removes all tokens that belong to the specified sequence and have positions in [p0, p1) + // Returns false if a partial sequence cannot be removed. Removing a whole sequence never fails // seq_id < 0 : match any sequence // p0 < 0 : [0, p1] // p1 < 0 : [p0, inf) @@ -594,34 +599,92 @@ extern "C" { // Returns the maximum size in bytes of the state (rng, logits, embedding // and kv_cache) - will often be smaller after compacting tokens - LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx); + LLAMA_API size_t llama_state_get_size(const struct llama_context * ctx); + LLAMA_API DEPRECATED(size_t llama_get_state_size(const struct llama_context * ctx), + "use llama_state_get_size instead"); // Copies the state to the specified destination address. // Destination needs to have allocated enough memory. // Returns the number of bytes copied - LLAMA_API size_t llama_copy_state_data( + LLAMA_API size_t llama_state_get_data( struct llama_context * ctx, uint8_t * dst); + LLAMA_API DEPRECATED(size_t llama_copy_state_data( + struct llama_context * ctx, + uint8_t * dst), + "use llama_state_get_data instead"); // Set the state reading from the specified address // Returns the number of bytes read - LLAMA_API size_t llama_set_state_data( + LLAMA_API size_t llama_state_set_data( struct llama_context * ctx, const uint8_t * src); + LLAMA_API DEPRECATED(size_t llama_set_state_data( + struct llama_context * ctx, + const uint8_t * src), + "use llama_state_set_data instead"); // Save/load session file - LLAMA_API bool llama_load_session_file( + LLAMA_API bool llama_state_load_file( struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out); + LLAMA_API DEPRECATED(bool llama_load_session_file( + struct llama_context * ctx, + const char * path_session, + llama_token * tokens_out, + size_t n_token_capacity, + size_t * n_token_count_out), + "use llama_state_load_file instead"); - LLAMA_API bool llama_save_session_file( + LLAMA_API bool llama_state_save_file( struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count); + LLAMA_API DEPRECATED(bool llama_save_session_file( + struct llama_context * ctx, + const char * path_session, + const llama_token * tokens, + size_t n_token_count), + "use llama_state_save_file instead"); + + // Get the exact size needed to copy the KV cache of a single sequence + LLAMA_API size_t llama_state_seq_get_size( + struct llama_context * ctx, + llama_seq_id seq_id); + + // Copy the KV cache of a single sequence into the specified buffer + LLAMA_API size_t llama_state_seq_get_data( + struct llama_context * ctx, + uint8_t * dst, + llama_seq_id seq_id); + + // Copy the sequence data (originally copied with `llama_state_seq_get_data`) into the specified sequence + // Returns: + // - Positive: Ok + // - Zero: Failed to load + LLAMA_API size_t llama_state_seq_set_data( + struct llama_context * ctx, + const uint8_t * src, + llama_seq_id dest_seq_id); + + LLAMA_API size_t llama_state_seq_save_file( + struct llama_context * ctx, + const char * filepath, + llama_seq_id seq_id, + const llama_token * tokens, + size_t n_token_count); + + LLAMA_API size_t llama_state_seq_load_file( + struct llama_context * ctx, + const char * filepath, + llama_seq_id dest_seq_id, + llama_token * tokens_out, + size_t n_token_capacity, + size_t * n_token_count_out); // // Decoding @@ -684,8 +747,9 @@ extern "C" { // Cols: n_vocab LLAMA_API float * llama_get_logits(struct llama_context * ctx); - // Logits for the ith token. Equivalent to: + // Logits for the ith token. For positive indices, Equivalent to: // llama_get_logits(ctx) + ctx->output_ids[i]*n_vocab + // Negative indicies can be used to access logits in reverse order, -1 is the last logit. // returns NULL for invalid ids. LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i); @@ -697,8 +761,9 @@ extern "C" { // Otherwise, returns NULL. LLAMA_API float * llama_get_embeddings(struct llama_context * ctx); - // Get the embeddings for the ith token. Equivalent to: + // Get the embeddings for the ith token. For positive indices, Equivalent to: // llama_get_embeddings(ctx) + ctx->output_ids[i]*n_embd + // Negative indicies can be used to access embeddings in reverse order, -1 is the last embedding. // shape: [n_embd] (1-dimensional) // returns NULL for invalid ids. LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i); @@ -721,6 +786,8 @@ extern "C" { // Special tokens LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence + LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification + LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line // Returns -1 if unknown, 1 for true or 0 for false. @@ -743,16 +810,16 @@ extern "C" { /// @param tokens The tokens pointer must be large enough to hold the resulting tokens. /// @return Returns the number of tokens on success, no more than n_tokens_max /// @return Returns a negative number on failure - the number of tokens that would have been returned - /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext. - /// Does not insert a leading space. + /// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated + /// as plaintext. Does not insert a leading space. LLAMA_API int32_t llama_tokenize( const struct llama_model * model, const char * text, int32_t text_len, llama_token * tokens, int32_t n_tokens_max, - bool add_bos, - bool special); + bool add_special, + bool parse_special); // Token Id -> Piece. // Uses the vocabulary in the provided context. diff --git a/scripts/gen-authors.sh b/scripts/gen-authors.sh new file mode 100755 index 000000000..3ef8391cc --- /dev/null +++ b/scripts/gen-authors.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +printf "# date: $(date)\n" > AUTHORS +printf "# this file is auto-generated by scripts/gen-authors.sh\n\n" >> AUTHORS + +git log --format='%an <%ae>' --reverse --date=short master | awk '!seen[$0]++' | sort >> AUTHORS + +# if necessary, update your name here. for example: jdoe -> John Doe +sed -i '' 's/^jdoe/John Doe/g' AUTHORS diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index 3ae7f460d..7c157eb4c 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -66,6 +66,8 @@ while read c; do tests/test-quantize-fns.cpp \ tests/test-quantize-perf.cpp \ tests/test-backend-ops.cpp \ + LICENSE \ + scripts/gen-authors.sh \ >> $SRC_LLAMA/ggml-src.patch done < $SRC_LLAMA/ggml-commits @@ -123,6 +125,9 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # tests/test-quantize-fns.cpp -> tests/test-quantize-fns.cpp # tests/test-quantize-perf.cpp -> tests/test-quantize-perf.cpp # tests/test-backend-ops.cpp -> tests/test-backend-ops.cpp + # + # LICENSE -> LICENSE + # scripts/gen-authors.sh -> scripts/gen-authors.sh cat ggml-src.patch | sed \ -e 's/src\/ggml\.c/ggml.c/g' \ @@ -156,6 +161,8 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/tests\/test-quantize-fns\.cpp/tests\/test-quantize-fns.cpp/g' \ -e 's/tests\/test-quantize-perf\.cpp/tests\/test-quantize-perf.cpp/g' \ -e 's/tests\/test-backend-ops\.cpp/tests\/test-backend-ops.cpp/g' \ + -e 's/LICENSE/LICENSE/g' \ + -e 's/scripts\/gen-authors\.sh/scripts\/gen-authors.sh/g' \ > ggml-src.patch.tmp mv ggml-src.patch.tmp ggml-src.patch diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 82195550e..d4400695b 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -bb8d8cff851b2de6fde4904be492d39458837e1a +98875cdb7e9ceeb726d1c196d2fecb3cbb59b93a diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index d8fdaadf7..80c43976f 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -31,3 +31,6 @@ cp -rpv ../ggml/include/ggml/ggml-backend.h ./ggml-backend.h cp -rpv ../ggml/tests/test-opt.cpp ./tests/test-opt.cpp cp -rpv ../ggml/tests/test-grad0.cpp ./tests/test-grad0.cpp cp -rpv ../ggml/tests/test-backend-ops.cpp ./tests/test-backend-ops.cpp + +cp -rpv ../LICENSE ./LICENSE +cp -rpv ../ggml/scripts/gen-authors.sh ./scripts/gen-authors.sh