From 71d6975559acfd6c8407a4ef8275a9979c737765 Mon Sep 17 00:00:00 2001 From: Henri Vasserman Date: Wed, 30 Aug 2023 19:14:53 +0300 Subject: [PATCH 01/87] [Docker] fix tools.sh argument passing. (#2884) * [Docker] fix tools.sh argument passing. This should allow passing multiple arguments to containers with the full image that are using the tools.sh frontend. Fix from https://github.com/ggerganov/llama.cpp/issues/2535#issuecomment-1697091734 --- .devops/tools.sh | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/.devops/tools.sh b/.devops/tools.sh index 2787c21fe..9d999315f 100755 --- a/.devops/tools.sh +++ b/.devops/tools.sh @@ -7,15 +7,12 @@ arg1="$1" # Shift the arguments to remove the first one shift -# Join the remaining arguments into a single string -arg2="$@" - if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then - python3 ./convert.py "$arg2" + python3 ./convert.py "$@" elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then - ./quantize "$arg2" + ./quantize "$@" elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then - ./main "$arg2" + ./main "$@" elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then echo "Converting PTH to GGML..." for i in `ls $1/$2/ggml-model-f16.bin*`; do @@ -27,7 +24,7 @@ elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then fi done elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then - ./server "$arg2" + ./server "$@" else echo "Unknown command: $arg1" echo "Available commands: " From 8afe2280009ecbfc9de2c93b8f41283dc810609a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 30 Aug 2023 21:46:19 +0200 Subject: [PATCH 02/87] CUDA: mul_mat_q=true llama_context_params default (#2912) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index fcd6f276a..95ee6ffe4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5287,7 +5287,7 @@ struct llama_context_params llama_context_default_params() { /*.progress_callback =*/ nullptr, /*.progress_callback_user_data =*/ nullptr, /*.low_vram =*/ false, - /*.mul_mat_q =*/ false, + /*.mul_mat_q =*/ true, /*.f16_kv =*/ true, /*.logits_all =*/ false, /*.vocab_only =*/ false, From 92d0b751a77a089e650983e9f1564ef4d31b32b9 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Thu, 31 Aug 2023 01:02:23 -0400 Subject: [PATCH 03/87] convert : fix python 3.8 support, modernize type annotations (#2916) * convert : fix python 3.8 support * convert : sort imports * convert : fix required parameters in convert-llama-ggmlv3-to-gguf * convert : fix mypy errors in convert-llama-ggmlv3-to-gguf * convert : use PEP 585 generics and PEP 604 unions Now that we have `from __future__ import annotations`, we can use this modern syntax in Python 3.7 instead of restricting support to Python 3.9 or 3.10 respectively. * gguf.py : a tuple is already a tuple * add mypy.ini * convert : add necessary `type: ignore` comments * gguf-py: bump version --- convert-falcon-hf-to-gguf.py | 25 +++--- convert-gptneox-hf-to-gguf.py | 22 ++--- convert-llama-7b-pth-to-gguf.py | 31 ++++--- convert-llama-ggmlv3-to-gguf.py | 18 ++-- convert-llama-hf-to-gguf.py | 33 +++---- convert-lora-to-ggml.py | 8 +- convert.py | 149 ++++++++++++++++---------------- gguf-py/gguf/gguf.py | 68 ++++++++------- gguf-py/pyproject.toml | 2 +- mypy.ini | 5 ++ 10 files changed, 193 insertions(+), 168 deletions(-) create mode 100644 mypy.ini diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index 0fdea70e1..ec786ff67 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-hf-to-gguf.py @@ -1,18 +1,21 @@ #!/usr/bin/env python3 # HF falcon--> gguf conversion -import gguf -import os -import sys -import struct +from __future__ import annotations + +import argparse import json +import os +import struct +import sys +from pathlib import Path +from typing import Any + +import gguf import numpy as np import torch -import argparse +from transformers import AutoTokenizer # type: ignore[import] -from typing import Any, List -from pathlib import Path -from transformers import AutoTokenizer def bytes_to_unicode(): # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py @@ -114,9 +117,9 @@ gguf_writer.add_file_type(ftype) print("gguf: get tokenizer metadata") -tokens: List[bytearray] = [] -scores: List[float] = [] -toktypes: List[int] = [] +tokens: list[bytearray] = [] +scores: list[float] = [] +toktypes: list[int] = [] tokenizer_json_file = dir_model / 'tokenizer.json' if not tokenizer_json_file.is_file(): diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py index 38e71e03b..852123d99 100755 --- a/convert-gptneox-hf-to-gguf.py +++ b/convert-gptneox-hf-to-gguf.py @@ -1,18 +1,20 @@ #!/usr/bin/env python3 # HF gptneox--> gguf conversion -import gguf -import os -import sys -import struct +from __future__ import annotations + +import argparse import json +import os +import struct +import sys +from pathlib import Path +from typing import Any + +import gguf import numpy as np import torch -import argparse - -from typing import Any, List -from pathlib import Path -from transformers import AutoTokenizer +from transformers import AutoTokenizer # type: ignore[import] # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py @@ -112,7 +114,7 @@ gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"]) print("gguf: get tokenizer metadata") -tokens: List[bytearray] = [] +tokens: list[bytearray] = [] tokenizer_json_file = dir_model / 'tokenizer.json' if not tokenizer_json_file.is_file(): diff --git a/convert-llama-7b-pth-to-gguf.py b/convert-llama-7b-pth-to-gguf.py index 6e973a116..6574c11dd 100755 --- a/convert-llama-7b-pth-to-gguf.py +++ b/convert-llama-7b-pth-to-gguf.py @@ -3,22 +3,25 @@ # Only models with a single datafile are supported, like 7B # HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model -import gguf -import os -import sys -import struct +from __future__ import annotations + +import argparse import json +import os +import struct +import sys +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import gguf import numpy as np import torch -import argparse +from sentencepiece import SentencePieceProcessor # type: ignore[import] -from typing import Any, List, TypeAlias -from pathlib import Path -from sentencepiece import SentencePieceProcessor +if TYPE_CHECKING: + from typing import TypeAlias -#NDArray = np.ndarray[Any, Any] -# compatible with python < 3.9 -NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' +NDArray: TypeAlias = 'np.ndarray[Any, Any]' def count_model_parts(dir_model: Path) -> int: @@ -129,9 +132,9 @@ if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in print("gguf: get tokenizer metadata") -tokens: List[bytes] = [] -scores: List[float] = [] -toktypes: List[int] = [] +tokens: list[bytes] = [] +scores: list[float] = [] +toktypes: list[int] = [] tokenizer_model_file = dir_model / 'tokenizer.model' if not tokenizer_model_file.is_file(): diff --git a/convert-llama-ggmlv3-to-gguf.py b/convert-llama-ggmlv3-to-gguf.py index c8e7f1761..3f39bc39e 100755 --- a/convert-llama-ggmlv3-to-gguf.py +++ b/convert-llama-ggmlv3-to-gguf.py @@ -1,10 +1,14 @@ #!/usr/bin/env python3 -import sys, struct, math, argparse +from __future__ import annotations + +import argparse +import math +import struct +import sys from pathlib import Path -import numpy as np - import gguf +import numpy as np # Note: Does not support GGML_QKK_64 QK_K = 256 @@ -72,7 +76,7 @@ class Vocab: class Tensor: def __init__(self): self.name = None - self.dims = () + self.dims: tuple[int, ...] = () self.dtype = None self.start_offset = 0 self.len_bytes = np.int64(0) @@ -119,7 +123,7 @@ class GGMLV3Model: offset += hp.load(data, offset) vocab = Vocab() offset += vocab.load(data, offset, hp.n_vocab) - tensors = [] + tensors: list[Tensor] = [] tensor_map = {} while offset < len(data): tensor = Tensor() @@ -305,8 +309,8 @@ def handle_metadata(cfg, hp): def handle_args(): parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF') - parser.add_argument('--input', '-i', type = Path, help = 'Input GGMLv3 filename') - parser.add_argument('--output', '-o', type = Path, help ='Output GGUF filename') + parser.add_argument('--input', '-i', type = Path, required = True, help = 'Input GGMLv3 filename') + parser.add_argument('--output', '-o', type = Path, required = True, help ='Output GGUF filename') parser.add_argument('--name', help = 'Set model name') parser.add_argument('--desc', help = 'Set model description') parser.add_argument('--gqa', type = int, default = 1, help = 'grouped-query attention factor (use 8 for LLaMA2 70B)') diff --git a/convert-llama-hf-to-gguf.py b/convert-llama-hf-to-gguf.py index ab94b5eab..c453c83c3 100755 --- a/convert-llama-hf-to-gguf.py +++ b/convert-llama-hf-to-gguf.py @@ -1,28 +1,31 @@ #!/usr/bin/env python3 # HF llama --> gguf conversion -import gguf -import os -import sys -import struct +from __future__ import annotations + +import argparse import json +import os +import struct +import sys +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import gguf import numpy as np import torch -import argparse +from sentencepiece import SentencePieceProcessor # type: ignore[import] -from typing import Any, List, Optional, TypeAlias -from pathlib import Path -from sentencepiece import SentencePieceProcessor +if TYPE_CHECKING: + from typing import TypeAlias -#NDArray = np.ndarray[Any, Any] -# compatible with python < 3.9 -NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' +NDArray: TypeAlias = 'np.ndarray[Any, Any]' # reverse HF permute back to original pth layout # https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py -def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray: +def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray: if n_kv_head is not None and n_head != n_kv_head: n_head //= n_kv_head @@ -136,9 +139,9 @@ if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in print("gguf: get tokenizer metadata") -tokens: List[bytes] = [] -scores: List[float] = [] -toktypes: List[int] = [] +tokens: list[bytes] = [] +scores: list[float] = [] +toktypes: list[int] = [] tokenizer_model_file = dir_model / 'tokenizer.model' if not tokenizer_model_file.is_file(): diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py index a00339b47..a937410dd 100755 --- a/convert-lora-to-ggml.py +++ b/convert-lora-to-ggml.py @@ -1,15 +1,17 @@ #!/usr/bin/env python3 +from __future__ import annotations + import json import os import re import struct import sys -from typing import Any, Dict, Sequence, BinaryIO +from typing import Any, BinaryIO, Sequence import numpy as np import torch -NUMPY_TYPE_TO_FTYPE: Dict[str, int] = {"float32": 0, "float16": 1} +NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1} HF_SUBLAYER_TO_GGML = { @@ -46,7 +48,7 @@ def translate_tensor_name(t: str) -> str: sys.exit(1) -def write_file_header(fout: BinaryIO, params: Dict[str, Any]) -> None: +def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None: fout.write(b"ggla"[::-1]) # magic (ggml lora) fout.write(struct.pack("i", 1)) # file version fout.write(struct.pack("i", params["r"])) diff --git a/convert.py b/convert.py index a7f4c2d75..9a39edb99 100755 --- a/convert.py +++ b/convert.py @@ -1,9 +1,8 @@ #!/usr/bin/env python3 +from __future__ import annotations -import gguf import argparse import concurrent.futures -from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor import copy import enum import faulthandler @@ -20,21 +19,23 @@ import struct import sys import time import zipfile -import numpy as np - from abc import ABCMeta, abstractmethod +from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path -from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set, Tuple, Type, TypeVar, Union) -from sentencepiece import SentencePieceProcessor # type: ignore +from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar + +import gguf +import numpy as np +from sentencepiece import SentencePieceProcessor # type: ignore[import] if TYPE_CHECKING: - from typing_extensions import TypeAlias + from typing import TypeAlias if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'): faulthandler.register(signal.SIGUSR1) -NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' +NDArray: TypeAlias = 'np.ndarray[Any, Any]' ARCH=gguf.MODEL_ARCH.LLAMA NAMES=gguf.MODEL_TENSOR_NAMES[ARCH] @@ -47,8 +48,8 @@ DEFAULT_CONCURRENCY = 8 @dataclass(frozen=True) class DataType: name: str - dtype: 'np.dtype[Any]' - valid_conversions: List[str] + dtype: np.dtype[Any] + valid_conversions: list[str] def elements_to_bytes(self, n_elements: int) -> int: return n_elements * self.dtype.itemsize @@ -65,7 +66,7 @@ DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_convers @dataclass(frozen=True) class QuantizedDataType(DataType): block_size: int - quantized_dtype: 'np.dtype[Any]' + quantized_dtype: np.dtype[Any] ggml_type: gguf.GGMLQuantizationType def quantize(self, arr: NDArray) -> NDArray: @@ -84,7 +85,7 @@ class Q8_0QuantizedDataType(QuantizedDataType): n_blocks = arr.size // self.block_size blocks = arr.reshape((n_blocks, self.block_size)) # Much faster implementation of block quantization contributed by @Cebtenzzre - def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[Tuple[Any, Any]]: + def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]: d = abs(blocks).max(axis = 1) / np.float32(127) with np.errstate(divide = 'ignore'): qs = (blocks / d[:, None]).round() @@ -98,13 +99,13 @@ DT_Q8_0 = Q8_0QuantizedDataType('Q8_0', quantized_dtype = np.dtype([('d', ' DataType: + def type_for_tensor(self, name: str, tensor: LazyTensor) -> DataType: dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self) if dt is None: raise ValueError(self) # 1D tensors are always F32. return dt if len(tensor.shape) > 1 else DT_F32 -GGML_FILE_TYPE_TO_DATA_TYPE: Dict[GGMLFileType, DataType] = { +GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = { GGMLFileType.AllF32 : DT_F32, GGMLFileType.MostlyF16 : DT_F16, GGMLFileType.MostlyQ8_0: DT_Q8_0, @@ -148,13 +149,13 @@ class Params: n_head_kv: int f_norm_eps: float - f_rope_freq_base: Optional[float] = None - f_rope_scale: Optional[float] = None + f_rope_freq_base: float | None = None + f_rope_scale: float | None = None - ftype: Optional[GGMLFileType] = None + ftype: GGMLFileType | None = None # path to the directory containing the model files - path_model: Optional['Path'] = None + path_model: Path | None = None @staticmethod def find_n_mult(n_ff: int, n_embd: int) -> int: @@ -166,7 +167,7 @@ class Params: raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).") @staticmethod - def guessed(model: 'LazyModel') -> 'Params': + def guessed(model: LazyModel) -> Params: # try transformer naming first n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape @@ -202,7 +203,7 @@ class Params: ) @staticmethod - def loadHFTransformerJson(model: 'LazyModel', config_path: 'Path') -> 'Params': + def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params: config = json.load(open(config_path)) n_vocab = config["vocab_size"] @@ -247,7 +248,7 @@ class Params: # LLaMA v2 70B params.json # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1 @staticmethod - def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params': + def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params: config = json.load(open(config_path)) n_vocab = config["vocab_size"] if "vocab_size" in config else -1 @@ -291,7 +292,7 @@ class Params: ) @staticmethod - def load(model_plus: 'ModelPlus') -> 'Params': + def load(model_plus: ModelPlus) -> Params: hf_config_path = model_plus.paths[0].parent / "config.json" orig_config_path = model_plus.paths[0].parent / "params.json" @@ -314,9 +315,9 @@ class Params: # class BpeVocab: - def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None: + def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None: self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read()) - added_tokens: Dict[str, int] + added_tokens: dict[str, int] if fname_added_tokens is not None: added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) else: @@ -335,9 +336,9 @@ class BpeVocab: self.fname_tokenizer = fname_tokenizer self.fname_added_tokens = fname_added_tokens - def bpe_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: tokenizer = self.bpe_tokenizer - from transformers.models.gpt2 import tokenization_gpt2 + from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import] byte_encoder = tokenization_gpt2.bytes_to_unicode() byte_decoder = {v: k for k, v in byte_encoder.items()} for i, item in enumerate(tokenizer): @@ -345,12 +346,12 @@ class BpeVocab: score: float = -i yield text, score, gguf.TokenType.USER_DEFINED - def added_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: score = -1000.0 yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED - def all_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: yield from self.bpe_tokens() yield from self.added_tokens() @@ -359,9 +360,9 @@ class BpeVocab: class SentencePieceVocab: - def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None: + def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None: self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) - added_tokens: Dict[str, int] + added_tokens: dict[str, int] if fname_added_tokens is not None: added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) else: @@ -380,7 +381,7 @@ class SentencePieceVocab: self.fname_tokenizer = fname_tokenizer self.fname_added_tokens = fname_added_tokens - def sentencepiece_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: tokenizer = self.sentencepiece_tokenizer for i in range(tokenizer.vocab_size()): piece = tokenizer.id_to_piece(i) @@ -404,19 +405,19 @@ class SentencePieceVocab: yield text, score, toktype - def added_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: score = -1000.0 yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED - def all_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: yield from self.sentencepiece_tokens() yield from self.added_tokens() def __repr__(self) -> str: return f"" -Vocab = Union[BpeVocab, SentencePieceVocab] +Vocab: TypeAlias = 'BpeVocab | SentencePieceVocab' # # data loading @@ -436,15 +437,15 @@ class Tensor(metaclass=ABCMeta): data_type: DataType @abstractmethod - def astype(self, data_type: DataType) -> 'Tensor': ... + def astype(self, data_type: DataType) -> Tensor: ... @abstractmethod - def permute(self, n_head: int, n_head_kv: int) -> 'Tensor': ... + def permute(self, n_head: int, n_head_kv: int) -> Tensor: ... @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) -> UnquantizedTensor: ... @abstractmethod - def part(self, n_part: int) -> 'UnquantizedTensor': ... + def part(self, n_part: int) -> UnquantizedTensor: ... @abstractmethod - def to_ggml(self) -> 'GGMLCompatibleTensor': ... + def to_ggml(self) -> GGMLCompatibleTensor: ... def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray: @@ -465,22 +466,22 @@ class UnquantizedTensor(Tensor): self.ndarray = bf16_to_fp32(self.ndarray) return UnquantizedTensor(self.ndarray.astype(dtype)) - def to_ggml(self) -> 'UnquantizedTensor': + def to_ggml(self) -> UnquantizedTensor: return self - 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) -> UnquantizedTensor: r = self.ndarray.shape[0] // 3 return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head_kv)) - def part(self, n_part: int) -> 'UnquantizedTensor': + def part(self, n_part: int) -> UnquantizedTensor: r = self.ndarray.shape[0] // 3 return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...]) - def permute(self, n_head: int, n_head_kv: int) -> 'UnquantizedTensor': + def permute(self, n_head: int, n_head_kv: int) -> UnquantizedTensor: return UnquantizedTensor(permute(self.ndarray, n_head, n_head_kv)) -def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray: +def load_unquantized(lazy_tensor: LazyTensor, expected_dtype: Any = None, convert: bool = False) -> NDArray: tensor = lazy_tensor.load() assert isinstance(tensor, UnquantizedTensor) @@ -496,13 +497,13 @@ def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, conv return tensor.ndarray -GGMLCompatibleTensor = Union[UnquantizedTensor] +GGMLCompatibleTensor = UnquantizedTensor @dataclass class LazyTensor: _load: Callable[[], Tensor] - shape: List[int] + shape: list[int] data_type: DataType description: str @@ -513,7 +514,7 @@ class LazyTensor: (self.data_type, ret.data_type, self.description) return ret - def astype(self, data_type: DataType) -> 'LazyTensor': + def astype(self, data_type: DataType) -> LazyTensor: self.validate_conversion_to(data_type) def load() -> Tensor: @@ -525,24 +526,24 @@ class LazyTensor: raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.') -LazyModel = Dict[str, LazyTensor] +LazyModel = dict[str, LazyTensor] @dataclass class ModelPlus: model: LazyModel - paths: List[Path] # Where this was read from. + paths: list[Path] # Where this was read from. format: Literal['ggml', 'torch', 'safetensors', 'none'] - vocab: Optional[Vocab] # For GGML models (which have vocab built in), the vocab. + vocab: Vocab | None # For GGML models (which have vocab built in), the vocab. -def merge_sharded(models: List[LazyModel]) -> LazyModel: +def merge_sharded(models: list[LazyModel]) -> LazyModel: # Original LLaMA models have each file contain one part of each tensor. # Use a dict instead of a set to preserve order. names = {name: None for model in models for name in model} def convert(name: str) -> LazyTensor: - lazy_tensors: List[LazyTensor] = [model[name] for model in models] + lazy_tensors: list[LazyTensor] = [model[name] for model in models] if len(lazy_tensors) == 1: # only one file; don't go through this procedure since there might # be quantized tensors @@ -570,7 +571,7 @@ def merge_sharded(models: List[LazyModel]) -> LazyModel: return {name: convert(name) for name in names} -def merge_multifile_models(models_plus: List[ModelPlus]) -> ModelPlus: +def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus: formats = set(mp.format for mp in models_plus) assert len(formats) == 1, "different formats?" format = formats.pop() @@ -674,7 +675,7 @@ class LazyUnpickler(pickle.Unpickler): def rebuild_from_type_v2(func, new_type, args, state): return func(*args) - CLASSES: Dict[Tuple[str, str], Any] = { + CLASSES: dict[tuple[str, str], Any] = { # getattr used here as a workaround for mypy not being smart enough to detrmine # the staticmethods have a __func__ attribute. ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'), @@ -707,15 +708,15 @@ def lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus: def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus: header_size, = struct.unpack(' LazyTensor: + def convert(info: dict[str, Any]) -> LazyTensor: data_type = SAFETENSORS_DATA_TYPES[info['dtype']] numpy_dtype = data_type.dtype - shape: List[int] = info['shape'] + shape: list[int] = info['shape'] begin, end = info['data_offsets'] assert 0 <= begin <= end <= len(byte_buf) assert end - begin == math.prod(shape) * numpy_dtype.itemsize @@ -754,7 +755,7 @@ def lazy_load_file(path: Path) -> ModelPlus: In = TypeVar('In') Out = TypeVar('Out') -def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: Optional[int] = None, use_processpool_executor: bool = False) -> Iterable[Out]: +def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: int | None = None, use_processpool_executor: bool = False) -> Iterable[Out]: '''Parallel map, but with backpressure. If the caller doesn't call `next` fast enough, this will stop calling `func` at some point rather than letting results pile up in memory. Specifically, there is a max of one @@ -763,13 +764,13 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc yield from map(func, iterable) # Not reached. iterable = iter(iterable) - executor_class: Union[Type[ThreadPoolExecutor], Type[ProcessPoolExecutor]] + executor_class: type[ThreadPoolExecutor] | type[ProcessPoolExecutor] if use_processpool_executor: executor_class = ProcessPoolExecutor else: executor_class = ThreadPoolExecutor with executor_class(max_workers = max_workers) as executor: - futures: List[concurrent.futures.Future[Out]] = [] + futures: list[concurrent.futures.Future[Out]] = [] done = False for _ in range(concurrency): try: @@ -893,13 +894,13 @@ class OutputFile: of.close() @staticmethod - def do_item(item: Tuple[str, LazyTensor]) -> Tuple[DataType, NDArray]: + def do_item(item: tuple[str, LazyTensor]) -> tuple[DataType, NDArray]: name, lazy_tensor = item tensor = lazy_tensor.load().to_ggml() return (lazy_tensor.data_type, tensor.ndarray) @staticmethod - def maybe_do_quantize(item: Tuple[DataType, NDArray]) -> NDArray: + def maybe_do_quantize(item: tuple[DataType, NDArray]) -> NDArray: dt, arr = item if not isinstance(dt, QuantizedDataType): return arr @@ -940,7 +941,7 @@ class OutputFile: of.close() -def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFileType: +def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType: wq_type = model[NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0)+".weight"].data_type if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32): @@ -960,7 +961,7 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM def convert_model_names(model: LazyModel, params: Params) -> LazyModel: tmap = gguf.TensorNameMap(ARCH, params.n_layer) - should_skip: Set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, [])) + should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, [])) tmp = model @@ -995,12 +996,12 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel: return out -def nth_multifile_path(path: Path, n: int) -> Optional[Path]: +def nth_multifile_path(path: Path, n: int) -> Path | None: '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return the nth path in the model. ''' # Support the following patterns: - patterns: List[Tuple[str, str]] = [ + patterns: list[tuple[str, str]] = [ # - x.00.pth, x.01.pth, etc. (r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'), # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc. @@ -1016,11 +1017,11 @@ def nth_multifile_path(path: Path, n: int) -> Optional[Path]: return None -def find_multifile_paths(path: Path) -> List[Path]: +def find_multifile_paths(path: Path) -> list[Path]: '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return the whole list of paths in the model. ''' - ret: List[Path] = [] + ret: list[Path] = [] for i in itertools.count(): nth_path = nth_multifile_path(path, i) if nth_path is None: @@ -1051,7 +1052,7 @@ def load_some_model(path: Path) -> ModelPlus: path = files[0] paths = find_multifile_paths(path) - models_plus: List[ModelPlus] = [] + models_plus: list[ModelPlus] = [] for path in paths: print(f"Loading model file {path}") models_plus.append(lazy_load_file(path)) @@ -1060,7 +1061,7 @@ def load_some_model(path: Path) -> ModelPlus: return model_plus -def load_vocab(path: Path, vocabtype: Optional[str]) -> Union[BpeVocab, SentencePieceVocab]: +def load_vocab(path: Path, vocabtype: str | None) -> Vocab: # Be extra-friendly and accept either a file or a directory. Also, if it's # a directory, it might be the model directory, and tokenizer.model might # be in the parent of that. @@ -1091,7 +1092,7 @@ def load_vocab(path: Path, vocabtype: Optional[str]) -> Union[BpeVocab, Sentence raise ValueError(f"Unsupported vocabulary type {vocabtype}") -def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path: +def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path: namestr = { GGMLFileType.AllF32: "f32", GGMLFileType.MostlyF16: "f16", @@ -1114,7 +1115,7 @@ def do_dump_model(model_plus: ModelPlus) -> None: print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") -def main(args_in: Optional[List[str]] = None) -> None: +def main(args_in: list[str] | None = None) -> None: parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index de3edbc99..b1bc4205b 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -1,16 +1,18 @@ #!/usr/bin/env python3 -import shutil -import sys -import struct -import tempfile -import numpy as np +from __future__ import annotations + import json import os -from pathlib import Path - +import shutil +import struct +import sys +import tempfile from enum import IntEnum, auto from io import BufferedWriter -from typing import Any, BinaryIO, Callable, IO, Dict, List, Optional, Sequence, Tuple, Union +from pathlib import Path +from typing import IO, Any, BinaryIO, Callable, Sequence + +import numpy as np # # constants @@ -103,7 +105,7 @@ class MODEL_TENSOR(IntEnum): FFN_NORM : int = auto() -MODEL_ARCH_NAMES: Dict[MODEL_ARCH, str] = { +MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.LLAMA: "llama", MODEL_ARCH.FALCON: "falcon", MODEL_ARCH.GPT2: "gpt2", @@ -112,7 +114,7 @@ MODEL_ARCH_NAMES: Dict[MODEL_ARCH, str] = { MODEL_ARCH.MPT: "mpt", } -MODEL_TENSOR_NAMES: Dict[MODEL_ARCH, Dict[MODEL_TENSOR, str]] = { +MODEL_TENSOR_NAMES: dict[MODEL_ARCH, dict[MODEL_TENSOR, str]] = { MODEL_ARCH.LLAMA: { MODEL_TENSOR.TOKEN_EMBD: "token_embd", MODEL_TENSOR.OUTPUT_NORM: "output_norm", @@ -158,7 +160,7 @@ MODEL_TENSOR_NAMES: Dict[MODEL_ARCH, Dict[MODEL_TENSOR, str]] = { } # tensors that will not be serialized -MODEL_TENSOR_SKIP: Dict[MODEL_ARCH, List[MODEL_TENSOR]] = { +MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_ARCH.LLAMA: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, @@ -167,7 +169,7 @@ MODEL_TENSOR_SKIP: Dict[MODEL_ARCH, List[MODEL_TENSOR]] = { class TensorNameMap: - mappings_cfg: Dict[MODEL_TENSOR, Tuple[str, ...]] = { + mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { # Token embeddings MODEL_TENSOR.TOKEN_EMBD: ( "gpt_neox.embed_in", # gptneox @@ -203,7 +205,7 @@ class TensorNameMap: ), } - block_mappings_cfg: Dict[MODEL_TENSOR, Tuple[str, ...]] = { + block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { # Attention norm MODEL_TENSOR.ATTN_NORM: ( "gpt_neox.layers.{bid}.input_layernorm", # gptneox @@ -298,9 +300,9 @@ class TensorNameMap: ), } - mapping: Dict[str, Tuple[MODEL_TENSOR, str]] + mapping: dict[str, tuple[MODEL_TENSOR, str]] - tensor_names: Dict[MODEL_TENSOR, str] + tensor_names: dict[MODEL_TENSOR, str] def __init__(self, arch: MODEL_ARCH, n_blocks: int): mapping = self.mapping = {} @@ -321,7 +323,7 @@ class TensorNameMap: key = key.format(bid = bid) mapping[key] = (tensor, tensor_name) - def get_type_and_name(self, key: str, try_suffixes: Sequence[str]) -> Optional[Tuple[MODEL_TENSOR, str]]: + def get_type_and_name(self, key: str, try_suffixes: Sequence[str]) -> tuple[MODEL_TENSOR, str] | None: result = self.mapping.get(key) if result is not None: return result @@ -332,13 +334,13 @@ class TensorNameMap: return (result[0], result[1] + suffix) return None - def get_name(self, key: str, try_suffixes: Sequence[str]) -> Optional[str]: + def get_name(self, key: str, try_suffixes: Sequence[str]) -> str | None: result = self.get_type_and_name(key, try_suffixes = try_suffixes) if result is None: return None return result[1] - def get_type(self, key: str, try_suffixes: Sequence[str]) -> Optional[MODEL_TENSOR]: + def get_type(self, key: str, try_suffixes: Sequence[str]) -> MODEL_TENSOR | None: result = self.get_type_and_name(key, try_suffixes = try_suffixes) if result is None: return None @@ -432,10 +434,10 @@ class GGUFWriter: ti_data = b"" ti_data_count = 0 use_temp_file: bool - temp_file: Optional[tempfile.SpooledTemporaryFile[bytes]] = None - tensors: List[Tuple[np.ndarray[Any, Any], int]] + temp_file: tempfile.SpooledTemporaryFile[bytes] | None = None + tensors: list[tuple[np.ndarray[Any, Any], int]] - def __init__(self, path: Union[os.PathLike[str], str], arch: str, use_temp_file = True): + def __init__(self, path: os.PathLike[str] | str, arch: str, use_temp_file = True): self.fout = open(path, "wb") self.arch = arch self.add_architecture() @@ -531,7 +533,7 @@ class GGUFWriter: GGUFValueType.FLOAT64: " int: return ((x + n - 1) // n) * n - def add_tensor_info(self, name: str, tensor_shape: Sequence[int], tensor_dtype: Union[np.dtype[np.float16], np.dtype[np.float32]], tensor_nbytes: int, raw_dtype: Optional[GGMLQuantizationType] = None): + def add_tensor_info(self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32], tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None): assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now" encoded_name = name.encode("utf8") @@ -580,7 +582,7 @@ class GGUFWriter: self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment) self.ti_data_count += 1 - def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Optional[Sequence[int]] = None, raw_dtype: Optional[GGMLQuantizationType] = None): + def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None): if self.use_temp_file and self.temp_file is None: fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024) fp.seek(0) @@ -600,7 +602,7 @@ class GGUFWriter: if pad != 0: self.temp_file.write(bytes([0] * pad)) - def write_padding(self, fp: BinaryIO, n: int, align: Optional[int] = None): + def write_padding(self, fp: BinaryIO, n: int, align: int | None = None): pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n if pad != 0: fp.write(bytes([0] * pad)) @@ -726,13 +728,13 @@ class GGUFWriter: def add_tokenizer_model(self, model: str): self.add_string(KEY_TOKENIZER_MODEL, model) - def add_token_list(self, tokens: Union[Sequence[str], Sequence[bytes], Sequence[bytearray]]): + def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]): self.add_array(KEY_TOKENIZER_LIST, tokens) - def add_token_merges(self, merges: Union[Sequence[str], Sequence[bytes], Sequence[bytearray]]): + def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]): self.add_array(KEY_TOKENIZER_MERGES, merges) - def add_token_types(self, types: Union[Sequence[TokenType], Sequence[int]]): + def add_token_types(self, types: Sequence[TokenType] | Sequence[int]): self.add_array(KEY_TOKENIZER_TOKEN_TYPE, types) def add_token_scores(self, scores: Sequence[float]): @@ -756,11 +758,11 @@ class GGUFWriter: class SpecialVocab: load_merges: bool = False - merges: List[str] = [] - special_token_types: Tuple[str, ...] = tuple(('bos', 'eos', 'unk', 'sep', 'pad')) - special_token_ids: Dict[str, int] = {} + merges: list[str] = [] + special_token_types: tuple[str, ...] = ('bos', 'eos', 'unk', 'sep', 'pad') + special_token_ids: dict[str, int] = {} - def __init__(self, path: Path, load_merges: bool = False, special_token_types: Optional[Tuple[str, ...]] = None): + def __init__(self, path: Path, load_merges: bool = False, special_token_types: tuple[str, ...] | None = None): self.special_token_ids = {} self.load_merges = load_merges if special_token_types is not None: @@ -821,7 +823,7 @@ class SpecialVocab: print(f'gguf: Adding {len(self.merges)} merge(s).') gw.add_token_merges(self.merges) for typ, tokid in self.special_token_ids.items(): - handler: Optional[Callable[[int], None]] = getattr(gw, f'add_{typ}_token_id', None) + handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None) if handler is None: print(f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping') continue diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index c66b069f9..26f792b14 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.2.1" +version = "0.3.1" description = "Write ML models in GGUF for GGML" authors = ["GGML "] packages = [ diff --git a/mypy.ini b/mypy.ini new file mode 100644 index 000000000..55c168f2d --- /dev/null +++ b/mypy.ini @@ -0,0 +1,5 @@ +[mypy] +strict = true +allow_untyped_calls = true +allow_untyped_defs = true +allow_incomplete_defs = true From e8422de39e4aa2f7e50574124b060a80607e654a Mon Sep 17 00:00:00 2001 From: DannyDaemonic Date: Thu, 31 Aug 2023 04:21:45 -0700 Subject: [PATCH 04/87] @vxiiduu's fix for PrefetchVirtualMemory (#2930) Reimplement fix for `PrefetchVirtualMemory`. Co-authored-by: vxiiduu <73044267+vxiiduu@users.noreply.github.com> --- llama.cpp | 27 ++++++++++++++++----------- 1 file changed, 16 insertions(+), 11 deletions(-) diff --git a/llama.cpp b/llama.cpp index 95ee6ffe4..98a5da963 100644 --- a/llama.cpp +++ b/llama.cpp @@ -611,20 +611,25 @@ struct llama_mmap { throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str())); } - #if _WIN32_WINNT >= _WIN32_WINNT_WIN8 if (prefetch) { - // Advise the kernel to preload the mapped memory - WIN32_MEMORY_RANGE_ENTRY range; - range.VirtualAddress = addr; - range.NumberOfBytes = (SIZE_T)size; - if (!PrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) { - fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n", - llama_format_win_err(GetLastError()).c_str()); + // PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it + BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG); + HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll"); + + // may fail on pre-Windows 8 systems + pPrefetchVirtualMemory = reinterpret_cast (GetProcAddress(hKernel32, "PrefetchVirtualMemory")); + + if (pPrefetchVirtualMemory) { + // advise the kernel to preload the mapped memory + WIN32_MEMORY_RANGE_ENTRY range; + range.VirtualAddress = addr; + range.NumberOfBytes = (SIZE_T)size; + if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) { + fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n", + llama_format_win_err(GetLastError()).c_str()); + } } } - #else - #pragma message("warning: You are building for pre-Windows 8; prefetch not supported") - #endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8 } ~llama_mmap() { From aeefac4ff760acea5afe66fbfe8d7eca1937b79c Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Thu, 31 Aug 2023 16:49:24 -0600 Subject: [PATCH 05/87] scripts: Use local gguf package when running from repo (#2927) * scripts: Use local gguf when running from repo --- convert-falcon-hf-to-gguf.py | 5 ++++- convert-gptneox-hf-to-gguf.py | 5 ++++- convert-llama-ggmlv3-to-gguf.py | 6 +++++- convert.py | 6 +++++- .../convert-train-checkpoint-to-gguf.py | 5 ++++- 5 files changed, 22 insertions(+), 5 deletions(-) diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index ec786ff67..271e58972 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-hf-to-gguf.py @@ -11,11 +11,14 @@ import sys from pathlib import Path from typing import Any -import gguf import numpy as np import torch from transformers import AutoTokenizer # type: ignore[import] +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) +import gguf + def bytes_to_unicode(): # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py index 852123d99..b9c8b4607 100755 --- a/convert-gptneox-hf-to-gguf.py +++ b/convert-gptneox-hf-to-gguf.py @@ -11,11 +11,14 @@ import sys from pathlib import Path from typing import Any -import gguf import numpy as np import torch from transformers import AutoTokenizer # type: ignore[import] +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) +import gguf + # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py diff --git a/convert-llama-ggmlv3-to-gguf.py b/convert-llama-ggmlv3-to-gguf.py index 3f39bc39e..08ba0c490 100755 --- a/convert-llama-ggmlv3-to-gguf.py +++ b/convert-llama-ggmlv3-to-gguf.py @@ -7,9 +7,13 @@ import struct import sys from pathlib import Path -import gguf import numpy as np +import os +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) +import gguf + # Note: Does not support GGML_QKK_64 QK_K = 256 # Items here are (block size, type size) diff --git a/convert.py b/convert.py index 9a39edb99..5cc3f6e66 100755 --- a/convert.py +++ b/convert.py @@ -25,10 +25,14 @@ from dataclasses import dataclass from pathlib import Path from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar -import gguf import numpy as np from sentencepiece import SentencePieceProcessor # type: ignore[import] +import os +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) +import gguf + if TYPE_CHECKING: from typing import TypeAlias diff --git a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py index 01b3ee92a..a527d6153 100644 --- a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py +++ b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py @@ -2,13 +2,16 @@ # train-text-from-scratch checkpoint --> gguf conversion import argparse -import gguf import os import struct import sys import numpy as np from pathlib import Path +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py' / 'gguf')) +import gguf + # gguf constants LLM_KV_OPTIMIZER_TYPE = "optimizer.type" LLM_KV_OPTIMIZER_TYPE_ADAM = "adam" From 528134dd0267838d9c0250cf1d9621631dff09b2 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 1 Sep 2023 01:32:09 +0200 Subject: [PATCH 06/87] remove convert-llama-7b-pth-to-gguf.py and convert-llama-hf-to-gguf.py (#2906) --- convert-llama-7b-pth-to-gguf.py | 261 ----------------------------- convert-llama-hf-to-gguf.py | 280 -------------------------------- 2 files changed, 541 deletions(-) delete mode 100755 convert-llama-7b-pth-to-gguf.py delete mode 100755 convert-llama-hf-to-gguf.py diff --git a/convert-llama-7b-pth-to-gguf.py b/convert-llama-7b-pth-to-gguf.py deleted file mode 100755 index 6574c11dd..000000000 --- a/convert-llama-7b-pth-to-gguf.py +++ /dev/null @@ -1,261 +0,0 @@ -#!/usr/bin/env python3 -# 7b pth llama --> gguf conversion -# Only models with a single datafile are supported, like 7B -# HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model - -from __future__ import annotations - -import argparse -import json -import os -import struct -import sys -from pathlib import Path -from typing import TYPE_CHECKING, Any - -import gguf -import numpy as np -import torch -from sentencepiece import SentencePieceProcessor # type: ignore[import] - -if TYPE_CHECKING: - from typing import TypeAlias - -NDArray: TypeAlias = 'np.ndarray[Any, Any]' - - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("consolidated."): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a PyTorch 7B LLaMA model to a GGML compatible file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") - parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "LlamaForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - sys.exit() - -# get number of model parts -num_parts = count_model_parts(dir_model) - -if num_parts > 1: - print("gguf: Only models with a single datafile are supported.") - - sys.exit() - -ARCH=gguf.MODEL_ARCH.LLAMA -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - - -print("gguf: get model metadata") - -block_count = hparams["num_hidden_layers"] -head_count = hparams["num_attention_heads"] - -if "num_key_value_heads" in hparams: - head_count_kv = hparams["num_key_value_heads"] -else: - head_count_kv = head_count - -if "_name_or_path" in hparams: - hf_repo = hparams["_name_or_path"] -else: - hf_repo = "" - -if "max_sequence_length" in hparams: - ctx_length = hparams["max_sequence_length"] -elif "max_position_embeddings" in hparams: - ctx_length = hparams["max_position_embeddings"] -else: - print("gguf: can not find ctx length parameter.") - - sys.exit() - - -gguf_writer.add_name(dir_model.name) -gguf_writer.add_source_hf_repo(hf_repo) -gguf_writer.add_tensor_data_layout("Meta AI original pth") -gguf_writer.add_context_length(ctx_length) -gguf_writer.add_embedding_length(hparams["hidden_size"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) -gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) -gguf_writer.add_head_count(head_count) -gguf_writer.add_head_count_kv(head_count_kv) -gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) - -if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]: - if "type" in hparams["rope_scaling"]: - if hparams["rope_scaling"]["type"] == "linear": - gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"]) - - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytes] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -tokenizer_model_file = dir_model / 'tokenizer.model' -if not tokenizer_model_file.is_file(): - print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr) - sys.exit(1) - -# vocab type sentencepiece -print("gguf: get sentencepiece tokenizer vocab and scores") - -tokenizer = SentencePieceProcessor(str(tokenizer_model_file)) - -for i in range(tokenizer.vocab_size()): - text: bytes - score: float - - piece = tokenizer.id_to_piece(i) - text = piece.encode("utf-8") - score = tokenizer.get_score(i) - - toktype = 1 # defualt to normal token type - if tokenizer.is_unknown(i): - toktype = 2 - if tokenizer.is_control(i): - toktype = 3 - - # toktype = 4 is user-defined = tokens from added_tokens.json - - if tokenizer.is_unused(i): - toktype = 5 - if tokenizer.is_byte(i): - toktype = 6 - - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - -added_tokens_file = dir_model / 'added_tokens.json' -if added_tokens_file.is_file(): - with open(added_tokens_file, "r", encoding="utf-8") as f: - addtokens_json = json.load(f) - - print("gguf: get added tokens") - - for key in addtokens_json: - tokens.append( key.encode("utf-8") ) - scores.append(-1000.0) - toktypes.append(4) # user-defined token type - -gguf_writer.add_tokenizer_model("llama") -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_scores(scores) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -# tensor info -print("gguf: get tensor metadata") - -part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts)) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") - - for name in model_part.keys(): - data = model_part[name] - - # we don't need these - if name == "rope.freqs": - continue - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - 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 ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert-llama-hf-to-gguf.py b/convert-llama-hf-to-gguf.py deleted file mode 100755 index c453c83c3..000000000 --- a/convert-llama-hf-to-gguf.py +++ /dev/null @@ -1,280 +0,0 @@ -#!/usr/bin/env python3 -# HF llama --> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import struct -import sys -from pathlib import Path -from typing import TYPE_CHECKING, Any - -import gguf -import numpy as np -import torch -from sentencepiece import SentencePieceProcessor # type: ignore[import] - -if TYPE_CHECKING: - from typing import TypeAlias - -NDArray: TypeAlias = 'np.ndarray[Any, Any]' - -# reverse HF permute back to original pth layout -# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py - - -def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray: - if n_kv_head is not None and n_head != n_kv_head: - n_head //= n_kv_head - - return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) - .swapaxes(1, 2) - .reshape(weights.shape)) - - -def count_model_parts(dir_model: str) -> int: - num_parts = 0 - - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a HuggingFace LLaMA model to a GGML compatible file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") - parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "LlamaForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit() - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH=gguf.MODEL_ARCH.LLAMA -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams["num_hidden_layers"] -head_count = hparams["num_attention_heads"] - -if "num_key_value_heads" in hparams: - head_count_kv = hparams["num_key_value_heads"] -else: - head_count_kv = head_count - -if "_name_or_path" in hparams: - hf_repo = hparams["_name_or_path"] -else: - hf_repo = "" - -if "max_sequence_length" in hparams: - ctx_length = hparams["max_sequence_length"] -elif "max_position_embeddings" in hparams: - ctx_length = hparams["max_position_embeddings"] -else: - print("gguf: can not find ctx length parameter.") - - sys.exit() - - -gguf_writer.add_name(dir_model.name) -gguf_writer.add_source_hf_repo(hf_repo) -gguf_writer.add_tensor_data_layout("Meta AI original pth") -gguf_writer.add_context_length(ctx_length) -gguf_writer.add_embedding_length(hparams["hidden_size"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) -gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) -gguf_writer.add_head_count(head_count) -gguf_writer.add_head_count_kv(head_count_kv) -gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) - -if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]: - if "type" in hparams["rope_scaling"]: - if hparams["rope_scaling"]["type"] == "linear": - gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"]) - - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytes] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -tokenizer_model_file = dir_model / 'tokenizer.model' -if not tokenizer_model_file.is_file(): - print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr) - sys.exit(1) - -# vocab type sentencepiece -print("gguf: get sentencepiece tokenizer vocab, scores and token types") - -tokenizer = SentencePieceProcessor(str(tokenizer_model_file)) - -for i in range(tokenizer.vocab_size()): - text: bytes - score: float - - piece = tokenizer.id_to_piece(i) - text = piece.encode("utf-8") - score = tokenizer.get_score(i) - - toktype = 1 # defualt to normal token type - if tokenizer.is_unknown(i): - toktype = 2 - if tokenizer.is_control(i): - toktype = 3 - - # toktype = 4 is user-defined = tokens from added_tokens.json - - if tokenizer.is_unused(i): - toktype = 5 - if tokenizer.is_byte(i): - toktype = 6 - - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - -added_tokens_file = dir_model / 'added_tokens.json' -if added_tokens_file.is_file(): - with open(added_tokens_file, "r", encoding="utf-8") as f: - addtokens_json = json.load(f) - - print("gguf: get added tokens") - - for key in addtokens_json: - tokens.append( key.encode("utf-8") ) - scores.append(-1000.0) - toktypes.append(4) # user-defined token type - - -gguf_writer.add_tokenizer_model("llama") -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_scores(scores) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") - - for name in model_part.keys(): - data = model_part[name] - - # we don't need these - if name.endswith(".rotary_emb.inv_freq"): - continue - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # reverse permute these - if name.endswith(".q_proj.weight"): - data = reverse_hf_permute(data, head_count) - if name.endswith(".k_proj.weight"): - data = reverse_hf_permute(data, head_count, head_count_kv) - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - 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 ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") From bce1fef328941499dc0acb76cc7fd7ac90449c2f Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Thu, 31 Aug 2023 22:13:51 -0400 Subject: [PATCH 07/87] convert : fix another python 3.8 issue (#2949) --- convert.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert.py b/convert.py index 5cc3f6e66..6c89b5ecc 100755 --- a/convert.py +++ b/convert.py @@ -530,7 +530,7 @@ class LazyTensor: raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.') -LazyModel = dict[str, LazyTensor] +LazyModel: TypeAlias = 'dict[str, LazyTensor]' @dataclass From e8d91589258f9204397a7ac5f9b3c857835c98f8 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Fri, 1 Sep 2023 11:15:57 +0300 Subject: [PATCH 08/87] metal: somewhat faster f16 x f32 matrix multiply kernel (#2951) * Somewhat faster f16 x f32 matrix multiply kernel * Better use 32 thread groups for f16 x f32 --------- Co-authored-by: Iwan Kawrakow --- ggml-metal.m | 2 +- ggml-metal.metal | 38 ++++++++++++++++++++++++++++---------- 2 files changed, 29 insertions(+), 11 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index e929c4b07..8c3c64f53 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -840,7 +840,7 @@ void ggml_metal_graph_compute( switch (src0t) { case GGML_TYPE_F16: { - nth0 = 64; + nth0 = 32; nth1 = 1; [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32]; } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index 82e1a0c7a..02db5323e 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -528,24 +528,42 @@ kernel void kernel_mul_mat_f16_f32( device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - sum[tpitg.x] = 0.0f; + uint ith = tpitg.x; + uint nth = tptg.x; - for (int i = tpitg.x; i < ne00; i += tptg.x) { - sum[tpitg.x] += (float) x[i] * (float) y[i]; + sum[ith] = 0.0f; + + for (int i = ith; i < ne00; i += nth) { + sum[ith] += (float) x[i] * (float) y[i]; } // accumulate the sum from all threads in the threadgroup threadgroup_barrier(mem_flags::mem_threadgroup); - for (uint i = tptg.x/2; i > 0; i /= 2) { - if (tpitg.x < i) { - sum[tpitg.x] += sum[tpitg.x + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith%4 == 0) { + for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i]; } - - if (tpitg.x == 0) { + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith%16 == 0) { + for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i]; + } + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith == 0) { + for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0]; } + + // Original implementation. Left behind commented out for now + //threadgroup_barrier(mem_flags::mem_threadgroup); + //for (uint i = tptg.x/2; i > 0; i /= 2) { + // if (tpitg.x < i) { + // sum[tpitg.x] += sum[tpitg.x + i]; + // } + // threadgroup_barrier(mem_flags::mem_threadgroup); + //} + // + //if (tpitg.x == 0) { + // dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0]; + //} } kernel void kernel_alibi_f32( From 18705a30ef3d6a89e1d7c6cb8cfe8633f760cb53 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Fri, 1 Sep 2023 05:03:49 -0400 Subject: [PATCH 09/87] llama2c : fix segfault and alloc-dealloc-mismatch (#2913) * llama2c : fix segfault if vocab is not found * llama2c : fix mismatch between new[] and delete * llama2c : fix basename on Windows * llama2c : use a destructor to prevent memory leaks --- .../convert-llama2c-to-ggml.cpp | 43 ++++++++++--------- 1 file changed, 23 insertions(+), 20 deletions(-) diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index e9e070b1f..0b03c9d2b 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -75,7 +75,7 @@ typedef struct { int seq_len; // max sequence length } Config; -typedef struct { +struct TransformerWeights { // token embedding table float* token_embedding_table; // (vocab_size, dim) // weights for rmsnorms @@ -97,7 +97,22 @@ typedef struct { // float* freq_cis_imag; // (seq_len, dim/2) // (optional) classifier weights for the logits, on the last layer float* wcls; -} TransformerWeights; + + ~TransformerWeights() { + delete[] token_embedding_table; + delete[] rms_att_weight; + delete[] rms_ffn_weight; + delete[] wq; + delete[] wk; + delete[] wv; + delete[] wo; + delete[] w1; + delete[] w2; + delete[] w3; + delete[] rms_final_weight; + delete[] wcls; + } +}; void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) { // we calloc instead of malloc to keep valgrind happy @@ -173,21 +188,6 @@ int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shar return 0; } -void free_weights(TransformerWeights* w) { - delete w->token_embedding_table; - delete w->rms_att_weight; - delete w->rms_ffn_weight; - delete w->wq; - delete w->wk; - delete w->wv; - delete w->wo; - delete w->w1; - delete w->w2; - delete w->w3; - delete w->rms_final_weight; - if (w->wcls) delete w->wcls; -} - void print_sample_weights(TransformerWeights *w){ printf("----- Quick print of first of the weight vales of all the variables\n"); printf("%f\n", w->token_embedding_table[0]); @@ -596,6 +596,10 @@ void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) // assume llama2.c vocabulary printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename); llama_file file(filename, "rb"); + if (!file.fp) { + fprintf(stderr, "error: %s: %s\n", strerror(errno), filename); + exit(1); + } const int n_vocab = config->vocab_size; /* uint32_t max_token_length = */ file.read_u32(); // unused vocab->id_to_token.resize(n_vocab); @@ -898,7 +902,7 @@ bool params_parse(int argc, char ** argv, struct train_params * params) { } std::string basename(const std::string &path) { - size_t pos = path.find_last_of("/"); + size_t pos = path.find_last_of("/\\"); if (pos == std::string::npos) { return path; } @@ -911,7 +915,7 @@ int main(int argc, char ** argv) { return 1; } Config config; - TransformerWeights weights; + TransformerWeights weights = {}; { FILE *file = fopen(params.fn_llama2c_model, "rb"); if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; } @@ -953,6 +957,5 @@ int main(int argc, char ** argv) { printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model); ggml_free(model.ctx); - free_weights(&weights); return 0; } From 4dcd47d71df8ca4edcc31302744bd93f0c31298e Mon Sep 17 00:00:00 2001 From: staviq Date: Fri, 1 Sep 2023 11:07:06 +0200 Subject: [PATCH 10/87] logs : fix mingw-like builds (fixes #2898) (#2911) * fix mingw-like builds * formatting * make LOG_COMPAT easier to override and extend * simplify win detection * fix for #2940 --- Makefile | 10 +++++----- common/log.h | 20 ++++++++++---------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/Makefile b/Makefile index b750540fe..b56df3d8a 100644 --- a/Makefile +++ b/Makefile @@ -79,6 +79,11 @@ ifdef LLAMA_SERVER_VERBOSE CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) endif +ifdef LLAMA_DISABLE_LOGS + CFLAGS += -DLOG_DISABLE_LOGS + CXXFLAGS += -DLOG_DISABLE_LOGS +endif # LLAMA_DISABLE_LOGS + # warnings CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ -Wmissing-prototypes -Werror=implicit-int @@ -343,11 +348,6 @@ k_quants.o: k_quants.c k_quants.h $(CC) $(CFLAGS) -c $< -o $@ endif # LLAMA_NO_K_QUANTS -ifdef LLAMA_DISABLE_LOGS - CFLAGS += -DLOG_DISABLE_LOGS - CXXFLAGS += -DLOG_DISABLE_LOGS -endif # LLAMA_DISABLE_LOGS - # # Print build information # diff --git a/common/log.h b/common/log.h index c1364187d..bf9fafd68 100644 --- a/common/log.h +++ b/common/log.h @@ -154,7 +154,7 @@ inline std::string log_filename_generator_impl(const std::string & log_file_base // #include "log.h" // #ifndef LOG_NO_TIMESTAMPS - #ifndef _WIN32 + #ifndef _MSC_VER #define LOG_TIMESTAMP_FMT "[%" PRIu64 "] " #define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count() #else @@ -167,7 +167,7 @@ inline std::string log_filename_generator_impl(const std::string & log_file_base #endif #ifdef LOG_TEE_TIMESTAMPS - #ifndef _WIN32 + #ifndef _MSC_VER #define LOG_TEE_TIMESTAMP_FMT "[%" PRIu64 "] " #define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count() #else @@ -187,7 +187,7 @@ inline std::string log_filename_generator_impl(const std::string & log_file_base // #include "log.h" // #ifndef LOG_NO_FILE_LINE_FUNCTION - #ifndef _WIN32 + #ifndef _MSC_VER #define LOG_FLF_FMT "[%24s:%5d][%24s] " #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ #else @@ -200,7 +200,7 @@ inline std::string log_filename_generator_impl(const std::string & log_file_base #endif #ifdef LOG_TEE_FILE_LINE_FUNCTION - #ifndef _WIN32 + #ifndef _MSC_VER #define LOG_TEE_FLF_FMT "[%24s:%5d][%24s] " #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ #else @@ -224,7 +224,7 @@ enum LogTriState // INTERNAL, DO NOT USE // USE LOG() INSTEAD // -#ifndef _WIN32 +#ifndef _MSC_VER #define LOG_IMPL(str, ...) \ { \ if (LOG_TARGET != nullptr) \ @@ -247,7 +247,7 @@ enum LogTriState // INTERNAL, DO NOT USE // USE LOG_TEE() INSTEAD // -#ifndef _WIN32 +#ifndef _MSC_VER #define LOG_TEE_IMPL(str, ...) \ { \ if (LOG_TARGET != nullptr) \ @@ -284,7 +284,7 @@ enum LogTriState // Main LOG macro. // behaves like printf, and supports arguments the exact same way. // -#ifndef _WIN32 +#ifndef _MSC_VER #define LOG(...) LOG_IMPL(__VA_ARGS__, "") #else #define LOG(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "") @@ -298,14 +298,14 @@ enum LogTriState // Secondary target can be changed just like LOG_TARGET // by defining LOG_TEE_TARGET // -#ifndef _WIN32 +#ifndef _MSC_VER #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "") #else #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "") #endif // LOG macro variants with auto endline. -#ifndef _WIN32 +#ifndef _MSC_VER #define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n") #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n") #else @@ -461,7 +461,7 @@ inline void log_test() LOG("13 Hello World this time in yet new file?\n") log_set_target(log_filename_generator("llama_autonamed", "log")); LOG("14 Hello World in log with generated filename!\n") -#ifdef _WIN32 +#ifdef _MSC_VER LOG_TEE("15 Hello msvc TEE without arguments\n") LOG_TEE("16 Hello msvc TEE with (%d)(%s) arguments\n", 1, "test") LOG_TEELN("17 Hello msvc TEELN without arguments\n") From 13268c533177a4dc76bce0b465645d74f0d51d55 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Sep 2023 13:42:41 +0300 Subject: [PATCH 11/87] metal : slight speed-up for add and mul kernels (#2917) --- ggml-metal.m | 20 ++++++++++++++++---- ggml-metal.metal | 32 ++++++++++++++++---------------- 2 files changed, 32 insertions(+), 20 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 8c3c64f53..4267db9be 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -680,6 +680,12 @@ void ggml_metal_graph_compute( } break; case GGML_OP_ADD: { + GGML_ASSERT(ggml_is_contiguous(src0)); + + // utilize float4 + GGML_ASSERT(ne00 % 4 == 0); + const int64_t nb = ne00/4; + if (ggml_nelements(src1) == ne10) { // src1 is a row [encoder setComputePipelineState:ctx->pipeline_add_row]; @@ -689,14 +695,20 @@ void ggml_metal_graph_compute( [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&nb length:sizeof(nb) atIndex:3]; - const int64_t n = ggml_nelements(dst); + const int64_t n = ggml_nelements(dst)/4; [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_OP_MUL: { + GGML_ASSERT(ggml_is_contiguous(src0)); + + // utilize float4 + GGML_ASSERT(ne00 % 4 == 0); + const int64_t nb = ne00/4; + if (ggml_nelements(src1) == ne10) { // src1 is a row [encoder setComputePipelineState:ctx->pipeline_mul_row]; @@ -706,9 +718,9 @@ void ggml_metal_graph_compute( [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&nb length:sizeof(nb) atIndex:3]; - const int64_t n = ggml_nelements(dst); + const int64_t n = ggml_nelements(dst)/4; [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index 02db5323e..8cdf0b9d2 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -25,9 +25,9 @@ typedef struct { } block_q8_0; kernel void kernel_add( - device const float * src0, - device const float * src1, - device float * dst, + device const float4 * src0, + device const float4 * src1, + device float4 * dst, uint tpig[[thread_position_in_grid]]) { dst[tpig] = src0[tpig] + src1[tpig]; } @@ -35,18 +35,18 @@ kernel void kernel_add( // assumption: src1 is a row // broadcast src1 into src0 kernel void kernel_add_row( - device const float * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, + device const float4 * src0, + device const float4 * src1, + device float4 * dst, + constant int64_t & nb, uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] + src1[tpig % ne00]; + dst[tpig] = src0[tpig] + src1[tpig % nb]; } kernel void kernel_mul( - device const float * src0, - device const float * src1, - device float * dst, + device const float4 * src0, + device const float4 * src1, + device float4 * dst, uint tpig[[thread_position_in_grid]]) { dst[tpig] = src0[tpig] * src1[tpig]; } @@ -54,12 +54,12 @@ kernel void kernel_mul( // assumption: src1 is a row // broadcast src1 into src0 kernel void kernel_mul_row( - device const float * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, + device const float4 * src0, + device const float4 * src1, + device float4 * dst, + constant int64_t & nb, uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] * src1[tpig % ne00]; + dst[tpig] = src0[tpig] * src1[tpig % nb]; } kernel void kernel_scale( From 5aec2cfaac386eb09aebb75b805860828f00de91 Mon Sep 17 00:00:00 2001 From: Tameem <113388789+AhmadTameem@users.noreply.github.com> Date: Fri, 1 Sep 2023 18:27:40 +0500 Subject: [PATCH 12/87] ggml : add RISC-V vector intrinsics support (#2929) * added support for RISCV CFLAGS & native compile + cross compile options * Add RISC-V Vector Intrinsics Support Added RVV intrinsics for following ggml_vec_dot_q4_0_q8_0 ggml_vec_dot_q4_1_q8_1 ggml_vec_dot_q5_0_q8_0 ggml_vec_dot_q5_1_q8_1 ggml_vec_dot_q8_0_q8_0 Co-authored-by: Sharafat Signed-off-by: Ahmad Tameem --------- Signed-off-by: Ahmad Tameem Co-authored-by: moiz.hussain Co-authored-by: Sharafat --- Makefile | 13 ++++ ggml.c | 227 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 240 insertions(+) diff --git a/Makefile b/Makefile index b56df3d8a..8f73297f4 100644 --- a/Makefile +++ b/Makefile @@ -35,6 +35,11 @@ ifndef UNAME_M UNAME_M := $(shell uname -m) endif +ifdef RISCV_CROSS_COMPILE +CC := riscv64-unknown-linux-gnu-gcc +CXX := riscv64-unknown-linux-gnu-g++ +endif + CCV := $(shell $(CC) --version | head -n 1) CXXV := $(shell $(CXX) --version | head -n 1) @@ -150,6 +155,9 @@ endif # Architecture specific # TODO: probably these flags need to be tweaked on some architectures # feel free to update the Makefile for your architecture and send a pull request or issue + +ifndef RISCV + ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64)) # Use all CPU extensions that are available: CFLAGS += -march=native -mtune=native @@ -198,6 +206,11 @@ ifneq ($(filter ppc64%,$(UNAME_M)),) endif endif +else + CFLAGS += -march=rv64gcv -mabi=lp64d + CXXFLAGS += -march=rv64gcv -mabi=lp64d +endif + ifndef LLAMA_NO_K_QUANTS CFLAGS += -DGGML_USE_K_QUANTS CXXFLAGS += -DGGML_USE_K_QUANTS diff --git a/ggml.c b/ggml.c index 46ce4a581..cf3955f7f 100644 --- a/ggml.c +++ b/ggml.c @@ -301,6 +301,10 @@ typedef double ggml_float; #endif #endif +#ifdef __riscv_v_intrinsic +#include +#endif + #ifdef __F16C__ #ifdef _MSC_VER @@ -2677,6 +2681,41 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * } *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (int i = 0; i < nb; i++) { + vuint8m1_t tx = __riscv_vle8_v_u8m1(x[i].qs, vl); + + vint8m1_t y0 = __riscv_vle8_v_i8m1(y[i].qs, vl); + vint8m1_t y1 = __riscv_vle8_v_i8m1(y[i].qs+16, vl); + + vuint8m1_t x_a = __riscv_vand_vx_u8m1(tx, 0x0F, vl); + vuint8m1_t x_l = __riscv_vsrl_vx_u8m1(tx, 0x04, vl); + + vint8m1_t x_ai = __riscv_vreinterpret_v_u8m1_i8m1(x_a); + vint8m1_t x_li = __riscv_vreinterpret_v_u8m1_i8m1(x_l); + + vint8m1_t v0 = __riscv_vsub_vx_i8m1(x_ai, 8, vl); + vint8m1_t v1 = __riscv_vsub_vx_i8m1(x_li, 8, vl); + + vint16m2_t vec_mul1 = __riscv_vwmul_vv_i16m2(v0, y0, vl); + vint16m2_t vec_mul2 = __riscv_vwmul_vv_i16m2(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul2, vec_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs1); + sumi += __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d); + } + + *s = sumf; #else // scalar float sumf = 0.0; @@ -2803,6 +2842,38 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * } *s = hsum_float_8(acc) + summs; +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (int i = 0; i < nb; i++) { + vuint8m1_t tx = __riscv_vle8_v_u8m1(x[i].qs, vl); + + vint8m1_t y0 = __riscv_vle8_v_i8m1(y[i].qs, vl); + vint8m1_t y1 = __riscv_vle8_v_i8m1(y[i].qs+16, vl); + + vuint8m1_t x_a = __riscv_vand_vx_u8m1(tx, 0x0F, vl); + vuint8m1_t x_l = __riscv_vsrl_vx_u8m1(tx, 0x04, vl); + + vint8m1_t v0 = __riscv_vreinterpret_v_u8m1_i8m1(x_a); + vint8m1_t v1 = __riscv_vreinterpret_v_u8m1_i8m1(x_l); + + vint16m2_t vec_mul1 = __riscv_vwmul_vv_i16m2(v0, y0, vl); + vint16m2_t vec_mul2 = __riscv_vwmul_vv_i16m2(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul2, vec_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs1); + sumi += __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; + } + + *s = sumf; #else // scalar float sumf = 0.0; @@ -3037,6 +3108,76 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * } *s = hsum_float_8(acc); +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + + uint32_t qh; + + // These temp values are for masking and shift operations + uint32_t temp_1[16] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}; + uint32_t temp_2[16] = {0x1, 0x2, 0x4, 0x8, 0x10, 0x20, 0x40, 0x80, + 0x100, 0x200, 0x400, 0x800, 0x1000, 0x2000, 0x4000, 0x8000}; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (int i = 0; i < nb; i++) { + memcpy(&qh, x[i].qh, sizeof(uint32_t)); + + // temporary registers + vuint32m4_t vt_1 = __riscv_vle32_v_u32m4(temp_2, vl); + vuint32m4_t vt_2 = __riscv_vle32_v_u32m4(temp_1, vl); + vuint32m4_t vt_3 = __riscv_vsll_vx_u32m4(vt_1, 16, vl); + vuint32m4_t vt_4 = __riscv_vadd_vx_u32m4(vt_2, 12, vl); + + // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; + vuint32m4_t xha_0 = __riscv_vand_vx_u32m4(vt_1, qh, vl); + vuint32m4_t xhr_0 = __riscv_vsrl_vv_u32m4(xha_0, vt_2, vl); + vuint32m4_t xhl_0 = __riscv_vsll_vx_u32m4(xhr_0, 4, vl); + + // ((qh & (1u << (j + 16))) >> (j + 12)); + vuint32m4_t xha_1 = __riscv_vand_vx_u32m4(vt_3, qh, vl); + vuint32m4_t xhl_1 = __riscv_vsrl_vv_u32m4(xha_1, vt_4, vl); + + // narrowing + vuint16m2_t xhc_0 = __riscv_vncvt_x_x_w_u16m2(xhl_0, vl); + vuint8m1_t xh_0 = __riscv_vncvt_x_x_w_u8m1(xhc_0, vl); + + vuint16m2_t xhc_1 = __riscv_vncvt_x_x_w_u16m2(xhl_1, vl); + vuint8m1_t xh_1 = __riscv_vncvt_x_x_w_u8m1(xhc_1, vl); + + // load + vuint8m1_t tx = __riscv_vle8_v_u8m1(x[i].qs, vl); + + vint8m1_t y0 = __riscv_vle8_v_i8m1(y[i].qs, vl); + vint8m1_t y1 = __riscv_vle8_v_i8m1(y[i].qs+16, vl); + + vuint8m1_t x_at = __riscv_vand_vx_u8m1(tx, 0x0F, vl); + vuint8m1_t x_lt = __riscv_vsrl_vx_u8m1(tx, 0x04, vl); + + vuint8m1_t x_a = __riscv_vor_vv_u8m1(x_at, xh_0, vl); + vuint8m1_t x_l = __riscv_vor_vv_u8m1(x_lt, xh_1, vl); + + vint8m1_t x_ai = __riscv_vreinterpret_v_u8m1_i8m1(x_a); + vint8m1_t x_li = __riscv_vreinterpret_v_u8m1_i8m1(x_l); + + vint8m1_t v0 = __riscv_vsub_vx_i8m1(x_ai, 16, vl); + vint8m1_t v1 = __riscv_vsub_vx_i8m1(x_li, 16, vl); + + vint16m2_t vec_mul1 = __riscv_vwmul_vv_i16m2(v0, y0, vl); + vint16m2_t vec_mul2 = __riscv_vwmul_vv_i16m2(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul2, vec_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs1); + sumi += __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi; + } + + *s = sumf; #else // scalar float sumf = 0.0; @@ -3293,6 +3434,72 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * } *s = hsum_float_8(acc) + summs; +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + + uint32_t qh; + + // These temp values are for shift operations + uint32_t temp_1[16] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (int i = 0; i < nb; i++) { + memcpy(&qh, x[i].qh, sizeof(uint32_t)); + + // temporary registers + vuint32m4_t vt_1 = __riscv_vle32_v_u32m4(temp_1, vl); + vuint32m4_t vt_2 = __riscv_vadd_vx_u32m4(vt_1, 12, vl); + + // load qh + vuint32m4_t vqh = __riscv_vmv_v_x_u32m4(qh, vl); + + // ((qh >> (j + 0)) << 4) & 0x10; + vuint32m4_t xhr_0 = __riscv_vsrl_vv_u32m4(vqh, vt_1, vl); + vuint32m4_t xhl_0 = __riscv_vsll_vx_u32m4(xhr_0, 4, vl); + vuint32m4_t xha_0 = __riscv_vand_vx_u32m4(xhl_0, 0x10, vl); + + // ((qh >> (j + 12)) ) & 0x10; + vuint32m4_t xhr_1 = __riscv_vsrl_vv_u32m4(vqh, vt_2, vl); + vuint32m4_t xha_1 = __riscv_vand_vx_u32m4(xhr_1, 0x10, vl); + + // narrowing + vuint16m2_t xhc_0 = __riscv_vncvt_x_x_w_u16m2(xha_0, vl); + vuint8m1_t xh_0 = __riscv_vncvt_x_x_w_u8m1(xhc_0, vl); + + vuint16m2_t xhc_1 = __riscv_vncvt_x_x_w_u16m2(xha_1, vl); + vuint8m1_t xh_1 = __riscv_vncvt_x_x_w_u8m1(xhc_1, vl); + + // load + vuint8m1_t tx = __riscv_vle8_v_u8m1(x[i].qs, vl); + + vint8m1_t y0 = __riscv_vle8_v_i8m1(y[i].qs, vl); + vint8m1_t y1 = __riscv_vle8_v_i8m1(y[i].qs+16, vl); + + vuint8m1_t x_at = __riscv_vand_vx_u8m1(tx, 0x0F, vl); + vuint8m1_t x_lt = __riscv_vsrl_vx_u8m1(tx, 0x04, vl); + + vuint8m1_t x_a = __riscv_vor_vv_u8m1(x_at, xh_0, vl); + vuint8m1_t x_l = __riscv_vor_vv_u8m1(x_lt, xh_1, vl); + + vint8m1_t v0 = __riscv_vreinterpret_v_u8m1_i8m1(x_a); + vint8m1_t v1 = __riscv_vreinterpret_v_u8m1_i8m1(x_l); + + vint16m2_t vec_mul1 = __riscv_vwmul_vv_i16m2(v0, y0, vl); + vint16m2_t vec_mul2 = __riscv_vwmul_vv_i16m2(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul2, vec_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs1); + sumi += __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; + } + + *s = sumf; #else // scalar float sumf = 0.0; @@ -3404,6 +3611,26 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * } *s = hsum_float_8(acc); +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + size_t vl = __riscv_vsetvl_e8m1(qk); + + for (int i = 0; i < nb; i++) { + // load elements + vint8m1_t bx = __riscv_vle8_v_i8m1(x[i].qs, vl); + vint8m1_t by = __riscv_vle8_v_i8m1(y[i].qs, vl); + + vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx, by, vl); + + vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl); + vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum); + + sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)); + } + + *s = sumf; #else // scalar float sumf = 0.0; From d8d6977f48f1fa402ade38ad32c5b5fb1358d059 Mon Sep 17 00:00:00 2001 From: Ben Siraphob Date: Fri, 1 Sep 2023 09:32:14 -0400 Subject: [PATCH 13/87] examples : add C grammar (#2357) --- grammars/c.gbnf | 42 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 grammars/c.gbnf diff --git a/grammars/c.gbnf b/grammars/c.gbnf new file mode 100644 index 000000000..4a0331dd2 --- /dev/null +++ b/grammars/c.gbnf @@ -0,0 +1,42 @@ +root ::= (declaration)* + +declaration ::= dataType identifier "(" parameter? ")" "{" statement* "}" + +dataType ::= "int" ws | "float" ws | "char" ws +identifier ::= [a-zA-Z_] [a-zA-Z_0-9]* + +parameter ::= dataType identifier + +statement ::= + ( dataType identifier ws "=" ws expression ";" ) | + ( identifier ws "=" ws expression ";" ) | + ( identifier ws "(" argList? ")" ";" ) | + ( "return" ws expression ";" ) | + ( "while" "(" condition ")" "{" statement* "}" ) | + ( "for" "(" forInit ";" ws condition ";" ws forUpdate ")" "{" statement* "}" ) | + ( "if" "(" condition ")" "{" statement* "}" ("else" "{" statement* "}")? ) | + ( singleLineComment ) | + ( multiLineComment ) + +forInit ::= dataType identifier ws "=" ws expression | identifier ws "=" ws expression +forUpdate ::= identifier ws "=" ws expression + +condition ::= expression relationOperator expression +relationOperator ::= ("<=" | "<" | "==" | "!=" | ">=" | ">") + +expression ::= term (("+" | "-") term)* +term ::= factor(("*" | "/") factor)* + +factor ::= identifier | number | unaryTerm | funcCall | parenExpression +unaryTerm ::= "-" factor +funcCall ::= identifier "(" argList? ")" +parenExpression ::= "(" ws expression ws ")" + +argList ::= expression ("," ws expression)* + +number ::= [0-9]+ + +singleLineComment ::= "//" [^\n]* "\n" +multiLineComment ::= "/*" ( [^*] | ("*" [^/]) )* "*/" + +ws ::= ([ \t\n]+) From ef156499721c67748cde01a5436cb6f0648bb4b4 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Fri, 1 Sep 2023 09:34:50 -0400 Subject: [PATCH 14/87] build : fix most gcc and clang warnings (#2861) * fix most gcc and clang warnings * baby-llama : remove commented opt_params_adam * fix some MinGW warnings * fix more MinGW warnings --- CMakeLists.txt | 5 +++++ Makefile | 7 ++++++- common/common.cpp | 6 ++++-- common/console.cpp | 1 + examples/baby-llama/baby-llama.cpp | 5 ----- examples/beam-search/beam-search.cpp | 8 +++++--- examples/server/server.cpp | 8 +++++--- k_quants.c | 8 ++------ llama.cpp | 4 ++-- 9 files changed, 30 insertions(+), 22 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index d6c1b3b33..1b7cce9f1 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -403,6 +403,7 @@ if (LLAMA_ALL_WARNINGS) -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int + -Wno-unused-function ) set(cxx_flags -Wall @@ -412,6 +413,10 @@ if (LLAMA_ALL_WARNINGS) -Wno-unused-function -Wno-multichar ) + if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU") + # g++ only + set(cxx_flags ${cxx_flags} -Wno-format-truncation) + endif() else() # todo : msvc endif() diff --git a/Makefile b/Makefile index 8f73297f4..ef1eef6ac 100644 --- a/Makefile +++ b/Makefile @@ -91,9 +91,14 @@ endif # LLAMA_DISABLE_LOGS # warnings CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ - -Wmissing-prototypes -Werror=implicit-int + -Wmissing-prototypes -Werror=implicit-int -Wno-unused-function CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar +ifeq '' '$(findstring clang++,$(CXX))' + # g++ only + CXXFLAGS += -Wno-format-truncation +endif + # OS specific # TODO: support Windows ifeq ($(UNAME_S),Linux) diff --git a/common/common.cpp b/common/common.cpp index ed09fc27d..41fc59ced 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -24,7 +24,9 @@ #if defined(_WIN32) #define WIN32_LEAN_AND_MEAN -#define NOMINMAX +#ifndef NOMINMAX +# define NOMINMAX +#endif #include #include #include @@ -1027,7 +1029,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l dump_string_yaml_multiline(stream, "grammar", params.grammar.c_str()); fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n"); fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); - fprintf(stream, "hellaswag_tasks: %ld # default: 400\n", params.hellaswag_tasks); + fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks); const auto logit_bias_eos = params.logit_bias.find(llama_token_eos(lctx)); const bool ignore_eos = logit_bias_eos != params.logit_bias.end() && logit_bias_eos->second == -INFINITY; diff --git a/common/console.cpp b/common/console.cpp index 8efa2a674..23545e5be 100644 --- a/common/console.cpp +++ b/common/console.cpp @@ -235,6 +235,7 @@ namespace console { int estimateWidth(char32_t codepoint) { #if defined(_WIN32) + (void)codepoint; return 1; #else return wcwidth(codepoint); diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp index 6fa55b319..a99ece9a6 100644 --- a/examples/baby-llama/baby-llama.cpp +++ b/examples/baby-llama/baby-llama.cpp @@ -1617,15 +1617,10 @@ int main(int argc, char ** argv) { float error_before_opt = ggml_get_f32_1d(e, 0); - struct ggml_opt_params opt_params_adam = ggml_opt_default_params(GGML_OPT_ADAM); struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS); - opt_params_adam.print_forward_graph = false; - opt_params_adam.print_backward_graph = false; opt_params_lbfgs.print_forward_graph = false; opt_params_lbfgs.print_backward_graph = false; - opt_params_adam.adam.n_iter = 16; opt_params_lbfgs.lbfgs.n_iter = 16; - // ggml_opt(ctx0, opt_params_adam, e); ggml_opt(ctx0, opt_params_lbfgs, e); // ggml_build_forward_expand(&gf, e); diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp index 42c7c7254..4d021434b 100644 --- a/examples/beam-search/beam-search.cpp +++ b/examples/beam-search/beam-search.cpp @@ -22,7 +22,9 @@ #include #elif defined (_WIN32) #define WIN32_LEAN_AND_MEAN -#define NOMINMAX +#ifndef NOMINMAX +# define NOMINMAX +#endif #include #include #endif @@ -73,7 +75,7 @@ void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_stat assert(0u < beams_state.n_beams); const llama_token * tokens = beams_state.beam_views[0].tokens; std::copy(tokens, tokens + n, callback_data.response.end() - n); - printf("%lu", n); + printf("%zu", n); } fflush(stdout); #if 1 // DEBUG: print current beams for this iteration @@ -145,7 +147,7 @@ int main(int argc, char ** argv) if (tokens_list.size() > max_tokens_list_size) { - fprintf( stderr , "%s: error: prompt too long (%lu tokens, max %lu)\n" , + fprintf( stderr , "%s: error: prompt too long (%zu tokens, max %zu)\n" , __func__ , tokens_list.size() , max_tokens_list_size ); return 1; } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b485a5ead..09eac2ec2 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -17,6 +17,8 @@ #include "completion.js.hpp" #include "json-schema-to-grammar.mjs.hpp" +#include + #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 #endif @@ -1038,7 +1040,7 @@ static json format_timings(llama_server_context &llama) { const auto timings = llama_get_timings(llama.ctx); - assert(timings.n_eval == llama.num_tokens_predicted); + assert(timings.n_eval == ptrdiff_t(llama.num_tokens_predicted)); return json{ {"prompt_n", timings.n_p_eval}, @@ -1239,7 +1241,7 @@ void beam_search_callback(void * callback_data, llama_beams_state beams_state) { const llama_token * tokens = beams_state.beam_views[0].tokens; const auto map = [](llama_token tok) { return completion_token_output{{},tok}; }; std::transform(tokens, tokens + n, llama.generated_token_probs.end() - n, map); - printf("%lu", n); + printf("%zu", n); } fflush(stdout); #if 0 // DEBUG: print current beams for this iteration @@ -1548,7 +1550,7 @@ int main(int argc, char **argv) svr.set_exception_handler([](const Request &, Response &res, std::exception_ptr ep) { - const auto * fmt = "500 Internal Server Error\n%s"; + const char fmt[] = "500 Internal Server Error\n%s"; char buf[BUFSIZ]; try { std::rethrow_exception(std::move(ep)); diff --git a/k_quants.c b/k_quants.c index 3a9b1dafd..3deeaedf7 100644 --- a/k_quants.c +++ b/k_quants.c @@ -183,13 +183,9 @@ static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t int ntry, float alpha) { float min = x[0]; float max = x[0]; - float sum_x = 0; - float sum_x2 = 0; for (int i = 1; i < n; ++i) { if (x[i] < min) min = x[i]; if (x[i] > max) max = x[i]; - sum_x += x[i]; - sum_x2 += x[i]*x[i]; } if (max == min) { for (int i = 0; i < n; ++i) L[i] = 0; @@ -2060,7 +2056,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri __m256 acc = _mm256_setzero_ps(); - uint32_t *aux; + const uint32_t *aux; for (int i = 0; i < nb; ++i) { @@ -2070,7 +2066,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri const int8_t * restrict q8 = y[i].qs; // Set up scales - aux = (uint32_t *)x[i].scales; + aux = (const uint32_t *)x[i].scales; __m128i scales128 = _mm_set_epi32( ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), diff --git a/llama.cpp b/llama.cpp index 98a5da963..5ca119238 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3600,7 +3600,7 @@ static void llama_grammar_advance_stack( std::vector> & new_stacks) { if (stack.empty()) { - new_stacks.push_back(stack); + new_stacks.emplace_back(stack); return; } @@ -3637,7 +3637,7 @@ static void llama_grammar_advance_stack( } case LLAMA_GRETYPE_CHAR: case LLAMA_GRETYPE_CHAR_NOT: - new_stacks.push_back(stack); + new_stacks.emplace_back(stack); break; default: // end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range From 49bb9cbe0f598bc43be539b0df8eafb2130cfad3 Mon Sep 17 00:00:00 2001 From: Konstantin Herud Date: Fri, 1 Sep 2023 15:36:14 +0200 Subject: [PATCH 15/87] docs : add java-llama.cpp to README.md (#2935) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index d727b0554..4e6a0957d 100644 --- a/README.md +++ b/README.md @@ -114,6 +114,7 @@ as the main playground for developing new features for the [ggml](https://github - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s) - Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj) - React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn) +- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp) **UI:** From ee8654bcd0146708988a703e54406d5b553712ea Mon Sep 17 00:00:00 2001 From: m3ndax Date: Fri, 1 Sep 2023 15:47:27 +0200 Subject: [PATCH 16/87] minor : add const qualifiers (#2853) * made the methods const # Conflicts: # examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp * made method const * Update convert-llama2c-to-ggml.cpp removed write_raw and write_u32 * llama2c : remove misleading const --------- Co-authored-by: Georgi Gerganov --- examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp | 2 +- llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 0b03c9d2b..0ee7adc52 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -637,7 +637,7 @@ void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) } } -void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, float * karpathy_weights){ +void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) { int ct; switch (gg_weights->n_dims){ case 1: diff --git a/llama.cpp b/llama.cpp index 5ca119238..23b251caf 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4393,7 +4393,7 @@ struct llama_logit_info { } return min_heap; } - float probability_from_logit(float logit) { + float probability_from_logit(float logit) const { return normalizer * std::exp(logit - max_l); } }; From 6c9c23429bf4e4fcaaddbebadc4638558430a7f2 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Fri, 1 Sep 2023 09:53:14 -0400 Subject: [PATCH 17/87] make : use unaligned vector moves on MinGW (#2945) Fixes #2922 --- Makefile | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/Makefile b/Makefile index ef1eef6ac..23f050c0d 100644 --- a/Makefile +++ b/Makefile @@ -177,6 +177,14 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64)) #CXXFLAGS += -mssse3 endif +# The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves. +# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412 +# https://github.com/ggerganov/llama.cpp/issues/2922 +ifneq '' '$(findstring mingw,$(shell $(CC) -dumpmachine))' + CFLAGS += -Xassembler -muse-unaligned-vector-move + CXXFLAGS += -Xassembler -muse-unaligned-vector-move +endif + ifneq ($(filter aarch64%,$(UNAME_M)),) # Apple M1, M2, etc. # Raspberry Pi 3, 4, Zero 2 (64-bit) From 0d5893668625456c94bbadfddc53fc69cd51c223 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Sep 2023 17:00:40 +0300 Subject: [PATCH 18/87] llama2c : rename function --- .../convert-llama2c-to-ggml.cpp | 28 +++++++++---------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 0ee7adc52..9e856c21a 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -637,7 +637,7 @@ void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) } } -void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) { +void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) { int ct; switch (gg_weights->n_dims){ case 1: @@ -674,13 +674,13 @@ void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, const float } void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename) { - // stuff AK weights into GG weights one by one. + // convert AK weights into GG weights one by one. // w->token_embedding_table -> model->tok_embeddings // float* -> struct ggml_tensor - stuff_karpathy_weights_into_gg(model->tok_embeddings, w->token_embedding_table); - stuff_karpathy_weights_into_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table); + convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table); + convert_weights_ak_to_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table); - stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight); + convert_weights_ak_to_gg(model->norm, w->rms_final_weight); //print_row(model->norm, 0); // for rms-att-weight @@ -690,18 +690,18 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod for (uint32_t i = 0; i < model->hparams.n_layer; ++i){ auto & layer = model->layers[i]; // 1d - stuff_karpathy_weights_into_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]); - stuff_karpathy_weights_into_gg(layer.ffn_norm , &w->rms_ffn_weight[i*row_length]); + convert_weights_ak_to_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]); + convert_weights_ak_to_gg(layer.ffn_norm , &w->rms_ffn_weight[i*row_length]); // from 3d matrix layer x dim x dim to 2d matrix dim x dim - stuff_karpathy_weights_into_gg(layer.wq , &w->wq[i*row_length*row_length]); - stuff_karpathy_weights_into_gg(layer.wk , &w->wk[i*row_length*row_length]); - stuff_karpathy_weights_into_gg(layer.wv , &w->wv[i*row_length*row_length]); - stuff_karpathy_weights_into_gg(layer.wo , &w->wo[i*row_length*row_length]); + convert_weights_ak_to_gg(layer.wq , &w->wq[i*row_length*row_length]); + convert_weights_ak_to_gg(layer.wk , &w->wk[i*row_length*row_length]); + convert_weights_ak_to_gg(layer.wv , &w->wv[i*row_length*row_length]); + convert_weights_ak_to_gg(layer.wo , &w->wo[i*row_length*row_length]); - stuff_karpathy_weights_into_gg(layer.w1 , &w->w1[i*row_length*n_ff]); - stuff_karpathy_weights_into_gg(layer.w2 , &w->w2[i*n_ff*row_length]); - stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]); + convert_weights_ak_to_gg(layer.w1 , &w->w1[i*row_length*n_ff]); + convert_weights_ak_to_gg(layer.w2 , &w->w2[i*n_ff*row_length]); + convert_weights_ak_to_gg(layer.w3 , &w->w3[i*row_length*n_ff]); } struct gguf_context * ctx = gguf_init_empty(); From 5d6f19f16b2173afe2d5c6aee2f5c9fc31038eba Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Fri, 1 Sep 2023 08:02:48 -0600 Subject: [PATCH 19/87] Allow quantize to only copy tensors, some other improvements (#2931) * Allow quantize tool to only copy tensors to allow repackaging models. * Slightly better logic when requantizing. * Change help message to go to `stdout`. --- examples/quantize/quantize.cpp | 24 +++++++++++++++++++----- llama.cpp | 25 +++++++++++++++++-------- llama.h | 1 + 3 files changed, 37 insertions(+), 13 deletions(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index df9a214fc..c174be069 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -35,6 +35,8 @@ static const std::vector QUANT_OPTIONS = { { "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", }, { "F16", LLAMA_FTYPE_MOSTLY_F16, "13.00G @ 7B", }, { "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", }, + // Note: Ensure COPY comes after F32 to avoid ftype 0 from matching. + { "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", }, }; @@ -71,12 +73,17 @@ bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std: // ./quantize [--allow-requantize] [--leave-output-tensor] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads] // void usage(const char * executable) { - fprintf(stderr, "usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); - fprintf(stderr, " --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); - fprintf(stderr, " --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); - fprintf(stderr, "\nAllowed quantization types:\n"); + printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); + printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); + printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); + printf("\nAllowed quantization types:\n"); for (auto & it : QUANT_OPTIONS) { - printf(" %2d or %-6s : %s\n", it.ftype, it.name.c_str(), it.desc.c_str()); + if (it.name != "COPY") { + printf(" %2d or ", it.ftype); + } else { + printf(" "); + } + printf("%-6s : %s\n", it.name.c_str(), it.desc.c_str()); } exit(1); } @@ -121,6 +128,9 @@ int main(int argc, char ** argv) { // export as [inp path]/ggml-model-[ftype].gguf fname_out = fpath + "ggml-model-" + ftype_str + ".gguf"; arg_idx++; + if (ftype_str == "COPY") { + params.only_copy = true; + } } else { fname_out = argv[arg_idx]; @@ -133,6 +143,10 @@ int main(int argc, char ** argv) { if (!try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) { fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]); return 1; + } else { + if (ftype_str == "COPY") { + params.only_copy = true; + } } arg_idx++; } diff --git a/llama.cpp b/llama.cpp index 23b251caf..3114d3311 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4683,6 +4683,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s llm_load_arch(*ml, model); llm_load_hparams(*ml, model, 0, 0, 0); + if (params->only_copy) { + ftype = model.ftype; + } + const size_t align = GGUF_DEFAULT_ALIGNMENT; struct gguf_context * ctx_out = gguf_init_empty(); @@ -4769,18 +4773,13 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // quantize only 2D tensors quantize &= (tensor->n_dims == 2); quantize &= params->quantize_output_tensor || name != "output.weight"; - quantize &= quantized_type != tensor->type; + quantize &= !params->only_copy; enum ggml_type new_type; void * new_data; size_t new_size; - if (!quantize) { - new_type = tensor->type; - new_data = tensor->data; - new_size = ggml_nbytes(tensor); - LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0); - } else { + if (quantize) { new_type = quantized_type; #ifdef GGML_USE_K_QUANTS // TODO: avoid hardcoded tensor names - use the TN_* constants @@ -4879,7 +4878,16 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } } #endif - + // If we've decided to quantize to the same type the tensor is already + // in then there's nothing to do. + quantize = tensor->type != new_type; + } + if (!quantize) { + new_type = tensor->type; + new_data = tensor->data; + 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); float * f32_data; @@ -5310,6 +5318,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { /*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1, /*.allow_requantize =*/ false, /*.quantize_output_tensor =*/ true, + /*.only_copy =*/ false, }; return result; diff --git a/llama.h b/llama.h index 6e5e1df63..422f28527 100644 --- a/llama.h +++ b/llama.h @@ -164,6 +164,7 @@ extern "C" { enum llama_ftype ftype; // quantize to this llama_ftype bool allow_requantize; // allow quantizing non-f32/f16 tensors bool quantize_output_tensor; // quantize output.weight + bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored } llama_model_quantize_params; // grammar types From 69fdbb9abc8907dd2a9ffdd840cba92d678a660a Mon Sep 17 00:00:00 2001 From: ZHAOKAI WANG Date: Fri, 1 Sep 2023 22:06:44 +0800 Subject: [PATCH 20/87] readme : quick start command fix (#2908) * quick start command fix * quick start win command fix --- examples/main/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/main/README.md b/examples/main/README.md index d555afdcc..2773fe976 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -34,7 +34,7 @@ For an interactive experience, try this command: #### Unix-based systems (Linux, macOS, etc.): ```bash -./main -m models/7B/ggml-model.bin -n -1 --color -r "User:" --in-prefix " " \ +./main -m models/7B/ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -i -p \ 'User: Hi AI: Hello. I am an AI chatbot. Would you like to talk? User: Sure! @@ -45,7 +45,7 @@ User:' #### Windows: ```powershell -main.exe -m models\7B\ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -e --prompt "User: Hi\nAI: Hello. I am an AI chatbot. Would you like to talk?\nUser: Sure!\nAI: What would you like to talk about?\nUser:" +main.exe -m models\7B\ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -i -e -p "User: Hi\nAI: Hello. I am an AI chatbot. Would you like to talk?\nUser: Sure!\nAI: What would you like to talk about?\nUser:" ``` The following command generates "infinite" text from a starting prompt (you can use `Ctrl-C` to stop it): From f04d0028444bc9b3d4225fba47e19d4c3aeb3741 Mon Sep 17 00:00:00 2001 From: Engininja2 <139037756+Engininja2@users.noreply.github.com> Date: Fri, 1 Sep 2023 15:33:19 -0600 Subject: [PATCH 21/87] cuda : vsubss4 for older versions of ROCm/clang (#2942) --- ggml-cuda.cu | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 5fd625630..8357f32f7 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -81,12 +81,29 @@ #if defined(GGML_USE_HIPBLAS) #define __CUDA_ARCH__ 1300 +#ifndef __has_builtin + #define __has_builtin(x) 0 +#endif + typedef int8_t int8x4_t __attribute__((ext_vector_type(4))); static __device__ __forceinline__ int __vsubss4(const int a, const int b) { const int8x4_t va = reinterpret_cast(a); const int8x4_t vb = reinterpret_cast(b); +#if __has_builtin(__builtin_elementwise_sub_sat) const int8x4_t c = __builtin_elementwise_sub_sat(va, vb); return reinterpret_cast(c); +#else + int8x4_t c; + int16_t tmp; +#pragma unroll + for (int i = 0; i < 4; i++) { + tmp = va[i] - vb[i]; + if(tmp > std::numeric_limits::max()) tmp = std::numeric_limits::max(); + if(tmp < std::numeric_limits::min()) tmp = std::numeric_limits::min(); + c[i] = tmp; + } + return reinterpret_cast(c); +#endif // __has_builtin(__builtin_elementwise_sub_sat) } static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { From 571083f508266c4eb5cb5457d836df5dd3c173ce Mon Sep 17 00:00:00 2001 From: Jhen-Jie Hong Date: Sat, 2 Sep 2023 08:31:46 +0800 Subject: [PATCH 22/87] server : avoid aniprompt in probabilities of final response (#2849) --- examples/server/server.cpp | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 09eac2ec2..94def943b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1379,7 +1379,13 @@ int main(int argc, char **argv) } } - const json data = format_final_response(llama, llama.generated_text, llama.generated_token_probs); + auto probs = llama.generated_token_probs; + if (llama.params.n_probs > 0 && llama.stopped_word) { + const std::vector stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false); + probs = std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size()); + } + + const json data = format_final_response(llama, llama.generated_text, probs); llama_print_timings(llama.ctx); @@ -1456,7 +1462,11 @@ int main(int argc, char **argv) if (!llama.has_next_token) { // Generation is done, send extra information. - const json data = format_final_response(llama, "", llama.generated_token_probs); + const json data = format_final_response( + llama, + "", + std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index) + ); const std::string str = "data: " + From 21f3d1be867b4d7be07c26f5da6e4bc69bcf4d27 Mon Sep 17 00:00:00 2001 From: Jhen-Jie Hong Date: Sat, 2 Sep 2023 20:23:45 +0800 Subject: [PATCH 23/87] k-quants : fix build on armv7 (android only) (#2920) * k-quants : fix build on armv7 * ggml : cleanup unused arm32 specific impl * k-quants : avoid some unused vzero / mzero define * ggml-alloc : use 4g for MEASURE_MAX_SIZE in 32-bit arm --- ggml-alloc.c | 7 +++++++ ggml.c | 46 ---------------------------------------------- k_quants.c | 40 +++++++++++++++++++++++++++++++++++----- 3 files changed, 42 insertions(+), 51 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index f07a4a217..459f121ca 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -284,7 +284,14 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) // address and size of the buffer when measuring // it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers static void * const MEASURE_BASE_ADDR = (void *) 0x1000; +#if defined(__ARM_NEON) && !defined(__aarch64__) +// 32-bit +// TODO: Use for 32-bit x86 as well +static const size_t MEASURE_MAX_SIZE = (1ULL<<32) - 1; // 4 GB +#else +// 64-bit static const size_t MEASURE_MAX_SIZE = 1ULL<<40; // 1 TB +#endif struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) { struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */); diff --git a/ggml.c b/ggml.c index cf3955f7f..38b1155c1 100644 --- a/ggml.c +++ b/ggml.c @@ -817,46 +817,6 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 #if !defined(__aarch64__) -inline static uint16_t vaddvq_u8(uint8x16_t v) { - return - (uint16_t)vgetq_lane_u8(v, 0) + (uint16_t)vgetq_lane_u8(v, 1) + - (uint16_t)vgetq_lane_u8(v, 2) + (uint16_t)vgetq_lane_u8(v, 3) + - (uint16_t)vgetq_lane_u8(v, 4) + (uint16_t)vgetq_lane_u8(v, 5) + - (uint16_t)vgetq_lane_u8(v, 6) + (uint16_t)vgetq_lane_u8(v, 7) + - (uint16_t)vgetq_lane_u8(v, 8) + (uint16_t)vgetq_lane_u8(v, 9) + - (uint16_t)vgetq_lane_u8(v, 10) + (uint16_t)vgetq_lane_u8(v, 11) + - (uint16_t)vgetq_lane_u8(v, 12) + (uint16_t)vgetq_lane_u8(v, 13) + - (uint16_t)vgetq_lane_u8(v, 14) + (uint16_t)vgetq_lane_u8(v, 15); -} - -inline static int16_t vaddvq_s8(int8x16_t v) { - return - (int16_t)vgetq_lane_s8(v, 0) + (int16_t)vgetq_lane_s8(v, 1) + - (int16_t)vgetq_lane_s8(v, 2) + (int16_t)vgetq_lane_s8(v, 3) + - (int16_t)vgetq_lane_s8(v, 4) + (int16_t)vgetq_lane_s8(v, 5) + - (int16_t)vgetq_lane_s8(v, 6) + (int16_t)vgetq_lane_s8(v, 7) + - (int16_t)vgetq_lane_s8(v, 8) + (int16_t)vgetq_lane_s8(v, 9) + - (int16_t)vgetq_lane_s8(v, 10) + (int16_t)vgetq_lane_s8(v, 11) + - (int16_t)vgetq_lane_s8(v, 12) + (int16_t)vgetq_lane_s8(v, 13) + - (int16_t)vgetq_lane_s8(v, 14) + (int16_t)vgetq_lane_s8(v, 15); -} - -inline static int32_t vaddvq_s16(int16x8_t v) { - return - (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) + - (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) + - (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) + - (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7); -} - -inline static uint32_t vaddvq_u16(uint16x8_t v) { - return - (uint32_t)vgetq_lane_u16(v, 0) + (uint32_t)vgetq_lane_u16(v, 1) + - (uint32_t)vgetq_lane_u16(v, 2) + (uint32_t)vgetq_lane_u16(v, 3) + - (uint32_t)vgetq_lane_u16(v, 4) + (uint32_t)vgetq_lane_u16(v, 5) + - (uint32_t)vgetq_lane_u16(v, 6) + (uint32_t)vgetq_lane_u16(v, 7); -} - inline static int32_t vaddvq_s32(int32x4_t v) { return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); } @@ -865,12 +825,6 @@ inline static float vaddvq_f32(float32x4_t v) { return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3); } -inline static float vminvq_f32(float32x4_t v) { - return - MIN(MIN(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), - MIN(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3))); -} - inline static float vmaxvq_f32(float32x4_t v) { return MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), diff --git a/k_quants.c b/k_quants.c index 3deeaedf7..4accd2480 100644 --- a/k_quants.c +++ b/k_quants.c @@ -13,6 +13,26 @@ // #include +#if !defined(__aarch64__) +inline static int32_t vaddvq_s16(int16x8_t v) { + return + (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) + + (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) + + (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) + + (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7); +} + +inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) { + int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a)); + int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b)); + return vcombine_s16(a0, b0); +} + +inline static int32_t vaddvq_s32(int32x4_t v) { + return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); +} +#endif + #else #ifdef __wasm_simd128__ @@ -1302,7 +1322,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t m3 = vdupq_n_u8(0x3); const uint8x16_t m4 = vdupq_n_u8(0xF); +#if defined(__ARM_FEATURE_DOTPROD) const int32x4_t vzero = vdupq_n_s32(0); +#endif int8x16x2_t q2bytes; uint8_t aux[16]; @@ -1608,7 +1630,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri #ifdef __ARM_NEON const uint8x16_t m3 = vdupq_n_u8(0x3); +#if defined(__ARM_FEATURE_DOTPROD) const int32x4_t vzero = vdupq_n_s32(0); +#endif int8x16x4_t q2bytes; @@ -2592,8 +2616,6 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const uint8_t * restrict q4 = x[i].qs; const int8_t * restrict q8 = y[i].qs; - //int32x4_t isum = mzero; - int32_t sumi1 = 0; int32_t sumi2 = 0; @@ -3092,9 +3114,11 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri #ifdef __ARM_NEON const uint8x16_t m4b = vdupq_n_u8(0xf); - const int32x4_t mzero = vdupq_n_s32(0); const uint8x16_t mone = vdupq_n_u8(1); const uint8x16_t mtwo = vdupq_n_u8(2); +#if defined(__ARM_FEATURE_DOTPROD) + const int32x4_t mzero = vdupq_n_s32(0); +#endif int8x16x4_t q5bytes; @@ -3437,8 +3461,10 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri #ifdef __ARM_NEON const uint8x16_t m4b = vdupq_n_u8(0xf); - const int32x4_t mzero = vdupq_n_s32(0); const uint8x16_t mh = vdupq_n_u8(16); +#if defined(__ARM_FEATURE_DOTPROD) + const int32x4_t mzero = vdupq_n_s32(0); +#endif int8x16x4_t q5bytes; uint8x16x4_t q5h; @@ -3656,7 +3682,9 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri float sum = 0; const uint8x16_t m4b = vdupq_n_u8(0xF); +#if defined(__ARM_FEATURE_DOTPROD) const int32x4_t vzero = vdupq_n_s32(0); +#endif //const int8x16_t m32s = vdupq_n_s8(32); const uint8x16_t mone = vdupq_n_u8(3); @@ -4045,8 +4073,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri float sum = 0; const uint8x16_t m4b = vdupq_n_u8(0xF); - const int32x4_t vzero = vdupq_n_s32(0); const int8x16_t m32s = vdupq_n_s8(32); +#if defined(__ARM_FEATURE_DOTPROD) + const int32x4_t vzero = vdupq_n_s32(0); +#endif const uint8x16_t mone = vdupq_n_u8(3); From 8b56b4f2c396eae1f4417e5a859557fed989e0ee Mon Sep 17 00:00:00 2001 From: Karsten Weiss Date: Sat, 2 Sep 2023 14:29:09 +0200 Subject: [PATCH 24/87] metal : show all Metal device instances in the system (#2952) * ggml_metal_init: Show all Metal device instances in the system Also show the default Metal device that was picked. * Update ggml-metal.m --------- Co-authored-by: Georgi Gerganov --- ggml-metal.m | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 4267db9be..88e7e1356 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -116,10 +116,24 @@ static NSString * const msl_library_source = @"see metal.metal"; struct ggml_metal_context * ggml_metal_init(int n_cb) { metal_printf("%s: allocating\n", __func__); - struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context)); + // Show all the Metal device instances in the system + NSArray * devices = MTLCopyAllDevices(); + id device; + NSString * s; + for (device in devices) { + s = [device name]; + metal_printf("%s: found device: %s\n", __func__, [s UTF8String]); + } + // Pick and show default Metal device + device = MTLCreateSystemDefaultDevice(); + s = [device name]; + metal_printf("%s: picking default device: %s\n", __func__, [s UTF8String]); + + // Configure context + struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context)); + ctx->device = device; ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); - ctx->device = MTLCreateSystemDefaultDevice(); ctx->queue = [ctx->device newCommandQueue]; ctx->n_buffers = 0; ctx->concur_list_len = 0; From 52315a421674ff64305dbf082f69e4ec77f0a3f3 Mon Sep 17 00:00:00 2001 From: bandoti <141645996+bandoti@users.noreply.github.com> Date: Sat, 2 Sep 2023 09:53:18 -0300 Subject: [PATCH 25/87] readme : update clblast instructions (#2903) * Update Windows CLBlast instructions * Update Windows CLBlast instructions * Remove trailing whitespace --- README.md | 40 +++++++++++++++++++++++++++++++++++----- 1 file changed, 35 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 4e6a0957d..5eda5f006 100644 --- a/README.md +++ b/README.md @@ -464,6 +464,8 @@ Building the program with BLAS support may lead to some performance improvements You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK). - For Ubuntu or Debian, the packages `opencl-headers`, `ocl-icd` may be needed. + - For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page. + -
Installing the OpenCL SDK from source @@ -481,10 +483,27 @@ Building the program with BLAS support may lead to some performance improvements ```
- Installing CLBlast: it may be found in your operating system's packages. + ##### Installing CLBlast + + Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages. + + Alternatively, they may be built from source. -
- If not, then installing from source: + Windows: + + ```cmd + set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64" + git clone https://github.com/CNugteren/CLBlast.git + mkdir CLBlast\build + cd CLBlast\build + cmake .. -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64 + cmake --build . --config Release + cmake --install . --prefix C:/CLBlast + ``` + + -
+ Unix: ```sh git clone https://github.com/CNugteren/CLBlast.git @@ -498,21 +517,32 @@ Building the program with BLAS support may lead to some performance improvements Where `/some/path` is where the built library will be installed (default is `/usr/local`).
- Building: + ##### Building Llama with CLBlast - Build with make: ```sh make LLAMA_CLBLAST=1 ``` - - CMake: + - CMake (Unix): ```sh mkdir build cd build cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_dir=/some/path cmake --build . --config Release ``` + - CMake (Windows): + ```cmd + set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast" + git clone https://github.com/ggerganov/llama.cpp + cd llama.cpp + mkdir build + cd build + cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64 + cmake --build . --config Release + cmake --install . --prefix C:/LlamaCPP + ``` - Running: + ##### Running Llama with CLBlast The CLBlast build supports `--gpu-layers|-ngl` like the CUDA version does. From 3358c381f6251bf6e65855e1c93bfaa9ec82ddb3 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sat, 2 Sep 2023 11:53:55 -0600 Subject: [PATCH 26/87] logging: Fix creating empty file even when disabled (#2966) * logging: Fix creating empty file even when disabled * Minor formatting fix Co-authored-by: staviq --------- Co-authored-by: staviq --- common/log.h | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/common/log.h b/common/log.h index bf9fafd68..0b9b01052 100644 --- a/common/log.h +++ b/common/log.h @@ -341,14 +341,14 @@ inline FILE *log_handler1_impl(bool change = false, LogTriState disable = LogTri } } + if (_disabled) + { + // Log is disabled + return nullptr; + } + if (_initialized) { - if (_disabled) - { - // Log is disabled - return nullptr; - } - // with fallback in case something went wrong return logfile ? logfile : stderr; } From bc054af97ac68a4b726e972cb283eb9565253ed5 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Sun, 3 Sep 2023 01:26:59 -0400 Subject: [PATCH 27/87] make : support overriding CFLAGS/CXXFLAGS/CPPFLAGS/LDFLAGS (#2886) * make : remove unused -DGGML_BIG_ENDIAN * make : put preprocessor stuff in CPPFLAGS * make : pass Raspberry Pi arch flags to g++ as well * make : support overriding CFLAGS/CXXFLAGS/CPPFLAGS/LDFLAGS * make : fix inverted conditional --- Makefile | 150 ++++++++++++++++++++++++------------------------------- 1 file changed, 66 insertions(+), 84 deletions(-) diff --git a/Makefile b/Makefile index 23f050c0d..e214970f8 100644 --- a/Makefile +++ b/Makefile @@ -67,21 +67,21 @@ OPT = -Ofast else OPT = -O3 endif -CFLAGS = -I. $(OPT) -std=c11 -fPIC -CXXFLAGS = -I. -I./common $(OPT) -std=c++11 -fPIC -LDFLAGS = +MK_CPPFLAGS = -I. -Icommon +MK_CFLAGS = $(CPPFLAGS) $(OPT) -std=c11 -fPIC +MK_CXXFLAGS = $(CPPFLAGS) $(OPT) -std=c++11 -fPIC +MK_LDFLAGS = ifdef LLAMA_DEBUG - CFLAGS += -O0 -g - CXXFLAGS += -O0 -g - LDFLAGS += -g + MK_CFLAGS += -O0 -g + MK_CXXFLAGS += -O0 -g + MK_LDFLAGS += -g else - CFLAGS += -DNDEBUG - CXXFLAGS += -DNDEBUG + MK_CPPFLAGS += -DNDEBUG endif ifdef LLAMA_SERVER_VERBOSE - CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) + MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) endif ifdef LLAMA_DISABLE_LOGS @@ -90,9 +90,9 @@ ifdef LLAMA_DISABLE_LOGS endif # LLAMA_DISABLE_LOGS # warnings -CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ - -Wmissing-prototypes -Werror=implicit-int -Wno-unused-function -CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar +MK_CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ + -Wmissing-prototypes -Werror=implicit-int -Wno-unused-function +MK_CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar ifeq '' '$(findstring clang++,$(CXX))' # g++ only @@ -101,29 +101,9 @@ endif # OS specific # TODO: support Windows -ifeq ($(UNAME_S),Linux) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),Darwin) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),FreeBSD) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),NetBSD) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),OpenBSD) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),Haiku) - CFLAGS += -pthread - CXXFLAGS += -pthread +ifneq '' '$(filter $(UNAME_S),Linux Darwin FreeBSD NetBSD OpenBSD Haiku)' + MK_CFLAGS += -pthread + MK_CXXFLAGS += -pthread endif # detect Windows @@ -149,12 +129,11 @@ ifeq ($(_WIN32),1) endif ifdef LLAMA_GPROF - CFLAGS += -pg - CXXFLAGS += -pg + MK_CFLAGS += -pg + MK_CXXFLAGS += -pg endif ifdef LLAMA_PERF - CFLAGS += -DGGML_PERF - CXXFLAGS += -DGGML_PERF + MK_CPPFLAGS += -DGGML_PERF endif # Architecture specific @@ -165,16 +144,16 @@ ifndef RISCV ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64)) # Use all CPU extensions that are available: - CFLAGS += -march=native -mtune=native - CXXFLAGS += -march=native -mtune=native + MK_CFLAGS += -march=native -mtune=native + MK_CXXFLAGS += -march=native -mtune=native # Usage AVX-only - #CFLAGS += -mfma -mf16c -mavx - #CXXFLAGS += -mfma -mf16c -mavx + #MK_CFLAGS += -mfma -mf16c -mavx + #MK_CXXFLAGS += -mfma -mf16c -mavx # Usage SSSE3-only (Not is SSE3!) - #CFLAGS += -mssse3 - #CXXFLAGS += -mssse3 + #MK_CFLAGS += -mssse3 + #MK_CXXFLAGS += -mssse3 endif # The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves. @@ -188,34 +167,33 @@ endif ifneq ($(filter aarch64%,$(UNAME_M)),) # Apple M1, M2, etc. # Raspberry Pi 3, 4, Zero 2 (64-bit) - CFLAGS += -mcpu=native - CXXFLAGS += -mcpu=native + MK_CFLAGS += -mcpu=native + MK_CXXFLAGS += -mcpu=native endif ifneq ($(filter armv6%,$(UNAME_M)),) # Raspberry Pi 1, Zero - CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access + MK_CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access + MK_CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access endif ifneq ($(filter armv7%,$(UNAME_M)),) # Raspberry Pi 2 - CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations + MK_CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations + MK_CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations endif ifneq ($(filter armv8%,$(UNAME_M)),) # Raspberry Pi 3, 4, Zero 2 (32-bit) - CFLAGS += -mfp16-format=ieee -mno-unaligned-access + MK_CFLAGS += -mfp16-format=ieee -mno-unaligned-access + MK_CXXFLAGS += -mfp16-format=ieee -mno-unaligned-access endif ifneq ($(filter ppc64%,$(UNAME_M)),) POWER9_M := $(shell grep "POWER9" /proc/cpuinfo) ifneq (,$(findstring POWER9,$(POWER9_M))) - CFLAGS += -mcpu=power9 - CXXFLAGS += -mcpu=power9 - endif - # Require c++23's std::byteswap for big-endian support. - ifeq ($(UNAME_M),ppc64) - CXXFLAGS += -std=c++23 -DGGML_BIG_ENDIAN + MK_CFLAGS += -mcpu=power9 + MK_CXXFLAGS += -mcpu=power9 endif endif @@ -225,12 +203,10 @@ else endif ifndef LLAMA_NO_K_QUANTS - CFLAGS += -DGGML_USE_K_QUANTS - CXXFLAGS += -DGGML_USE_K_QUANTS + MK_CPPFLAGS += -DGGML_USE_K_QUANTS OBJS += k_quants.o ifdef LLAMA_QKK_64 - CFLAGS += -DGGML_QKK_64 - CXXFLAGS += -DGGML_QKK_64 + MK_CPPFLAGS += -DGGML_QKK_64 endif endif @@ -238,31 +214,32 @@ ifndef LLAMA_NO_ACCELERATE # Mac M1 - include Accelerate framework. # `-framework Accelerate` works on Mac Intel as well, with negliable performance boost (as of the predict time). ifeq ($(UNAME_S),Darwin) - CFLAGS += -DGGML_USE_ACCELERATE - LDFLAGS += -framework Accelerate + MK_CPPFLAGS += -DGGML_USE_ACCELERATE + MK_LDFLAGS += -framework Accelerate endif endif # LLAMA_NO_ACCELERATE ifdef LLAMA_MPI - CFLAGS += -DGGML_USE_MPI -Wno-cast-qual - CXXFLAGS += -DGGML_USE_MPI -Wno-cast-qual + MK_CPPFLAGS += -DGGML_USE_MPI + MK_CFLAGS += -Wno-cast-qual + MK_CXXFLAGS += -Wno-cast-qual OBJS += ggml-mpi.o endif # LLAMA_MPI ifdef LLAMA_OPENBLAS - CFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags openblas) - LDFLAGS += $(shell pkg-config --libs openblas) + MK_CPPFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags-only-I openblas) + MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) + MK_LDFLAGS += $(shell pkg-config --libs openblas) endif # LLAMA_OPENBLAS ifdef LLAMA_BLIS - CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis - LDFLAGS += -lblis -L/usr/local/lib + MK_CPPFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis + MK_LDFLAGS += -lblis -L/usr/local/lib endif # LLAMA_BLIS ifdef LLAMA_CUBLAS - CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include - CXXFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include - LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib + MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include + MK_LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib OBJS += ggml-cuda.o NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math ifdef LLAMA_CUDA_NVCC @@ -313,14 +290,15 @@ endif # LLAMA_CUBLAS ifdef LLAMA_CLBLAST - CFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags clblast OpenCL) - CXXFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags clblast OpenCL) + MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL) + MK_CFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) + MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) # Mac provides OpenCL as a framework ifeq ($(UNAME_S),Darwin) - LDFLAGS += -lclblast -framework OpenCL + MK_LDFLAGS += -lclblast -framework OpenCL else - LDFLAGS += $(shell pkg-config --libs clblast OpenCL) + MK_LDFLAGS += $(shell pkg-config --libs clblast OpenCL) endif OBJS += ggml-opencl.o @@ -335,10 +313,9 @@ ifdef LLAMA_HIPBLAS LLAMA_CUDA_DMMV_X ?= 32 LLAMA_CUDA_MMV_Y ?= 1 LLAMA_CUDA_KQUANTS_ITER ?= 2 - CFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS - CXXFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS - LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib - LDFLAGS += -lhipblas -lamdhip64 -lrocblas + MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS + MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib + MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS)) HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) HIPFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) @@ -353,10 +330,9 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h endif # LLAMA_HIPBLAS ifdef LLAMA_METAL - CFLAGS += -DGGML_USE_METAL #-DGGML_METAL_NDEBUG - CXXFLAGS += -DGGML_USE_METAL - LDFLAGS += -framework Foundation -framework Metal -framework MetalKit - OBJS += ggml-metal.o + MK_CPPFLAGS += -DGGML_USE_METAL #-DGGML_METAL_NDEBUG + MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit + OBJS += ggml-metal.o endif # LLAMA_METAL ifdef LLAMA_METAL @@ -369,11 +345,17 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h $(CC) $(CFLAGS) -c $< -o $@ endif # LLAMA_MPI -ifdef LLAMA_NO_K_QUANTS +ifndef LLAMA_NO_K_QUANTS k_quants.o: k_quants.c k_quants.h $(CC) $(CFLAGS) -c $< -o $@ endif # LLAMA_NO_K_QUANTS +# combine build flags with cmdline overrides +override CPPFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) +override CFLAGS := $(MK_CFLAGS) $(CFLAGS) +override CXXFLAGS := $(MK_CXXFLAGS) $(CXXFLAGS) +override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS) + # # Print build information # From 2753415afdaf22a18c49608bd9d93cfffc05d435 Mon Sep 17 00:00:00 2001 From: kchro3 <62481661+kchro3@users.noreply.github.com> Date: Sat, 2 Sep 2023 22:27:25 -0700 Subject: [PATCH 28/87] swift : add missing c file to Package.swift (#2978) --- Package.swift | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Package.swift b/Package.swift index 73d027c70..2c07710cd 100644 --- a/Package.swift +++ b/Package.swift @@ -12,7 +12,7 @@ let package = Package( name: "llama", path: ".", exclude: ["ggml-metal.metal"], - sources: ["ggml.c", "llama.cpp"], + sources: ["ggml.c", "llama.cpp", "ggml-alloc.c"], publicHeadersPath: "spm-headers", cSettings: [.unsafeFlags(["-Wno-shorten-64-to-32"]), .define("GGML_USE_ACCELERATE")], linkerSettings: [ From c42f0ec6b344e14bd81c8612ab1445b3ff77358b Mon Sep 17 00:00:00 2001 From: momonga <115213907+mmnga@users.noreply.github.com> Date: Sun, 3 Sep 2023 14:36:28 +0900 Subject: [PATCH 29/87] examples : fix gpt-neox (#2943) Co-authored-by: mmnga --- examples/gptneox-wip/gptneox-main.cpp | 13 ++++---- llama.cpp | 46 +++++++++++++++++++++++++-- 2 files changed, 51 insertions(+), 8 deletions(-) diff --git a/examples/gptneox-wip/gptneox-main.cpp b/examples/gptneox-wip/gptneox-main.cpp index 04af50245..6291523f2 100644 --- a/examples/gptneox-wip/gptneox-main.cpp +++ b/examples/gptneox-wip/gptneox-main.cpp @@ -660,9 +660,10 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 ggml_tensor * gpt_neox_ff( const gpt_neox_block &block, ggml_context * ctx0, - ggml_tensor * inp) { + ggml_tensor * inp, + const gpt_neox_hparams &hparams) { - ggml_tensor * cur = ggml_norm(ctx0, inp); + ggml_tensor * cur = ggml_norm(ctx0, inp, hparams.norm_eps); cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, block.ln_2_g, cur), cur), ggml_repeat(ctx0, block.ln_2_b, cur)); cur = ggml_mul_mat(ctx0, block.c_mlp_fc_w, cur); @@ -753,7 +754,7 @@ bool gpt_neox_eval( // self-attention { { - cur = ggml_norm(ctx0, inpL); + cur = ggml_norm(ctx0, inpL, hparams.norm_eps); cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.blocks[il].ln_1_g, cur), cur), @@ -844,7 +845,7 @@ bool gpt_neox_eval( if (hparams.par_res == 0) { struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpL); - cur = gpt_neox_ff(model.blocks[il], ctx0, inpFF); + cur = gpt_neox_ff(model.blocks[il], ctx0, inpFF, hparams); // input for next layer inpL = ggml_add(ctx0, cur, inpFF); @@ -853,7 +854,7 @@ bool gpt_neox_eval( // this is independent of the self-attention result, so it could be done in parallel to the self-attention // note here we pass inpL instead of cur - cur = gpt_neox_ff(model.blocks[il], ctx0, inpL); + cur = gpt_neox_ff(model.blocks[il], ctx0, inpL, hparams); // layer input + FF cur = ggml_add(ctx0, cur, inpFF); @@ -867,7 +868,7 @@ bool gpt_neox_eval( // norm { - inpL = ggml_norm(ctx0, inpL); + inpL = ggml_norm(ctx0, inpL, hparams.norm_eps); // inpL = ln_f_g*inpL + ln_f_b inpL = ggml_add(ctx0, diff --git a/llama.cpp b/llama.cpp index 3114d3311..2b0cf30f6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -325,6 +325,44 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_GPT2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + }, + }, + { + LLM_ARCH_GPTJ, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + }, + }, + { + LLM_ARCH_GPTNEOX, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, + { + LLM_ARCH_MPT, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + }, + }, + { + LLM_ARCH_UNKNOWN, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + }, + }, }; static llm_arch llm_arch_from_string(const std::string & name) { @@ -1605,9 +1643,13 @@ static void llm_load_hparams( GGUF_GET_KEY(ctx, hparams.n_rot, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT)); - if (hparams.n_rot != hparams.n_embd / hparams.n_head) { - throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head)); + if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) { + if (hparams.n_rot != hparams.n_embd / hparams.n_head) { + throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head)); + } } + // gpt-neox n_rot = rotary_pct * (n_embd / n_head) + // gpt-j n_rot = rotary_dim } // arch-specific KVs From 340af42f09a80e32f4998857b4f0543e41124525 Mon Sep 17 00:00:00 2001 From: Ido S Date: Sun, 3 Sep 2023 08:50:51 +0300 Subject: [PATCH 30/87] docs : add `catai` to `README.md` (#2967) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 5eda5f006..0cfd94db4 100644 --- a/README.md +++ b/README.md @@ -120,6 +120,7 @@ as the main playground for developing new features for the [ggml](https://github - [nat/openplayground](https://github.com/nat/openplayground) - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) +- [withcatai/catai](https://github.com/withcatai/catai) --- From cff7b0bf07cb46e1ad4fd199f6bdeb538925c8c4 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sat, 2 Sep 2023 23:52:13 -0600 Subject: [PATCH 31/87] convert.py : BPE fixes (#2938) * convert.py: BPE fixes? * Remove unnecessary conditional in addl token error handling --- convert.py | 32 ++++++++++++++++++++++++++++---- 1 file changed, 28 insertions(+), 4 deletions(-) diff --git a/convert.py b/convert.py index 6c89b5ecc..5a7483b43 100755 --- a/convert.py +++ b/convert.py @@ -323,15 +323,27 @@ class BpeVocab: self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read()) added_tokens: dict[str, int] if fname_added_tokens is not None: + # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) else: - added_tokens = {} + # Fall back to trying to find the added tokens in tokenizer.json + tokenizer_json_file = fname_tokenizer.parent / 'tokenizer.json' + if not tokenizer_json_file.is_file(): + added_tokens = {} + else: + tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8")) + added_tokens = dict( + (item['content'], item['id']) + for item in tokenizer_json.get('added_tokens', []) + # Added tokens here can be duplicates of the main vocabulary. + if item['content'] not in self.bpe_tokenizer ) vocab_size: int = len(self.bpe_tokenizer) expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) actual_ids = sorted(added_tokens.values()) if expected_ids != actual_ids: - raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}") + expected_end_id = vocab_size + len(actual_ids) - 1 + raise Exception(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}") items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) self.added_tokens_list = [text for (text, idx) in items] @@ -345,10 +357,22 @@ class BpeVocab: from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import] byte_encoder = tokenization_gpt2.bytes_to_unicode() byte_decoder = {v: k for k, v in byte_encoder.items()} + score = 0.0 for i, item in enumerate(tokenizer): text: bytes = item.encode("utf-8") - score: float = -i - yield text, score, gguf.TokenType.USER_DEFINED + # FIXME: These shouldn't be hardcoded, but it's probably better than the current behavior? + if i <= 258 and text.startswith(b'<') and text.endswith(b'>'): + if i == 0 and text == b'': + toktype = gguf.TokenType.UNKNOWN + elif i == 1 or i == 2: + toktype = gguf.TokenType.CONTROL + elif i >= 3 and text.startswith(b'<0x'): + toktype = gguf.TokenType.BYTE + else: + toktype = gguf.TokenType.NORMAL + else: + toktype = gguf.TokenType.NORMAL + yield text, score, toktype def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: From 6a31a3bd9806c85ed08266f6ab65181da0f30d03 Mon Sep 17 00:00:00 2001 From: kchro3 <62481661+kchro3@users.noreply.github.com> Date: Sat, 2 Sep 2023 23:21:05 -0700 Subject: [PATCH 32/87] swift : add support for k-quants (#2983) --- Package.swift | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/Package.swift b/Package.swift index 2c07710cd..96f52c4f0 100644 --- a/Package.swift +++ b/Package.swift @@ -12,9 +12,18 @@ let package = Package( name: "llama", path: ".", exclude: ["ggml-metal.metal"], - sources: ["ggml.c", "llama.cpp", "ggml-alloc.c"], + sources: [ + "ggml.c", + "llama.cpp", + "ggml-alloc.c", + "k_quants.c" + ], publicHeadersPath: "spm-headers", - cSettings: [.unsafeFlags(["-Wno-shorten-64-to-32"]), .define("GGML_USE_ACCELERATE")], + cSettings: [ + .unsafeFlags(["-Wno-shorten-64-to-32"]), + .define("GGML_USE_K_QUANTS"), + .define("GGML_USE_ACCELERATE") + ], linkerSettings: [ .linkedFramework("Accelerate") ] From ca82cf7bac0c91d03e3d320b3a865dd006f854ac Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 3 Sep 2023 11:06:22 +0300 Subject: [PATCH 33/87] metal : more optimizations (#2959) * Very minor speedup via simd-group synchronization in f16 x f32 * Another very minor speedup on metal * Quite significant PP speedup on metal * Another attempt * Minor * Massive improvement for TG for fp16 * ~4-5% improvement for Q8_0 TG on metal --------- Co-authored-by: Iwan Kawrakow Co-authored-by: Georgi Gerganov --- ggml-metal.m | 22 +++-- ggml-metal.metal | 226 ++++++++++++++++++++++++++++++----------------- 2 files changed, 163 insertions(+), 85 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 88e7e1356..d0d23442e 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -76,6 +76,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(norm); GGML_METAL_DECL_KERNEL(mul_mat_f16_f32); + GGML_METAL_DECL_KERNEL(mul_mat_f16_f32_1row); GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32); GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32); GGML_METAL_DECL_KERNEL(mul_mat_q8_0_f32); @@ -219,6 +220,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(norm); GGML_METAL_ADD_KERNEL(mul_mat_f16_f32); + GGML_METAL_ADD_KERNEL(mul_mat_f16_f32_1row); GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32); GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32); GGML_METAL_ADD_KERNEL(mul_mat_q8_0_f32); @@ -284,6 +286,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(rms_norm); GGML_METAL_DEL_KERNEL(norm); GGML_METAL_DEL_KERNEL(mul_mat_f16_f32); + GGML_METAL_DEL_KERNEL(mul_mat_f16_f32_1row); GGML_METAL_DEL_KERNEL(mul_mat_q4_0_f32); GGML_METAL_DEL_KERNEL(mul_mat_q4_1_f32); GGML_METAL_DEL_KERNEL(mul_mat_q8_0_f32); @@ -868,7 +871,11 @@ void ggml_metal_graph_compute( { nth0 = 32; nth1 = 1; - [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32]; + if (ne11 * ne12 < 4) { + [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32_1row]; + } else { + [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32]; + } } break; case GGML_TYPE_Q4_0: { @@ -920,8 +927,8 @@ void ggml_metal_graph_compute( GGML_ASSERT(ne02 == 1); GGML_ASSERT(ne12 == 1); - nth0 = 2; - nth1 = 32; + nth0 = 4; //1; + nth1 = 8; //32; [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_K_f32]; } break; case GGML_TYPE_Q5_K: @@ -969,9 +976,12 @@ void ggml_metal_graph_compute( [encoder setBytes:&gqa length:sizeof(gqa) atIndex:17]; if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q8_0 || - src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) { + src0t == GGML_TYPE_Q2_K) {// || src0t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } + else if (src0t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else if (src0t == GGML_TYPE_Q3_K) { #ifdef GGML_QKK_64 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -985,8 +995,8 @@ void ggml_metal_graph_compute( else if (src0t == GGML_TYPE_Q6_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else { - [encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + int64_t ny = (ne11 + 3)/4; + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } } } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index 8cdf0b9d2..3fa311b40 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -133,19 +133,24 @@ kernel void kernel_soft_max( threadgroup_barrier(mem_flags::mem_threadgroup); } - // broadcast - if (tpitg[0] == 0) { - buf[0] = buf[0]; - } + //// broadcast - not needed. There is a threadgroup barrier above in the last iteration of + // the loop, and when that is done, buf[0] has the correct (synchronized) value + //if (tpitg[0] == 0) { + // buf[0] = buf[0]; + //} - threadgroup_barrier(mem_flags::mem_threadgroup); + //threadgroup_barrier(mem_flags::mem_threadgroup); const float max = buf[0]; // parallel sum buf[tpitg[0]] = 0.0f; for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) { - buf[tpitg[0]] += exp(psrc0[i00] - max); + const float exp_psrc0 = exp(psrc0[i00] - max); + buf[tpitg[0]] += exp_psrc0; + // Remember the result of exp here. exp is expensive, so we really do not + // whish to compute it twice. + pdst[i00] = exp_psrc0; } // reduce @@ -157,17 +162,18 @@ kernel void kernel_soft_max( threadgroup_barrier(mem_flags::mem_threadgroup); } - // broadcast - if (tpitg[0] == 0) { - buf[0] = buf[0]; - } + // broadcast - not needed, see above + //// broadcast + //if (tpitg[0] == 0) { + // buf[0] = buf[0]; + //} - threadgroup_barrier(mem_flags::mem_threadgroup); + //threadgroup_barrier(mem_flags::mem_threadgroup); const float sum = buf[0]; for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) { - pdst[i00] = exp(psrc0[i00] - max) / sum; + pdst[i00] /= sum; } } @@ -214,25 +220,27 @@ kernel void kernel_norm( } threadgroup_barrier(mem_flags::mem_threadgroup); } - // broadcast - if (tpitg == 0) { - sum[0] /= ne00; - } - threadgroup_barrier(mem_flags::mem_threadgroup); + //// broadcast + //if (tpitg == 0) { + // sum[0] /= ne00; + //} + //threadgroup_barrier(mem_flags::mem_threadgroup); const float mean = sum[0]; - // recenter + // recenter and VARIANCE device float * y = dst + tgpig*ne00; - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - y[i00] = x[i00] - mean; - } - - // VARIANCE - // parallel sum sum[tpitg] = 0.0f; for (int i00 = tpitg; i00 < ne00; i00 += ntg) { + y[i00] = x[i00] - mean; sum[tpitg] += y[i00] * y[i00]; } + + //// VARIANCE + //// parallel sum + //sum[tpitg] = 0.0f; + //for (int i00 = tpitg; i00 < ne00; i00 += ntg) { + // sum[tpitg] += y[i00] * y[i00]; + //} // reduce threadgroup_barrier(mem_flags::mem_threadgroup); for (uint i = ntg/2; i > 0; i /= 2) { @@ -241,11 +249,11 @@ kernel void kernel_norm( } threadgroup_barrier(mem_flags::mem_threadgroup); } - // broadcast - if (tpitg == 0) { - sum[0] /= ne00; - } - threadgroup_barrier(mem_flags::mem_threadgroup); + //// broadcast + //if (tpitg == 0) { + // sum[0] /= ne00; + //} + //threadgroup_barrier(mem_flags::mem_threadgroup); const float variance = sum[0]; const float scale = 1.0f/sqrt(variance + eps); @@ -435,6 +443,8 @@ kernel void kernel_mul_mat_q4_1_f32( mul_vec_q_n_f32(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,gqa,tgpig,tiisg,sgitg); } +#define NB_Q8_0 8 + kernel void kernel_mul_mat_q8_0_f32( device const void * src0, device const float * src1, @@ -463,30 +473,30 @@ kernel void kernel_mul_mat_q8_0_f32( device const block_q8_0 * x = (device const block_q8_0 *) src0 + offset0; device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; - float yl[16]; + float yl[NB_Q8_0]; float sumf[nr]={0.f}; - const int ix = tiisg/2; - const int il = tiisg%2; + const int ix = tiisg/4; + const int il = tiisg%4; - device const float * yb = y + ix * QK8_0 + 16*il; + device const float * yb = y + ix * QK8_0 + NB_Q8_0*il; - // each thread in a SIMD group deals with half a block. - for (int ib = ix; ib < nb; ib += nw/2) { - for (int i = 0; i < 16; ++i) { + // each thread in a SIMD group deals with NB_Q8_0 quants at a time + for (int ib = ix; ib < nb; ib += nw/4) { + for (int i = 0; i < NB_Q8_0; ++i) { yl[i] = yb[i]; } for (int row = 0; row < nr; row++) { - device const int8_t * qs = x[ib+row*nb].qs + 16*il; + device const int8_t * qs = x[ib+row*nb].qs + NB_Q8_0*il; float sumq = 0.f; - for (int iq = 0; iq < 16; ++iq) { + for (int iq = 0; iq < NB_Q8_0; ++iq) { sumq += qs[iq] * yl[iq]; } sumf[row] += sumq*x[ib+row*nb].d; } - yb += QK8_0 * 16; + yb += NB_Q8_0 * nw; } for (int row = 0; row < nr; ++row) { @@ -497,6 +507,60 @@ kernel void kernel_mul_mat_q8_0_f32( } } +kernel void kernel_mul_mat_f16_f32_1row( + device const char * src0, + device const char * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]]) { + + const int64_t r0 = tgpig.x; + const int64_t r1 = tgpig.y; + const int64_t im = tgpig.z; + + device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + + float sumf = 0; + if (ne00 < 128) { + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } else { + device const half4 * x4 = (device const half4 *) x; + device const float4 * y4 = (device const float4 *) y; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k]; + } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) sumf += (float) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } + +} + +#define N_F16_F32 4 + kernel void kernel_mul_mat_f16_f32( device const char * src0, device const char * src1, @@ -515,55 +579,58 @@ kernel void kernel_mul_mat_f16_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - threadgroup float * sum [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpig[[thread_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 tptg[[threads_per_threadgroup]]) { + uint tiisg[[thread_index_in_simdgroup]]) { const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; + const int64_t rb = N_F16_F32*tgpig.y; const int64_t im = tgpig.z; - device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); - uint ith = tpitg.x; - uint nth = tptg.x; + if (ne00 < 128) { + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } - sum[ith] = 0.0f; + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - for (int i = ith; i < ne00; i += nth) { - sum[ith] += (float) x[i] * (float) y[i]; + float sumf = 0; + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } + + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } + } else { + device const half4 * x4 = (device const half4 *)x; + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } + + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const float4 * y4 = (device const float4 *) y; + + float sumf = 0; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k]; + } + + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) sumf += (float) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } } - // accumulate the sum from all threads in the threadgroup - threadgroup_barrier(mem_flags::mem_threadgroup); - if (ith%4 == 0) { - for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - if (ith%16 == 0) { - for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - if (ith == 0) { - for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; - dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0]; - } - - // Original implementation. Left behind commented out for now - //threadgroup_barrier(mem_flags::mem_threadgroup); - //for (uint i = tptg.x/2; i > 0; i /= 2) { - // if (tpitg.x < i) { - // sum[tpitg.x] += sum[tpitg.x + i]; - // } - // threadgroup_barrier(mem_flags::mem_threadgroup); - //} - // - //if (tpitg.x == 0) { - // dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0]; - //} } kernel void kernel_alibi_f32( @@ -1262,7 +1329,8 @@ kernel void kernel_mul_mat_q4_K_f32( const int r0 = tgpig.x; const int r1 = tgpig.y; const int r2 = tgpig.z; - const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + //const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + const int first_row = r0 * N_DST; const int ib_row = first_row * nb; const uint offset0 = r2/gqa*(nb*ne0); device const block_q4_K * x = (device const block_q4_K *) src0 + ib_row + offset0; From 6460f758dbd472653296044d36bed8c4554988f5 Mon Sep 17 00:00:00 2001 From: Wentai Zhang Date: Sun, 3 Sep 2023 16:46:44 +0800 Subject: [PATCH 34/87] opencl : fix a bug in ggml_cl_pool_malloc() for ggml_cl_mul_mat_f32() (#2955) Co-authored-by: Wentai Zhang --- ggml-opencl.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index eb214a836..3d50a7f08 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1493,7 +1493,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr if (src0->backend == GGML_BACKEND_GPU) { // NOLINT d_X = (cl_mem) src0->data; } else { - d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size); + d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size); } cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); From afc43d5f82588d2ed71ea104e8262f5e5da13980 Mon Sep 17 00:00:00 2001 From: Alon Date: Sun, 3 Sep 2023 11:48:49 +0300 Subject: [PATCH 35/87] cov : add Code Coverage and codecov.io integration (#2928) * update .gitignore * makefile: add coverage support (lcov, gcovr) * add code-coverage workflow * update code coverage workflow * wun on ubuntu 20.04 * use gcc-8 * check why the job hang * add env vars * add LLAMA_CODE_COVERAGE=1 again * - add CODECOV_TOKEN - add missing make lcov-report * install lcov * update make file -pb flag * remove unused GGML_NITER from workflows * wrap coverage output files in COV_TARGETS --- .github/workflows/build.yml | 1 - .github/workflows/code-coverage.yml | 36 +++++++++++++++++++++++++++++ .gitignore | 7 ++++++ Makefile | 22 +++++++++++++++++- 4 files changed, 64 insertions(+), 2 deletions(-) create mode 100644 .github/workflows/code-coverage.yml diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 20fd8c2b5..9d0a6c222 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -18,7 +18,6 @@ on: env: BRANCH_NAME: ${{ github.head_ref || github.ref_name }} GGML_NLOOP: 3 - GGML_NITER: 1 GGML_N_THREADS: 1 jobs: diff --git a/.github/workflows/code-coverage.yml b/.github/workflows/code-coverage.yml new file mode 100644 index 000000000..392db8a08 --- /dev/null +++ b/.github/workflows/code-coverage.yml @@ -0,0 +1,36 @@ +name: Code Coverage +on: [push, pull_request] + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + +jobs: + run: + runs-on: ubuntu-20.04 + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Dependencies + run: | + sudo apt-get update + sudo apt-get install build-essential gcc-8 lcov + + - name: Build + run: CC=gcc-8 make -j LLAMA_CODE_COVERAGE=1 tests + + - name: Run tests + run: CC=gcc-8 make test + + - name: Generate coverage report + run: | + make coverage + make lcov-report + + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v3 + env: + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} + with: + files: lcov-report/coverage.info diff --git a/.gitignore b/.gitignore index 8b5f45a2d..f9244fadc 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,10 @@ *.exe *.dll *.log +*.gcov +*.gcno +*.gcda +*.dot .DS_Store .build/ .cache/ @@ -17,6 +21,9 @@ .vs/ .vscode/ +lcov-report/ +gcovr-report/ + build*/ out/ tmp/ diff --git a/Makefile b/Makefile index e214970f8..c042bf0e5 100644 --- a/Makefile +++ b/Makefile @@ -4,6 +4,9 @@ BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-tex # Binaries only useful for tests TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1 +# Code coverage output files +COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report + default: $(BUILD_TARGETS) test: @@ -23,6 +26,18 @@ test: all: $(BUILD_TARGETS) $(TEST_TARGETS) +coverage: ## Run code coverage + gcov -pb tests/*.cpp + +lcov-report: coverage ## Generate lcov report + mkdir -p lcov-report + lcov --capture --directory . --output-file lcov-report/coverage.info + genhtml lcov-report/coverage.info --output-directory lcov-report + +gcovr-report: coverage ## Generate gcovr report + mkdir -p gcovr-report + gcovr --root . --html --html-details --output gcovr-report/coverage.html + ifndef UNAME_S UNAME_S := $(shell uname -s) endif @@ -84,6 +99,11 @@ ifdef LLAMA_SERVER_VERBOSE MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) endif + +ifdef LLAMA_CODE_COVERAGE + CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase '' +endif + ifdef LLAMA_DISABLE_LOGS CFLAGS += -DLOG_DISABLE_LOGS CXXFLAGS += -DLOG_DISABLE_LOGS @@ -399,7 +419,7 @@ libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) clean: - rm -vf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h $(BUILD_TARGETS) $(TEST_TARGETS) + rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) # # Examples From d9151e6f570eb20bfd54427bd8a337d9b1a08018 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Sep 2023 12:40:56 +0300 Subject: [PATCH 36/87] metal : revert 6af0bab until we fix it This restores the generated text to be the same as before #2959 --- ggml-metal.metal | 74 ++++++++++++------------------------------------ 1 file changed, 18 insertions(+), 56 deletions(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index 3fa311b40..1d324e466 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -536,27 +536,14 @@ kernel void kernel_mul_mat_f16_f32_1row( device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); float sumf = 0; - if (ne00 < 128) { - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; - } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } - } else { - device const half4 * x4 = (device const half4 *) x; - device const float4 * y4 = (device const float4 *) y; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k]; - } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - for (int i = 4*(ne00/4); i < ne00; ++i) sumf += (float) x[i] * y[i]; - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } } #define N_F16_F32 4 @@ -588,49 +575,24 @@ kernel void kernel_mul_mat_f16_f32( device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); - if (ne00 < 128) { - for (int row = 0; row < N_F16_F32; ++row) { - int r1 = rb + row; - if (r1 >= ne11) { - break; - } - - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - - float sumf = 0; - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; - } - - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; } - } else { - device const half4 * x4 = (device const half4 *)x; - for (int row = 0; row < N_F16_F32; ++row) { - int r1 = rb + row; - if (r1 >= ne11) { - break; - } - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - device const float4 * y4 = (device const float4 *) y; + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - float sumf = 0; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k]; - } + float sumf = 0; + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - for (int i = 4*(ne00/4); i < ne00; ++i) sumf += (float) x[i] * y[i]; - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; } } - } kernel void kernel_alibi_f32( From 37301347767d555d0a66c043ce4ef6ead8e61c55 Mon Sep 17 00:00:00 2001 From: opparco Date: Sun, 3 Sep 2023 19:18:09 +0900 Subject: [PATCH 37/87] llama : fix bpe tokenize from byte (#2889) --- llama.cpp | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 2b0cf30f6..c97c1462f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3366,9 +3366,15 @@ struct llm_tokenizer_bpe { std::string byte_str(1, *j); auto token_multibyte = vocab.token_to_id.find(byte_str); if (token_multibyte == vocab.token_to_id.end()) { - fprintf(stderr,"ERROR: byte not found in vocab: '%s'\n", byte_str.c_str()); + try { + llama_token token_byte = llama_byte_to_token(vocab, *j); + output.push_back(token_byte); + } catch (const std::out_of_range & err) { + fprintf(stderr,"ERROR: byte not found in vocab: '%s'\n", byte_str.c_str()); + } + } else { + output.push_back((*token_multibyte).second); } - output.push_back((*token_multibyte).second); } } else { output.push_back((*token).second); From 73a12a6344d5da4d8e2eba5d12221b8bc6895931 Mon Sep 17 00:00:00 2001 From: Alon Date: Sun, 3 Sep 2023 13:19:01 +0300 Subject: [PATCH 38/87] cov : disable comment in PRs (#2989) --- codecov.yml | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 codecov.yml diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 000000000..a301c5b2c --- /dev/null +++ b/codecov.yml @@ -0,0 +1,14 @@ +comment: off + +coverage: + status: + project: + default: + target: auto + threshold: 0 + base: auto + patch: + default: + target: auto + threshold: 0 + base: auto From b7f2aa9e512c3be2e863d877cbb1056d7c4a03f8 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Sep 2023 13:23:33 +0300 Subject: [PATCH 39/87] metal : restore 363f0bf and fix reduce in F16_F32 kernels (#2986) --- ggml-metal.metal | 76 ++++++++++++++++++++++++++++++++++++------------ 1 file changed, 57 insertions(+), 19 deletions(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index 1d324e466..119fcbeb6 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -536,14 +536,27 @@ kernel void kernel_mul_mat_f16_f32_1row( device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); float sumf = 0; - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; + if (ne00 < 128) { + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } else { + device const half4 * x4 = (device const half4 *) x; + device const float4 * y4 = (device const float4 *) y; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k]; + } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } } #define N_F16_F32 4 @@ -570,29 +583,54 @@ kernel void kernel_mul_mat_f16_f32( uint tiisg[[thread_index_in_simdgroup]]) { const int64_t r0 = tgpig.x; - const int64_t rb = N_F16_F32*tgpig.y; + const int64_t rb = tgpig.y*N_F16_F32; const int64_t im = tgpig.z; device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); - for (int row = 0; row < N_F16_F32; ++row) { - int r1 = rb + row; - if (r1 >= ne11) { - break; + if (ne00 < 128) { + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } + + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + + float sumf = 0; + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } + + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } } + } else { + device const half4 * x4 = (device const half4 *)x; + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const float4 * y4 = (device const float4 *) y; - float sumf = 0; - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; - } + float sumf = 0; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k]; + } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } } } + } kernel void kernel_alibi_f32( From 6519e9c99cffbad19b31bcba86df48c500628c09 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sun, 3 Sep 2023 04:38:43 -0600 Subject: [PATCH 40/87] gguf(python): Fix special vocab handling when id < 0 (#2984) --- gguf-py/gguf/gguf.py | 4 ++-- gguf-py/pyproject.toml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index b1bc4205b..d377cd56d 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -801,7 +801,7 @@ class SpecialVocab: else: continue for maybe_token_id in (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content): - if isinstance(maybe_token_id, int): + if isinstance(maybe_token_id, int) and maybe_token_id >= 0: self.special_token_ids[typ] = maybe_token_id break return True @@ -814,7 +814,7 @@ class SpecialVocab: config = json.load(f) for typ in self.special_token_types: maybe_token_id = config.get(f'{typ}_token_id') - if isinstance(maybe_token_id, int): + if isinstance(maybe_token_id, int) and maybe_token_id >= 0: self.special_token_ids[typ] = maybe_token_id return True diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index 26f792b14..8da60de1b 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.3.1" +version = "0.3.2" description = "Write ML models in GGUF for GGML" authors = ["GGML "] packages = [ From 8f429fa5111901f9646cf998643ac5310846d487 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Sep 2023 13:42:56 +0300 Subject: [PATCH 41/87] perplexity : fix ETA by warming up the model with an empty run --- common/common.cpp | 8 ++++++++ examples/main/main.cpp | 8 -------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 41fc59ced..a1c3dc780 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -752,6 +752,14 @@ std::tuple llama_init_from_gpt_par params.logit_bias[llama_token_eos(lctx)] = -INFINITY; } + { + LOG("warming up the model with an empty run\n"); + + const std::vector tmp = { llama_token_bos(lctx), }; + llama_eval(lctx, tmp.data(), tmp.size(), 0, params.n_threads); + llama_reset_timings(lctx); + } + return std::make_tuple(model, lctx); } diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 7117db4b0..db98312ca 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -492,14 +492,6 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; - { - LOG("warming up the model with an empty run\n"); - - const std::vector tmp = { llama_token_bos(ctx), }; - llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads); - llama_reset_timings(ctx); - } - while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict if (embd.size() > 0) { From 47068e517004d90f13c16352bb3b4cafd53a00cd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Sep 2023 15:12:08 +0300 Subject: [PATCH 42/87] speculative : PoC for speeding-up inference via speculative sampling (#2926) * speculative : initial example * speculative : print encoding speed * speculative : add --draft CLI arg --- common/common.cpp | 140 ++++++++++++++++ common/common.h | 36 +++++ examples/CMakeLists.txt | 1 + examples/main/main.cpp | 136 +++------------- examples/speculative/CMakeLists.txt | 8 + examples/speculative/speculative.cpp | 234 +++++++++++++++++++++++++++ 6 files changed, 440 insertions(+), 115 deletions(-) create mode 100644 examples/speculative/CMakeLists.txt create mode 100644 examples/speculative/speculative.cpp diff --git a/common/common.cpp b/common/common.cpp index a1c3dc780..313821375 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -305,6 +305,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.n_keep = std::stoi(argv[i]); + } else if (arg == "--draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_draft = std::stoi(argv[i]); } else if (arg == "--chunks") { if (++i >= argc) { invalid_param = true; @@ -317,6 +323,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.model = argv[i]; + } else if (arg == "-md" || arg == "--model-draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.model_draft = argv[i]; } else if (arg == "-a" || arg == "--alias") { if (++i >= argc) { invalid_param = true; @@ -638,6 +650,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); + fprintf(stdout, " --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft); fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); if (llama_mlock_supported()) { fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); @@ -669,6 +682,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); fprintf(stdout, " -m FNAME, --model FNAME\n"); fprintf(stdout, " model path (default: %s)\n", params.model.c_str()); + fprintf(stdout, " -md FNAME, --model-draft FNAME\n"); + fprintf(stdout, " draft model for speculative decoding (default: %s)\n", params.model.c_str()); fprintf(stdout, " -ld LOGDIR, --logdir LOGDIR\n"); fprintf(stdout, " path under which to save YAML logs (no logging if unset)\n"); fprintf(stdout, "\n"); @@ -832,6 +847,130 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector & last_tokens, + std::vector & candidates, + int idx) { + const int n_ctx = llama_n_ctx(ctx); + const int n_vocab = llama_n_vocab(ctx); + + const float temp = params.temp; + const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k; + const float top_p = params.top_p; + const float tfs_z = params.tfs_z; + const float typical_p = params.typical_p; + const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; + const float repeat_penalty = params.repeat_penalty; + const float alpha_presence = params.presence_penalty; + const float alpha_frequency = params.frequency_penalty; + const int mirostat = params.mirostat; + const float mirostat_tau = params.mirostat_tau; + const float mirostat_eta = params.mirostat_eta; + const bool penalize_nl = params.penalize_nl; + + llama_token id = 0; + + float * logits = llama_get_logits(ctx) + idx * n_vocab; + + // Apply params.logit_bias map + for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { + logits[it->first] += it->second; + } + + candidates.clear(); + 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 cur_p = { candidates.data(), candidates.size(), false }; + + if (ctx_guidance) { + llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale); + } + + // apply penalties + if (!last_tokens.empty()) { + const float nl_logit = logits[llama_token_nl(ctx)]; + const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx); + + llama_sample_repetition_penalty(ctx, &cur_p, + last_tokens.data() + last_tokens.size() - last_n_repeat, + last_n_repeat, repeat_penalty); + llama_sample_frequency_and_presence_penalties(ctx, &cur_p, + last_tokens.data() + last_tokens.size() - last_n_repeat, + last_n_repeat, alpha_frequency, alpha_presence); + + if (!penalize_nl) { + for (size_t idx = 0; idx < cur_p.size; idx++) { + if (cur_p.data[idx].id == llama_token_nl(ctx)) { + cur_p.data[idx].logit = nl_logit; + break; + } + } + } + } + + if (grammar != NULL) { + llama_sample_grammar(ctx, &cur_p, grammar); + } + + if (temp <= 0) { + // Greedy sampling + id = llama_sample_token_greedy(ctx, &cur_p); + } else { + if (mirostat == 1) { + static float mirostat_mu = 2.0f * mirostat_tau; + const int mirostat_m = 100; + llama_sample_temperature(ctx, &cur_p, temp); + id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); + } else if (mirostat == 2) { + static float mirostat_mu = 2.0f * mirostat_tau; + llama_sample_temperature(ctx, &cur_p, temp); + id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu); + } else { + // Temperature sampling + llama_sample_top_k (ctx, &cur_p, top_k, 1); + llama_sample_tail_free (ctx, &cur_p, tfs_z, 1); + llama_sample_typical (ctx, &cur_p, typical_p, 1); + llama_sample_top_p (ctx, &cur_p, top_p, 1); + llama_sample_temperature(ctx, &cur_p, temp); + + { + const int n_top = 10; + LOG("top %d candidates:\n", n_top); + + for (int i = 0; i < n_top; i++) { + const llama_token id = cur_p.data[i].id; + LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p); + } + } + + id = llama_sample_token(ctx, &cur_p); + + LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str()); + } + } + // printf("`%d`", candidates_p.size); + + if (grammar != NULL) { + llama_grammar_accept_token(ctx, grammar, id); + } + + return id; +} + +// +// YAML utils +// + // returns true if successful, false otherwise bool create_directory_with_parents(const std::string & path) { #ifdef _WIN32 @@ -1070,6 +1209,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta); fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str()); + fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false"); fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers); diff --git a/common/common.h b/common/common.h index 5a379688e..105fb09e4 100644 --- a/common/common.h +++ b/common/common.h @@ -32,6 +32,7 @@ struct gpt_params { int32_t n_ctx = 512; // context size int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_draft = 16; // number of tokens to draft during speculative decoding int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_gpu_layers = 0; // number of layers to store in VRAM int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors @@ -63,6 +64,7 @@ struct gpt_params { float cfg_scale = 1.f; // How strong is guidance std::string model = "models/7B/ggml-model-f16.gguf"; // model path + std::string model_draft = ""; // draft model for speculative decoding std::string model_alias = "unknown"; // model alias std::string prompt = ""; std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state @@ -156,6 +158,40 @@ std::string llama_detokenize_bpe( llama_context * ctx, const std::vector & tokens); +// +// Sampling utils +// + +// this is a common sampling function used across the examples for convenience +// it can serve as a starting point for implementing your own sampling function +// +// required: +// - ctx: context to use for sampling +// - params: sampling parameters +// +// optional: +// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL +// - grammar: grammar to use for sampling, ignore if NULL +// - last_tokens: needed for repetition penalty, ignore if empty +// - idx: sample from llama_get_logits(ctx) + idx * n_vocab +// +// returns: +// - token: sampled token +// - candidates: vector of candidate tokens +// +llama_token llama_sample_token( + struct llama_context * ctx, + struct llama_context * ctx_guidance, + struct llama_grammar * grammar, + const struct gpt_params & params, + const std::vector & last_tokens, + std::vector & candidates, + int idx = 0); + +// +// YAML utils +// + bool create_directory_with_parents(const std::string & path); void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data); void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data); diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 6e65eb087..884c42764 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -23,6 +23,7 @@ else() add_subdirectory(train-text-from-scratch) add_subdirectory(convert-llama2c-to-ggml) add_subdirectory(simple) + add_subdirectory(speculative) add_subdirectory(embd-input) add_subdirectory(llama-bench) add_subdirectory(beam-search) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index db98312ca..922b9a980 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -116,7 +116,7 @@ int main(int argc, char ** argv) { #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("main", "log")); LOG_TEE("Log start\n"); - log_dump_cmdline(argc,argv); + log_dump_cmdline(argc, argv); #endif // LOG_DISABLE_LOGS // TODO: Dump params ? @@ -425,8 +425,9 @@ int main(int argc, char ** argv) { LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); LOG_TEE("\n\n"); + struct llama_grammar * grammar = NULL; grammar_parser::parse_state parsed_grammar; - llama_grammar * grammar = NULL; + if (!params.grammar.empty()) { parsed_grammar = grammar_parser::parse(params.grammar.c_str()); // will be empty (default) if there are parse errors @@ -450,8 +451,8 @@ int main(int argc, char ** argv) { } // TODO: replace with ring-buffer - std::vector last_n_tokens(n_ctx); - std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); + std::vector last_tokens(n_ctx); + std::fill(last_tokens.begin(), last_tokens.end(), 0); if (params.interactive) { const char *control_message; @@ -492,6 +493,11 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; + const int n_vocab = llama_n_vocab(ctx); + + std::vector candidates; + candidates.reserve(n_vocab); + while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict if (embd.size() > 0) { @@ -529,8 +535,8 @@ int main(int argc, char ** argv) { LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance); - // insert n_left/2 tokens at the start of embd from last_n_tokens - embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size()); + // insert n_left/2 tokens at the start of embd from last_tokens + embd.insert(embd.begin(), last_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_tokens.end() - embd.size()); LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd)); @@ -629,20 +635,6 @@ int main(int argc, char ** argv) { embd_guidance.clear(); if ((int) embd_inp.size() <= n_consumed && !is_interacting) { - const float temp = params.temp; - const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k; - const float top_p = params.top_p; - const float tfs_z = params.tfs_z; - const float typical_p = params.typical_p; - const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; - const float repeat_penalty = params.repeat_penalty; - const float alpha_presence = params.presence_penalty; - const float alpha_frequency = params.frequency_penalty; - const int mirostat = params.mirostat; - const float mirostat_tau = params.mirostat_tau; - const float mirostat_eta = params.mirostat_eta; - const bool penalize_nl = params.penalize_nl; - // 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; @@ -651,98 +643,12 @@ int main(int argc, char ** argv) { LOG("saved session to %s\n", path_session.c_str()); } - llama_token id = 0; + const llama_token id = llama_sample_token(ctx, ctx_guidance, grammar, params, last_tokens, candidates); - { - auto logits = llama_get_logits(ctx); - auto n_vocab = llama_n_vocab(ctx); + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(id); - // Apply params.logit_bias map - for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { - logits[it->first] += it->second; - } - - 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 cur_p = { candidates.data(), candidates.size(), false }; - - if (ctx_guidance) { - llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale); - } - - // Apply penalties - float nl_logit = logits[llama_token_nl(ctx)]; - auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx); - llama_sample_repetition_penalty(ctx, &cur_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, repeat_penalty); - llama_sample_frequency_and_presence_penalties(ctx, &cur_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, alpha_frequency, alpha_presence); - if (!penalize_nl) { - for (size_t idx = 0; idx < cur_p.size; idx++) { - if (cur_p.data[idx].id == llama_token_nl(ctx)) { - cur_p.data[idx].logit = nl_logit; - break; - } - } - } - - if (grammar != NULL) { - llama_sample_grammar(ctx, &cur_p, grammar); - } - - if (temp <= 0) { - // Greedy sampling - id = llama_sample_token_greedy(ctx, &cur_p); - } else { - if (mirostat == 1) { - static float mirostat_mu = 2.0f * mirostat_tau; - const int mirostat_m = 100; - llama_sample_temperature(ctx, &cur_p, temp); - id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); - } else if (mirostat == 2) { - static float mirostat_mu = 2.0f * mirostat_tau; - llama_sample_temperature(ctx, &cur_p, temp); - id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu); - } else { - // Temperature sampling - llama_sample_top_k (ctx, &cur_p, top_k, 1); - llama_sample_tail_free (ctx, &cur_p, tfs_z, 1); - llama_sample_typical (ctx, &cur_p, typical_p, 1); - llama_sample_top_p (ctx, &cur_p, top_p, 1); - llama_sample_temperature(ctx, &cur_p, temp); - - { - const int n_top = 10; - LOG("top %d candidates:\n", n_top); - - for (int i = 0; i < n_top; i++) { - const llama_token id = cur_p.data[i].id; - LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p); - } - } - - id = llama_sample_token(ctx, &cur_p); - - LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str()); - } - } - // printf("`%d`", candidates_p.size); - - if (grammar != NULL) { - llama_grammar_accept_token(ctx, grammar, id); - } - - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(id); - - LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_n_tokens)); - } + LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_tokens)); embd.push_back(id); @@ -758,8 +664,8 @@ int main(int argc, char ** argv) { LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); while ((int) embd_inp.size() > n_consumed) { embd.push_back(embd_inp[n_consumed]); - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(embd_inp[n_consumed]); + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(embd_inp[n_consumed]); ++n_consumed; if ((int) embd.size() >= params.n_batch) { break; @@ -792,7 +698,7 @@ int main(int argc, char ** argv) { // check for reverse prompt if (params.antiprompt.size()) { std::string last_output; - for (auto id : last_n_tokens) { + for (auto id : last_tokens) { last_output += llama_token_to_piece(ctx, id); } @@ -823,7 +729,7 @@ int main(int argc, char ** argv) { } // deal with end of text token in interactive mode - if (last_n_tokens.back() == llama_token_eos(ctx)) { + if (last_tokens.back() == llama_token_eos(ctx)) { LOG("found EOS token\n"); if (params.interactive) { @@ -925,7 +831,7 @@ int main(int argc, char ** argv) { if (grammar != NULL) { llama_grammar_free(grammar); - std::vector grammar_rules( parsed_grammar.c_rules()); + std::vector grammar_rules(parsed_grammar.c_rules()); grammar = llama_grammar_init( grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); diff --git a/examples/speculative/CMakeLists.txt b/examples/speculative/CMakeLists.txt new file mode 100644 index 000000000..6c5c9456e --- /dev/null +++ b/examples/speculative/CMakeLists.txt @@ -0,0 +1,8 @@ +set(TARGET speculative) +add_executable(${TARGET} speculative.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) +if(TARGET BUILD_INFO) + add_dependencies(${TARGET} BUILD_INFO) +endif() diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp new file mode 100644 index 000000000..f0400c13f --- /dev/null +++ b/examples/speculative/speculative.cpp @@ -0,0 +1,234 @@ +#ifndef _GNU_SOURCE +#define _GNU_SOURCE +#endif + +#include "build-info.h" + +#include "common.h" +#include "llama.h" + +#include +#include +#include +#include + +int main(int argc, char ** argv) { + gpt_params params; + + if (gpt_params_parse(argc, argv, params) == false) { + return 1; + } + + if (params.model_draft.empty()) { + fprintf(stderr, "%s: error: --model-draft is required\n", __func__); + return 1; + } + +#ifndef LOG_DISABLE_LOGS + log_set_target(log_filename_generator("speculative", "log")); + LOG_TEE("Log start\n"); + log_dump_cmdline(argc, argv); +#endif // LOG_DISABLE_LOGS + + // init llama.cpp + llama_backend_init(params.numa); + + llama_model * model_tgt = NULL; + llama_model * model_dft = NULL; + + llama_context * ctx_tgt = NULL; + llama_context * ctx_dft = NULL; + + // load the target model + params.perplexity = true; // HACK: enable logits_all = true + std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params); + + // load the draft model + params.model = params.model_draft; + std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params); + + // tokenize the prompt + std::vector inp; + inp = ::llama_tokenize(ctx_tgt, params.prompt, true); + + const int max_context_size = llama_n_ctx(ctx_tgt); + const int max_tokens_list_size = max_context_size - 4; + + if ((int) inp.size() > max_tokens_list_size) { + fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size); + return 1; + } + + fprintf(stderr, "\n\n"); + + for (auto id : inp) { + fprintf(stderr, "%s", llama_token_to_piece(ctx_tgt, id).c_str()); + } + + fflush(stderr); + + const int n_input = inp.size(); + + const auto t_enc_start = ggml_time_us(); + + // eval the prompt with both models + llama_eval(ctx_tgt, inp.data(), int(inp.size() - 1), 0, params.n_threads); + llama_eval(ctx_tgt, &inp.back(), 1, inp.size() - 1, params.n_threads); + llama_eval(ctx_dft, inp.data(), int(inp.size()), 0, params.n_threads); + + const auto t_enc_end = ggml_time_us(); + + // the 2 models should have the same vocab + const int n_ctx = llama_n_ctx(ctx_tgt); + const int n_vocab = llama_n_vocab(ctx_tgt); + //GGML_ASSERT(n_vocab == llama_n_vocab(ctx_dft)); + + // how many tokens to draft each time + const int n_draft = params.n_draft; + + int n_predict = 0; + int n_drafted = 0; + int n_accept = 0; + + int n_past_tgt = inp.size(); + int n_past_dft = inp.size(); + + std::vector drafted; + + std::vector last_tokens(n_ctx); + std::fill(last_tokens.begin(), last_tokens.end(), 0); + + for (auto & id : inp) { + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(id); + } + + std::vector candidates; + candidates.reserve(n_vocab); + + // used to determine end of generation + bool has_eos = false; + + const auto t_dec_start = ggml_time_us(); + + while (true) { + LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted)); + + // sample from the drafted tokens if any + int i_dft = 0; + while (true) { + const llama_token id = llama_sample_token(ctx_tgt, NULL, NULL, params, last_tokens, candidates, i_dft); + + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(id); + + //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, last_tokens)); + + const std::string token_str = llama_token_to_piece(ctx_tgt, id); + printf("%s", token_str.c_str()); + fflush(stdout); + + if (id == llama_token_eos(ctx_tgt)) { + has_eos = true; + } + + ++n_predict; + + if (i_dft < (int) drafted.size() && id == drafted[i_dft]) { + LOG("drafted token %d accepted\n", id); + ++n_accept; + ++n_past_tgt; + ++n_past_dft; + ++i_dft; + + continue; + } + + // the drafted token was rejected or we are out of drafted tokens + llama_eval(ctx_dft, &id, 1, n_past_dft, params.n_threads); + ++n_past_dft; + + drafted.clear(); + drafted.push_back(id); + + break; + } + + if (n_predict > params.n_predict || has_eos) { + break; + } + + // sample n_draft tokens from the draft model picking the best token + int n_past_cur = n_past_dft; + for (int i = 0; i < n_draft; ++i) { + float * logits = llama_get_logits(ctx_dft); + + candidates.clear(); + 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 cur_p = { candidates.data(), candidates.size(), false }; + + // computes softmax and sorts the candidates + llama_sample_softmax(ctx_dft, &cur_p); + + for (int i = 0; i < 3; ++i) { + LOG(" - draft candidate %d: %d (%.3f)\n", i, cur_p.data[i].id, cur_p.data[i].p); + } + + // too low probability, stop drafting + if (cur_p.data[0].p < 2*cur_p.data[1].p) { + break; + } + + drafted.push_back(cur_p.data[0].id); + ++n_drafted; + + if (i < n_draft - 1) { + // evaluate the drafted token on the draft model + llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads); + ++n_past_cur; + } + } + + // evaluate the target model on the drafted tokens + llama_eval(ctx_tgt, drafted.data(), drafted.size(), n_past_tgt, params.n_threads); + ++n_past_tgt; + + drafted.erase(drafted.begin()); + } + + auto t_dec_end = ggml_time_us(); + + LOG_TEE("\n\n"); + + LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f)); + LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f)); + + // TODO: make sure these numbers are computed correctly + LOG_TEE("\n"); + LOG_TEE("n_draft = %d\n", n_draft); + LOG_TEE("n_predict = %d\n", n_predict); + LOG_TEE("n_drafted = %d\n", n_drafted); + LOG_TEE("n_accept = %d\n", n_accept); + LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); + + LOG_TEE("\ndraft:\n"); + llama_print_timings(ctx_dft); + + LOG_TEE("\ntarget:\n"); + llama_print_timings(ctx_tgt); + + llama_free(ctx_tgt); + llama_free_model(model_tgt); + + llama_free(ctx_dft); + llama_free_model(model_dft); + + llama_backend_free(); + + fprintf(stderr, "\n\n"); + + return 0; +} From cf9b08485c4c2d4d945c6e74fe20f273a38b6104 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 3 Sep 2023 20:34:09 +0200 Subject: [PATCH 43/87] ggml-alloc : use virtual memory for measurement (#2973) * ggml-alloc : use virtual memory for measurement * compatibility fixes for MAP_ANONYMOUS * fallback to fixed address for systems without virtual memory --- ggml-alloc.c | 123 ++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 97 insertions(+), 26 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index 459f121ca..c1939a4b7 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -1,3 +1,8 @@ +// defines MAP_ANONYMOUS +#ifndef _GNU_SOURCE +#define _GNU_SOURCE +#endif + #include "ggml-alloc.h" #include "ggml.h" #include @@ -6,6 +11,26 @@ #include #include +#ifdef __has_include + #if __has_include() + #include + #if defined(_POSIX_MAPPED_FILES) + #include + #include + #endif + #endif +#endif + +#if defined(_WIN32) + #define WIN32_LEAN_AND_MEAN + #ifndef NOMINMAX + #define NOMINMAX + #endif + #include + #include +#endif + + #define UNUSED(x) (void)(x) #define MAX(a, b) ((a) > (b) ? (a) : (b)) #define GGML_MAX_CONCUR (2*GGML_MAX_NODES) @@ -99,19 +124,24 @@ static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tens } #endif - -static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +static size_t ggml_allocr_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { return ggml_nbytes(tensor); UNUSED(alloc); } +// check if a tensor is allocated by this buffer +static bool ggml_allocr_is_own(struct ggml_allocr * alloc, const struct ggml_tensor * tensor) { + void * ptr = tensor->data; + return ptr >= alloc->data && (char *)ptr < (char *)alloc->data + alloc->max_size; +} + void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { #ifdef GGML_ALLOCATOR_DEBUG GGML_ASSERT(ggml_is_view(tensor) == false); // views generally get data pointer from one of their sources GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated #endif - size_t size = ggml_allocator_get_alloc_size(alloc, tensor); + size_t size = ggml_allocr_get_alloc_size(alloc, tensor); size = aligned_offset(NULL, size, alloc->alignment); AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size); @@ -177,17 +207,17 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) } // this is a very naive implementation, but for our case the number of free blocks should be very small -static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { void * ptr = tensor->data; - if (ptr < alloc->data || (char*)ptr >= (char*)alloc->data + alloc->max_size) { + if (ggml_allocr_is_own(alloc, tensor) == false) { // the tensor was not allocated in this buffer // this can happen because the graph allocator will try to free weights and other tensors from different buffers // the easiest way to deal with this is just to ignore it return; } - size_t size = ggml_allocator_get_alloc_size(alloc, tensor); + size_t size = ggml_allocr_get_alloc_size(alloc, tensor); size = aligned_offset(NULL, size, alloc->alignment); AT_PRINTF("%s: freeing %s (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, size, alloc->n_free_blocks); @@ -281,24 +311,64 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) return alloc; } -// address and size of the buffer when measuring -// it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers -static void * const MEASURE_BASE_ADDR = (void *) 0x1000; -#if defined(__ARM_NEON) && !defined(__aarch64__) -// 32-bit -// TODO: Use for 32-bit x86 as well -static const size_t MEASURE_MAX_SIZE = (1ULL<<32) - 1; // 4 GB +// OS specific functions to allocate and free uncommitted virtual memory +static void * alloc_vmem(size_t size) { +#if defined(_WIN32) + return VirtualAlloc(NULL, size, MEM_RESERVE, PAGE_NOACCESS); +#elif defined(_POSIX_MAPPED_FILES) + return mmap(NULL, size, PROT_NONE, MAP_PRIVATE | MAP_ANON, -1, 0); #else -// 64-bit -static const size_t MEASURE_MAX_SIZE = 1ULL<<40; // 1 TB + // use a fixed address for other platforms + uintptr_t base_addr = (uintptr_t)-size - 0x100; + return (void *)base_addr; #endif +} + +static void free_vmem(void * base_addr, size_t size) { +#if defined(_WIN32) + VirtualFree(base_addr, 0, MEM_RELEASE); + UNUSED(size); +#elif defined(_POSIX_MAPPED_FILES) + munmap(base_addr, size); +#else + // nothing to do + UNUSED(base_addr); + UNUSED(size); +#endif +} + +// allocate uncommitted virtual memory to measure the size of the graph +static void alloc_measure_vmem(void ** base_addr, size_t * size) { + // 1TB for 64-bit, 1GB for 32-bit + *size = sizeof(void *) == 4 ? 1ULL<<30 : 1ULL<<40; + do { + *base_addr = alloc_vmem(*size); + if (*base_addr != NULL) { + AT_PRINTF("allocated %.2f GB of virtual memory for measure buffer at %p\n", *size / 1024.0 / 1024.0 / 1024.0, *base_addr); + return; + } + // try again with half the size + *size /= 2; + } while (*size > 0); + + GGML_ASSERT(!"failed to allocate virtual memory for measure buffer"); +} + +static void free_measure_vmem(void * base_addr, size_t size) { + free_vmem(base_addr, size); +} struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) { struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */); + void * base_addr; + size_t size; + + alloc_measure_vmem(&base_addr, &size); + *alloc = (struct ggml_allocr){ - /*.data = */ MEASURE_BASE_ADDR, - /*.size = */ MEASURE_MAX_SIZE, + /*.data = */ base_addr, + /*.size = */ size, /*.alignment = */ alignment, /*.n_free_blocks = */ 0, /*.free_blocks = */ {{0}}, @@ -318,6 +388,9 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) { } void ggml_allocr_free(struct ggml_allocr * alloc) { + if (alloc->measure) { + free_measure_vmem(alloc->data, alloc->size); + } free(alloc); } @@ -387,8 +460,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) } // if the node's data is external, then we cannot re-use it - if ((char *) parent->data < (char *) alloc->data || - (char *) parent->data >= ((char *) alloc->data + alloc->size)) { + if (ggml_allocr_is_own(alloc, parent) == false) { AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data); continue; } @@ -422,7 +494,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) } } -static size_t ggml_allocator_alloc_graph_tensors_n( +static size_t ggml_allocr_alloc_graph_tensors_n( struct ggml_allocr * alloc, struct ggml_cgraph ** graphs, int n_graphs, struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) { @@ -500,11 +572,10 @@ static size_t ggml_allocator_alloc_graph_tensors_n( AT_PRINTF("\n"); } - // update parents // update immediately if there is no parse_seq // update only at barriers if there is parse_seq - if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] == -1) { + if ((alloc->parse_seq_len == 0) || alloc->parse_seq[ind] == -1) { int update_start = alloc->parse_seq_len ? last_barrier_pos : ind; int update_end = alloc->parse_seq_len ? ind : ind + 1; for (int i = update_start; i < update_end; i++) { @@ -528,12 +599,12 @@ static size_t ggml_allocator_alloc_graph_tensors_n( view_src_hn->n_views -= 1; AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views); if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) { - ggml_allocator_free_tensor(alloc, view_src); + ggml_allocr_free_tensor(alloc, view_src); } } else { if (parent->data != node->data) { - ggml_allocator_free_tensor(alloc, parent); + ggml_allocr_free_tensor(alloc, parent); } } } @@ -550,7 +621,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n( for (int i = 0; outputs[g][i] != NULL; i++) { struct ggml_tensor * output = outputs[g][i]; AT_PRINTF("output: %s\n", output->name); - ggml_allocator_free_tensor(alloc, output); + ggml_allocr_free_tensor(alloc, output); } } } @@ -559,5 +630,5 @@ static size_t ggml_allocator_alloc_graph_tensors_n( } size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) { - return ggml_allocator_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL); + return ggml_allocr_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL); } From 35195689cd835464779c247b1c22ab9247418fd1 Mon Sep 17 00:00:00 2001 From: Jiahao Li Date: Mon, 4 Sep 2023 14:53:30 +0800 Subject: [PATCH 44/87] 2x faster (rms) norm cuda kernels (3.7% e2e improvement) (#2985) * 2x faster (rms) norm cuda kernels * Fix code style --- ggml-cuda.cu | 89 ++++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 66 insertions(+), 23 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 8357f32f7..d2dbf824e 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -464,58 +464,91 @@ static __global__ void silu_f32(const float * x, float * dst, const int k) { dst[i] = x[i] / (1.0f + expf(-x[i])); } +static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32); + a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32); + } + return a; +} + +template static __global__ void norm_f32(const float * x, float * dst, const int ncols) { const int row = blockIdx.x*blockDim.y + threadIdx.y; const int tid = threadIdx.x; const float eps = 1e-5f; - float mean = 0.0f; - float var = 0.0f; + float2 mean_var = make_float2(0.f, 0.f); - for (int col = tid; col < ncols; col += WARP_SIZE) { + for (int col = tid; col < ncols; col += block_size) { const float xi = x[row*ncols + col]; - mean += xi; - var += xi * xi; + mean_var.x += xi; + mean_var.y += xi * xi; } // sum up partial sums -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - mean += __shfl_xor_sync(0xffffffff, mean, mask, 32); - var += __shfl_xor_sync(0xffffffff, var, mask, 32); + mean_var = warp_reduce_sum(mean_var); + if (block_size > WARP_SIZE) { + __shared__ float2 s_sum[32]; + int warp_id = threadIdx.x / WARP_SIZE; + int lane_id = threadIdx.x % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = mean_var; + } + __syncthreads(); + mean_var = s_sum[lane_id]; + mean_var = warp_reduce_sum(mean_var); } - mean /= ncols; - var = var / ncols - mean * mean; - const float inv_var = rsqrtf(var + eps); + const float mean = mean_var.x / ncols; + const float var = mean_var.y / ncols - mean * mean; + const float inv_std = rsqrtf(var + eps); - for (int col = tid; col < ncols; col += WARP_SIZE) { - dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_var; + for (int col = tid; col < ncols; col += block_size) { + dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std; } } +static __device__ __forceinline__ float warp_reduce_sum(float x) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + x += __shfl_xor_sync(0xffffffff, x, mask, 32); + } + return x; +} + +template static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) { const int row = blockIdx.x*blockDim.y + threadIdx.y; const int tid = threadIdx.x; float tmp = 0.0f; // partial sum for thread in warp - for (int col = tid; col < ncols; col += WARP_SIZE) { + for (int col = tid; col < ncols; col += block_size) { const float xi = x[row*ncols + col]; tmp += xi * xi; } // sum up partial sums -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); + tmp = warp_reduce_sum(tmp); + if (block_size > WARP_SIZE) { + __shared__ float s_sum[32]; + int warp_id = threadIdx.x / WARP_SIZE; + int lane_id = threadIdx.x % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = tmp; + } + __syncthreads(); + tmp = s_sum[lane_id]; + tmp = warp_reduce_sum(tmp); } const float mean = tmp / ncols; const float scale = rsqrtf(mean + eps); - for (int col = tid; col < ncols; col += WARP_SIZE) { + for (int col = tid; col < ncols; col += block_size) { dst[row*ncols + col] = scale * x[row*ncols + col]; } } @@ -4203,14 +4236,24 @@ static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_ static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % WARP_SIZE == 0); - const dim3 block_dims(WARP_SIZE, 1, 1); - norm_f32<<>>(x, dst, ncols); + if (ncols < 1024) { + const dim3 block_dims(WARP_SIZE, 1, 1); + norm_f32<<>>(x, dst, ncols); + } else { + const dim3 block_dims(1024, 1, 1); + norm_f32<1024><<>>(x, dst, ncols); + } } static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) { GGML_ASSERT(ncols % WARP_SIZE == 0); - const dim3 block_dims(WARP_SIZE, 1, 1); - rms_norm_f32<<>>(x, dst, ncols, eps); + if (ncols < 1024) { + const dim3 block_dims(WARP_SIZE, 1, 1); + rms_norm_f32<<>>(x, dst, ncols, eps); + } else { + const dim3 block_dims(1024, 1, 1); + rms_norm_f32<1024><<>>(x, dst, ncols, eps); + } } static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) { From e4386f417faf894f6706eec005e24d142b577fcb Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Mon, 4 Sep 2023 10:28:55 +0200 Subject: [PATCH 45/87] server : add a subtle loading animation to the edit box (#2466) * editorconfig: add override for the server HTML (which already is 2-space indented) * server: add a subtle loading animation to the edit box --- .editorconfig | 3 + examples/server/index.html.hpp | 3970 +++++++++++++++-------------- examples/server/public/index.html | 41 +- 3 files changed, 2058 insertions(+), 1956 deletions(-) diff --git a/.editorconfig b/.editorconfig index 135a7e4bc..f8245b85c 100644 --- a/.editorconfig +++ b/.editorconfig @@ -17,3 +17,6 @@ indent_style = tab [prompts/*.txt] insert_final_newline = unset + +[examples/server/public/*] +indent_size = 2 diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 84e6f97ba..f30232929 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -210,1024 +210,1120 @@ unsigned char index_html[] = { 0x66, 0x6f, 0x6e, 0x74, 0x2d, 0x73, 0x69, 0x7a, 0x65, 0x3a, 0x20, 0x38, 0x30, 0x25, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x38, 0x38, 0x38, 0x3b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x40, 0x6d, - 0x65, 0x64, 0x69, 0x61, 0x20, 0x28, 0x70, 0x72, 0x65, 0x66, 0x65, 0x72, - 0x73, 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x2d, 0x73, 0x63, 0x68, 0x65, - 0x6d, 0x65, 0x3a, 0x20, 0x64, 0x61, 0x72, 0x6b, 0x29, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x70, 0x6f, 0x70, 0x6f, 0x76, - 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@media (prefers-color-scheme: dark) { + .loading { + --loading-color-1: #22222200; + --loading-color-2: #222222ff; + } .popover-content { background-color: black; } @@ -321,7 +343,10 @@ const llamaStats = signal(null) const controller = signal(null) - const generating = computed(() => controller.value == null ) + // currently generating a completion? + const generating = computed(() => controller.value != null) + + // has the user started a chat? const chatStarted = computed(() => session.value.transcript.length > 0) const transcriptUpdate = (transcript) => { @@ -430,11 +455,19 @@ return html`
-